Following ESA's successful launch of its first Artificial Intelligence (AI) Earth observation mission ɸ-Sat-1 (pronounced Phi-Sat-1), the European Space Agency (ESA) follows-up with the next innovative state-of-the-art technology: ɸ-Sat-2 (Phi-Sat-2).

The federal government took several important steps that prioritized AI development and deployment and positioned the United States to strengthen its global AI leadership, beginning with President Trump's "Executive Order on Maintaining American Leadership in Artificial Intelligence," which set the tone for the rest of the year. February 11: President Trump issued Executive Order 13859, "Maintaining American Leadership in Artificial Intelligence," which launched the American AI Initiative, the official U.S. national AI strategy. The initiative includes five pillars: investing in AI research and development, making federal AI resources more available, setting standards for AI, training an AI workforce, and promoting a pro-innovation international environment. The executive order stresses the importance of "continued American leadership" in AI to "maintaining the economic and national security of the United States," as President Trump wrote in a press release accompanying the order. April 2: The Food and Drug Administration (FDA) released a proposed regulatory framework for AI-based software as a medical device, including as a tool for disease detection, diagnosis, targeted therapies, or personalized medicine.

From autonomous vehicles to virtual assistants, artificial intelligence is becoming increasingly present in our daily lives, and yet we are really just at the beginning of the curve. A powerful, transformative technology though it is, dealing with vast amounts of data, applications are already triggering unease in the public and the continued adoption of the game-changing technology must be balanced with heightened scrutiny towards policy, regulation, and ethics. The need for more stringent oversight is demonstrated by the increasing reliance we place on this technology in our daily lives -- in the case of driverless cars, we'd be placing our lives in the hands of AI. But it's also demonstrated in use by businesses and organizations. In the case of law enforcement, Flaws, or incompleteness in the data used by facial recognition systems in law enforcement, for example, can lead to racial profiling or misidentification of suspects, or add to the sense of an invasive surveillance culture at best.

A closed-form solution exists in two-class linear discriminant analysis (LDA), which discriminates two Gaussian-distributed classes in a multi-dimensional feature space. In this work, we interpret the multilayer perceptron (MLP) as a generalization of a two-class LDA system so that it can handle an input composed by multiple Gaussian modalities belonging to multiple classes. Besides input layer $l_{in}$ and output layer $l_{out}$, the MLP of interest consists of two intermediate layers, $l_1$ and $l_2$. We propose a feedforward design that has three stages: 1) from $l_{in}$ to $l_1$: half-space partitionings accomplished by multiple parallel LDAs, 2) from $l_1$ to $l_2$: subspace isolation where one Gaussian modality is represented by one neuron, 3) from $l_2$ to $l_{out}$: class-wise subspace mergence, where each Gaussian modality is connected to its target class. Through this process, we present an automatic MLP design that can specify the network architecture (i.e., the layer number and the neuron number at a layer) and all filter weights in a feedforward one-pass fashion. This design can be generalized to an arbitrary distribution by leveraging the Gaussian mixture model (GMM). Experiments are conducted to compare the performance of the traditional backpropagation-based MLP (BP-MLP) and the new feedforward MLP (FF-MLP).

We explore the use of multiple deep learning models for detecting flaws in software programs. Current, standard approaches for flaw detection rely on a single representation of a software program (e.g., source code or a program binary). We illustrate that, by using techniques from multimodal deep learning, we can simultaneously leverage multiple representations of software programs to improve flaw detection over single representation analyses. Specifically, we adapt three deep learning models from the multimodal learning literature for use in flaw detection and demonstrate how these models outperform traditional deep learning models.

Narratives are fundamental to our perception of the world and are pervasive in all activities that involve the representation of events in time. Yet, modern online information systems do not incorporate narratives in their representation of events occurring over time. This article aims to bridge this gap, combining the theory of narrative representations with the data from modern online systems. We make three key contributions: a theory-driven computational representation of narratives, a novel extraction algorithm to obtain these representations from data, and an evaluation of our approach. In particular, given the effectiveness of visual metaphors, we employ a route map metaphor to design a narrative map representation. The narrative map representation illustrates the events and stories in the narrative as a series of landmarks and routes on the map. Each element of our representation is backed by a corresponding element from formal narrative theory, thus providing a solid theoretical background to our method. Our approach extracts the underlying graph structure of the narrative map using a novel optimization technique focused on maximizing coherence while respecting structural and coverage constraints. We showcase the effectiveness of our approach by performing a user evaluation to assess the quality of the representation, metaphor, and visualization. Evaluation results indicate that the Narrative Map representation is a powerful method to communicate complex narratives to individuals. Our findings have implications for intelligence analysts, computational journalists, and misinformation researchers.

