Ultrasound is widely employed for clinical intervention and diagnosis, due to its advantages of offering non-invasive, radiation-free, and real-time imaging. However, the accessibility of this dexterous procedure is limited due to the substantial training and expertise required of operators. The robotic ultrasound (RUS) offers a viable solution to address this limitation; nonetheless, achieving human-level proficiency remains challenging. Learning from demonstrations (LfD) methods have been explored in RUS, which learns the policy prior from a dataset of offline demonstrations to encode the mental model of the expert sonographer. However, active engagement of experts, i.e. Coaching, during the training of RUS has not been explored thus far. Coaching is known for enhancing efficiency and performance in human training. This paper proposes a coaching framework for RUS to amplify its performance. The framework combines DRL (self-supervised practice) with sparse expert's feedback through coaching. The DRL employs an off-policy Soft Actor-Critic (SAC) network, with a reward based on image quality rating. The coaching by experts is modeled as a Partially Observable Markov Decision Process (POMDP), which updates the policy parameters based on the correction by the expert. The validation study on phantoms showed that coaching increases the learning rate by $25\%$ and the number of high-quality image acquisition by $74.5\%$.

The conventional approach to the fronthaul design for cell-free massive MIMO system follows the compress-and-precode (CP) paradigm. Accordingly, encoded bits and precoding coefficients are shared by the distributed unit (DU) on the fronthaul links, and precoding takes place at the radio units (RUs). Previous theoretical work has shown that CP can be potentially improved by a significant margin by precode-and-compress (PC) methods, in which all baseband processing is carried out at the DU, which compresses the precoded signals for transmission on the fronthaul links. The theoretical performance gain of PC methods are particularly pronounced when the DU implements multivariate quantization (MQ), applying joint quantization across the signals for all the RUs. However, existing solutions for MQ are characterized by a computational complexity that grows exponentially with the sum-fronthaul capacity from the DU to all RUs. This work sets out to design scalable MQ strategies for PC-based cell-free massive MIMO systems. For the low-fronthaul capacity regime, we present alpha-parallel MQ (alpha-PMQ), whose complexity is exponential only in the fronthaul capacity towards an individual RU, while performing close to full MQ. alpha-PMQ tailors MQ to the topology of the network by allowing for parallel local quantization steps for RUs that do not interfere too much with each other. For the high-fronthaul capacity regime, we then introduce neural MQ, which replaces the exhaustive search in MQ with gradient-based updates for a neural-network-based decoder, attaining a complexity that grows linearly with the sum-fronthaul capacity. Numerical results demonstrate that the proposed scalable MQ strategies outperform CP for both the low and high-fronthaul capacity regimes at the cost of increased computational complexity at the DU (but not at the RUs).

ACM Fellow Daniela Rus has been dreaming of robots since she was a child, imagining mechanical shoes to help her jump higher. As director of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology (MIT), Rus has done pioneering work in modular robots, soft robotics, novel neural networks, and more. Her talk on the future of robotics and AI was featured at a recent TED conference, and this year she released a pair of books for the general public, including The Mind's Mirror: Risk and Reward in the Age of AI. Throughout her career, Rus has maintained a dual focus on improving both the bodies and the brains of intelligent machines. This traces back to her Ph.D. thesis, when she discovered the algorithms she'd developed for dexterous manipulation were too advanced for the robotic hands of the day.

Agitation is one of the most prevalent symptoms in people with dementia (PwD) that can place themselves and the caregiver's safety at risk. Developing objective agitation detection approaches is important to support health and safety of PwD living in a residential setting. In a previous study, we collected multimodal wearable sensor data from 17 participants for 600 days and developed machine learning models for detecting agitation in one-minute windows. However, there are significant limitations in the dataset, such as imbalance problem and potential imprecise labelsas the occurrence of agitation is much rarer in comparison to the normal behaviours. In this paper, we first implemented different undersampling methods to eliminate the imbalance problem, and came to the conclusion that only 20% of normal behaviour data were adequate to train a competitive agitation detection model. Then, we designed a weighted undersampling method to evaluate the manual labeling mechanism given the ambiguous time interval assumption. After that, the postprocessing method of cumulative class re-decision (CCR) was proposed based on the historical sequential information and continuity characteristic of agitation, improving the decision-making performance for the potential application of agitation detection system. The results showed that a combination of undersampling and CCR improved F1-score and other metrics to varying degrees with less training time and data.

To enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (O-RAN). So far, however, the applicability of O-RAN in controlling and optimizing RAN functions has not been widely investigated. In this paper, we jointly optimize the flow-split distribution, congestion control and scheduling (JFCS) to enable an intelligent traffic steering application in O-RAN. Combining tools from network utility maximization and stochastic optimization, we introduce a multi-layer optimization framework that provides fast convergence, long-term utility-optimality and significant delay reduction compared to the state-of-the-art and baseline RAN approaches. Our main contributions are three-fold: i) we propose the novel JFCS framework to efficiently and adaptively direct traffic to appropriate radio units; ii) we develop low-complexity algorithms based on the reinforcement learning, inner approximation and bisection search methods to effectively solve the JFCS problem in different time scales; and iii) the rigorous theoretical performance results are analyzed to show that there exists a scaling factor to improve the tradeoff between delay and utility-optimization. Collectively, the insights in this work will open the door towards fully automated networks with enhanced control and flexibility. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the convergence rate, long-term utility-optimality and delay reduction.

