Large Language Model Augmented Narrative Driven Recommendations

Narrative-driven recommendation (NDR) presents an information access problem where users solicit recommendations with verbose descriptions of their preferences and context, for example, travelers soliciting recommendations for points of interest while describing their likes/dislikes and travel circumstances. These requests are increasingly important with the rise of natural language-based conversational interfaces for search and recommendation systems. However, NDR lacks abundant training data for models, and current platforms commonly do not support these requests. Fortunately, classical user-item interaction datasets contain rich textual data, e.g., reviews, which often describe user preferences and context - this may be used to bootstrap training for NDR models. In this work, we explore using large language models (LLMs) for data augmentation to train NDR models. We use LLMs for authoring synthetic narrative queries from user-item interactions with few-shot prompting and train retrieval models for NDR on synthetic queries and user-item interaction data. Our experiments demonstrate that this is an effective strategy for training small-parameter retrieval models that outperform other retrieval and LLM baselines for narrative-driven recommendation.Comment: Pre-prin

Editable User Profiles for Controllable Text Recommendation

Methods for making high-quality recommendations often rely on learning latent representations from interaction data. These methods, while performant, do not provide ready mechanisms for users to control the recommendation they receive. Our work tackles this problem by proposing LACE, a novel concept value bottleneck model for controllable text recommendations. LACE represents each user with a succinct set of human-readable concepts through retrieval given user-interacted documents and learns personalized representations of the concepts based on user documents. This concept based user profile is then leveraged to make recommendations. The design of our model affords control over the recommendations through a number of intuitive interactions with a transparent user profile. We first establish the quality of recommendations obtained from LACE in an offline evaluation on three recommendation tasks spanning six datasets in warm-start, cold-start, and zero-shot setups. Next, we validate the controllability of LACE under simulated user interactions. Finally, we implement LACE in an interactive controllable recommender system and conduct a user study to demonstrate that users are able to improve the quality of recommendations they receive through interactions with an editable user profile.Comment: Accepted to SIGIR 2023; Pre-print, camera-ready to follo

AUTOPAYMENTS VIA ACCOUNT ABSTRACTION

The present disclosure focuses to simplify the user’s ability to make autopayments without making use of the private key associated with the user while using a non-custodial wallet. The present disclosure describes that without making use of user’s private key, a smart contract can make an autopayment on behalf of the user to the merchant to whom the user wishes to make the payment. In other words, the smart contract will make the automatic payment to merchants associated with the user if the merchant’s details are present in the allowed list of the user, else, the smart contract may reject the transaction

Augmented Reality Technology to Facilitate Proficiency in Emergency Medical Procedures

Background: Augmented reality (AR) conveys an experience during which the user’s real-time environment is enhanced by computer-generated perceptual information; it is being investigated as a solution to enhance medical education and clinical practice. There is little literature on its utility for teaching emergency procedures. Methods: A within-subjects trial was performed comparing traditional training to AR guidance for two emergency procedures. Lay-subjects and emergency medical technicians received video training and AR guidance for performing bag-valve-mask ventilation and needle-decompression. Subjects performed both procedures in a simulation setting after each training modality. Subject performance, acceptability and usability were analyzed. Results: There was no difference in procedural performance between lay or EMT subjects for AR training, and no difference in subject-reported usefulness between the AR and control training. Conclusion: AR mediated guidance for emergency medical procedures is feasible and efficacious. Subject performance after AR training was statistically undistinguishable from a didactic educational modality

Scalable Off-Chain Auctions

Blockchain auction plays an important role in the price discovery of digital assets (e.g. NFTs). However, despite their importance, implementing auctions directly on blockchains such as Ethereum incurs scalability issues. In particular, the on-chain transactions scale poorly with the number of bidders, leading to network congestion, increased transaction fees, and slower transaction confirmation time. This lack of scalability significantly hampers the ability of the system to handle large-scale, high-speed auctions that are common in today\u27s economy. In this work, we build a protocol where an auctioneer can conduct sealed bid auctions that run entirely off-chain when parties behave honestly, and in the event that $k$ bidders deviate (e.g., do not open their sealed bid) from an $n$-party auction protocol, then the on-chain complexity is only $O(k \log n)$. This improves over existing solutions that require $O(n)$ on-chain complexity, even if a single bidder deviates from the protocol. In the event of a malicious auctioneer, our protocol still guarantees that the auction will successfully terminate. We implement our protocol and show that it offers significant efficiency improvements compared to existing on-chain solutions. Our use of zkSnark to achieve scalability also ensures that the on-chain contract and other participants do not acquire any information about the bidders\u27 identities and their respective bids, except for the winner and the winning bid amount

An Improved Canine Genome and a Comprehensive Catalogue of Coding Genes and Non-Coding Transcripts

The domestic dog, Canis familiaris, is a well-established model system for mapping trait and disease loci. While the original draft sequence was of good quality, gaps were abundant particularly in promoter regions of the genome, negatively impacting the annotation and study of candidate genes. Here, we present an improved genome build, canFam3.1, which includes 85 MB of novel sequence and now covers 99.8% of the euchromatic portion of the genome. We also present multiple RNA-Sequencing data sets from 10 different canine tissues to catalog ∼175,000 expressed loci. While about 90% of the coding genes previously annotated by EnsEMBL have measurable expression in at least one sample, the number of transcript isoforms detected by our data expands the EnsEMBL annotations by a factor of four. Syntenic comparison with the human genome revealed an additional ∼3,000 loci that are characterized as protein coding in human and were also expressed in the dog, suggesting that those were previously not annotated in the EnsEMBL canine gene set. In addition to ∼20,700 high-confidence protein coding loci, we found ∼4,600 antisense transcripts overlapping exons of protein coding genes, ∼7,200 intergenic multi-exon transcripts without coding potential, likely candidates for long intergenic non-coding RNAs (lincRNAs) and ∼11,000 transcripts were reported by two different library construction methods but did not fit any of the above categories. Of the lincRNAs, about 6,000 have no annotated orthologs in human or mouse. Functional analysis of two novel transcripts with shRNA in a mouse kidney cell line altered cell morphology and motility. All in all, we provide a much-improved annotation of the canine genome and suggest regulatory functions for several of the novel non-coding transcripts

Focused Ion Beam Fabrication

Contains reports on eight research projects.DARPA/Naval Electronics Systems Command (Contract MDA 903-85-C-0215)DARPA/U.S. Army Research Office (Contract DAAL03-88-K-0108)U.S. Army Research Office (Contract DAAL03-87-K-0126)Charles Stark Draper LaboratoryInternational Business Machines Corporation - Research Division, General Technologies DivisionU.S. Air ForceDARP