Split federated learning (SFL) is a compute-efficient paradigm in distributed machine learning (ML), where components of large ML models are outsourced to remote servers. A significant challenge in SFL, particularly when deployed over wireless channels, is the susceptibility of transmitted model parameters to adversarial jamming that could jeopardize the learning process. This is particularly pronounced for word embedding parameters in large language models (LLMs), which are crucial for language understanding. In this paper, rigorous insights are provided into the influence of jamming LLM word embeddings in SFL by deriving an expression for the ML training loss divergence and showing that it is upper-bounded by the mean squared error (MSE). Based on this analysis, a physical layer framework is developed for resilient SFL with LLMs (R-SFLLM) over wireless networks. R-SFLLM leverages wireless sensing data to gather information on the jamming directions-of-arrival (DoAs) for the purpose of devising a novel, sensing-assisted anti-jamming strategy while jointly optimizing beamforming, user scheduling, and resource allocation. Extensive experiments using BERT and RoBERTa models demonstrate R-SFLLM's effectiveness, achieving close-to-baseline performance across various natural language processing (NLP) tasks and datasets. The proposed methodology further introduces an adversarial training component, where controlled noise exposure significantly enhances the LLM's resilience to perturbed parameters during training. The results show that more noise-sensitive models, such as RoBERTa, benefit from this feature, especially when resource allocation is unfair. It is also shown that worst-case jamming in particular translates into worst-case model outcomes, thereby necessitating the need for jamming-resilient SFL protocols.

Contextualized Image Captioning (CIC) evolves traditional image captioning into a more complex domain, necessitating the ability for multimodal reasoning. It aims to generate image captions given specific contextual information. This paper further introduces a novel domain of Controllable Contextualized Image Captioning (Ctrl-CIC). Unlike CIC, which solely relies on broad context, Ctrl-CIC accentuates a user-defined highlight, compelling the model to tailor captions that resonate with the highlighted aspects of the context. We present two approaches, Prompting-based Controller (P-Ctrl) and Recalibration-based Controller (R-Ctrl), to generate focused captions. P-Ctrl conditions the model generation on highlight by prepending captions with highlight-driven prefixes, whereas R-Ctrl tunes the model to selectively recalibrate the encoder embeddings for highlighted tokens. Additionally, we design a GPT-4V empowered evaluator to assess the quality of the controlled captions alongside standard assessment methods. Extensive experimental results demonstrate the efficient and effective controllability of our method, charting a new direction in achieving user-adaptive image captioning. Code is available at https://github.com/ShunqiM/Ctrl-CIC .

This commentary piece reviews the recent Open AI Democratic Inputs programme, which funded 10 teams to design procedures for public participation in generative AI. While applauding the technical innovations in these projects, we identify several shared assumptions including the generality of LLMs, extracting abstract values, soliciting solutions not problems and equating participation with democracy. We call instead for AI participation which involves specific communities and use cases and solicits concrete problems to be remedied. We also find it important that these communities have a stake in the outcome, including ownership of data or models.

Global data assimilation enables weather forecasting at all scales and provides valuable data for studying the Earth system. However, the computational demands of physics-based algorithms used in operational systems limits the volume and diversity of observations that are assimilated. Here, we present "EarthNet", a multi-modal foundation model for data assimilation that learns to predict a global gap-filled atmospheric state solely from satellite observations. EarthNet is trained as a masked autoencoder that ingests a 12 hour sequence of observations and learns to fill missing data from other sensors. We show that EarthNet performs a form of data assimilation producing a global 0.16 degree reanalysis dataset of 3D atmospheric temperature and humidity at a fraction of the time compared to operational systems. It is shown that the resulting reanalysis dataset reproduces climatology by evaluating a 1 hour forecast background state against observations. We also show that our 3D humidity predictions outperform MERRA-2 and ERA5 reanalyses by 10% to 60% between the middle troposphere and lower stratosphere (5 to 20 km altitude) and our 3D temperature and humidity are statistically equivalent to the Microwave integrated Retrieval System (MiRS) observations at nearly every level of the atmosphere. Our results indicate significant promise in using EarthNet for high-frequency data assimilation and global weather forecasting.

