308 research outputs found
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
-stationary point of the above problem within
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
Out of Thin Air: Exploring Data-Free Adversarial Robustness Distillation
Adversarial Robustness Distillation (ARD) is a promising task to solve the
issue of limited adversarial robustness of small capacity models while
optimizing the expensive computational costs of Adversarial Training (AT).
Despite the good robust performance, the existing ARD methods are still
impractical to deploy in natural high-security scenes due to these methods rely
entirely on original or publicly available data with a similar distribution. In
fact, these data are almost always private, specific, and distinctive for
scenes that require high robustness. To tackle these issues, we propose a
challenging but significant task called Data-Free Adversarial Robustness
Distillation (DFARD), which aims to train small, easily deployable, robust
models without relying on data. We demonstrate that the challenge lies in the
lower upper bound of knowledge transfer information, making it crucial to
mining and transferring knowledge more efficiently. Inspired by human
education, we design a plug-and-play Interactive Temperature Adjustment (ITA)
strategy to improve the efficiency of knowledge transfer and propose an
Adaptive Generator Balance (AGB) module to retain more data information. Our
method uses adaptive hyperparameters to avoid a large number of parameter
tuning, which significantly outperforms the combination of existing techniques.
Meanwhile, our method achieves stable and reliable performance on multiple
benchmarks.Comment: Accepted by AAAI2
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
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