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With rich visual data, such as images, becoming readily associated with items, visually-aware recommendation systems (VARS) have been widely used in different applications. Recent studies have shown that VARS are vulnerable to item-image adversarial attacks, which add human-imperceptible perturbations to the clean images associated with those items. Attacks on VARS pose new security challenges to a wide range of applications, such as e-commerce and social media, where VARS are widely used. How to secure VARS from such adversarial attacks becomes a critical problem. Currently, there is still a lack of systematic studies on how to design defense strategies against visual attacks on VARS. In this article, we attempt to fill this gap by proposing anadversarial image denoising and detectionframework to secure VARS. Our proposed method can simultaneously (1) secure VARS from adversarial attacks characterized bylocalperturbations by image denoising based onglobalvision transformers; and (2) accurately detect adversarial examples using a novel contrastive learning approach. Meanwhile, our framework is designed to be used as both a filter and a detector so that they can bejointlytrained to improve the flexibility of our defense strategy to a variety of attacks and VARS models. Our approach is uniquely tailored for VARS, addressing the distinct challenges in scenarios where adversarial attacks can differ across industries, for instance, causing misclassification in e-commerce or misrepresentation in real estate. We have conducted extensive experimental studies with two popular attack methods (FGSM and PGD). Our experimental results on two real-world datasets show that our defense strategy against visual attacks is effective and outperforms existing methods on different attacks. Moreover, our method demonstrates high accuracy in detecting adversarial examples, complementing its robustness across various types of adversarial attacks. 

Abstract How the brain encodes, recognizes, and memorizes general visual objects is a fundamental question in neuroscience. Here, we investigated the neural processes underlying visual object perception and memory by recording from 3173 single neurons in the human amygdala and hippocampus across four experiments. We employed both passive-viewing and recognition memory tasks involving a diverse range of naturalistic object stimuli. Our findings reveal a region-based feature code for general objects, where neurons exhibit receptive fields in the high-level visual feature space. This code can be validated by independent new stimuli and replicated across all experiments, including fixation-based analyses with large natural scenes. This region code explains the long-standing visual category selectivity, preferentially enhances memory of encoded stimuli, predicts memory performance, encodes image memorability, and exhibits intricate interplay with memory contexts. Together, region-based feature coding provides an important mechanism for visual object processing in the human brain. 

We have revisited the Kittel model that describes antiferroelectricity (AFE) in terms of two sublattices of spontaneous polarization with antiparallel couplings. By constructing a comprehensive phase diagram including the antiferroelectric, ferroelectric, and paraelectric phases in the parameter space, we have identified an AFE phase with stable antipolar states and metastable polar states (SAMP) when the coupling between the two sublattices is weak. We find that the metastability of the polar state in the SAMP AFE phase can lead to apparent ferroelectric behavior, depending on the measurement timescale—for example, the frequency of the applied electric field. This explains the observed ferroelectric behavior of orthorhombic hafnia, which is predicted to be antipolar from density functional theory calculations. 

The atomic structures at epitaxial film–substrate interfaces determine the scalability of thin films and can result in new phenomena. However, it is challenging to control the structure of the interface. In this work, we report the strong tunability of the epitaxial interface of improper ferroelectric hexagonal ferrites deposited on spinel ferrites, achieving the artificial selection of two types of interfaces that are related by a 90° rotation of in-plane epitaxial relations and feature either disordered or hybrid reconstruction. The hybrid-type interface exhibits characteristic structures of both hexagonal ferrites and spinel ferrites, which remove the critical thickness for improper ferroelectricity. This tunable interfacial structure provides critical insight into controlling interfacial clamping to maintain robust improper ferroelectricity at the two-dimensional limit.