The administration of U.S. President Joe Biden plans next month to broaden curbs on U.S shipments to China of semiconductors used for artificial intelligence and chipmaking tools, several people familiar with the matter have said. The Commerce Department intends to publish new regulations based on restrictions communicated in letters earlier this year to three U.S. companies -- KLA Corp., Lam Research Corp. and Applied Materials Inc -- the people said, speaking on condition of anonymity. The planned new rules have not previously been reported. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

The energy transition requires critical minerals. Though the U.S. has plentiful resources of its own, the country has largely relied on foreign sources. That's in part because one major roadblock to accessing American critical mineral deposits is that they remain largely unmapped. That may be about to change, though. The Department of Defense and the U.S. Geological Survey have issued two separate challenges to explore using artificial intelligence and machine learning to expedite USGS' task of assessing the availability and mining potential of 50 critical minerals.

In-brief New rules drafted by the European Union aimed at regulating AI could prevent developers from releasing open-source models, according to American think tank Brookings. The proposed EU AI Act, yet to be signed into law, states that open source developers have to ensure their AI software is accurate, secure, and be transparent about risk and data use in clear technical documentation. Brookings argues that if a private company were to deploy the public model or use it in a product, and it somehow gets in trouble due to some unforeseen or uncontrollable effects from the model, the company would then probably try to blame the open source developers and sue them. It might force the open source community to think twice about releasing their code, and would, unfortunately, mean the development of AI will be driven by private companies. Proprietary code is difficult to analyse and build upon, meaning innovation will be hampered.

WASHINGTON, Sept 11(Reuters) - The Biden administration plans next month to broaden curbs on U.S shipments to China of semiconductors used for artificial intelligence and chipmaking tools, several people familiar with the matter said. The Commerce Department intends to publish new regulations based on restrictions communicated in letters earlier this year to three U.S. companies -- KLA Corp (KLAC.O), Lam Research Corp (LRCX.O) and Applied Materials Inc (AMAT.O), the people said, speaking on the condition of anonymity. The plan for new rules has not been previously reported. The letters, which the companies publicly acknowledged, forbade them from exporting chipmaking equipment to Chinese factories that produce advanced semiconductors with sub-14 nanometer processes unless the sellers obtain Commerce Department licenses. The rules would also codify restrictions in Commerce Department letters sent to Nvidia Corp (NVDA.O) and Advanced Micro Devices (AMD.O) last month instructing them to halt shipments of several artificial intelligence computing chips to China unless they obtain licenses.

Risk assessment of roadways is commonly practiced based on historical crash data. Information on driver behaviors and real-time traffic situations is sometimes missing. In this paper, the Safe Route Mapping (SRM) model, a methodology for developing dynamic risk heat maps of roadways, is extended to consider driver behaviors when making predictions. An Android App is designed to gather drivers' information and upload it to a server. On the server, facial recognition extracts drivers' data, such as facial landmarks, gaze directions, and emotions. The driver's drowsiness and distraction are detected, and driving performance is evaluated. Meanwhile, dynamic traffic information is captured by a roadside camera and uploaded to the same server. A longitudinal-scanline-based arterial traffic video analytics is applied to recognize vehicles from the video to build speed and trajectory profiles. Based on these data, a LightGBM model is introduced to predict conflict indices for drivers in the next one or two seconds. Then, multiple data sources, including historical crash counts and predicted traffic conflict indicators, are combined using a Fuzzy logic model to calculate risk scores for road segments. The proposed SRM model is illustrated using data collected from an actual traffic intersection and a driving simulation platform. The prediction results show that the model is accurate, and the added driver behavior features will improve the model's performance. Finally, risk heat maps are generated for visualization purposes. The authorities can use the dynamic heat map to designate safe corridors and dispatch law enforcement and drivers for early warning and trip planning.

