Competitive Coevolution through Evolutionary Complexification

Two major goals in machine learning are the discovery and improvement of solutions to complex problems. In this paper, we argue that complexification, i.e. the incremental elaboration of solutions through adding new structure, achieves both these goals. We demonstrate the power of complexification through the NeuroEvolution of Augmenting Topologies (NEAT) method, which evolves increasingly complex neural network architectures. NEAT is applied to an open-ended coevolutionary robot duel domain where robot controllers compete head to head. Because the robot duel domain supports a wide range of strategies, and because coevolution benefits from an escalating arms race, it serves as a suitable testbed for studying complexification. When compared to the evolution of networks with fixed structure, complexifying evolution discovers significantly more sophisticated strategies. The results suggest that in order to discover and improve complex solutions, evolution, and search in general, should be allowed to complexify as well as optimize

Evolving symmetric and modular neural networks for distributed control

Problems such as the design of distributed controllers are character-ized by modularity and symmetry. However, the symmetries use-ful for solving them are often difficult to determine analytically. This paper presents a nature-inspired approach called Evolution of Network Symmetry and mOdularity (ENSO) to solve such prob-lems. It abstracts properties of generative and developmental sys-tems, and utilizes group theory to represent symmetry and search for it systematically, making it more evolvable than randomly mu-tating symmetry. This approach is evaluated by evolving controllers for a quadruped robot in physically realistic simulations. On flat ground, the resulting controllers are as effective as those having hand-designed symmetries. However, they are significantly faster when evolved on inclined ground, where the appropriate symmetries are difficult to determine manually. The group-theoretic symmetry mutations of ENSO were also significantly more effective at evolv-ing such controllers than random symmetry mutations. Thus, ENSO is a promising approach for evolving modular and symmetric solu-tions to distributed control problems, as well as multiagent systems in general

Treatment of Displaced Olecranon Fractures: A Systematic Review

Background and Aims:The incidence of olecranon fractures is rising. Displaced fractures are usually operated either by tension band wiring or plate fixation. The aim of this review is to evaluate the best current evidence on the management of displaced olecranon fractures.Materials and Methods:Randomized controlled trials were systematically gathered in May 2018 from CENTRAL, MEDLINE, Embase, CINAHL, Scopus, and PEDro databases. The methodological quality of articles was assessed according to the Cochrane Collaboration’s domain-based framework. Prospero database registration number: CRD42018096650.Results:Of 1518 identified records, finally, 5 were relevant. Four trials were found on tension band wiring: two compared tension band wiring with plate fixation (n = 108), one compared plate fixation with an olecranon memory connector (n = 40), and one trial compared tension band wiring with a modified tension band wiring called Cable Pin System (n = 62). In addition, one trial compared operative and conservative treatment in elderly (n = 19). The risk of bias was considered low in two and high in three of the trials. The follow-up time was 5–36 months, and outcome measures varied from patient-rated and physician-rated measures to radiological outcomes. In the analysis, there was no difference between tension band wiring and plate fixation. The data were insufficient for further quantitative analysis.Conclusion:No differences were found in clinical or patient-rated outcome measures between the two most frequent fixation methods (tension band wiring and plate fixation) of displaced olecranon fractures. Current data are not sufficient to evaluate other treatment methods; however, conservative treatment might serve as an option for selected patients in the elderly population.</div

Neural-Symbolic Learning and Reasoning: Contributions and Challenges

The goal of neural-symbolic computation is to integrate robust connectionist learning and sound symbolic reasoning. With the recent advances in connectionist learning, in particular deep neural networks, forms of representation learning have emerged. However, such representations have not become useful for reasoning. Results from neural-symbolic computation have shown to offer powerful alternatives for knowledge representation, learning and reasoning in neural computation. This paper recalls the main contributions and discusses key challenges for neural-symbolic integration which have been identified at a recent Dagstuhl seminar

p62/SQSTM1 regulates cellular oxygen sensing by attenuating PHD3 activity through aggregate sequestration and enhanced degradation

The hypoxia-inducible factor (HIF) prolyl hydroxylase PHD3 regulates cellular responses to hypoxia. In normoxia the expression of PHD3 is low and it occurs in cytosolic aggregates. SQSTM1/p62 (p62) recruits proteins into cytosolic aggregates, regulates metabolism and protein degradation and is downregulated by hypoxia. Here we show that p62 determines the localization, expression and activity of PHD3. In normoxia PHD3 interacted with p62 in cytosolic aggregates, and p62 was required for PHD3 aggregation that was lost upon transfer to hypoxia, allowing PHD3 to be expressed evenly throughout the cell. In line with this, p62 enhanced the normoxic degradation of PHD3. Depletion of p62 in normoxia led to elevated PHD3 levels, whereas forced p62 expression in hypoxia downregulated PHD3. The loss of p62 resulted in enhanced interaction of PHD3 with HIF-alpha and reduced HIF-alpha levels. The data demonstrate p62 is a critical regulator of the hypoxia response and PHD3 activity, by inducing PHD3 aggregation and degradation under normoxia

