Few-Shot Learning with a Strong Teacher

Few-shot learning (FSL) aims to train a strong classifier using limited labeled examples. Many existing works take the meta-learning approach, sampling few-shot tasks in turn and optimizing the few-shot learner's performance on classifying the query examples. In this paper, we point out two potential weaknesses of this approach. First, the sampled query examples may not provide sufficient supervision for the few-shot learner. Second, the effectiveness of meta-learning diminishes sharply with increasing shots (i.e., the number of training examples per class). To resolve these issues, we propose a novel objective to directly train the few-shot learner to perform like a strong classifier. Concretely, we associate each sampled few-shot task with a strong classifier, which is learned with ample labeled examples. The strong classifier has a better generalization ability and we use it to supervise the few-shot learner. We present an efficient way to construct the strong classifier, making our proposed objective an easily plug-and-play term to existing meta-learning based FSL methods. We validate our approach in combinations with many representative meta-learning methods. On several benchmark datasets including miniImageNet and tiredImageNet, our approach leads to a notable improvement across a variety of tasks. More importantly, with our approach, meta-learning based FSL methods can consistently outperform non-meta-learning based ones, even in a many-shot setting, greatly strengthening their applicability

Devastating chest wall necrotizing fasciitis following pigtail catheter drainage

SummaryPigtail catheter for drainage of pleural effusion has gained popularity. Complication related to the insertion of these small-bore catheter is low. In this report, we highlight two cases with devastating necrotizing fasciitis of chest wall following pigtail catheter insertion

Dual Associated Encoder for Face Restoration

Restoring facial details from low-quality (LQ) images has remained a challenging problem due to its ill-posedness induced by various degradations in the wild. The existing codebook prior mitigates the ill-posedness by leveraging an autoencoder and learned codebook of high-quality (HQ) features, achieving remarkable quality. However, existing approaches in this paradigm frequently depend on a single encoder pre-trained on HQ data for restoring HQ images, disregarding the domain gap between LQ and HQ images. As a result, the encoding of LQ inputs may be insufficient, resulting in suboptimal performance. To tackle this problem, we propose a novel dual-branch framework named DAEFR. Our method introduces an auxiliary LQ branch that extracts crucial information from the LQ inputs. Additionally, we incorporate association training to promote effective synergy between the two branches, enhancing code prediction and output quality. We evaluate the effectiveness of DAEFR on both synthetic and real-world datasets, demonstrating its superior performance in restoring facial details.Comment: Technical Repor

A deep learning approach for lower back-pain risk prediction during manual lifting

Occupationally-induced back pain is a leading cause of reduced productivity in industry. Detecting when a worker is lifting incorrectly and at increased risk of back injury presents significant possible benefits. These include increased quality of life for the worker due to lower rates of back injury and fewer workers' compensation claims and missed time for the employer. However, recognizing lifting risk provides a challenge due to typically small datasets and subtle underlying features in accelerometer and gyroscope data. A novel method to classify a lifting dataset using a 2D convolutional neural network (CNN) and no manual feature extraction is proposed in this paper; the dataset consisted of 10 subjects lifting at various relative distances from the body with 720 total trials. The proposed deep CNN displayed greater accuracy (90.6%) compared to an alternative CNN and multilayer perceptron (MLP). A deep CNN could be adapted to classify many other activities that traditionally pose greater challenges in industrial environments due to their size and complexity.Comment: 21 pages, 10 figure

Dual blockage of STAT3 and ERK1/2 eliminates radioresistant GBM cells.

Radiotherapy (RT) is the major modality for control of glioblastoma multiforme (GBM), the most aggressive brain tumor in adults with poor prognosis and low patient survival rate. To improve the RT efficacy on GBM, the mechanism causing tumor adaptive radioresistance which leads to the failure of tumor control and lethal progression needs to be further elucidated. Here, we conducted a comparative analysis of RT-treated recurrent tumors versus primary counterparts in GBM patients, RT-treated orthotopic GBM tumors xenografts versus untreated tumors and radioresistant GBM cells versus wild type cells. The results reveal that activation of STAT3, a well-defined redox-sensitive transcriptional factor, is causally linked with GBM adaptive radioresistance. Database analysis also agrees with the worse prognosis in GBM patients due to the STAT3 expression-associated low RT responsiveness. However, although the radioresistant GBM cells can be resensitized by inhibition of STAT3, a fraction of radioresistant cells can still survive the RT combined with STAT3 inhibition or CRISPR/Cas9-mediated STAT3 knockout. A complementally enhanced activation of ERK1/2 by STAT3 inhibition is identified responsible for the survival of the remaining resistant tumor cells. Dual inhibition of ERK1/2 and STAT3 remarkably eliminates resistant GBM cells and inhibits tumor regrowth. These findings demonstrate a previously unknown feature ofSTAT3-mediated ERK1/2 regulation and an effective combination of two targets in resensitizing GBM to RT

