GRIM: GRaph-based Interactive narrative visualization for gaMes

Dialogue-based Role Playing Games (RPGs) require powerful storytelling. The narratives of these may take years to write and typically involve a large creative team. In this work, we demonstrate the potential of large generative text models to assist this process. \textbf{GRIM}, a prototype \textbf{GR}aph-based \textbf{I}nteractive narrative visualization system for ga\textbf{M}es, generates a rich narrative graph with branching storylines that match a high-level narrative description and constraints provided by the designer. Game designers can interactively edit the graph by automatically generating new sub-graphs that fit the edits within the original narrative and constraints. We illustrate the use of \textbf{GRIM} in conjunction with GPT-4, generating branching narratives for four well-known stories with different contextual constraints

Recognition of HIV-1 Peptides by Host CTL Is Related to HIV-1 Similarity to Human Proteins

Background: While human immunodeficiency virus type 1 (HIV-1)-specific cytotoxic T lymphocytes preferentially target specific regions of the viral proteome, HIV-1 features that contribute to immune recognition are not well understood. One hypothesis is that similarities between HIV and human proteins influence the host immune response, i.e., resemblance between viral and host peptides could preclude reactivity against certain HIV epitopes. Methodology/Principal Findings: We analyzed the extent of similarity between HIV-1 and the human proteome. Proteins from the HIV-1 B consensus sequence from 2001 were dissected into overlapping k-mers, which were then probed against a non-redundant database of the human proteome in order to identify segments of high similarity. We tested the relationship between HIV-1 similarity to host encoded peptides and immune recognition in HIV-infected individuals, and found that HIV immunogenicity could be partially modulated by the sequence similarity to the host proteome. ELISpot responses to peptides spanning the entire viral proteome evaluated in 314 individuals showed a trend indicating an inverse relationship between the similarity to the host proteome and the frequency of recognition. In addition, analysis of responses by a group of 30 HIV-infected individuals against 944 overlapping peptides representing a broad range of individual HIV-1B Nef variants, affirmed that the degree of similarity to the host was significantly lower for peptides with reactive epitopes than for those that were not recognized. Conclusions/Significance: Our results suggest that antigenic motifs that are scarcely represented in human proteins might represent more immunogenic CTL targets not selected against in the host. This observation could provide guidance in the design of more effective HIV immunogens, as sequences devoid of host-like features might afford superior immune reactivity

Generalized Feature Detection Using the Karhunen-Loeve Transform and Expansion Matching

This paper presents anovel generalized feature extraction method based on the Expansion Matching (EXM) method and the Karhunen-Loeve (KL) transform. This yields an e cient method to locate a large variety of features with reduced numberof ltering operations. The EXM method is used to design optimal detectors for di erent features. The KL representation is used to de ne anoptimal basis for representing these EXM feature detectors with minimum truncation error. Input images are then analyzed with the the resulting KLbases. The KL coe cients obtained from the analysis are used to e ciently reconstruct the response due to anycombination of feature detectors. The method is applied to real images and successfully extracts avariety of arc and edge features as well as complex junction features formed by combining two or more arc or line features.

On The Use Of The Karhunen-Loeve Transform And Expansion Matching For Generalized Feature Detection

A novel generalized feature extraction method based on the Expansion Matching (EXM) method and the KarhunenLoeve (KL) transform is presented. This yields an efficient method to locate a large variety of features with a single pass of parallel filtering operations. The EXM method is used to design optimal detectors for different features. The KL representation is used to define an optimal basis for representing these EXM feature detectors with minimum truncation error. Input images are then analyzed with the the resulting KL bases. The KL coefficients obtained from the analysis are used to efficiently reconstruct the response due to any combination of feature detectors. The method is successfully applied to real images and extracts a variety of arc and edge feature as well as more complex junction features formed by combining two or more arcs or line features. 1. INTRODUCTION An important preprocessing step in many computer vision and image understanding tasks involves the extraction ..

Frequency of recognition of HIV-1 B consensus 2001 peptides as a function of their intrinsic disorder prediction score.

<p>The vertical axis corresponds to the percentage of HIV-1 infected individuals that recognized each of the 410 peptides spanning the HIV-1 B consensus 2001 proteome. The horizontal axis corresponds to the disorder prediction score for each peptide, calculated using predictions of order/disorder made with the VSL1 predictor (PONDR®).</p

HIV-1 nonamers with high similarities to human proteins.

<p>The level of similarity to host proteins for these HIV nonamers differed by 3 standard deviations from the level of similarity found with randomized nonamers. Amino acid changes are in bold italics and lower case.</p

Frequency of recognition of HIV-1 B consensus 2001 peptides as a function of their similarity to human proteins.

<p>410 peptides spanning the HIV-1 B consensus 2001 proteome were tested by ELISpot using PBMC from a cohort of 314 HIV-1 infected individuals. The vertical axis corresponds to the percentage of individuals who recognized a given peptide. The horizontal axis corresponds to the number of matches to the human proteome for each peptide. Matches were derived by dissecting the 410 peptides into overlapping 6-mers offset by one residue, and then scored against the RefSeq protein sequence database. Matches were normalized to account for the length of the starting peptide (ranging from 15–20 AA in length).</p

Magnitude of the ELISpot responses elicited by HIV-1 B Nef peptides as a function of their similarity to human peptides.

<p>The vertical axis corresponds to the mean number of spot-forming cells counted in ELISpot assays. The horizontal axis corresponds to Nef peptides that have 2, 3 or 4 differences with their closest human peptides.</p