Large Language Models as Annotators: Enhancing Generalization of NLP Models at Minimal Cost

State-of-the-art supervised NLP models achieve high accuracy but are also susceptible to failures on inputs from low-data regimes, such as domains that are not represented in training data. As an approximation to collecting ground-truth labels for the specific domain, we study the use of large language models (LLMs) for annotating inputs and improving the generalization of NLP models. Specifically, given a budget for LLM annotations, we present an algorithm for sampling the most informative inputs to annotate and retrain the NLP model. We find that popular active learning strategies such as uncertainty-based sampling do not work well. Instead, we propose a sampling strategy based on the difference in prediction scores between the base model and the finetuned NLP model, utilizing the fact that most NLP models are finetuned from a base model. Experiments with classification (semantic similarity) and ranking (semantic search) tasks show that our sampling strategy leads to significant gains in accuracy for both the training and target domains

Model Sparsification Can Simplify Machine Unlearning

Recent data regulations necessitate machine unlearning (MU): The removal of the effect of specific examples from the model. While exact unlearning is possible by conducting a model retraining with the remaining data from scratch, its computational cost has led to the development of approximate but efficient unlearning schemes. Beyond data-centric MU solutions, we advance MU through a novel model-based viewpoint: sparsification via weight pruning. Our results in both theory and practice indicate that model sparsity can boost the multi-criteria unlearning performance of an approximate unlearner, closing the approximation gap, while continuing to be efficient. With this insight, we develop two new sparsity-aware unlearning meta-schemes, termed `prune first, then unlearn' and `sparsity-aware unlearning'. Extensive experiments show that our findings and proposals consistently benefit MU in various scenarios, including class-wise data scrubbing, random data scrubbing, and backdoor data forgetting. One highlight is the 77% unlearning efficacy gain of fine-tuning (one of the simplest approximate unlearning methods) in the proposed sparsity-aware unlearning paradigm. Codes are available at https://github.com/OPTML-Group/Unlearn-Sparse

Biocompatibility of a Magnetic Tunnel Junction Sensor Array for the Detection of Neuronal Signals in Culture

Magnetoencephalography has been established nowadays as a crucial in vivo technique for clinical and diagnostic applications due to its unprecedented spatial and temporal resolution and its non-invasive methods. However, the innate nature of the biomagnetic signals derived from active biological tissue is still largely unknown. One alternative possibility for in vitro analysis is the use of magnetic sensor arrays based on Magnetoresistance. However, these sensors have never been used to perform long-term in vitro studies mainly due to critical biocompatibility issues with neurons in culture. In this study, we present the first biomagnetic chip based on magnetic tunnel junction (MTJ) technology for cell culture studies and show the biocompatibility of these sensors. We obtained a full biocompatibility of the system through the planarization of the sensors and the use of a three-layer capping of SiO2/Si3N4/SiO2. We grew primary neurons up to 20 days on the top of our devices and obtained proper functionality and viability of the overlying neuronal networks. At the same time, MTJ sensors kept their performances unchanged for several weeks in contact with neurons and neuronal medium. These results pave the way to the development of high performing biomagnetic sensing technology for the electrophysiology of in vitro systems, in analogy with Multi Electrode Arrays

Safety and Effectiveness of Hydroxychloroquine and Azithromycin Combination Therapy for Treatment of Hospitalized Patients with COVID-19: A Propensity-Matched Study