Theory of d+idd + id Second-Order Topological Superconductors

Topological superconductors are a class of unconventional superconducting materials featuring sub-gap zero-energy Majorana bound modes that hold promise as a building block for topological quantum computing. In this work, we study the realization of second-order topology that defines anomalous gapless boundary modes in a two-orbital superconductor with spin-orbital couplings. We reveal a time-reversal symmetry-breaking second-order topological superconducting phase with $d+id$-wave orbital-dependent paring without the need for the external magnetic field. Remarkably, this orbital-active $d$-wave paring gives rise to anomalous zero-energy Majorana corner modes, which is in contrast to conventional chiral $d$-wave pairing, accommodating one-dimensional Majorana edge modes. Our work not only reveals a unique mechanism of time-reversal symmetry breaking second-order topological superconductors but also bridges the gap between second-order topology and orbital-dependent pairings.Comment: 5+ pages, 5 figure

Stage-Specific Expression of TNFα Regulates Bad/Bid-Mediated Apoptosis and RIP1/ROS-Mediated Secondary Necrosis in Birnavirus-Infected Fish Cells

Infectious pancreatic necrosis virus (IPNV) can induce Bad-mediated apoptosis followed by secondary necrosis in fish cells, but it is not known how these two types of cell death are regulated by IPNV. We found that IPNV infection can regulate Bad/Bid-mediated apoptotic and Rip1/ROS-mediated necrotic death pathways via the up-regulation of TNFα in zebrafish ZF4 cells. Using a DNA microarray and quantitative RT-PCR analyses, two major subsets of differentially expressed genes were characterized, including the innate immune response gene TNFα and the pro-apoptotic genes Bad and Bid. In the early replication stage (0–6 h post-infection, or p.i.), we observed that the pro-inflammatory cytokine TNFα underwent a rapid six-fold induction. Then, during the early-middle replication stages (6–12 h p.i.), TNFα level was eight-fold induction and the pro-apoptotic Bcl-2 family members Bad and Bid were up-regulated. Furthermore, specific inhibitors of TNFα expression (AG-126 or TNFα-specific siRNA) were used to block apoptotic and necrotic death signaling during the early or early-middle stages of IPNV infection. Inhibition of TNFα expression dramatically reduced the Bad/Bid-mediated apoptotic and Rip1/ROS-mediated necrotic cell death pathways and rescued host cell viability. Moreover, we used Rip1-specific inhibitors (Nec-1 and Rip1-specific siRNA) to block Rip1 expression. The Rip1/ROS-mediated secondary necrotic pathway appeared to be reduced in IPNV-infected fish cells during the middle-late stage of infection (12–18 h p.i.). Taken together, our results indicate that IPNV triggers two death pathways via up-stream induction of the pro-inflammatory cytokine TNFα, and these results may provide new insights into the pathogenesis of RNA viruses

Rab25-Mediated EGFR Recycling Causes Tumor Acquired Radioresistance.

Tumor acquired radioresistance remains as the major limit in cancer radiotherapy (RT). Rab25, a receptor recycling protein, has been reported to be enhanced in tumors with aggressive phenotype and chemotherapy resistance. In this study, elevated Rab25 expression was identified in an array of radioresistant human cancer cell lines, in vivo radioresistant xenograft tumors. Clinical investigation confirmed that Rab25 expression was also associated with a worse prognosis in patients with lung adenocarcinoma (LUAD) and nasopharyngeal carcinoma (NPC). Enhanced activities of EGFR were observed in both NPC and LUAD radioresistant cells. Rab25 interacts with EGFR to enhance EGFR recycling to cell surface and to decrease degradation in cytoplasm. Inhibition of Rab25 showed synergized radiosensitivity with reduced aggressive phenotype. This study provides the clinical and experimental evidence that Rab25 is a potential therapeutic target to alleviate the hyperactive EGFR signaling and to prevent RT-acquired tumor resistance in patients with LUAD and NPC