Positive selection determines T cell receptor V beta 14 gene usage by CD8+ T cells.

We report here a mAb, 14-2, reactive with TCRs that include V beta 14. The frequency of V beta 14+ T cells varies with CD4 and CD8 subset and is controlled by the H-2 genes. Thus CD8+ T cells from H-2b mice include approximately 2.3% V beta 14+ T cells while CD8+ T cells from mice expressing K kappa include greater than 8% V beta 14+ T cells. In all strains examined, 7-8% of CD4+ T cells express V beta 14. The frequent usage of V beta 14 in CD8+ T cells of K kappa-expressing mice is a result of preferential positive selection of V beta 14+ CD8+ T cells as demonstrated by analysis of radiation chimeras. These studies demonstrate that H-2-dependent positive selection occurs in unmanipulated mice. Furthermore, the results imply that positive selection, and possibly H-2 restriction, can be strongly influenced by a V beta domain, with some independence from the beta-junctional sequence and alpha chain

The nature of design and style in music

Thesis (M.A.)--Boston University, 1939. This item was digitized by the Internet Archive

A Cost-Effective Method to Prevent Data Exfiltration from LLM Prompt Responses

Large language models (LLMs) are susceptible to security risks wherein malicious attackers can manipulate LLMs by poisoning their training data or using malicious text prompts or queries designed to cause the LLM to return output that includes sensitive or confidential information, e.g., that is part of the LLM training dataset. This disclosure describes the use of a data loss prevention (DLP) system to protect LLMs against data exfiltration. The DLP system can be configured to detect specific data types that are to be prevented from being leaked. The LLM output, generated in response to a query from an application or user, is passed through the DLP system which generates a risk score for the LLM output. If the risk score is above a predefined threshold, the LLM output is provided to an additional pre-trained model that has been trained to detect sensitive or confidential data. The output is modified to block, mask, redact, or otherwise remove the sensitive data. The modified output is provided to the application or user. In certain cases, the output may indicate that no response can be provided due to a policy violation

An Investigation of Learning in a T-Maze with Relevant Drives Satisfied

Psychologists agree, in general, that the role of motivation in determining the behavior of organisms is an important one. However, different interpretations arise when one attempts to specify precisely what this role may be. Some psychologists believe that motivation serves only as a forcer or activator of behavior. Others hold that in addition to being an activator of behavior motivation must be reduced for learning to occur. Thus, field theorists such as Tolman and Leeper state that the acquisition of learning is not a function of the number of reinforcements, or drive state reductions. Rather, acquisition is dependent upon the temporal contiguity of the organism\u27s perceptions of successive stimuli, or signs and their significates. Once acquired, however, utilization of learning in the performance of a task is activated by an organism\u27s drive state

Automatically Detecting Expensive Prompts and Configuring Firewall Rules to Mitigate Denial of Service Attacks on Large Language Models

Denial of service attacks on generative artificial intelligence systems, e.g., large language models (LLMs), can include sending LLMs requests that include expensive prompts designed to consume computing resources and degrade model performance. This disclosure describes techniques to automatically detect such prompts and then configure firewall rules that prevent such prompts in subsequent requests from reaching the LLM. Per the techniques, prompts provided to an LLM are matched against input and output token size as well as resource utilization to identify prompts that deviate significantly from a baseline. Expensive prompts are identified, and semantically similar prompts are automatically generated using the same LLM or another model. A subset of the generated prompts that are semantics similar to expensive prompts are identified by comparing respective vector embeddings. The subset of prompts and the received expensive prompts are provided to a pre-trained LLM that generates firewall rules, e.g., web application firewall (WAF) rules. Incoming requests from applications are evaluated based on the rules, and expensive prompts are blocked from reaching the LLM or are rate-limited