Machine Reading Comprehension (MRC) models tend to take advantage of spurious correlations (also known as dataset bias or annotation artifacts in the research community). Consequently, these models may perform the MRC task without fully comprehending the given context and question, which is undesirable since it may result in low robustness against distribution shift. The main focus of this paper is answer-position bias, where a significant percentage of training questions have answers located solely in the first sentence of the context. We propose a Single-Sentence Reader as a new approach for addressing answer position bias in MRC. Remarkably, in our experiments with six different models, our proposed Single-Sentence Readers trained on biased dataset achieve results that nearly match those of models trained on normal dataset, proving their effectiveness in addressing the answer position bias. Our study also discusses several challenges our Single-Sentence Readers encounter and proposes a potential solution.

Predicting vehicle trajectories is crucial for ensuring automated vehicle operation efficiency and safety, particularly on congested multi-lane highways. In such dynamic environments, a vehicle's motion is determined by its historical behaviors as well as interactions with surrounding vehicles. These intricate interactions arise from unpredictable motion patterns, leading to a wide range of driving behaviors that warrant in-depth investigation. This study presents the Graph-based Interaction-aware Multi-modal Trajectory Prediction (GIMTP) framework, designed to probabilistically predict future vehicle trajectories by effectively capturing these interactions. Within this framework, vehicles' motions are conceptualized as nodes in a time-varying graph, and the traffic interactions are represented by a dynamic adjacency matrix. To holistically capture both spatial and temporal dependencies embedded in this dynamic adjacency matrix, the methodology incorporates the Diffusion Graph Convolutional Network (DGCN), thereby providing a graph embedding of both historical states and future states. Furthermore, we employ a driving intention-specific feature fusion, enabling the adaptive integration of historical and future embeddings for enhanced intention recognition and trajectory prediction. This model gives two-dimensional predictions for each mode of longitudinal and lateral driving behaviors and offers probabilistic future paths with corresponding probabilities, addressing the challenges of complex vehicle interactions and multi-modality of driving behaviors. Validation using real-world trajectory datasets demonstrates the efficiency and potential.

We propose to apply several gradient estimation techniques to enable the differentiation of programs with discrete randomness in High Energy Physics. Such programs are common in High Energy Physics due to the presence of branching processes and clustering-based analysis. Thus differentiating such programs can open the way for gradient based optimization in the context of detector design optimization, simulator tuning, or data analysis and reconstruction optimization. We discuss several possible gradient estimation strategies, including the recent Stochastic AD method, and compare them in simplified detector design experiments. In doing so we develop, to the best of our knowledge, the first fully differentiable branching program.

The robotaxi is recording me sitting in the backseat, and I am recording it. Someone in the neighboring car is recording us both. It's an unusually hot day in San Francisco, and I am in a self-driving car named Charcuterie, operated by Cruise. Next to me is William Riggs, a professor at the University of San Francisco who studies self-driving cars. The front seats are both empty, and the wheel silently shifts as the car maneuvers itself along a thoroughfare next to Golden Gate Park.

The Pentagon plans to field thousands of drones and other high-tech military equipment within the next two years as the United States military turns to "autonomous systems" to counter China's numerical edge in terms of personnel and weaponry, a senior defence official said. US Deputy Secretary of Defense Kathleen Hicks told a military technology conference in Washington, DC on Monday that the "imperative to innovate" was crucial at a time of strategic competition with China, a rival who Hick described as being very different to the "relatively slow and lumbering" competitors the US faced during the Cold War. While US forces were engaged in fighting for 20 years in Iraq and Afghanistan, "the PRC [People's Republic of China] worked with focus and determination to build a modern military, carefully crafting it to blunt the operational advantages we've enjoyed for decades", Hicks said in a speech. In a candid address that highlighted Washington's view of the military threat posed by China and its ability to out-scale the US military, Hicks said the US maintained an advantage owing to its ability "to imagine, create and master the future character of warfare". Beijing's main military advantage is "mass: more ships, more missiles, more people", she said.