HIF prolyl hydroxylase PHD3 regulates translational machinery and glucose metabolism in clear cell renal cell carcinoma

Background: A key feature of clear cell renal cell carcinoma (ccRCC) is the inactivation of the von Hippel-Lindau tumour suppressor protein (pVHL) that leads to the activation of hypoxia-inducible factor (HIF) pathway also in well-oxygenated conditions. Important regulator of HIF-a, prolyl hydroxylase PHD3, is expressed in high amounts in ccRCC. Although several functions and downstream targets for PHD3 in cancer have been suggested, the role of elevated PHD3 expression in ccRCC is not clear.Methods: To gain insight into the functions of high PHD3 expression in ccRCC, we used PHD3 knockdown by siRNA in 786-O cells under normoxic and hypoxic conditions and performed discovery mass spectrometry (LC-MS/MS) of the purified peptide samples. The LC-MS/MS results were analysed by label- free quantification of proteome data using a peptide-level expression-change averaging procedure and subsequent gene ontology enrichment analysis.Results: Our data reveals an intriguingly widespread effect of PHD3 knockdown with 91 significantly regulated proteins. Under hypoxia, the response to PHD3 silencing was wider than under normoxia illustrated by both the number of regulated proteins and by the range of protein expression levels. The main cellular functions regulated by PHD3 expression were glucose metabolism, protein translation and messenger RNA (mRNA) processing. PHD3 silencing led to downregulation of most glycolytic enzymes from glucose transport to lactate production supported by the reduction in extracellular acidification and lactate production and increase in cellular oxygen consumption rate. Moreover, upregulation of mRNA processing-related proteins and alteration in a number of ribosomal proteins was seen as a response to PHD3 silencing. Further studies on upstream effectors of the translational machinery revealed a possible role for PHD3 in regulation of mTOR pathway signalling.Conclusions: Our findings suggest crucial involvement of PHD3 in the maintenance of key cellular functions including glycolysis and protein synthesis in ccRCC

NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networks

Deep Neural Networks (DNNs) have made significant improvements to reach the desired accuracy to be employed in a wide variety of Machine Learning (ML) applications. Recently the Google Brain's team demonstrated the ability of Capsule Networks (CapsNets) to encode and learn spatial correlations between different input features, thereby obtaining superior learning capabilities compared to traditional (i.e., non-capsule based) DNNs. However, designing CapsNets using conventional methods is a tedious job and incurs significant training effort. Recent studies have shown that powerful methods to automatically select the best/optimal DNN model configuration for a given set of applications and a training dataset are based on the Neural Architecture Search (NAS) algorithms. Moreover, due to their extreme computational and memory requirements, DNNs are employed using the specialized hardware accelerators in IoT-Edge/CPS devices. In this paper, we propose NASCaps, an automated framework for the hardware-aware NAS of different types of DNNs, covering both traditional convolutional DNNs and CapsNets. We study the efficacy of deploying a multi-objective Genetic Algorithm (e.g., based on the NSGA-II algorithm). The proposed framework can jointly optimize the network accuracy and the corresponding hardware efficiency, expressed in terms of energy, memory, and latency of a given hardware accelerator executing the DNN inference. Besides supporting the traditional DNN layers, our framework is the first to model and supports the specialized capsule layers and dynamic routing in the NAS-flow. We evaluate our framework on different datasets, generating different network configurations, and demonstrate the tradeoffs between the different output metrics. We will open-source the complete framework and configurations of the Pareto-optimal architectures at https://github.com/ehw-fit/nascaps.Comment: To appear at the IEEE/ACM International Conference on Computer-Aided Design (ICCAD '20), November 2-5, 2020, Virtual Event, US

Fabrication of a thin silicon detector with excellent thickness uniformity

We have fabricated and tested a thin silicon detector with the specific goal of having a very good thickness uniformity. SOI technology was used in the detector fabrication. The detector was designed to be used as a Delta E detector in a silicon telescope for measuring solar energetic particles in space. The detector thickness was specified to be 20 mu m with an rms thickness uniformity of +/- 0.5%. The active area consists of three separate elements, a round centre area and two surrounding annular segments. A new method was developed for measuring the thickness uniformity based on a modified Fizeau interferometer. The thickness uniformity specification was well met with the measured rms thickness variation of 43 nm. The detector was electrically characterized by measuring the I-V and C-V curves and the performance was verified using a Am-241 alpha source. (C) 2015 Elsevier B.V. All rights reserved.</p