326 research outputs found
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
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