This escalating trend is projected to continue as multiple companies, including SpaceX, Amazon, and Astra Space, plan to launch large constellations of hundreds to thousands of satellites. The resulting dense and complex operating environment elevates the risk of collisions and debris generation, posing substantial challenges for space operators. Not only does this situation threaten the safety of flight and mission success in the short run, but it also jeopardizes the long-term viability of the LEO environment for scientific, commercial, and national security uses. Hence, understanding and modeling the evolution of the space environment is crucial for ensuring its sustainability and informing strategies for effective space traffic management. A variety of models have emerged to examine this evolution and calculate the orbital capacity, which is referred to as the number of satellites that can feasibly be situated in LEO [1]. Traditional proprietary models, developed by organizations like NASA's LEGEND [2], ESA's DELTA [3], JAXA's IMPACT [4] among others [5, 6], have been complemented by newer open-source initiatives such as the MIT Orbital Capacity Tool (MOCAT) and its various versions [7, 8]. Most of these models operate by propagating all the ASOs forward in time, utilizing physical models of spacecraft dynamics. This methodology incorporates factors such as atmospheric drag, solar radiation pressure, third-body perturbations, and space weather, in addition to simulated collisions and explosions.

Large Language Models (LLMs) are attracting significant research attention due to their instruction-following abilities, allowing users and developers to leverage LLMs for a variety of tasks. However, LLMs are vulnerable to prompt-injection attacks: a class of attacks that hijack the model's instruction-following abilities, changing responses to prompts to undesired, possibly malicious ones. In this work, we introduce Jatmo, a method for generating task-specific models resilient to prompt-injection attacks. Jatmo leverages the fact that LLMs can only follow instructions once they have undergone instruction tuning. It harnesses a teacher instruction-tuned model to generate a task-specific dataset, which is then used to fine-tune a base model (i.e., a non-instruction-tuned model). Jatmo only needs a task prompt and a dataset of inputs for the task: it uses the teacher model to generate outputs. For situations with no pre-existing datasets, Jatmo can use a single example, or in some cases none at all, to produce a fully synthetic dataset. Our experiments on seven tasks show that Jatmo models provide similar quality of outputs on their specific task as standard LLMs, while being resilient to prompt injections. The best attacks succeeded in less than 0.5% of cases against our models, versus 87% success rate against GPT-3.5-Turbo. We release Jatmo at https://github.com/wagner-group/prompt-injection-defense.

The in-context learning ability of large language models (LLMs) enables them to generalize to novel downstream tasks with relatively few labeled examples. However, they require enormous computational resources to be deployed. Alternatively, smaller models can solve specific tasks if fine-tuned with enough labeled examples. These examples, however, are expensive to obtain. In pursuit of the best of both worlds, we study synthetic data generation of fine-tuning training data via fine-tuned teacher LLMs to improve the downstream performance of much smaller models. In four text classification and two text generation tasks, we find that both data generation and annotation dramatically improve the respective downstream model's performance, occasionally necessitating only a minor fraction of the original training dataset.

Causal inference in a sub-population involves identifying the causal effect of an intervention on a specific subgroup, which is distinguished from the whole population through the influence of systematic biases in the sampling process. However, ignoring the subtleties introduced by sub-populations can either lead to erroneous inference or limit the applicability of existing methods. We introduce and advocate for a causal inference problem in sub-populations (henceforth called s-ID), in which we merely have access to observational data of the targeted sub-population (as opposed to the entire population). Existing inference problems in sub-populations operate on the premise that the given data distributions originate from the entire population, thus, cannot tackle the s-ID problem. To address this gap, we provide necessary and sufficient conditions that must hold in the causal graph for a causal effect in a sub-population to be identifiable from the observational distribution of that sub-population. Given these conditions, we present a sound and complete algorithm for the s-ID problem.

By leveraging the representations learned in the past, we aim to augment the features Recent deep learning techniques have shown remarkable at each incremental stage to address data deficiency in progress in various computer vision tasks including image the classes belonging to old tasks. To this end, inspired by classification (He et al. 2016; Hu, Shen, and Sun 2018), object adversarial attacks, we adjust the feature representations of detection (Liu et al. 2016; Redmon et al. 2016; Zhu et al. training examples to resemble representations from specific 2021c), semantic segmentation (Chen et al. 2017; Long, target classes that are different from their original classes. Shelhamer, and Darrell 2015; Noh, Hong, and Han 2015), These perturbed features allow a new classifier to maintain and many others. Behind this success is an implicit assumption the decision boundaries for the classes learned up to the that the whole dataset with a predefined set of classes previous stages. Note that this is a novel perspective different should be given in a batch. However, this assumption is from conventional adversarial attack methods (Carlini unlikely to hold in the real-world scenarios which change and Wagner 2017; Goodfellow, Shlens, and Szegedy 2017; dynamically over time. This limits the applicability to realworld Madry et al. 2018; Moosavi-Dezfooli, Fawzi, and Frossard problems because deep neural networks trained under 2016; Zhao, Dua, and Singh 2018), which focus on deceiving changing data distribution often suffer from catastrophic forgetting, models. One may consider generating additional features meaning that the models lose the ability to maintain for each class using the exemplars with the same class labels.

