Semi-Supervised SAR ATR Framework with Transductive Auxiliary Segmentation

Convolutional neural networks (CNNs) have achieved high performance in synthetic aperture radar (SAR) automatic target recognition (ATR). However, the performance of CNNs depends heavily on a large amount of training data. The insufficiency of labeled training SAR images limits the recognition performance and even invalidates some ATR methods. Furthermore, under few labeled training data, many existing CNNs are even ineffective. To address these challenges, we propose a Semi-supervised SAR ATR Framework with transductive Auxiliary Segmentation (SFAS). The proposed framework focuses on exploiting the transductive generalization on available unlabeled samples with an auxiliary loss serving as a regularizer. Through auxiliary segmentation of unlabeled SAR samples and information residue loss (IRL) in training, the framework can employ the proposed training loop process and gradually exploit the information compilation of recognition and segmentation to construct a helpful inductive bias and achieve high performance. Experiments conducted on the MSTAR dataset have shown the effectiveness of our proposed SFAS for few-shot learning. The recognition performance of 94.18\% can be achieved under 20 training samples in each class with simultaneous accurate segmentation results. Facing variances of EOCs, the recognition ratios are higher than 88.00\% when 10 training samples each class

Lantern-shaped screw loaded with autologous bone for treating osteonecrosis of the femoral head

Background: Treatment for osteonecrosis of the femoral head (ONFH) in young individuals remains controversial. We developed a lantern-shaped screw, which was designed to provide mechanical support for the femoral head to prevent its collapse, for the treatment of ONFH. The purpose of this study was to investigate the efficacy and safety of the lantern-shaped screw loaded with autologous bone for the treatment of pre-collapse stages of ONFH. Methods: Thirty-two patients were randomly divided into two groups: the lantern-shaped screw group (core decompression and lantern-shaped screw loaded with autogenous bone) and the control group (core decompression and autogenous bone graft). During 36 months follow-up after surgery, treatment results in patients were assessed by X-ray and computed tomography (CT) scanning as well as functional recovery Harris hip score (HHS). Results: Successful clinical results were achieved in 15 of 16 hips (94%) in the lantern-shaped screw group compared with 10 of 16 hips (63%) in the control group (p = 0.0325). Successful radiological results were achieved in 14 of 16 hips (88%) in the lantern-shaped screw group compared with 8 of 16 hips (50%) in the control group (P = 0.0221). Conclusion: The lantern-shaped screw loaded with autologous bone for the treatment of pre-collapse stages of ONFH is effective and results in preventing progression of ONFH and reducing the risk of femoral head collapse

Improving Domain Adaptation through Extended-Text Reading Comprehension

To enhance the domain-specific capabilities of large language models, continued pre-training on a domain-specific corpus is a prevalent method. Recent work demonstrates that adapting models using reading comprehension data formatted by regex-based patterns can significantly improve performance on domain-specific tasks. However, regex-based patterns are incapable of parsing raw corpora using domain-specific knowledge. Furthermore, the question and answer pairs are extracted directly from the corpus in predefined formats offers limited context. To address this limitation, we improve reading comprehension via LLM and clustering. LLM focuses on leveraging domain knowledge within the corpus to refine comprehension stage, while clustering supplies relevant knowledge by extending the context to enrich reading stage. Additionally, our method incorporates parameter-efficient fine-tuning to improve the efficiency of domain adaptation. In comparison to AdaptLLM, our method achieves an improvement exceeding 5% in domain-specific tasks. Our code will available at https://github.com/microsoft/LMOps.Comment: Work in Progres

Search for light dark matter from atmosphere in PandaX-4T

We report a search for light dark matter produced through the cascading decay of $\eta$ mesons, which are created as a result of inelastic collisions between cosmic rays and Earth's atmosphere. We introduce a new and general framework, publicly accessible, designed to address boosted dark matter specifically, with which a full and dedicated simulation including both elastic and quasi-elastic processes of Earth attenuation effect on the dark matter particles arriving at the detector is performed. In the PandaX-4T commissioning data of 0.63 tonne$\cdot$year exposure, no significant excess over background is observed. The first constraints on the interaction between light dark matter generated in the atmosphere and nucleus through a light scalar mediator are obtained. The lowest excluded cross-section is set at $5.9 \times 10^{-37}{\rm cm^2}$ for dark matter mass of $0.1$ MeV$/c^2$ and mediator mass of 300 MeV$/c^2$. The lowest upper limit of $\eta$ to dark matter decay branching ratio is $1.6 \times 10^{-7}$

A Search for Light Fermionic Dark Matter Absorption on Electrons in PandaX-4T

We report a search on a sub-MeV fermionic dark matter absorbed by electrons with an outgoing active neutrino using the 0.63 tonne-year exposure collected by PandaX-4T liquid xenon experiment. No significant signals are observed over the expected background. The data are interpreted into limits to the effective couplings between such dark matter and electrons. For axial-vector or vector interactions, our sensitivity is competitive in comparison to existing astrophysical bounds on the decay of such dark matter into photon final states. In particular, we present the first direct detection limits for an axial-vector (vector) interaction which are the strongest in the mass range from 25 to 45 (35 to 50) keV/c$^2$

