The discovery of novel drug-target (DT) interactions is an important step in the drug development process. The majority of computer techniques for predicting DT interactions have focused on binary classification, with the goal of determining whether or not a DT pair interacts. Protein-ligand interactions, on the other hand, assume a continuous range of binding strength values, also known as binding affinity, and forecasting this value remains a difficulty. As the amount of affinity data in DT knowledge-bases grows, advanced learning techniques such as deep learning architectures can be used to predict binding affinities. In this paper, we present a deep-learningbased methodology for predicting DT binding affinities using just sequencing information from both targets and drugs. The results show that the proposed deep learning-based model that uses the 1D representations of targets and drugs is an effective approach for drug target binding affinity prediction and it does not require additional chemical domain knowledge to work with. The model in which high-level representations of a drug and a target are constructed via CNNs that uses residual skip connections and also with an additional stream to create a highlevel combined representation of the drug-target pair achieved the best Concordance Index (CI) [1] performance in one of the largest benchmark datasets [2], outperforming the recent state-of-the-art method AttentionDTA [3] and many other machine-learning [4,5] and deep-learning [6-10] based baseline methods for DT binding affinity prediction that uses the 1D representations of targets and drugs.

In complex large-scale systems such as climate, important effects are caused by a combination of confounding processes that are not fully observable. The identification of sources from observations of system state is vital for attribution and prediction, which inform critical policy decisions. The difficulty of these types of inverse problems lies in the inability to isolate sources and the cost of simulating computational models. Surrogate models may enable the many-query algorithms required for source identification, but data challenges arise from high dimensionality of the state and source, limited ensembles of costly model simulations to train a surrogate model, and few and potentially noisy state observations for inversion due to measurement limitations. The influence of auxiliary processes adds an additional layer of uncertainty that further confounds source identification. We introduce a framework based on (1) calibrating deep neural network surrogates to the flow maps provided by an ensemble of simulations obtained by varying sources, and (2) using these surrogates in a Bayesian framework to identify sources from observations via optimization. Focusing on an atmospheric dispersion exemplar, we find that the expressive and computationally efficient nature of the deep neural network operator surrogates in appropriately reduced dimension allows for source identification with uncertainty quantification using limited data. Introducing a variable wind field as an auxiliary process, we find that a Bayesian approximation error approach is essential for reliable source inversion when uncertainty due to wind stresses the algorithm.

Rapid advances in artificial intelligence (AI) have the potential to significantly increase the productivity, quality, and profitability in future manufacturing systems. Traditional mass-production will give way to personalized production, with each item made to order, at the low cost and high-quality consumers have come to expect. Manufacturing systems will have the intelligence to be resilient to multiple disruptions, from small-scale machine breakdowns, to large-scale natural disasters. Products will be made with higher precision and lower variability. While gains have been made towards the development of these factories of the future, many challenges remain to fully realize this vision. To consider the challenges and opportunities associated with this topic, a panel of experts from Industry, Academia, and Government was invited to participate in an active discussion at the 2022 Modeling, Estimation and Control Conference (MECC) held in Jersey City, New Jersey from October 3- 5, 2022. The panel discussion focused on the challenges and opportunities to more fully integrate AI into manufacturing systems. Three overarching themes emerged from the panel discussion. First, to be successful, AI will need to work seamlessly, and in an integrated manner with humans (and vice versa). Second, significant gaps in the infrastructure needed to enable the full potential of AI into the manufacturing ecosystem, including sufficient data availability, storage, and analysis, must be addressed. And finally, improved coordination between universities, industry, and government agencies can facilitate greater opportunities to push the field forward. This article briefly summarizes these three themes, and concludes with a discussion of promising directions.

This paper proposes a methodology for discovering meaningful properties in data by exploring the latent space of unsupervised deep generative models. We combine manipulation of individual latent variables to extreme values outside the training range with methods inspired by causal inference into an approach we call causal disentanglement with extreme values (CDEV) and show that this approach yields insights for model interpretability. Using this technique, we can infer what properties of unknown data the model encodes as meaningful. We apply the methodology to test what is meaningful in the communication system of sperm whales, one of the most intriguing and understudied animal communication systems. We train a network that has been shown to learn meaningful representations of speech and test whether we can leverage such unsupervised learning to decipher the properties of another vocal communication system for which we have no ground truth. The proposed technique suggests that sperm whales encode information using the number of clicks in a sequence, the regularity of their timing, and audio properties such as the spectral mean and the acoustic regularity of the sequences. Some of these findings are consistent with existing hypotheses, while others are proposed for the first time. We also argue that our models uncover rules that govern the structure of communication units in the sperm whale communication system and apply them while generating innovative data not shown during training. This paper suggests that an interpretation of the outputs of deep neural networks with causal methodology can be a viable strategy for approaching data about which little is known and presents another case of how deep learning can limit the hypothesis space. Finally, the proposed approach combining latent space manipulation and causal inference can be extended to other architectures and arbitrary datasets.

