Enhancing Infrared Small Target Detection Robustness with Bi-Level Adversarial Framework

The detection of small infrared targets against blurred and cluttered backgrounds has remained an enduring challenge. In recent years, learning-based schemes have become the mainstream methodology to establish the mapping directly. However, these methods are susceptible to the inherent complexities of changing backgrounds and real-world disturbances, leading to unreliable and compromised target estimations. In this work, we propose a bi-level adversarial framework to promote the robustness of detection in the presence of distinct corruptions. We first propose a bi-level optimization formulation to introduce dynamic adversarial learning. Specifically, it is composited by the learnable generation of corruptions to maximize the losses as the lower-level objective and the robustness promotion of detectors as the upper-level one. We also provide a hierarchical reinforced learning strategy to discover the most detrimental corruptions and balance the performance between robustness and accuracy. To better disentangle the corruptions from salient features, we also propose a spatial-frequency interaction network for target detection. Extensive experiments demonstrate our scheme remarkably improves 21.96% IOU across a wide array of corruptions and notably promotes 4.97% IOU on the general benchmark. The source codes are available at https://github.com/LiuZhu-CV/BALISTD.Comment: 9 pages, 6 figure

Generative Input: Towards Next-Generation Input Methods Paradigm

Since the release of ChatGPT, generative models have achieved tremendous success and become the de facto approach for various NLP tasks. However, its application in the field of input methods remains under-explored. Many neural network approaches have been applied to the construction of Chinese input method engines(IMEs).Previous research often assumed that the input pinyin was correct and focused on Pinyin-to-character(P2C) task, which significantly falls short of meeting users' demands. Moreover, previous research could not leverage user feedback to optimize the model and provide personalized results. In this study, we propose a novel Generative Input paradigm named GeneInput. It uses prompts to handle all input scenarios and other intelligent auxiliary input functions, optimizing the model with user feedback to deliver personalized results. The results demonstrate that we have achieved state-of-the-art performance for the first time in the Full-mode Key-sequence to Characters(FK2C) task. We propose a novel reward model training method that eliminates the need for additional manual annotations and the performance surpasses GPT-4 in tasks involving intelligent association and conversational assistance. Compared to traditional paradigms, GeneInput not only demonstrates superior performance but also exhibits enhanced robustness, scalability, and online learning capabilities

Sulfur signaling pathway in cardiovascular disease

Hydrogen sulfide (H2S) and sulfur dioxide (SO2), recognized as endogenous sulfur-containing gas signaling molecules, were the third and fourth molecules to be identified subsequent to nitric oxide and carbon monoxide (CO), and exerted diverse biological effects on the cardiovascular system. However, the exact mechanisms underlying the actions of H2S and SO2 have remained elusive until now. Recently, novel post-translational modifications known as S-sulfhydration and S-sulfenylation, induced by H2S and SO2 respectively, have been proposed. These modifications involve the chemical alteration of specific cysteine residues in target proteins through S-sulfhydration and S-sulfenylation, respectively. H2S induced S-sulfhydrylation can have a significant impact on various cellular processes such as cell survival, apoptosis, cell proliferation, metabolism, mitochondrial function, endoplasmic reticulum stress, vasodilation, anti-inflammatory response and oxidative stress in the cardiovascular system. Alternatively, S-sulfenylation caused by SO2 serves primarily to maintain vascular homeostasis. Additional research is warranted to explore the physiological function of proteins with specific cysteine sites, despite the considerable advancements in comprehending the role of H2S-induced S-sulfhydration and SO2-induced S-sulfenylation in the cardiovascular system. The primary objective of this review is to present a comprehensive examination of the function and potential mechanism of S-sulfhydration and S-sulfenylation in the cardiovascular system. Proteins that undergo S-sulfhydration and S-sulfenylation may serve as promising targets for therapeutic intervention and drug development in the cardiovascular system. This could potentially expedite the future development and utilization of drugs related to H2S and SO2

Combined Scaling for Open-Vocabulary Image Classification

We present a combined scaling method - named BASIC - that achieves 85.7% top-1 accuracy on the ImageNet ILSVRC-2012 validation set without learning from any labeled ImageNet example. This accuracy surpasses best published similar models - CLIP and ALIGN - by 9.3%. Our BASIC model also shows significant improvements in robustness benchmarks. For instance, on 5 test sets with natural distribution shifts such as ImageNet-{A,R,V2,Sketch} and ObjectNet, our model achieves 84.3% top-1 average accuracy, only a small drop from its original ImageNet accuracy. To achieve these results, we scale up the contrastive learning framework of CLIP and ALIGN in three dimensions: data size, model size, and batch size. Our dataset has 6.6B noisy image-text pairs, which is 4x larger than ALIGN, and 16x larger than CLIP. Our largest model has 3B weights, which is 3.75x larger in parameters and 8x larger in FLOPs than ALIGN and CLIP. Finally, our batch size is 65536 which is 2x more than CLIP and 4x more than ALIGN. We encountered two main challenges with the scaling rules of BASIC. First, the main challenge with implementing the combined scaling rules of BASIC is the limited memory of accelerators, such as GPUs and TPUs. To overcome the memory limit, we propose two simple methods which make use of gradient checkpointing and model parallelism. Second, while increasing the dataset size and the model size has been the defacto method to improve the performance of deep learning models like BASIC, the effect of a large contrastive batch size on such contrastive-trained image-text models is not well-understood. To shed light on the benefits of large contrastive batch sizes, we develop a theoretical framework which shows that larger contrastive batch sizes lead to smaller generalization gaps for image-text models such as BASIC

