From evolutionary ecosystem simulations to computational models of human behavior

We have a wide breadth of computational tools available today that enable a more ethical approach to the study of human cognition and behavior. We argue that the use of computer models to study evolving ecosystems provides a rich source of inspiration, as they enable the study of complex systems that change over time. Often employing a combination of genetic algorithms and agent-based models, these methods span theoretical approaches from games to complexification, nature-inspired methods from studies of self-replication to the evolution of eyes, and evolutionary ecosystems of humans, from entire economies to the effects of personalities in teamwork. The review of works provided here illustrates the power of evolutionary ecosystem simulations and how they enable new insights for researchers. They also demonstrate a novel methodology of hypothesis exploration: building a computational model that encapsulates a hypothesis of human cognition enables it to be tested under different conditions, with its predictions compared to real data to enable corroboration. Such computational models of human behavior provide us with virtual test labs in which unlimited experiments can be performed. This article is categorized under: Computer Science and Robotics > Artificial Intelligence

Mastl kinase, a promising therapeutic target, promotes cancer recurrence.

Mastl kinase promotes mitotic progression and cell cycle reentry after DNA damage. We report here that Mastl is frequently upregulated in various types of cancer. This upregulation was correlated with cancer progression in breast and oral cancer, poor patient survival in breast cancer, and tumor recurrence in head and neck squamous cell carcinoma. We further investigated the role of Mastl in tumor resistance using cell lines derived from the initial and recurrent tumors of the same head and neck squamous cell carcinoma patients. Ectopic expression of Mastl in the initial tumor cells strongly promoted cell proliferation in the presence of cisplatin by attenuating DNA damage signaling and cell death. Mastl knockdown in recurrent tumor cells re-sensitized their response to cancer therapy in vitro and in vivo. Finally, Mastl targeting specifically potentiated cancer cells to cell death in chemotherapy while sparing normal cells. Thus, this study revealed that Mastl upregulation is involved in cancer progression and tumor recurrence after initial cancer therapy, and validated Mastl as a promising target to increase the therapeutic window

Using a Variational Autoencoder to Learn Valid Search Spaces of Safely Monitored Autonomous Robots for Last-Mile Delivery

The use of autonomous robots for delivery of goods to customers is an exciting new way to provide a reliable and sustainable service. However, in the real world, autonomous robots still require human supervision for safety reasons. We tackle the real-world problem of optimizing autonomous robot timings to maximize deliveries, while ensuring that there are never too many robots running simultaneously so that they can be monitored safely. We assess the use of a recent hybrid machine-learning-optimization approach COIL (constrained optimization in learned latent space) and compare it with a baseline genetic algorithm for the purposes of exploring variations of this problem. We also investigate new methods for improving the speed and efficiency of COIL. We show that only COIL can find valid solutions where appropriate numbers of robots run simultaneously for all problem variations tested. We also show that when COIL has learned its latent representation, it can optimize 10% faster than the GA, making it a good choice for daily re-optimization of robots where delivery requests for each day are allocated to robots while maintaining safe numbers of robots running at once

COIL: Constrained optimization in learned latent space: learning representations for valid solutions

Constrained optimization problems can be difficult because their search spaces have properties not conducive to search, e.g., multimodality, discontinuities, or deception. To address such difficulties, considerable research has been performed on creating novel evolutionary algorithms or specialized genetic operators. However, if the representation that defined the search space could be altered such that it only permitted valid solutions that satisfied the constraints, the task of finding the optimal would be made more feasible without any need for specialized optimization algorithms. We propose Constrained Optimization in Latent Space (COIL), which uses a VAE to generate a learned latent representation from a dataset comprising samples from the valid region of the search space according to a constraint, thus enabling the optimizer to find the objective in the new space defined by the learned representation. Preliminary experiments show promise: compared to an identical GA using a standard representation that cannot meet the constraints or find fit solutions, COIL with its learned latent representation can perfectly satisfy different types of constraints while finding high-fitness solutions

Regularity scalable image coding based on wavelet singularity detection

In this paper, we propose an adaptive algorithm for scalable wavelet image coding, which is based on the general feature, the regularity, of images. In pattern recognition or computer vision, regularity of images is estimated from the oriented wavelet coefficients and quantified by the Lipschitz exponents. To estimate the Lipschitz exponents, evaluating the interscale evolution of the wavelet transform modulus sum (WTMS) over the directional cone of influence was proven to be a better approach than tracing the wavelet transform modulus maxima (WTMM). This is because the irregular sampling nature of the WTMM complicates the reconstruction process. Moreover, examples were found to show that the WTMM representation cannot uniquely characterize a signal. It implies that the reconstruction of signal from its WTMM may not be consistently stable. Furthermore, the WTMM approach requires much more computational effort. Therefore, we use the WTMS approach to estimate the regularity of images from the separable wavelet transformed coefficients. Since we do not concern about the localization issue, we allow the decimation to occur when we evaluate the interscale evolution. After the regularity is estimated, this information is utilized in our proposed adaptive regularity scalable wavelet image coding algorithm. This algorithm can be simply embedded into any wavelet image coders, so it is compatible with the existing scalable coding techniques, such as the resolution scalable and signal-to-noise ratio (SNR) scalable coding techniques, without changing the bitstream format, but provides more scalable levels with higher peak signal-to-noise ratios (PSNRs) and lower bit rates. In comparison to the other feature-based wavelet scalable coding algorithms, the proposed algorithm outperforms them in terms of visual perception, computational complexity and coding efficienc

Large scale structure around a z=2.1 cluster

The most prodigious starburst galaxies are absent in massive galaxy clusters today, but their connection with large scale environments is less clear at $z\gtrsim2$. We present a search of large scale structure around a galaxy cluster core at $z=2.095$ using a set of spectroscopically confirmed galaxies. We find that both color-selected star-forming galaxies (SFGs) and dusty star-forming galaxies (DSFGs) show significant overdensities around the $z=2.095$ cluster. A total of 8 DSFGs (including 3 X-ray luminous active galactic nuclei, AGNs) and 34 SFGs are found within a 10 arcmin radius (corresponds to $\sim$15 cMpc at $z\sim2.1$) from the cluster center and within a redshift range of $\Delta z=0.02$, which leads to galaxy overdensities of $\delta_{\rm DSFG}\sim12.3$ and $\delta_{\rm SFG}\sim2.8$. The cluster core and the extended DSFG- and SFG-rich structure together demonstrate an active cluster formation phase, in which the cluster is accreting a significant amount of material from large scale structure while the more mature core may begin to virialize. Our finding of this DSFG-rich structure, along with a number of other protoclusters with excess DSFGs and AGNs found to date, suggest that the overdensities of these rare sources indeed trace significant mass overdensities. However, it remains puzzling how these intense star formers are triggered concurrently. Although an increased probability of galaxy interactions and/or enhanced gas supply can trigger the excess of DSFGs, our stacking analysis based on 850 $\mu$m images and morphological analysis based on rest-frame optical imaging do not show such enhancements of merger fraction and gas content in this structure.Comment: 11 pages, 4 figures, ApJ accepte