Evaluating the worst-case performance of a reinforcement learning (RL) agent under the strongest/optimal adversarial perturbations on state observations (within some constraints) is crucial for understanding the robustness of RL agents. However, finding the optimal adversary is challenging, in terms of both whether we can find the optimal attack and how efficiently we can find it. Existing works on adversarial RL either use heuristics-based methods that may not find the strongest adversary, or directly train an RL-based adversary by treating the agent as a part of the environment, which can find the optimal adversary but may become intractable in a large state space. This paper introduces a novel attacking method to find the optimal attacks through collaboration between a designed function named "actor" and an RL-based learner named "director". The actor crafts state perturbations for a given policy perturbation direction, and the director learns to propose the best policy perturbation directions. Our proposed algorithm, PA-AD, is theoretically optimal and significantly more efficient than prior RL-based works in environments with large state spaces. Empirical results show that our proposed PA-AD universally outperforms state-of-the-art attacking methods in various Atari and MuJoCo environments. By applying PA-AD to adversarial training, we achieve state-of-the-art empirical robustness in multiple tasks under strong adversaries. The codebase is released at https://github.com/umd-huang-lab/paad_adv_rl.

Autonomous Underwater Vehicles (AUVs) conduct regular visual surveys of marine environments to characterise and monitor the composition and diversity of the benthos. The use of machine learning classifiers for this task is limited by the low numbers of annotations available and the many fine-grained classes involved. In addition to these challenges, there are domain shifts between image sets acquired during different AUV surveys due to changes in camera systems, imaging altitude, illumination and water column properties leading to a drop in classification performance for images from a different survey where some or all these elements may have changed. This paper proposes a framework to improve the performance of a benthic morphospecies classifier when used to classify images from a different survey compared to the training data. We adapt the SymmNet state-of-the-art Unsupervised Domain Adaptation method with an efficient bilinear pooling layer and image scaling to normalise spatial resolution, and show improved classification accuracy. We test our approach on two datasets with images from AUV surveys with different imaging payloads and locations. The results show that generic domain adaptation can be enhanced to produce a significant increase in accuracy for images from an AUV survey that differs from the training images.

In Part One, the authors addressed the industries most affected by AI, and began the discussion on U.S. federal and state regulations to expect in 2023. Part Two, continues the discussion on potential federal AI regulation and what companies can do to prepare.

In a turbulent world, the need to grow in a stable and peaceful environment demands the necessity of a military force that would ensure national security against both internal and external challenges. Gaining knowledge on the Nation's military strength will help in knowing the ability of a nation's readiness to secure and defend itself from the foe powers. Will artificial intelligence in defense pave the way to the new age of defense? Yes, progressing in AI allows new capabilities that will affect military strategies assertively. Blinx's Military Ranking Predictor AI application helps you to identify the powerful nations based on their ranking and the users could get a global overview of how countries are beefing up their military strength.

In the years leading up to, and through, World War II, animal behaviorist researchers thoroughly embraced motion picture technology as a means to better capture the daily experiences of their test subjects -- whether exploring the nuances of contemporary chimpanzee society or running macabre rat-eat-rat survival experiments to determine the Earth's "carrying capacity." However, once the studies had run their course, much of that scientific content was simply shelved. In his new book, The Celluloid Specimen: Moving Image Research into Animal Life, Seattle University Assistant Professor of Film Studies Dr. Ben Schultz-Figueroa, pulls these historic archives out of the vacuum of academic research to examine how they have influenced America's scientific and moral compasses since. In the excerpt below, Schultz-Figueroa recounts the Allied war effort to guide precision aerial munitions towards their targets using live pigeons as onboard targeting reticles. Excerpted from The Celluloid Specimen: Moving Image Research into Animal Life by Ben Schultz-Figueroa, published by the University of California Press.

Abstract: Deep learning models are vulnerable to adversarial examples. Transfer-based adversarial attacks attract tremendous attention as they can identify the weaknesses of deep learning models in a black-box manner. An effective strategy to improve the transferability of adversarial examples is attacking an ensemble of models. However, previous works simply average the outputs of different models, lacking an in-depth analysis on how and why model ensemble can strongly improve the transferability. In this work, we rethink the ensemble in adversarial attacks and define the common weakness of model ensemble with the properties of the flatness of loss landscape and the closeness to the local optimum of each model.