Many compression techniques have been proposed to reduce the communication overhead of Federated Learning training procedures. However, these are typically designed for compressing model updates, which are expected to decay throughout training. As a result, such methods are inapplicable to downlink (i.e., from the parameter server to clients) compression in the cross-device setting, where heterogeneous clients $\textit{may appear only once}$ during training and thus must download the model parameters. Accordingly, we propose $\textsf{DoCoFL}$ -- a new framework for downlink compression in the cross-device setting. Importantly, $\textsf{DoCoFL}$ can be seamlessly combined with many uplink compression schemes, rendering it suitable for bi-directional compression. Through extensive evaluation, we show that $\textsf{DoCoFL}$ offers significant bi-directional bandwidth reduction while achieving competitive accuracy to that of a baseline without any compression.

Deep reinforcement learning (DRL) is a promising method to learn control policies for robots only from demonstration and experience. To cover the whole dynamic behaviour of the robot, DRL training is an active exploration process typically performed in simulation environments. Although this simulation training is cheap and fast, applying DRL algorithms to real-world settings is difficult. If agents are trained until they perform safely in simulation, transferring them to physical systems is difficult due to the sim-to-real gap caused by the difference between the simulation dynamics and the physical robot. In this paper, we present a method of online training a DRL agent to drive autonomously on a physical vehicle by using a model-based safety supervisor. Our solution uses a supervisory system to check if the action selected by the agent is safe or unsafe and ensure that a safe action is always implemented on the vehicle. With this, we can bypass the sim-to-real problem while training the DRL algorithm safely, quickly, and efficiently. We compare our method with conventional learning in simulation and on a physical vehicle. We provide a variety of real-world experiments where we train online a small-scale vehicle to drive autonomously with no prior simulation training. The evaluation results show that our method trains agents with improved sample efficiency while never crashing, and the trained agents demonstrate better driving performance than those trained in simulation.

American action movie villains have always acted as a sort of paranoia litmus test, capturing a snapshot of the particular anxieties plaguing the country and its citizens at any given time. In the 1990s and '00s, with the Red Menace long forgotten, movies leaned heavily on the awful "bad Arab" trope, pulling their villains from the Middle East. Other recent smash-'em-ups have made bad guys out of rogue spies, shadowy cyber terrorists, and self-interested arms dealers, all common players in the global news landscape. But for Mission: Impossible--Dead Reckoning Part One, out this week, writers Bruce Geller, Erik Jendresen, and Christopher McQuarrie (who also directed the movie) made their big bad--known as The Entity--out of a slightly more amorphous fear: that of an all-powerful, all-seeing, sentient AI. It has access to anything with an online network and can use those evil techno powers to manipulate everything from global military superpowers to a grandma with a gun.

NATO leaders are holding their annual summit as Ukraine looks to the security alliance for support in its attempt to push back invading Russian forces. The Vilnius communique, however, while emphasising NATO's support for Ukraine, gave no clear timetable on when the country might be able to join the alliance, in a major disappointment for Ukrainian President Volodymyr Zelenskyy, who had travelled to the Lithuanian capital. "Ukraine's future is in NATO," the leaders said in the joint statement on Tuesday. "We will be in a position to extend an invitation to Ukraine to join the alliance when allies agree and conditions are met," the declaration said, without specifying the conditions. The communique also touched on the Asia Pacific, with the leaders of Australia, Japan, New Zealand and South Korea all attending as NATO allies. It said China was a challenge to NATO's interests, security and values with its "ambitions and coercive policies" triggering a furious response from Beijing. And it accused Beijing and Moscow of "mutually reinforcing attempts to undercut the rules-based international order". China has said it wants peace in Ukraine, but has not condemned Russia's full scale invasion since it began in February 2022. NATO is a defensive Alliance. It is the unique, essential and indispensable transatlantic forum to consult, coordinate and act on all matters related to our individual and collective security. We reaffirm our iron-clad commitment to defend each other and every inch of Allied territory at all times, protect our one billion citizens, and safeguard our freedom and democracy, in accordance with Article 5 of the Washington Treaty. We will continue to ensure our collective defence from all threats, no matter where they stem from, based on a 360-degree approach, to fulfil NATO's three core tasks of deterrence and defence, crisis prevention and management, and cooperative security. We adhere to international law and to the purposes and principles of the Charter of the United Nations and are committed to upholding the rules-based international order. This Summit marks a milestone in strengthening our Alliance. We look forward to our valuable exchanges with the Heads of State and Government of Australia, Japan, New Zealand, and the Republic of Korea, as well as the President of the European Council and the President of the European Commission at this Summit. We also welcome the engagements with the Foreign Ministers of Georgia and the Republic of Moldova, and with the Deputy Foreign Minister of Bosnia and Herzegovina, as we continue to consult closely on the implementation of NATO's tailored support measures. This is an historic step for Finland and for NATO. For many years, we worked closely as partners; we now stand together as Allies. NATO membership makes Finland safer, and NATO stronger. Every nation has the right to choose its own security arrangements.

Very few social media studies have been done on South African user-generated content during the COVID-19 pandemic and even fewer using hand-labelling over automated methods. Vaccination is a major tool in the fight against the pandemic, but vaccine hesitancy jeopardizes any public health effort. In this study, sentiment analysis on South African tweets related to vaccine hesitancy was performed, with the aim of training AI-mediated classification models and assessing their reliability in categorizing UGC. A dataset of 30000 tweets from South Africa were extracted and hand-labelled into one of three sentiment classes: positive, negative, neutral. The machine learning models used were LSTM, bi-LSTM, SVM, BERT-base-cased and the RoBERTa-base models, whereby their hyperparameters were carefully chosen and tuned using the WandB platform. We used two different approaches when we pre-processed our data for comparison: one was semantics-based, while the other was corpus-based. The pre-processing of the tweets in our dataset was performed using both methods, respectively. All models were found to have low F1-scores within a range of 45$\%$-55$\%$, except for BERT and RoBERTa which both achieved significantly better measures with overall F1-scores of 60$\%$ and 61$\%$, respectively. Topic modelling using an LDA was performed on the miss-classified tweets of the RoBERTa model to gain insight on how to further improve model accuracy.

