Unlearnable Clusters: Towards Label-agnostic Unlearnable Examples

There is a growing interest in developing unlearnable examples (UEs) against visual privacy leaks on the Internet. UEs are training samples added with invisible but unlearnable noise, which have been found can prevent unauthorized training of machine learning models. UEs typically are generated via a bilevel optimization framework with a surrogate model to remove (minimize) errors from the original samples, and then applied to protect the data against unknown target models. However, existing UE generation methods all rely on an ideal assumption called label-consistency, where the hackers and protectors are assumed to hold the same label for a given sample. In this work, we propose and promote a more practical label-agnostic setting, where the hackers may exploit the protected data quite differently from the protectors. E.g., a m-class unlearnable dataset held by the protector may be exploited by the hacker as a n-class dataset. Existing UE generation methods are rendered ineffective in this challenging setting. To tackle this challenge, we present a novel technique called Unlearnable Clusters (UCs) to generate label-agnostic unlearnable examples with cluster-wise perturbations. Furthermore, we propose to leverage VisionandLanguage Pre-trained Models (VLPMs) like CLIP as the surrogate model to improve the transferability of the crafted UCs to diverse domains. We empirically verify the effectiveness of our proposed approach under a variety of settings with different datasets, target models, and even commercial platforms Microsoft Azure and Baidu PaddlePaddle. Code is available at \url{https://github.com/jiamingzhang94/Unlearnable-Clusters}.Comment: CVPR202

Composite vertical structures and spatiotemporal characteristics of abnormal eddies in the Japan/East Sea: a synergistic investigation using satellite altimetry and Argo profiles

Mesoscale eddies are omnipresent and play an important role in regulating Earth’s climate and ocean circulation in the global ocean. Here using the combination of satellite altimetry products and Argo float profile data, two types of abnormal eddies are investigated: WCEs(warm cyclonic eddies) and CAEs(cold anticyclonic eddies) with different cores than conventional eddies in the Japan/East Sea. By applying a classification method based on the calculation of the heat content anomalies in the upper ocean, it was found that 10% of the eddies that captured the Argo float profiles exhibited obvious abnormal features. Subsequently, their spatiotemporal distributions and characteristics were analyzed statistically. Three-dimensional structures of abnormal eddies were obtained via the composite analysis method, showing that the warm/cold and light/dense core of the composite WCE/CAE is confined to the upper 100 m of the ocean with a maximum temperature anomaly of approximately +1.0(-1.1)°C. The composite WCE had a double-core salinity structure with a salty core above 50 m and an inferior fresh core. Meanwhile composite CAE had a fresh single-core with a maximum magnitude of -0.05 psu. Abnormal eddies are pervasive in the Japan/East sea, a revaluation of the role of these eddies in ocean circulation and climate systems, such as heat and salt transport, air and sea interaction, and variability in mixed layer depth, is of great importance

3D printed milk protein food simulant: improving the printing performance of milk protein concentration by incorporating whey protein isolate

This paper aimed to establish a milk protein based 3D printing food simulant and investigated the effect of whey protein isolate (WPI) concentration on the printing performance of milk protein concentrate (MPC). WPI and MPC powders at different ratios were prepared in paste (35 wt%, total dry matter content). The rheological properties and water distribution of protein matrix prepared with different MPC/WPI ratios were characterized with a rheometer and low field nuclear magnetic resonance (LF-NMR), respectively. Moreover, the variations in the microstructure of printed objects were observed with a scanning electron microscope (SEM). The printed objects showed different appearance and physical properties; the printing fidelity was also evaluated by measuring the geometric accuracy of printed objects. The rheological and texture data showed that the presence of WPI could reduce the apparent viscosity and soften the MPC paste, benefiting the printing process. The results showed that the milk powder paste mixture prepared with MPC/WPI at a ratio of 5/2 was the most desirable material for extrusion-based 3D printing, which could be successfully printed and matched the designed 3D model best. Industrial relevance: 3D printing in food sector has been an attractive and emerging technology owing to its potential advantages, such as customized food designs, personalized and digitalized nutrition, simplifying supply chain and so on. This paper established a high protein food simulant for 3D printing, optimized its printing performance with whey protein isolate, and studied the physicochemical property of prepared protein pastes. The overall results indicated that milk protein powders could be the promising materials for the application in food 3D printing. In flowing studies or practical production, the glycerol could be replaced by ingredients such as syrup, honey etc. This study may give more insights into 3D printing applied in food sector and facilitate the further developments of 3D food printing

The rational dose for MaXingShiGan decoction is crucial for its clinical effectiveness in treating bronchial pneumonia: three randomized, double-blind, dose-parallel controlled clinical studies