Seaweed biomass offers significant potential for climate mitigation, but large-scale, autonomous open-ocean farms are required to fully exploit it. Such farms typically have low propulsion and are heavily influenced by ocean currents. We want to design a controller that maximizes seaweed growth over months by taking advantage of the non-linear time-varying ocean currents for reaching high-growth regions. The complex dynamics and underactuation make this challenging even when the currents are known. This is even harder when only short-term imperfect forecasts with increasing uncertainty are available. We propose a dynamic programming-based method to efficiently solve for the optimal growth value function when true currents are known. We additionally present three extensions when as in reality only forecasts are known: (1) our methods resulting value function can be used as feedback policy to obtain the growth-optimal control for all states and times, allowing closed-loop control equivalent to re-planning at every time step hence mitigating forecast errors, (2) a feedback policy for long-term optimal growth beyond forecast horizons using seasonal average current data as terminal reward, and (3) a discounted finite-time Dynamic Programming (DP) formulation to account for increasing ocean current estimate uncertainty. We evaluate our approach through 30-day simulations of floating seaweed farms in realistic Pacific Ocean current scenarios. Our method demonstrates an achievement of 95.8% of the best possible growth using only 5-day forecasts. This confirms the feasibility of using low-power propulsion and optimal control for enhanced seaweed growth on floating farms under real-world conditions.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. At a small section of the Fukushima Daiichi nuclear plant's central control room, the treated water transfer switch is on. A graph on a computer monitor nearby shows a steady decrease of water levels as treated radioactive wastewater is diluted and released into the Pacific Ocean. In the coastal area of the plant, two seawater pumps are in action, gushing torrents of seawater through sky blue pipes into the big header where the treated water, which comes down through a much thinner black pipe from the hilltop tanks, is diluted hundreds of times before the release.

It is a sparkling, sunny August morning in 2043, as your Air China flight from Beijing touches down gracefully (and almost silently) at LAX. The sleek plane is one of a new generation of hydrogen-powered wide-body jets manufactured by Commercial Aircraft Corp. of China -- the kind of innovation that helped the state-owned company sail past Boeing and Airbus in the 2030s to become the world's largest aerospace group. Starting with the Inflation Reduction Act in 2022, the last two decades have seen massive efforts to clean up transportation all around the United States and throughout the world. Back in the early 2020s, transportation accounted for 29% of America's greenhouse gas emissions, but that number has been steadily dwindling to almost zero -- resulting in cleaner cities everywhere. Not only have electric and hydrogen-powered vehicles replaced gas-guzzling cars, but many people have forsaken car-ownership altogether, in favor of much more economic and widely available solutions like e-bikes, robo-taxis and public transit.

In the last decade we have seen more robotics innovation becoming real products and companies than in the entire history of robotics. Furthermore, the greater Silicon Valley and San Francisco Bay Area is at the center of this'Cambrian Explosion in Robotics' as Dr Gill Pratt, Director of Robotics at Toyota Research Institute described it. In fact, two of the very first robots were developed right here. In 1969 at Stanford, Vic Sheinman designed the first electric robot arm able to be computer controlled. After successful pilots and interest from General Motors, Unimation acquired the concept and released the PUMA or Programmable Universal Machine for Assembly.

RNN-based methods have faced challenges in the Long-term Time Series Forecasting (LTSF) domain when dealing with excessively long look-back windows and forecast horizons. Consequently, the dominance in this domain has shifted towards Transformer, MLP, and CNN approaches. The substantial number of recurrent iterations are the fundamental reasons behind the limitations of RNNs in LTSF. To address these issues, we propose two novel strategies to reduce the number of iterations in RNNs for LTSF tasks: Segment-wise Iterations and Parallel Multi-step Forecasting (PMF). RNNs that combine these strategies, namely SegRNN, significantly reduce the required recurrent iterations for LTSF, resulting in notable improvements in forecast accuracy and inference speed. Extensive experiments demonstrate that SegRNN not only outperforms SOTA Transformer-based models but also reduces runtime and memory usage by more than 78%. These achievements provide strong evidence that RNNs continue to excel in LTSF tasks and encourage further exploration of this domain with more RNN-based approaches. The source code is coming soon.