Our work focuses on training RL agents on multiple visually diverse environments to improve observational generalization performance. In prior methods, policy and value networks are separately optimized using a disjoint network architecture to avoid interference and obtain a more accurate value function. We identify that a value network in the multi-environment setting is more challenging to optimize and prone to memorizing the training data than in the conventional single-environment setting. In addition, we find that appropriate regularization on the value network is necessary to improve both training and test performance. To this end, we propose Delayed-Critic Policy Gradient (DCPG), a policy gradient algorithm that implicitly penalizes value estimates by optimizing the value network less frequently with more training data than the policy network. This can be implemented using a single unified network architecture. Furthermore, we introduce a simple self-supervised task that learns the forward and inverse dynamics of environments using a single discriminator, which can be jointly optimized with the value network. Our proposed algorithms significantly improve observational generalization performance and sample efficiency on the Procgen Benchmark.

Adversarial Training is the most effective approach for improving the robustness of Deep Neural Networks (DNNs). However, compared to the large body of research in optimizing the adversarial training process, there are few investigations into how architecture components affect robustness, and they rarely constrain model capacity. Thus, it is unclear where robustness precisely comes from. In this work, we present the first large-scale systematic study on the robustness of DNN architecture components under fixed parameter budgets. Through our investigation, we distill 18 actionable robust network design guidelines that empower model developers to gain deep insights. We demonstrate these guidelines' effectiveness by introducing the novel Robust Architecture (RobArch) model that instantiates the guidelines to build a family of top-performing models across parameter capacities against strong adversarial attacks. RobArch achieves the new state-of-the-art AutoAttack accuracy on the RobustBench ImageNet leaderboard. The code is available at $\href{https://github.com/ShengYun-Peng/RobArch}{\text{this url}}$.

In this thesis a comprehensive verification framework is proposed to contend with some important issues in composability verification and a verification process is suggested to verify composability of different kinds of systems models, such as reactive, real-time and probabilistic systems. With an assumption that all these systems are concurrent in nature in which different composed components interact with each other simultaneously, the requirements for the extensive techniques for the structural and behavioral analysis becomes increasingly challenging. The proposed verification framework provides methods, techniques and tool support for verifying composability at its different levels. These levels are defined as foundations of consistent model composability. Each level is discussed in detail and an approach is presented to verify composability at that level. In particular we focus on the Dynamic-Semantic Composability level due to its significance in the overall composability correctness and also due to the level of difficulty it poses in the process. In order to verify composability at this level we investigate the application of three different approaches namely (i) Petri Nets based Algebraic Analysis (ii) Colored Petri Nets (CPN) based State-space Analysis and (iii) Communicating Sequential Processes based Model Checking. All three approaches attack the problem of verifying dynamic-semantic composability in different ways however they all share the same aim i.e., to confirm the correctness of a composed model with respect to its requirement specifications.

Clustering is a fundamental unsupervised learning problem where a dataset is partitioned into clusters that consist of nearby points in a metric space. A recent variant, fair clustering, associates a color with each point representing its group membership and requires that each color has (approximately) equal representation in each cluster to satisfy group fairness. In this model, the cost of the clustering objective increases due to enforcing fairness in the algorithm. The relative increase in the cost, the ''price of fairness,'' can indeed be unbounded. Therefore, in this paper we propose to treat an upper bound on the clustering objective as a constraint on the clustering problem, and to maximize equality of representation subject to it. We consider two fairness objectives: the group utilitarian objective and the group egalitarian objective, as well as the group leximin objective which generalizes the group egalitarian objective. We derive fundamental lower bounds on the approximation of the utilitarian and egalitarian objectives and introduce algorithms with provable guarantees for them. For the leximin objective we introduce an effective heuristic algorithm. We further derive impossibility results for other natural fairness objectives. We conclude with experimental results on real-world datasets that demonstrate the validity of our algorithms.

Misinformation threatens modern society by promoting distrust in science, changing narratives in public health, heightening social polarization, and disrupting democratic elections and financial markets, among a myriad of other societal harms. To address this, a growing cadre of professional fact-checkers and journalists provide high-quality investigations into purported facts. However, these largely manual efforts have struggled to match the enormous scale of the problem. In response, a growing body of Natural Language Processing (NLP) technologies have been proposed for more scalable fact-checking. Despite tremendous growth in such research, however, practical adoption of NLP technologies for fact-checking still remains in its infancy today. In this work, we review the capabilities and limitations of the current NLP technologies for fact-checking. Our particular focus is to further chart the design space for how these technologies can be harnessed and refined in order to better meet the needs of human fact-checkers. To do so, we review key aspects of NLP-based fact-checking: task formulation, dataset construction, modeling, and human-centered strategies, such as explainable models and human-in-the-loop approaches. Next, we review the efficacy of applying NLP-based fact-checking tools to assist human fact-checkers. We recommend that future research include collaboration with fact-checker stakeholders early on in NLP research, as well as incorporation of human-centered design practices in model development, in order to further guide technology development for human use and practical adoption. Finally, we advocate for more research on benchmark development supporting extrinsic evaluation of human-centered fact-checking technologies.

