Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems

Calibration is defined as the ratio of the average predicted click rate to the true click rate. The optimization of calibration is essential to many online advertising recommendation systems because it directly affects the downstream bids in ads auctions and the amount of money charged to advertisers. Despite its importance, calibration optimization often suffers from a problem called "maximization bias". Maximization bias refers to the phenomenon that the maximum of predicted values overestimates the true maximum. The problem is introduced because the calibration is computed on the set selected by the prediction model itself. It persists even if unbiased predictions can be achieved on every datapoint and worsens when covariate shifts exist between the training and test sets. To mitigate this problem, we theorize the quantification of maximization bias and propose a variance-adjusting debiasing (VAD) meta-algorithm in this paper. The algorithm is efficient, robust, and practical as it is able to mitigate maximization bias problems under covariate shifts, neither incurring additional online serving costs nor compromising the ranking performance. We demonstrate the effectiveness of the proposed algorithm using a state-of-the-art recommendation neural network model on a large-scale real-world dataset

A study on the memory value of industrial heritage based on space narrative - a case of urban renewal in Shanghai

When the process of urban renewal is accelerating, the issue of preservation and utilization of cultural heritage in cities, particularly, the preservation and adaptive reuse of industrial heritage has sparked enthusiastic discussions in academia. Industrial heritage is the physical carrier symbolizing industrial civilization, and its physical presence is often the focus of people’s attention. The underlined historical, scientific, and artistic values, especially the memory and emotional values, namely its intangible value as the place spirit of industrial heritage, are often not given enough attention. However, these values are becoming increasingly important in shaping urban culture. Taking the preservation and adaptive reuse of industrial heritage in Shanghai in recent years as an example, this paper explores taking the intangible memory value of industrial sites as a narrative text to construct a narrative space for interpreting the memory and emotion of urban culture and to inherit industrial culture and place spirit. The adaptive reuse of industrial heritage will perpetuate and create new memory values through space narrative, preserve the city’s historical features, and shape the urban culture

Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals

High sample complexity has long been a challenge for RL. On the other hand, humans learn to perform tasks not only from interaction or demonstrations, but also by reading unstructured text documents, e.g., instruction manuals. Instruction manuals and wiki pages are among the most abundant data that could inform agents of valuable features and policies or task-specific environmental dynamics and reward structures. Therefore, we hypothesize that the ability to utilize human-written instruction manuals to assist learning policies for specific tasks should lead to a more efficient and better-performing agent. We propose the Read and Reward framework. Read and Reward speeds up RL algorithms on Atari games by reading manuals released by the Atari game developers. Our framework consists of a QA Extraction module that extracts and summarizes relevant information from the manual and a Reasoning module that evaluates object-agent interactions based on information from the manual. An auxiliary reward is then provided to a standard A2C RL agent, when interaction is detected. Experimentally, various RL algorithms obtain significant improvement in performance and training speed when assisted by our design

Temporally Disentangled Representation Learning under Unknown Nonstationarity

In unsupervised causal representation learning for sequential data with time-delayed latent causal influences, strong identifiability results for the disentanglement of causally-related latent variables have been established in stationary settings by leveraging temporal structure. However, in nonstationary setting, existing work only partially addressed the problem by either utilizing observed auxiliary variables (e.g., class labels and/or domain indexes) as side information or assuming simplified latent causal dynamics. Both constrain the method to a limited range of scenarios. In this study, we further explored the Markov Assumption under time-delayed causally related process in nonstationary setting and showed that under mild conditions, the independent latent components can be recovered from their nonlinear mixture up to a permutation and a component-wise transformation, without the observation of auxiliary variables. We then introduce NCTRL, a principled estimation framework, to reconstruct time-delayed latent causal variables and identify their relations from measured sequential data only. Empirical evaluations demonstrated the reliable identification of time-delayed latent causal influences, with our methodology substantially outperforming existing baselines that fail to exploit the nonstationarity adequately and then, consequently, cannot distinguish distribution shifts.Comment: NeurIPS 202

Super Normal: Sustainability in type design

This thesis is set out to explore the manifestations and characteristics of Super Normal type design in the age of mechanical reproduction. Originally developed from furniture and product design, Super Normal, here, is the first time to be applied within the context of typography as a way to design a good and sustainable typeface. Containing both a written part and an artistic production, the thesis combined the knowledge of type design, product design, and human behavior. A design project Normal Sans was undertaken to further investigate the comprehensions of Super Normal. From the view of engaging timelessness and functionality, the Super Normal typefaces are able to decrease confusing attributes and lengthen their service lives to improve sustainability. According to the literature review, from the perspective of type design, the research was framed into two phases: case study and design project. In the case study phase, ten Super Normal typeface cases are analyzed to identify how they visually communicate and operate through a relatively long time. Among them, both serif & sans-serif and print & web categories are included. Applying a general introduction and a four-point framework, the chosen typefaces were reduced to abstract forms to measure their motivation, legibility, readability, aesthetics, and adaptability. In the design project, the newly designed typeface Normal Sans was emphasized to practice the findings and data, which were calculated and collected in the case study. The outcome takes the form of a humanist sans-serif typeface family that consists of Roman, Bold, Light proportions with their corresponding Italics. Based on the type design knowledge with the experimental research, the thesis is considered to take a new step forward in understanding the concept of Super Normal in typography. Moreover, it verifies the visual manifestations and characteristics of the sustainable concept and demonstrates the basic guidelines for creating a Super Normal typeface

A study on the memory value of industrial heritage based on space narrative - a case of urban renewal in Shanghai

