Legged robots offer mobility where wheeled platforms fail, such as stairs, rubble, soft substrates, and cluttered indoor-outdoor settings, enabling applications in inspection, search and rescue, agriculture, and planetary exploration [1]. Robust locomotion control is therefore a foundational capability for practical quadrupedal systems, underpinning safe navigation and dependable operation across diverse terrains and disturbances [2]. Deep reinforcement learning (DRL) has emerged as a compelling paradigm for such control because it optimizes closed-loop policies through interaction and can produce adaptive behaviors [3]. A substantial body of prior work has focused on training blind controllers that rely exclusively on proprioceptive inputs such as inertial measurement units (IMUs) and joint feedback [4]. While these blind policies can traverse uneven and unknown terrains through large-scale simulation and domain randomization, they inherently lack foresight: without exteroceptive input, they respond only upon contact and struggle to proactively avoid obstacles or plan foot placement on irregular ground. Vision complements proprioception by providing anticipatory geometric information, enabling early detection of distant obstacles and terrain changes [5]. As a result, cross-modal policies that integrate proprioception with depth imaging have gained prominence, facilitating safer and more efficient locomotion through earlier trajectory adjustments. Most existing cross-modal pipelines adopt multilayer perceptrons (MLPs) for the proprioceptive encoder and for the decision head that fuses proprioception with vision.

As the particle physics community needs higher and higher precisions in order to test our current model of the subatomic world, larger and larger datasets are necessary. With upgrades scheduled for the detectors of colliding-beam experiments around the world, and specifically at the Large Hadron Collider at CERN, more collisions and more complex interactions are expected. This directly implies an increase in data produced and consequently in the computational resources needed to process them. At CERN, the amount of data produced is gargantuan. This is why the data have to be heavily filtered and selected in real time before being permanently stored. This data can then be used to perform physics analyses, in order to expand our current understanding of the universe and improve the Standard Model of physics. This real-time filtering, known as triggering, involves complex processing happening often at frequencies as high as 40 MHz. This thesis contributes to understanding how machine learning models can be efficiently deployed in such environments, in order to maximize throughput and minimize energy consumption. Inevitably, modern hardware designed for such tasks and contemporary algorithms are needed in order to meet the challenges posed by the stringent, high-frequency data rates. In this work, I present our graph neural network-based pipeline, developed for charged particle track reconstruction at the LHCb experiment at CERN. The pipeline was implemented end-to-end inside LHCb's first-level trigger, entirely on GPUs. Its performance was compared against the classical tracking algorithms currently in production at LHCb. The pipeline was also accelerated on the FPGA architecture, and its performance in terms of power consumption and processing speed was compared against the GPU implementation.

Remote sensing change detection is essential for monitoring urban expansion, disaster assessment, and resource management, offering timely, accurate, and large-scale insights into dynamic landscape transformations. While deep learning has revolutionized change detection, the increasing complexity and computational demands of modern models have not necessarily translated into significant accuracy gains. Instead of following this trend, this study explores a more efficient approach, focusing on lightweight models that maintain high accuracy while minimizing resource consumption, which is an essential requirement for on-satellite processing. To this end, we propose FlickCD, which means quick flick then get great results, pushing the boundaries of the performance-resource trade-off. FlickCD introduces an Enhanced Difference Module (EDM) to amplify critical feature differences between temporal phases while suppressing irrelevant variations such as lighting and weather changes, thereby reducing computational costs in the subsequent change decoder. Additionally, the FlickCD decoder incorporates Local-Global Fusion Blocks, leveraging Shifted Window Self-Attention (SWSA) and Efficient Global Self-Attention (EGSA) to effectively capture semantic information at multiple scales, preserving both coarse- and fine-grained changes. Extensive experiments on four benchmark datasets demonstrate that FlickCD reduces computational and storage overheads by more than an order of magnitude while achieving state-of-the-art (SOTA) performance or incurring only a minor (<1% F1) accuracy trade-off. The implementation code is publicly available at https://github.com/xulsh8/FlickCD.

Large Language Models (LLMs) display remarkable capabilities to understand or even produce political discourse, but have been found to consistently display a progressive left-leaning bias. At the same time, so-called persona or identity prompts have been shown to produce LLM behavior that aligns with socioeconomic groups that the base model is not aligned with. In this work, we analyze whether zero-shot persona prompting with limited information can accurately predict individual voting decisions and, by aggregation, accurately predict positions of European groups on a diverse set of policies. We evaluate if predictions are stable towards counterfactual arguments, different persona prompts and generation methods. Finally, we find that we can simulate voting behavior of Members of the European Parliament reasonably well with a weighted F1 score of approximately 0.793. Our persona dataset of politicians in the 2024 European Parliament and our code are available at https://github.com/dess-mannheim/european_parliament_simulation.

