New monotonicity for pp-capacitary functions in 33-manifolds with nonnegative scalar curvature

In this paper, we derive general monotone quantities and geometric inequalities associated with $p$-capacitary functions in asymptotically flat $3$-manifolds with simple topology and nonnegative scalar curvature. The inequalities become equalities on the spatial Schwarzschild manifolds outside rotationally symmetric spheres. This generalizes Miao's result \cite{M} from $p=2$ to $p\in (1, 3)$. As applications, we recover mass-to-$p$-capacity and $p$-capacity-to-area inequalities due to Bray-Miao \cite{BM} and Xiao \cite{Xiao}.Comment: 30 pages. Any comments are welcome

Disentangled Contrastive Collaborative Filtering

Recent studies show that graph neural networks (GNNs) are prevalent to model high-order relationships for collaborative filtering (CF). Towards this research line, graph contrastive learning (GCL) has exhibited powerful performance in addressing the supervision label shortage issue by learning augmented user and item representations. While many of them show their effectiveness, two key questions still remain unexplored: i) Most existing GCL-based CF models are still limited by ignoring the fact that user-item interaction behaviors are often driven by diverse latent intent factors (e.g., shopping for family party, preferred color or brand of products); ii) Their introduced non-adaptive augmentation techniques are vulnerable to noisy information, which raises concerns about the model's robustness and the risk of incorporating misleading self-supervised signals. In light of these limitations, we propose a Disentangled Contrastive Collaborative Filtering framework (DCCF) to realize intent disentanglement with self-supervised augmentation in an adaptive fashion. With the learned disentangled representations with global context, our DCCF is able to not only distill finer-grained latent factors from the entangled self-supervision signals but also alleviate the augmentation-induced noise. Finally, the cross-view contrastive learning task is introduced to enable adaptive augmentation with our parameterized interaction mask generator. Experiments on various public datasets demonstrate the superiority of our method compared to existing solutions. Our model implementation is released at the link https://github.com/HKUDS/DCCF.Comment: Published as a SIGIR'23 full pape

Distinguishing Look-Alike Innocent and Vulnerable Code by Subtle Semantic Representation Learning and Explanation

Though many deep learning (DL)-based vulnerability detection approaches have been proposed and indeed achieved remarkable performance, they still have limitations in the generalization as well as the practical usage. More precisely, existing DL-based approaches (1) perform negatively on prediction tasks among functions that are lexically similar but have contrary semantics; (2) provide no intuitive developer-oriented explanations to the detected results. In this paper, we propose a novel approach named SVulD, a function-level Subtle semantic embedding for Vulnerability Detection along with intuitive explanations, to alleviate the above limitations. Specifically, SVulD firstly trains a model to learn distinguishing semantic representations of functions regardless of their lexical similarity. Then, for the detected vulnerable functions, SVulD provides natural language explanations (e.g., root cause) of results to help developers intuitively understand the vulnerabilities. To evaluate the effectiveness of SVulD, we conduct large-scale experiments on a widely used practical vulnerability dataset and compare it with four state-of-the-art (SOTA) approaches by considering five performance measures. The experimental results indicate that SVulD outperforms all SOTAs with a substantial improvement (i.e., 23.5%-68.0% in terms of F1-score, 15.9%-134.8% in terms of PR-AUC and 7.4%-64.4% in terms of Accuracy). Besides, we conduct a user-case study to evaluate the usefulness of SVulD for developers on understanding the vulnerable code and the participants' feedback demonstrates that SVulD is helpful for development practice.Comment: Accepted By FSE'2

4-[1-(Hydroxy­imino)ethyl]-N-(4-nitro­benzyl­idene)aniline

In the title compound, C15H13N3O3, the dihedral angle formed by the two benzene rings is 44.23 (2)°. The crystal structure is stabilized by aromatic π–π stacking inter­actions, with centroid-centroid distances of 3.825 (3) and 3.870 (4) Å between the aniline and the nitro­benzene rings of neighbouring mol­ecules, respectively. In addition, the stacked mol­ecules exhibit inter­molecular C—H⋯N and C—H⋯O inter­actions

