Food effect summarization from New Drug Application (NDA) is an essential component of product-specific guidance (PSG) development and assessment. However, manual summarization of food effect from extensive drug application review documents is time-consuming, which arouses a need to develop automated methods. Recent advances in large language models (LLMs) such as ChatGPT and GPT-4, have demonstrated great potential in improving the effectiveness of automated text summarization, but its ability regarding the accuracy in summarizing food effect for PSG assessment remains unclear. In this study, we introduce a simple yet effective approach, iterative prompting, which allows one to interact with ChatGPT or GPT-4 more effectively and efficiently through multi-turn interaction. Specifically, we propose a three-turn iterative prompting approach to food effect summarization in which the keyword-focused and length-controlled prompts are respectively provided in consecutive turns to refine the quality of the generated summary. We conduct a series of extensive evaluations, ranging from automated metrics to FDA professionals and even evaluation by GPT-4, on 100 NDA review documents selected over the past five years. We observe that the summary quality is progressively improved throughout the process. Moreover, we find that GPT-4 performs better than ChatGPT, as evaluated by FDA professionals (43% vs. 12%) and GPT-4 (64% vs. 35%). Importantly, all the FDA professionals unanimously rated that 85% of the summaries generated by GPT-4 are factually consistent with the golden reference summary, a finding further supported by GPT-4 rating of 72% consistency. These results strongly suggest a great potential for GPT-4 to draft food effect summaries that could be reviewed by FDA professionals, thereby improving the efficiency of PSG assessment cycle and promoting the generic drug product development.

Neural operator architectures employ neural networks to approximate operators mapping between Banach spaces of functions; they may be used to accelerate model evaluations via emulation, or to discover models from data. Consequently, the methodology has received increasing attention over recent years, giving rise to the rapidly growing field of operator learning. The first contribution of this paper is to prove that for general classes of operators which are characterized only by their $C^r$- or Lipschitz-regularity, operator learning suffers from a curse of dimensionality, defined precisely here in terms of representations of the infinite-dimensional input and output function spaces. The result is applicable to a wide variety of existing neural operators, including PCA-Net, DeepONet and the FNO. The second contribution of the paper is to prove that the general curse of dimensionality can be overcome for solution operators defined by the Hamilton-Jacobi equation; this is achieved by leveraging additional structure in the underlying solution operator, going beyond regularity. To this end, a novel neural operator architecture is introduced, termed HJ-Net, which explicitly takes into account characteristic information of the underlying Hamiltonian system. Error and complexity estimates are derived for HJ-Net which show that this architecture can provably beat the curse of dimensionality related to the infinite-dimensional input and output function spaces.

The passage retrieval problem, which is of central The principal limitation to this approach is its dependence importance in search engine optimization and text on explicit term matches between the analytics, entails the following: given a set of documents query and the context. In many cases, the correct and a query, determine which document best context-query pair may have no words in common.

In this paper, both empirically and theoretically, we show that several AI-text detectors are not reliable in practical scenarios. Empirically, we show that paraphrasing attacks, where a light paraphraser is applied on top of a large language model (LLM), can break a whole range of detectors, including ones using watermarking schemes as well as neural network-based detectors and zero-shot classifiers. Our experiments demonstrate that retrieval-based detectors, designed to evade paraphrasing attacks, are still vulnerable to recursive paraphrasing. We then provide a theoretical impossibility result indicating that as language models become more sophisticated and better at emulating human text, the performance of even the best-possible detector decreases. For a sufficiently advanced language model seeking to imitate human text, even the best-possible detector may only perform marginally better than a random classifier. Our result is general enough to capture specific scenarios such as particular writing styles, clever prompt design, or text paraphrasing. We also extend the impossibility result to include the case where pseudorandom number generators are used for AI-text generation instead of true randomness. We show that the same result holds with a negligible correction term for all polynomial-time computable detectors. Finally, we show that even LLMs protected by watermarking schemes can be vulnerable against spoofing attacks where adversarial humans can infer hidden LLM text signatures and add them to human-generated text to be detected as text generated by the LLMs, potentially causing reputational damage to their developers. We believe these results can open an honest conversation in the community regarding the ethical and reliable use of AI-generated text.

Deep R Programming is a comprehensive and in-depth introductory course on one of the most popular languages for data science. It equips ambitious students, professionals, and researchers with the knowledge and skills to become independent users of this potent environment so that they can tackle any problem related to data wrangling and analytics, numerical computing, statistics, and machine learning. This textbook is a non-profit project. Its online and PDF versions are freely available at .

Effective resistance (ER) is an attractive way to interrogate the structure of graphs. It is an alternative to computing the eigen-vectors of the graph Laplacian. Graph laplacians are used to find low dimensional structures in high dimensional data. Here too, ER based analysis has advantages over eign-vector based methods. Unfortunately Von Luxburg et al. (2010) show that, when vertices correspond to a sample from a distribution over a metric space, the limit of the ER between distant points converges to a trivial quantity that holds no information about the structure of the graph. We show that by using scaling resistances in a graph with $n$ vertices by $n^2$, one gets a meaningful limit of the voltages and of effective resistances. We also show that by adding a "ground" node to a metric graph one gets a simple and natural way to compute all of the distances from a chosen point to all other points.

Republican presidential candidate Francis Suarez appeared to admit during a Tuesday morning radio interview about national security that he does not know what a Uyghur is. The admission from Suarez came during an appearance on The Hugh Hewitt Show, where Hewitt asked Suarez, "Will you be talking about the Uyghurs in your campaign?" "The what," Suarez, the current mayor of Miami, responded. Republican presidential candidate and Mayor of Miami Francis Suarez delivers remarks at the Faith and Freedom Road to Majority conference on June 23, 2023, in Washington, DC. (Drew Angerer/Getty Images) "What's a Uyghur," Suarez inquired further. Moving on from the question due to Suarez's inability to identify what a Uyghur is, Hewitt told the mayor, "You've got to get smart on that."

Deepfakes, facial recognition and existential threat: politicians, watchdogs and the public must confront daunting issues when it comes to regulating artificial intelligence. Tech regulation has a history of lagging the industry, with the the UK's online safety bill and the EU's Digital Services Act only just arriving almost two decades after the launch of Facebook. AI is streaking ahead as well. ChatGPT already has more than 100 million users, the pope is in a puffer jacket and an array of experts have warned that the AI race is getting out of control. But at least the European Union, as is often the case with tech, is making a start with the AI Act.

DEATH PENALTY CHARGES - Idaho murder suspect Bryan Kohberger will be in court today for the first time since state announced it will seek death penalty. MERCY FOR MERCENARIES - Russia drops charges against Prigozhin, other participants of Wagner Group rebellion. SACKED OVER SCIENCE - College allegedly fired biology professor teaching sex is determined by chromosomes X and Y. Continue reading … TECH'LOVE' - Wimbledon teams up with IBM to introduce generative AI video commentary and highlight clips. NORMANDY MOMENT - AI companies are risking US national security by working with China, writes Patrick Murphy. NO COP OUT - Florida's largest police union reveals the candidate it's endorsing for president.

Lt. Gen. Keith Kellogg, former Pence National Security adviser, weighs in on reports that China is working to establish a military base in Cuba. This month, 79 years ago, Allied troops stormed the beaches of Normandy in World War II. The greatest amphibious invasion in human history was a product of unprecedented levels of planning, heroism, sacrifice, and new technology. Scientists, service members, and industrialists came together to develop and build underwater pipelines, artificial harbors, specialized landing craft, and tide prediction equipment. Everyone had a job to do – and everyone did it as one team in the fight.