Adversarial erasing attention for person re-identification in camera networks under complex environments

Person re-identification (Re-ID) in camera networks under complex environments has achieved promising performance using deep feature representations. However, most approaches usually ignore to learn features from non-salient parts of pedestrian, which results in an incomplete pedestrian representation. In this paper, we propose a novel person Re-ID method named Adversarial Erasing Attention (AEA) to mine discriminative completed features using an adversarial way. Specifically, the proposed AEA consists of the basic network and the complementary network. On the one hand, original pedestrian images are used to train the basic network in order to extract global and local deep features. On the other hand, to learn features complementary to the basic network, we propose the adversarial erasing operation, that locates non-salient areas with the help of attention map, to generate erased pedestrian images. Then, we utilize them to train the complementary network and adopt the dynamic strategy to match the dynamic status of AEA in the learning process. Hence, the diversity of training samples is enriched and the complementary network could discover new clues when learning deep features. Finally, we combine the features learned from the basic and complementary networks to represent the pedestrian image. Experiments on three databases (Market1501, CUHK03 and DukeMTMC-reID) demonstrate the proposed AEA achieves great performances

Graph-based Alignment and Uniformity for Recommendation

Collaborative filtering-based recommender systems (RecSys) rely on learning representations for users and items to predict preferences accurately. Representation learning on the hypersphere is a promising approach due to its desirable properties, such as alignment and uniformity. However, the sparsity issue arises when it encounters RecSys. To address this issue, we propose a novel approach, graph-based alignment and uniformity (GraphAU), that explicitly considers high-order connectivities in the user-item bipartite graph. GraphAU aligns the user/item embedding to the dense vector representations of high-order neighbors using a neighborhood aggregator, eliminating the need to compute the burdensome alignment to high-order neighborhoods individually. To address the discrepancy in alignment losses, GraphAU includes a layer-wise alignment pooling module to integrate alignment losses layer-wise. Experiments on four datasets show that GraphAU significantly alleviates the sparsity issue and achieves state-of-the-art performance. We open-source GraphAU at https://github.com/YangLiangwei/GraphAU.Comment: 4 page

Contextual Collaboration: Uniting Collaborative Filtering with Pre-trained Language Models

Traditional recommender systems have predominantly relied on identity representations (IDs) to characterize users and items. In contrast, the emergence of pre-trained language model (PLM) en-coders has significantly enriched the modeling of contextual item descriptions. While PLMs excel in addressing few-shot, zero-shot, and unified modeling scenarios, they often overlook the critical collaborative filtering signal. This omission gives rise to two pivotal challenges: (1) Collaborative Contextualization, aiming for the seamless integration of collaborative signals with contextual representations. (2) The necessity to bridge the representation gap between ID-based and contextual representations while preserving their contextual semantics. In this paper, we introduce CollabContext, a novel model that skillfully merges collaborative filtering signals with contextual representations, aligning these representations within the contextual space while retaining essential contextual semantics. Experimental results across three real-world datasets showcase substantial improvements. Through its capability in collaborative contextualization, CollabContext demonstrates remarkable enhancements in recommendation performance, particularly in cold-start scenarios. The code is available after the conference accepts the paper

Group Identification via Transitional Hypergraph Convolution with Cross-view Self-supervised Learning

With the proliferation of social media, a growing number of users search for and join group activities in their daily life. This develops a need for the study on the group identification (GI) task, i.e., recommending groups to users. The major challenge in this task is how to predict users' preferences for groups based on not only previous group participation of users but also users' interests in items. Although recent developments in Graph Neural Networks (GNNs) accomplish embedding multiple types of objects in graph-based recommender systems, they, however, fail to address this GI problem comprehensively. In this paper, we propose a novel framework named Group Identification via Transitional Hypergraph Convolution with Graph Self-supervised Learning (GTGS). We devise a novel transitional hypergraph convolution layer to leverage users' preferences for items as prior knowledge when seeking their group preferences. To construct comprehensive user/group representations for GI task, we design the cross-view self-supervised learning to encourage the intrinsic consistency between item and group preferences for each user, and the group-based regularization to enhance the distinction among group embeddings. Experimental results on three benchmark datasets verify the superiority of GTGS. Additional detailed investigations are conducted to demonstrate the effectiveness of the proposed framework.Comment: 11 pages. Accepted by CIKM'2

Urolithiasis location and size and the association with microhematuria and stone-related symptoms.

PURPOSE: To conduct a study to assess the association between calculus location and size and the incidence of both microhematuria and symptoms of urolithiasis in a urology office environment. PATIENTS AND METHODS: After Institutional Review Board approval, a prospective study was conducted with data from 100 consecutive patients who presented to our office with documented urolithiasis. The location (caliceal, pelvic, or ureteral) and size ( RESULTS: A total of 111 stones were found in the study population resulting in a 45.9% incidence of microhematuria. In patients with renal pelvic and ureteral stones, 67.6% demonstrated microhematuria vs 36.4% with caliceal stones, P=0.0035. For stones ≥ 8 mm, 62.5% were positive for microhematuria vs 29.1% of stones \u3c8 \u3emm, P=0.0006. Ureteral or renal pelvic stones caused the most symptoms (70.6%) compared with caliceal stones (16.9%), P=0.0001. In those patients who reported pain associated with urolithiasis, 65.6% had concomitant microhematuria vs 36.8% in those without pain, P=0.0097. CONCLUSIONS: Urinary calculus location and size are associated with the incidence of microhematuria and stone-related symptoms. Pain related to urolithiasis may be a positive predictor for the presence of microhematuria

Robotic-assistance does not enhance standard laparoscopic technique for right-sided donor nephrectomy.

