Accelerating Diffusion-based Combinatorial Optimization Solvers by Progressive Distillation

Graph-based diffusion models have shown promising results in terms of generating high-quality solutions to NP-complete (NPC) combinatorial optimization (CO) problems. However, those models are often inefficient in inference, due to the iterative evaluation nature of the denoising diffusion process. This paper proposes to use progressive distillation to speed up the inference by taking fewer steps (e.g., forecasting two steps ahead within a single step) during the denoising process. Our experimental results show that the progressively distilled model can perform inference 16 times faster with only 0.019% degradation in performance on the TSP-50 dataset

Research on the Impact of Returnee Executives' Strategic CSR Orientation on Corporate Value

An increasing number of studies have focused on the impact of the increase in the proportion of overseas returnees in enterprise management. How do executives with an overseas background affect corporate value? Based on Upper Echelons Theory, this paper studies the influence of overseas returnees on enterprise value from the perspective of strategic corporate social responsibility. Based on the sample composed of companies listed in Shanghai and Shenzhen stock exchange, the research shows that executives from overseas through strengthening strategic corporate social responsibility, make the enterprise social responsibility incorporated into the development of business strategy, finally promoting the ascension of the enterprise value. Namely, the strategic corporate social responsibility orientation plays an intermediary role between executives from overseas and enterprise value. This study further correlates the characteristics of overseas returnees with the strategic decisions of enterprises, deepens the understanding of the realization mechanism of overseas executives' promotion of corporate value, enriches the research results of factors influencing corporate social responsibility, to improve the active construction of local enterprises' social responsibility by changing the structure of the executive

Speech-to-Speech Translation with Discrete-Unit-Based Style Transfer

Direct speech-to-speech translation (S2ST) with discrete self-supervised representations has achieved remarkable accuracy, but is unable to preserve the speaker timbre of the source speech during translation. Meanwhile, the scarcity of high-quality speaker-parallel data poses a challenge for learning style transfer between source and target speech. We propose an S2ST framework with an acoustic language model based on discrete units from a self-supervised model and a neural codec for style transfer. The acoustic language model leverages self-supervised in-context learning, acquiring the ability for style transfer without relying on any speaker-parallel data, thereby overcoming the issue of data scarcity. By using extensive training data, our model achieves zero-shot cross-lingual style transfer on previously unseen source languages. Experiments show that our model generates translated speeches with high fidelity and style similarity. Audio samples are available at http://stylelm.github.io/ .Comment: 5 pages, 1 figure. submitted to ICASSP 202

High throughput detection of M6P/IGF2R intronic hypermethylation and LOH in ovarian cancer

Cell surface mannose 6-phosphate/insulin-like growth factor II receptors (M6P/IGF2R) bind and target exogenous insulin-like growth factor II (IGF2) to the prelysosomes where it is degraded. Loss of heterozygosity (LOH) for M6P/IGF2R is found in cancers, with mutational inactivation of the remaining allele. We exploited the normal allele-specific differential methylation of the M6P/IGF2R intron 2 CpG island to rapidly evaluate potential LOH in ovarian cancers, since every normal individual is informative. To this end, we developed a method for bisulfite modification of genomic DNA in 96-well format that allows for rapid methylation profiling. We identified ovarian cancers with M6P/IGF2R LOH, but unexpectedly also found frequent abnormal acquisition of methylation on the paternally inherited allele at intron 2. These results demonstrate the utility of our high-throughput method of bisulfite modification for analysis of large sample numbers. They further show that the methylation status of the intron 2 CpG island may be a useful indicator of LOH and biomarker of disease

