Second-order analytic solutions for re-entry trajectories

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76344/1/AIAA-1993-3679-680.pd

Higher-order analytic solutions for critical cases of ballistic entry

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77179/1/AIAA-1996-3425-378.pd

Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search Classification

With the devastating outbreak of COVID-19, vaccines are one of the crucial lines of defense against mass infection in this global pandemic. Given the protection they provide, vaccines are becoming mandatory in certain social and professional settings. This paper presents a classification model for detecting COVID-19 vaccination related search queries, a machine learning model that is used to generate search insights for COVID-19 vaccinations. The proposed method combines and leverages advancements from modern state-of-the-art (SOTA) natural language understanding (NLU) techniques such as pretrained Transformers with traditional dense features. We propose a novel approach of considering dense features as memory tokens that the model can attend to. We show that this new modeling approach enables a significant improvement to the Vaccine Search Insights (VSI) task, improving a strong well-established gradient-boosting baseline by relative +15% improvement in F1 score and +14% in precision.Comment: EMNLP 202

Chronic Invasive Aspergillosis caused by Aspergillus viridinutans

Aspergillus viridinutans, a mold phenotypically resembling A. fumigatus, was identified by gene sequence analyses from 2 patients. Disease was distinct from typical aspergillosis, being chronic and spreading in a contiguous manner across anatomical planes. We emphasize the recognition of fumigati-mimetic molds as agents of chronic or refractory aspergillosis

Confident Adaptive Language Modeling

Recent advances in Transformer-based large language models (LLMs) have led to significant performance improvements across many tasks. These gains come with a drastic increase in the models' size, potentially leading to slow and costly use at inference time. In practice, however, the series of generations made by LLMs is composed of varying levels of difficulty. While certain predictions truly benefit from the models' full capacity, other continuations are more trivial and can be solved with reduced compute. In this work, we introduce Confident Adaptive Language Modeling (CALM), a framework for dynamically allocating different amounts of compute per input and generation timestep. Early exit decoding involves several challenges that we address here, such as: (1) what confidence measure to use; (2) connecting sequence-level constraints to local per-token exit decisions; and (3) attending back to missing hidden representations due to early exits in previous tokens. Through theoretical analysis and empirical experiments on three diverse text generation tasks, we demonstrate the efficacy of our framework in reducing compute -- potential speedup of up to $\times 3$ -- while provably maintaining high performance

DSI++: Updating Transformer Memory with New Documents

Differentiable Search Indices (DSIs) encode a corpus of documents in model parameters and use the same model to answer user queries directly. Despite the strong performance of DSI models, deploying them in situations where the corpus changes over time is computationally expensive because reindexing the corpus requires re-training the model. In this work, we introduce DSI++, a continual learning challenge for DSI to incrementally index new documents while being able to answer queries related to both previously and newly indexed documents. Across different model scales and document identifier representations, we show that continual indexing of new documents leads to considerable forgetting of previously indexed documents. We also hypothesize and verify that the model experiences forgetting events during training, leading to unstable learning. To mitigate these issues, we investigate two approaches. The first focuses on modifying the training dynamics. Flatter minima implicitly alleviate forgetting, so we optimize for flatter loss basins and show that the model stably memorizes more documents ($+12\%$). Next, we introduce a generative memory to sample pseudo-queries for documents and supplement them during continual indexing to prevent forgetting for the retrieval task. Extensive experiments on novel continual indexing benchmarks based on Natural Questions (NQ) and MS MARCO demonstrate that our proposed solution mitigates forgetting significantly. Concretely, it improves the average Hits@10 by $+21.1\%$ over competitive baselines for NQ and requires $6$ times fewer model updates compared to re-training the DSI model for incrementally indexing five corpora in a sequence.Comment: Accepted at EMNLP 2023 main conferenc

Infections in secondary immunodeficiency patients treated with Privigen® or Hizentra®: a retrospective US administrative claims study in patients with hematological malignancies

B cell-derived lymphoproliferative disorders are associated with secondary immunodeficiency (SID); some patients require immunoglobulin replacement therapy (IgRT) to mitigate infections. Using IQVIA’s PharMetrics® Plus database, patients with SID who received IgPro10/IgPro20 in the 12 months post-diagnosis (IgRT users) were matched to patients with SID not receiving IgRT (non-IgRT users). The risk of severe infection was compared using within-patient change from baseline to follow-up as well as between cohorts. Overall, 277 IgRT users were matched to 1019 non-IgRT users. Before IgRT, more IgRT users experienced any bacterial infection (88.4% vs. 72.9%; p<.0001) or ≥1 severe bacterial infection (SBI) (42.2% vs. 31.8%; p=.0011) vs. non-IgRT users. During follow-up, risk of SBI among IgRT users (21.7%) reached parity with non-IgRT users (21.2%). IgRT was associated with a reduction in SBIs to levels comparable with the lower ‘baseline infection risk’ of non-IgRT users. These criteria help define SID patients who may benefit from IgRT

Risk factors for severe infections in secondary immunodeficiency: a retrospective US administrative claims study in patients with hematological malignancies

Real-world data are lacking to identify patients with secondary immunodeficiency (SID) who may benefit most from anti-infective interventions. This retrospective analysis used the IQVIA PharMetrics® Plus database to assess baseline characteristics associated with risk of severe infections post-SID diagnosis in patients with hematological malignancies. In 4066 patients included, the mean number of any and severe infections per patient in the one-year pre-SID diagnosis period was 9.5 and 0.7, respectively. Post-SID diagnosis, the mean annualized number of any and severe infections was 19.1 and 1.5, respectively. Receiver operating characteristic curve analysis identified a threshold (cutoff) of three bacterial infections at baseline as optimally predictive of severe infections post-SID diagnosis. Multivariate analysis indicated that hospitalizations, infections (≥3), or antibiotic use pre-SID diagnosis were predictive of severe infections post-SID diagnosis. Evaluation of these risk factors could inform clinical decisions regarding which patients may benefit from prophylactic anti-infective treatment, including immunoglobulin replacement if warranted