Based on the tremendous success of pre-trained language models (PrLMs) for source code comprehension tasks, current literature studies either ways to further improve the performance (generalization) of PrLMs, or their robustness against adversarial attacks. However, they have to compromise on the trade-off between the two aspects and none of them consider improving both sides in an effective and practical way. To fill this gap, we propose Semantic-Preserving Adversarial Code Embeddings (SPACE) to find the worst-case semantic-preserving attacks while forcing the model to predict the correct labels under these worst cases. Experiments and analysis demonstrate that SPACE can stay robust against state-of-the-art attacks while boosting the performance of PrLMs for code.

Twelve years ago, the European Union began with the gradual phase-out of energy-inefficient incandescent light bulbs under the Ecodesign Directive. In this work, we implement an agent-based simulation to model the consumer behaviour in the EU lighting market with the goal to explain consumer behaviour and explore alternative policies. Agents are based on the Consumat II model, have individual preferences based on empirical market research, gather experience from past actions, and socially interact with each other in a dynamic environment. Our findings suggest that the adoption of energy-friendly lighting alternatives was hindered by a low level of consumer interest combined with high-enough levels of satisfaction about incandescent bulbs and that information campaigns can partially address this. These findings offer insight into both individual-level driving forces of behaviour and society-level outcomes in a niche market. With this, our work demonstrates the strengths of agent-based models for policy generation and evaluation.

In this work I study the problem of adversarial perturbations to rewards, in a Multi-armed bandit (MAB) setting. Specifically, I focus on an adversarial attack to a UCB type best-arm identification policy applied to a stochastic MAB. The UCB attack presented in [1] results in pulling a target arm K very often. I used the attack model of [1] to derive the sample complexity required for selecting target arm K as the best arm. I have proved that the stopping condition of UCB based best-arm identification algorithm given in [2], can be achieved by the target arm K in T rounds, where T depends only on the total number of arms and $\sigma$ parameter of $\sigma^2-$ sub-Gaussian random rewards of the arms.

In this paper we address the application of pre-processing techniques to multi-channel time series data with varying lengths, which we refer to as the alignment problem, for downstream machine learning. The misalignment of multi-channel time series data may occur for a variety of reasons, such as missing data, varying sampling rates, or inconsistent collection times. We consider multi-channel time series data collected from the MIT SuperCloud High Performance Computing (HPC) center, where different job start times and varying run times of HPC jobs result in misaligned data. This misalignment makes it challenging to build AI/ML approaches for tasks such as compute workload classification. Building on previous supervised classification work with the MIT SuperCloud Dataset, we address the alignment problem via three broad, low overhead approaches: sampling a fixed subset from a full time series, performing summary statistics on a full time series, and sampling a subset of coefficients from time series mapped to the frequency domain. Our best performing models achieve a classification accuracy greater than 95%, outperforming previous approaches to multi-channel time series classification with the MIT SuperCloud Dataset by 5%. These results indicate our low overhead approaches to solving the alignment problem, in conjunction with standard machine learning techniques, are able to achieve high levels of classification accuracy, and serve as a baseline for future approaches to addressing the alignment problem, such as kernel methods.

With the growing rates of cyber-attacks and cyber espionage, the need for better and more powerful intrusion detection systems (IDS) is even more warranted nowadays. The basic task of an IDS is to act as the first line of defense, in detecting attacks on the internet. As intrusion tactics from intruders become more sophisticated and difficult to detect, researchers have started to apply novel Machine Learning (ML) techniques to effectively detect intruders and hence preserve internet users' information and overall trust in the entire internet network security. Over the last decade, there has been an explosion of research on intrusion detection techniques based on ML and Deep Learning (DL) architectures on various cyber security-based datasets such as the DARPA, KDDCUP'99, NSL-KDD, CAIDA, CTU-13, UNSW-NB15. In this research, we review contemporary literature and provide a comprehensive survey of different types of intrusion detection technique that applies Support Vector Machines (SVMs) algorithms as a classifier. We focus only on studies that have been evaluated on the two most widely used datasets in cybersecurity namely: the KDDCUP'99 and the NSL-KDD datasets. We provide a summary of each method, identifying the role of the SVMs classifier, and all other algorithms involved in the studies. Furthermore, we present a critical review of each method, in tabular form, highlighting the performances measures, strengths, and limitations, of each of the methods surveyed.