For Multivariate Time Series Forecasting (MTSF), recent deep learning applications show that univariate models frequently outperform multivariate ones. To address the difficiency in multivariate models, we introduce a method to Construct Auxiliary Time Series (CATS) that functions like a 2D temporal-contextual attention mechanism, which generates Auxiliary Time Series (ATS) from Original Time Series (OTS) to effectively represent and incorporate inter-series relationships for forecasting. Key principles of ATS - continuity, sparsity, and variability - are identified and implemented through different modules. Even with a basic 2-layer MLP as core predictor, CATS achieves state-of-the-art, significantly reducing complexity and parameters compared to previous multivariate models, marking it an efficient and transferable MTSF solution.

Paid autonomous vehicle service is coming to Los Angeles, thanks to a decision by California regulators today to allow Alphabet subsidiary Waymo to operate in the city. Under the new ruling, Waymo is also permitted to launch service in a large section of the San Francisco Peninsula. The decision by the California Public Utilities Commission will likely prove controversial. It comes over the protest of local governments and agencies, including the Los Angeles Department of Transportation, the San Francisco County Transportation Authority, the city of South San Francisco, and the County of San Mateo. All argued that local government and citizens should have more input and oversight over the expanded autonomous taxi service.

Recent studies have demonstrated remarkable performance in time series forecasting. However, due to the partially-observed nature of real-world applications, solely focusing on the target of interest, so-called endogenous variables, is usually insufficient to guarantee accurate forecasting. Notably, a system is often recorded into multiple variables, where the exogenous series can provide valuable external information for endogenous variables. Thus, unlike prior well-established multivariate or univariate forecasting that either treats all the variables equally or overlooks exogenous information, this paper focuses on a practical setting, which is time series forecasting with exogenous variables. We propose a novel framework, TimeXer, to utilize external information to enhance the forecasting of endogenous variables. With a deftly designed embedding layer, TimeXer empowers the canonical Transformer architecture with the ability to reconcile endogenous and exogenous information, where patch-wise self-attention and variate-wise cross-attention are employed. Moreover, a global endogenous variate token is adopted to effectively bridge the exogenous series into endogenous temporal patches. Experimentally, TimeXer significantly improves time series forecasting with exogenous variables and achieves consistent state-of-the-art performance in twelve real-world forecasting benchmarks.

Recent advancements have underscored the impact of deep learning techniques on multivariate time series forecasting (MTSF). Generally, these techniques are bifurcated into two categories: Channel-independence and Channel-mixing approaches. Although Channel-independence methods typically yield better results, Channel-mixing could theoretically offer improvements by leveraging inter-variable correlations. Nonetheless, we argue that the integration of uncorrelated information in channel-mixing methods could curtail the potential enhancement in MTSF model performance. To substantiate this claim, we introduce the Cross-variable Decorrelation Aware feature Modeling (CDAM) for Channel-mixing approaches, aiming to refine Channel-mixing by minimizing redundant information between channels while enhancing relevant mutual information. Furthermore, we introduce the Temporal correlation Aware Modeling (TAM) to exploit temporal correlations, a step beyond conventional single-step forecasting methods. This strategy maximizes the mutual information between adjacent sub-sequences of both the forecasted and target series. Combining CDAM and TAM, our novel framework significantly surpasses existing models, including those previously considered state-of-the-art, in comprehensive tests.

The number of humpback whales in the North Pacific Ocean fell by 20 per cent between 2012 and 2021, according to a study that used artificial intelligence to identify individual whales from photos of their tails. The decline coincided with a massive marine heatwave sometimes called the blob, which began in 2013 and lasted until 2016. The unprecedented intensity of the blob was almost certainly the result of global warming. The findings suggest that around 7000 whales starved to death because of the marine heatwave, says Ted Cheeseman at Southern Cross University in Australia. The blob is known to have caused mass die-offs of many other animals, such as seabirds.

