Adversarial Training Towards Robust Multimedia Recommender System

With the prevalence of multimedia content on the Web, developing recommender solutions that can effectively leverage the rich signal in multimedia data is in urgent need. Owing to the success of deep neural networks in representation learning, recent advance on multimedia recommendation has largely focused on exploring deep learning methods to improve the recommendation accuracy. To date, however, there has been little effort to investigate the robustness of multimedia representation and its impact on the performance of multimedia recommendation. In this paper, we shed light on the robustness of multimedia recommender system. Using the state-of-the-art recommendation framework and deep image features, we demonstrate that the overall system is not robust, such that a small (but purposeful) perturbation on the input image will severely decrease the recommendation accuracy. This implies the possible weakness of multimedia recommender system in predicting user preference, and more importantly, the potential of improvement by enhancing its robustness. To this end, we propose a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning. The idea is to train the model to defend an adversary, which adds perturbations to the target image with the purpose of decreasing the model's accuracy. We conduct experiments on two representative multimedia recommendation tasks, namely, image recommendation and visually-aware product recommendation. Extensive results verify the positive effect of adversarial learning and demonstrate the effectiveness of our AMR method. Source codes are available in https://github.com/duxy-me/AMR.Comment: TKD

Perturbation-Tolerant Structural Controllability for Linear Systems

This paper proposes a novel notion named perturbation-tolerant structural controllability (PTSC) to study controllability preservation for a structured linear system under structured perturbations. To be precise, we consider a structured system whose entries can be classified into three categories: fixed zero entries, unknown generic entries whose values are fixed but unknown, and perturbed entries that can take arbitrary complex values. Such a system is PTSC if, for almost all values of the unknown generic entries in the parameter space, the corresponding controllable system realizations can preserve controllability under arbitrary complex-valued perturbations with their zero/nonzero structure prescribed by the perturbed entries. It is proven genericity exists in this notion, that is, depending on the structure of the structured system, for almost all of its controllable realizations, either there exists an addable structured perturbation prescribed by the perturbed entries so that the resulting system is uncontrollable, or there is not such a perturbation. We give a decomposition-based necessary and sufficient condition for a single-input linear system, ensuring PTSC, whose verification has polynomial time complexity; we then present some intuitive graph-theoretic conditions for PTSC. For the multi-input case, we provide some necessary conditions for PTSC. As an application, our results can serve as some feasibility conditions for the conventional structured controllability radius problems from a generic view.Comment: Fix some typos. arXiv admin note: substantial text overlap with arXiv:2103.1190

Resolution-Enhanced All-Optical Analog-to-Digital Converter Employing Cascade Optical Quantization Operation

In this paper, a cascade optical quantization scheme is proposed to realize all-optical analog-to-digital converter with efficiently enhanced quantization resolution and achievable high analog bandwidth of larger than 20 GHz. Employing the cascade structure of an unbalanced Mach-zehnder modulator and a specially designed optical directional coupler, we predict the enhancement of number-of-bits can be up to 1.59-bit. Simulation results show that a 25 GHz RF signal is efficiently digitalized with the signal-tonoise ratio of 33.58 dB and effective-number-of-bits of 5.28-bit

OCCL: a Deadlock-free Library for GPU Collective Communication

Various distributed deep neural network (DNN) training technologies lead to increasingly complicated use of collective communications on GPU. The deadlock-prone collectives on GPU force researchers to guarantee that collectives are enqueued in a consistent order on each GPU to prevent deadlocks. In complex distributed DNN training scenarios, manual hardcoding is the only practical way for deadlock prevention, which poses significant challenges to the development of artificial intelligence. This paper presents OCCL, which is, to the best of our knowledge, the first deadlock-free collective communication library for GPU supporting dynamic decentralized preemption and gang-scheduling for collectives. Leveraging the preemption opportunity of collectives on GPU, OCCL dynamically preempts collectives in a decentralized way via the deadlock-free collective execution framework and allows dynamic decentralized gang-scheduling via the stickiness adjustment scheme. With the help of OCCL, researchers no longer have to struggle to get all GPUs to launch collectives in a consistent order to prevent deadlocks. We implement OCCL with several optimizations and integrate OCCL with a distributed deep learning framework OneFlow. Experimental results demonstrate that OCCL achieves comparable or better latency and bandwidth for collectives compared to NCCL, the state-of-the-art. When used in distributed DNN training, OCCL can improve the peak training throughput by up to 78% compared to statically sequenced NCCL, while introducing overheads of less than 6.5% across various distributed DNN training approaches