SPA: A Graph Spectral Alignment Perspective for Domain Adaptation

Unsupervised domain adaptation (UDA) is a pivotal form in machine learning to extend the in-domain model to the distinctive target domains where the data distributions differ. Most prior works focus on capturing the inter-domain transferability but largely overlook rich intra-domain structures, which empirically results in even worse discriminability. In this work, we introduce a novel graph SPectral Alignment (SPA) framework to tackle the tradeoff. The core of our method is briefly condensed as follows: (i)-by casting the DA problem to graph primitives, SPA composes a coarse graph alignment mechanism with a novel spectral regularizer towards aligning the domain graphs in eigenspaces; (ii)-we further develop a fine-grained message propagation module -- upon a novel neighbor-aware self-training mechanism -- in order for enhanced discriminability in the target domain. On standardized benchmarks, the extensive experiments of SPA demonstrate that its performance has surpassed the existing cutting-edge DA methods. Coupled with dense model analysis, we conclude that our approach indeed possesses superior efficacy, robustness, discriminability, and transferability. Code and data are available at: https://github.com/CrownX/SPA.Comment: NeurIPS 2023 camera read

Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering

Graph collaborative filtering, which learns user and item representations through message propagation over the user-item interaction graph, has been shown to effectively enhance recommendation performance. However, most current graph collaborative filtering models mainly construct the interaction graph on a single behavior domain (e.g. click), even though users exhibit various types of behaviors on real-world platforms, including actions like click, cart, and purchase. Furthermore, due to variations in user engagement, there exists an imbalance in the scale of different types of behaviors. For instance, users may click and view multiple items but only make selective purchases from a small subset of them. How to alleviate the behavior imbalance problem and utilize information from the multiple behavior graphs concurrently to improve the target behavior conversion (e.g. purchase) remains underexplored. To this end, we propose IMGCF, a simple but effective model to alleviate behavior data imbalance for multi-behavior graph collaborative filtering. Specifically, IMGCF utilizes a multi-task learning framework for collaborative filtering on multi-behavior graphs. Then, to mitigate the data imbalance issue, IMGCF improves representation learning on the sparse behavior by leveraging representations learned from the behavior domain with abundant data volumes. Experiments on two widely-used multi-behavior datasets demonstrate the effectiveness of IMGCF.Comment: accepted by ICDM2023 Worksho

Traceability analysis for low-voltage distribution network abnormal line loss using a data-driven power flow model

The abnormal behavior of end-users is one of the main causes of abnormal line loss in distribution networks. The integration of a large amount of distributed renewable energy into a low-voltage distribution network (LVDN) complicates line loss analysis. Traceability analysis for abnormal line loss aims to identify the specific end-user responsible for the anomaly in line loss. This paper proposes, for LVDNs with incomplete topology and line parameters, a practical traceability analysis approach using a data-driven power flow model. A data-driven power flow model based on a neural network is first established to capture the power flow mapping relationship without topology and line parameter information. A backpropagation algorithm is then presented to correct the actual power consumption data according to the measured voltage data. By comparing actual power consumption data with measured power data, users with abnormal behavior can be accurately identified and tracked. Finally, the effectiveness of the proposed approach is verified by actual data

Automatic Detection of Hard Exudates in Color Retinal Images Using Dynamic Threshold and SVM Classification: Algorithm Development and Evaluation

Diabetic retinopathy (DR) is one of the most common causes of visual impairment. Automatic detection of hard exudates (HE) from retinal photographs is an important step for detection of DR. However, most of existing algorithms for HE detection are complex and inefficient. We have developed and evaluated an automatic retinal image processing algorithm for HE detection using dynamic threshold and fuzzy C-means clustering (FCM) followed by support vector machine (SVM) for classification. The proposed algorithm consisted of four main stages: (i) imaging preprocessing; (ii) localization of optic disc (OD); (iii) determination of candidate HE using dynamic threshold in combination with global threshold based on FCM; and (iv) extraction of eight texture features from the candidate HE region, which were then fed into an SVM classifier for automatic HE classification. The proposed algorithm was trained and cross-validated (10 fold) on a publicly available e-ophtha EX database (47 images) on pixel-level, achieving the overall average sensitivity, PPV, and F-score of 76.5%, 82.7%, and 76.7%. It was tested on another independent DIARETDB1 database (89 images) with the overall average sensitivity, specificity, and accuracy of 97.5%, 97.8%, and 97.7%, respectively. In summary, the satisfactory evaluation results on both retinal imaging databases demonstrated the effectiveness of our proposed algorithm for automatic HE detection, by using dynamic threshold and FCM followed by an SVM for classification