Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction

Conversion rate prediction is critical to many online applications such as digital display advertising. To capture dynamic data distribution, industrial systems often require retraining models on recent data daily or weekly. However, the delay of conversion behavior usually leads to incorrect labeling, which is called delayed feedback problem. Existing work may fail to introduce the correct information about false negative samples due to data sparsity and dynamic data distribution. To directly introduce the correct feedback label information, we propose an Unbiased delayed feedback Label Correction framework (ULC), which uses an auxiliary model to correct labels for observed negative feedback samples. Firstly, we theoretically prove that the label-corrected loss is an unbiased estimate of the oracle loss using true labels. Then, as there are no ready training data for label correction, counterfactual labeling is used to construct artificial training data. Furthermore, since counterfactual labeling utilizes only partial training data, we design an embedding-based alternative training method to enhance performance. Comparative experiments on both public and private datasets and detailed analyses show that our proposed approach effectively alleviates the delayed feedback problem and consistently outperforms the previous state-of-the-art methods.Comment: accepted by KDD 202

Word Searching in Scene Image and Video Frame in Multi-Script Scenario using Dynamic Shape Coding

Retrieval of text information from natural scene images and video frames is a challenging task due to its inherent problems like complex character shapes, low resolution, background noise, etc. Available OCR systems often fail to retrieve such information in scene/video frames. Keyword spotting, an alternative way to retrieve information, performs efficient text searching in such scenarios. However, current word spotting techniques in scene/video images are script-specific and they are mainly developed for Latin script. This paper presents a novel word spotting framework using dynamic shape coding for text retrieval in natural scene image and video frames. The framework is designed to search query keyword from multiple scripts with the help of on-the-fly script-wise keyword generation for the corresponding script. We have used a two-stage word spotting approach using Hidden Markov Model (HMM) to detect the translated keyword in a given text line by identifying the script of the line. A novel unsupervised dynamic shape coding based scheme has been used to group similar shape characters to avoid confusion and to improve text alignment. Next, the hypotheses locations are verified to improve retrieval performance. To evaluate the proposed system for searching keyword from natural scene image and video frames, we have considered two popular Indic scripts such as Bangla (Bengali) and Devanagari along with English. Inspired by the zone-wise recognition approach in Indic scripts[1], zone-wise text information has been used to improve the traditional word spotting performance in Indic scripts. For our experiment, a dataset consisting of images of different scenes and video frames of English, Bangla and Devanagari scripts were considered. The results obtained showed the effectiveness of our proposed word spotting approach.Comment: Multimedia Tools and Applications, Springe

Health-Related Quality of Life and Health Service Use among Multimorbid Middle-Aged and Older-Aged Adults in China: A Cross-Sectional Study in Shandong Province