Objective: Evaluate the impact of adjusting the overall dose, Gypsum Fibrosum [Mineral; Gypsum] (ShiGao, SG) dose, and Prunus armeniaca L. [Rosaceae; Semen Armeniacae Amarum] (KuXingRen, KXR) dose on the efficacy of MaXingShiGan Decoction (MXSG) in treating children with bronchial pneumonia (Wind-heat Blocking the Lung), in order to provide strategy supported by high-quality evidence for the selection of rational clinical doses of MXSG.Methods: Based on the basic dose of MXSG, we conducted three randomized, double-blind, dose parallel controlled, multicenter clinical trials, involving adjustments to the overall dose, SG dose, and KXR dose, and included 120 children with bronchial pneumonia (Wind-heat Blocking the Lung) respectively. And the patients were divided into low, medium, and high dose groups in a 1:1:1 ratio, with 40 cases in each group. The intervention period lasted for 10 days. The primary outcome was the clinical cured rate, while the secondary outcomes included the effectiveness in alleviating major symptoms of bronchial pneumonia (including fever, cough, dyspnea, and phlegm congestion). And the occurrence of adverse events was recorded.Results: We first recorded and analyzed the baseline characteristics of the three studies, including age, gender, height, and so on. The results indicated that there were no significant differences among the dose groups within each study. For the study adjusting the overall dose of MXSG, the results showed that both the medium-dose group and high-dose group had significantly higher clinical cured rates compared to the low-dose group (Chi-square value 9.01, p = 0.0111). However, there was no significant benefit between the high-dose group and the medium-dose group (81.58% vs. 81.08%). Regarding phlegm congestion, excluding fever, cough, and dyspnea, both the medium-dose group and high-dose group had significantly higher clinical cured rates than the low-dose group (Chi-square value 6.31, p = 0.0426), and there was no significant benefit between the high-dose group and the medium-dose group (69.23% vs. 75.00%). A total of 5 adverse events were observed, of which only 1 case in the medium-dose group was possibly related to the experimental medication. For the study adjusted the SG dose in MXSG, the results showed that the high-dose group had the highest clinical cured rate, but the inter-group difference was not statistically significant (Chi-square value 3.36, p = 0.1864). The area under the curve (AUC) for cough in the medium-dose group was significantly lower than in the low-dose group and high-dose group (F-test value 3.14, p = 0.0471). Although no significant differences were observed in fever and dyspnea among the groups, the AUC in the high-dose group was lower than in the medium-dose and low-dose groups. In comparing the complete defervescence time, both the high-dose group (p < 0.0001) and the medium-dose group (p = 0.0015) achieved faster than the low-dose group. The high-dose group slightly outperformed the medium-dose group (0.50 (0.50, 0.80) vs. 0.80 (0.40, 1.40)), although the difference was not significant. In the medium-dose group, 1 adverse event was observed, but it was not related to the experimental medication. For the study adjusted the KXR dose in MXSG, the results showed that both the medium-dose group and high-dose group had significantly higher cured rates compared to the low-dose group (Chi-square value 47.05, p < 0.0001). However, there was no significant benefit comparing the high-dose group to the medium-dose group (90.00% vs. 92.50%). Regarding clinical symptoms, the results indicated that for cough (F-test value 3.16, p = 0.0460) and phlegm congestion (F-test value 3.84, p = 0.0243), the AUC for both the medium-dose group and high-dose group were significantly lower than in the low-dose group. Although there was benefit in the high-dose group compared to the medium-dose group, it was not statistically significant. No adverse events were observed during the study period.Conclusion: The synthesis of the three conducted clinical studies collectively indicates that for children with bronchial pneumonia (Wind-heat Blocking the Lung), the basic clinical dose of MXSG may represents an optimal intervention dose based on the accumulated clinical experience of doctors. If the dose is insufficient, the clinical effects might be compromised, but using a higher dose does not significantly enhance benefits. Concerning different symptoms, increasing the overall formula’s dose has a favorable impact on improving phlegm congestion, increasing the SG is effective in improving symptoms such as fever, cough, and dyspnea, while higher dose of KXR is effective in alleviating cough and phlegm congestion. These findings suggest that for MXSG, achieving the optimal intervention dose is crucial to achieve better clinical efficacy. For the SG and KXR, if certain symptoms are more severe, increasing the dose can be considered within safe limits, can lead to significant clinical benefits in symptom improvement. This also explains why the dose of MXSG might vary among clinical doctors, while maintaining a balance between safety and effectiveness. Of course, our study is still exploratory clinical trials, and further studies are needed to confirm our findings.Clinical Trial Registration:https://www.chictr.org.cn/index.html; Identifier: ChiCTR-TRC-13003093, ChiCTR-TRC-13003099

