Real-time Bidding for Online Advertising: Measurement and Analysis

The real-time bidding (RTB), aka programmatic buying, has recently become the fastest growing area in online advertising. Instead of bulking buying and inventory-centric buying, RTB mimics stock exchanges and utilises computer algorithms to automatically buy and sell ads in real-time; It uses per impression context and targets the ads to specific people based on data about them, and hence dramatically increases the effectiveness of display advertising. In this paper, we provide an empirical analysis and measurement of a production ad exchange. Using the data sampled from both demand and supply side, we aim to provide first-hand insights into the emerging new impression selling infrastructure and its bidding behaviours, and help identifying research and design issues in such systems. From our study, we observed that periodic patterns occur in various statistics including impressions, clicks, bids, and conversion rates (both post-view and post-click), which suggest time-dependent models would be appropriate for capturing the repeated patterns in RTB. We also found that despite the claimed second price auction, the first price payment in fact is accounted for 55.4% of total cost due to the arrangement of the soft floor price. As such, we argue that the setting of soft floor price in the current RTB systems puts advertisers in a less favourable position. Furthermore, our analysis on the conversation rates shows that the current bidding strategy is far less optimal, indicating the significant needs for optimisation algorithms incorporating the facts such as the temporal behaviours, the frequency and recency of the ad displays, which have not been well considered in the past.Comment: Accepted by ADKDD '13 worksho

A Computationally Efficient Hybrid Neural Network Architecture for Porous Media: Integrating CNNs and GNNs for Improved Permeability Prediction

Subsurface fluid flow, essential in various natural and engineered processes, is largely governed by a rock's permeability, which describes its ability to allow fluid passage. While convolutional neural networks (CNNs) have been employed to estimate permeability from high-resolution 3D rock images, our novel visualization technology reveals that they occasionally miss higher-level characteristics, such as nuanced connectivity and flow paths, within porous media. To address this, we propose a novel fusion model to integrate CNN with the graph neural network (GNN), which capitalizes on graph representations derived from pore network model to capture intricate relational data between pores. The permeability prediction accuracy of the fusion model is superior to the standalone CNN, whereas its total parameter number is nearly two orders of magnitude lower than the latter. This innovative approach not only heralds a new frontier in the research of digital rock property predictions, but also demonstrates remarkable improvements in prediction accuracy and efficiency, emphasizing the transformative potential of hybrid neural network architectures in subsurface fluid flow research

NeRRF: 3D Reconstruction and View Synthesis for Transparent and Specular Objects with Neural Refractive-Reflective Fields

Neural radiance fields (NeRF) have revolutionized the field of image-based view synthesis. However, NeRF uses straight rays and fails to deal with complicated light path changes caused by refraction and reflection. This prevents NeRF from successfully synthesizing transparent or specular objects, which are ubiquitous in real-world robotics and A/VR applications. In this paper, we introduce the refractive-reflective field. Taking the object silhouette as input, we first utilize marching tetrahedra with a progressive encoding to reconstruct the geometry of non-Lambertian objects and then model refraction and reflection effects of the object in a unified framework using Fresnel terms. Meanwhile, to achieve efficient and effective anti-aliasing, we propose a virtual cone supersampling technique. We benchmark our method on different shapes, backgrounds and Fresnel terms on both real-world and synthetic datasets. We also qualitatively and quantitatively benchmark the rendering results of various editing applications, including material editing, object replacement/insertion, and environment illumination estimation. Codes and data are publicly available at https://github.com/dawning77/NeRRF

Highly secretory expression of recombinant cowpea chlorotic mottle virus capsid proteins in Pichia pastoris and in-vitro encapsulation of ruthenium nanoparticles for catalysis

The applications of viral protein cages have expanded rapidly into the fields of bionanotechnology and materials science. However, the low-cost production of viral capsid proteins (CPs) on a large scale is always a challenge. Herein, we develop a highly efficient expression system by constructing recombinant Pichia pastoris cells as a “factory” for the secretion of soluble cowpea chlorotic mottle virus (CCMV) CPs. Under optimal induction conditions (0.9 mg/mL of methanol concentration at 30 °C for 96 h), a high yield of approximately 95 mg/L of CCMV CPs was harvested from the fermentation supernatant with CPs purity >90%, which has significantly simplified the rest of the purification process. The resultant CPs are employed to encapsulate Ruthenium (Ru) nanoparticles (NPs) via in-vitro self-assembly to prepare hybrid nanocatalyst, i.e. Ru@virus-like particles (VLPs). The catalytic activity over Ru@VLPs was evaluated by reducing 4-nitrophenol (4-NP) to 4-aminophenol (4-AP). The results indicate that, with the protection of protein cages, Ru NPs were highly stabilized during the catalytic reaction. This results in enhanced catalytic activity (reaction rate constant k = 0.14 min−1) in comparison with unsupported citrate-stabilized Ru NPs (Ru-CA) (k = 0.08 min−1). Additionally, comparatively lower activation energy over Ru@VLPs (approximately 32 kJ/mol) than that over Ru-CA (approximately 39 kJ/mol) could be attributed to the synergistic effect between Ru NPs and some functional groups such as amino groups (–NH2) on CPs that weakened the activation barrier of 4-NP reduction. Therefore, enhanced activity and decreased activation energy over Ru@VLPs demonstrated the superiority of Ru@VLPs to unsupported Ru-CA

HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models

Large language models (LLMs), such as ChatGPT, are prone to generate hallucinations, i.e., content that conflicts with the source or cannot be verified by the factual knowledge. To understand what types of content and to which extent LLMs are apt to hallucinate, we introduce the Hallucination Evaluation benchmark for Large Language Models (HaluEval), a large collection of generated and human-annotated hallucinated samples for evaluating the performance of LLMs in recognizing hallucination. To generate these samples, we propose a ChatGPT-based two-step framework, i.e., sampling-then-filtering. Besides, we also hire some human labelers to annotate the hallucinations in ChatGPT responses. The empirical results suggest that ChatGPT is likely to generate hallucinated content in specific topics by fabricating unverifiable information (i.e., about $19.5\%$ responses). Moreover, existing LLMs face great challenges in recognizing the hallucinations in texts. However, our experiments also prove that providing external knowledge or adding reasoning steps can help LLMs recognize hallucinations. Our benchmark can be accessed at https://github.com/RUCAIBox/HaluEval.Comment: Accepted to EMNLP 2023 Main Conference (Long Paper

Communication-Efficient Cooperative Multi-Agent PPO via Regulated Segment Mixture in Internet of Vehicles

Multi-Agent Reinforcement Learning (MARL) has become a classic paradigm to solve diverse, intelligent control tasks like autonomous driving in Internet of Vehicles (IoV). However, the widely assumed existence of a central node to implement centralized federated learning-assisted MARL might be impractical in highly dynamic scenarios, and the excessive communication overheads possibly overwhelm the IoV system. Therefore, in this paper, we design a communication efficient cooperative MARL algorithm, named RSM-MAPPO, to reduce the communication overheads in a fully distributed architecture. In particular, RSM-MAPPO enhances the multi-agent Proximal Policy Optimization (PPO) by incorporating the idea of segment mixture and augmenting multiple model replicas from received neighboring policy segments. Afterwards, RSM-MAPPO adopts a theory-guided metric to regulate the selection of contributive replicas to guarantee the policy improvement. Finally, extensive simulations in a mixed-autonomy traffic control scenario verify the effectiveness of the RSM-MAPPO algorithm

DPF: Learning Dense Prediction Fields with Weak Supervision

Nowadays, many visual scene understanding problems are addressed by dense prediction networks. But pixel-wise dense annotations are very expensive (e.g., for scene parsing) or impossible (e.g., for intrinsic image decomposition), motivating us to leverage cheap point-level weak supervision. However, existing pointly-supervised methods still use the same architecture designed for full supervision. In stark contrast to them, we propose a new paradigm that makes predictions for point coordinate queries, as inspired by the recent success of implicit representations, like distance or radiance fields. As such, the method is named as dense prediction fields (DPFs). DPFs generate expressive intermediate features for continuous sub-pixel locations, thus allowing outputs of an arbitrary resolution. DPFs are naturally compatible with point-level supervision. We showcase the effectiveness of DPFs using two substantially different tasks: high-level semantic parsing and low-level intrinsic image decomposition. In these two cases, supervision comes in the form of single-point semantic category and two-point relative reflectance, respectively. As benchmarked by three large-scale public datasets PASCALContext, ADE20K and IIW, DPFs set new state-of-the-art performance on all of them with significant margins. Code can be accessed at https://github.com/cxx226/DPF

Effects of Rock Fragments on the Soil Physicochemical Properties and Vegetation on the Northeastern Tibetan Plateau

Stony soils are very widely distributed and contain abundant rock fragments (>2 mm), which impose major effects on soil properties and plant growth. However, the role of rock fragments is still often neglected, which can lead to an inadequate understanding of the interaction between plants and soil. Undisturbed soil columns were collected from three alpine grasslands on the Qilian Mountain, and the X-ray computed tomography method was applied to investigate the characteristics of rock fragments. The results showed there was significant difference in number density, volumetric content and surface area density of rock fragment among the three grasslands, and followed the order of alpine meadow > alpine steppe > alpine desert steppe. In addition, the soil organic carbon, total nitrogen, total phosphorus, available phosphorus, N-NH4+, and N-NO3− contents in fine earth all increased with increasing number density, volumetric content and surface area density but to different degrees. Furthermore, positive correlations were observed between the rock shape factor and belowground biomass (R2 = 0.531, p < 0.05), between the rock volumetric content and aboveground biomass (R2 = 0.527, p < 0.05), and between number density and Simpson’s index (R2 = 0.875, p < 0.05). Our findings suggest that within a certain range, the increase in rock fragment content is conducive to soil nutrient accumulation and soil water storage and circulation and changes plant features, which contributes to the growth of plants. In addition, rock fragments should be given more consideration when investigating the relationships between soil and vegetation and their response to climate change in future studies

The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models

In the era of large language models (LLMs), hallucination (i.e., the tendency to generate factually incorrect content) poses great challenge to trustworthy and reliable deployment of LLMs in real-world applications. To tackle the LLM hallucination, three key questions should be well studied: how to detect hallucinations (detection), why do LLMs hallucinate (source), and what can be done to mitigate them (mitigation). To address these challenges, this work presents a systematic empirical study on LLM hallucination, focused on the the three aspects of hallucination detection, source and mitigation. Specially, we construct a new hallucination benchmark HaluEval 2.0, and designs a simple yet effective detection method for LLM hallucination. Furthermore, we zoom into the different training or utilization stages of LLMs and extensively analyze the potential factors that lead to the LLM hallucination. Finally, we implement and examine a series of widely used techniques to mitigate the hallucinations in LLMs. Our work has led to several important findings to understand the hallucination origin and mitigate the hallucinations in LLMs. Our code and data can be accessed at https://github.com/RUCAIBox/HaluEval-2.0.Comment: 24 pages, 8 figures, 13 table