GPT-generated Text Detection: Benchmark Dataset and Tensor-based Detection Method

As natural language models like ChatGPT become increasingly prevalent in applications and services, the need for robust and accurate methods to detect their output is of paramount importance. In this paper, we present GPT Reddit Dataset (GRiD), a novel Generative Pretrained Transformer (GPT)-generated text detection dataset designed to assess the performance of detection models in identifying generated responses from ChatGPT. The dataset consists of a diverse collection of context-prompt pairs based on Reddit, with human-generated and ChatGPT-generated responses. We provide an analysis of the dataset's characteristics, including linguistic diversity, context complexity, and response quality. To showcase the dataset's utility, we benchmark several detection methods on it, demonstrating their efficacy in distinguishing between human and ChatGPT-generated responses. This dataset serves as a resource for evaluating and advancing detection techniques in the context of ChatGPT and contributes to the ongoing efforts to ensure responsible and trustworthy AI-driven communication on the internet. Finally, we propose GpTen, a novel tensor-based GPT text detection method that is semi-supervised in nature since it only has access to human-generated text and performs on par with fully-supervised baselines.Comment: 4 pages, 2 figures, published in the WWW 2024 Short Papers Trac

Small, porous polyacrylate beads

Uniformly-shaped, porous, round beads are prepared by the co-polymerization of an acrylic monomer and a cross-linking agent in the presence of 0.05 to 5% by weight of an aqueous soluble polymer such as polyethylene oxide. Cross-linking proceeds at high temperature above about 50.degree.C or at a lower temperature with irradiation. Beads of even shape and even size distribution of less than 2 micron diameter are formed. The beads will find use as adsorbents in chromatography and as markers for studies of cell surface receptors

Crosslinked, porous, polyacrylate beads

Uniformly-shaped, porous, round beads are prepared by the co-polymerization of an acrylic monomer and a cross-linking agent in the presence of 0.05 to 5% by weight of an aqueous soluble polymer such as polyethylene oxide. Cross-linking proceeds at high temperature above about 50.degree.C or at a lower temperature with irradiation. Beads of even shape and even size distribution of less than 2 micron diameter are formed. The beads will find use as adsorbents in chromatography and as markers for studies of cell surface receptors

Protein specific polymeric immunomicrospheres

Small, round, bio-compatible microspheres capable of covalently bonding proteins and having a uniform diameter below about 3500 A are prepared by substantially instantaneously initiating polymerization of an aqueous emulsion containing no more than 35% total monomer including an acrylic monomer substituted with a covalently bondable group such as hydroxyl, amino or carboxyl and a minor amount of a cross-linking agent

Preparation of small bio-compatible microspheres

Small, round, bio-compatible microspheres capable of covalently bonding proteins and having a uniform diameter below about 3500 A are prepared by substantially instantaneously initiating polymerization of an aqueous emulsion containing no more than 35% total monomer including an acrylic monomer substituted with a covalently bondable group such a hydroxyl, amino or carboxyl and a minor amount of a cross-linking agent

Coating for gasifiable carbon-graphite fibers

A thin, uniform, firmly adherent coating of metal gasification catalyst is applied to a carbon-graphite fiber by first coating the fiber with a film-forming polymer containing functional moieties capable of reaction with the catalytic metal ions. Multivalent metal cations such as calcium cross-link the polymer such as a polyacrylic acid to insolubilize the film by forming catalytic metal macro-salt links between adjacent polymer chains. The coated fibers are used as reinforcement for resin composites and will gasify upon combustion without evolving conductive airborne fragments

