Quantitative mass spectrometry has revolutionized proteomics by enabling simultaneous quantification of thousands of proteins. Pooling patient-derived data from multiple institutions enhances statistical power but raises significant privacy concerns. Here we introduce FedProt, the first privacy-preserving tool for collaborative differential protein abundance analysis of distributed data, which utilizes federated learning and additive secret sharing. In the absence of a multicenter patient-derived dataset for evaluation, we created two, one at five centers from LFQ E.coli experiments and one at three centers from TMT human serum. Evaluations using these datasets confirm that FedProt achieves accuracy equivalent to DEqMS applied to pooled data, with completely negligible absolute differences no greater than $\text{$4 \times 10^{-12}$}$. In contrast, -log10(p-values) computed by the most accurate meta-analysis methods diverged from the centralized analysis results by up to 25-27. FedProt is available as a web tool with detailed documentation as a FeatureCloud App.

The integration of new modalities into frontier AI systems offers exciting capabilities, but also increases the possibility such systems can be adversarially manipulated in undesirable ways. In this work, we focus on a popular class of vision-language models (VLMs) that generate text outputs conditioned on visual and textual inputs. We conducted a large-scale empirical study to assess the transferability of gradient-based universal image "jailbreaks" using a diverse set of over 40 open-parameter VLMs, including 18 new VLMs that we publicly release. Overall, we find that transferable gradient-based image jailbreaks are extremely difficult to obtain. When an image jailbreak is optimized against a single VLM or against an ensemble of VLMs, the jailbreak successfully jailbreaks the attacked VLM(s), but exhibits little-to-no transfer to any other VLMs; transfer is not affected by whether the attacked and target VLMs possess matching vision backbones or language models, whether the language model underwent instruction-following and/or safety-alignment training, or many other factors. Only two settings display partially successful transfer: between identically-pretrained and identically-initialized VLMs with slightly different VLM training data, and between different training checkpoints of a single VLM. Leveraging these results, we then demonstrate that transfer can be significantly improved against a specific target VLM by attacking larger ensembles of "highly-similar" VLMs. These results stand in stark contrast to existing evidence of universal and transferable text jailbreaks against language models and transferable adversarial attacks against image classifiers, suggesting that VLMs may be more robust to gradient-based transfer attacks.

Large language models (LLMs) increasingly find their way into the most diverse areas of our everyday lives. They indirectly influence people's decisions or opinions through their daily use. Therefore, understanding how and which moral judgements these LLMs make is crucial. However, morality is not universal and depends on the cultural background. This raises the question of whether these cultural preferences are also reflected in LLMs when prompted in different languages or whether moral decision-making is consistent across different languages. So far, most research has focused on investigating the inherent values of LLMs in English. While a few works conduct multilingual analyses of moral bias in LLMs in a multilingual setting, these analyses do not go beyond atomic actions. To the best of our knowledge, a multilingual analysis of moral bias in dilemmas has not yet been conducted. To address this, our paper builds on the moral machine experiment (MME) to investigate the moral preferences of five LLMs, Falcon, Gemini, Llama, GPT, and MPT, in a multilingual setting and compares them with the preferences collected from humans belonging to different cultures. To accomplish this, we generate 6500 scenarios of the MME and prompt the models in ten languages on which action to take. Our analysis reveals that all LLMs inhibit different moral biases to some degree and that they not only differ from the human preferences but also across multiple languages within the models themselves. Moreover, we find that almost all models, particularly Llama 3, divert greatly from human values and, for instance, prefer saving fewer people over saving more.

Event Factuality Detection (EFD) task determines the factuality of textual events, i.e., classifying whether an event is a fact, possibility, or impossibility, which is essential for faithfully understanding and utilizing event knowledge. However, due to the lack of high-quality large-scale data, event factuality detection is under-explored in event understanding research, which limits the development of EFD community. To address these issues and provide faithful event understanding, we introduce MAVEN-Fact, a large-scale and high-quality EFD dataset based on the MAVEN dataset. MAVEN-Fact includes factuality annotations of 112,276 events, making it the largest EFD dataset. Extensive experiments demonstrate that MAVEN-Fact is challenging for both conventional fine-tuned models and large language models (LLMs). Thanks to the comprehensive annotations of event arguments and relations in MAVEN, MAVEN-Fact also supports some further analyses and we find that adopting event arguments and relations helps in event factuality detection for fine-tuned models but does not benefit LLMs. Furthermore, we preliminarily study an application case of event factuality detection and find it helps in mitigating event-related hallucination in LLMs. Our dataset and codes can be obtained from \url{https://github.com/lcy2723/MAVEN-FACT}

