Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss

Contrastive Learning (CL) has achieved impressive performance in self-supervised learning tasks, showing superior generalization ability. Inspired by the success, adopting CL into collaborative filtering (CF) is prevailing in semi-supervised top-K recommendations. The basic idea is to routinely conduct heuristic-based data augmentation and apply contrastive losses (e.g., InfoNCE) on the augmented views. Yet, some CF-tailored challenges make this adoption suboptimal, such as the issue of out-of-distribution, the risk of false negatives, and the nature of top-K evaluation. They necessitate the CL-based CF scheme to focus more on mining hard negatives and distinguishing false negatives from the vast unlabeled user-item interactions, for informative contrast signals. Worse still, there is limited understanding of contrastive loss in CF methods, especially w.r.t. its generalization ability. To bridge the gap, we delve into the reasons underpinning the success of contrastive loss in CF, and propose a principled Adversarial InfoNCE loss (AdvInfoNCE), which is a variant of InfoNCE, specially tailored for CF methods. AdvInfoNCE adaptively explores and assigns hardness to each negative instance in an adversarial fashion and further utilizes a fine-grained hardness-aware ranking criterion to empower the recommender's generalization ability. Training CF models with AdvInfoNCE, we validate the effectiveness of AdvInfoNCE on both synthetic and real-world benchmark datasets, thus showing its generalization ability to mitigate out-of-distribution problems. Given the theoretical guarantees and empirical superiority of AdvInfoNCE over most contrastive loss functions, we advocate its adoption as a standard loss in recommender systems, particularly for the out-of-distribution tasks. Codes are available at https://github.com/LehengTHU/AdvInfoNCE.Comment: Accepted to NeurIPS 202

Video Super-Resolution Transformer with Masked Inter&Intra-Frame Attention

Recently, Vision Transformer has achieved great success in recovering missing details in low-resolution sequences, i.e., the video super-resolution (VSR) task. Despite its superiority in VSR accuracy, the heavy computational burden as well as the large memory footprint hinder the deployment of Transformer-based VSR models on constrained devices. In this paper, we address the above issue by proposing a novel feature-level masked processing framework: VSR with Masked Intra and inter frame Attention (MIA-VSR). The core of MIA-VSR is leveraging feature-level temporal continuity between adjacent frames to reduce redundant computations and make more rational use of previously enhanced SR features. Concretely, we propose an intra-frame and inter-frame attention block which takes the respective roles of past features and input features into consideration and only exploits previously enhanced features to provide supplementary information. In addition, an adaptive block-wise mask prediction module is developed to skip unimportant computations according to feature similarity between adjacent frames. We conduct detailed ablation studies to validate our contributions and compare the proposed method with recent state-of-the-art VSR approaches. The experimental results demonstrate that MIA-VSR improves the memory and computation efficiency over state-of-the-art methods, without trading off PSNR accuracy. The code is available at https://github.com/LabShuHangGU/MIA-VSR.Comment: Accepted by CVPR 202

Diagnosis and segmentation effect of the ME-NBI-based deep learning model on gastric neoplasms in patients with suspected superficial lesions - a multicenter study

BackgroundEndoscopically visible gastric neoplastic lesions (GNLs), including early gastric cancer and intraepithelial neoplasia, should be accurately diagnosed and promptly treated. However, a high rate of missed diagnosis of GNLs contributes to the potential risk of the progression of gastric cancer. The aim of this study was to develop a deep learning-based computer-aided diagnosis (CAD) system for the diagnosis and segmentation of GNLs under magnifying endoscopy with narrow-band imaging (ME-NBI) in patients with suspected superficial lesions.MethodsME-NBI images of patients with GNLs in two centers were retrospectively analysed. Two convolutional neural network (CNN) modules were developed and trained on these images. CNN1 was trained to diagnose GNLs, and CNN2 was trained for segmentation. An additional internal test set and an external test set from another center were used to evaluate the diagnosis and segmentation performance.ResultsCNN1 showed a diagnostic performance with an accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 90.8%, 92.5%, 89.0%, 89.4% and 92.2%, respectively, and an area under the curve (AUC) of 0.928 in the internal test set. With CNN1 assistance, all endoscopists had a higher accuracy than for an independent diagnosis. The average intersection over union (IOU) between CNN2 and the ground truth was 0.5837, with a precision, recall and the Dice coefficient of 0.776, 0.983 and 0.867, respectively.ConclusionsThis CAD system can be used as an auxiliary tool to diagnose and segment GNLs, assisting endoscopists in more accurately diagnosing GNLs and delineating their extent to improve the positive rate of lesion biopsy and ensure the integrity of endoscopic resection

Control of a Wheelchair-Mounted 6DOF Assistive Robot With Chin and Finger Joysticks

