Behavioral Analysis of Cuttlefish Traveling Waves and Its Implications for Neural Control

SummaryTraveling waves (from action potential propagation to swimming body motions or intestinal peristalsis) are ubiquitous phenomena in biological systems and yet are diverse in form, function, and mechanism. An interesting such phenomenon occurs in cephalopod skin, in the form of moving pigmentation patterns called “passing clouds” [1]. These dynamic pigmentation patterns result from the coordinated activation of large chromatophore arrays [2]. Here, we introduce a new model system for the study of passing clouds, Metasepia tullbergi, in which wave displays are very frequent and thus amenable to laboratory investigations. The mantle of Metasepia contains four main regions of wave travel, each supporting a different propagation direction. The four regions are not always active simultaneously, but those that are show synchronized activity and maintain a constant wavelength and a period-independent duty cycle, despite a large range of possible periods (from 1.5 s to 10 s). The wave patterns can be superposed on a variety of other ongoing textural and chromatic patterns of the skin. Finally, a traveling wave can even disappear transiently and reappear in a different position (“blink”), revealing ongoing but invisible propagation. Our findings provide useful clues about classes of likely mechanisms for the generation and propagation of these traveling waves. They rule out wave propagation mechanisms based on delayed excitation from a pacemaker [3] but are consistent with two other alternatives, such as coupled arrays of central pattern generators [3] and dynamic attractors on a network with circular topology [4]

IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies

Effective offline RL methods require properly handling out-of-distribution actions. Implicit Q-learning (IQL) addresses this by training a Q-function using only dataset actions through a modified Bellman backup. However, it is unclear which policy actually attains the values represented by this implicitly trained Q-function. In this paper, we reinterpret IQL as an actor-critic method by generalizing the critic objective and connecting it to a behavior-regularized implicit actor. This generalization shows how the induced actor balances reward maximization and divergence from the behavior policy, with the specific loss choice determining the nature of this tradeoff. Notably, this actor can exhibit complex and multimodal characteristics, suggesting issues with the conditional Gaussian actor fit with advantage weighted regression (AWR) used in prior methods. Instead, we propose using samples from a diffusion parameterized behavior policy and weights computed from the critic to then importance sampled our intended policy. We introduce Implicit Diffusion Q-learning (IDQL), combining our general IQL critic with the policy extraction method. IDQL maintains the ease of implementation of IQL while outperforming prior offline RL methods and demonstrating robustness to hyperparameters. Code is available at https://github.com/philippe-eecs/IDQL.Comment: 11 Pages, 6 Figures, 3 Table

The underestimated giants: operant conditioning, visual discrimination and long-term memory in giant tortoises

Relatively little is known about cognition in turtles, and most studies have focused on aquatic animals. Almost nothing is known about the giant land tortoises. These are visual animals that travel large distances in the wild, interact with each other and with their environment, and live extremely long lives. Here, we show that Galapagos and Seychelle tortoises, housed in a zoo environment, readily underwent operant conditioning and we provide evidence that they learned faster when trained in the presence of a group rather than individually. The animals readily learned to distinguish colors in a two-choice discrimination task. However, since each animal was assigned its own individual colour for this task, the presence of the group had no obvious effect on the speed of learning. When tested 95 days after the initial training, all animals remembered the operant task. When tested in the discrimination task, most animals relearned the task up to three times faster than naive animals. Remarkably, animals that were tested 9 years after the initial training still retained the operant conditioning. As animals remembered the operant task, but needed to relearn the discrimination task constitutes the first evidence for a differentiation between implicit and explicit memory in tortoises. Our study is a first step towards a wider appreciation of the cognitive abilities of these unique animals

Assessment of Mesophotic Coral Ecosystem Connectivity for Proposed Expansion of a Marine Sanctuary in the Northwest Gulf of Mexico: Larval Dynamics

In coral reef ecosystems, mesophotic coral habitat (\u3e30 m to the end of the photic zone) are extensions of shallow reefs and contribute to the persistence of coral reef populations. In the North West Gulf of Mexico (NW GOM), the Flower Garden Banks National Marine Sanctuary (FGBNMS) is an isolated reef ecosystem comprising contiguous shallow and mesophotic reefs habitats on two central banks along the margin of the continental shelf. A future expansion of the sanctuary is proposed to include additional mesophotic banks and aims at building a network of protected areas in the NW GOM to ensure the persistence of the coral reef populations inhabiting the sanctuary. To evaluate the feasibility of this expansion and investigate the overall dynamics of coral species in the region, we studied the patterns of larval connectivity of Montastraea cavernosa, a common depth generalist coral species, using a larval dispersal modeling approach. Our results highlighted larval exports from the NW GOM banks to the northeastern and southwestern GOM, larval connectivity between all banks investigated in this study, and the potential for exporting larvae from mesophotic to shallower reefs. Our study associated with Studivan and Voss (2018; associate manuscript) demonstrates the relevance of combining modeling and genetic methods to consider both demographic and genetic timescales for the evaluation of the connectivity dynamics of marine populations. In the case of the NW GOM, both studies support the future management plan for expanding FGBNMS

Influence of bed materials on the performance of the Nong Bua dual fluidized bed gasification power plant in Thailand

Bed materials and their catalytic activity are two main parameters that affect the performance of the dual fluidized bed (DFB) gasification system in terms of product gas composition and tar levels. Two sources of bed materials were used for the operation of a commercial DFB gasification system in Thailand, using woodchips as a biomass feedstock. One source of the bed materials was the calcined olivine which had been used in the Gussing Plant, Austria, and the other activated bed material was a mixture of fresh Chinese olivine and used Austrian olivine with additives of biomass ash, calcium hydroxide and dolomite. These bed materials were collected and analysed for morphological and chemical composition using a scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS) and X-ray fluorescence spectroscopy (XRF). The product gas was cleaned in a scrubber to remove tars, from which the samples were collected for gravimetric tar analysis. Its composition data was automatically recorded at the operation site before it entered the gas engine. From the SEM, EDS and XRF analyses, calcium-rich layers around the bed materials were observed on the activated bed material. The inner layers of bed materials collected were homogeneous. Biomass ash, which was generally added to the bed materials, had significant calcium and potassium content. These calcium-rich layers of the bed materials, from the calcium hydroxide, biomass ash and dolomite, influenced system performance, which was determined by observing lower tar concentration and higher hydrogen concentration in the product gas

Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs

Convolutional Neural Networks (CNN) are becoming a common presence in many applications and services, due to their superior recognition accuracy. They are increasingly being used on mobile devices, many times just by porting large models designed for server space, although several model compression techniques have been considered. One model compression technique intended to reduce computations is channel pruning. Mobile and embedded systems now have GPUs which are ideal for the parallel computations of neural networks and for their lower energy cost per operation. Specialized libraries perform these neural network computations through highly optimized routines. As we find in our experiments, these libraries are optimized for the most common network shapes, making uninstructed channel pruning inefficient. We evaluate higher level libraries, which analyze the input characteristics of a convolutional layer, based on which they produce optimized OpenCL (Arm Compute Library and TVM) and CUDA (cuDNN) code. However, in reality, these characteristics and subsequent choices intended for optimization can have the opposite effect. We show that a reduction in the number of convolutional channels, pruning 12% of the initial size, is in some cases detrimental to performance, leading to 2× slowdown. On the other hand, we also find examples where performance-aware pruning achieves the intended results, with performance speedups of 3× with cuDNN and above 10× with Arm Compute Library and TVM. Our findings expose the need for hardware-instructed neural network pruning