Manipulation of ionized impurity scattering for achieving high thermoelectric performance in n-type Mg

Achieving higher carrier mobility plays a pivotal role for obtaining potentially high thermoelectric performance. In principle, the carrier mobility is governed by the band structure as well as by the carrier scattering mechanism. Here, we demonstrate that by manipulating the carrier scattering mechanism in n-type Mg[subscript 3]Sb[subscript 2 ]-based materials, a substantial improvement in carrier mobility, and hence the power factor, can be achieved. In this work, Fe, Co, Hf, and Ta are doped on the Mg site of Mg[subscript 3.2]Sb[subscript 1.5]Bi[subscript 0.49]Te [subscript 0.01], where the ionized impurity scattering crosses over to mixed ionized impurity and acoustic phonon scattering. A significant improvement in Hall mobility from ∼16 to ∼81 cm 2 ·V[superscript −1]·s[superscript − 1] is obtained, thus leading to a notably enhanced power factor of ∼13 μW·cm [superscript −1]·K [superscript −2] from ∼5 μW·cm[superscript −1]·K[superscript −2]. A simultaneous reduction in thermal conductivity is also achieved. Collectively, a figure of merit (ZT) of ∼1.7 is obtained at 773 K in Mg[subscript 3.1]Co[subscript 0.1]Sb[subscript 1.5]Bi[subscript 0.49]Te [subscript 0.01]. The concept of manipulating the carrier scattering mechanism to improve the mobility should also be applicable to other material systems. Keywords: thermoelectric; carrier scattering mechanism; ionized impurity scattering; n-type; Mg[subscript 3]Sb[subscript 2]; defect

High CD8+tumor-infiltrating lymphocytes indicate severe exhaustion and poor prognosis in angioimmunoblastic T-cell lymphoma

BackgroundExhaustion of CD8+ tumor-infiltrating lymphocytes (TILs), characterized by the overexpression of immune checkpoints (IC), is a major impediment to anti-tumor immunity. However, the exhaustion status of CD8+TILs in angioimmunoblastic T cell lymphoma (AITL) remains unclear. Therefore, we aimed to elucidate the exhaustion status of CD8+TILs in AITL and its influence on prognosis.MethodsThe correlation between CD8+TILs and IC expression in AITL was analyzed using single-cell RNA sequencing (n = 2), flow cytometry (n = 20), and RNA sequencing (n = 20). Biological changes related to CD8+TILs exhaustion at different cytotoxic T lymphocyte (CTL) levels (mean expression levels of CD8A, CD8B, GZMA, GZMB, and PRF1) in AITL were evaluated using RNA sequencing (n = 20) and further validated using the GEO dataset (n = 51). The impact of CD8 protein expression and CTL levels on patient prognosis was analyzed using flow cytometry and RNA sequencing, respectively.ResultsOur findings demonstrated that the higher the infiltration of CD8+TILs, the higher was the proportion of exhausted CD8+TILs characterized by the overexpression of multiple IC. This was accompanied by extensive exhaustion-related biological changes, which suggested severe exhaustion in CD8+TILs and may be one of the main reasons for the poor prognosis of patients with high CD8+TILs and CTL.ConclusionOur study comprehensively reveals the exhaustion status of CD8+TILs and their potential negative impact on AITL prognosis, which facilitates further mechanistic studies and is valuable for guiding immunotherapy strategies

The dual role of glioma exosomal microRNAs: glioma eliminates tumor suppressor miR-1298-5p via exosomes to promote immunosuppressive effects of MDSCs

Clear evidence shows that tumors could secrete microRNAs (miRNAs) via exosomes to modulate the tumor microenvironment (TME). However, the mechanisms sorting specific miRNAs into exosomes are still unclear. In order to study the biological function and characterization of exosomal miRNAs, we performed whole-transcriptome sequencing in 59 patients’ whole-course cerebrospinal fluid (CSF) small extracellular vesicles (sEV) and matched glioma tissue samples. The results demonstrate that miRNAs could be divided into exosome-enriched miRNAs (ExomiRNAs) and intracellular-retained miRNAs (CLmiRNAs), and exosome-enriched miRNAs generally play a dual role. Among them, miR-1298-5p was enriched in CSF exosomes and suppressed glioma progression in vitro and vivo experiments. Interestingly, exosomal miR-1298-5p could promote the immunosuppressive effects of myeloid-derived suppressor cells (MDSCs) to facilitate glioma. Therefore, we found miR-1298-5p had different effects on glioma cells and MDSCs. Mechanically, downstream signaling pathway analyses showed that miR-1298-5p plays distinct roles in glioma cells and MDSCs via targeting SETD7 and MSH2, respectively. Moreover, reverse verification was performed on the intracellular-retained miRNA miR-9-5p. Thus, we confirmed that tumor-suppressive miRNAs in glioma cells could be eliminated through exosomes and target tumor-associated immune cells to induce tumor-promoting phenotypes. Glioma could get double benefit from it. These findings uncover the mechanisms that glioma selectively sorts miRNAs into exosomes and modulates tumor immunity.publishedVersio