Influence operations are large-scale efforts to manipulate public opinion. The rapid detection and disruption of these operations is critical for healthy public discourse. Emergent AI technologies may enable novel operations which evade current detection methods and influence public discourse on social media with greater scale, reach, and specificity. New methods with inductive learning capacity will be needed to identify these novel operations before they indelibly alter public opinion and events. We develop an inductive learning framework which: 1) determines content- and graph-based indicators that are not specific to any operation; 2) uses graph learning to encode abstract signatures of coordinated manipulation; and 3) evaluates generalization capacity by training and testing models across operations originating from Russia, China, and Iran. We find that this framework enables strong cross-operation generalization while also revealing salient indicators$\unicode{x2013}$illustrating a generic approach which directly complements transductive methodologies, thereby enhancing detection coverage.

The open-radio access network (O-RAN) embraces cloudification and network function virtualization for base-band function processing by dis-aggregated radio units (RUs), distributed units (DUs), and centralized units (CUs). These enable the cloud-RAN vision in full, where multiple mobile network operators (MNOs) can install their proprietary or open RUs, but lease on-demand computational resources for DU-CU functions from commonly available open-clouds via open x-haul interfaces. In this paper, we propose and compare the performances of min-max fairness and Vickrey-Clarke-Groves (VCG) auction-based x-haul and DU-CU resource allocation mechanisms to create a multi-tenant O-RAN ecosystem that is sustainable for small, medium, and large MNOs. The min-max fair approach minimizes the maximum OPEX of RUs through cost-sharing proportional to their demands, whereas the VCG auction-based approach minimizes the total OPEX for all resources utilized while extracting truthful demands from RUs. We consider time-wavelength division multiplexed (TWDM) passive optical network (PON)-based x-haul interfaces where PON virtualization technique is used to flexibly provide optical connections among RUs and edge-clouds at macro-cell RU locations as well as open-clouds at the central office locations. Moreover, we design efficient heuristics that yield significantly better economic efficiency and network resource utilization than conventional greedy resource allocation algorithms and reinforcement learning-based algorithms.

When you challenge a computer to play a chess game, interact with a smart assistant, type a question into ChatGPT, or create artwork on DALL-E, you're interacting with a program that computer scientists would classify as artificial intelligence. But defining artificial intelligence can get complicated, especially when other terms like "robotics" and "machine learning" get thrown into the mix. To help you understand how these different fields and terms are related to one another, we've put together a quick guide. Artificial intelligence is a field of study, much like chemistry or physics, that kicked off in 1956. "Artificial intelligence is about the science and engineering of making machines with human-like characteristics in how they see the world, how they move, how they play games, even how they learn," says Daniela Rus, director of the computer science and artificial intelligence laboratory (CSAIL) at MIT. "Artificial intelligence is made up of many subcomponents, and there are all kinds of algorithms that solve various problems in artificial intelligence."

Abstract--In distributed multiple-input multiple-output (D-allocate their power among users to optimize the system's To overcome the complexity problem, Bashar et al. [3] In this study, we investigate the potential effects of adversarial attacks targeting AI-driven power control systems in I. We explain the main constraints of the adversary Deep learning is expected to be an important enabler for resulting from the distributed nature of wireless domain and many wireless communication challenges in 6G. Deep neural focus only on the possible practical scenarios to observe the networks (DNNs) are being proposed to handle a wide range severity of adversarial attack threats. We work on attacks based of wireless communication tasks including encoding/decoding on universal adversarial perturbation (UAP) which are not operations, spectrum sensing and RF signal classification. We propose a novel modified UAP (m-D-MIMO is a new network type considered for 6G communication UAP) technique that crafts a specific perturbation for each systems where many radio units (RUs) are geographically input where there is only a partial knowledge about some of distributed in a region to increase the coverage input entries.

Is a human-shaped robot like Optimus, the Tesla Bot, the right path to travel if we want to achieve useful robots and automated help in all aspects of our working and personal lives? That's basically the promise of Optimus, which Tesla CEO Elon Musk says will usher in "a fundamental transformation for civilization as we know it." One thing is undeniable: there's an abiding appeal to human shaped and human sized robots, as Irena Cronin, CEO of Infinite Retina, recently told me. Part of the value of copying the human form: the world is built for humans. Cars, homes, machines, factories, warehouses ... all of our built environment is designed by and for human beings as inhabitants, operators, drivers, or workers. And Tesla's not the only one attempting to create humaniform robots.