Measuring hidden phenotype: Quantifying the shape of barley seeds using the Euler Characteristic Transform

Shape plays a fundamental role in biology. Traditional phenotypic analysis methods measure some features but fail to measure the information embedded in shape comprehensively. To extract, compare, and analyze this information embedded in a robust and concise way, we turn to Topological Data Analysis (TDA), specifically the Euler Characteristic Transform. TDA measures shape comprehensively using mathematical representations based on algebraic topology features. To study its use, we compute both traditional and topological shape descriptors to quantify the morphology of 3121 barley seeds scanned with X-ray Computed Tomography (CT) technology at 127 micron resolution. The Euler Characteristic Transform measures shape by analyzing topological features of an object at thresholds across a number of directional axes. A Kruskal-Wallis analysis of the information encoded by the topological signature reveals that the Euler Characteristic Transform picks up successfully the shape of the crease and bottom of the seeds. Moreover, while traditional shape descriptors can cluster the seeds based on their accession, topological shape descriptors can cluster them further based on their panicle. We then successfully train a support vector machine (SVM) to classify 28 different accessions of barley based exclusively on the shape of their grains. We observe that combining both traditional and topological descriptors classifies barley seeds better than using just traditional descriptors alone. This improvement suggests that TDA is thus a powerful complement to traditional morphometrics to comprehensively describe a multitude of “hidden” shape nuances which are otherwise not detected.

Perfect Trees: Designing Energy-Optimal Symmetric Encryption Primitives

Energy efficiency is critical in battery-driven devices, and designing energyoptimal symmetric-key ciphers is one of the goals for the use of ciphers in such environments. In the paper by Banik et al. (IACR ToSC 2018), stream ciphers were identified as ideal candidates for low-energy solutions. One of the main conclusions of this paper was that Trivium, when implemented in an unrolled fashion, was by far the most energy-efficient way of encrypting larger quantity of data. In fact, it was shown that as soon as the number of databits to be encrypted exceeded 320 bits, Trivium consumed the least amount of energy on STM 90 nm ASIC circuits and outperformed the Midori family of block ciphers even in the least energy hungry ECB mode (Midori was designed specifically for energy efficiency).In this work, we devise the first heuristic energy model in the realm of stream ciphers that links the underlying algebraic topology of the state update function to the consumptive behaviour. The model is then used to derive a metric that exhibits a heavy negative correlation with the energy consumption of a broad range of stream cipher architectures, i.e., the families of Trivium-like, Grain-like and Subterranean-like constructions. We demonstrate that this correlation is especially pronounced for Trivium-like ciphers which leads us to establish a link between the energy consumption and the security guarantees that makes it possible to find several alternative energy-optimal versions of Trivium that meet the requirements but consume less energy. We present two such designs Trivium-LE(F) and Trivium-LE(S) that consume around 15% and 25% less energy respectively making them the to date most energy-efficient encryption primitives. They inherit the same security level as Trivium, i.e., 80-bit security. We further present Triad-LE as an energy-efficient variant satisfying a higher security level. The simplicity and wide applicability of our model has direct consequences for the conception of future hardware-targeted stream ciphers as for the first time it is possible to optimize for energy during the design phase. Moreover, we extend the reach of our model beyond plain encryption primitives and propose a novel energy-efficient message authentication code Trivium-LE-MAC.

Embeddings of locally compact abelian p-groups in Hawaiian groups

We show that locally compact abelian p-groups can be embedded in the first Hawaiian group on a compact path connected subspace of the Euclidean space of dimension four. This result gives a new geometric interpretation for the classification of locally compact abelian groups which are rich in commuting closed subgroups. It is then possible to introduce the idea of an algebraic topology for topologically modular locally compact groups via the geometry of the Hawaiian earring. Among other things, we find applications for locally compact groups which are just noncompact.

Characterizing Protein Conformational Spaces using Dimensionality Reduction and Algebraic Topology

Datasets representing the conformational landscapes of protein structures are high dimensional and hence present computational challenges. Efficient and effective dimensionality reduction of these datasets is therefore paramount to our ability to analyze the conformational landscapes of proteins and extract important information regarding protein folding, conformational changes and binding. Representing the structures with fewer attributes that capture the most variance of the data, makes for quicker and precise analysis of these structures. In this work we make use of dimensionality reduction methods for reducing the number of instances and for feature reduction. The reduced dataset that is obtained is then subjected to topological and quantitative analysis. In this step we perform hierarchical clustering to obtain different sets of conformation clusters that may correspond to intermediate structures. The structures represented by these conformations are then analyzed by studying their high dimension topological properties to identify truly distinct conformations and holes in the conformational space that may represent high energy barriers. Our results show that the clusters closely follow known experimental results about intermediate structures, as well as binding and folding events.

A Constructive Treatment to Elemental Life Forms through Mathematical Philosophy

The quest to understand the natural and the mathematical as well as philosophical principles of dynamics of life forms are ancient in the human history of science. In ancient times, Pythagoras and Plato, and later, Copernicus and Galileo, correctly observed that the grand book of nature is written in the language of mathematics. Platonism, Aristotelian logism, neo-realism, monadism of Leibniz, Hegelian idealism and others have made efforts to understand reasons of existence of life forms in nature and the underlying principles through the lenses of philosophy and mathematics. In this paper, an approach is made to treat the similar question about nature and existential life forms in view of mathematical philosophy. The approach follows constructivism to formulate an abstract model to understand existential life forms in nature and its dynamics by selectively combining the elements of various schools of thoughts. The formalisms of predicate logic, probabilistic inference and homotopy theory of algebraic topology are employed to construct a structure in local time-scale horizon and in cosmological time-scale horizon. It aims to resolve the relative and apparent conflicts present in various thoughts in the process, and it has made an effort to establish a logically coherent interpretation.

Persistent homology approach distinguishes potential pattern between “Early” and “Not Early” hepatic decompensation groups using MRI modalities

Primary sclerosis cholangitis (PSC) predisposes individuals to liver failure, but it is challenging for radiologists examining radiologic images to predict which patients with PSC will ultimately develop liver failure. Motivated by algebraic topology, a topological data analysis - inspired framework was adopted in the study of the imaging pattern between the “Early Decompensation” and “Not Early” groups. The results demonstrate that the proposed methodology discriminates “Early Decompensation” and “Not Early” groups. Our study is the first attempt to provide a topological representation-based method into early hepatic decompensation and not early groups.