Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users

Static recommendation methods like collaborative filtering suffer from the inherent limitation of performing real-time personalization for cold-start users. Online recommendation, e.g., multi-armed bandit approach, addresses this limitation by interactively exploring user preference online and pursuing the exploration-exploitation (EE) trade-off. However, existing bandit-based methods model recommendation actions homogeneously. Specifically, they only consider the items as the arms, being incapable of handling the item attributes, which naturally provide interpretable information of user's current demands and can effectively filter out undesired items. In this work, we consider the conversational recommendation for cold-start users, where a system can both ask the attributes from and recommend items to a user interactively. This important scenario was studied in a recent work. However, it employs a hand-crafted function to decide when to ask attributes or make recommendations. Such separate modeling of attributes and items makes the effectiveness of the system highly rely on the choice of the hand-crafted function, thus introducing fragility to the system. To address this limitation, we seamlessly unify attributes and items in the same arm space and achieve their EE trade-offs automatically using the framework of Thompson Sampling. Our Conversational Thompson Sampling (ConTS) model holistically solves all questions in conversational recommendation by choosing the arm with the maximal reward to play. Extensive experiments on three benchmark datasets show that ConTS outperforms the state-of-the-art methods Conversational UCB (ConUCB) and Estimation-Action-Reflection model in both metrics of success rate and average number of conversation turns.Comment: TOIS 202

Simulation of droplet impacting a square solid obstacle in microchannel with different wettability by using high density ratio pseudopotential multiplerelaxation- time (MRT) lattice Boltzmann Method (LBM)

In this paper, a pseudopotential high density ratio (DR) lattice Boltzmann Model was developed by incorporating multi-relaxation-time (MRT) collision matrix, large DR external force term, surface tension adjustment external force term and solid-liquid pseudopotential force. It was found that the improved model can precisely capture the two-phase interface at high DR. Besides, the effects of initial Reynolds number, Weber number, solid wall contact angle (CA), ratio of obstacle size to droplet diameter ( 1 χ ), ratio of channel width to droplet diameter ( 2 χ ) on the deformation and breakup of droplet when impacting on a square obstacle were investigated. The results showed that with the Reynolds number increasing, the droplet will fall along the obstacle and then spread along both sides of the obstacle. Besides, by increasing Weber number, the breakup of the liquid film will be delayed and the liquid film will be stretched to form an elongated ligament. With decreasing of the wettability of solid particle (CA→ 180°), the droplet will surround the obstacle and then detach from the obstacle. When 1 χ is greater than 0.5, the droplet will spread along both sides of the obstacle quickly; otherwise, the droplet will be ruptured earlier. Furthermore, when 2 χ decreases, the droplet will spread earlier and then fall along the wall more quickly; otherwise, the droplet will expand along both sides of the obstacle. Moreover, increasing the hydrophilicity of the microchannel, the droplet will impact the channel more rapidly and infiltrate the wall along the upstream and downstream simultaneously; on the contrary, the droplet will wet downstream only

Exploring Decision-based Black-box Attacks on Face Forgery Detection

Face forgery generation technologies generate vivid faces, which have raised public concerns about security and privacy. Many intelligent systems, such as electronic payment and identity verification, rely on face forgery detection. Although face forgery detection has successfully distinguished fake faces, recent studies have demonstrated that face forgery detectors are very vulnerable to adversarial examples. Meanwhile, existing attacks rely on network architectures or training datasets instead of the predicted labels, which leads to a gap in attacking deployed applications. To narrow this gap, we first explore the decision-based attacks on face forgery detection. However, applying existing decision-based attacks directly suffers from perturbation initialization failure and low image quality. First, we propose cross-task perturbation to handle initialization failures by utilizing the high correlation of face features on different tasks. Then, inspired by using frequency cues by face forgery detection, we propose the frequency decision-based attack. We add perturbations in the frequency domain and then constrain the visual quality in the spatial domain. Finally, extensive experiments demonstrate that our method achieves state-of-the-art attack performance on FaceForensics++, CelebDF, and industrial APIs, with high query efficiency and guaranteed image quality. Further, the fake faces by our method can pass face forgery detection and face recognition, which exposes the security problems of face forgery detectors

