48 research outputs found
Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems
With the growing privacy concerns in recommender systems, recommendation
unlearning, i.e., forgetting the impact of specific learned targets, is getting
increasing attention. Existing studies predominantly use training data, i.e.,
model inputs, as the unlearning target. However, we find that attackers can
extract private information, i.e., gender, race, and age, from a trained model
even if it has not been explicitly encountered during training. We name this
unseen information as attribute and treat it as the unlearning target. To
protect the sensitive attribute of users, Attribute Unlearning (AU) aims to
degrade attacking performance and make target attributes indistinguishable. In
this paper, we focus on a strict but practical setting of AU, namely
Post-Training Attribute Unlearning (PoT-AU), where unlearning can only be
performed after the training of the recommendation model is completed. To
address the PoT-AU problem in recommender systems, we design a two-component
loss function that consists of i) distinguishability loss: making attribute
labels indistinguishable from attackers, and ii) regularization loss:
preventing drastic changes in the model that result in a negative impact on
recommendation performance. Specifically, we investigate two types of
distinguishability measurements, i.e., user-to-user and
distribution-to-distribution. We use the stochastic gradient descent algorithm
to optimize our proposed loss. Extensive experiments on three real-world
datasets demonstrate the effectiveness of our proposed methods
In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems
Recommender systems are typically biased toward a small group of users,
leading to severe unfairness in recommendation performance, i.e., User-Oriented
Fairness (UOF) issue. The existing research on UOF is limited and fails to deal
with the root cause of the UOF issue: the learning process between advantaged
and disadvantaged users is unfair. To tackle this issue, we propose an
In-processing User Constrained Dominant Sets (In-UCDS) framework, which is a
general framework that can be applied to any backbone recommendation model to
achieve user-oriented fairness. We split In-UCDS into two stages, i.e., the
UCDS modeling stage and the in-processing training stage. In the UCDS modeling
stage, for each disadvantaged user, we extract a constrained dominant set (a
user cluster) containing some advantaged users that are similar to it. In the
in-processing training stage, we move the representations of disadvantaged
users closer to their corresponding cluster by calculating a fairness loss. By
combining the fairness loss with the original backbone model loss, we address
the UOF issue and maintain the overall recommendation performance
simultaneously. Comprehensive experiments on three real-world datasets
demonstrate that In-UCDS outperforms the state-of-the-art methods, leading to a
fairer model with better overall recommendation performance
Experimental evidence on the Altshuler-Aronov-Spivak interference of the topological surface states in the exfoliated Bi2Te3 nanoflakes
Here we demonstrate the Altshuler-Aronov-Spivak (AAS) interference of the
topological surface states on the exfoliated Bi2Te3 microflakes by a flux
period of h/2e in their magnetoresistance oscillations and its weak field
character. Both the osillations with the period of h/e and h/2e are observed.
The h/2e-period AAS oscillation gradually dominates with increasing the sample
widths and the temperatures. This reveals the transition of the Dirac Fermions'
transport to the diffusive regime.Comment: version 3;Applied Physics Letters in pres
A cytomegalovirus peptide-specific antibody alters natural killer cell homeostasis and ss shared in several autoimmune diseases
Human cytomegalovirus (hCMV), a ubiquitous beta-herpesvirus, has been associated with several autoimmune diseases. However, the direct role of hCMV in inducing autoimmune disorders remains unclear. Here we report the identification of an autoantibody that recognizes a group of peptides with a conserved motif matching the Pp150 protein of hCMV (anti-Pp150) and is shared among patients with various autoimmune diseases. Anti-Pp150 also recognizes the single-pass membrane protein CIP2A and induces the death of CD56bright NK cells, a natural killer cell subset whose expansion is correlated with autoimmune disease. Consistent with this finding, the percentage of circulating CD56bright NK cells is reduced in patients with several autoimmune diseases and negatively correlates with anti-Pp150 concentration. CD56bright NK cell death occurs via both antibody- and complement-dependent cytotoxicity. Our findings reveal that a shared hCMV-induced autoantibody is involved in the decrease of CD56bright NK cells and may thus contribute to the onset of autoimmune disorders
Long-range imaging LiDAR with multiple denoising technologies
The ability to capture and record high-resolution images over long distances is essential for a wide range of applications, including connected and autonomous vehicles, defense and security operations, as well as agriculture and mining industries. Here, we demonstrate a self-assembled bistatic long-range imaging LiDAR system. Importantly, to achieve high signal-to-noise ratio (SNR) data, we employed a comprehensive suite of denoising methods including temporal, spatial, spectral, and polarization filtering. With the aid of these denoising technologies, our system has been validated to possess the capability of imaging under various complex usage conditions. In terms of distance performance, the test results achieved ranges of over 4000 m during daylight with clear weather, 19,200 m at night, 6700 m during daylight with haze, and 2000 m during daylight with rain. Additionally, it offers an angular resolution of 0.01 mrad. These findings demonstrate the potential to offer comprehensive construction strategies and operational methodologies to individuals seeking long-range LiDAR data
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Zinc finger protein Zfp335 is required for the formation of the naïve T cell compartment
The generation of naïve T lymphocytes is critical for immune function yet the mechanisms governing their maturation remain incompletely understood. Through ENU mutagenesis screening, we identified a mouse mutant, bloto, characterized by a deficiency in naïve T cells. The causative genetic lesion was identified as a hypomorphic mutation in the zinc finger protein Zfp335. Zfp335bloto/bloto mice had a paucity of naïve T lymphocytes due to a cell-intrinsic defect in mature thymocytes, as well as in peripheral T cells that have recently undergone thymic egress. The T cell defect in Zfp335bloto/bloto mice could not be explained by altered thymic selection, proliferation or Bcl2-dependent survival. ChIP-seq analysis revealed that Zfp335 binds primarily to promoter regions, and we identified a set of target genes in thymocytes that are enriched in categories related to protein metabolism, mitochondrial function, and transcriptional regulation. Our analysis also showed that Zfp335bloto occupancy at a subset of sites was significantly decreased, and that this reduced binding correlated with deregulated target gene expression. Restoring the expression of one target, Ankle2, partially rescued T cell maturation. Finally, we identified a new DNA recognition motif and demonstrated that it was bound by Zfp335 in vitro. We suggest that Zfp335 binds directly to DNA in a sequence-specific manner to control gene transcription and in doing so, functions as an essential regulator of late-stage intrathymic and post-thymic T cell maturation