Search for: All records

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. 

Abstract The principles behind the sharp, singular structures in a crumpled sheet are well understood. Here we discuss more general ways of exploiting such sharp structures to control the shape of a sheet by deforming or forcing it elsewhere. Often, the induced shape leads to further sharp structures—“sub-singularities.” Though weaker and softer than the primary singularities, they nevertheless provide robust ways of shaping a sheet. In simple cases, we understand the reason for these and their strength. This paper surveys a broad range of other sub-structure phenomena and reports recent progress in understanding some of them. 

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. 

Arctic systems are warming at four times the global average, causing permafrost—permanently frozen soil, ice, organic matter, and bedrock—to thaw. Permafrost thaw exposes previously unavailable soil carbon and nutrients to decomposition—a process mediated by microbes—which releases greenhouse gases such as carbon dioxide and methane into the atmosphere. While it is well established that thaw alters the composition and function of the permafrost microbiome, patterns revealing common responses to thaw across different permafrost soil types have not yet emerged. In this study, we address how permafrost thaw impacts microbiome diversity, alters species abundance, and contributes to carbon flux in the Arctic. We sampled peat-like, mineral, and organic-mineral permafrost from three locations in central and northern Alaska. We assessed their abiotic soil properties and microbiome characteristics before and after a 3-month laboratory microcosm incubation. Across all sites, prokaryotic biomass increased following thaw, measured as 16S rRNA gene copy number. This change in biomass was positively correlated with cumulative respiration, indicating an increase in microbial activity post-thaw. We evaluated the thaw response of microbial taxa across three sites, identifying taxa that significantly increased in abundance post-thaw. Common responders shared across all sites belonged to the familiesBeijerinckiaceae,Burkholderiaceae,Clostridiaceae,Oxalobacteraceae,Pseudomonadaceae, andSporichthyaceae, indicating a common set of taxa that consistently respond to thaw regardless of site-specific conditions. Alpha diversity decreased with thaw across all sites, likely reflecting the increased dominance of specific thaw-responsive taxa that may be driving post-thaw biogeochemistry and increased respiration. Taken together, we deepen the understanding of different permafrost microbiomes and their response to thaw, which has implications for the permafrost–climate feedback and enables more accurate predictions of how Arctic ecosystem structure and function respond to change. 

Summary Theory has shown that time lags in the regulation of symbiotic nitrogen (N) fixation (SNF) can be important to the competitive dynamics and ecosystem consequences of N‐fixing trees, but measurements of these time lags are lacking.Here, we used a novel method to measure SNF in seedlings of four N‐fixing tree species that represent tropical and temperate origins and actinorhizal and rhizobial symbiotic associations, each grown under warm and cold temperature regimes. We added N to previously N‐poor pots to induce downregulation and flushed N out of previously N‐rich pots to induce upregulation.It took 31–51 d for SNF to decline by 95%, with faster downregulation in temperate species and at warm temperatures. Upregulation by 95% took 108–138 d in total, including 21–57 d after SNF was first detectable. SNF started earlier in rhizobial symbioses, but increased faster once it started in actinorhizal symbioses.These results suggest that time lags in regulating SNF represent a significant constraint on facultative SNF and can lead to large losses of available N from ecosystems, providing a resolution to the paradox of sustained N richness. 

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. 

Abstract. Permafrost degradation in Arctic lowlands is a critical geomorphic process, increasingly driven by climate warming and infrastructure development. This study applies an integrated geophysical and surveying approach – Electrical Resistivity Tomography (ERT), Ground Penetrating Radar (GPR), and thaw probing – to characterize near-surface permafrost variability across four land use types in Utqiaġvik, Alaska: gravel road, snow fence, residential building and undisturbed tundra. Results reveal pronounced heterogeneity in thaw depths (0.2 to >1 m) and ice content, shaped by both natural features such as ice wedges and frost heave and anthropogenic disturbances. Roads and snow fences altered surface drainage and snow accumulation, promoting differential thaw, deeper active layers, and localized ground deformation. Buildings in permafrost regions alter the local thermal regime through multiple interacting factors – for example, solar radiation, thermal leakage, snow cover dynamics, and surface disturbance – among others. ERT identified high-resistivity zones (>1,000 Ω·m) interpreted as ice-rich permafrost and low-resistivity features (<5 Ω·m) likely associated with cryopegs or thaw zones. GPR delineated subsurface stratigraphy and supported interpretation of ice-rich layers and permafrost features. These findings underscore the strong spatial coupling between surface infrastructure and subsurface thermal and hydrological regimes in ice-rich permafrost. Geophysical methods revealed subsurface features and thaw depth variations across different land use types in Utqiaġvik, highlighting how infrastructure alters permafrost conditions. These findings support localized assessment of ground stability in Arctic environments. 

Abstract A reexamination of period-finding algorithms is prompted by new large-area astronomical sky surveys that can identify billions of individual sources having a thousand or more observations per source. This large increase in data necessitates fast and efficient period detection algorithms. In this paper, we provide an initial description of an algorithm that is being used for the detection of periodic behavior in a sample of 1.5 billion objects using light curves generated from Zwicky Transient Facility (ZTF) data. We call this algorithm “Fast Periodicity Weighting” (FPW), derived using a Gaussian Process formalism. Periodic sources in ZTF show a wide variety of waveforms, some quite complex, including eclipsing objects, sinusoidally varying objects also exhibiting eclipses, objects with cyclotron emission at various phases, and accreting objects with complex waveforms. A major advantage of the FPW algorithm is that it is sensitive to a broad range of waveforms. We describe the FPW algorithm and its application to ZTF, and provide efficient code for both CPU and GPU. 

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.