703 research outputs found
Soft Prompt Tuning for Augmenting Dense Retrieval with Large Language Models
Dense retrieval (DR) converts queries and documents into dense embeddings and
measures the similarity between queries and documents in vector space. One of
the challenges in DR is the lack of domain-specific training data. While DR
models can learn from large-scale public datasets like MS MARCO through
transfer learning, evidence shows that not all DR models and domains can
benefit from transfer learning equally. Recently, some researchers have
resorted to large language models (LLMs) to improve the zero-shot and few-shot
DR models. However, the hard prompts or human-written prompts utilized in these
works cannot guarantee the good quality of generated weak queries. To tackle
this, we propose soft prompt tuning for augmenting DR (SPTAR): For each task,
we leverage soft prompt-tuning to optimize a task-specific soft prompt on
limited ground truth data and then prompt the LLMs to tag unlabeled documents
with weak queries, yielding enough weak document-query pairs to train
task-specific dense retrievers. We design a filter to select high-quality
example document-query pairs in the prompt to further improve the quality of
weak tagged queries. To the best of our knowledge, there is no prior work
utilizing soft prompt tuning to augment DR models. The experiments demonstrate
that SPTAR outperforms the unsupervised baselines BM25 and the recently
proposed LLMs-based augmentation method for DR.Comment: fix typo InPairs which should be InPar
Large-scale single-photon imaging
Benefiting from its single-photon sensitivity, single-photon avalanche diode
(SPAD) array has been widely applied in various fields such as fluorescence
lifetime imaging and quantum computing. However, large-scale high-fidelity
single-photon imaging remains a big challenge, due to the complex hardware
manufacture craft and heavy noise disturbance of SPAD arrays. In this work, we
introduce deep learning into SPAD, enabling super-resolution single-photon
imaging over an order of magnitude, with significant enhancement of bit depth
and imaging quality. We first studied the complex photon flow model of SPAD
electronics to accurately characterize multiple physical noise sources, and
collected a real SPAD image dataset (64 32 pixels, 90 scenes, 10
different bit depth, 3 different illumination flux, 2790 images in total) to
calibrate noise model parameters. With this real-world physical noise model, we
for the first time synthesized a large-scale realistic single-photon image
dataset (image pairs of 5 different resolutions with maximum megapixels, 17250
scenes, 10 different bit depth, 3 different illumination flux, 2.6 million
images in total) for subsequent network training. To tackle the severe
super-resolution challenge of SPAD inputs with low bit depth, low resolution,
and heavy noise, we further built a deep transformer network with a
content-adaptive self-attention mechanism and gated fusion modules, which can
dig global contextual features to remove multi-source noise and extract
full-frequency details. We applied the technique on a series of experiments
including macroscopic and microscopic imaging, microfluidic inspection, and
Fourier ptychography. The experiments validate the technique's state-of-the-art
super-resolution SPAD imaging performance, with more than 5 dB superiority on
PSNR compared to the existing methods
Endothelial Cells Regulate Physiological Cardiomyocyte Growth via VEGFR2-Mediated Paracrine Signaling
Background: Heart failure, which is a major global health problem, is often preceded by pathological cardiac hypertrophy. The expansion of the cardiac vasculature, to maintain adequate supply of oxygen and nutrients, is a key determinant of whether the heart grows in a physiological compensated manner or a pathological decompensated manner. Bidirectional endothelial cell (EC)-cardiomyocyte (CMC) cross talk via cardiokine and angiocrine signaling plays an essential role in the regulation of cardiac growth and homeostasis. Currently, the mechanisms involved in the EC-CMC interaction are not fully understood, and very little is known about the EC-derived signals involved. Understanding how an excess of angiogenesis induces cardiac hypertrophy and how ECs regulate CMC homeostasis could provide novel therapeutic targets for heart failure. Methods: Genetic mouse models were used to delete vascular endothelial growth factor (VEGF) receptors, adeno-associated viral vectors to transduce the myocardium, and pharmacological inhibitors to block VEGF and ErbB signaling in vivo. Cell culture experiments were used for mechanistic studies, and quantitative polymerase chain reaction, microarrays, ELISA, and immunohistochemistry were used to analyze the cardiac phenotypes. Results: Both EC deletion of VEGF receptor (VEGFR)-1 and adeno-associated viral vector-mediated delivery of the VEGFR1-specific ligands VEGF-B or placental growth factor into the myocardium increased the coronary vasculature and induced CMC hypertrophy in adult mice. The resulting cardiac hypertrophy was physiological, as indicated by preserved