The enclosed data release consists of a subset of the calibration data from the Majorana Demonstrator experiment. Each Majorana event is accompanied by raw Germanium detector waveforms, pulse shape discrimination cuts, and calibrated final energies, all shared in an HDF5 file format along with relevant metadata. This release is specifically designed to support the training and testing of Artificial Intelligence (AI) and Machine Learning (ML) algorithms upon our data. This document is structured as follows. Section I provides an overview of the dataset's content and format; Section II outlines the location of this dataset and the method for accessing it; Section III presents the NPML Machine Learning Challenge associated with this dataset; Section IV contains a disclaimer from the Majorana collaboration regarding the use of this dataset; Appendix A contains technical details of this data release. Please direct questions about the material provided within this release to liaobo77@ucsd.edu (A. Li).

A 2019 survey found that 73 of 84 prominent AI strategy documents referenced transparency or explainability [81]. Influential intergovernmental bodies such as United Nations agencies and the Organization for Economic Cooperation and Development (OECD) have put forth transparency and explainability as key mechanisms for ensuring that algorithmic systems produce beneficial outcomes and uphold "democratic values" [121, 143]. Algorithmic transparency and explainability can serve many purposes, but some of the most important are legal in nature: allowing lawmakers to understand and craft effective rules for algorithmic systems, enabling a broader set of stakeholders to be aware of (and obtain redress from) algorithmic harms, and assisting regulators in exercising meaningful oversight over the use of algorithms [81, 109]. To serve these objectives, transparency measures and explanation techniques must be developed with an understanding of the specific goals, constraints, and incentives of policymakers. This paper aims to help bridge the gap between policymakers and the explanation research community, helping researchers to better understand and respond to the needs of policymakers. To this end, it provides case studies illustrating five uses for algorithmic transparency and explanation in policy settings. These case studies (Table 1) were selected to span four axes: the spectrum from explanation to transparency (including both requirements for specific explanation techniques, like those developed by the machine learning research community, and broader forms of transparency requirements); different jurisdictions (including U.S. federal regulators, U.S. states, and the EU); policy actors with differing technical and financial capacities; and a diverse array of policy approaches (including prescriptive technical rules, process-oriented rules, nonbinding guidelines, and modifications to legal procedures). Building on these case studies, this paper argues that explanation techniques developed by the research community can be too complex, too uncertain, or too restricted to satisfy the constraints that policymakers and the law operate under in practice. As a result, explanation is often limited in its ability to enable meaningful public policy solutions to algorithmic harms.

The myriad complex systems with multiway interactions motivate the extension of graph-based pairwise connections to higher-order relations. In particular, the simplicial complex has inspired generalizations of graph neural networks (GNNs) to simplicial complex-based models. Learning on such systems requires large amounts of data, which can be expensive or impossible to obtain. We propose data augmentation of simplicial complexes through both linear and nonlinear mixup mechanisms that return mixtures of existing labeled samples. In addition to traditional pairwise mixup, we present a convex clustering mixup approach for a data-driven relationship among several simplicial complexes. We theoretically demonstrate that the resultant synthetic simplicial complexes interpolate among existing data with respect to homomorphism densities. Our method is demonstrated on both synthetic and real-world datasets for simplicial complex classification.

Several fundamental problems in science and engineering consist of global optimization tasks involving unknown high-dimensional (black-box) functions that map a set of controllable variables to the outcomes of an expensive experiment. Bayesian Optimization (BO) techniques are known to be effective in tackling global optimization problems using a relatively small number objective function evaluations, but their performance suffers when dealing with high-dimensional outputs. To overcome the major challenge of dimensionality, here we propose a deep learning framework for BO and sequential decision making based on bootstrapped ensembles of neural architectures with randomized priors. Using appropriate architecture choices, we show that the proposed framework can approximate functional relationships between design variables and quantities of interest, even in cases where the latter take values in high-dimensional vector spaces or even infinite-dimensional function spaces. In the context of BO, we augmented the proposed probabilistic surrogates with re-parameterized Monte Carlo approximations of multiple-point (parallel) acquisition functions, as well as methodological extensions for accommodating black-box constraints and multi-fidelity information sources. We test the proposed framework against state-of-the-art methods for BO and demonstrate superior performance across several challenging tasks with high-dimensional outputs, including a constrained multi-fidelity optimization task involving shape optimization of rotor blades in turbo-machinery.

More than 20 tech and civil society leaders, including the chief executives of five of the 10 biggest U.S. companies, appeared at a closed-door Senate meeting on Wednesday to shape how artificial intelligence is regulated. The meeting, which was organized by Senate Majority Leader Chuck Schumer, included a prestigious, and possibly combustible, mix of personalities with diverging views on how to write the rules for AI. The CEOs of Alphabet, Microsoft, Meta Platforms and OpenAI were invited to appear alongside rivals and industry critics to discuss possible guardrails for AI that balance the risks and rewards of the technology. Areas of disagreement were apparent throughout the morning session, according to several people who were in the room. Meta CEO Mark Zuckerberg, OpenAI CEO Sam Altman and Microsoft co-founder Bill Gates offered diverging views on the risks of open-source AI research, according to people in the room.

