Colo-SCRL: Self-Supervised Contrastive Representation Learning for Colonoscopic Video Retrieval

Colonoscopic video retrieval, which is a critical part of polyp treatment, has great clinical significance for the prevention and treatment of colorectal cancer. However, retrieval models trained on action recognition datasets usually produce unsatisfactory retrieval results on colonoscopic datasets due to the large domain gap between them. To seek a solution to this problem, we construct a large-scale colonoscopic dataset named Colo-Pair for medical practice. Based on this dataset, a simple yet effective training method called Colo-SCRL is proposed for more robust representation learning. It aims to refine general knowledge from colonoscopies through masked autoencoder-based reconstruction and momentum contrast to improve retrieval performance. To the best of our knowledge, this is the first attempt to employ the contrastive learning paradigm for medical video retrieval. Empirical results show that our method significantly outperforms current state-of-the-art methods in the colonoscopic video retrieval task.Comment: Accepted by ICME 202

Towards Discriminative Representation with Meta-learning for Colonoscopic Polyp Re-Identification

Colonoscopic Polyp Re-Identification aims to match the same polyp from a large gallery with images from different views taken using different cameras and plays an important role in the prevention and treatment of colorectal cancer in computer-aided diagnosis. However, traditional methods for object ReID directly adopting CNN models trained on the ImageNet dataset usually produce unsatisfactory retrieval performance on colonoscopic datasets due to the large domain gap. Additionally, these methods neglect to explore the potential of self-discrepancy among intra-class relations in the colonoscopic polyp dataset, which remains an open research problem in the medical community. To solve this dilemma, we propose a simple but effective training method named Colo-ReID, which can help our model to learn more general and discriminative knowledge based on the meta-learning strategy in scenarios with fewer samples. Based on this, a dynamic Meta-Learning Regulation mechanism called MLR is introduced to further boost the performance of polyp re-identification. To the best of our knowledge, this is the first attempt to leverage the meta-learning paradigm instead of traditional machine learning to effectively train deep models in the task of colonoscopic polyp re-identification. Empirical results show that our method significantly outperforms current state-of-the-art methods by a clear margin.Comment: arXiv admin note: text overlap with arXiv:2307.1062

The ecological impact of pest-induced tree dieback on insect biodiversity in Yunnan pine plantations, China

China has recently announced a reform of forestry policy, with a major goal being to transform from plantation to heterogeneous forests, which have higher resistance to pests and disease and house more biodiversity. One driver of reform is increased intensity and frequency of pest-induced tree-dieback events. To inform management, we ask what effects these events have on insect biodiversity in Pinus yunnanensis monocultures in Yunnan province, the province with one of the highest proportions of forest cover in China. We sampled aerial arthropods (mostly insect) biodiversity along gradients of Pinus yunnanensis dieback severity using Malaise traps and used metabarcoding to characterise the insect community. We used MS-GDM (‘multi-site generalized dissimilarity modelling of zeta diversity’), zeta-decline analysis, and iNEXT (‘Interpolation and extrapolation for species diversity’) to assess community change as functions of forest-structure covariates. Metabarcoding of Malaise-trapped insects reveals that bark-beetle induced forest dieback does not result in detectable differences in species diversity but does result in compositional change, with the biggest turnover occurring between 0% and infested-0%-open-canopy forests and 20%-infested-20%-open-canopy forests. Zeta-decline analysis found that the insect community in low-infestation forests is characterized by a stochastic assembly, while in high-infestation forests, the community structure is consistent with niche assembly. Our results thus suggest that bark-beetle dieback mimics natural forest-gap dynamics, consistent with the interpretation of bark beetles as a keystone species in European conifer forests, where it has been proposed that forest heterogeneity can be created efficiently by allowing natural disturbances, including bark-beetle outbreaks, to proceed naturally, without being mitigated by deadwood removal and dense replanting. In Yunnan’s situation, and given predicted increases in bark-beetle dieback severity and frequency, this strategy should probably be supplemented with anthropogenic treatments, such as deadwood enhancement and planting of multiple tree species, to accelerate the succession of plantations into heterogeneous forests

