Spry Squared is a Minority and Woman Owned Small Business headquartered in Denver, Colorado with offices across the United States of America. We are an experienced federal government and commercial service provider with security cleared personnel working on various projects across the USA and the globe. Spry Squared provides organizations with Best in Class Enterprise Solutions, Managed IT Services, Cybersecurity Solutions, IT Professional Services, Recruiting Services, Project/Program Management and technology products. We are your strategic partner and value-added reseller, solving complex business challenges by leveraging technology solutions that reduce costs, optimize productivity and minimize risk. Spry Squared is looking for a strong technical Data Engineer to join a team of highly empowered System Administrators, Developers, and Engineers to support the ETL workflows on classified data source networks (NIPR, SIPR, JWICS).

Welcome to Startups Weekly, a nuanced take on this week's startup news and trends. To get this in your inbox, subscribe here. It's Kyle, filling in this issue for Natasha, who's taking a much needed break from the news cycle (and the spectacle that's become Twitter). While it's my first Startups Weekly column, you've likely seen me on TC here and there, covering chiefly venture, AI and enterprise-related items. It's a real pleasure to round up this week's startup news -- partially because it doesn't center around Musk shenanigans.

Harnessing the incredible potential of artificial intelligence (AI) for its invaluable impact on the human race is an incredibly powerful guiding vision. AI is part of everyday life, guiding us today. In this article, I am going to discuss how AI is fundamentally shaping smarter decisions that impact our world. By embracing AI as a national imperative and making AI solutions relevant and accessible to all, we can charge forward and change the way businesses all over the world make their most important decisions. Companies that harnessed the potential of AI were in a position during the pandemic to weather the storm and continue to thrive despite the pandemic's massive impact on consumer behavior.

Ukrainian President Volodymyr Zelenskyy has backed a fundraising campaign to help Ukraine build a naval drone fleet to protect cities against Russian missiles launched from warships on the Black Sea. United24, an initiative Zelenskyy launched to raise charitable donations following Russia's invasion in February, said Ukraine needed 100 drones operating from the sea, each of which costs 10 million hryvnias (around $274,000). The fundraising site said that since the invasion began, Russian has launched over 4,500 missiles into Ukraine and "every fifth strike came from the sea". "We must defend the waters of our seas and peaceful cities from Russian missiles launched from ships," Zelenskyy wrote on the Telegram messaging app on Friday. "Naval drones will also help unblock the corridor for civilian ships transporting grain for the world," he said.

AI stocks may be excellent long-term investments. The global artificial intelligence industry is expected to grow from $59.7 billion in 2021 to $422.4 billion by 2028, according to Zion Market Research. Virtually every industry is being disrupted by AI, automation and robotics. Whether it be machine learning, smart applications and appliances, digital assistants or autonomous vehicles, companies that aren't investing in AI products and services risk becoming obsolete. Countless companies stand to benefit from AI, but a handful of stocks have AI and automation as a central part of their businesses.

In response to growing recognition of the social impact of new AI-based technologies, major AI and ML conferences and journals now encourage or require papers to include ethics impact statements and undergo ethics reviews. This move has sparked heated debate concerning the role of ethics in AI research, at times devolving into name-calling and threats of "cancellation." We diagnose this conflict as one between atomist and holist ideologies. Among other things, atomists believe facts are and should be kept separate from values, while holists believe facts and values are and should be inextricable from one another. With the goal of reducing disciplinary polarization, we draw on numerous philosophical and historical sources to describe each ideology's core beliefs and assumptions. Finally, we call on atomists and holists within the ever-expanding data science community to exhibit greater empathy during ethical disagreements and propose four targeted strategies to ensure AI research benefits society.