Global Structure-Aware Diffusion Process for Low-Light Image Enhancement

This paper studies a diffusion-based framework to address the low-light image enhancement problem. To harness the capabilities of diffusion models, we delve into this intricate process and advocate for the regularization of its inherent ODE-trajectory. To be specific, inspired by the recent research that low curvature ODE-trajectory results in a stable and effective diffusion process, we formulate a curvature regularization term anchored in the intrinsic non-local structures of image data, i.e., global structure-aware regularization, which gradually facilitates the preservation of complicated details and the augmentation of contrast during the diffusion process. This incorporation mitigates the adverse effects of noise and artifacts resulting from the diffusion process, leading to a more precise and flexible enhancement. To additionally promote learning in challenging regions, we introduce an uncertainty-guided regularization technique, which wisely relaxes constraints on the most extreme regions of the image. Experimental evaluations reveal that the proposed diffusion-based framework, complemented by rank-informed regularization, attains distinguished performance in low-light enhancement. The outcomes indicate substantial advancements in image quality, noise suppression, and contrast amplification in comparison with state-of-the-art methods. We believe this innovative approach will stimulate further exploration and advancement in low-light image processing, with potential implications for other applications of diffusion models. The code is publicly available at https://github.com/jinnh/GSAD.Comment: Accepted to NeurIPS 202

In vitro and in vivo antitumor properties of 7-epidocetaxel, a major impurity of docetaxel

Purpose: To investigate the antitumor properties and toxicity of 7-epi docetaxel (7-epi DTX) as an active pharmaceutical ingredient, and in formulations.Methods: Docetaxel-loaded albumin nanoparticles (DTX NPs) were prepared by freeze-drying, while 7- epi DTX was detected and isolated by high performance liquid chromatography (HPLC). Their antitumor properties were evaluated in vitro in CT26 cells and in vivo in BALB/c sk-ov-3 xenograft nude mice model. The tissues were histological examined.Results: The in vivo antitumor effects of DTX NPs at different doses of 7-epi DTX were similar. Moreover, the in vitro anti-cancer effect of 7-epi DTX was comparable to that of DTX. However, the in vivo antitumor effectiveness of 7-epi DTX was inferior to that of DTX. In toxicity studies, 7-epi DTX did not elicit any acute toxic effects both as active pharmaceutical ingredients, and as a component of formulations.Conclusion: The results indicate that 7-epi DTX does not elicit acute toxic effects both as an active pharmaceutical ingredient and in bulk formulations. The antitumor property of 7-epi DTX is less than that of DTX.Keywords: 7-Epidocetaxel, Impurity, Antitumor properties, Toxicit

Novel Microfiber Sensor and Its Biosensing Application for Detection of hCG Based on a Singlemode-Tapered Hollow Core-Singlemode Fiber Structure

A novel microfiber sensor is proposed and demonstrated based on a singlemode-tapered hollow core -singlemode (STHS) fiber structure. Experimentally a STHS with taper waist diameter of 26.5 μm has been fabricated and RI sensitivity of 816, 1601.86, and 4775.5 nm/RIU has been achieved with RI ranges from 1.3335 to 1.3395 , from 1.369 to 1.378, and from 1.409 to 1.4175 respectively, which agrees very well with simulated RI sensitivity of 885, 1517, and 4540 nm/RIU at RI ranges from 1.3335 to 1.337, from 1.37 to 1.374, and from 1.41 to 1.414 . The taper waist diameter has impact on both temperature and strain sensitivity of the sensor structure: (1) the smaller the waist diameter, the higher the temperature sensitivity, and experimentally 26.82 pm/°C has been achieved with a taper waist diameter of 21.4 μm; (2) as waist diameter decrease, strain sensitivity increase and 7.62 pm/με has been achieved with a taper diameter of 20.3 μm. The developed sensor was then functionalized for human chorionic gonadotropin (hCG) detection as an example for biosensing application. Experimentally for hCG concentration of 5 mIU/ml, the sensor has 0.5 nm wavelength shift, equivalent to limit of detection (LOD) of 0.6 mIU/ml by defining 3 times of the wavelength variation (0.06 nm) as measurement limit. The biosensor demonstrated relatively good reproducibility and specificity, which has potential for real medical diagnostics and other applications