INTRODUCTION: We sought to determine the effectiveness and safety of hydroxychloroquine-azithromycin (HCQ-AZM) therapy in hospitalized patients with COVID-19. METHODS: This was a retrospective cohort study of 613 patients hospitalized (integrated health system involving three hospitals) for RT-PCR-confirmed COVID-19 infection between March 1, 2020 and April 25, 2020. Intervention was treatment with HCQ-AZM in hospitalized patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Outcomes of interest were in-hospital all-cause mortality, cardiovascular mortality, pulseless electrical activity (PEA) arrest, non-lethal arrhythmias, and length of hospital stay. Secondary measures included in-hospital corrected QT (QTc) interval parameters and serum biomarkers levels. RESULTS: Propensity-matched groups were composed of 173 patients given HCQ-AZM and 173 matched patients who did not receive treatment. There was no significant difference in in-hospital mortality (odds ratio [OR] 1.52; 95% confidence interval [CI] 0.80-2.89; p = 0.2), PEA arrest (OR 1.68, CI 0.68-4.15; p = 0.27), or incidence of non-lethal arrhythmias (10.4% vs. 6.8%; p = 0.28). Length of hospital stay (10.5 ± 7.4 vs. 5.8 ± 6.1; p \u3c 0.001), peak CRP levels (252 ± 136 vs. 166 ± 124; p \u3c 0.0001), and degree of QTc interval prolongation was higher for the HCQ-AZM group (28 ± 32 vs. 9 ± 32; p \u3c 0.0001), but there was no significant difference in incidence of sustained ventricular arrhythmias (2.8% vs. 1.7%; p = 0.52). HCQ-AZM was stopped in 10 patients because of QT interval prolongation and 1 patient because of drug-related polymorphic ventricular tachycardia. CONCLUSION: In this propensity-matched study, there was no difference in in-hospital mortality, life-threatening arrhythmias, or incidence of PEA arrest between the HCQ-AZM and untreated control groups. QTc intervals were longer in patients receiving HCQ-AZM, but only one patient developed drug-related ventricular tachycardia

On-treatment comparison between corrective His bundle pacing and biventricular pacing for cardiac resynchronization: A secondary analysis of His-SYNC

Background The His-SYNC pilot trial was the first randomized comparison between His bundle pacing in lieu of a left ventricular lead for cardiac resynchronization therapy (His-CRT) and biventricular pacing (BiV-CRT), but was limited by high rates of crossover. Objective To evaluate the results of the His-SYNC pilot trial utilizing treatment-received (TR) and per-protocol (PP) analyses. Methods The His-SYNC pilot was a multicenter, prospective, single-blinded, randomized, controlled trial comparing His-CRT vs BiV-CRT in patients meeting standard indications for CRT (eg, NYHA II–IV patients with QRS >120 ms). Crossovers were required based on prespecified criteria. The primary endpoints analyzed included improvement in QRS duration, left ventricular ejection fraction (LVEF), and freedom from cardiovascular (CV) hospitalization and mortality. Results Among 41 patients enrolled (aged 64 ± 13 years, 38% female, LVEF 28%, QRS 168 ± 18 ms), 21 were randomized to His-CRT and 20 to BiV-CRT. Crossover occurred in 48% of His-CRT and 26% of BiV-CRT. The most common reason for crossover from His-CRT was inability to correct QRS owing to nonspecific intraventricular conduction delay (n = 5). Patients treated with His-CRT demonstrated greater QRS narrowing compared to BiV (125 ± 22 ms vs 164 ± 25 ms [TR], P < .001;124 ± 19 ms vs 162 ± 24 ms [PP], P < .001). A trend toward higher echocardiographic response was also observed (80 vs 57% [TR], P = .14; 91% vs 54% [PP], P = .078). No significant differences in CV hospitalization or mortality were observed. Conclusions Patients receiving His-CRT on-treatment demonstrated superior electrical resynchronization and a trend toward higher echocardiographic response than BiV-CRT. Larger prospective studies may be justifiable with refinements in patient selection and implantation techniques to minimize crossovers

Biocompatibility of a Magnetic Tunnel Junction Sensor Array for the Detection of Neuronal Signals in Culture

Magnetoencephalography has been established nowadays as a crucial in vivo technique for clinical and diagnostic applications due to its unprecedented spatial and temporal resolution and its non-invasive methods. However, the innate nature of the biomagnetic signals derived from active biological tissue is still largely unknown. One alternative possibility for in vitro analysis is the use of magnetic sensor arrays based on Magnetoresistance. However, these sensors have never been used to perform long-term in vitro studies mainly due to critical biocompatibility issues with neurons in culture. In this study, we present the first biomagnetic chip based on magnetic tunnel junction (MTJ) technology for cell culture studies and show the biocompatibility of these sensors. We obtained a full biocompatibility of the system through the planarization of the sensors and the use of a three-layer capping of SiO2/Si3N4/SiO2. We grew primary neurons up to 20 days on the top of our devices and obtained proper functionality and viability of the overlying neuronal networks. At the same time, MTJ sensors kept their performances unchanged for several weeks in contact with neurons and neuronal medium. These results pave the way to the development of high performing biomagnetic sensing technology for the electrophysiology of in vitro systems, in analogy with Multi Electrode Arrays