Content-based Unrestricted Adversarial Attack

Unrestricted adversarial attacks typically manipulate the semantic content of an image (e.g., color or texture) to create adversarial examples that are both effective and photorealistic, demonstrating their ability to deceive human perception and deep neural networks with stealth and success. However, current works usually sacrifice unrestricted degrees and subjectively select some image content to guarantee the photorealism of unrestricted adversarial examples, which limits its attack performance. To ensure the photorealism of adversarial examples and boost attack performance, we propose a novel unrestricted attack framework called Content-based Unrestricted Adversarial Attack. By leveraging a low-dimensional manifold that represents natural images, we map the images onto the manifold and optimize them along its adversarial direction. Therefore, within this framework, we implement Adversarial Content Attack based on Stable Diffusion and can generate high transferable unrestricted adversarial examples with various adversarial contents. Extensive experimentation and visualization demonstrate the efficacy of ACA, particularly in surpassing state-of-the-art attacks by an average of 13.3-50.4% and 16.8-48.0% in normally trained models and defense methods, respectively

An Efficient Alternating Riemannian/Projected Gradient Descent Ascent Algorithm for Fair Principal Component Analysis

Fair principal component analysis (FPCA), a ubiquitous dimensionality reduction technique in signal processing and machine learning, aims to find a low-dimensional representation for a high-dimensional dataset in view of fairness. The FPCA problem involves optimizing a non-convex and non-smooth function over the Stiefel manifold. The state-of-the-art methods for solving the problem are subgradient methods and semidefinite relaxation-based methods. However, these two types of methods have their obvious limitations and thus are only suitable for efficiently solving the FPCA problem in special scenarios. This paper aims at developing efficient algorithms for solving the FPCA problem in general, especially large-scale, settings. In this paper, we first transform FPCA into a smooth non-convex linear minimax optimization problem over the Stiefel manifold. To solve the above general problem, we propose an efficient alternating Riemannian/projected gradient descent ascent (ARPGDA) algorithm, which performs a Riemannian gradient descent step and an ordinary projected gradient ascent step at each iteration. We prove that ARPGDA can find an $\varepsilon$-stationary point of the above problem within $\mathcal{O}(\varepsilon^{-3})$ iterations. Simulation results show that, compared with the state-of-the-art methods, our proposed ARPGDA algorithm can achieve a better performance in terms of solution quality and speed for solving the FPCA problems.Comment: 5 pages, 8 figures, submitted for possible publicatio

Lifelong Embedding Learning and Transfer for Growing Knowledge Graphs

Existing knowledge graph (KG) embedding models have primarily focused on static KGs. However, real-world KGs do not remain static, but rather evolve and grow in tandem with the development of KG applications. Consequently, new facts and previously unseen entities and relations continually emerge, necessitating an embedding model that can quickly learn and transfer new knowledge through growth. Motivated by this, we delve into an expanding field of KG embedding in this paper, i.e., lifelong KG embedding. We consider knowledge transfer and retention of the learning on growing snapshots of a KG without having to learn embeddings from scratch. The proposed model includes a masked KG autoencoder for embedding learning and update, with an embedding transfer strategy to inject the learned knowledge into the new entity and relation embeddings, and an embedding regularization method to avoid catastrophic forgetting. To investigate the impacts of different aspects of KG growth, we construct four datasets to evaluate the performance of lifelong KG embedding. Experimental results show that the proposed model outperforms the state-of-the-art inductive and lifelong embedding baselines.Comment: Accepted in the 37th AAAI Conference on Artificial Intelligence (AAAI 2023

Boosting the Transferability of Adversarial Attacks with Global Momentum Initialization