Throughout the last decade, many assistive robots for people with disabilities have been developed; however, researchers have not fully utilized these robotic technologies to entirely create independent living conditions for people with disabilities, particularly in relation to activities of daily living (ADLs). An assistive system can help satisfy the demands of regular ADLs for people with disabilities. With an increasing shortage of caregivers and a growing number of individuals with impairments and the elderly, assistive robots can help meet future healthcare demands. One of the critical aspects of designing these assistive devices is to improve functional independence while providing an excellent human–machine interface. People with limited upper limb function due to stroke, spinal cord injury, cerebral palsy, amyotrophic lateral sclerosis, and other conditions find the controls of assistive devices such as power wheelchairs difficult to use. Thus, the objective of this research was to design a multimodal control method for robotic self-assistance that could assist individuals with disabilities in performing self-care tasks on a daily basis. In this research, a control framework for two interchangeable operating modes with a finger joystick and a chin joystick is developed where joysticks seamlessly control a wheelchair and a wheelchair-mounted robotic arm. Custom circuitry was developed to complete the control architecture. A user study was conducted to test the robotic system. Ten healthy individuals agreed to perform three tasks using both (chin and finger) joysticks for a total of six tasks with 10 repetitions each. The control method has been tested rigorously, maneuvering the robot at different velocities and under varying payload (1–3.5 lb) conditions. The absolute position accuracy was experimentally found to be approximately 5 mm. The round-trip delay we observed between the commands while controlling the xArm was 4 ms. Tests performed showed that the proposed control system allowed individuals to perform some ADLs such as picking up and placing items with a completion time of less than 1 min for each task and 100% success

Experimental Study on the Comparison between Network Microstructure Titanium Matrix Composites and Ti6Al4V on EDM Milling

Network microstructure titanium matrix composites (NMTMCs), featuring Ti6Al4V as the matrix and network-distributed TiB whiskers (TiBw) as reinforcement, exhibit remarkable potential for diverse applications due to their superior physical properties. Due to the difficulty in machining titanium matrix composites, electrical discharge machining (EDM) stands as one of the preferred machining techniques for NMTMCs. Nevertheless, the compromised surface quality and the recast layer significantly impact the performance of the workpiece machined by EDM. Therefore, for the purpose of enhancing the surface quality and restraining the defects of NMTMCs, this study conducted comparative EDM milling experiments between NMTMCs and Ti6Al4V to analyze the effects of discharge capacitance, charging current, and pulse interval on the surface roughness, recast layer thickness, recast layer uniformity, and surface microcrack density of both materials. The results indicated that machining energy significantly influences workpiece surface quality. Furthermore, comparative experiments exploring the influence of network reinforcement on EDM milling revealed that NMTMCs have a higher melting point, leading to an accumulation phenomenon in low-energy machining where the reinforcement could not be completely removed. The residual reinforcement in the recasting layer had an adsorption effect on molten metal affecting the thermal conductivity and uniformity within the recasting layer. Finally, specific guidelines are put forward for optimizing the material’s surface roughness, recast layer thickness, and uniformity, along with minimizing microcrack density, which attain a processing effect that features a roughness of Ra 0.9 μm, an average recast layer thickness of 6 μm with a range of 8 μm, and a surface microcrack density of 0.08 μm−1

Coordinated scheduling of intercell production and intercell transportation in the equipment manufacturing industry

<p>Intercell moves are caused by exceptional parts which need to be processed in multiple cells. Intercell cooperation disrupts the cellular manufacturing philosophy of creating independent cells, but is essential to lower the costs for enterprises. This article addresses an intercell scheduling problem considering limited transportation capability. To solve this problem, a two-stage ant colony optimization approach is proposed, in which pre-scheduling and re-scheduling are performed sequentially. To evaluate and optimize the interaction of production and transportation, a transportation benefit function is presented, according to which the scheduling solutions are adjusted. The computational results show that the transportation benefit function is more effective than other strategies, and the proposed approach has significant advantages over CPLEX in both the production dimension and the transportation dimension.</p

Flexible Image Reconstruction in the Orbital Angular Momentum Holography with Binarized Airy Lens

The orbital angular momentum (OAM) holography has been marked a path to achieving ultrahigh capacity holographic information systems. However, the practical applicability of the OAM holography is limited by the complicated optical setup and unadjustable image intensity and position. Here, a decoding method is proposed by using a binarized phase map derived from an autofocusing Airy beam. By adjusting the parameters of the phase map, the position and intensity distribution of the reconstructed image become flexibly adjustable. In addition, the cross-talk between different image channels can be effectively reduced thanks to the abruptly autofocusing capability of the Airy beams. As a result, the quality and practicability of the OAM holography can be greatly enhanced