When the process of urban renewal is accelerating, the issue of preservation and utilization of cultural heritage in cities, particularly, the preservation and adaptive reuse of industrial heritage has sparked enthusiastic discussions in academia. Industrial heritage is the physical carrier symbolizing industrial civilization, and its physical presence is often the focus of people’s attention. The underlined historical, scientific, and artistic values, especially the memory and emotional values, namely its intangible value as the place spirit of industrial heritage, are often not given enough attention. However, these values are becoming increasingly important in shaping urban culture. Taking the preservation and adaptive reuse of industrial heritage in Shanghai in recent years as an example, this paper explores taking the intangible memory value of industrial sites as a narrative text to construct a narrative space for interpreting the memory and emotion of urban culture and to inherit industrial culture and place spirit. The adaptive reuse of industrial heritage will perpetuate and create new memory values through space narrative, preserve the city’s historical features, and shape the urban culture

A Morphological Feature-Oriented Algorithm for Extracting Impervious Surface Areas Obscured by Vegetation in Collaboration with OSM Road Networks in Urban Areas

Remote sensing is the primary way to extract the impervious surface areas (ISAs). However, the obstruction of vegetation is a long-standing challenge that prevents the accurate extraction of urban ISAs. Currently, there are no general and systematic methods to solve the problem. In this paper, we present a morphological feature-oriented algorithm, which can make use of the OSM road network information to remove the obscuring effects when the ISAs are extracted. Very high resolution (VHR) images of Wuhan, China, were used in experiments to verify the effectiveness of the proposed algorithm. Experimental results show that the proposed algorithm can improve the accuracy and completeness of ISA extraction by our previous deep learning-based algorithm. In the proposed algorithm, the overall accuracy (OA) is 86.64%. The results show that the proposed algorithm is feasible and can extract the vegetation-obscured ISAs effectively and precisely

The Change Pattern and Its Dominant Driving Factors of Wetlands in the Yellow River Delta Based on Sentinel-2 Images

There were significant differences in the dominant driving factors of the change process of different types of wetlands in the Yellow River delta. In addition, to our knowledge, the optimal classification feature sets with the Random Forest algorithm for wetlands in the Yellow River delta were least explored. In this paper, the wetland information in the study area was extracted based on a Random Forest algorithm with de-feature variable redundancy, and then the change process of wetland and its dominant factors from 2015 to 2021 was monitored and analyzed using the Geodetector and gravity center model. The results showed that (1) the optimal variable sets composed of red edge indexes based on the Random Forest algorithm had the highest classification accuracy, with the overall accuracy and Kappa coefficient of 95.75% and 0.93. (2) During 2015–2021, a large area of natural wetland in the Yellow River delta was transformed into an artificial wetland. The wetlands showed an overall development direction of “northwest–southeast” along the Yellow River. (3) The interaction between vegetation coverage and accumulated temperature had the largest explanatory power of the change in the natural wetland area. The interaction between solar radiation and DEM had the largest explanatory power for the change in the artificial wetland area. The research results could better provide decisions for wetland protection and restoration in the Yellow River delta

Regulation of dielectric and microwave absorption properties of needle-punched polyimide/carbon fiber nonwoven composites in X-band

The application of nonwoven fabrics in the field of microwave absorption is becoming more and more extensive because of its advantages of light weight, short manufacturing process and strong designability. In this article, polyimide and carbon fiber (PI/CF) nonwoven fabrics were prepared by needle punching process, and then they were immersed in water-based polyurethane (WPU) solution containing different contents of short carbon fiber (SCF, 0–3 mm) to prepare a series of PI/CF/WPU composites. As could be seen from the microscopic morphology that the materials were well impregnated by WPU, and the prepared composites had dense structures. The tensile strength and elongation at break of pure WPU were 13.4 MPa and 161.9%, respectively. The addition of 2 wt.% SCFs in WPU increased the tensile strength to 14.4 MPa by 7.4%, while the addition of 5 wt.% SCFs decreased the tensile strength by 8.2%. Adding 2 wt.% and 5 wt.% SCFs to WPU had little effect on improving the dielectric of pure WPU. But when the PI/CF nonwoven fabrics were added, the dielectric properties of the material greatly improved. The real and imaginary parts of the PI/CF/WPU were 20.2 and 9.5 at 8.2 GHz without SCF addition, 30.4 and 15.7 with 2 wt.% SCFs addition, and 47.2 and 182.8 with 5 wt.% SCFs addition. For PI/CF/WPU without SCF, the reflectivity in the entire X-band was lower than −10 dB in the thickness of 2–2.2 mm. When the thickness was 2.2 mm, the reflectivity reached the minimum value of −25 dB at 12.4 GHz

A novel remote sensing monitoring index of salinization based on three-dimensional feature space model and its application in the Yellow River Delta of China

AbstractPrevious studies were mostly conducted based on two-dimensional feature space to monitor salinization, while studies on dense long-term salinization monitoring based on three-dimensional feature space have not been reported. Based on Landsat TM/ETM+/OLI images and three-dimensional feature space method, this study introduced six typical salinization surface parameters, including NDVI, salinity index, MSAVI, surface albedo, iron oxide index, wetness index to construct eight different feature space monitoring index. The optimal soil salinization monitoring index model was proposed base on field observed data and then the evolution process of salinization in Yellow River Delta (YRD) were analyzed and revealed during 1984–2022. The salinization monitoring index model of MSAVI-Albedo-IFe2O3 feature space had the highest accuracy with R2 = 0.93 and RMSE = 0.678g/kg. The spatial distribution of salinization in YRD showed an increasing trend from inland southwest to coastal northeast and the salinization intensity showed an increasing trend during 1984–2022 due to the implements of agricultural measures such as planting salt-tolerant crops, microbial remediation and fertility improvement. The rate of salinization deterioration in the northeast part was greater than others. Zones of salinization improvement were mainly located in cultivated land of the southwest parts