(1) Background: The management of multiple chronic diseases challenges China’s health system, but current research has neglected how multimorbidity is associated with poor health-related quality of life (HRQOL) and high health service demands by middle-aged and older adults. (2) Methods: A cross-sectional study was conducted in Shandong province, China in 2018 across three age groups: Middle-aged (45 to 59 years), young-old (60 to 74 years), and old-old (75 or above years). The information about socio-economic, health-related behaviors, HRQOL, and health service utilization was collected via face-to-face structured questionnaires. The EQ-5D-3L instrument, comprising a health description system and a visual analog scale (VAS), was used to measure participants’ HRQOL, and χ2 tests and the one-way ANOVA test were used to analyze differences in socio-demographic factors and HRQOL among the different age groups. Logistic regression models estimated the associations between lifestyle factors, health service utilization, and multimorbidity across age groups. (3) Results: There were 17,867 adults aged 45 or above in our sample, with 9259 (51.82%) female and 65.60% living in rural areas. Compared with the middle-aged adults, the young-old and old-old were more likely to be single and to have a lower level of education and income, with the old-old having lower levels than the young-old (P < 0.001). We found that 2465 (13.80%) suffered multimorbidities of whom 75.21% were older persons (aged 60 or above). As age increased, both the mean values of EQ-5D utility and the VAS scale decreased, displaying an inverse trend to the increase in the number of chronic diseases (P < 0.05). Ex-smokers and physical check-ups for middle or young-old respondents and overweight/obesity for all participants (P < 0.05) were positively correlated with multimorbidity. Drinking within the past month for all participants (P < 0.001), and daily tooth-brushing for middle (P < 0.05) and young-old participants (P < 0.001), were negatively associated with multimorbidity. Multimorbidities increased service utilization including outpatient and inpatient visits and taking self-medicine; and the probability of health utilization was the lowest for the old-old multimorbid patients (P < 0.001). (4) Conclusions: The prevalence and decline in HRQOL of multimorbid middle-aged and older-aged people were severe in Shandong province. Old patients also faced limited access to health services. We recommend early prevention and intervention to address the prevalence of middle-aged and old-aged multimorbidity. Further, the government should set-up special treatment channels for multiple chronic disease sufferers, improve medical insurance policies for the older-aged groups, and set-up multiple chronic disease insurance to effectively alleviate the costs of medical utilization caused by economic pressure for outpatients and inpatients with chronic diseases

Navigating surface reconstruction of spinel oxides for electrochemical water oxidation

Understanding and mastering the structural evolution of water oxidation electrocatalysts lays the foundation to finetune their catalytic activity. Herein, we demonstrate that surface reconstruction of spinel oxides originates from the metal-oxygen covalency polarity in the MT–O–MO backbone. A stronger MO–O covalency relative to MT–O covalency is found beneficial for a more thorough reconstruction towards oxyhydroxides. The structure-reconstruction relationship allows precise prediction of the reconstruction ability of spinel pre-catalysts, based on which the reconstruction degree towards the in situ generated oxyhydroxides can be controlled. The investigations of oxyhydroxides generated from spinel pre-catalysts with the same reconstruction ability provide guidelines to navigate the cation selection in spinel pre-catalysts design. This work reveals the fundamentals for manipulating the surface reconstruction of spinel pre-catalysts for water oxidation

Decoding the dopamine transporter imaging for the differential diagnosis of parkinsonism using deep learning.

PURPOSE This work attempts to decode the discriminative information in dopamine transporter (DAT) imaging using deep learning for the differential diagnosis of parkinsonism. METHODS This study involved 1017 subjects who underwent DAT PET imaging ([11C]CFT) including 43 healthy subjects and 974 parkinsonian patients with idiopathic Parkinson's disease (IPD), multiple system atrophy (MSA) or progressive supranuclear palsy (PSP). We developed a 3D deep convolutional neural network to learn distinguishable DAT features for the differential diagnosis of parkinsonism. A full-gradient saliency map approach was employed to investigate the functional basis related to the decision mechanism of the network. Furthermore, deep-learning-guided radiomics features and quantitative analysis were compared with their conventional counterparts to further interpret the performance of deep learning. RESULTS The proposed network achieved area under the curve of 0.953 (sensitivity 87.7%, specificity 93.2%), 0.948 (sensitivity 93.7%, specificity 97.5%), and 0.900 (sensitivity 81.5%, specificity 93.7%) in the cross-validation, together with sensitivity of 90.7%, 84.1%, 78.6% and specificity of 88.4%, 97.5% 93.3% in the blind test for the differential diagnosis of IPD, MSA and PSP, respectively. The saliency map demonstrated the most contributed areas determining the diagnosis located at parkinsonism-related regions, e.g., putamen, caudate and midbrain. The deep-learning-guided binding ratios showed significant differences among IPD, MSA and PSP groups (P < 0.001), while the conventional putamen and caudate binding ratios had no significant difference between IPD and MSA (P = 0.24 and P = 0.30). Furthermore, compared to conventional radiomics features, there existed average above 78.1% more deep-learning-guided radiomics features that had significant differences among IPD, MSA and PSP. CONCLUSION This study suggested the developed deep neural network can decode in-depth information from DAT and showed potential to assist the differential diagnosis of parkinsonism. The functional regions supporting the diagnosis decision were generally consistent with known parkinsonian pathology but provided more specific guidance for feature selection and quantitative analysis

