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Efficient design of genomic perturbation experiments is crucial for accelerating drug discovery and therapeutic target identification, yet exhaustive perturbation of the human genome remains infeasible due to the vast search space of potential genetic interactions and experimental constraints. Bayesian optimization (BO) has emerged as a powerful framework for selecting informative interventions, but existing approaches often fail to exploit domain-specific biological prior knowledge. We propose Biology-Informed Bayesian Optimization (BioBO), a method that integrates Bayesian optimization with multimodal gene embeddings and enrichment analysis, a widely used tool for gene prioritization in biology, to enhance surrogate modeling and acquisition strategies. BioBO combines biologically grounded priors with acquisition functions in a principled framework, which biases the search toward promising genes while maintaining the ability to explore uncertain regions. Through experiments on established public benchmarks and datasets, we demonstrate that BioBO improves labeling efficiency by 25-40%, and consistently outperforms conventional BO by identifying top-performing perturbations more effectively. Moreover, by incorporating enrichment analysis, BioBO yields pathway-level explanations for selected perturbations, offering mechanistic interpretability that links designs to biologically coherent regulatory circuits. In vitro cellular experimentation with genomic interventions is a critical step in early-stage drug discovery and target prioritization. By perturbing genes and observing cellular responses, researchers can infer gene function and identify potential therapeutic targets (Chan et al., 2022; Bock et al., 2022).

Predicting the stock market is a big challenge for the machine learning world. It is known how difficult it is to have accurate and consistent predictions with ML models. Some architectures are able to capture the movement of stocks but almost never are able to be launched to the production world. We present Achilles, with a classical architecture of LSTM(Long Short Term Memory) neural network this model is able to predict the Gold vs USD commodity. With the predictions minute-per-minute of this model we implemented a trading bot to run during 23 days of testing excluding weekends. At the end of the testing period we generated $1623.52 in profit with the methodology used. The results of our method demonstrate Machine Learning can successfully be implemented to predict the Gold vs USD commodity.

In this paper, we study the harmlessness alignment problem of multimodal large language models (MLLMs). We conduct a systematic empirical analysis of the harmlessness performance of representative MLLMs and reveal that the image input poses the alignment vulnerability of MLLMs. Inspired by this, we propose a novel jailbreak method named HADES, which hides and amplifies the harmfulness of the malicious intent within the text input, using meticulously crafted images. Experimental results show that HADES can effectively jailbreak existing MLLMs, which achieves an average Attack Success Rate (ASR) of 90.26% for LLaVA-1.5 and 71.60% for Gemini Pro Vision. Our code and data are available at https://github.com/RUCAIBox/HADES. Warning: this paper contains example data that may be offensive.

Synthetic data is seen as the most promising solution to share individual-level data while preserving privacy. Shadow modeling-based Membership Inference Attacks (MIAs) have become the standard approach to evaluate the privacy risk of synthetic data. While very effective, they require a large number of datasets to be created and models trained to evaluate the risk posed by a single record. The privacy risk of a dataset is thus currently evaluated by running MIAs on a handful of records selected using ad-hoc methods. We here propose what is, to the best of our knowledge, the first principled vulnerable record identification technique for synthetic data publishing, leveraging the distance to a record's closest neighbors. We show our method to strongly outperform previous ad-hoc methods across datasets and generators. We also show evidence of our method to be robust to the choice of MIA and to specific choice of parameters. Finally, we show it to accurately identify vulnerable records when synthetic data generators are made differentially private. The choice of vulnerable records is as important as more accurate MIAs when evaluating the privacy of synthetic data releases, including from a legal perspective. We here propose a simple yet highly effective method to do so. We hope our method will enable practitioners to better estimate the risk posed by synthetic data publishing and researchers to fairly compare ever improving MIAs on synthetic data.

