BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning

Label-noise learning (LNL) aims to increase the model's generalization given training data with noisy labels. To facilitate practical LNL algorithms, researchers have proposed different label noise types, ranging from class-conditional to instance-dependent noises. In this paper, we introduce a novel label noise type called BadLabel, which can significantly degrade the performance of existing LNL algorithms by a large margin. BadLabel is crafted based on the label-flipping attack against standard classification, where specific samples are selected and their labels are flipped to other labels so that the loss values of clean and noisy labels become indistinguishable. To address the challenge posed by BadLabel, we further propose a robust LNL method that perturbs the labels in an adversarial manner at each epoch to make the loss values of clean and noisy labels again distinguishable. Once we select a small set of (mostly) clean labeled data, we can apply the techniques of semi-supervised learning to train the model accurately. Empirically, our experimental results demonstrate that existing LNL algorithms are vulnerable to the newly introduced BadLabel noise type, while our proposed robust LNL method can effectively improve the generalization performance of the model under various types of label noise. The new dataset of noisy labels and the source codes of robust LNL algorithms are available at https://github.com/zjfheart/BadLabels

NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels

Adversarial training (AT) formulated as the minimax optimization problem can effectively enhance the model's robustness against adversarial attacks. The existing AT methods mainly focused on manipulating the inner maximization for generating quality adversarial variants or manipulating the outer minimization for designing effective learning objectives. However, empirical results of AT always exhibit the robustness at odds with accuracy and the existence of the cross-over mixture problem, which motivates us to study some label randomness for benefiting the AT. First, we thoroughly investigate noisy labels (NLs) injection into AT's inner maximization and outer minimization, respectively and obtain the observations on when NL injection benefits AT. Second, based on the observations, we propose a simple but effective method -- NoiLIn that randomly injects NLs into training data at each training epoch and dynamically increases the NL injection rate once robust overfitting occurs. Empirically, NoiLIn can significantly mitigate the AT's undesirable issue of robust overfitting and even further improve the generalization of the state-of-the-art AT methods. Philosophically, NoiLIn sheds light on a new perspective of learning with NLs: NLs should not always be deemed detrimental, and even in the absence of NLs in the training set, we may consider injecting them deliberately. Codes are available in https://github.com/zjfheart/NoiLIn.Comment: Accepted at Transactions on Machine Learning Research (TMLR) at June 202

Patterns of de novo metastasis and survival outcomes by age in breast cancer patients: a SEER population-based study

BackgroundThe role of age in metastatic disease, including breast cancer, remains obscure. This study was conducted to determine the role of age in patients with de novo metastatic breast cancer.MethodsBreast cancer patients diagnosed with distant metastases between 2010 and 2019 were retrieved from the Surveillance, Epidemiology, and End Results database. Comparisons were performed between young (aged ≤ 40 years), middle-aged (41–60 years), older (61–80 years), and the oldest old (> 80 years) patients. Adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) were estimated using multivariate Cox proportional hazard models. Survival analysis was performed by the Kaplan–Meier method.ResultsThis study included 24155 (4.4% of all patients) de novo metastatic breast cancer patients. The number of young, middle-aged, older, and the oldest old patients were 195 (8.3%), 9397 (38.9%), 10224 (42.3%), and 2539 (10.5%), respectively. The 5-year OS rate was highest in the young (42.1%), followed by middle-aged (34.8%), older (28.3%), and the oldest old patients (11.8%). Multivariable Cox regression analysis showed that middle-aged (aHR, 1.18; 95% CI, 1.10–1.27), older (aHR, 1.42; 95% CI, 1.32–1.52), and the oldest old patients (aHR, 2.15; 95% CI, 1.98–2.33) had worse OS than young patients. Consistently, middle-aged (aHR, 1.16; 95% CI, 1.08–1.25), older (aHR, 1.32; 95% CI, 1.23–1.43), and the oldest old patients (aHR, 1.86; 95% CI, 1.71–2.03) had worse BCSS than young patients.ConclusionThis study provided clear evidence that de novo metastatic breast cancer had an age-specific pattern. Age was an independent risk factor for mortality in patients with de novo metastatic breast cancer

Numerical simulation of hydraulic fracture propagation under energy supplement conditions

After the long-term production, due to the influence of low-pressure and low-stress fields in the near-well area, the reversion and propagation of new fractures after temporary plugging is short. It is difficult for the new fracture to extend to the remaining oil enrichment areas on both sides of the primary fractures, resulting in a low increase in the bandwidth of the fracture group after repeated fracturing, which affects the reservoir utilization. In the early stage of repeated fracturing, a large amount of pre-fracturing fluid is injected to supplement the energy of the fractures and rapidly increase the pore pressure in the local range, weakening rock strength and change the pore structure. In addition, the combination of energy replenishment and reservoir stimulation, coupled reconstruction of the seepage field and stress field, promotes the effective propagation of new fractures. However, in the process of increasing formation energy, the propagation law of hydraulic fractures and natural fractures is not clear. In this paper, the model of tight sandstone reservoir in the HQ block of Ordos Basin was established with the finite element software ABAQUS, based on the effective stress principle and the theoretical method of fluid-solid coupling numerical simulation. The propagation of a single hydraulic fracture and the interaction between hydraulic fracture and natural fracture under the condition of energy increase was investigated to better guide the field operation. The results show that for every 1 MPa pressure increase in a single hydraulic fracture, the fracture length increases by 0.62 m and the maximum fracture width decreases by 0.09 mm. When the formation energy increases by 6 MPa, the time for the hydraulic fracture to reach the intersection point with the natural fracture is shortened by 10 %, and the length of the natural fracture is 2.16 times compared with the case of 3 MPa energy increase

Meta-analysis of the detection of plant pigment concentrations using hyperspectral remotely sensed data

Passive optical hyperspectral remote sensing of plant pigments offers potential for understanding plant ecophysiological processes across a range of spatial scales. Following a number of decades of research in this field, this paper undertakes a systematic meta-analysis of 85 articles to determine whether passive optical hyperspectral remote sensing techniques are sufficiently well developed to quantify individual plant pigments, which operational solutions are available for wider plant science and the areas which now require greater focus. The findings indicate that predictive relationships are strong for all pigments at the leaf scale but these decrease and become more variable across pigment types at the canopy and landscape scales. At leaf scale it is clear that specific sets of optimal wavelengths can be recommended for operational methodologies: total chlorophyll and chlorophyll a quantification is based on reflectance in the green (550–560nm) and red edge (680–750nm) regions; chlorophyll b on the red, (630–660nm), red edge (670–710nm) and the near-infrared (800–810nm); carotenoids on the 500–580nm region; and anthocyanins on the green (550–560nm), red edge (700–710nm) and near-infrared (780–790nm). For total chlorophyll the optimal wavelengths are valid across canopy and landscape scales and there is some evidence that the same applies for chlorophyll a