Some of the most powerful people in America assembled in Washington, DC, today to help shape the future of artificial intelligence (AI) safeguards. The unprecedented meeting took place as the US Senate gears up to draft legislation that will regulate the rapidly advancing AI industry, which many of the world's best minds fear could destroy humanity if left unchecked. The gathering brought 22 of the most influential voices in the tech sector - who had a combined net worth of over $400billion - and 100 senators under one roof, bridging the gap between Silicon Valley and the nation's capital. The high-profile event included notorious AI critic Elon Musk, who today called for tighter regulation of AI, as well as Mark Zuckerberg, Bill Gates and the CEOs of Google and IBM. The private meeting was a crash course for legislators on how best to regulate AI: a technical achievement which some of these same industry leaders likened to the'extinction'-level risk of nuclear weapons.

A delegation of top tech leaders including Sundar Pichai, Elon Musk, Mark Zuckerberg and Sam Altman convened in Washington on Wednesday for the first of nine meetings with US senators to discuss the rise of artificial intelligence and how it should be regulated. Billed as an "AI safety forum," the closed door meeting was organized by the Democratic senator Chuck Schumer who called it "one of the most important conversations of the year". The forum comes as the federal government explores new and existing avenues to regulate AI. "It will be a meeting unlike any other that we have seen in the Senate in a very long time, perhaps ever: a coming together of top voices in business, civil rights, defense, research, labor, the arts, all together, in one room, having a much-needed conversation about how Congress can tackle AI," Schumer said when announcing the forum. Several AI experts and other industry leaders are also in attendance, at the listening sessions, including Bill Gates; the Motion Picture Association CEO, Charles Rivkin; the former Google CEO Eric Schmidt; the Center for Humane Technology co-founder Tristan Harris; and Deborah Raji, a researcher at University of California, Berkeley. Some labor and civil liberties groups are also represented among the 22 attendees including Elizabeth Shuler, the president of the labor union AFL-CIO; Randi Weingarten, the president of the American Federation of Teachers; Janet Murguía, the president of UnidosUS; and Maya Wiley, the president and CEO of the Leadership Conference on Civil & Human Rights.

Sen. Elizabeth Warren said AI should be regulated to protect privacy and safety following a closed door hearing with tech leaders. Following a closed Senate AI forum with tech giants, union leaders and artificial intelligence experts, Sen. Elizabeth Warren, D-Mass., told reporters Wednesday AI should be regulated to protect privacy. She also criticized the decision to keep media and the public from viewing the hearing. "I do not understand why the press has been barred from this meeting," Warren said. "What most of the people have said is we want innovation, but we have got to protect safety."

Sen. Mike Rounds, R-S.D., weighs in on whether Ukraine should be given NATO membership and President Biden's decision to send cluster bombs to Ukraine on'Your World.' EXCLUSIVE: Sen. Mike Rounds, R-S.D., told a group of tech leaders, union leaders and artificial intelligence experts on Wednesday that AI's rapid advancement has inspired calls for "a new Manhattan-like project" and how the government should regulate AI -- if at all -- is still a matter of debate. Rounds, along with Senate Majority Leader Chuck Schumer, is leading the first in a series of bipartisan AI Insight Forums designed to help lawmakers get ahead of AI as it permeates everyday life. Wednesday's session saw the attendance of Meta's Mark Zuckerberg, X owner Elon Musk, AFL-CIO union boss Elizabeth Shuler and others. "Today, we stand at the beginning of a journey of monumental change. While Artificial Intelligence has been around in various forms for years, recent advances in the most cutting-edge models have shown us just how capable the technology has become," Rounds told the closed-door meeting, according to prepared comments obtained exclusively by Fox News Digital.

Behind closed doors today, the US Senate will grill nearly two dozen tech executives including Tesla CEO and longtime AI critic Elon Musk, ChatGPT-maker and staunch AI defender Sam Altman and Microsoft founder Bill Gates, on how best to regulate AI. Democrat Senate Majority Leader Chuck Schumer, who spearheaded the effort for today's Senate'AI Insight Forum,' described the all-day debate on the implications of artificial intelligence as an'all-hands-on-deck moment for Congress.' 'For Congress to legislate on artificial intelligence,' Senator Schumer said Tuesday, 'is for us to engage in one of the most complex and important subjects Congress has ever faced.' Senator Schumer is set to moderate the forum on how Congress should set artificial intelligence safeguards, which runs from 10AM to 5PM, for its first half. Republican Senator Mike Rounds of South Dakota will help moderate the forum.