Computing systems are tightly integrated today into our professional, social, and private lives. An important consequence of this growing ubiquity of computing is that it can have significant ethical implications of which computing professionals should take account. In most real-world scenarios, it is not immediately obvious how particular technical choices during the design and use of computing systems could be viewed from an ethical perspective. This article provides a perspective on the ethical challenges within semiconductor chip design, IoT applications, and the increasing use of artificial intelligence in the design processes, tools, and hardware-software stacks of these systems.

Energy consumption of deep artificial neural networks (ANNs) with thousands of neurons poses a problem not only during training [1], but also during inference [2]. Among other alternatives [3, 4, 5], hardware implementations of spiking neural networks (SNNs) [6, 7, 8, 9, 10] have been proposed as an energy-efficient solution, not only for large centralized applications, but also for computing in edge devices [11, 12, 13]. In SNNs neurons communicate by ultra-short pulses, called action potentials or spikes, that can be considered as point-like events in continuous time. In deep multi-layer SNNs, if a neuron in layer n fires a spike, this event causes a change in the voltage trajectory of neurons in layer n + 1. If, after some time, the trajectory of a neuron in layer n + 1 reaches a threshold value, then this neuron fires a spike. While there is no general consensus on how to best decode spike trains in biology [14, 15, 16], multiple pieces of evidence indicate that immediately after an onset of a stimulus, populations of neurons in auditory, visual, or tactile sensory areas respond in such a way that the timing of the first spike of each neuron after stimulus onset contains a high amount of information about the stimulus features [17, 18, 19]. These and similar observations have triggered the idea that, immediately after stimulus onset, an initial wave of activity is triggered and travels across several brain areas in the sensory processing stream [20, 21, 22, 23, 24]. We take inspiration from these observations and assume in this paper that information is encoded in the exact spike times of each neuron and that spikes are transmitted in a wave-like manner across layers of a deep feedforward neural network. Specifically, we use coding by time-to-first-spike (TTFS) [15], a timing-based code originally proposed in neuroscience [15, 17, 18, 22], which has recently attracted substantial attention in the context of neuromorphic implementations [8, 9, 10, 25, 26, 27, 28, 29, 30].

Optimal Power Flow (OPF) is a very traditional research area within the power systems field that seeks for the optimal operation point of electric power plants, and which needs to be solved every few minutes in real-world scenarios. However, due to the nonconvexities that arise in power generation systems, there is not yet a fast, robust solution technique for the full Alternating Current Optimal Power Flow (ACOPF). In the last decades, power grids have evolved into a typical dynamic, non-linear and large-scale control system, known as the power system, so searching for better and faster ACOPF solutions is becoming crucial. Appearance of Graph Neural Networks (GNN) has allowed the natural use of Machine Learning (ML) algorithms on graph data, such as power networks. On the other hand, Deep Reinforcement Learning (DRL) is known for its powerful capability to solve complex decision-making problems. Although solutions that use these two methods separately are beginning to appear in the literature, none has yet combined the advantages of both. We propose a novel architecture based on the Proximal Policy Optimization algorithm with Graph Neural Networks to solve the Optimal Power Flow. The objective is to design an architecture that learns how to solve the optimization problem and that is at the same time able to generalize to unseen scenarios. We compare our solution with the DCOPF in terms of cost after having trained our DRL agent on IEEE 30 bus system and then computing the OPF on that base network with topology changes

The rapid development of remote sensing technologies have gained significant attention due to their ability to accurately localize, classify, and segment objects from aerial images. These technologies are commonly used in unmanned aerial vehicles (UAVs) equipped with high-resolution cameras or sensors to capture data over large areas. This data is useful for various applications, such as monitoring and inspecting cities, towns, and terrains. In this paper, we presented a method for classifying and segmenting city road traffic dashed lines from aerial images using deep learning models such as U-Net and SegNet. The annotated data is used to train these models, which are then used to classify and segment the aerial image into two classes: dashed lines and non-dashed lines. However, the deep learning model may not be able to identify all dashed lines due to poor painting or occlusion by trees or shadows. To address this issue, we proposed a method to add missed lines to the segmentation output. We also extracted the x and y coordinates of each dashed line from the segmentation output, which can be used by city planners to construct a CAD file for digital visualization of the roads.

