We thank reviewers for detailed comments and suggestions. We will address all comments in the revision. AIStats'19) considered the problem of learning an optimal action but ignored the contextual information. In this work, we incorporated the contextual information, which is readily available in many applications. The idea might look incremental.

Accurate remote state estimation is a fundamental component of many autonomous and networked dynamical systems, where multiple decision-making agents interact and communicate over shared, bandwidth-constrained channels. These communication constraints introduce an additional layer of complexity, namely, the decision of when to communicate. This results in a fundamental trade-off between estimation accuracy and communication resource usage. Traditional extensions of classical estimation algorithms (e.g., the Kalman filter) treat the absence of communication as 'missing' information. However, silence itself can carry implicit information about the system's state, which, if properly interpreted, can enhance the estimation quality even in the absence of explicit communication. Leveraging this implicit structure, however, poses significant analytical challenges, even in relatively simple systems. In this paper, we propose CALM (Communication-Aware Learning and Monitoring), a novel learning-based framework that jointly addresses the dual challenges of communication scheduling and estimator design. Our approach entails learning not only when to communicate but also how to infer useful information from periods of communication silence. We perform comparative case studies on multiple benchmarks to demonstrate that CALM is able to decode the implicit coordination between the estimator and the scheduler to extract information from the instances of 'silence' and enhance the estimation accuracy.

We thank all reviewers for their valuable comments. Let us first provide a concise recap of our contributions. We propose an efficient post-processing algorithm (Alg. 1) which can be applied on top of any We highlight that contributions i) and iii) are, up to our knowledge, unique. We now address specific points raised by the reviewers, which will be included in the final version upon acceptance. Let us highlight two key differences between "Wasserstein Fair This is an interesting direction of future research. The main difficulty in such an extension for, e.g., Equalized Odds is due to the conditioning on the Notice also that DP is used in several papers, including Jiang et al. discussed above. Let us disagree that the notion of DP is naive. As stated in the paper (ll. However a form of this assumption is rather classical in non-parametric statistics (see e.g., "Fast learning In our settings As. 4.2 is mostly technical and can be "choice of sigma is left to the user ."

We thank the reviewers for their supportive and insightful comments. We address their questions/concerns below. As noted by R2&R3, existing results for self-supervised methods have mainly been obtained on ImageNet. R2: 1- "The extensions from 2D to 3D seem relatively natural. . . Extending CPC to 3D was not straightforward.

We are very grateful to the careful and throughout reviews from all reviewers. Reviewer_1 The reviewer comments that our novel results are both practically and theoretically important, and also suggests that we provide more discussion and examples on our technical conditions such as the decomposability condition, subspace compatibility constant etc. We have done so, though due to space limitations we have pushed examples illustrating our conditions to the supplementary material, in the section on proof ingredients for the results in section 5. In detail, for each model, we check and illustrate why those aforementioned technical conditions hold in every subsection of Section B. We will try to illustrate these comments in the main paper. Reviewer_2 The reviewer suggests giving more illustration about why the existing theoretical framework for regularization does not work for inference with latent variables. Briefly, the reason is as follows.

Dual-energy computed tomography (DECT) has been widely used to obtain quantitative elemental composition of imaged subjects for personalized and precise medical diagnosis. Compared with existing high-end DECT leveraging advanced X-ray source and/or detector technologies, the use of the sequentially-scanning data acquisition scheme to implement DECT may make broader impact on clinical practice because this scheme requires no specialized hardware designs. However, since the concentration of iodinated contrast agent in the imaged subject varies over time, sequentially-scanned data sets acquired at two tube potentials are temporally inconsistent. As existing material decomposition approaches for DECT assume that the data sets acquired at two tube potentials are temporally consistent, the violation of this assumption results in inaccurate quantification accuracy of iodine concentration. In this work, we developed a technique to achieve sequentially-scanning DECT imaging using high temporal resolution image reconstruction and temporal extrapolation, ACCELERATION in short, to address the technical challenge induced by temporal inconsistency of sequentially-scanned data sets and improve iodine quantification accuracy in sequentially-scanning DECT. ACCELERATION has been validated and evaluated using numerical simulation data sets generated from clinical human subject exams. Results demonstrated the improvement of iodine quantification accuracy using ACCELERATION.

Artistic performances involving robotic systems present unique technical challenges akin to those encountered in other field deployments. In this paper, we delve into the orchestration of robotic artistic performances, focusing on the complexities inherent in communication protocols and localization methods. Through our case studies and experimental insights, we demonstrate the breadth of technical requirements for this type of deployment, and, most importantly, the significant contributions of working closely with non-experts.

Socially assistive robots (SARs) have shown great success in providing personalized cognitive-affective support for user populations with special needs such as older adults, children with autism spectrum disorder (ASD), and individuals with mental health challenges. The large body of work on SAR demonstrates its potential to provide at-home support that complements clinic-based interventions delivered by mental health professionals, making these interventions more effective and accessible. However, there are still several major technical challenges that hinder SAR-mediated interactions and interventions from reaching human-level social intelligence and efficacy. With the recent advances in large language models (LLMs), there is an increased potential for novel applications within the field of SAR that can significantly expand the current capabilities of SARs. However, incorporating LLMs introduces new risks and ethical concerns that have not yet been encountered, and must be carefully be addressed to safely deploy these more advanced systems. In this work, we aim to conduct a brief survey on the use of LLMs in SAR technologies, and discuss the potentials and risks of applying LLMs to the following three major technical challenges of SAR: 1) natural language dialog; 2) multimodal understanding; 3) LLMs as robot policies.

Ethereum Co-Founder Vitalik Buterin shared his musing on an "underdiscussed, but nevertheless very important" aspect of the Ethereum ecosystem in a recent blog post this weekend. The post entitled "How will Ethereum's multi-client philosophy interact with ZK-EVMs?" focused on the technical challenges, trade-offs, and potential solutions for creating a multi-client ecosystem for ZK-EVMs. Vitalik believes ZK-EVMs will evolve to become an essential part of Ethereum's layer-1 security and verification process in the future. Zero Knowledge (ZK) technology allows developers to prove the authenticity of a transaction or message without revealing any additional information. Thus, it allows one party to convince another that a message is true without disclosing any knowledge beyond the message's validity.

Beating the human world champions by 2050 is an ambitious goal of the Humanoid League that provides a strong incentive for RoboCup teams to further improve and develop their systems. In this paper, we present upgrades of our system which enabled our team NimbRo to win the Soccer Tournament, the Drop-in Games, and the Technical Challenges in the Humanoid AdultSize League of RoboCup 2022. Strong performance in these competitions resulted in the Best Humanoid award in the Humanoid League. The mentioned upgrades include: hardware upgrade of the vision module, balanced walking with Capture Steps, and the introduction of phase-based in-walk kicks.