The complementary contributions of academia and industry to AI research

Artificial intelligence (AI) has seen tremendous development in industry and academia. However, striking recent advances by industry have stunned the world, inviting a fresh perspective on the role of academic research in this field. Here, we characterize the impact and type of AI produced by both environments over the last 25 years and establish several patterns. We find that articles published by teams consisting exclusively of industry researchers tend to get greater attention, with a higher chance of being highly cited and citation-disruptive, and several times more likely to produce state-of-the-art models. In contrast, we find that exclusively academic teams publish the bulk of AI research and tend to produce higher novelty work, with single papers having several times higher likelihood of being unconventional and atypical. The respective impact-novelty advantages of industry and academia are robust to controls for subfield, team size, seniority, and prestige. We find that academic-industry collaborations struggle to replicate the novelty of academic teams and tend to look similar to industry teams. Together, our findings identify the unique and nearly irreplaceable contributions that both academia and industry make toward the healthy progress of AI.Comment: 28 pages, 7 figure

HeteFedRec: Federated Recommender Systems with Model Heterogeneity

Owing to the nature of privacy protection, federated recommender systems (FedRecs) have garnered increasing interest in the realm of on-device recommender systems. However, most existing FedRecs only allow participating clients to collaboratively train a recommendation model of the same public parameter size. Training a model of the same size for all clients can lead to suboptimal performance since clients possess varying resources. For example, clients with limited training data may prefer to train a smaller recommendation model to avoid excessive data consumption, while clients with sufficient data would benefit from a larger model to achieve higher recommendation accuracy. To address the above challenge, this paper introduces HeteFedRec, a novel FedRec framework that enables the assignment of personalized model sizes to participants. In HeteFedRec, we present a heterogeneous recommendation model aggregation strategy, including a unified dual-task learning mechanism and a dimensional decorrelation regularization, to allow knowledge aggregation among recommender models of different sizes. Additionally, a relation-based ensemble knowledge distillation method is proposed to effectively distil knowledge from heterogeneous item embeddings. Extensive experiments conducted on three real-world recommendation datasets demonstrate the effectiveness and efficiency of HeteFedRec in training federated recommender systems under heterogeneous settings

Lightweight Embeddings for Graph Collaborative Filtering

Graph neural networks (GNNs) are currently one of the most performant collaborative filtering methods. Meanwhile, owing to the use of an embedding table to represent each user/item as a distinct vector, GNN-based recommenders have inherited the long-standing defect of parameter inefficiency. As a common practice for scalable embeddings, parameter sharing enables the use of fewer embedding vectors (i.e., meta-embeddings). When assigning meta-embeddings, most existing methods are a heuristically designed, predefined mapping from each user's/item's ID to the corresponding meta-embedding indexes, thus simplifying the optimization problem into learning only the meta-embeddings. However, in the context of GNN-based collaborative filtering, such a fixed mapping omits the semantic correlations between entities that are evident in the user-item interaction graph, leading to suboptimal recommendation performance. To this end, we propose Lightweight Embeddings for Graph Collaborative Filtering (LEGCF), a parameter-efficient embedding framework dedicated to GNN-based recommenders. LEGCF innovatively introduces an assignment matrix as an extra learnable component on top of meta-embeddings. To jointly optimize these two heavily entangled components, aside from learning the meta-embeddings by minimizing the recommendation loss, LEGCF further performs efficient assignment update by enforcing a novel semantic similarity constraint and finding its closed-form solution based on matrix pseudo-inverse. The meta-embeddings and assignment matrix are alternately updated, where the latter is sparsified on the fly to ensure negligible storage overhead. Extensive experiments on three benchmark datasets have verified LEGCF's smallest trade-off between size and performance, with consistent accuracy gain over state-of-the-art baselines. The codebase of LEGCF is available in https://github.com/xurong-liang/LEGCF.Comment: Accepted by SIGIR '2

FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset

We present FaceVerse, a fine-grained 3D Neural Face Model, which is built from hybrid East Asian face datasets containing 60K fused RGB-D images and 2K high-fidelity 3D head scan models. A novel coarse-to-fine structure is proposed to take better advantage of our hybrid dataset. In the coarse module, we generate a base parametric model from large-scale RGB-D images, which is able to predict accurate rough 3D face models in different genders, ages, etc. Then in the fine module, a conditional StyleGAN architecture trained with high-fidelity scan models is introduced to enrich elaborate facial geometric and texture details. Note that different from previous methods, our base and detailed modules are both changeable, which enables an innovative application of adjusting both the basic attributes and the facial details of 3D face models. Furthermore, we propose a single-image fitting framework based on differentiable rendering. Rich experiments show that our method outperforms the state-of-the-art methods.Comment: https://github.com/LizhenWangT/FaceVers

Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation

Federated recommender systems (FedRecs) have gained significant attention for their potential to protect user's privacy by keeping user privacy data locally and only communicating model parameters/gradients to the server. Nevertheless, the currently existing architecture of FedRecs assumes that all users have the same 0-privacy budget, i.e., they do not upload any data to the server, thus overlooking those users who are less concerned about privacy and are willing to upload data to get a better recommendation service. To bridge this gap, this paper explores a user-governed data contribution federated recommendation architecture where users are free to take control of whether they share data and the proportion of data they share to the server. To this end, this paper presents a cloud-device collaborative graph neural network federated recommendation model, named CDCGNNFed. It trains user-centric ego graphs locally, and high-order graphs based on user-shared data in the server in a collaborative manner via contrastive learning. Furthermore, a graph mending strategy is utilized to predict missing links in the graph on the server, thus leveraging the capabilities of graph neural networks over high-order graphs. Extensive experiments were conducted on two public datasets, and the results demonstrate the effectiveness of the proposed method

