Prompt injection attacks have emerged as a serious threat in curr ent large language models (LLMs), where adversaries may alter model behav ior by injecting malicious instructions into the prompt [2]. Existing approach es - such as input sanitization, fixed prompt templates, and heuristic-based filtering - often mix trusted system instructions with untrusted us er inputs, leading to brittle defenses that are easily circumvented. For examp le, an adversary could include a cleverly worded request that causes the model to "forget its internal guidelines," thereby triggering unintended beh avior. Our PICO (Prompt Isolation and Cybersecurity Oversight) propos al circumvents these limitations, first of all, by architecturally segregat ing the system prompt and user input into distinct channels. In doing so, we ensure that the trusted instructions remain intact while only the untruste d user input is subject to adaptation. Furthermore, we augment the mode l with a dedicated Security Expert Agent and a Cybersecurity Knowledge G raph [4] to provide supplemental, domain-specific signals that reinforce the invariant. In what follows, we first present a mathematical formalization of th e PICO security strategy, and then we describe its concrete realiza tion, both via PICO-based retraining of transformer models from the bottom up, and via a more efficient if less ideal fine-tuning strategy. We flesh out theapproach by considering how it would be expected to handle two specific example situations, including a basic prompt injection and then a subtler Policy Puppetry attack.

Previous approaches for automatic lay summarisation are exclusively reliant on the source article that, given it is written for a technical audience (e.g., researchers), is unlikely to explicitly define all technical concepts or state all of the background information that is relevant for a lay audience. We address this issue by augmenting eLife, an existing biomedical lay summarisation dataset, with article-specific knowledge graphs, each containing detailed information on relevant biomedical concepts. Using both automatic and human evaluations, we systematically investigate the effectiveness of three different approaches for incorporating knowledge graphs within lay summarisation models, with each method targeting a distinct area of the encoder-decoder model architecture. Our results confirm that integrating graph-based domain knowledge can significantly benefit lay summarisation by substantially increasing the readability of generated text and improving the explanation of technical concepts.

Becoming a data scientist is considered a prestigious trait. Back in 2012, Harvard Business Review called'data scientist' the sexiest job of the 21st century, and the growing trend of roles in the industry seems to be confirming that statement. To confirm this sexiness is still ongoing, the info from Glassdoor shows being a data scientist is the second-best job in America in 2021. To get such a prestigious job, you have to go through rigorous job interviews. Data science questions asked can be very broad and complex. This is expected, considering the role of a data scientist usually incorporates so many areas.

If you want to have a career in data science, knowing Python is a must. Python is the most popular programming language in data science, especially when it comes to machine learning and artificial intelligence. To help you in your data science career, I've prepared the main Python concepts tested in the data science interview. Later on, I will discuss two main interview question types that cover those concepts you're required to know as a data scientist. I'll also show you several example questions and give you solutions to push you in the right direction.

Some involve coding, some are drag-and-drop, some are difficult for beginners, some have no coding at all. All of these tools will help you with data visualization. But one of the most overlooked but critical practical functions of a data scientist has been included under this heading: summarisation. Summarisation means the practical result of your data science workflow. What does the result of your analysis mean for the operation of the business or the research problem that you are currently working on? How do you convert your result to the maximum improvement for your business? Can you measure the impact this result will have on the profit of your enterprise?

Some involve coding, some are drag-and-drop, some are difficult for beginners, some have no coding at all. All of these tools will help you with data visualization. But one of the most overlooked but critical practical functions of a data scientist has been included under this heading: summarisation. Summarisation means the practical result of your data science workflow. What does the result of your analysis mean for the operation of the business or the research problem that you are currently working on? How do you convert your result to the maximum improvement for your business? Can you measure the impact this result will have on the profit of your enterprise?