Deep neural networks are vulnerable to adversarial examples, which attach human invisible perturbations to benign inputs. Simultaneously, adversarial examples exhibit transferability under different models, which makes practical black-box attacks feasible. However, existing methods are still incapable of achieving desired transfer attack performance. In this work, from the perspective of gradient optimization and consistency, we analyze and discover the gradient elimination phenomenon as well as the local momentum optimum dilemma. To tackle these issues, we propose Global Momentum Initialization (GI) to suppress gradient elimination and help search for the global optimum. Specifically, we perform gradient pre-convergence before the attack and carry out a global search during the pre-convergence stage. Our method can be easily combined with almost all existing transfer methods, and we improve the success rate of transfer attacks significantly by an average of 6.4% under various advanced defense mechanisms compared to state-of-the-art methods. Eventually, we achieve an attack success rate of 95.4%, fully illustrating the insecurity of existing defense mechanisms

The nuclear factor-κB inhibitor pyrrolidine dithiocarbamate reduces polyinosinic-polycytidilic acid-induced immune response in pregnant rats and the behavioral defects of their adult offspring

<p>Abstract</p> <p>Background</p> <p>Epidemiological studies have indicated that maternal infection during pregnancy may lead to a higher incidence of schizophrenia in the offspring. It is assumed that the maternal infection increases the immune response, leading to neurodevelopmental disorders in the offspring. Maternal polyinosinic-polycytidilic acid (PolyI:C) treatment induces a wide range of characteristics in the offspring mimicking some schizophrenia symptoms in humans. These observations are consistent with the neurodevelopmental hypothesis of schizophrenia.</p> <p>Methods</p> <p>We examined whether suppression of the maternal immune response could prevent neurodevelopmental disorders in adult offspring. PolyI:C or saline was administered to early pregnant rats to mimic maternal infection, and the maternal immune response represented by tumor necrosis factor alpha (TNF-α) and interleukin-10 (IL-10) levels was determined by enzyme-linked immunosorbent assays (ELISA). The NF-κB inhibitor pyrrolidine dithiocarbamate (PDTC) was used to suppress the maternal immune response. Neurodevelopmental disorders in adult offspring were examined by prepulse inhibition (PPI), passive avoidance, and active avoidance tests.</p> <p>Results</p> <p>PolyI:C administration to early pregnant rats led to elevated serum cytokine levels as shown by massive increases in serum TNF-α and IL-10 levels. The adult offspring showed defects in prepulse inhibition, and passive avoidance and active avoidance tests. PDTC intervention in early pregnant rats suppressed cytokine increases and reduced the severity of neurodevelopmental defects in adult offspring.</p> <p>Conclusions</p> <p>Our findings suggest that PDTC can suppress the maternal immune response induced by PolyI:C and partially prevent neurodevelopmental disorders of adult offspring.</p

ALL IN ONE NETWORK FOR DRIVER ATTENTION MONITORING

Nowadays, driver drowsiness and driver distraction is considered as a major risk for fatal road accidents around the world. As a result, driver monitoring identifying is emerging as an essential function of automotive safety systems. Its basic features include head pose, gaze direction, yawning and eye state analysis. However, existing work has investigated algorithms to detect these tasks separately and was usually conducted under laboratory environments. To address this problem, we propose a multi-task learning CNN framework which simultaneously solve these tasks. The network is implemented by sharing common features and parameters of highly related tasks. Moreover, we propose Dual-Loss Block to decompose the pose estimation task into pose classification and coarse-to-fine regression and Objectcentric Aware Block to reduce orientation estimation errors. Thus, with such novel designs, our model not only achieves SOA results but also reduces the complexity of integrating into automotive safety systems. It runs at 10 fps on vehicle embedded systems which marks a momentous step for this field. More importantly, to facilitate other researchers, we publish our dataset FDUDrivers which contains 20000 images of 100 different drivers and covers various real driving environments. FDUDrivers might be the first comprehensive dataset regarding driver attention monitorin