Isolation and Impartial Aggregation: A Paradigm of Incremental Learning without Interference

This paper focuses on the prevalent stage interference and stage performance imbalance of incremental learning. To avoid obvious stage learning bottlenecks, we propose a new incremental learning framework, which leverages a series of stage-isolated classifiers to perform the learning task at each stage, without interference from others. To be concrete, to aggregate multiple stage classifiers as a uniform one impartially, we first introduce a temperature-controlled energy metric for indicating the confidence score levels of the stage classifiers. We then propose an anchor-based energy self-normalization strategy to ensure the stage classifiers work at the same energy level. Finally, we design a voting-based inference augmentation strategy for robust inference. The proposed method is rehearsal-free and can work for almost all incremental learning scenarios. We evaluate the proposed method on four large datasets. Extensive results demonstrate the superiority of the proposed method in setting up new state-of-the-art overall performance. Code is available at https://github.com/iamwangyabin/ESN

Analysis and Prediction of Dockless Shared Bike Demand Evolving Around Urban Rail Transit Stations: Case Study in Shenzhen, China

Abstract The emergence of dockless shared bikes (DSB) has led to their use as an important transfer mode to urban rail transit (URT) stations. However, in highly populated areas such as subway stations in peak hours, there is increasing concern about the imbalance between the demand and supply of shared bikes. To promote smoother subway transfer trips using shared bikes, it is very important to estimate the DSB demand, especially the disparity in the volume of bike pick-up and drop-off demand around subway stations. This research first utilizes the Shenzhen metro usage data and DSB usage data, analyzes data regarding subway and shared bike usage, discusses their potential transfer uses, and finds great disparity in DSB demand between different subway stations. The catchment area method is used to estimate bike usage as a potential transfer mode to the subway, where the catchment area is defined as a radius of 150 m from the subway station center. The DSB trip demand is categorized into two types: pick-up and drop-off. The most recent deep learning method, adaptive graph convolutional recurrent network (AGCRN), is used to predict the DSB demand more accurately because of its ability in enabling the modeling of relationships between entities in a self-adapted graph, and the prediction is compared with long short-term memory (LSTM), spatiotemporal neural network (STNN), diffusion convolutional recurrent neural network (DCRNN), and Graph WaveNet. Results show that methods with graphs (STNN, DCRNN, Graph WaveNet, and AGCRN) perform better than LSTM, and methods with adaptive graphs (Graph WaveNet and AGCRN) outperform methods with static graphs in terms of mean absolute error (MAE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE). DSB prediction results show that AGCRN performs the best in this study. More data, particularly land use data and URT station volume data, are expected to improve the predictive accuracy of the method due to potentially improved graph representation of station characteristics and subway station volume correlations. And with more accurate prediction results, it will be possible to achieve a better balancing strategy for bike operation optimization for better bike usage, and thus for a higher transfer rate of DSB to subway

The time window for pre-emptive transjugular intrahepatic portosystemic shunt could be extended to 5 days

As recommended by Baveno VII consensus, the utilization of pre-emptive transjugular intrahepatic portosystemic shunt (pTIPS) has been considered as standard therapeutic approach for the management of acute variceal bleeding (AVB) associated with cirrhosis., but the 72-h window for pTIPS is too narrow. This study aimed to compare the clinical outcomes between patients who received <72 h pTIPS and 72 h-5d pTIPS. In this study, a total of 63 cirrhotic patients with AVB who underwent pTIPS between October 2016 and December 2021 were included in this retrospective study. They were divided into <72 h group (n = 32) and 72 h-5d group (n = 31), based on the timing of the intervention. The Kaplan-Meier curves demonstrated that there were no significant differences in the cumulative incidence of death (22.3% ± 7.4% vs. 19.9% ± 7.3%, log-rank P = 0.849), variceal rebleeding (9.7% ± 5.3% vs. 17.8% ± 7.3%, log-rank P = 0.406), OHE (28.5% ± 8.0% vs. 23.9% ± 8.0%, log-rank P = 0.641) and shunt dysfunction (8.6% ± 6.0% vs. 17.4% ± 8.1%, log-rank P = 0.328) between <72 h and 72 h-5d groups. In the total cohort, sarcopenia was identified as an independent risk factor for mortality (HR = 11.268, 95% CI = 1.435–88.462, P = 0.021) and OHE(HR = 12.504, 95% CI = 1.598–97.814, P = 0.016). In conclusion, the clinical outcomes of cirrhotic patients with AVB who underwent pTIPS within the 72-h to 5-day window were found to be comparable to those treated within the 72-h window