Large Vision Language Models (VLMs) extend and enhance the perceptual abilities of Large Language Models (LLMs). Despite offering new possibilities for LLM applications, these advancements raise significant security and ethical concerns, particularly regarding the generation of harmful content. While LLMs have undergone extensive security evaluations with the aid of red teaming frameworks, VLMs currently lack a well-developed one. To fill this gap, we introduce Arondight, a standardized red team framework tailored specifically for VLMs. Arondight is dedicated to resolving issues related to the absence of visual modality and inadequate diversity encountered when transitioning existing red teaming methodologies from LLMs to VLMs. Our framework features an automated multi-modal jailbreak attack, wherein visual jailbreak prompts are produced by a red team VLM, and textual prompts are generated by a red team LLM guided by a reinforcement learning agent. To enhance the comprehensiveness of VLM security evaluation, we integrate entropy bonuses and novelty reward metrics. These elements incentivize the RL agent to guide the red team LLM in creating a wider array of diverse and previously unseen test cases. Our evaluation of ten cutting-edge VLMs exposes significant security vulnerabilities, particularly in generating toxic images and aligning multi-modal prompts. In particular, our Arondight achieves an average attack success rate of 84.5\% on GPT-4 in all fourteen prohibited scenarios defined by OpenAI in terms of generating toxic text. For a clearer comparison, we also categorize existing VLMs based on their safety levels and provide corresponding reinforcement recommendations. Our multimodal prompt dataset and red team code will be released after ethics committee approval. CONTENT WARNING: THIS PAPER CONTAINS HARMFUL MODEL RESPONSES.

NASA scientists say pure sulfur has been found on Mars for the first time after the Curiosity rover inadvertently uncovered a cluster of yellow crystals when it drove over a rock. And it looks like the area is filled with it. It's an unexpected discovery -- while minerals containing sulfur have been observed on the Red Planet, elemental sulfur on its own has never been seen there before. "It forms in only a narrow range of conditions that scientists haven't associated with the history of this location," according to NASA. Curiosity cracked open the rock on May 30 while driving in a region known as the Gediz Vallis channel, where similar rocks were seen all around.

Israeli military personnel may have also missed the drone simply because of human error and "contributed to the Houthis achieving a lucky shot," said Fabian Hinz, an expert at the International Institute for Strategic Studies in London. Residents said the sound of the blast had awakened them in the predawn hours. Yochai Afek, 35, said he had looked out his bedroom window to see his car in flames. Thinking that an air-conditioner had fallen on his vehicle, he and his wife ran outside with a fire extinguisher and a hose, and were surprised to find a large crowd outside at 3 a.m. "We didn't understand why the whole neighborhood came out to the streets because of a fallen AC unit," Mr. Afek said.

One immediate, short-term response, some analysts said, might be a cease-fire deal between Hamas and Israel, a move that could halt attacks from Hamas's allies, like the Houthis and Hezbollah in Lebanon. While the Houthis' opposition to Israel long preceded the war in Gaza, the group had rarely attacked Israeli interests before it began. A truce in Gaza could "prompt some kind of a lull for a while" in Yemen and Lebanon, said Relik Shafir, a former general in the Israeli Air Force. But while mediators say they are edging closer to sealing a Gaza cease-fire, key gaps between Israel and Hamas remain, and parts of Prime Minister Benjamin Netanyahu's right-wing coalition oppose compromising on Hamas's main demands. In the long term, the Houthis also remain committed to Israel's total destruction and would most likely not be placated for long by a temporary truce in Gaza or an end to Israel's occupation of the West Bank. The Houthis are a Yemeni Shiite militia that over the past decade seized control of large parts of western Yemen, including its capital, Sana, and Red Sea coastline.

I didn't know I was dead until I saw it on Google. When I searched my name, there it was: a picture of my smiling face next to the text "Tom Faber was a physicist and publisher, and he was a university lecturer at Cambridge for 35 years". Apparently I died on 27 July 2004, aged 77. This was news to me. The problem was the picture. When you search the name of a notable person, Google may create what it calls a "knowledge panel", a little box with basic information taken from Wikipedia. Somewhere along the way, the algorithm had confused pictures of my face with the biography of another man who shared my name. According to his obituary, he was "a distinguished physicist with a literary hinterland". Google provides a feedback form to resolve this type of bug. I filled it in several times, but it made no difference.

A Russian missile hit a children's playground in Ukraine's southern city of Mykolaiv, killing a child and two adults, and injuring five others, Ukrainian President Volodymyr Zelenskyy said on Telegram. Vitaliy Kim, the regional governor, praised residents who rushed to the site to help before medics arrived and said another missile had landed elsewhere in the region. Russian drones launched in an overnight attack on Ukraine hit energy infrastructure facilities in two regions in the country's north, the Reuters news agency reported, citing Ukrainian regional officials. One of the Shahed drones hit an energy facility in the Sumy region while another struck a site in the Chernihiv region. There were no immediate reports of casualties.