Not All Instances Contribute Equally: Instance-adaptive Class Representation Learning for Few-Shot Visual Recognition

Few-shot visual recognition refers to recognize novel visual concepts from a few labeled instances. Many few-shot visual recognition methods adopt the metric-based meta-learning paradigm by comparing the query representation with class representations to predict the category of query instance. However, current metric-based methods generally treat all instances equally and consequently often obtain biased class representation, considering not all instances are equally significant when summarizing the instance-level representations for the class-level representation. For example, some instances may contain unrepresentative information, such as too much background and information of unrelated concepts, which skew the results. To address the above issues, we propose a novel metric-based meta-learning framework termed instance-adaptive class representation learning network (ICRL-Net) for few-shot visual recognition. Specifically, we develop an adaptive instance revaluing network with the capability to address the biased representation issue when generating the class representation, by learning and assigning adaptive weights for different instances according to their relative significance in the support set of corresponding class. Additionally, we design an improved bilinear instance representation and incorporate two novel structural losses, i.e., intra-class instance clustering loss and inter-class representation distinguishing loss, to further regulate the instance revaluation process and refine the class representation. We conduct extensive experiments on four commonly adopted few-shot benchmarks: miniImageNet, tieredImageNet, CIFAR-FS, and FC100 datasets. The experimental results compared with the state-of-the-art approaches demonstrate the superiority of our ICRL-Net

Parameter Efficient Multi-task Model Fusion with Partial Linearization

Large pre-trained models have enabled significant advances in machine learning and served as foundation components. Model fusion methods, such as task arithmetic, have been proven to be powerful and scalable to incorporate fine-tuned weights from different tasks into a multi-task model. However, efficiently fine-tuning large pre-trained models on multiple downstream tasks remains challenging, leading to inefficient multi-task model fusion. In this work, we propose a novel method to improve multi-task fusion for parameter-efficient fine-tuning techniques like LoRA fine-tuning. Specifically, our approach partially linearizes only the adapter modules and applies task arithmetic over the linearized adapters. This allows us to leverage the the advantages of model fusion over linearized fine-tuning, while still performing fine-tuning and inference efficiently. We demonstrate that our partial linearization technique enables a more effective fusion of multiple tasks into a single model, outperforming standard adapter tuning and task arithmetic alone. Experimental results demonstrate the capabilities of our proposed partial linearization technique to effectively construct unified multi-task models via the fusion of fine-tuned task vectors. We evaluate performance over an increasing number of tasks and find that our approach outperforms standard parameter-efficient fine-tuning techniques. The results highlight the benefits of partial linearization for scalable and efficient multi-task model fusion

Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling

Conversion rate (CVR) prediction is one of the most critical tasks for digital display advertising. Commercial systems often require to update models in an online learning manner to catch up with the evolving data distribution. However, conversions usually do not happen immediately after a user click. This may result in inaccurate labeling, which is called delayed feedback problem. In previous studies, delayed feedback problem is handled either by waiting positive label for a long period of time, or by consuming the negative sample on its arrival and then insert a positive duplicate when a conversion happens later. Indeed, there is a trade-off between waiting for more accurate labels and utilizing fresh data, which is not considered in existing works. To strike a balance in this trade-off, we propose Elapsed-Time Sampling Delayed Feedback Model (ES-DFM), which models the relationship between the observed conversion distribution and the true conversion distribution. Then we optimize the expectation of true conversion distribution via importance sampling under the elapsed-time sampling distribution. We further estimate the importance weight for each instance, which is used as the weight of loss function in CVR prediction. To demonstrate the effectiveness of ES-DFM, we conduct extensive experiments on a public data and a private industrial dataset. Experimental results confirm that our method consistently outperforms the previous state-of-the-art results.Comment: This paper has been accepted by AAAI 202

Cardiac involvement in patients recovered from COVID-2019 identified using magnetic resonance imaging

OBJECTIVES This study evaluated cardiac involvement in patients recovered from coronavirus disease-2019 (COVID-19) using cardiac magnetic resonance (CMR).BACKGROUND Myocardial injury caused by COVID-19 was previously reported in hospitalized patients. It is unknown if there is sustained cardiac involvement after patients' recovery from COVID-19.METHODS Twenty-six patients recovered from COVID-19 who reported cardiac symptoms and underwent CMR examinations were retrospectively included. CMR protocols consisted of conventional sequences (cine, T2-weighted imaging, and late gadolinium enhancement [LGE]) and quantitative mapping sequences (T1, T2, and extracellular volume [ECV] mapping). Edema ratio and LGE were assessed in post-COVID-19 patients. Cardiac function, native T1/T2, and ECV were quantitatively evaluated and compared with controls.RESULTS Fifteen patients (58%) had abnormal CMR findings on conventional CMR sequences: myocardial edema was found in 14 (54%) patients and LGE was found in 8 (31%) patients. Decreased right ventricle functional parameters including ejection fraction, cardiac index, and stroke volume/body surface area were found in patients with positive conventional CMR findings. Using quantitative mapping, global native T1, T2, and ECV were all found to be significantly elevated in patients with positive conventional CMR findings, compared with patients without positive findings and controls (median [interquartile range]: native T1 1,271 ms [1,243 to 1,298 ms] vs. 1,237 ms [1,216 to 1,262 ms] vs. 1,224 ms [1,217 to 1,245 ms]; mean +/- SD: T2 42.7 +/- 3.1 ms vs. 38.1 ms +/- 2.4 vs. 39.1 ms +/- 3.1; median [interquartile range]: 28.2% [24.8% to 36.2%] vs. 24.8% [23.1% to 25.4%] vs. 23.7% [22.2% to 25.2%]; p = 0.002; p < 0.001, and p =0.002, respectively).CONCLUSIONS Cardiac involvement was found in a proportion of patients recovered from COVID-19. CMR manifestation included myocardial edema, fibrosis, and impaired right ventricle function. Attention should be paid to the possible myocardial involvement in patients recovered from COVID-19 with cardiac symptoms. (C) 2020 by the American College of Cardiology Foundation.Cardiovascular Aspects of Radiolog