cardiac function and exercise capacity and lack of pathological gene activation. These changes were mediated by increased VEGF signaling via endothelial VEGFR2, because the effects of VEGF-B and placental growth factor on both angiogenesis and CMC growth were fully inhibited by treatment with antibodies blocking VEGFR2 or by endothelial deletion of VEGFR2. To identify activated pathways downstream of VEGFR2, whole-genome transcriptomics and secretome analyses were performed, and the Notch and ErbB pathways were shown to be involved in transducing signals for EC-CMC cross talk in response to angiogenesis. Pharmacological or genetic blocking of ErbB signaling also inhibited part of the VEGF-B-induced effects in the heart. Conclusions: This study reveals that cross talk between the EC VEGFR2 and CMC ErbB signaling pathways coordinates CMC hypertrophy with angiogenesis, contributing to physiological cardiac growth.Peer reviewe
OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System
Automated machine learning (AutoML) seeks to build ML models with minimal
human effort. While considerable research has been conducted in the area of
AutoML in general, aiming to take humans out of the loop when building
artificial intelligence (AI) applications, scant literature has focused on how
AutoML works well in open-environment scenarios such as the process of training
and updating large models, industrial supply chains or the industrial
metaverse, where people often face open-loop problems during the search
process: they must continuously collect data, update data and models, satisfy
the requirements of the development and deployment environment, support massive
devices, modify evaluation metrics, etc. Addressing the open-environment issue
with pure data-driven approaches requires considerable data, computing
resources, and effort from dedicated data engineers, making current AutoML
systems and platforms inefficient and computationally intractable.
Human-computer interaction is a practical and feasible way to tackle the
problem of open-environment AI. In this paper, we introduce OmniForce, a
human-centered AutoML (HAML) system that yields both human-assisted ML and
ML-assisted human techniques, to put an AutoML system into practice and build
adaptive AI in open-environment scenarios. Specifically, we present OmniForce
in terms of ML version management; pipeline-driven development and deployment
collaborations; a flexible search strategy framework; and widely provisioned
and crowdsourced application algorithms, including large models. Furthermore,
the (large) models constructed by OmniForce can be automatically turned into
remote services in a few minutes; this process is dubbed model as a service
(MaaS). Experimental results obtained in multiple search spaces and real-world
use cases demonstrate the efficacy and efficiency of OmniForce
Disruption of a GATA4/Ankrd1 Signaling Axis in Cardiomyocytes Leads to Sarcomere Disarray: Implications for Anthracycline Cardiomyopathy
Doxorubicin (Adriamycin) is an effective anti-cancer drug, but its clinical usage is limited by a dose-dependent cardiotoxicity characterized by widespread sarcomere disarray and loss of myofilaments. Cardiac ankyrin repeat protein (CARP, ANKRD1) is a transcriptional regulatory protein that is extremely susceptible to doxorubicin; however, the mechanism(s) of doxorubicin-induced CARP depletion and its specific role in cardiomyocytes have not been completely defined. We report that doxorubicin treatment in cardiomyocytes resulted in inhibition of CARP transcription, depletion of CARP protein levels, inhibition of myofilament gene transcription, and marked sarcomere disarray. Knockdown of CARP with small interfering RNA (siRNA) similarly inhibited myofilament gene transcription and disrupted cardiomyocyte sarcomere structure. Adenoviral overexpression of CARP, however, was unable to rescue the doxorubicin-induced sarcomere disarray phenotype. Doxorubicin also induced depletion of the cardiac transcription factor GATA4 in cardiomyocytes. CARP expression is regulated in part by GATA4, prompting us to examine the relationship between GATA4 and CARP in cardiomyocytes. We show in co-transfection experiments that GATA4 operates upstream of CARP by activating the proximal CARP promoter. GATA4-siRNA knockdown in cardiomyocytes inhibited CARP expression and myofilament gene transcription, and induced extensive sarcomere disarray. Adenoviral overexpression of GATA4 (AdV-GATA4) in cardiomyocytes prior to doxorubicin exposure maintained GATA4 levels, modestly restored CARP levels, and attenuated sarcomere disarray. Interestingly, siRNA-mediated depletion of CARP completely abolished the Adv-GATA4 rescue of the doxorubicin-induced sarcomere phenotype. These data demonstrate co-dependent roles for GATA4 and CARP in regulating sarcomere gene expression and maintaining sarcomeric organization in cardiomyocytes in culture. The data further suggests that concurrent depletion of GATA4 and CARP in cardiomyocytes by doxorubicin contributes in large part to myofibrillar disarray and the overall pathophysiology of anthracycline cardiomyopathy
- …