Large language models (LLMs) have demonstrated impressive performance on many tasks. However, to achieve optimal performance, specially designed prompting methods are still needed. These methods either rely on task-specific few-shot examples that require a certain level of domain knowledge, or are designed to be simple but only perform well on a few types of tasks. In this work, we attempt to introduce the concept of generalist prompting, which operates on the design principle of achieving optimal or near-optimal performance on a wide range of tasks while eliminating the need for manual selection and customization of prompts tailored to specific problems. Furthermore, we propose MeMo (Mental Models), an innovative prompting method that is simple-designed yet effectively fulfills the criteria of generalist prompting. MeMo distills the cores of various prompting methods into individual mental models and allows LLMs to autonomously select the most suitable mental models for the problem, achieving or being near to the state-of-the-art results on diverse tasks such as STEM, logical reasoning, and commonsense reasoning in zero-shot settings. We hope that the insights presented herein will stimulate further exploration of generalist prompting methods for LLMs.

China has stepped up its diplomatic and military pressure against Taiwan, alarming U.S. officials and allies in the region that Beijing is looking to take back the island by force. If projections of a Chinese military invasion to retake Taiwan are accurate, the U.S. can utilize artificial intelligence (AI) and other technology that will indicate to forces in the region that China isn't engaging in yet another provocative military exercise but is launching the invasion so many predict. According to experts, AI and machine learning (ML) can help the U.S. and its allies in the region improve the speed and efficiency of war plan development, intelligence assessments and targeting effectiveness. An MV-22 Osprey from the "Ugly Angels" of Marine Medium Tiltrotor Squadron 362 flies by the aircraft carrier USS Nimitz in the South China Sea Feb. 11, 2023. WHAT IS ARTIFICIAL INTELLIGENCE (AI)?

To address the challenges of out-of-control generation in generative models for machine reading comprehension (MRC), we introduce the Question-Attended Span Extraction (QASE) module. Integrated during the fine-tuning of pre-trained generative language models (PLMs), QASE enables these PLMs to match SOTA extractive methods and outperform leading LLMs like GPT-4 in MRC tasks, without significant increases in computational costs.

Large language models (LLMs) have recently experienced remarkable growth in both their capabilities (OpenAI, 2023; Gemini Team et al., 2023; Touvron et al., 2023) and their applications in various fields (NLLB Team et al., 2022; Thirunavukarasu et al., 2023; Schick et al., 2023; Bubeck et al., 2023). As LLMs become increasingly complex and are deployed in safety-critical environments (Singhal et al., 2022; Li et al., 2023; Maddela et al., 2023), it is essential to thoroughly understand their robustness to different inputs. Indeed, the susceptibility of LLMs to user inputs and adversarial prompts -- prompts crafted to mislead the model or exploit its weaknesses, potentially leading to unsafe, biased, or incorrect outputs -- poses a significant challenge (Perez et al., 2022; Wei et al., 2023; Zou et al., 2023). Identifying these vulnerabilities and subsequently mitigating such risks is therefore vital to ensure the safe and reliable operation of LLMs in the real world. Current methods for identifying adversarial prompts aimed at "attacking" LLMs and eliciting undesirable outputs are limited by several factors.

Enforcing safety while preventing overly conservative behaviors is essential for autonomous vehicles to achieve high task performance. In this paper, we propose a barrier-enhanced homotopic parallel trajectory optimization (BHPTO) approach with over-relaxed alternating direction method of multipliers (ADMM) for real-time integrated decision-making and planning. To facilitate safety interactions between the ego vehicle (EV) and surrounding vehicles, a spatiotemporal safety module exhibiting bi-convexity is developed on the basis of barrier function. Varying barrier coefficients are adopted for different time steps in a planning horizon to account for the motion uncertainties of surrounding HVs and mitigate conservative behaviors. Additionally, we exploit the discrete characteristics of driving maneuvers to initialize nominal behavior-oriented free-end homotopic trajectories based on reachability analysis, and each trajectory is locally constrained to a specific driving maneuver while sharing the same task objectives. By leveraging the bi-convexity of the safety module and the kinematics of the EV, we formulate the BHPTO as a bi-convex optimization problem. Then constraint transcription and over-relaxed ADMM are employed to streamline the optimization process, such that multiple trajectories are generated in real time with feasibility guarantees. Through a series of experiments, the proposed development demonstrates improved task accuracy, stability, and consistency in various traffic scenarios using synthetic and real-world traffic datasets.