Interesting method that will undoubtedly find an audience. Current application limited, but likely to extend to other, more general cases. Reasonably well written, though could be improved.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper proposes an estimation strategy for recovering the parameters of a finite state Markov chain given observed stationary frequencies of some states. Although the problem proposed is potentially very interesting, the paper does not appear to be in a mature state. Some fundamental issues are not adequately addressed, while the evaluation is limited, and the writing quality is not strong. Note that there is an uncountable set of ergodic transition models that can exactly match a given subset of stationary frequencies when the number of observed stationary state frequencies is small relative to the total number of states.

Reliable uncertainty quantification remains a major obstacle to the deployment of deep learning models under distributional shift. Existing post-hoc approaches that retrofit pretrained models either inherit misplaced confidence or merely reshape predictions, without teaching the model when to be uncertain. We introduce GUIDE, a lightweight evidential learning meta-model approach that attaches to a frozen deep learning model and explicitly learns how and when to be uncertain. GUIDE identifies salient internal features via a calibration stage, and then employs these features to construct a noise-driven curriculum that teaches the model how and when to express uncertainty. GUIDE requires no retraining, no architectural modifications, and no manual intermediate-layer selection to the base deep learning model, thus ensuring broad applicability and minimal user intervention. The resulting model avoids distilling overconfidence from the base model, improves out-of-distribution detection by ~77% and adversarial attack detection by ~80%, while preserving in-distribution performance. Across diverse benchmarks, GUIDE consistently outperforms state-of-the-art approaches, evidencing the need for actively guiding uncertainty to close the gap between predictive confidence and reliability.

Appendix A provides the proofs omitted from Section 4.1, describing the method adopted Appendix B provides the proofs omitted from Section 4.2, describing the method adopted Appendix D provides some additional experimental results. A Proofs omitted from Section 4.1 B Proofs omitted from Section 4.2 Theorem 2. Let t [T ] and δ (0, 1). The proof follows the reasoning outlined in Section 4.2. Before proving Theorem 3, we need to show the following technical lemma. Lemma 4. Let f (τ):= This is reasonable in practice, since a new player can always be profiled according to a number of user classes.

In this paper, we present asymmetric pivot manipulation for picking up rigid hollow objects to achieve a hole grasp. The pivot motion, executed by a position-controlled robotic arm, enables the gripper to effectively grasp hollow objects placed horizontally such that one gripper finger is positioned inside the object's hole, while the other contacts its outer surface along the length. Hole grasp is widely employed by humans to manipulate hollow objects, facilitating precise placement and enabling efficient subsequent operations, such as tightly packing objects into trays or accurately inserting them into narrow machine slots in manufacturing processes. Asymmetric pivoting for hole grasping is applicable to hollow objects of various sizes and hole shapes, including bottles, cups, and ducts. We investigate the variable parameters that satisfy the force balance conditions for successful grasping configurations. Our method can be implemented using a commercially available parallel-jaw gripper installed directly on a robot arm without modification. Experimental verification confirmed that hole grasp can be achieved using our proposed asymmetric pivot manipulation for various hollow objects, demonstrating a high success rate. Two use cases, namely aligning and feeding hollow cylindrical objects, were experimentally demonstrated on the testbed to clearly showcase the advantages of the hole grasp approach.

Audio classification is paramount in a variety of applications including surveillance, healthcare monitoring, and environmental analysis. Traditional methods frequently depend on intricate signal processing algorithms and manually crafted features, which may fall short in fully capturing the complexities of audio patterns. Neuromorphic computing, inspired by the architecture and functioning of the human brain, presents a promising alternative for audio classification tasks. This survey provides an exhaustive examination of the current state-of-the-art in neuromorphic-based audio classification. It delves into the crucial components of neuromorphic systems, such as Spiking Neural Networks (SNNs), memristors, and neuromorphic hardware platforms, highlighting their advantages in audio classification. Furthermore, the survey explores various methodologies and strategies employed in neuromorphic audio classification, including event-based processing, spike-based learning, and bio-inspired feature extraction. It examines how these approaches address the limitations of traditional audio classification methods, particularly in terms of energy efficiency, real-time processing, and robustness to environmental noise. Additionally, the paper conducts a comparative analysis of different neuromorphic audio classification models and benchmarks, evaluating their performance metrics, computational efficiency, and scalability. By providing a comprehensive guide for researchers, engineers and practitioners, this survey aims to stimulate further innovation and advancements in the evolving field of neuromorphic audio classification.

The initial scores for this paper were: 4: An okay submission, but not good enough; a reject. The main concerns of the negative reviewers were: - issues about the problem formulation - only weak baselines are considered; results below the state-of-the-art - limited novelty - missing citations - only relatively minor improvements obtained by the proposed approach The positive reviewer also acknowledges the issues with experimental evaluation (the proposed method is shown to help weak baselines that are overall below the state-of-the-art), but finds the idea of the paper interesting, original and standing out. The authors provide a rebuttal. In the follow-up discussion among the reviewers, R3 acknowledges that some of their concerns have been addressed but remains borderline negative (5) as they think the rebuttal does not alleviate the concerns regarding the overall low results and some ablations are still missing. R2 agrees on the issues with experimental evaluation pointed by R1 R3 but maintains that "given that the problem and the method are interesting and that there are no good dataset to study them, I would recommend accept."