Neuromorphic computing is an emerging computing paradigm that moves away from batched processing towards the online, event-driven, processing of streaming data. Neuromorphic chips, when coupled with spike-based sensors, can inherently adapt to the "semantics" of the data distribution by consuming energy only when relevant events are recorded in the timing of spikes and by proving a low-latency response to changing conditions in the environment. This paper proposes an end-toend design for a neuromorphic wireless Internet-of-Things system that integrates spike-based sensing, processing, and communication. In the proposed NeuroComm system, each sensing device is equipped with a neuromorphic sensor, a spiking neural network (SNN), and an impulse radio (IR) transmitter with multiple antennas. Transmission takes place over a shared fading channel to a receiver equipped with a multi-antenna impulse radio receiver and with an SNN. In order to enable adaptation of the receiver to the fading channel conditions, we introduce a hypernetwork to control the weights of the decoding SNN using pilots. Pilots, encoding SNNs, decoding SNN, and hypernetwork are jointly trained across multiple channel realizations. The proposed system is shown to significantly improve over conventional frame-based digital solutions, as well as over alternative non-adaptive training methods, in terms of time-to-accuracy and energy consumption metrics. The work of Osvaldo Simeone and Nicolas Skatchkovsky was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme, grant agreement No. 725731, by an Open Fellowship of the EPSRC with reference EP/W024101/1, and by the European Union through project CENTRIC (101096379), and the work by Jiechen Chen was funded by the China Scholarship Council and King's College London for their Joint Full-Scholarship (K-CSC) under Grant CSC202108440223.

After nearly four decades in space, NASA's retried Earth Radiation Budget Satellite (ERBS) is about to fall from the sky. On Friday, the agency said the likelihood of wreckage from ERBS harming anyone on Earth is "very low." NASA expects most of the 5,400-pound satellite will burn up upon re-entry. Earlier this week, the Defense Department predicted ERBS would re-enter the Earth's atmosphere on Sunday at approximately 6:40PM ET, give or take 17 hours. While it may be a household name, the Earth Radiation Budget Satellite had anything but a dull history.

Deceit and media manipulation have always been a part of wartime communications, but never before has it been possible for nearly any actor in a conflict to generate realistic audio, video, and text of their opponent's political officials and military leaders. As artificial intelligence (AI) grows more sophisticated and the cost of computing continues to drop, the challenge deepfakes pose to online information environments during armed conflict will only grow. To navigate that challenge, security officials and policymakers need a far greater understanding of how the technology works and the myriad ways it can be used in international armed conflict. Deepfakes can be leveraged for a wide range of purposes, including falsifying orders from military leaders, sowing confusion among the public and armed forces, and lending legitimacy to wars and uprisings. While these tactics can and often will fail, their potential to impact an adversary's communications and messaging mean that security and intelligence officials will inevitably use them in a wide range of operations.

After almost four decades in space, a retired NASA satellite is about to fall to earth. The chances of falling satellite debris posing a risk to "anyone on Earth is very low", NASA said in a statement on Saturday. Most of the 2,450kg (5,400lb) satellite will burn up on re-entry but some pieces are expected to survive, according to NASA. The United States space agency put the odds of injury from falling debris at about one-in-9,400. The science satellite is expected to come down on Sunday night at approximately 18:40 EST (23:40 GMT), give or take 17 hours, according to the US defence department.

Will robots take over our jobs? This Cornell University and King's College London collaboration examines how artificial intelligence (AI) has influenced major knowledge-intensive services sectors, such as telecommunications and health care -- and how governments, employers and workers have responded to the challenges that smart technologies pose for the world of work. Taking the United States and the United Kingdom as our case studies, we will explore a wide range of emerging issues and countervailing forces (e.g., public policies, professional associations, vocational training systems, licensing bodies and laws, unions and labor market regulation). The study aims to be the first to systematically map these issues in the United States and the United Kingdom, with the goal of launching a mixed-methods project that covers a broader set of country cases. In so doing, the collaboration leverages the interdisciplinary expertise of our institutions to inform policy debates at the intersection of AI and work.