Increasing response time of emergency vehicles (EVs) could lead to an immensurable loss of property and life. On this account, tactical decision making for EV's microscopic control remains an indispensable issue to be improved. Our approach verifies that deep reinforcement learning could complement rule-based methods in generalization. It reveals that deterministic avoidance strategy for common vehicles at a low speed benefits EVs a lot, nevertheless, when at a high velocity, DQN breaks the deadlock of reduced safe distance and brings boldness to EVs in lane changing. Besides, a novel DQN method with speed-adaptive compact state space (SC-DQN) is put forward to fit in EVs' high-speed feature and generalize in various road topologies. All Above is implemented in SUMO emulator, where common vehicles are modeled rule-based whereas EVs are intelligently controlled.

Clustering algorithms play a fundamental role as tools in decision-making and sensible automation processes. Due to the widespread use of these applications, a robustness analysis of this family of algorithms against adversarial noise has become imperative. To the best of our knowledge, however, only a few works have currently addressed this problem. In an attempt to fill this gap, in this work, we propose a black-box adversarial attack for crafting adversarial samples to test the robustness of clustering algorithms. We formulate the problem as a constrained minimization program, general in its structure and customizable by the attacker according to her capability constraints. We do not assume any information about the internal structure of the victim clustering algorithm, and we allow the attacker to query it as a service only. In the absence of any derivative information, we perform the optimization with a custom approach inspired by the Abstract Genetic Algorithm (AGA). In the experimental part, we demonstrate the sensibility of different single and ensemble clustering algorithms against our crafted adversarial samples on different scenarios. Furthermore, we perform a comparison of our algorithm with a state-of-the-art approach showing that we are able to reach or even outperform its performance. Finally, to highlight the general nature of the generated noise, we show that our attacks are transferable even against supervised algorithms such as SVMs, random forests and neural networks.

Random Fourier features enable researchers to build feature map to learn the spectral distribution of the underlying kernel. Current distribution-based methods follow a two-stage scheme: they first learn and optimize the feature map by solving the kernel alignment problem, then learn a linear classifier on the features. However, since the ideal kernel in kernel alignment problem is not necessarily optimal in classification tasks, the generalization performance of the random features learned in this two-stage manner can perhaps be further improved. To address this issue, we propose an end-to-end, one-stage kernel learning approach, called generative random Fourier features, which jointly learns the features and the classifier. A generative network is involved to implicitly learn and to sample from the distribution of the latent kernel. Random features are then built via the generative weights and followed by a linear classifier parameterized as a full-connected layer. We jointly train the generative network and the classifier by solving the empirical risk minimization problem for a one-stage solution. Straightly minimizing the loss between predictive and true labels brings better generalization performance. Besides, this end-to-end strategy allows us to increase the depth of features, resulting in multi-layer architecture and exhibiting strong linear-separable pattern. Empirical results demonstrate the superiority of our method in classification tasks over other two-stage kernel learning methods. Finally, we investigate the robustness of proposed method in defending adversarial attacks, which shows that the randomization and resampling mechanism associated with the learned distribution can alleviate the performance decrease brought by adversarial examples.

The state has suspended Boston-based COVID-19 testing lab Orig3n Laboratory after it produced nearly 400 false positive results. Public health officials became aware in early August of an "unusually high positivity rate" among the lab's test results and requested that Orig3n stop testing for the virus as of Aug. 8. Specimens were sent to an independent lab to be retested as part of a state Department of Public Health investigation, and the results showed at least 383 false positives. On Aug. 27, the state Department of Public Health notified Orig3n of "three significant certification deficiencies that put patients at immediate risk of harm," according to a DPH spokeswoman. They included the failure of the lab's director to provide overall management, issues with the extraction phase of testing, and a failure to meet analytic requirements such as documenting the daily sanitizing of equipment used for coronavirus testing. A statement of deficiency was issued on Sept. 4. The lab must now respond with a written plan of correction by Sept. 14, "and if action is not taken it can face sanctions," DPH said.