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We examine the collective behavior of single cells in microbial systems to provide insights into the origin of the biological clock. Microfluidics has opened a window onto how single cells can synchronize their behavior. Four hypotheses are proposed to explain the origin of the clock from the synchronized behavior of single cells. These hypotheses depend on the presence or absence of a communication mechanism between the clocks in single cells and the presence or absence of a stochastic component in the clock mechanism. To test these models, we integrate physical models for the behavior of the clocks in single cells or filaments with new approaches to measuring clocks in single cells. As an example, we provide evidence for a quorum-sensing signal both with microfluidics experiments on single cells and with continuousin vivometabolism NMR (CIVM-NMR). We also provide evidence for the stochastic component in clocks of single cells. Throughout this study, ensemble methods from statistical physics are used to characterize the clock at both the single-cell level and the macroscopic scale of 10⁶cells. 

Reinforcement Learning from Human Feedback (RLHF) has become the predominant approach for language model (LM) alignment. At its core, RLHF uses a margin-based loss for preference optimization, specifying ideal LM behavior only by the difference between preferred and dispreferred responses. In this paper, we identify a common pitfall of margin-based methods -- the under-specification of ideal LM behavior on preferred and dispreferred responses individually, which leads to two unintended consequences as the margin increases: (1) The probability of dispreferred (e.g., unsafe) responses may increase, resulting in potential safety alignment failures. (2) The probability of preferred responses may decrease, even when those responses are ideal. We demystify the reasons behind these problematic behaviors: margin-based losses couple the change in the preferred probability to the gradient of the dispreferred one, and vice versa, often preventing the preferred probability from increasing while the dispreferred one decreases, and thus causing a synchronized increase or decrease in both probabilities. We term this effect, inherent in margin-based objectives, gradient entanglement. Formally, we derive conditions for general margin-based alignment objectives under which gradient entanglement becomes concerning: the inner product of the gradients of preferred and dispreferred log-probabilities is large relative to the individual gradient norms. We theoretically investigate why such inner products can be large when aligning language models and empirically validate our findings. Empirical implications of our framework extend to explaining important differences in the training dynamics of various preference optimization algorithms, and suggesting potential algorithm designs to mitigate the under-specification issue of margin-based methods and thereby improving language model alignment. 

Abstract. The hydrology of thawing permafrost affects the fate of the vast amount of permafrost carbon due to its controls on waterlogging, redox status, and transport. However, regional mapping of soil water storage in the soil layer that experiences the annual freeze-thaw cycle above permafrost, known as the active layer, remains a formidable challenge over remote arctic regions. This study shows that Interferometric Synthetic Aperture Radar (InSAR) observations can be used to estimate the amount of soil water originating from the active layer seasonal thaw. Our ALOS InSAR results, validated by in situ observations, show that the thickness of the soil water that experiences the annual freeze-thaw cycle ranges from 0 to 75 cm in a 60-by-100-km area near the Toolik Field Station on the North Slope of Alaska. Notably, the spatial distribution of the soil water correlates with surface topography and land vegetation cover types. We found that pixel-mismatching of the topographic map and radar images is the primary error source in the Toolik ALOS InSAR data. The amount of pixel misregistration, the local slope, and the InSAR perpendicular baseline influence the observed errors in InSAR Line-Of-Sight (LOS) distance measurements non-linearly. For most of the study area with a percent slope of less than 5%, the LOS error from pixel misregistration is less than 1 cm, translating to less than 14 cm of error in the soil water estimates. 

Abstract. The hydrology of thawing permafrost affects the fate of the vast amount of permafrost carbon, due to its controls on waterlogging, redox status, and transport. However, regional mapping of soil water storage in the soil layer that experiences the annual freeze–thaw cycle above permafrost, known as the active layer, remains a formidable challenge over remote Arctic regions. This study shows that interferometric synthetic aperture radar (InSAR) observations can be used to estimate the amount of soil water originating from the active-layer seasonal thaw. Our ALOS InSAR results, validated by in situ observations, show that the equivalent thickness of the soil water that experiences the annual freeze–thaw cycle ranges from 0 to 75 cm in a 60 km×100 km area near the Toolik Field Station on the North Slope of Alaska. Notably, the spatial distribution of the soil water correlates with surface topography and land vegetation cover types. We found that pixel-mismatching of the topographic map and radar images is the primary error source in the Toolik ALOS InSAR data. The amount of pixel misregistration, the local slope, and the InSAR perpendicular baseline influence the observed errors in InSAR line-of-sight (LOS) distance measurements. For most of the study area with a percent slope of less than 5 %, the LOS error from pixel misregistration is less than 1 cm, translating to less than 14 cm of error in the soil water estimates.