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  1. Planar magnetic microswimmers are well-suited for in vivo biomedical applications due to their cost-effective mass production through standard photolithography techniques. The precise control of their motion in diverse environments is a critical aspect of their application. This study demonstrates the control of these swimmers individually and as a swarm, exploring navigation through channels and showcasing their functional capabilities for future biomedical settings. We also introduce the capability of microswimmers for surface motion, complementing their traditional fluid-based propulsion and extending their functionality. Our research reveals that microswimmers with varying magnetization directions exhibit unique trajectory patterns, enabling complex swarm tasks. This study further delves into the behavior of these microswimmers in intricate environments, assessing their adaptability and potential for advanced applications. The findings suggest that these microswimmers could be pivotal in areas such as targeted drug delivery and precision medical procedures, marking significant progress in the biomedical and micro-robotic fields and offering new insights into their control and behavior in diverse environments. 
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    Free, publicly-accessible full text available June 27, 2025
  2. Free, publicly-accessible full text available June 27, 2025
  3. Free, publicly-accessible full text available February 16, 2025
  4. We consider the question of Gaussian mean testing, a fundamental task in high-dimensional distribution testing and signal processing, subject to adversarial corruptions of the samples. We focus on the relative power of different adversaries, and show that, in contrast to the common wisdom in robust statistics, there exists a strict separation between adaptive adversaries (strong contamination) and oblivious ones (weak contamination) for this task. Specifically, we resolve both the information-theoretic and computational landscapes for robust mean testing. In the exponential-time setting, we establish the tight sample complexity of testing N(0,I) against N(αv,I), where ∥v∥2=1, with an ε-fraction of adversarial corruptions, to be Θ~(max(d√α2,dε3α4,min(d2/3ε2/3α8/3,dεα2))) while the complexity against adaptive adversaries is Θ~(max(d√α2,dε2α4)) which is strictly worse for a large range of vanishing ε,α. To the best of our knowledge, ours is the first separation in sample complexity between the strong and weak contamination models. In the polynomial-time setting, we close a gap in the literature by providing a polynomial-time algorithm against adaptive adversaries achieving the above sample complexity Θ~(max(d−−√/α2,dε2/α4)), and a low-degree lower bound (which complements an existing reduction from planted clique) suggesting that all efficient algorithms require this many samples, even in the oblivious-adversary setting. 
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  5. Work on scaling laws has found that large language models (LMs) show predictable improvements to overall loss with increased scale (model size, training data, and compute). Here, we present evidence for the claim that LMs may show inverse scaling, or worse task performance with increased scale, e.g., due to flaws in the training objective and data. We present empirical evidence of inverse scaling on 11 datasets collected by running a public contest, the Inverse Scaling Prize, with a substantial prize pool. Through analysis of the datasets, along with other examples found in the literature, we identify four potential causes of inverse scaling: (i) preference to repeat memorized sequences over following in-context instructions, (ii) imitation of undesirable patterns in the training data, (iii) tasks containing an easy distractor task which LMs could focus on, rather than the harder real task, and (iv) correct but misleading few-shot demonstrations of the task. We release the winning datasets at https://inversescaling.com/data to allow for further investigation of inverse scaling. Our tasks have helped drive the discovery of U-shaped and inverted-U scaling trends, where an initial trend reverses, suggesting that scaling trends are less reliable at predicting the behavior of larger-scale models than previously understood. Overall, our results suggest that there are tasks for which increased model scale alone may not lead to progress, and that more careful thought needs to go into the data and objectives for training language models. 
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    Free, publicly-accessible full text available February 28, 2025
  6. As evidence grows supporting the importance of non-cognitive factors in learning, computer-assisted learning platforms increasingly incorporate non-academic interventions to influence student learning and learning related-behaviors. Non-cognitive interventions often attempt to influence students’ mindset, motivation, or metacognitive reflection to impact learning behaviors and outcomes. In the current paper, we analyze data from five experiments, involving seven treatment conditions embedded in mastery-based learning activities hosted on a computer-assisted learning platform focused on middle school mathematics. Each treatment condition embodied a specific non-cognitive theoretical perspective. Over seven school years, 20,472 students participated in the experiments. We estimated the effects of each treatment condition on students’ response time, hint usage, likelihood of mastering knowledge components, learning efficiency, and post-tests performance. Our analyses reveal a mix of both positive and negative treatment effects on student learning behaviors and performance. Few interventions impacted learning as assessed by the post-tests. These findings highlight the difficulty in positively influencing student learning behaviors and outcomes using non-cognitive interventions. 
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  7. Work on scaling laws has found that large language models (LMs) show predictable improvements to overall loss with increased scale (model size, training data, and compute). Here, we present evidence for the claim that LMs may show inverse scaling, or worse task performance with increased scale, e.g., due to flaws in the training objective and data. We present empirical evidence of inverse scaling on 11 datasets collected by running a public contest, the Inverse Scaling Prize, with a substantial prize pool. Through analysis of the datasets, along with other examples found in the literature, we identify four potential causes of inverse scaling: (i) preference to repeat memorized sequences over following in-context instructions, (ii) imitation of undesirable patterns in the training data, (iii) tasks containing an easy distractor task which LMs could focus on, rather than the harder real task, and (iv) correct but misleading few-shot demonstrations of the task. We release the winning datasets at inversescaling.com/data to allow for further investigation of inverse scaling. Our tasks have helped drive the discovery of U-shaped and inverted-U scaling trends, where an initial trend reverses, suggesting that scaling trends are less reliable at predicting the behavior of larger-scale models than previously understood. Overall, our results suggest that there are tasks for which increased model scale alone may not lead to progress, and that more careful thought needs to go into the data and objectives for training language models. 
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  8. Abstract

    A very high‐spatial resolution (∼21–23 m pixel at 85 km altitude) OH airglow imager at the Andes Lidar Observatory at Cerro Pachón, Chile observed considerable ducted wave activity on the night of 29–30 October 2016. This instrument was collocated with a Na wind‐temperature lidar that provided data revealing the occurrence of strong ducts. A large field of view OH and greenline airglow imager showed waves present over a vertical extent consistent with the altitudes of the ducting features identified in the lidar profiles. While waves that appeared to be ducted were seen in all imagers throughout the observation interval, the wave train seen in the OH images at earlier times had a distinct leading nonsinusoidal phase followed by several, lower‐amplitude, more sinusoidal phases, suggesting a likely bore. The leading phase exhibited significant dissipation via small‐scale secondary instabilities suggesting vortex rings that progressed rapidly to smaller scales and turbulence (the latter not fully resolved) thereafter. The motions of these small‐scale features were consistent with their location in the duct at or below ∼83–84 km. Bore dissipation caused a momentum flux divergence and a local acceleration of the mean flow within the duct along the direction of the initial bore propagation. A number of these features are consistent with mesospheric bores observed or modeled in previous studies.

     
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  9. In a neuron network, synapses update individually using local information, allowing for entirely decentralized learning. In contrast, elements in an artificial neural network are typically updated simultaneously using a central processor. Here, we investigate the feasibility and effect of desynchronous learning in a recently introduced decentralized, physics-driven learning network. We show that desynchronizing the learning process does not degrade the performance for a variety of tasks in an idealized simulation. In experiment, desynchronization actually improves the performance by allowing the system to better explore the discretized state space of solutions. We draw an analogy between desynchronization and mini-batching in stochastic gradient descent and show that they have similar effects on the learning process. Desynchronizing the learning process establishes physics-driven learning networks as truly fully distributed learning machines, promoting better performance and scalability in deployment. 
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