Conformal prediction is a statistical tool for producing prediction regions of machine learning models that are valid with high probability. However, applying conformal prediction to time series data leads to conservative prediction regions. In fact, to obtain prediction regions over $T$ time steps with confidence $1-\delta$, {previous works require that each individual prediction region is valid} with confidence $1-\delta/T$. We propose an optimization-based method for reducing this conservatism to enable long horizon planning and verification when using learning-enabled time series predictors. Instead of considering prediction errors individually at each time step, we consider a parameterized prediction error over multiple time steps. By optimizing the parameters over an additional dataset, we find prediction regions that are not conservative. We show that this problem can be cast as a mixed integer linear complementarity program (MILCP), which we then relax into a linear complementarity program (LCP). Additionally, we prove that the relaxed LP has the same optimal cost as the original MILCP. Finally, we demonstrate the efficacy of our method on case studies using pedestrian trajectory predictors and F16 fighter jet altitude predictors.

Lately, propelled by the phenomenal advances around the transformer architecture, the legal NLP field has enjoyed spectacular growth. To measure progress, well curated and challenging benchmarks are crucial. However, most benchmarks are English only and in legal NLP specifically there is no multilingual benchmark available yet. Additionally, many benchmarks are saturated, with the best models clearly outperforming the best humans and achieving near perfect scores. We survey the legal NLP literature and select 11 datasets covering 24 languages, creating LEXTREME. To provide a fair comparison, we propose two aggregate scores, one based on the datasets and one on the languages. The best baseline (XLM-R large) achieves both a dataset aggregate score a language aggregate score of 61.3. This indicates that LEXTREME is still very challenging and leaves ample room for improvement. To make it easy for researchers and practitioners to use, we release LEXTREME on huggingface together with all the code required to evaluate models and a public Weights and Biases project with all the runs.

Artificial Intelligence is now recognized as a general-purpose technology with ample impact on human life. This work aims at understanding the evolution of AI and, in particular Machine learning, from the perspective of researchers' contributions to the field. In order to do so, we present several measures allowing the analyses of AI and machine learning researchers' impact, influence, and leadership over the last decades. This work also contributes, to a certain extent, to shed new light on the history and evolution of AI by exploring the dynamics involved in the field's evolution by looking at papers published at the flagship AI and machine learning conferences since the first International Joint Conference on Artificial Intelligence (IJCAI) held in 1969. AI development and evolution have led to increasing research output, reflected in the number of articles published over the last sixty years. We construct comprehensive citation collaboration and paper-author datasets and compute corresponding centrality measures to carry out our analyses. These analyses allow a better understanding of how AI has reached its current state of affairs in research. Throughout the process, we correlate these datasets with the work of the ACM Turing Award winners and the so-called two AI winters the field has gone through. We also look at self-citation trends and new authors' behaviors. Finally, we present a novel way to infer the country of affiliation of a paper from its organization. Therefore, this work provides a deep analysis of Artificial Intelligence history from information gathered and analysed from large technical venues datasets and suggests novel insights that can contribute to understanding and measuring AI's evolution.

Machine learning, particularly graph learning, is gaining increasing recognition for its transformative impact across various fields. One such promising application is in the realm of molecule design and discovery, notably within the pharmaceutical industry. Our survey offers a comprehensive overview of state-of-the-art methods in molecule design, particularly focusing on \emph{de novo} drug design, which incorporates (deep) graph learning techniques. We categorize these methods into three distinct groups: \emph{i)} \emph{all-at-once}, \emph{ii)} \emph{fragment-based}, and \emph{iii)} \emph{node-by-node}. Additionally, we introduce some key public datasets and outline the commonly used evaluation metrics for both the generation and optimization of molecules. In the end, we discuss the existing challenges in this field and suggest potential directions for future research.

They're balloons – but not as we know them. Scientists at New Mexico's Los Alamos National Laboratory are working on a'vacuum balloon' with a hard shell that could eventually carry humans and travel'as fast as a commercial airliner'. Miles Beaux, a physicist at the lab, told DailyMail.com in an exclusive interview that if his experiments are successful the craft could be used for transport, surveillance, and even for parcel delivery drones. Beaux and his chemist colleague Chris Hamilton have been making small, hollow spheres out of a super-lightweight material called aerogel, then sucking the air out of them in an attempt to create a solid ball that is lighter than the surrounding atmosphere – allowing it to hover. The'vacuum balloons' would trump traditional helium or hydrogen balloons, which slowly lose their lift, and could potentially carry objects in the air indefinitely.