Unnatural language processing: How do language models handle machine-generated prompts?

Language model prompt optimization research has shown that semantically and grammatically well-formed manually crafted prompts are routinely outperformed by automatically generated token sequences with no apparent meaning or syntactic structure, including sequences of vectors from a model's embedding space. We use machine-generated prompts to probe how models respond to input that is not composed of natural language expressions. We study the behavior of models of different sizes in multiple semantic tasks in response to both continuous and discrete machine-generated prompts, and compare it to the behavior in response to human-generated natural-language prompts. Even when producing a similar output, machine-generated and human prompts trigger different response patterns through the network processing pathways, including different perplexities, different attention and output entropy distributions, and different unit activation profiles. We provide preliminary insight into the nature of the units activated by different prompt types, suggesting that only natural language prompts recruit a genuinely linguistic circuit.Comment: Findings of EMNLP 2023 Camera-Read

Referential communication in heterogeneous communities of pre-trained visual deep networks

As large pre-trained image-processing neural networks are being embedded in autonomous agents such as self-driving cars or robots, the question arises of how such systems can communicate with each other about the surrounding world, despite their different architectures and training regimes. As a first step in this direction, we systematically explore the task of \textit{referential communication} in a community of heterogeneous state-of-the-art pre-trained visual networks, showing that they can develop, in a self-supervised way, a shared protocol to refer to a target object among a set of candidates. This shared protocol can also be used, to some extent, to communicate about previously unseen object categories of different granularity. Moreover, a visual network that was not initially part of an existing community can learn the community's protocol with remarkable ease. Finally, we study, both qualitatively and quantitatively, the properties of the emergent protocol, providing some evidence that it is capturing high-level semantic features of objects

Vitamin E family: Role in the pathogenesis and treatment of Alzheimer's disease

AbstractIntroductionVitamin E family, composed by tocopherols and tocotrienols, is a group of compounds with neuroprotective properties. The exact role in the pathogenesis and the benefit of vitamin E as treatment for Alzheimer's disease (AD) are still under debate.MethodsA literature search in PubMed, Medline, and Cochrane databases has been carried out. All types of studies, from bench and animal models to clinical, were included.ResultsHigh plasma vitamin E levels are associated with better cognitive performance, even if clear evidence of their ability to prevent or delay cognitive decline in AD is still lacking. Each vitamin E form is functionally unique and shows specific biological functions. Tocotrienols seem to have superior antioxidant and anti-inflammatory properties compared with tocopherols.DiscussionThe benefit of vitamin E as a treatment for AD is still under debate, mainly because of the inconsistent findings from observational studies and the methodological limitations of clinical trials

Semantic Press

In this paper Semantic Press, a tool for the automatic press review, is introduced. It is based on Text Mining technologies and is tailored to meet the needs of the eGovernment and eParticipation communities. First, a general description of the application demands emerging from the eParticipation and eGovernment sectors is offered. Then, an introduction to the framework of the automatic analysis and classification of newspaper content is provided, together with a description of the technologies underlying it

integration of enhanced optical tracking techniques and imaging in igrt

Patient setup/Optical tracking/IGRT/Treatment surveillance. In external beam radiotherapy, modern technologies for dynamic dose delivery and beam conformation provide high selectivity in radiation dose administration to the pathological volume. A comparable accuracy level is needed in the 3-D localization of tumor and organs at risk (OARs), in order to accomplish the planned dose distribution in the reality of each irradiation session. In-room imaging techniques for patient setup verification and tumor targeting may benefit of the combined daily use of optical tracking technologies, supported by techniques for the detection and compensation of organ motion events. Multiple solutions to enhance the use of optical tracking for the on-line correction of target localization uncertainties are described, with specific emphasis on the compensation of setup errors, breathing movements and non-rigid deformations. The final goal is the implementation of customized protocols where appropriate external landmarks, to be tracked in real-time by means of noninvasive optical devices, are selected as a function of inner target localization. The presented methodology features high accuracy in patient setup optimization, also providing a valuable tool for on-line patient surveillance, taking into account both breathing and deformation effects. The methodic application of optical tracking is put forward to represent a reliable and low cost procedure for the reduction of safety margins, once the patient-specific correlation between external landmarks and inner structures has been established. Therefore, the integration of optical tracking with in-room imaging devices is proposed as a way to gain higher confidence in the framework of Image Guided Radiation Therapy (IGRT) treatments