ConaCLIP: Exploring Distillation of Fully-Connected Knowledge Interaction Graph for Lightweight Text-Image Retrieval

Large-scale pre-trained text-image models with dual-encoder architectures (such as CLIP) are typically adopted for various vision-language applications, including text-image retrieval. However,these models are still less practical on edge devices or for real-time situations, due to the substantial indexing and inference time and the large consumption of computational resources. Although knowledge distillation techniques have been widely utilized for uni-modal model compression, how to expand them to the situation when the numbers of modalities and teachers/students are doubled has been rarely studied. In this paper, we conduct comprehensive experiments on this topic and propose the fully-Connected knowledge interaction graph (Cona) technique for cross-modal pre-training distillation. Based on our findings, the resulting ConaCLIP achieves SOTA performances on the widely-used Flickr30K and MSCOCO benchmarks under the lightweight setting. An industry application of our method on an e-commercial platform further demonstrates the significant effectiveness of ConaCLIP.Comment: ACL 2023 Industry Trac

DiffTune: Auto-Tuning through Auto-Differentiation

The performance of robots in high-level tasks depends on the quality of their lower-level controller, which requires fine-tuning. However, the intrinsically nonlinear dynamics and controllers make tuning a challenging task when it is done by hand. In this paper, we present DiffTune, a novel, gradient-based automatic tuning framework. We formulate the controller tuning as a parameter optimization problem. Our method unrolls the dynamical system and controller as a computational graph and updates the controller parameters through gradient-based optimization. The gradient is obtained using sensitivity propagation, which is the only method for gradient computation when tuning for a physical system instead of its simulated counterpart. Furthermore, we use $\mathcal{L}_1$ adaptive control to compensate for the uncertainties (that unavoidably exist in a physical system) such that the gradient is not biased by the unmodelled uncertainties. We validate the DiffTune on a Dubin's car and a quadrotor in challenging simulation environments. In comparison with state-of-the-art auto-tuning methods, DiffTune achieves the best performance in a more efficient manner owing to its effective usage of the first-order information of the system. Experiments on tuning a nonlinear controller for quadrotor show promising results, where DiffTune achieves 3.5x tracking error reduction on an aggressive trajectory in only 10 trials over a 12-dimensional controller parameter space.Comment: Minkyung Kim and Lin Song contributed equally to this wor

Hetero-bimetallic Lanthanide-Coinage Metal Compounds Featuring Possible Metal-Metal Interactions in the Excited State

Heterometallic complexes, combining metals of the outer rims of the d-block, for example lanthanides(III) (Ln) and coinage metals(I) (M) are scarcely reported, synthetically challenging and highly interesting in terms of their interactions. In this context, we synthesized hetero-bimetallic Ln−M compounds ligated by the phosphine functionalized amidinate system (N,N’-bis[(2-diphenylphosphino)phenyl]formamidinate, “dpfam”). The resulting compounds [dpfam3LnM][OTf] (Ln = La, Nd and M = Ag, Au) feature a close proximity of the two metal centres and were investigated experimentally by photoluminescence spectroscopy and quantum chemical calculations. The latter showed rare La−Au interactions for the first excited state

Rethinking Causal Relationships Learning in Graph Neural Networks

Graph Neural Networks (GNNs) demonstrate their significance by effectively modeling complex interrelationships within graph-structured data. To enhance the credibility and robustness of GNNs, it becomes exceptionally crucial to bolster their ability to capture causal relationships. However, despite recent advancements that have indeed strengthened GNNs from a causal learning perspective, conducting an in-depth analysis specifically targeting the causal modeling prowess of GNNs remains an unresolved issue. In order to comprehensively analyze various GNN models from a causal learning perspective, we constructed an artificially synthesized dataset with known and controllable causal relationships between data and labels. The rationality of the generated data is further ensured through theoretical foundations. Drawing insights from analyses conducted using our dataset, we introduce a lightweight and highly adaptable GNN module designed to strengthen GNNs' causal learning capabilities across a diverse range of tasks. Through a series of experiments conducted on both synthetic datasets and other real-world datasets, we empirically validate the effectiveness of the proposed module

Effect of multiple clinical factors on recurrent angina after percutaneous coronary intervention: A retrospective study from 398 ST-segment elevation myocardial infarction patients

Recurrent angina (RA) has an important influence on health status of patients after percutaneous coronary intervention (PCI). This study aimed to retrospectively investigate the effect of multiple clinical factors on both short-term and long-term development of RA.A total of 398 ST-segment elevation myocardial infarction (STEMI) patients were studied for up to 12 months. The primary clinical outcome, RA, was assessed at 1-month and 12-month. In multivariate analyses, the effect of clinical factors, including baseline demographics, medical history, infarction-related arteries, procedural characteristics of PCI, and the use of medicines, was investigated in patients with and without RA.The Logistic regression analysis showed that the patients with treatment through radial approach PCI (odds ratio [OR]: 0.42, 95% confidence interval [CI]: 0.18-0.96, P < 0.05) were less likely to have RA during 1-month assessment. During 12 months after PCI, male patients (OR: 0.53, 95% CI: 0.29-0.96, P < 0.05), and/or those treated with radial approach PCI (OR: 0.45, 95% CI: 0.21-0.97, P < 0.05) were less likely to have RA, whereas the patients with infarction related artery (IRA) in left anterior descending (LAD) (OR: 2.41, 95% CI: 1.20-4.84, P < 0.01) were more likely to have RA at follow-up. The Cox regression analysis further revealed that the patients with infarction of the LAD artery (hazard ratio [HR]: 2.08, 95% CI: 1.10-3.92, P < 0.05), but not with treatment through radial artery during PCI (HR: 0.42, 95% CI: 0.18-0.96, P < 0.05) had higher potential of development of RA during 12 months after PCI.We studied the effects of multiple clinical factors on the development of RA after PCI. Our findings suggest that patients with infarction of the LAD artery, and/or treatment not through radial artery during PCI were associated with higher risk of RA and may require close follow-up

