Here's what the bill aims to achieve, and how it crossed many hurdles en route to becoming law. The Take It Down Act was borne out of the suffering--and then activism--of a handful of teenagers. In October 2023, 14-year-old Elliston Berry of Texas and 15-year-old Francesca Mani of New Jersey each learned that classmates had used AI software to fabricate nude images of them and female classmates. The tools that had been used to humiliate them were relatively new: products of the generative AI boom in which virtually any image could be created with the click of a button. Pornographic and sometimes violent deepfake images of Taylor Swift and others soon spread across the internet.

Transportation Sec. Sean Duffy indicated the Trump administration is committed to "radical transparency." In a video message about the Federal Aviation Administration doing "drone-detection testing" in New Jersey, Transportation Sec. Sean Duffy indicated that the Trump administration is committed to "radical transparency," juxtaposing that approach with what he referred to as the Biden administration's "drone fiasco." The FAA noted in a post on its website last week that the testing is slated to occur "in Cape May, New Jersey, between April 14-25." "The FAA will operate several large drones and more than 100 commercial off-the-shelf drones during the two-week period. Testing will take place over the water and near the Cape May Ferry Terminal during the daytime on weekdays only. The public should not fly recreational drones near this area during the test period," the post stated.

'The Big Weekend Show' co-hosts discuss Tinder user traffic peaking during'Dating Sunday.' A New Jersey woman is accused of hiring a man she met on Tinder to kill her police officer ex-boyfriend and his daughter, according to authorities. Camden County Prosecutor Grace C. MacAulay charged Jaclyn Diiorio, 26, with two counts of attempted first-degree murder, one count of conspiracy to commit murder and one count of third-degree possession of a controlled dangerous substance in connection with the alleged crime. Diiorio, of Runnemede, allegedly told a confidential informant she met on Tinder that she wanted her ex, a 53-year-old Philadelphia Police Department officer, and his 19-year-old daughter killed, Gloucester New Jersey Township Police said in a news release. The informant and Diiorio allegedly exchanged several phone calls and text messages after meeting on the dating app and later in person at a Wawa, according to court documents obtained by Fox News Digital.

We study a long-run mean-variance team stochastic game (MV-TSG), where each agent shares a common mean-variance objective for the system and takes actions independently to maximize it. MV-TSG has two main challenges. First, the variance metric is neither additive nor Markovian in a dynamic setting. Second, simultaneous policy updates of all agents lead to a non-stationary environment for each individual agent. Both challenges make dynamic programming inapplicable. In this paper, we study MV-TSGs from the perspective of sensitivity-based optimization. The performance difference and performance derivative formulas for joint policies are derived, which provide optimization information for MV-TSGs. We prove the existence of a deterministic Nash policy for this problem. Subsequently, we propose a Mean-Variance Multi-Agent Policy Iteration (MV-MAPI) algorithm with a sequential update scheme, where individual agent policies are updated one by one in a given order. We prove that the MV-MAPI algorithm converges to a first-order stationary point of the objective function. By analyzing the local geometry of stationary points, we derive specific conditions for stationary points to be (local) Nash equilibria, and further, strict local optima. To solve large-scale MV-TSGs in scenarios with unknown environmental parameters, we extend the idea of trust region methods to MV-MAPI and develop a multi-agent reinforcement learning algorithm named Mean-Variance Multi-Agent Trust Region Policy Optimization (MV-MATRPO). We derive a performance lower bound for each update of joint policies. Finally, numerical experiments on energy management in multiple microgrid systems are conducted.

We propose a new framework for formulating optimal transport distances between Markov chains. Previously known formulations studied couplings between the entire joint distribution induced by the chains, and derived solutions via a reduction to dynamic programming (DP) in an appropriately defined Markov decision process. This formulation has, however, not led to particularly efficient algorithms so far, since computing the associated DP operators requires fully solving a static optimal transport problem, and these operators need to be applied numerous times during the overall optimization process. In this work, we develop an alternative perspective by considering couplings between a "flattened" version of the joint distributions that we call discounted occupancy couplings, and show that calculating optimal transport distances in the full space of joint distributions can be equivalently formulated as solving a linear program (LP) in this reduced space. This LP formulation allows us to port several algorithmic ideas from other areas of optimal transport theory. In particular, our formulation makes it possible to introduce an appropriate notion of entropy regularization into the optimization problem, which in turn enables us to directly calculate optimal transport distances via a Sinkhorn-like method we call Sinkhorn Value Iteration (SVI). We show both theoretically and empirically that this method converges quickly to an optimal coupling, essentially at the same computational cost of running vanilla Sinkhorn in each pair of states. Along the way, we point out that our optimal transport distance exactly matches the common notion of bisimulation metrics between Markov chains, and thus our results also apply to computing such metrics, and in fact our algorithm turns out to be significantly more efficient than the best known methods developed so far for this purpose.