Immune Checkpoint Inhibitors-Related Thyroid Dysfunction: Epidemiology, Clinical Presentation, Possible Pathogenesis, and Management

Immune checkpoint inhibitors (ICIs) are a group of drugs employed in the treatment of various types of malignant tumors and improve the therapeutic effect. ICIs blocks negative co-stimulatory molecules, such as programmed cell death gene-1 (PD-1) and its ligand (PD-L1) and cytotoxic T-lymphocyte-associated antigen-4 (CTLA-4), reactivating the recognition and killing effect of the immune system on tumors. However, the reactivation of the immune system can also lead to the death of normal organs, tissues, and cells, eventually leading to immune-related adverse events (IRAEs). IRAEs involve various organs and tissues and also cause thyroid dysfunction. This article reviews the epidemiology, clinical manifestations, possible pathogenesis, and management of ICIs-related thyroid dysfunction

Nitration of TRPM2 as a Molecular Switch Induces Autophagy during Brain Pericyte Injury

Aims: Dysfunction of neurovascular pericytes underlies breakdown of the blood-brain barrier, but the molecular mechanisms are largely unknown. In this study, we evaluated the role of the transient receptor potential melastatin-related 2 (TRPM2) channel and autophagy during brain pericyte injury both in vitro and in vivo. Results: A rapid induction in autophagy in human brain vascular pericytes, in the zinc oxide nanoparticles (ZnO-NP)-induced cell stress model, was paralleled with an increase in the expression of the TRPM2-S truncated isoform, which was abolished by treatment with a nitric oxide synthase inhibitor and a peroxynitrite scavenger. Furthermore, Y1485 in the C-terminus of the TRPM2 protein was identified as the tyrosine nitration substrate by mass spectrometry. Overexpression of the Y1485S TRPM2 mutant reduced LC3-II accumulation and pericyte injury induced by ZnO-NP. Consistently, LC3-II accumulation was reduced and pericytes were better preserved in intact brain microvessels of the TRPM2 knockout mice after ZnO-NP-induced vascular injury. Innovation and Conclusions: Our present study has revealed a novel mechanism of autophagy disturbance secondary to nitrosative stress-induced tyrosine nitration of TRPM2 during pericyte injury

Fabrication and characterisation of electrospun silk fibroin/gelatin scaffolds crosslinked with glutaraldehyde vapour

Bombyx mori silk fibroin (SF) /gelatin nanofibre mats with different blend ratios of 100/0, 90/10 and 70/30 were prepared by electrospinning and crosslinked with glutaraldehyde (GTA) vapour at room temperature. GTA was shown to induce the conformational transition of SFs from random coils to β-sheets along with increasing nanofibre diameters with the addition of gelatin into SFs. It was found that by increasing the gelatin content, crosslinking degree was enhanced from 34% for pure SF nanofibre mats to 43% for SF/gelatin counterparts at the blend ratio of 70/30, which directly affected mechanical properties, porosity, and water uptake capacity (WUC) of prepared nanofibre mats. The addition of 10 and 30 wt% gelatin into SFs improved tensile strengths of SF/gelatin nanofibre mats by 10 and 27% along with significant increases in Young’s modulus by 1.1 and 1.3 times, respectively, as opposed to plain SF counterparts. However, both porosity and WUC were found to decrease from 62 and 405% for pristine SF nanofibre mats to 47% and 232% for SF/gelatin counterparts at the blend ratio of 70/30 accordingly. To further evaluate the combined effect of GTA crosslinking and gelatin content on biological response of SF/gelatin scaffolds, the proliferation assay using 3T3 mouse fibroblast was conducted. In comparison with pure SFs, cell proliferation rate was lower for SF/gelatin constructs, which declined when the gelatin content increased. These results indicated that the adverse effect of GTA crosslinking on cell response may be ascribed to imposed changes in morphology and physiochemical properties of SF/gelatin nanofibre mats. Although crosslinking could be used to improve mechanical properties of nanofibre mats, it reduced their capacity to support the cell activity. GTA optimisation is required to further modulate the physico-chemical properties of SF/gelatin nanofibre mats in order to obtain stable materials with favourable bioactive properties and promote cellular responses for tissue engineering applications