High index contrast photonic platforms for on-chip Raman spectroscopy

Nanophotonic waveguide enhanced Raman spectroscopy (NWERS) is a sensing technique that uses a highly confined waveguide mode to excite and collect the Raman scattered signal from molecules in close vicinity of the waveguide. The most important parameters defining the figure of merit of an NWERS sensor include its ability to collect the Raman signal from an analyte, i.e. "the Raman conversion efficiency" and the amount of "Raman background" generated from the guiding material. Here, we compare different photonic integrated circuit (PIC) platforms capable of on-chip Raman sensing in terms of the aforementioned parameters. Among the four photonic platforms under study, tantalum oxide and silicon nitride waveguides exhibit high signal collection efficiency and low Raman background. In contrast, the performance of titania and alumina waveguides suffers from a strong Raman background and a weak signal collection efficiency, respectively

Deletion of FgHOG1 Is Suppressive to the mgv1 Mutant by Stimulating Gpmk1 Activation and Avoiding Intracellular Turgor Elevation in Fusarium graminearum

Fusarium head blight caused by Fusarium graminearum is an important disease of wheat and barley. Previous studies have showed that all three MAP kinase genes, MGV1, FgHOG1, and GPMK1, are involved in regulating hyphal growth, sexual reproduction, plant infection, and stress responses in this pathogen. To determine the relationship between the Mgv1 and FgHog1 pathways, in this study, we generated and characterized the mgv1 Fghog1 double mutant. Deletion of FgHOG1 partially rescued the defects of the mgv1 mutant in vegetative growth and cell wall integrity but had no effects on its defects in plant infection and DON production. The mgv1 Fghog1 mutant grew faster and was more tolerant to cell wall stressors than the mgv1 mutant. Swollen compartments and cell burst were observed frequently in the mgv1 mutant but rarely in the mgv1 Fghog1 mutant when treated with fungicide fludioxonil or cell wall stressor Congo red. Conversely, the deletion of MGV1 also alleviated the hyperosmotic sensitivity of the Fghog1 mutant in vegetative growth. TGY assays indicated increased phosphorylation of FgHog1 in the mgv1 mutant, and TEY assays further revealed elevated activation of Gpmk1 in the mgv1 Fghog1 double mutant, particularly under cell wall stress conditions. Overall, our data showed that deletion of FgHOG1 partially suppressed the defects of the mgv1 mutant, possibly by affecting genes related to cell wall integrity and osmoregulation via the over-activation of Gpmk1 MAP kinase and avoiding intracellular turgor elevation

A Novel Reassortant Avian H7N6 Influenza Virus Is Transmissible in Guinea Pigs via Respiratory Droplets

Since 2013, H7N9 and H5N6 avian influenza viruses (AIVs) have caused sporadic human infections and deaths and continued to circulate in the poultry industry. Since 2014, H7N6 viruses which might be reassortants of H7N9 and H5N6 viruses, have been isolated in China. However, the biological properties of H7N6 viruses are unknown. Here, we characterize the receptor binding preference, pathogenicity and transmissibility of a H7N6 virus A/chicken/Hubei/00095/2017(H7N6) (abbreviated HB95), and a closely related H7N9 virus, A/chicken/Hubei/00093/2017(H7N9) (abbreviated HB93), which were isolated from poultry in Hubei Province, China, in 2017. Phylogenetic analyses demonstrated that the hemagglutinin (HA) gene of HB95 is closely related to those of HB93 and human-origin H7N9 viruses, and that the neuraminidase (NA) gene of HB95 shared the highest nucleotide similarity with those of H5N6 viruses. HB95 and HB93 had binding affinity for human-like α2, 6-linked sialic acid receptors and were virulent in mice without prior adaptation. In addition, in guinea pig model, HB93 was transmissible by direct contact, but HB95 was transmissible via respiratory droplets. These results revealed the potential threat to public health posed by H7N6 influenza viruses and emphasized the need for continued surveillance of the circulation of this subtype in poultry

A Force Measurement Method Based on Flexible PDMS Grating

With the rapid development of flexible materials, various high-performance biocompatible flexible sensors have been proposed for specific measurement applications. Among these materials, polydimethylsiloxane (PDMS) is one of the most popular polymers by curing the mixture of pre-polymer (base) and cross-linker (curing agent). In this paper, a force measurement method based on PDMS grating is introduced. The PDMS grating is cast from a commercial master grating, which is precise, low-cost, and easy to follow. The elastic modulus can be controlled by the curing temperature and the mixing ratio. The PDMS grating is tested using a tension testing machine. As the stretching force increases, the grating line-spacing simultaneously increases and the diffraction light spot shifts. By capturing the light spot shift using a camera, the relationship between light spot position and stretching force is established and evaluated. Experimental results show that the linearity (R2) of the proposed method is better than 0.998, adding that the sensitivity is ~0.5&ndash;0.7N/mm and the accuracy is up to 0.05N