Deep Learning for Link Prediction in Dynamic Networks using Weak Estimators

Link prediction is the task of evaluating the probability that an edge exists in a network, and it has useful applications in many domains. Traditional approaches rely on measuring the similarity between two nodes in a static context. Recent research has focused on extending link prediction to a dynamic setting, predicting the creation and destruction of links in networks that evolve over time. Though a difficult task, the employment of deep learning techniques have shown to make notable improvements to the accuracy of predictions. To this end, we propose the novel application of weak estimators in addition to the utilization of traditional similarity metrics to inexpensively build an effective feature vector for a deep neural network. Weak estimators have been used in a variety of machine learning algorithms to improve model accuracy, owing to their capacity to estimate changing probabilities in dynamic systems. Experiments indicate that our approach results in increased prediction accuracy on several real-world dynamic networks

The Rho GTPases Rac1, Cdc42, and RhoA Regulate APP Transport to Lysosomes and Aβ Production

Alzheimer’s Disease (AD) is characterized by Beta-Amyloid (Aβ) plaques within the brain. Aβ peptides are produced by the cleavage of Amyloid Precursor Protein (APP). Our lab has previously discovered a novel pathway for APP internalization mediated by ADP-ribosylation factor 6 (Arf6). This pathway resembles macropinocytosis, transporting cell surface APP directly to lysosomes, a possible site for Aβ production. We set out to characterize the effectors downstream of Arf6. In SN56 and N2A cells we co-transfected HA-tagged APP (to label cell-surface APP) with compartment markers, to visualize APP trafficking. We used dominant negative and constitutively active mutants, pharmacological inhibitors, and siRNA for Rac1, Cdc42, and RhoA to determine their roles in APP macropinocytosis. APP trafficking to lysosomes was reduced after knockdown of Rac1, Cdc42, and RhoA, and inhibition of this transport reduced production of Aβ40 and Aβ42. Our findings indicate a role for Rac1, Cdc42, and RhoA in Aβ production

Asking More Informative Questions for Grounded Retrieval

When a model is trying to gather information in an interactive setting, it benefits from asking informative questions. However, in the case of a grounded multi-turn image identification task, previous studies have been constrained to polar yes/no questions, limiting how much information the model can gain in a single turn. We present an approach that formulates more informative, open-ended questions. In doing so, we discover that off-the-shelf visual question answering (VQA) models often make presupposition errors, which standard information gain question selection methods fail to account for. To address this issue, we propose a method that can incorporate presupposition handling into both question selection and belief updates. Specifically, we use a two-stage process, where the model first filters out images which are irrelevant to a given question, then updates its beliefs about which image the user intends. Through self-play and human evaluations, we show that our method is successful in asking informative open-ended questions, increasing accuracy over the past state-of-the-art by 14%, while resulting in 48% more efficient games in human evaluations

Symbolic Planning and Code Generation for Grounded Dialogue

Large language models (LLMs) excel at processing and generating both text and code. However, LLMs have had limited applicability in grounded task-oriented dialogue as they are difficult to steer toward task objectives and fail to handle novel grounding. We present a modular and interpretable grounded dialogue system that addresses these shortcomings by composing LLMs with a symbolic planner and grounded code execution. Our system consists of a reader and planner: the reader leverages an LLM to convert partner utterances into executable code, calling functions that perform grounding. The translated code's output is stored to track dialogue state, while a symbolic planner determines the next appropriate response. We evaluate our system's performance on the demanding OneCommon dialogue task, involving collaborative reference resolution on abstract images of scattered dots. Our system substantially outperforms the previous state-of-the-art, including improving task success in human evaluations from 56% to 69% in the most challenging setting.Comment: Accepted to EMNLP 202