Sciences for The 2.5-meter Wide Field Survey Telescope (WFST)

The Wide Field Survey Telescope (WFST) is a dedicated photometric survey facility under construction jointly by the University of Science and Technology of China and Purple Mountain Observatory. It is equipped with a primary mirror of 2.5m in diameter, an active optical system, and a mosaic CCD camera of 0.73 Gpix on the main focus plane to achieve high-quality imaging over a field of view of 6.5 square degrees. The installation of WFST in the Lenghu observing site is planned to happen in the summer of 2023, and the operation is scheduled to commence within three months afterward. WFST will scan the northern sky in four optical bands (u, g, r, and i) at cadences from hourly/daily to semi-weekly in the deep high-cadence survey (DHS) and the wide field survey (WFS) programs, respectively. WFS reaches a depth of 22.27, 23.32, 22.84, and 22.31 in AB magnitudes in a nominal 30-second exposure in the four bands during a photometric night, respectively, enabling us to search tremendous amount of transients in the low-z universe and systematically investigate the variability of Galactic and extragalactic objects. Intranight 90s exposures as deep as 23 and 24 mag in u and g bands via DHS provide a unique opportunity to facilitate explorations of energetic transients in demand for high sensitivity, including the electromagnetic counterparts of gravitational-wave events detected by the second/third-generation GW detectors, supernovae within a few hours of their explosions, tidal disruption events and luminous fast optical transients even beyond a redshift of 1. Meanwhile, the final 6-year co-added images, anticipated to reach g about 25.5 mag in WFS or even deeper by 1.5 mag in DHS, will be of significant value to general Galactic and extragalactic sciences. The highly uniform legacy surveys of WFST will also serve as an indispensable complement to those of LSST which monitors the southern sky.Comment: 46 pages, submitted to SCMP

Systematic Calibration for Ultra-High Accuracy Inertial Measurement Units

An inertial navigation system (INS) has been widely used in challenging GPS environments. With the rapid development of modern physics, an atomic gyroscope will come into use in the near future with a predicted accuracy of 5 × 10−6°/h or better. However, existing calibration methods and devices can not satisfy the accuracy requirements of future ultra-high accuracy inertial sensors. In this paper, an improved calibration model is established by introducing gyro g-sensitivity errors, accelerometer cross-coupling errors and lever arm errors. A systematic calibration method is proposed based on a 51-state Kalman filter and smoother. Simulation results show that the proposed calibration method can realize the estimation of all the parameters using a common dual-axis turntable. Laboratory and sailing tests prove that the position accuracy in a five-day inertial navigation can be improved about 8% by the proposed calibration method. The accuracy can be improved at least 20% when the position accuracy of the atomic gyro INS can reach a level of 0.1 nautical miles/5 d. Compared with the existing calibration methods, the proposed method, with more error sources and high order small error parameters calibrated for ultra-high accuracy inertial measurement units (IMUs) using common turntables, has a great application potential in future atomic gyro INSs

A Novel Gravity Compensation Method for High Precision Free-INS Based on “Extreme Learning Machine”

In recent years, with the emergency of high precision inertial sensors (accelerometers and gyros), gravity compensation has become a major source influencing the navigation accuracy in inertial navigation systems (INS), especially for high-precision INS. This paper presents preliminary results concerning the effect of gravity disturbance on INS. Meanwhile, this paper proposes a novel gravity compensation method for high-precision INS, which estimates the gravity disturbance on the track using the extreme learning machine (ELM) method based on measured gravity data on the geoid and processes the gravity disturbance to the height where INS has an upward continuation, then compensates the obtained gravity disturbance into the error equations of INS to restrain the INS error propagation. The estimation accuracy of the gravity disturbance data is verified by numerical tests. The root mean square error (RMSE) of the ELM estimation method can be improved by 23% and 44% compared with the bilinear interpolation method in plain and mountain areas, respectively. To further validate the proposed gravity compensation method, field experiments with an experimental vehicle were carried out in two regions. Test 1 was carried out in a plain area and Test 2 in a mountain area. The field experiment results also prove that the proposed gravity compensation method can significantly improve the positioning accuracy. During the 2-h field experiments, the positioning accuracy can be improved by 13% and 29% respectively, in Tests 1 and 2, when the navigation scheme is compensated by the proposed gravity compensation method

