Decoupled DETR For Few-shot Object Detection

Few-shot object detection (FSOD), an efficient method for addressing the severe data-hungry problem, has been extensively discussed. Current works have significantly advanced the problem in terms of model and data. However, the overall performance of most FSOD methods still does not fulfill the desired accuracy. In this paper we improve the FSOD model to address the severe issue of sample imbalance and weak feature propagation. To alleviate modeling bias from data-sufficient base classes, we examine the effect of decoupling the parameters for classes with sufficient data and classes with few samples in various ways. We design a base-novel categories decoupled DETR (DeDETR) for FSOD. We also explore various types of skip connection between the encoder and decoder for DETR. Besides, we notice that the best outputs could come from the intermediate layer of the decoder instead of the last layer; therefore, we build a unified decoder module that could dynamically fuse the decoder layers as the output feature. We evaluate our model on commonly used datasets such as PASCAL VOC and MSCOCO. Our results indicate that our proposed module could achieve stable improvements of 5% to 10% in both fine-tuning and meta-learning paradigms and has outperformed the highest score in recent works

Few-shot Object Detection with Refined Contrastive Learning

Due to the scarcity of sampling data in reality, few-shot object detection (FSOD) has drawn more and more attention because of its ability to quickly train new detection concepts with less data. However, there are still failure identifications due to the difficulty in distinguishing confusable classes. We also notice that the high standard deviation of average precisions reveals the inconsistent detection performance. To this end, we propose a novel FSOD method with Refined Contrastive Learning (FSRC). A pre-determination component is introduced to find out the Resemblance Group (GR) from novel classes which contains confusable classes. Afterwards, refined contrastive learning (RCL) is pointedly performed on this group of classes in order to increase the inter-class distances among them. In the meantime, the detection results distribute more uniformly which further improve the performance. Experimental results based on PASCAL VOC and COCO datasets demonstrate our proposed method outperforms the current state-of-the-art research. FSRC can not only decouple the relevance of confusable classes to get a better performance, but also makes predictions more consistent by reducing the standard deviation of the AP of classes to be detected

I: Thin-Film Nanoporous Anodic Alumina For Nanobiotechnology Ii: Microscale Cd4+ Cell Biosensor With Single-Cell Resolution For Diagnosis Of Hiv Infection

Two research topics were presented in this dissertation. The first topic focused on nanoporous anodic alumina thin films and their applications in nanobiotechnology. It discussed in details the fabrication and the characterizations of nanoporous anodic alumina thin films on silicon substrates. Two methods were presented for fabricating freestanding PAA thin films with open pores. In the first method, an alumina thin film was fabricated on a Si3N4-coated silicon substrate. A partly freestanding structure was achieved by removing the silicon substrates using KOH anisotropic etch. The second method, named as "double-layer anodization", was developed for fabricating partly or fully freestanding alumina thin films by utilizing a sacrificial metal layer. The confined diffusion of small organic molecules in alumina nanopores was investigated. The diffusion system was built upon the silicon-based freestanding alumina thin film. The molecular diffusion in alumina pores was modeled as a one-dimensional Fickian flow, based on which the diffusion dynamics was characterized. A novel DNA biosensor utilizing the large surface area of alumina films was developed. The device was based on a metal-alumina-metal vertical structure. The top and bottom metal films served as electrodes while the sandwiched alumina membrane served as a porous dielectric layer. Single-stranded DNA oligonucleotides were attached to the sidewalls of the alumina pores through chemical modifications. The DNA hybridization process was sensed by measuring the impedance spectrum of the alumina membrane between the two electrodes. The second topic was the research of a cell biosensor for the precise quantification of human CD4+ cells, orientated for the development of affordable point-of-care diagnostic tools for analyzing the HIV-infection status of AIDS patients. With this motivation, an impedance biosensor was developed, based on an array of cell-sized working electrode pixels. Each electrode pixels was able to independently detect the existence of one single cell on the electrode surface. The cell counting was digitalized by the electrode pixelation, being independent of the cell concentration. With this sensor, the detection of CD4+ cells at single-cell resolution was demonstrated

Replicated Risk Variants for Major Psychiatric Disorders May Serve as Potential Therapeutic Targets for the Shared Depressive Endophenotype

Genome-wide association studies (GWASs) have reported numerous associations between risk variants and major psychiatric disorders (MPDs) including schizophrenia (SCZ), bipolar disorder (BPD), major depressive disorder (MDD) and others. We reviewed all of the published GWASs, and extracted the genome-wide significant (p\u3c10) and replicated associations between risk SNPs and MPDs. We found the associations of 6 variants located in 6 genes, including L type voltage-gated calcium channel (LTCCs) subunit alpha1 C gene (), that were genome-wide significant ( ) and replicated at single-point level across at least two GWASs. Among them, the associations between MPDs and rs1006737 within are most robust. Thus, as a next step, the expression of the replicated risk genes in human hippocampus was analyzed. We found had significant mRNA expression in human hippocampus in two independent cohorts. Finally, we tried to elucidate the roles of venlafaxine and ω-3 PUFAs in the mRNA expression regulation of the replicated risk genes in hippocampus. We used cDNA chip-based microarray profiling to explore the transcriptome-wide mRNA expression regulation by ω-3 PUFAs (0.72/kg/d) and venlafaxine (0.25/kg/d) treatment in chronic mild stress (CMS) rats. ω-3 PUFAs and venlafaxine treatment elicited significant up-regulation. We concluded that might confer the genetic vulnerability to the shared depressive symptoms across MPDs and CACNA1C might be the therapeutic target for depressive endophenotype as well

Transcription factor ISL1 is essential for pacemaker development and function

The sinoatrial node (SAN) maintains a rhythmic heartbeat; therefore, a better understanding of factors that drive SAN development and function is crucial to generation of potential therapies, such as biological pacemakers, for sinus arrhythmias. Here, we determined that the LIM homeodomain transcription factor ISL1 plays a key role in survival, proliferation, and function of pacemaker cells throughout development. Analysis of several Isl1 mutant mouse lines, including animals harboring an SAN-specific Isl1 deletion, revealed that ISL1 within SAN is a requirement for early embryonic viability. RNA-sequencing (RNA-seq) analyses of FACS-purified cells from ISL1-deficient SANs revealed that a number of genes critical for SAN function, including those encoding transcription factors and ion channels, were downstream of ISL1. Chromatin immunoprecipitation assays performed with anti-ISL1 antibodies and chromatin extracts from FACS-purified SAN cells demonstrated that ISL1 directly binds genomic regions within several genes required for normal pacemaker function, including subunits of the L-type calcium channel, Ank2, and Tbx3. Other genes implicated in abnormal heart rhythm in humans were also direct ISL1 targets. Together, our results demonstrate that ISL1 regulates approximately one-third of SAN-specific genes, indicate that a combination of ISL1 and other SAN transcription factors could be utilized to generate pacemaker cells, and suggest ISL1 mutations may underlie sick sinus syndrome