Cloud providers can greatly benefit from accurate workload prediction. However, the workload of cloud servers is highly variable, with occasional heavy workload bursts. This makes workload prediction challenging. There are mainly two categories of workload prediction methods: statistical methods and neural-network-based ones. The former ones rely on strong mathematical assumptions and have reported low accuracy when predicting highly variable workload. The latter ones offer higher overall accuracy, yet they are vulnerable to data imbalance between heavy workload and common one. This impairs the prediction accuracy of neural network-based models on heavy workload. Either the overall inaccuracy of statistic methods or the heavy-workload inaccuracy of neural-network-based models can cause service level agreement violations. Thus, we propose PePNet to improve overall especially heavy workload prediction accuracy. It has two distinctive characteristics: (i) A Periodicity-Perceived Mechanism to detect the existence of periodicity and the length of one period automatically, without any priori knowledge. Furthermore, it fuses periodic information adaptively, which is suitable for periodic, lax periodic and aperiodic time series. (ii) An Achilles' Heel Loss Function iteratively optimizing the most under-fitting part in predicting sequence for each step, which significantly improves the prediction accuracy of heavy load. Extensive experiments conducted on Alibaba2018, SMD dataset and Dinda's dataset demonstrate that PePNet improves MAPE for overall workload by 20.0% on average, compared with state-of-the-art methods. Especially, PePNet improves MAPE for heavy workload by 23.9% on average.

A few months ago, Generative Pre-Trained Transformer-3, or GPT-3, the biggest artificial intelligence (AI) model in history and the most powerful language model ever, was launched with much fanfare by OpenAI, a San Francisco-based AI lab. Over the last few years, one of the biggest trends in natural language processing (NLP) has been the increasing size of language models (LMs), as measured by the size of training data and the number of parameters. The 2018-released BERT, which was then considered the best-in-class NLP model, was trained on a dataset of 3 billion words. The XLNet model that outperformed BERT was based on a training set of 32 billion words. Shortly thereafter, GPT-2 was trained on a dataset of 40 billion words. Dwarfing all these, GPT-3 was trained on a weighted dataset of roughly 500 billion words.

This article is part of a special report on artificial intelligence, The AI Issue. Europe's plan to ride a new wave of AI innovation into a technological renaissance relies on companies sharing their data with researchers and entrepreneurs. But will the companies play along? According to interviews with industry groups representing Silicon Valley, European tech companies and Germany's industrial base, the answer for now is: maybe, but only to a limited extent, and even then only when sharing data will not benefit rivals. "We haven't seen any single company speaking up in public saying it was a great idea," said Alexandre Roure of the Computer and Communications Industry Association (CCIA), a tech lobby whose members include Google and Facebook.

AI has the potential to add $15.7 trillion to the global GDP by 2030 due to labor productivity improvement and automation. Enterprise AI adoption is accelerating in the market, with the projected value of AI (hardware, software, services) to reach $190.61 billion by 2025. The primordial ingredient that is required to make AI deployments successful is the access and availability of quality and clean data to train and feed machine learning and analytical models. Enterprise data architectures have been deployed and developed over decades, leading to a hodge-podge of different technologies, physical and logical architectures, formats and frameworks in organizations. Since machine learning (ML) algorithms rely on data to learn from to evolve prediction models, the need for sufficient, quality, prepared data is paramount.

According to a recent report from AI research and advisory firm Cognilytica, over 80% of the time spent in AI projects are spent dealing with and wrangling data. Even more importantly, and perhaps surprisingly, is how human-intensive much of this data preparation work is. In order for supervised forms of machine learning to work, especially the multi-layered deep learning neural network approaches, they must be fed large volumes of examples of correct data that is appropriately annotated, or "labeled", with the desired output result. For example, if you're trying to get your machine learning algorithm to correctly identify cats inside of images, you need to feed that algorithm thousands of images of cats, appropriately labeled as cats, with the images not having any extraneous or incorrect data that will throw the algorithm off as you build the model.