Link Prediction with Non-Contrastive Learning

A recent focal area in the space of graph neural networks (GNNs) is graph self-supervised learning (SSL), which aims to derive useful node representations without labeled data. Notably, many state-of-the-art graph SSL methods are contrastive methods, which use a combination of positive and negative samples to learn node representations. Owing to challenges in negative sampling (slowness and model sensitivity), recent literature introduced non-contrastive methods, which instead only use positive samples. Though such methods have shown promising performance in node-level tasks, their suitability for link prediction tasks, which are concerned with predicting link existence between pairs of nodes (and have broad applicability to recommendation systems contexts) is yet unexplored. In this work, we extensively evaluate the performance of existing non-contrastive methods for link prediction in both transductive and inductive settings. While most existing non-contrastive methods perform poorly overall, we find that, surprisingly, BGRL generally performs well in transductive settings. However, it performs poorly in the more realistic inductive settings where the model has to generalize to links to/from unseen nodes. We find that non-contrastive models tend to overfit to the training graph and use this analysis to propose T-BGRL, a novel non-contrastive framework that incorporates cheap corruptions to improve the generalization ability of the model. This simple modification strongly improves inductive performance in 5/6 of our datasets, with up to a 120% improvement in Hits@50--all with comparable speed to other non-contrastive baselines and up to 14x faster than the best-performing contrastive baseline. Our work imparts interesting findings about non-contrastive learning for link prediction and paves the way for future researchers to further expand upon this area.Comment: ICLR 2023. 19 pages, 6 figure

Linkless Link Prediction via Relational Distillation

Graph Neural Networks (GNNs) have shown exceptional performance in the task of link prediction. Despite their effectiveness, the high latency brought by non-trivial neighborhood data dependency limits GNNs in practical deployments. Conversely, the known efficient MLPs are much less effective than GNNs due to the lack of relational knowledge. In this work, to combine the advantages of GNNs and MLPs, we start with exploring direct knowledge distillation (KD) methods for link prediction, i.e., predicted logit-based matching and node representation-based matching. Upon observing direct KD analogs do not perform well for link prediction, we propose a relational KD framework, Linkless Link Prediction (LLP), to distill knowledge for link prediction with MLPs. Unlike simple KD methods that match independent link logits or node representations, LLP distills relational knowledge that is centered around each (anchor) node to the student MLP. Specifically, we propose rank-based matching and distribution-based matching strategies that complement each other. Extensive experiments demonstrate that LLP boosts the link prediction performance of MLPs with significant margins, and even outperforms the teacher GNNs on 7 out of 8 benchmarks. LLP also achieves a 70.68x speedup in link prediction inference compared to GNNs on the large-scale OGB dataset

Node Duplication Improves Cold-start Link Prediction

Graph Neural Networks (GNNs) are prominent in graph machine learning and have shown state-of-the-art performance in Link Prediction (LP) tasks. Nonetheless, recent studies show that GNNs struggle to produce good results on low-degree nodes despite their overall strong performance. In practical applications of LP, like recommendation systems, improving performance on low-degree nodes is critical, as it amounts to tackling the cold-start problem of improving the experiences of users with few observed interactions. In this paper, we investigate improving GNNs' LP performance on low-degree nodes while preserving their performance on high-degree nodes and propose a simple yet surprisingly effective augmentation technique called NodeDup. Specifically, NodeDup duplicates low-degree nodes and creates links between nodes and their own duplicates before following the standard supervised LP training scheme. By leveraging a ''multi-view'' perspective for low-degree nodes, NodeDup shows significant LP performance improvements on low-degree nodes without compromising any performance on high-degree nodes. Additionally, as a plug-and-play augmentation module, NodeDup can be easily applied to existing GNNs with very light computational cost. Extensive experiments show that NodeDup achieves 38.49%, 13.34%, and 6.76% improvements on isolated, low-degree, and warm nodes, respectively, on average across all datasets compared to GNNs and state-of-the-art cold-start methods

Nanowire sensor, sensor array, and method for making the same

The present invention relates to a nanowire sensor and method for forming the same. More specifically, the nanowire sensor comprises at least one nanowire formed on a substrate, with a sensor receptor disposed on a surface of the nanowire, thereby forming a receptor-coated nanowire. The nanowire sensor can be arranged as a sensor sub-unit comprising a plurality of homogeneously receptor-coated nanowires. A plurality of sensor subunits can be formed to collectively comprise a nanowire sensor array. Each sensor subunit in the nanowire sensor array can be formed to sense a different stimulus, allowing a user to sense a plurality of stimuli. Additionally, each sensor subunit can be formed to sense the same stimuli through different aspects of the stimulus. The sensor array is fabricated through a variety of techniques, such as by creating nanopores on a substrate and electrodepositing nanowires within the nanopores