Transcriptome Analysis Reveals a Comprehensive Insect Resistance Response Mechanism in Cotton to Infestation by the Phloem Feeding Insect Bemisia Tabaci (Whitefly)

The whitefly (Bemisia tabaci) causes tremendous damage to cotton production worldwide. However, very limited information is available about how plants perceive and defend themselves from this destructive pest. In this study, the transcriptomic differences between two cotton cultivars that exhibit either strong resistance (HR) or sensitivity (ZS) to whitefly were compared at different time points (0, 12, 24 and 48 h after infection) using RNA‐Seq. Approximately one billion paired‐end reads were obtained by Illumina sequencing technology. Gene ontology and KEGG pathway analysis indicated that the cotton transcriptional response to whitefly infestation involves genes encoding protein kinases, transcription factors, metabolite synthesis, and phytohormone signalling. Furthermore, a weighted gene co‐expression network constructed from RNA‐Seq datasets showed that WRKY40 and copper transport protein are hub genes that may regulate cotton defenses to whitefly infestation. Silencing GhMPK3 by virus‐induced gene silencing (VIGS) resulted in suppression of the MPK‐WRKY‐JA and ET pathways and lead to enhanced whitefly susceptibility, suggesting that the candidate insect resistant genes identified in this RNA‐Seq analysis are credible and offer significant utility. Taken together, this study provides comprehensive insights into the cotton defense system to whitefly infestation and has identified several candidate genes for control of phloem‐feeding pests

Effect of Spice Essential Oil Combinations on the Quality and Safety of Air-Dried Catfish Sausage

In order to explore the effects of different combinations of spice essential oils on the quality and safety of air-dried catfish sausages, sausages were prepared from marinated catfish surimi added with a 1:1 (m/m) blend of clove and star anise (CA), clove and perilla (CP), star anise and perilla (AP) or clove, star anise and perilla essential oil (CAP) at 0.03% or none as a control. Moisture content, water activity (aw), pH, color difference, thiobarbituric acid reactive substance (TBARS) value, biological amine content, N-nitrosamine content, and microbial load were measured. Microbial community structure was analyzed by 16S rDNA high-throughput sequencing. The results showed that the moisture content of the AP group was 27.13%, and the aw value was 0.765. The L* and a* values of the CA group were high, indicating typical characteristics of air-dried sausages. The pH of the four treatment groups was higher than that of the control group, and followed the descending order of CAP > AP > CA > CP. There was no significant difference in the inhibition of fat oxidation among different essential oil combinations (P > 0.05), but the TBARS value of air-dried sausages was significantly reduced by all combinations (P < 0.05). The contents of biogenic amine and N-nitrosamine in the AP group were low. The total number of bacteria, and the number of Enterobacteriaceae and Aeromonas were significantly lower in the AP group than in the control group (P < 0.05). High-throughput sequencing results showed low species richness in the AP and CAP groups and low relative abundance of pathogenic bacteria in the AP group. Overall analysis showed that AP was superior to the other groups in improving the quality and safety of air-dried catfish sausages

Characterization of Montmorillonite–Biochar Composite and Its Application in the Removal of Atrazine in Aqueous Solution and Soil

Atrazine is a widely used triazine herbicide, which poses a serious threat to human health and aquatic ecosystem. A montmorillonite–biochar composite (MMT/BC) was prepared for atrazine remediation. Biochar samples were characterized by using scanning electron microscope (SEM), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), and X-ray photoelectron spectrometer (XPS). Structural and morphological analysis of raw biochar (BC) and MMT/BC showed that MMT particles have been successfully coated on the surface of biochar. Sorption experiments in aqueous solution indicated that the MMT/BC has higher removal capacity of atrazine compared to BC (about 3.2 times). The sorption of atrazine on the MMT/BC was primarily controlled by both physisorption and chemisorption mechanisms. The amendment of MMT/BC increased the sorption capacity of soils and delayed the degradation of atrazine. Findings from this work indicate that the MMT/BC composite can effectively improve the sorption capacity of atrazine in aquatic environment and farmland soil and reduce the environmental risk.Characterization of Montmorillonite–Biochar Composite and Its Application in the Removal of Atrazine in Aqueous Solution and SoilpublishedVersio

Memory-enhancing effect of Rhodiola rosea L extract on aged mice

Purpose: The memory-enhancing effects of Rhodiola rosea L. extract (RRLE) on normal aged mice were assessed.Methods: In the open-field test, the effect of RRLE (150 and 300 mg/kg) on mouse locomotive activities was evaluated by investigating the extract’s influence on CAT and AchE activities in the brain tissue of mice.Results: Compared with aged group, high dose of RRLE reduced the total distance (3212.4 ± 123.1 cm, p &lt; 0.05) significantly, increased catalase (CAT) activity (101.4 ± 12.2 U/mg pro, p &lt; 0.05), and inhibited acetyl cholinesterase (AChE) activity (0.94 ± 0.12 U/mg pro, p &lt; 0.05) in the brain tissue of aged mice.Conclusion: The results show that RRLE improves the memory functions of aged mice probably by increasing CAT activity while decreasing AChE activity.Keywords: Rhodiola rosea, Memory function, Catalase, Acetyl cholinesterase, Open-field tes