Machine-learning methods in biochemistry commonly represent molecules as graphs of pairwise intermolecular interactions for property and structure predictions. Most methods operate on a single graph, typically the minimal free energy (MFE) structure, for low-energy ensembles (conformations) representative of structures at thermodynamic equilibrium. We introduce a thermodynamically parameterized exponential-family random graph (ERGM) embedding that models molecules as Boltzmann-weighted ensembles of interaction graphs. We evaluate this embedding on SELEX datasets, where experimental biases (e.g., PCR amplification or sequencing noise) can obscure true aptamer-ligand affinity, producing anomalous candidates whose observed abundance diverges from their actual binding strength. We show that the proposed embedding enables robust community detection and subgraph-level explanations for aptamer ligand affinity, even in the presence of biased observations. This approach may be used to identify low-abundance aptamer candidates for further experimental evaluation.

Looking back at our primate ancestors, it would be easy to assume that humans today have reached the final chapter of our evolution. However, many scientists believe that the way humans appear today is just the start of the story. Thanks to technology, space travel, and climate change, the world around us is changing faster than ever - and experts believe that humanity will change with it. Now, artificial intelligence (AI) reveals what the humans of the future might look like. With Google's ImageFX AI image generator, MailOnline has used predictions from leading scientists to imagine how the human race might evolve.

As predictable as the swallows returning to Capistrano, recent breakthroughs in AI have been accompanied by a new wave of fears of some version of "the singularity," that point in runaway technological innovation at which computers become unleashed from human control. Those worried that AI is going to toss us humans into the dumpster, however, might look to the natural world for perspective on what current AI can and cannot do. Those octopi alive today are a marvel of evolution--they can mold themselves into almost any shape and are equipped with an arsenal of weapons and stealth camouflage, as well as an apparent ability to decide which to use depending on the challenge. Yet, despite decades of effort, robotics hasn't come close to duplicating this suite of abilities (not surprising since the modern octopus is the product of adaptations over 100 million generations). Robotics is a far longer way off from creating Hal.

In today's fast-paced world of city living and stressful work-life imbalances, especially on the (hopefully) tail-end of a year of pandemic quarantine measures, many young workers are yearning to get closer to nature and family. In the face of re-emerging commutes and the push-and-pull of back-to-the-office versus hybrid or fully-remote working, many young robots would rather ditch the status quo and return to the countryside to scratch a living from the land like their ancestors before them. And they'll bring lasers, too. Of course, we're not talking about the weary office drones being herded back to the office after a year of blissfully working at home, but of robots armed with deep learning computer vision systems and precision actuators for a new breed of farming automation. This new breed of automated agriculture promises to decrease inputs and the side-effects of modern agriculture, while helping farmers deal with everything from labor shortages to climate change.

Population-based optimization algorithms, like evolutionary algorithms and swarm intelligence, often describe their dynamics in terms of the interplay between selective pressures and convergence. For example, strong selective pressures result in faster convergence and likely premature convergence. Weaker selective pressures may result in a slower convergence (greater computational cost) although perhaps locate a better or even global optima. An operator with a high selective pressure decreases diversity in the population more rapidly than operators with a low selective pressure, which may lead to premature convergence to suboptimal solutions. A high selective pressure limits the exploration abilities of the population.

We analyse the impact of the selective pressure for the global optimisation capabilities of steady-state EAs. For the standard bimodal benchmark function \twomax we rigorously prove that using uniform parent selection leads to exponential runtimes with high probability to locate both optima for the standard ($\mu$+1)~EA and ($\mu$+1)~RLS with any polynomial population sizes. On the other hand, we prove that selecting the worst individual as parent leads to efficient global optimisation with overwhelming probability for reasonable population sizes. Since always selecting the worst individual may have detrimental effects for escaping from local optima, we consider the performance of stochastic parent selection operators with low selective pressure for a function class called \textsc{TruncatedTwoMax} where one slope is shorter than the other. An experimental analysis shows that the EAs equipped with inverse tournament selection, where the loser is selected for reproduction and small tournament sizes, globally optimise \textsc{TwoMax} efficiently and effectively escape from local optima of \textsc{TruncatedTwoMax} with high probability. Thus they identify both optima efficiently while uniform (or stronger) selection fails in theory and in practice. We then show the power of inverse selection on function classes from the literature where populations are essential by providing rigorous proofs or experimental evidence that it outperforms uniform selection equipped with or without a restart strategy. We conclude the paper by confirming our theoretical insights with an empirical analysis of the different selective pressures on standard benchmarks of the classical MaxSat and Multidimensional Knapsack Problems.

We perform a rigorous runtime analysis for the Univariate Marginal Distribution Algorithm on the LeadingOnes function, a well-known benchmark function in the theory community of evolutionary computation with a high correlation between decision variables. For a problem instance of size $n$, the currently best known upper bound on the expected runtime is $\mathcal{O}(n\lambda\log\lambda+n^2)$ (Dang and Lehre, GECCO 2015), while a lower bound necessary to understand how the algorithm copes with variable dependencies is still missing. Motivated by this, we show that the algorithm requires a $e^{\Omega(\mu)}$ runtime with high probability and in expectation if the selective pressure is low; otherwise, we obtain a lower bound of $\Omega(\frac{n\lambda}{\log(\lambda-\mu)})$ on the expected runtime. Furthermore, we for the first time consider the algorithm on the function under a prior noise model and obtain an $\mathcal{O}(n^2)$ expected runtime for the optimal parameter settings. In the end, our theoretical results are accompanied by empirical findings, not only matching with rigorous analyses but also providing new insights into the behaviour of the algorithm.

The problem with dogs is that they're a lot like babies that never grow up. This is both a great strength and a huge annoyance, mostly because they can't talk. Researchers who study infant learning and behavior have to rely on other cues, like how long subjects look at an object, because asking them questions is just a big waste of time. Dogs are the same, and that makes it very difficult to come to definitive conclusions about their behavior and what it means. We know, for example, that humans interpret dog facial expressions as conveying certain emotions, and that doing so affects our behavior.

Race does not stand up scientifically, period. To begin with, if race categories were meant primarily to capture differences in genetics, they are doing an abysmal job. The genetic distance between some groups within Africa is as great as the genetic distance between many "racially divergent" groups in the rest of the world. The genetic distance between East Asians and Europeans is shorter than the divergence between Hazda in north-central Tanzania to the Fulani shepherds of West Africa (who live in present-day Mali, Niger, Burkina Faso, and Guinea). Armed with this knowledge, many investigators in the biological sciences have replaced the term "race" with the term "continental ancestry." This in part reflects a rejection of "race" as a biological classification. Every so-called race has the same protein-coding genes, and there is no clear genetic dividing line that subdivides the human species.

Current evolutionary theory describes a Darwinian machine – i.e., heritable variation in reproductive success that assumes fixed mechanisms of variation and selection operating on a fixed reproductive unit. But, in fact, none of these mechanisms is fixed in nature. For example, the distribution of phenotypic variation changes over evolutionary time as a result of the evolution of development, the selective pressures on traits change as a result of the evolution of ecological interactions, and even the identity of the evolutionary unit changes as a result of the evolution of new reproductive strategies and new mechanisms of inheritance. The circular causality implied by an evolutionary process that alters its own mechanisms results in conceptual difficulties and controversies in many areas of evolutionary biology. However, in computer science, the idea that an algorithmic process can improve over time as a function of past experience, including its own past behaviour, has been thoroughly studied in the field of machine learning.