Association of Metabolic Dysfunction-Associated Fatty Liver Disease With Left Ventricular Diastolic Function and Cardiac Morphology

Background and AimNon-alcoholic fatty liver disease (NAFLD) is closely related to cardiovascular diseases (CVD). A newly proposed definition is metabolic dysfunction-associated fatty liver disease (MAFLD), which was changed from NAFLD. The clinical effect of this change on abnormalities of cardiac structure and function is yet unknown. We aimed to examine whether MAFLD is associated with left ventricular (LV) diastolic dysfunction (LVDD) and cardiac remolding and further identify the impact of different subgroups and severity of MAFLD.MethodWe evaluated 228 participants without known CVDs. Participants were categorized by the presence of MAFLD and the normal group. Then, patients with MAFLD were subclassified into three subgroups: MAFLD patients with diabetes (diabetes subgroup), overweight/obesity patients (overweight/obesity subgroup), and lean/normal-weight patients who had two metabolic risk abnormalities (lean metabolic dysfunction subgroup). Furthermore, the severity of hepatic steatosis was assessed by transient elastography (FibroScan®) with a controlled attenuation parameter (CAP), and patients with MAFLD were divided into normal, mild, moderate, and severe hepatic steatosis groups based on CAP value. Cardiac structure and function were examined by echocardiography.ResultsLVDD was significantly more prevalent in the MAFLD group (24.6% vs. 60.8%, p &lt; 0.001) compared to the normal group. The overweight subgroup and diabetes subgroup were significantly associated with signs of cardiac remolding, including interventricular septum thickness, LV posterior wall thickness, left atrial diameter (all p &lt; 0.05), relative wall thickness, and LV mass index (all p &lt; 0.05). Additionally, moderate-to-to severe steatosis patients had higher risks for LVDD and cardiac remolding (all p-values &lt; 0.05).ConclusionMAFLD was associated with LVDD and cardiac remolding, especially in patients with diabetes, overweight patients, and moderate-to-to severe steatosis patients. This study provides theoretical support for the precise prevention of cardiovascular dysfunction in patients with MAFLD

Differential diagnosis of parkinsonism based on deep metabolic imaging indices.

The clinical presentations of early idiopathic Parkinson's disease (PD) substantially overlap with those of atypical parkinsonian syndromes like multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). This study aimed to develop metabolic imaging indices based on deep learning to support the differential diagnosis of these conditions. Methods: A benchmark Huashan parkinsonian PET imaging (HPPI, China) database including 1275 parkinsonian patients and 863 non-parkinsonian subjects with 18F-FDG PET images was established to support artificial intelligence development. A 3D deep convolutional neural network was developed to extract deep metabolic imaging (DMI) indices, which was blindly evaluated in an independent cohort with longitudinal follow-up from the HPPI, and an external German cohort of 90 parkinsonian patients with different imaging acquisition protocols. Results: The proposed DMI indices had less ambiguity space in the differential diagnosis. They achieved sensitivities of 98.1%, 88.5%, and 84.5%, and specificities of 90.0%, 99.2%, and 97.8% for the diagnosis of PD, MSA, and PSP in the blind test cohort. In the German cohort, They resulted in sensitivities of 94.1%, 82.4%, 82.1%, and specificities of 84.0%, 99.9%, 94.1% respectively. Employing the PET scans independently achieved comparable performance to the integration of demographic and clinical information into the DMI indices. Conclusion: The DMI indices developed on the HPPI database show potential to provide an early and accurate differential diagnosis for parkinsonism and is robust when dealing with discrepancies between populations and imaging acquisitions