A brain-computer interface (BCI) is a system that allows a person to communicate or control the surroundings without depending on the brain's normal output pathways of peripheral nerves and muscles. A lot of successful applications have arisen utilizing the advantages of BCI to assist disabled people with so-called assistive technology. Considering using BCI has fewer limitations and huge potential, this project has been proposed to control the movement of an electronic wheelchair via brain signals. The goal of this project is to help disabled people, especially paralyzed people suffering from motor disabilities, improve their life qualities. In order to realize the project stated above, Steady-State Visual Evoked Potential (SSVEP) is involved. It can be easily elicited in the visual cortical with the same frequency as the one is being focused by the subject. There are two important parts in this project. One is to process the EEG signals and another one is to make a visual stimulator using hardware. The EEG signals are processed in Matlab using the algorithm of Butterworth Infinite Impulse Response (IIR) bandpass filter (for preprocessing) and Fast Fourier Transform (FFT) (for feature extraction). Besides, a harmonics-based classification method is proposed and applied in the classification part. Moreover, the design of the visual stimulator combines LEDs as flickers and LCDs as information displayers on one panel. Microcontrollers are employed to control the SSVEP visual stimuli panel. This project is evaluated by subjects with different races and ages. Experimental results show the system is easy to be operated and it can achieve approximately a minimum 1-second time delay. So it demonstrates that this SSVEP-based BCI-controlled wheelchair has a huge potential to be applied to disabled people in the future.

Autonomous Vehicles (AVs) have emerged as a promising solution by replacing human drivers with advanced computer-aided decision-making systems. However, for AVs to effectively navigate the road, they must possess the capability to predict the future behavior of nearby traffic participants, similar to the predictive driving abilities of human drivers. Building upon existing literature is crucial to advance the field and develop a comprehensive understanding of trajectory prediction methods in the context of automated driving. To address this need, we have undertaken a comprehensive review that focuses on trajectory prediction methods for AVs, with a particular emphasis on machine learning techniques including deep learning and reinforcement learning-based approaches. We have extensively examined over two hundred studies related to trajectory prediction in the context of AVs. The paper begins with an introduction to the general problem of predicting vehicle trajectories and provides an overview of the key concepts and terminology used throughout. After providing a brief overview of conventional methods, this review conducts a comprehensive evaluation of several deep learning-based techniques. Each method is summarized briefly, accompanied by a detailed analysis of its strengths and weaknesses. The discussion further extends to reinforcement learning-based methods. This article also examines the various datasets and evaluation metrics that are commonly used in trajectory prediction tasks. Encouraging an unbiased and objective discussion, we compare two major learning processes, considering specific functional features. By identifying challenges in the existing literature and outlining potential research directions, this review significantly contributes to the advancement of knowledge in the domain of AV trajectory prediction.

In this paper, we present a systematic literature review on deep generative models for physiological signals, particularly electrocardiogram, electroencephalogram, photoplethysmogram and electromyogram. Compared to the existing review papers, we present the first review that summarizes the recent state-of-the-art deep generative models. By analysing the state-of-the-art research related to deep generative models along with their main applications and challenges, this review contributes to the overall understanding of these models applied to physiological signals. Additionally, by highlighting the employed evaluation protocol and the most used physiological databases, this review facilitates the assessment and benchmarking of deep generative models.

Autonomous, agile quadrotor flight raises fundamental challenges for robotics research in terms of perception, planning, learning, and control. A versatile and standardized platform is needed to accelerate research and let practitioners focus on the core problems. To this end, we present Agilicious, a co-designed hardware and software framework tailored to autonomous, agile quadrotor flight. It is completely open-source and open-hardware and supports both model-based and neural-network--based controllers. Also, it provides high thrust-to-weight and torque-to-inertia ratios for agility, onboard vision sensors, GPU-accelerated compute hardware for real-time perception and neural-network inference, a real-time flight controller, and a versatile software stack. In contrast to existing frameworks, Agilicious offers a unique combination of flexible software stack and high-performance hardware. We compare Agilicious with prior works and demonstrate it on different agile tasks, using both model-based and neural-network--based controllers. Our demonstrators include trajectory tracking at up to 5g and 70 km/h in a motion-capture system, and vision-based acrobatic flight and obstacle avoidance in both structured and unstructured environments using solely onboard perception. Finally, we demonstrate its use for hardware-in-the-loop simulation in virtual-reality environments. Thanks to its versatility, we believe that Agilicious supports the next generation of scientific and industrial quadrotor research.

This study aims to determine the appropriate size of the Mongolian general corpus. This study used the Heaps function and Type Token Ratio to determine the appropriate size of the Mongolian general corpus. The sample corpus of 906,064 tokens comprised texts from 10 domains of newspaper politics, economy, society, culture, sports, world articles and laws, middle and high school literature textbooks, interview articles, and podcast transcripts. First, we estimated the Heaps function with this sample corpus. Next, we observed changes in the number of types and TTR values while increasing the number of tokens by one million using the estimated Heaps function. As a result of observation, we found that the TTR value hardly changed when the number of tokens exceeded from 39 to 42 million. Thus, we conclude that an appropriate size for a Mongolian general corpus is from 39 to 42 million tokens.