Monthly Archives: March 2025

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A new perspective of considering convex optimization problems based on electric circuit theory

March 6, 2025 10:41 ,

Stephen P. Boyd, Tetiana Parshakova, Ernest K. Ryu, Jaewook J. Suh, Optimization Algorithm Design via Electric Circuits, NeurIPS 2024 spotlight, 25 Sept 2024, Last Modified: 14 Jan 2025, https://openreview.net/forum?id=9Jmt1eER9P.

We present a novel methodology for convex optimization algorithm design using ideas from electric RLC circuits. Given an optimization problem, the first stage of the methodology is to design an appropriate electric circuit whose continuous-time dynamics converge to the solution of the optimization problem at hand. Then, the second stage is an automated, computer-assisted discretization of the continuous-time dynamics, yielding a provably convergent discrete-time algorithm. Our methodology recovers many classical (distributed) optimization algorithms and enables users to quickly design and explore a wide range of new algorithms with convergence guarantees.

Posted in: Mathematics , Tagged: , ,

On the innate ability of vertebrates for number recognition and the one of distinguishing ratios of numbers

March 6, 2025 10:33 ,

Elena Lorenzi, Dmitry Kobylkov, Giorgio Vallortigara, Is there an innate sense of number in the brain?, Cerebral Cortex, Volume 35, Issue 2, February 2025, DOI: 10.1093/cercor/bhaf004.

The approximate number system or «sense of number» is a crucial, presymbolic mechanism enabling animals to estimate quantities, which is essential for survival in various contexts (eg estimating numerosities of social companions, prey, predators, and so on). Behavioral studies indicate that a sense of number is widespread across vertebrates and invertebrates. Specific brain regions such as the intraparietal sulcus and prefrontal cortex in primates, or equivalent areas in birds and fish, are involved in numerical estimation, and their activity is modulated by the ratio of quantities. Data gathered across species strongly suggest similar evolutionary pressures for number estimation pointing to a likely common origin, at least across vertebrates. On the other hand, few studies have investigated the origins of the sense of number. Recent findings, however, have shown that numerosity-selective neurons exist in newborn animals, such as domestic chicks and zebrafish, supporting the hypothesis of an innateness of the approximate number system. Control-rearing experiments on visually naïve animals further support the notion that the sense of number is innate and does not need any specific instructive experience in order to be triggered.

Posted in: Psycho-physiological bases of engineering , Tagged:

It seems that the human brain working memory uses pointers

March 6, 2025 10:21 ,

Edward Awh, Edward K. Vogel, Working memory needs pointers, Trends in Cognitive Sciences, Volume 29, Issue 3, 2025, Pages 230-241, DOI: 10.1016/j.tics.2024.12.006.

Cognitive neuroscience has converged on a definition of working memory (WM) as a capacity-limited system that maintains highly accessible representations via stimulus-specific neural patterns. We argue that this standard definition may be incomplete. We highlight the fundamental need to recognize specific instances or tokens and to bind those tokens to the surrounding context. We propose that contextual binding is supported by spatiotemporal ‘pointers’ and that pointers are the source of neural signals that track the number of stored items, independent of their content. These content-independent pointers may provide a productive perspective for understanding item-based capacity limits in WM and the role of WM as a gateway for long-term storage.

Posted in: Psycho-physiological bases of engineering , Tagged:

Planning tasks under uncertainty that have a maximum time to be finished

March 6, 2025 10:17 ,

Michal Staniaszek, Lara Brudermüller, Yang You, Raunak Bhattacharyya, Bruno Lacerda, Nick Hawes, Time-bounded planning with uncertain task duration distributions, Robotics and Autonomous Systems, Volume 186, 2025, DOI: 10.1016/j.robot.2025.104926.

We consider planning problems where a robot must gather reward by completing tasks at each of a large set of locations while constrained by a time bound. Our focus is problems where the context under which each task will be executed can be predicted, but is not known in advance. Here, the term context refers to the conditions under which the task is executed, and can be related to the robot’s internal state (e.g., how well it is localised?), or the environment itself (e.g., how dirty is the floor the robot must clean?). This context has an impact on the time required to execute the task, which we model probabilistically. We model the problem of time-bounded planning for tasks executed under uncertain contexts as a Markov decision process with discrete time in the state, and propose variants on this model which allow adaptation to different robotics domains. Due to the intractability of the general model, we propose simplifications to allow planning in large domains. The key idea behind these simplifications is constraining navigation using a solution to the travelling salesperson problem. We evaluate our models on maps generated from real-world environments and consider two domains with different characteristics: UV disinfection, and cleaning. We evaluate the effect of model variants and simplifications on performance, and show that policies obtained for our models outperform a rule-based baseline, as well as a model which does not consider context. We also evaluate our models in a real robot experiment where a quadruped performs simulated inspection tasks in an industrial environment.

Posted in: Robot task planning , Tagged: