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Abstract The learning and recognition of object features from unregulated input has been a longstanding challenge for artificial intelligence systems. Brains, on the other hand, are adept at learning stable sensory representations given noisy observations, a capacity mediated by a cascade of signal conditioning steps informed by domain knowledge. The olfactory system, in particular, solves a source separation and denoising problem compounded by concentration variability, environmental interference, and unpredictably correlated sensor affinities using a plastic network that requires statistically well-behaved input. We present a data-blind neuromorphic signal conditioning strategy, based on the biological system architecture, that normalizes and quantizes analog data into spike-phase representations, thereby transforming uncontrolled sensory input into a regular form with minimal information loss. Normalized input is delivered to a column of spiking principal neurons via heterogeneous synaptic weights; this gain diversification strategy regularizes neuronal utilization, yoking total activity to the network’s operating range and rendering internal representations robust to uncontrolled open-set stimulus variance. To dynamically optimize resource utilization while balancing activity regularization and resolution, we supplement this mechanism with a data-aware calibration strategy in which the range and density of the quantization weights adapt to accumulated input statistics. 

Diffusion-enhanced hydride synthesis enables the modern solidstate chemist to achieve their Hephaestian aspirations through design of experiments methods and the mechanistic knowledge gleaned fromin situpowder X-ray diffraction data. 

This paper synthesizes three domains of literature to develop a conceptual framework for knowledge integration in cross-disciplinary and cross-sectoral collaborations: (1) studies of inter- and transdisciplinarity, (2) studies of knowledge co-production in sustainability research, and (3) studies focusing on factors influencing knowledge integration in the Science of Team Science field. Combining a scoping review methodology with a cited reference search approach, we identify eight dimensions of knowledge integration: types of knowledge integrated, competencies and education required to practice knowledge integration, organizational structure, types of actor involvement, stages of collaboration, contextual factors, processes and mechanisms of knowledge integration, and types of knowledge integration outcomes. We structure these dimensions across four interconnected components of collaboration: knowledge gathering (inputs), structural dynamics and collaborative dynamics (processes), and integrative outcomes (outputs). We identify the different types of knowledge mobilized in cross-disciplinary collaborations – epistemic, experiential, contextual, cultural, applied, specialized, knowledge for systemic change, and normative knowledge - and link them to the structural features (e.g., team composition, governance) and collaborative dynamics (e.g., stakeholder engagement, interaction frequency, and roles) of cross-disciplinary teams that influence the processes and outcomes of knowledge integration. This framework is intended to function as a heuristic to prompt teams to adapt it to specific contexts, projects, and team configurations. It can also be used a scaffold for designing and evaluating knowledge integration efforts in diverse collaborative settings. 

This article introduces the new angle-damped dihedral torsion (ADDT), angle-damped linear dihedral (ADLD), angle-damped cosine only (ADCO), and constant amplitude dihedral torsion (CADT) model potentials. 

Perceptual judgments of the environment emerge from the concerted activity of neural populations in decision-making areas downstream of the sensory cortex. When the sensory input is ambiguous, perceptual judgments can be biased by prior expectations shaped by environmental regularities. These effects are examples of Bayesian inference, a reasoning method in which prior knowledge is leveraged to optimize uncertain decisions. However, it is not known how decision-making circuits combine sensory signals and prior expectations to form a perceptual decision. Here, we study neural population activity in the prefrontal cortex of macaque monkeys trained to report perceptual judgments of ambiguous visual stimuli under two different stimulus distributions. We isolate the component of the neural population response that represents the formation of the perceptual decision (the decision variable, DV), and find that its dynamical evolution reflects the integration of sensory signals and prior expectations. Prior expectations impact the DV’s trajectory both before and during stimulus presentation such that DV trajectories with a smaller dynamic range result in more biased and less sensitive perceptual decisions. We show that these results resemble a specific variant of Bayesian inference known as approximate hierarchical inference. Our findings expand our understanding of the mechanisms by which prefrontal circuits can execute Bayesian inference. 

The presence and nature of low-frequency (0.1–10 mHz) Alfvénic waves in the corona have been established over the past decade, with many of these results coming from coronagraphic observations of the infrared Fexiiiline. The Cryo-NIRSP instrument situated at DKIST has recently begun acquiring science-quality data of the same Fexiiiline, with at least a factor of 9 improvement in spatial resolution, a factor of 30 increase in temporal resolution, and an increase in signal-to-noise ratio, when compared to the majority of previously available data. Here we present an analysis of 1 s cadence sit-and-stare data from Cryo-NIRSP, examining the Doppler velocity fluctuations associated with the Fexiii1074 nm coronal line. We are able to confirm previous results of Alfvénic waves in the corona and explore a new frequency regime. The data reveal that the power-law behavior of the Doppler velocity power spectrum extends to higher frequencies. This result appears to challenge some models of photospheric-driven Alfvénic waves that predict a lack of high-frequency wave power in the corona owing to strong chromospheric damping. Moreover, the high-frequency waves do not transport as much energy as their low-frequency counterparts, with less time-averaged energy per frequency interval. We are also able to confirm the incompressible nature of the fluctuations with little coherence between the line amplitude and Doppler velocity time series.