Enhancing the Reduction of High-Aluminum Iron Ore by Synergistic Reducing with High-Manganese Iron Ore

How to utilize low grade complex iron resources is an issue that has attracted much attention due to the continuous and huge consumption of iron ores in China. High-aluminum iron ore is a refractory resource and is difficult to upgrade by separating iron and alumina. An innovative technology involving synergistic reducing and synergistic smelting a high-aluminum iron ore containing 41.92% Fetotal, 13.74% Al2O3, and 13.96% SiO2 with a high-manganese iron ore assaying 9.24% Mntotal is proposed. The synergistic reduction process is presented and its enhancing mechanism is discussed. The results show that the generation of hercynite (FeAl2O4) and fayalite (Fe2SiO4) leads to a low metallization degree of 66.49% of the high-aluminum iron ore. Over 90% of the metallization degree is obtained by synergistic reducing with 60% of the high-manganese iron ore. The mechanism of synergistic reduction can be described as follows: MnO from the high-manganese ore chemically combines with Fe2SiO4 and FeAl2O4 to generate Mn2SiO4, MnAl2O4 and FeO, resulting in higher activity of FeO, which can be reduced to Fe in a CO atmosphere. The main products of the synergistic reduction process consist of Fe, Mn2SiO4, and MnAl2O4

Upgrading of High-Aluminum Hematite-Limonite Ore by High Temperature Reduction-Wet Magnetic Separation Process

The huge consumption of iron ores in China has attracted much attention to utilizing low grade complex iron resources, such as high-aluminum hematite-limonite ore, which is a refractory resource and difficult to upgrade by traditional physical concentration processes due to the superfine size and close dissemination of iron minerals with gangue minerals. An innovative technology for a high temperature reduction-magnetic separation process was studied to upgrade a high-aluminum iron ore assaying 41.92% Fetotal, 13.74% Al2O3 and 13.96% SiO2. The optimized results show that the final metal iron powder, assaying 90.46% Fetotal, was manufactured at an overall iron recovery of 90.25% under conditions as follows: balling the high aluminum iron ore with 15% coal blended and at 0.3 basicity, reducing the dried pellets at 1350 °C for 25 min with a total C/Fe mass ratio of 1.0, grinding the reduced pellets up to 95%, passing at 0.074 mm and magnetically separating the ground product in a Davis Tube at a 0.10-T magnetic field intensity. The metal iron powder can be used as the burden for an electric arc furnace (EAF). Meanwhile, the nonmagnetic tailing is suitable to produce ceramic, which mainly consists of anorthite and corundum. An efficient way has been found to utilize high-aluminum iron resources

Clinicopathological and prognostic value of hypoxia-inducible factor-1α in patients with bone tumor: a systematic review and meta-analysis

Abstract Background Recently, many studies have shown the role of hypoxia-inducible factor-1α (HIF-1α) expression in the outcome of bone tumor. However, the results remain inconclusive. It is necessary to carry out a meta-analysis of all the current available data to clarify the relationship between HIF-1α and survival or clinicopathological features of bone tumor. Methods PubMed, Cochrane Library, Web of Science, China National Knowledge Internet, and Wanfang databases were used to search the relationship between HIF-1α and bone tumor. Articles investigating clinicopathological and prognostic value of HIF-1α in bone tumor patients were enrolled in this meta-analysis. Overlapping articles, duplicate data, reviews, case reports, and letters without original data were excluded. The pooled risk ratios (RRs) and hazard ratios (HRs) were used to evaluate the clinicopathological and prognostic value of HIF-1α on bone tumor patients, respectively. Results A total of 28 studies including 1443 patients were included in this meta-analysis, which were involved in three different types of bone tumor including 3 chondrosarcomas, 2 giant cell tumors of bone, and 23 osteosarcomas. Our results showed that high expression levels of HIF-1α were associated with poorer OS (overall survival) (HR = 2.61, 95% CI 2.11–3.23, P <  0.001) and shorter DFS (disease-free survival) (HR = 2.02, 95% CI 1.41–2.89, P <  0.001) in bone tumor. In addition, this study also analyzed the role of HIF-1α expression in clinicopathological features, which were closely related with the severity of bone tumor, including differentiation, clinical stage, metastasis, and microvessel density. Our results indicated that HIF-1α overexpression was significantly associated with differentiation (RR = 1.56, 95% CI 1.00–2.43, P = 0.049), clinical stage (RR = 1.75, 95% CI 1.25–2.45, P = 0.001), metastasis (RR = 1.78, 95% CI 1.58–2.00, P <  0.001), and microvessel density (SMD = 2.34, 95% CI 1.35–3.34, P <  0.001) of bone tumor. Conclusions HIF-1α overexpression indicated an unfavorable factor for OS and DFS in bone tumor, suggesting that HIF-1α may serve as a potential prognostic marker for bone tumor