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