Jailbreak attacks cause large language models (LLMs) to generate harmful, unethical, or otherwise objectionable content. Evaluating these attacks presents a number of challenges, which the current collection of benchmarks and evaluation techniques do not adequately address. First, there is no clear standard of practice regarding jailbreaking evaluation. Second, existing works compute costs and success rates in incomparable ways. And third, numerous works are not reproducible, as they withhold adversarial prompts, involve closed-source code, or rely on evolving proprietary APIs. To address these challenges, we introduce JailbreakBench, an open-sourced benchmark with the following components: (1) an evolving repository of state-of-the-art adversarial prompts, which we refer to as jailbreak artifacts; (2) a jailbreaking dataset comprising 100 behaviors -- both original and sourced from prior work (Zou et al., 2023; Mazeika et al., 2023, 2024) -- which align with OpenAI's usage policies; (3) a standardized evaluation framework at https://github.com/JailbreakBench/jailbreakbench that includes a clearly defined threat model, system prompts, chat templates, and scoring functions; and (4) a leaderboard at https://jailbreakbench.github.io/ that tracks the performance of attacks and defenses for various LLMs. We have carefully considered the potential ethical implications of releasing this benchmark, and believe that it will be a net positive for the community.

Randomized smoothing is a defensive technique to achieve enhanced robustness against adversarial examples which are small input perturbations that degrade the performance of neural network models. Conventional randomized smoothing adds random noise with a fixed noise level for every input sample to smooth out adversarial perturbations. This paper proposes a new variational framework that uses a per-sample noise level suitable for each input by introducing a noise level selector. Our experimental results demonstrate enhancement of empirical robustness against adversarial attacks. We also provide and analyze the certified robustness for our sample-wise smoothing method.

We introduce a new training algorithm for variety of deep neural networks that utilize random complex exponential activation functions. Our approach employs a Markov Chain Monte Carlo sampling procedure to iteratively train network layers, avoiding global and gradient-based optimization while maintaining error control. It consistently attains the theoretical approximation rate for residual networks with complex exponential activation functions, determined by network complexity. Additionally, it enables efficient learning of multiscale and high-frequency features, producing interpretable parameter distributions. Despite using sinusoidal basis functions, we do not observe Gibbs phenomena in approximating discontinuous target functions.

The United States Food and Drug Administration's (FDA's) Premarket Notification 510(K) pathway allows manufacturers to gain approval for a medical device by demonstrating its substantial equivalence to another legally marketed device. However, the inherent ambiguity of this regulatory procedure has led to high recall rates for many devices cleared through this pathway. This trend has raised significant concerns regarding the efficacy of the FDA's current approach, prompting a reassessment of the 510(K) regulatory framework. In this paper, we develop a combined human-algorithm approach to assist the FDA in improving its 510(k) medical device clearance process by reducing the risk of potential recalls and the workload imposed on the FDA. We first develop machine learning methods to estimate the risk of recall of 510(k) medical devices based on the information available at the time of submission. We then propose a data-driven clearance policy that recommends acceptance, rejection, or deferral to FDA's committees for in-depth evaluation. We conduct an empirical study using a unique large-scale dataset of over 31,000 medical devices and 12,000 national and international manufacturers from over 65 countries that we assembled based on data sources from the FDA and Centers for Medicare and Medicaid Service (CMS). A conservative evaluation of our proposed policy based on this data shows a 38.9% improvement in the recall rate and a 43.0% reduction in the FDA's workload. Our analyses also indicate that implementing our policy could result in significant annual cost-savings ranging between \$2.4 billion and \$2.7 billion, which highlights the value of using a holistic and data-driven approach to improve the FDA's current 510(K) medical device evaluation pathway.

Former U.S. ambassador to NATO Kay Bailey Hutchinson discusses Biden's recent effort to show American allies that he is fit to serve as president and Ukrainian President Zelenskyy's concern about delaying action against Russia. Struggling with manpower shortages, overwhelming odds and uneven international assistance, Ukraine hopes to find a strategic edge against Russia in an abandoned warehouse or a factory basement. An ecosystem of laboratories in hundreds of secret workshops is leveraging innovation to create a robot army that Ukraine hopes will kill Russian troops and save its own wounded soldiers and civilians. Defense startups across Ukraine -- about 250 according to industry estimates -- are creating the killing machines at secret locations that typically look like rural car repair shops. Employees at a startup run by entrepreneur Andrii Denysenko can put together an unmanned ground vehicle called the Odyssey in four days at a shed used by the company.

A new space suit designed by scientists at Weill Cornell Medicine and Cornell University, inspired by Frank Herbert's Dune, could make spacewalks longer and less disgusting by recycling expelled urine in a special filtration backpack. The traditional NASA spacesuit design has been in circulation since the 1970s and only has an absorbent polymer to catch astronauts' urine. This outdated waste system has also led to hygiene and medical issues, like urinary tract infections (UTIs) and gastrointestinal problems, for astronauts. The new'stillsuits' have a "vacuum-based external catheter leading to a combined forward-reverse osmosis unit" astronauts carry on their back. The suits were designed for future NASA space missions, including the Artemis II and Artemis III missions, both headed to the moon.