Comments and other data left on a PDF detailing Homeland Security's proposal to build "mega" detention and processing centers reveal the personnel involved in its creation. A PDF that Department of Homeland Security officials provided to New Hampshire governor Kelly Ayotte's office about a new effort to build "mega" detention and processing centers across the United States contains embedded comments and metadata identifying the people who worked on it. The seemingly accidental exposure of the identities of DHS personnel who crafted Immigration and Customs Enforcement's mega detention center plan lands amid widespread public pushback against the expansion of ICE detention centers and the department's brutal immigration enforcement tactics. Metadata in the document, which concerns ICE's "Detention Reengineering Initiative" (DRI), lists as its author Jonathan Florentino, the director of ICE's Newark, New Jersey, Field Office of Enforcement and Removal Operations. In a note embedded on top of an FAQ question, "What is the average length of stay for the aliens?"

Robby Starbuck speaks in an interview in New York in March. Robby Starbuck speaks in an interview in New York in March. Critics condemn Robby Starbuck, appointed in lawsuit settlement, for'peddling lies and pushing extremism' A prominent anti-DEI campaigner appointed by Meta in August as an adviser on AI bias has spent the weeks since his appointment spreading disinformation about shootings, transgender people, vaccines, crime, and protests. Robby Starbuck, 36, of Nashville, was appointed in August as an adviser by Meta - owner of Facebook, Instagram, WhatsApp, and other tech platforms - in an August lawsuit settlement. Since his appointment, Starbuck has baselessly claimed that individual shooters in the US were motivated by leftist ideology, described faith-based protest groups as communists, and without evidence tied Democratic lawmakers to murders.

Langevin dynamics (LD) has been proven to be a powerful technique for optimizing a non-convex objective as an efficient algorithm to find local minima while eventually visiting a global minimum on longer time-scales.

We consider a multi-robot setting, where we have a fleet of multi-capacity autonomous robots that must service spatially distributed pickup-and-delivery requests with fixed maximum wait times. Requests can be either scheduled ahead of time or they can enter the system in real-time. In this setting, stability for a routing policy is defined as the cost of the policy being uniformly bounded over time. Most previous work either solve the problem offline to theoretically maintain stability or they consider dynamically arriving requests at the expense of the theoretical guarantees on stability. In this paper, we aim to bridge this gap by proposing a novel proactive rollout-based routing framework that adapts to real-time demand while still provably maintaining the stability of the learned routing policy. We derive provable stability guarantees for our method by proposing a fleet sizing algorithm that obtains a sufficiently large fleet that ensures stability by construction. To validate our theoretical results, we consider a case study on real ride requests for Harvard's evening Van System. We also evaluate the performance of our framework using the currently deployed smaller fleet size. In this smaller setup, we compare against the currently deployed routing algorithm, greedy heuristics, and Monte-Carlo-Tree-Search-based algorithms. Our empirical results show that our framework maintains stability when we use the sufficiently large fleet size found in our theoretical results. For the smaller currently deployed fleet size, our method services 6% more requests than the closest baseline while reducing median passenger wait times by 33%.

Adapting pre-trained deep learning models to new and unknown environments is a difficult challenge in underwater acoustic localization. We show that although pre-trained models have performance that suffers from mismatch between the training and test data, they generally exhibit a higher ``implied uncertainty'' in environments where there is more mismatch. Leveraging this notion of implied uncertainty, we partition the test samples into more certain and less certain sets, and implement an estimation method using the certain samples to improve the labeling for uncertain samples, which helps to adapt the model. We use an efficient method to quantify model prediction uncertainty, and an innovative approach to adapt a pre-trained model to unseen underwater environments at test time. This eliminates the need for labeled data from the target environment or the original training data. This adaptation is enhanced by integrating an independent estimate based on the received signal energy. We validate the approach extensively using real experimental data, as well as synthetic data consisting of model-generated signals with real ocean noise. The results demonstrate significant improvements in model prediction accuracy, underscoring the potential of the method to enhance underwater acoustic localization in diverse, noisy, and unknown environments.

Optimization models have been applied to solve a wide variety of decision-making problems. These models are usually developed by optimization experts but are used by practitioners without optimization expertise in various application domains. As a result, practitioners often struggle to interact with and draw useful conclusions from optimization models independently. To fill this gap, we introduce OptiChat, a natural language dialogue system designed to help practitioners interpret model formulation, diagnose infeasibility, analyze sensitivity, retrieve information, evaluate modifications, and provide counterfactual explanations. By augmenting large language models (LLMs) with functional calls and code generation tailored for optimization models, we enable seamless interaction and minimize the risk of hallucinations in OptiChat. We develop a new dataset to evaluate OptiChat's performance in explaining optimization models. Experiments demonstrate that OptiChat effectively bridges the gap between optimization models and practitioners, delivering autonomous, accurate, and instant responses.

The presence of non-convexity in smooth optimization problems arising from deep learning have sparked new smoothness conditions in the literature and corresponding convergence analyses. We discuss these smoothness conditions, order them, provide conditions for determining whether they hold, and evaluate their applicability to training a deep linear neural network for binary classification.

In pursuit of a more sustainable and cost-efficient last mile, parcel lockers have gained a firm foothold in the parcel delivery landscape. To fully exploit their potential and simultaneously ensure customer satisfaction, successful management of the locker's limited capacity is crucial. This is challenging as future delivery requests and pickup times are stochastic from the provider's perspective. In response, we propose to dynamically control whether the locker is presented as an available delivery option to each incoming customer with the goal of maximizing the number of served requests weighted by their priority. Additionally, we take different compartment sizes into account, which entails a second type of decision as parcels scheduled for delivery must be allocated. We formalize the problem as an infinite-horizon sequential decision problem and find that exact methods are intractable due to the curses of dimensionality. In light of this, we develop a solution framework that orchestrates multiple algorithmic techniques rooted in Sequential Decision Analytics and Reinforcement Learning, namely cost function approximation and an offline trained parametric value function approximation together with a truncated online rollout. Our innovative approach to combine these techniques enables us to address the strong interrelations between the two decision types. As a general methodological contribution, we enhance the training of our value function approximation with a modified version of experience replay that enforces structure in the value function. Our computational study shows that our method outperforms a myopic benchmark by 13.7% and an industry-inspired policy by 12.6%.