OBJECTIVE: To examine donor and recipient outcomes after right-sided robotic-assisted laparoscopic donor nephrectomy (RALDN) compared with standard laparoscopic donor nephrectomy (LDN) and to determine whether robotic-assistance enhances LDN. MATERIALS & METHODS: From December 2005 to January 2011, 25 patients underwent right-sided LDN or RALDN. An IRB-approved retrospective review was performed of both donor and recipient medical charts. Primary endpoints included both intraoperative and postoperative outcomes. RESULTS: Twenty right-sided LDNs and 5 RALDNs were performed during the study period. Neither estimated blood loss (76.4 mL vs. 30 mL, P = .07) nor operative time (231 min vs. 218 min, P = .61) were significantly different between either group (LDN vs. RALDN). Warm ischemia time for LDN was 2.6 min vs. 3.8 min for RALDN (P = .44). Donor postoperative serum estimated glomerular filtration rates (eGFR) were similar (53 vs. 59.6 mL/min/1.73 m2, LDN vs. RALDN, P = .26). For the recipient patients, posttransplant eGFR were similar at 6 months (53.4 vs. 59.8 mL/min/1.73 m2, LDN vs. RALDN, P = .53). CONCLUSION: In this study, robotic-assistance did not improve outcomes associated with LDN. Larger prospective studies are needed to confirm any perceived benefit of RALDN

Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering

Collaborative filtering (CF) is a widely employed technique that predicts user preferences based on past interactions. Negative sampling plays a vital role in training CF-based models with implicit feedback. In this paper, we propose a novel perspective based on the sampling area to revisit existing sampling methods. We point out that current sampling methods mainly focus on Point-wise or Line-wise sampling, lacking flexibility and leaving a significant portion of the hard sampling area un-explored. To address this limitation, we propose Dimension Independent Mixup for Hard Negative Sampling (DINS), which is the first Area-wise sampling method for training CF-based models. DINS comprises three modules: Hard Boundary Definition, Dimension Independent Mixup, and Multi-hop Pooling. Experiments with real-world datasets on both matrix factorization and graph-based models demonstrate that DINS outperforms other negative sampling methods, establishing its effectiveness and superiority. Our work contributes a new perspective, introduces Area-wise sampling, and presents DINS as a novel approach that achieves state-of-the-art performance for negative sampling. Our implementations are available in PyTorch

DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation

Graph Neural Network (GNN) based recommender systems have been attracting more and more attention in recent years due to their excellent performance in accuracy. Representing user-item interactions as a bipartite graph, a GNN model generates user and item representations by aggregating embeddings of their neighbors. However, such an aggregation procedure often accumulates information purely based on the graph structure, overlooking the redundancy of the aggregated neighbors and resulting in poor diversity of the recommended list. In this paper, we propose diversifying GNN-based recommender systems by directly improving the embedding generation procedure. Particularly, we utilize the following three modules: submodular neighbor selection to find a subset of diverse neighbors to aggregate for each GNN node, layer attention to assign attention weights for each layer, and loss reweighting to focus on the learning of items belonging to long-tail categories. Blending the three modules into GNN, we present DGRec(Diversified GNN-based Recommender System) for diversified recommendation. Experiments on real-world datasets demonstrate that the proposed method can achieve the best diversity while keeping the accuracy comparable to state-of-the-art GNN-based recommender systems.Comment: 9 pages, WSDM 202

Hierarchical rose-petal surfaces delay the early-stage bacterial biofilm growth

A variety of natural surfaces exhibit antibacterial properties; as a result significant efforts in the past decade have been dedicated towards fabrication of biomimetic surfaces that can help control biofilm growth. Examples of such surfaces include rose petals, which possess hierarchical structures like the micro-papillae measuring tens of microns and nano-folds that range in the size of 700 ±100 nm. We duplicated the natural structures on rose-petal surfaces via a simple UV-curable nanocasting technique, and tested the efficacy of these artificial surfaces in preventing biofilm growth using clinically relevant bacteria strains. The rose-petal structured surfaces exhibited hydrophobicity (contact angle~130.8º ±4.3º) and high contact angle hysteresis (~91.0° ±4.9°). Water droplets on rose-petal replicas evaporated following the constant contact line mode, indicating the likely coexistence of both Cassie and Wenzel states (Cassie-Baxter impregnating wetting state). Fluorescent microscopy and image analysis revealed the significantly lower attachment of Staphylococcus epidermidis (86.1± 6.2% less) and Pseudomonas aeruginosa (85.9 ±3.2% less) on the rose-petal structured surfaces, compared with flat surfaces over a period of 2 hours. Extensive biofilm matrix was observed in biofilms formed by both species on flat surfaces after prolonged growth (several days), but was less apparent on rose-petal biomimetic surfaces. In addition, the biomass of S. epidermidis (63.2 ±9.4% less) and P. aeruginosa (76.0 ±10.0% less) biofilms were significantly reduced on the rose-petal structured surfaces, in comparison to the flat surfaces. By comparing P. aeruginosa growth on representative unitary nano-pillars, we demonstrated that hierarchical structures are more effective in delaying biofilm growth. The mechanisms are two-fold: 1) the nano-folds across the hemispherical micro-papillae restrict initial attachment of bacterial cells and delay the direct contacts of cells via cell alignment, and 2) the hemispherical micro-papillae arrays isolate bacterial clusters and inhibit the formation of a fibrous network. The hierarchical features on rose petal surfaces may be useful for developing strategies to control biofilm formation in medical and industrial contexts