Representation Learning with Large Language Models for Recommendation

Recommender systems have seen significant advancements with the influence of deep learning and graph neural networks, particularly in capturing complex user-item relationships. However, these graph-based recommenders heavily depend on ID-based data, potentially disregarding valuable textual information associated with users and items, resulting in less informative learned representations. Moreover, the utilization of implicit feedback data introduces potential noise and bias, posing challenges for the effectiveness of user preference learning. While the integration of large language models (LLMs) into traditional ID-based recommenders has gained attention, challenges such as scalability issues, limitations in text-only reliance, and prompt input constraints need to be addressed for effective implementation in practical recommender systems. To address these challenges, we propose a model-agnostic framework RLMRec that aims to enhance existing recommenders with LLM-empowered representation learning. It proposes a recommendation paradigm that integrates representation learning with LLMs to capture intricate semantic aspects of user behaviors and preferences. RLMRec incorporates auxiliary textual signals, develops a user/item profiling paradigm empowered by LLMs, and aligns the semantic space of LLMs with the representation space of collaborative relational signals through a cross-view alignment framework. This work further establish a theoretical foundation demonstrating that incorporating textual signals through mutual information maximization enhances the quality of representations. In our evaluation, we integrate RLMRec with state-of-the-art recommender models, while also analyzing its efficiency and robustness to noise data. Our implementation codes are available at https://github.com/HKUDS/RLMRec.Comment: Published as a WWW'24 full pape

An FPGA-Integrated Time-to-Digital Converter Based on a Ring Oscillator for Programmable Delay Line Resolution Measurement

We describe the architecture of a time-to-digital converter (TDC), specially intended to measure the delay resolution of a programmable delay line (PDL). The configuration, which consists of a ring oscillator, a frequency divider (FD), and a period measurement circuit (PMC), is implemented in a field programmable gate array (FPGA) device. The ring oscillator realized in loop containing a PDL and a look-up table (LUT) generates periodic oscillatory pulses. The FD amplifies the oscillatory period from nanosecond range to microsecond range. The time-to-digital conversion is based on counting the number of clock cycles between two consecutive pulses of the FD by the PMC. Experiments have been conducted to verify the performance of the TDC. The achieved relative errors for four PDLs are within 0.50%-1.21% and the TDC has an equivalent resolution of about 0.4 ps

Neurobiological Changes of Schizotypy: Evidence From Both Volume-Based Morphometric Analysis and Resting-State Functional Connectivity

The current study sought to examine the underlying brain changes in individuals with high schizotypy by integrating networks derived from brain structural and functional imaging. Individuals with high schizotypy (n = 35) and low schizotypy (n = 34) controls were screened using the Schizotypal Personality Questionnaire and underwent brain structural and resting-state functional magnetic resonance imaging on a 3T scanner. Voxel-based morphometric analysis and graph theory-based functional network analysis were conducted. Individuals with high schizotypy showed reduced gray matter (GM) density in the insula and the dorsolateral prefrontal gyrus. The graph theoretical analysis showed that individuals with high schizotypy showed similar global properties in their functional networks as low schizotypy individuals. Several hubs of the functional network were identified in both groups, including the insula, the lingual gyrus, the postcentral gyrus, and the rolandic operculum. More hubs in the frontal lobe and fewer hubs in the occipital lobe were identified in individuals with high schizotypy. By comparing the functional connectivity between clusters with abnormal GM density and the whole brain, individuals with high schizotypy showed weaker functional connectivity between the left insula and the putamen, but stronger connectivity between the cerebellum and the medial frontal gyrus. Taken together, our findings suggest that individuals with high schizotypy present changes in terms of GM and resting-state functional connectivity, especially in the frontal lobe

4,4′-Dimethyl-1,1′-[ethyl­enedioxy­bis(nitrilo­methyl­idyne)]dibenzene

The Schiff base, C18H20N2O2, which lies about an inversion centre, adopts a linear conformation. The mol­ecules are packed by C—H⋯π inter­actions, forming a two-dimensional supra­molecular network

2,2′-{1,1′-[Butane-1,4-diyl­bis(oxy­nitrilo)]di­ethylidyne}di-1-naphthol

The title compound, C28H28N2O4, was synthesized by the reaction of 2-acetyl-1-naphthol with 1,4-bis­(amino­oxy)butane in ethanol. The molecule, which lies about an inversion centre, adopts a linear structure, in which the oxime groups and naphthalene ring systems assume an anti conformation. The intra­molecular inter­planar distance between parallel naphthalene rings is 1.054 (3) Å. Intra­molecular O—H⋯N hydrogen bonds are formed between the oxime nitro­gen and hydr­oxy groups