We introduce a method to successively locate equilibria (steady states) of dynamical systems on Riemannian manifolds. The manifolds need not be characterized by an a priori known atlas or by the zeros of a smooth map. Instead, they can be defined by point-clouds and sampled as needed through an iterative process. If the manifold is an Euclidean space, our method follows isoclines, curves along which the direction of the vector field $X$ is constant. For a generic vector field $X$, isoclines are smooth curves and every equilibrium lies on isoclines. We generalize the definition of isoclines to Riemannian manifolds through the use of parallel transport: generalized isoclines are curves along which the directions of $X$ are parallel transports of each other. As in the Euclidean case, generalized isoclines of generic vector fields $X$ are smooth curves that connect equilibria of $X$. Our algorithm can be regarded as an extension of the method of Newton trajectories to the manifold setting when the manifold is unknown. This work is motivated by computational statistical mechanics, specifically high dimensional (stochastic) differential equations that model the dynamics of molecular systems. Often, these dynamics concentrate near low-dimensional manifolds and have transitions (saddle points with a single unstable direction) between metastable equilibria. We employ iteratively sampled data and isoclines to locate these saddle points. Coupling a black-box sampling scheme (e.g., Markov chain Monte Carlo) with manifold learning techniques (diffusion maps in the case presented here), we show that our method reliably locates equilibria of $X$.

A successful machine learning (ML) algorithm often relies on a large amount of high-quality data to train well-performed models. Supervised learning approaches, such as deep learning techniques, generate high-quality ML functions for real-life applications, however with large costs and human efforts to label training data. Recent advancements in federated learning (FL) allow multiple data owners or organisations to collaboratively train a machine learning model without sharing raw data. In this light, vertical FL allows organisations to build a global model when the participating organisations have vertically partitioned data. Further, in the vertical FL setting the participating organisation generally requires fewer resources compared to sharing data directly, enabling lightweight and scalable distributed training solutions. However, privacy protection in vertical FL is challenging due to the communication of intermediate outputs and the gradients of model update. This invites adversary entities to infer other organisations underlying data. Thus, in this paper, we aim to explore how to protect the privacy of individual organisation data in a differential privacy (DP) setting. We run experiments with different real-world datasets and DP budgets. Our experimental results show that a trade-off point needs to be found to achieve a balance between the vertical FL performance and privacy protection in terms of the amount of perturbation noise.

Deep learning models have demonstrated superior performance in various healthcare applications. However, the major limitation of these deep models is usually the lack of high-quality training data due to the private and sensitive nature of this field. In this study, we propose a novel textual data augmentation method to generate artificial clinical notes in patients' Electronic Health Records (EHRs) that can be used as additional training data for patient outcomes prediction. Essentially, we fine-tune the generative language model GPT-2 to synthesize labeled text with the original training data. More specifically, We propose a teacher-student framework where we first pre-train a teacher model on the original data, and then train a student model on the GPT-augmented data under the guidance of the teacher. We evaluate our method on the most common patient outcome, i.e., the 30-day readmission rate. The experimental results show that deep models can improve their predictive performance with the augmented data, indicating the effectiveness of the proposed architecture.

When performing complex tasks, humans naturally reason at multiple temporal and spatial resolutions simultaneously. We contend that for an artificially intelligent agent to effectively model human teammates, i.e., demonstrate computational theory of mind (ToM), it should do the same. In this paper, we present an approach for integrating high and low-resolution spatial and temporal information to predict human behavior in real time and evaluate it on data collected from human subjects performing simulated urban search and rescue (USAR) missions in a Minecraft-based environment. Our model composes neural networks for high and low-resolution feature extraction with a neural network for behavior prediction, with all three networks trained simultaneously. The high-resolution extractor encodes dynamically changing goals robustly by taking as input the Manhattan distance difference between the humans' Minecraft avatars and candidate goals in the environment for the latest few actions, computed from a high-resolution gridworld representation. In contrast, the low-resolution extractor encodes participants' historical behavior using a historical state matrix computed from a low-resolution graph representation. Through supervised learning, our model acquires a robust prior for human behavior prediction, and can effectively deal with long-term observations. Our experimental results demonstrate that our method significantly improves prediction accuracy compared to approaches that only use high-resolution information.