Low Rank and Sparse Fourier Structure in Recurrent Networks Trained on Modular Addition Akshay Rangamani Dept. of Data Science New Jersey Institute of T echnology Newark, NJ, USA akshay.rangamani@njit.edu Abstract --Modular addition tasks serve as a useful test bed for observing empirical phenomena in deep learning, including the phenomenon of grokking. Prior work has shown that one-layer transformer architectures learn Fourier Multiplication circuits to solve modular addition tasks. In this paper, we show that Recurrent Neural Networks (RNNs) trained on modular addition tasks also use a Fourier Multiplication strategy. We identify low rank structures in the model weights, and attribute model components to specific Fourier frequencies, resulting in a sparse representation in the Fourier space. We also show empirically that the RNN is robust to removing individual frequencies, while the performance degrades drastically as more frequencies are ablated from the model.

Online (bipartite) matching under known stationary arrivals is a fundamental model that has been studied extensively with the objective of maximizing the total number of customers served. We instead study the objective of maximizing the minimum matching rate across all online types, which is referred to as long-run (individual) fairness. For Online Matching under long-run Fairness (OM-LF) with a single offline agent, we show that the first-come-first-serve (FCFS) policy is 1-competitive, i.e., matching any optimal clairvoyant. For the general case of OM-LF: We present a sampling algorithm (SAMP) and show that (1) SAMP is of competitiveness of at least 1 1/e and (2) it is asymptotically optimal with competitiveness approaching one in different regimes when either all offline agents have a sufficiently large matching capacity, or all online types have a sufficiently large arrival rate, or highly imbalance between the total offline matching capacity and the number of online arrivals. To complement the competitive results, we show the following hardness results for OM-LF: (1) Any non-rejecting policy (matching every arriving online agent if possible) is no more than 1/2-competitive; (2) Any (randomized) policy is no more than ( 3 1)-competitive; (3) SAMP can be no more than (1 1/e)- competitive suggesting the tightness of competitive analysis for SAMP. We stress that all hardness results mentioned here are independent of any benchmarks. We also consider a few extensions of OM-LF by proposing a few variants of fairness metrics, including long-run group-level fairness and short-run fairness, and we devise related algorithms with provable competitive performance.

Mysterious glowing orbs have been spotted flying off the coast of Florida - months after New Jersey was invaded by drones. Residents of Daytona Beach have described the unidentified objects rising directly from the ocean and flying over the surface of the water. One extremely viral video from March 17 at around 10pm captured what appeared to be a large object moving toward land, and the flare of light surrounding it gradually dissipating to reveal the shape of an aircraft. While many have dismissed it as simply a passenger plane, locals have shared similar videos online claiming the objects moved in unconventional ways. One particularly extraordinary theory has surfaced on social media, where locals say a'group of whistleblowers' claiming to be'military personnel and sailors' told them the US Navy discovered a'huge' underwater mothership that they believe is producing the orbs.

Active Inference (AIF) is emerging as a powerful framework for decision-making under uncertainty, yet its potential in engineering applications remains largely unexplored. In this work, we propose a novel dual-layer AIF architecture that addresses both building-level and community-level energy management. By leveraging the free energy principle, each layer adapts to evolving conditions and handles partial observability without extensive sensor information and respecting data privacy. We validate the continuous AIF model against both a perfect optimization baseline and a reinforcement learning-based approach. We also test the community AIF framework under extreme pricing scenarios. The results highlight the model's robustness in handling abrupt changes. This study is the first to show how a distributed AIF works in engineering. It also highlights new opportunities for privacy-preserving and uncertainty-aware control strategies in engineering applications.

We study the canonical problem of linear regression under (ε, δ)-differential privacy when the datapoints are sampled i.i.d.