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Plant biomass is a fundamental ecosystem attribute that is sensitive to rapid climatic changes occurring in the Arctic. Nevertheless, measuring plant biomass in the Arctic is logistically challenging and resource intensive. Lack of accessible field data hinders efforts to understand the amount, composition, distribution, and changes in plant biomass in these northern ecosystems. Here, we presentThe Arctic plant aboveground biomass synthesis dataset, which includes field measurements of lichen, bryophyte, herb, shrub, and/or tree aboveground biomass (g m⁻²) on 2,327 sample plots from 636 field sites in seven countries. We created the synthesis dataset by assembling and harmonizing 32 individual datasets. Aboveground biomass was primarily quantified by harvesting sample plots during mid- to late-summer, though tree and often tall shrub biomass were quantified using surveys and allometric models. Each biomass measurement is associated with metadata including sample date, location, method, data source, and other information. This unique dataset can be leveraged to monitor, map, and model plant biomass across the rapidly warming Arctic.

 

The self-assembly of complex structures from a set of non-identical building blocks is a hallmark of soft matter and biological systems, including protein complexes, colloidal clusters, and DNA-based assemblies. Predicting the dependence of the equilibrium assembly yield on the concentrations and interaction energies of building blocks is highly challenging, owing to the difficulty of computing the entropic contributions to the free energy of the many structures that compete with the ground state configuration. While these calculations yield well known results for spherically symmetric building blocks, they do not hold when the building blocks have internal rotational degrees of freedom. Here we present an approach for solving this problem that works with arbitrary building blocks, including proteins with known structure and complex colloidal building blocks. Our algorithm combines classical statistical mechanics with recently developed computational tools for automatic differentiation. Automatic differentiation allows efficient evaluation of equilibrium averages over configurations that would otherwise be intractable. We demonstrate the validity of our framework by comparison to molecular dynamics simulations of simple examples, and apply it to calculate the yield curves for known protein complexes and for the assembly of colloidal shells.

 

In recent work, Backendal, Haller, and Paterson identified several exploitable vulnerabilities in the cloud storage provider MEGA. They demonstrated an RSA key recovery attack in which a malicious server could recover a client’s private RSA key after 512 client login attempts. We show how to exploit additional information revealed by MEGA’s protocol vulnerabilities to give an attack that requires only six client logins to recover the secret key. Our optimized attack combines several cryptanalytic techniques. In particular, we formulate and give a solution to a variant of the hidden number problem with small unknown multipliers, which may be of independent interest. We show that our lattice construction for this problem can be used to give improved results for the implicit factorization problem of May and Ritzenhofen. 

The potential impact of autonomous robots on everyday life is evident in emerging applications such as precision agriculture, search and rescue, and infrastructure inspection. However, such applications necessitate operation in unknown and unstructured environments with a broad and sophisticated set of objectives, all under strict computation and power limitations. We therefore argue that the computational kernels enabling robotic autonomy must bescheduledandoptimizedto guarantee timely and correct behavior, while allowing for reconfiguration of scheduling parameters at runtime. In this paper, we consider a necessary first step towards this goal ofcomputational awarenessin autonomous robots: an empirical study of a base set of computational kernels from the resource management perspective. Specifically, we conduct a data-driven study of the timing, power, and memory performance of kernels for localization and mapping, path planning, task allocation, depth estimation, and optical flow, across three embedded computing platforms. We profile and analyze these kernels to provide insight into scheduling and dynamic resource management for computation-aware autonomous robots. Notably, our results show that there is a correlation of kernel performance with a robot’s operational environment, justifying the notion of computation-aware robots and why our work is a crucial step towards this goal.

 

The shape and relative size of an ocular lens affect the focal length of the eye, with consequences for visual acuity and sensitivity. Lenses are typically spherical in aquatic animals with camera-type eyes and axially flattened in terrestrial species to facilitate vision in optical media with different refractive indices. Frogs and toads (Amphibia: Anura) are ecologically diverse, with many species shifting from aquatic to terrestrial ecologies during metamorphosis. We quantified lens shape and relative size using 179 micro X-ray computed tomography scans of 126 biphasic anuran species and tested for correlations with life stage, environmental transitions, adult habits and adult activity patterns. Across broad phylogenetic diversity, tadpole lenses are more spherical than those of adults. Biphasic species with aquatic larvae and terrestrial adults typically undergo ontogenetic changes in lens shape, whereas species that remain aquatic as adults tend to retain more spherical lenses after metamorphosis. Further, adult lens shape is influenced by adult habit; notably, fossorial adults tend to retain spherical lenses following metamorphosis. Finally, lens size relative to eye size is smaller in aquatic and semiaquatic species than other adult ecologies. Our study demonstrates how ecology shapes visual systems, and the power of non-invasive imaging of museum specimens for studying sensory evolution.