A New Norm-Observed Calibration Method Based on Improved Differential Evolution Algorithm for SINS

It is vital for a strapdown inertial navigation system (SINS) to be calibrated before normal use. In this paper, a new kind of norm-observed calibration method is proposed. Considering that the norm of the output of accelerometers and gyroscopes can be exactly the norm of local acceleration of gravity and Earth rotation angular velocity, respectively, optimization function about all-parameter calibration and the corresponding 24-position calibration path is established. Differential evolutionary algorithm (DE) is supposed to be the best option in parameter identification due to its strong search and fast convergence abilities. However, the high-dimensional individual vector from calibration error equations restrains the algorithm’s optimum speed and accuracy. To overcome this drawback, improved DE (IDE) optimization is specially designed: First, current “DE/rand/1” and “DE/current-to-best/1” mutation strategies are combined as one with complementary advantages and overall balance during the whole optimization process. Next, with the increase of the evolutionary generation, the mutation factor can adjust itself according to the convergence situation. Multiple identification tests prove that our IDE optimization has rapid convergence and high repeatability. Besides, certain motivation of external angular velocity is added to the gyroscope calibration, and a series of dynamic observation paths is formed, further improving the optimization accuracy. The final static navigation experiment shows that SINS with calibration parameters solved by IDE has better performance over other identification methods, which further explains that our novel method is more accurate and reliable in parameter identification

A Fuzzy Evaluation of Tool Materials in the Turning of Marine Steels

To recommend one suitable tool material for the cutting of marine steels under special conditions and requirements in emergency rescues of capsized steel ships, the cermet tools, cemented carbide tools and coated carbide tools were evaluated using a fuzzy evaluation method concerning cutting force, cutting temperature, surface roughness and tool wear. Experimental results indicate that the tool cutting performance was diverse and difficult to evaluate with a single evaluation index. The cemented carbide tools presented bad cutting performance with severe wear. Compared with the cemented carbide tools, the cermet tools showed excellent wear resistance with about 60.3% smaller tool flank wear value and good surface quality with about 46.8% smaller surface roughness. The coated carbide tools presented low cutting temperatures about 15.6% smaller than those of the cermet tools. The result of fuzzy evaluation demonstrates that the cermet tools presented the best cutting performance, followed by the coated carbide tools, and then the cemented carbide tools. The cermet tools are recommended to cut marine steels in emergency rescues of capsized steel ships

Surface Integrity and Corrosion Resistance of 42CrMo4 High-Strength Steel Strengthened by Hard Turning

To improve the surface corrosion resistance of 42CrMo4 high-strength steel used in a marine environment, this article studied the effects of hard turning on the surface integrity and corrosion resistance of 42CrMo4 high-strength steel through the single factor experimental method, namely hard turning, polarization corrosion, electrochemical impedance spectroscopy, potentiodynamic polarization curve, and salt spray tests. The results indicated that the surface integrity was modified by the hard turning, with a surface roughness lower than Ra 0.8 μm, decreased surface microhardness, fine and uniform surface microstructure, and dominant surface residual compressive stress. The hard turning process was feasible to strengthen the surface corrosion resistance of 42CrMo4 high-strength steel. The better corrosion resistance of the surface layer than that of the substrate material can be ascribed to the uniform carbides and compact microstructure. The corrosion resistance varied with cutting speeds as a result of the changed surface microhardness and residual compressive stress, varied with feed rates as a result of the changed surface roughness, and varied with cutting depths as a result of the changed surface residual compressive stress, respectively. The surface integrity with smaller surface roughness and microhardness and bigger surface residual compressive stress was beneficial for corrosion resistance