There's a lot of talk about ChatGPT these days, and some people talk about the monetary costs of running the model, but not many people talk about the environmental costs of the model. Increasing levels of greenhouse gases in the atmosphere due to human activities are a major driver of climate change [8]. The information and communications technology (ICT) sector and the data center industry are responsible for a relatively large share of global greenhouse gas emissions [9]. We -- users and developers of digital tools that run in data centers -- therefore need to do our part to contribute towards reducing the carbon footprint of digital activities, thereby mitigating climate change. To this end, it is first and foremost important that we become aware that even digital products require energy to develop and consume, thus they have a carbon footprint.

Penn State researchers are looking for a safer and more efficient way to use machine learning in the real world. Using a simulated high-rise office building, they developed and tested a new reinforcement learning algorithm aimed at improving energy consumption and occupant comfort in a real-world setting. Greg Pavlak, assistant professor of architectural engineering at Penn State, presented the results from the paper he co-authored, "Constrained Differentiable Cross-Entropy Method for Safe Model-Based Reinforcement Learning," at the Association for Computing Machinery International Conference on Systems for Energy-Efficient Built Environments (BuildSys) Conference, which was held Nov. 9-10 in Boston. "Reinforcement learning agents explore their environments to learn optimal actions through trial and error," Pavlak said. "Due to challenges in simulating the complexities of the real world, there is a growing trend to train reinforcement learning agents directly in the real world instead of in simulation."

The task we consider is portfolio construction in a speculative market, a fundamental problem in modern finance. While various empirical works now exist to explore deep learning in finance, the theory side is almost non-existent. In this work, we focus on developing a theoretical framework for understanding the use of data augmentation for deep-learning-based approaches to quantitative finance. The proposed theory clarifies the role and necessity of data augmentation for finance; moreover, our theory implies that a simple algorithm of injecting a random noise of strength $\sqrt{|r_{t-1}|}$ to the observed return $r_{t}$ is better than not injecting any noise and a few other financially irrelevant data augmentation techniques.

An autonomous experimentation platform in manufacturing is supposedly capable of conducting a sequential search for finding suitable manufacturing conditions for advanced materials by itself or even for discovering new materials with minimal human intervention. The core of the intelligent control of such platforms is the policy directing sequential experiments, namely, to decide where to conduct the next experiment based on what has been done thus far. Such policy inevitably trades off exploitation versus exploration and the current practice is under the Bayesian optimization framework using the expected improvement criterion or its variants. We discuss whether it is beneficial to trade off exploitation versus exploration by measuring the element and degree of surprise associated with the immediate past observation. We devise a surprise-reacting policy using two existing surprise metrics, known as the Shannon surprise and Bayesian surprise. Our analysis shows that the surprise-reacting policy appears to be better suited for quickly characterizing the overall landscape of a response surface or a design place under resource constraints. We argue that such capability is much needed for futuristic autonomous experimentation platforms. We do not claim that we have a fully autonomous experimentation platform, but believe that our current effort sheds new lights or provides a different view angle as researchers are racing to elevate the autonomy of various primitive autonomous experimentation systems.

Many business workflows require extracting important fields from form-like documents (e.g. bank statements, bills of lading, purchase orders, etc.). Recent techniques for automating this task work well only when trained with large datasets. In this work we propose a novel data augmentation technique to improve performance when training data is scarce, e.g. 10-250 documents. Our technique, which we call FieldSwap, works by swapping out the key phrases of a source field with the key phrases of a target field to generate new synthetic examples of the target field for use in training. We demonstrate that this approach can yield 1-7 F1 point improvements in extraction performance.

Quantum computing (QC) promises significant advantages on certain hard computational tasks over classical computers. However, current quantum hardware, also known as noisy intermediate-scale quantum computers (NISQ), are still unable to carry out computations faithfully mainly because of the lack of quantum error correction (QEC) capability. A significant amount of theoretical studies have provided various types of QEC codes; one of the notable topological codes is the surface code, and its features, such as the requirement of only nearest-neighboring two-qubit control gates and a large error threshold, make it a leading candidate for scalable quantum computation. Recent developments of machine learning (ML)-based techniques especially the reinforcement learning (RL) methods have been applied to the decoding problem and have already made certain progress. Nevertheless, the device noise pattern may change over time, making trained decoder models ineffective. In this paper, we propose a continual reinforcement learning method to address these decoding challenges. Specifically, we implement double deep Q-learning with probabilistic policy reuse (DDQN-PPR) model to learn surface code decoding strategies for quantum environments with varying noise patterns. Through numerical simulations, we show that the proposed DDQN-PPR model can significantly reduce the computational complexity. Moreover, increasing the number of trained policies can further improve the agent's performance. Our results open a way to build more capable RL agents which can leverage previously gained knowledge to tackle QEC challenges.