While Generative AI stands to be one of the fastest adopted technologies ever, studies have made evident that the usage of Large Language Models (LLMs) puts significant burden on energy grids and our environment. It may prove a hindrance to the Sustainability goals of any organization. A crucial step in any Sustainability strategy is monitoring or estimating the energy consumption of various components. While there exist multiple tools for monitoring energy consumption, there is a dearth of tools/frameworks for estimating the consumption or carbon emissions. Current drawbacks of both monitoring and estimation tools include high input data points, intrusive nature, high error margin, etc. We posit that leveraging emerging LLM benchmarks and related data points can help overcome aforementioned challenges while balancing accuracy of the emission estimations. To that extent, we discuss the challenges of current approaches and present our evolving framework, R-ICE, which estimates prompt level inference carbon emissions by leveraging existing state-of-the-art(SOTA) benchmark. This direction provides a more practical and non-intrusive way to enable emerging use-cases like dynamic LLM routing, carbon accounting, etc. Our promising validation results suggest that benchmark-based modelling holds great potential for inference emission estimation and warrants further exploration from the scientific community.

Climate control is crucial for greenhouse production as it directly affects crop growth and resource use. Reinforcement learning (RL) has received increasing attention in this field, but still faces challenges, including limited training efficiency and high reliance on initial learning conditions. Interactive RL, which combines human (grower) input with the RL agent's learning, offers a potential solution to overcome these challenges. However, interactive RL has not yet been applied to greenhouse climate control and may face challenges related to imperfect inputs. Therefore, this paper aims to explore the possibility and performance of applying interactive RL with imperfect inputs into greenhouse climate control, by: (1) developing three representative interactive RL algorithms tailored for greenhouse climate control (reward shaping, policy shaping and control sharing); (2) analyzing how input characteristics are often contradicting, and how the trade-offs between them make grower's inputs difficult to perfect; (3) proposing a neural network-based approach to enhance the robustness of interactive RL agents under limited input availability; (4) conducting a comprehensive evaluation of the three interactive RL algorithms with imperfect inputs in a simulated greenhouse environment. The demonstration shows that interactive RL incorporating imperfect grower inputs has the potential to improve the performance of the RL agent. RL algorithms that influence action selection, such as policy shaping and control sharing, perform better when dealing with imperfect inputs, achieving 8.4% and 6.8% improvement in profit, respectively. In contrast, reward shaping, an algorithm that manipulates the reward function, is sensitive to imperfect inputs and leads to a 9.4% decrease in profit. This highlights the importance of selecting an appropriate mechanism when incorporating imperfect inputs.

In recent years, ensuring safety, efficiency, and comfort in interactive autonomous driving has become a critical challenge. Traditional model-based techniques, such as game-theoretic methods and robust control, are often overly conservative or computationally intensive. Conversely, learning-based approaches typically require extensive training data and frequently exhibit limited interpretability and generalizability. Simpler strategies, such as Risk Potential Fields (RPF), provide lightweight alternatives with minimal data demands but are inherently static and struggle to adapt effectively to dynamic traffic conditions. To overcome these limitations, we propose the Evolutionary Risk Potential Field (ERPF), a novel approach that dynamically updates risk assessments in dynamical scenarios based on historical obstacle proximity data. We introduce a Risk-Ellipse construct that combines longitudinal reach and lateral uncertainty into a unified spatial temporal collision envelope. Additionally, we define an adaptive Evolution Factor metric, computed through sigmoid normalization of Time to Collision (TTC) and Time-Window-of-Hazard (TWH), which dynamically adjusts the dimensions of the ellipse axes in real time. This adaptive risk metric is integrated seamlessly into a Model Predictive Control (MPC) framework, enabling autonomous vehicles to proactively address complex interactive driving scenarios in terms of uncertain driving of surrounding vehicles. Comprehensive comparative experiments demonstrate that our ERPF-MPC approach consistently achieves smoother trajectories, higher average speeds, and collision-free navigation, offering a robust and adaptive solution suitable for complex interactive driving environments.

Technology Engineering The'Star Trek' technology that came to real life Breakthroughs, discoveries, and DIY tips sent every weekday. To celebrate Star Trek Day on September 8, the European Space Agency (ESA) released a video of the Star Trek technology that's made it real-life space. So while we still don't have teleporters or deflector shields, ISS astronauts kind of have tricorders like the one used by Captain Christopher Pike in the first episode of the original series. We've also seen the development of technology that resembles Replicators, VISOR, and PADDs. The original premiered on network television in the United States on September 8, 1966.

The world is full of extraordinary young people brimming with ideas for how to crack tough problems. Every year, we recognize 35 such individuals from around the world--all of whom are under the age of 35. These scientists, inventors, and entrepreneurs are working to help mitigate climate change, accelerate scientific progress, and alleviate human suffering from disease. Some are launching companies while others are hard at work in academic labs. They were selected from hundreds of nominees by expert judges and our newsroom staff. Get to know them all--including our 2025 Innovator of the Year-- in these profiles .

How Trump's policies are affecting early-career scientists--in their own words Every year, we recognize extraordinary young researchers on our Innovators Under 35 list. Recent honorees told us how they're faring under the new administration. Every year celebrates accomplished young scientists, entrepreneurs, and inventors from around the world in our Innovators Under 35 list . We've just published the 2025 edition . This year, though, the context is pointedly different: The US scientific community finds itself in an unprecedented position, with the very foundation of its work under attack . Since Donald Trump took office in January, his administration has fired top government scientists, targeted universities individually and academia more broadly, and made substantial funding cuts to the country's science and technology infrastructure .