This paper presents an algorithm to solve the Soft k-Means problem globally. Unlike Fuzzy c-Means, Soft k-Means (SkM) has a matrix factorization-type objective and has been shown to have a close relation with the popular probability decomposition-type clustering methods, e.g., Left Stochastic Clustering (LSC). Though some work has been done for solving the Soft k-Means problem, they usually use an alternating minimization scheme or the projected gradient descent method, which cannot guarantee global optimality since the non-convexity of SkM. In this paper, we present a sufficient condition for a feasible solution of Soft k-Means problem to be globally optimal and show the output of the proposed algorithm satisfies it. Moreover, for the Soft k-Means problem, we provide interesting discussions on stability, solutions non-uniqueness, and connection with LSC. Then, a new model, named Minimal Volume Soft k-Means (MVSkM), is proposed to address the solutions non-uniqueness issue. Finally, experimental results support our theoretical results.

Spectral clustering is an effective methodology for unsupervised learning. Most traditional spectral clustering algorithms involve a separate two-step procedure and apply the transformed new representations for the final clustering results. Recently, much progress has been made to utilize the non-negative feature property in real-world data and to jointly learn the representation and clustering results. However, to our knowledge, no previous work considers a unified model that incorporates the important multi-view information with those properties, which severely limits the performance of existing methods. In this paper, we formulate a novel clustering model, which exploits the non-negative feature property and, more importantly, incorporates the multi-view information into a unified joint learning framework: the unified multi-view orthonormal non-negative graph based clustering framework (Umv-ONGC). Then, we derive an effective three-stage iterative solution for the proposed model and provide analytic solutions for the three sub-problems from the three stages. We also explore, for the first time, the multi-model non-negative graph-based approach to clustering data based on deep features. Extensive experiments on three benchmark data sets demonstrate the effectiveness of the proposed method.

随着信息时代的蓬勃发展，日常生成大量数据。由于这些数据的大规模和高维度，通常很难在实际应用中实现更好的决策。因此，迫切需要一种有效的大数据分析方法。对于功能工程，功能选择似乎是一个重要的研究内容，预计可以从候选人中选择“出色”功能。可以通过特征选择来实现不同的功能，例如降低维度，模型效应改进和模型性能改进。在许多分类任务中，研究人员发现，如果数据来自同一类，通常它们似乎彼此接近。因此，局部紧凑性对于评估功能至关重要。在此手稿中，我们提出了一种快速无监督的特征选择方法，称为紧凑型评分（CSUFS），以选择所需的功能。为了证明效率和准确性，通过进行广泛的实验选择了几个数据集。后来，通过解决聚类任务来揭示我们方法的有效性和优势。在这里，性能由几个众所周知的评估指标表示，而效率则由相应的运行时间反映。正如模拟结果所揭示的那样，与现有算法相比，我们提出的算法似乎更准确和有效。

许多研究人员已经观察到多任务学习，该研究人员假设不同的任务可以共享低级别常见且潜在的子空间。它意味着学习多个任务，而不是独立学习它们。在本文中，我们提出了两种基于两种正则化术语的新型多任务学习制剂，这可以通过最小化恰好k $最小的奇异值来学习最佳共享潜在子空间。所提出的正则化术语比追踪规范更短的等级最小化的近似。但是解决了确切等级最小化问题是一个np难的问题。因此，我们设计一种新颖的基于重量的迭代策略来解决我们的模型，可以通过设置大惩罚参数来显微地处理确切的等级最小化问题。基准数据集的实验结果表明，我们的方法可以正确地恢复跨任务共享的低级结构，而优于相关的多任务学习方法。

近年来，Hyperspectral异常检测（有）已成为一个积极的主题，在军事和平民中发挥着重要作用。作为经典的方法，基于协作的探测器（CRD）引起了广泛的关注和深入研究。尽管CRD方法具有良好的性能，但其计算成本主要由滑动双窗策略产生的，对于广泛的应用来说太高了。此外，需要多次重复测试来确定一旦数据集更改，需要重置的双窗口的大小，并且不能以先验知识预先识别。为了缓解这一问题，我们提出了一种新的集合和随机协同代表性的探测器（ERCRD），其包括两个密切相关的阶段。首先，我们处理CRD（RCRD）上的随机子采样，以获得多个检测结果而不是滑动双窗策略，这显着降低了计算复杂性，并使其在实际应用中更加可行。其次，使用集合学习来优化RCRD的多个结果，这充当各种“专家”提供丰富的互补信息，以更好地定位不同的异常。这样的两个阶段形成有机和理论探测器，这不仅可以提高有方法的准确性和稳定性，而且还可以提高其泛化能力。四个真实高光谱数据集的实验表现出这一提出的Ercrd方法的准确性和效率与最多的最先进的方法相比。

To facilitate research on text generation, this paper presents a comprehensive and unified library, TextBox 2.0, focusing on the use of pre-trained language models (PLMs). To be comprehensive, our library covers $13$ common text generation tasks and their corresponding $83$ datasets and further incorporates $45$ PLMs covering general, translation, Chinese, dialogue, controllable, distilled, prompting, and lightweight PLMs. We also implement $4$ efficient training strategies and provide $4$ generation objectives for pre-training new PLMs from scratch. To be unified, we design the interfaces to support the entire research pipeline (from data loading to training and evaluation), ensuring that each step can be fulfilled in a unified way. Despite the rich functionality, it is easy to use our library, either through the friendly Python API or command line. To validate the effectiveness of our library, we conduct extensive experiments and exemplify four types of research scenarios. The project is released at the link: https://github.com/RUCAIBox/TextBox.

The booming development and huge market of micro-videos bring new e-commerce channels for merchants. Currently, more micro-video publishers prefer to embed relevant ads into their micro-videos, which not only provides them with business income but helps the audiences to discover their interesting products. However, due to the micro-video recording by unprofessional equipment, involving various topics and including multiple modalities, it is challenging to locate the products related to micro-videos efficiently, appropriately, and accurately. We formulate the microvideo-product retrieval task, which is the first attempt to explore the retrieval between the multi-modal and multi-modal instances. A novel approach named Multi-Queue Momentum Contrast (MQMC) network is proposed for bidirectional retrieval, consisting of the uni-modal feature and multi-modal instance representation learning. Moreover, a discriminative selection strategy with a multi-queue is used to distinguish the importance of different negatives based on their categories. We collect two large-scale microvideo-product datasets (MVS and MVS-large) for evaluation and manually construct the hierarchical category ontology, which covers sundry products in daily life. Extensive experiments show that MQMC outperforms the state-of-the-art baselines. Our replication package (including code, dataset, etc.) is publicly available at https://github.com/duyali2000/MQMC.

There is increasing adoption of artificial intelligence in drug discovery. However, existing works use machine learning to mainly utilize the chemical structures of molecules yet ignore the vast textual knowledge available in chemistry. Incorporating textual knowledge enables us to realize new drug design objectives, adapt to text-based instructions, and predict complex biological activities. We present a multi-modal molecule structure-text model, MoleculeSTM, by jointly learning molecule's chemical structures and textual descriptions via a contrastive learning strategy. To train MoleculeSTM, we construct the largest multi-modal dataset to date, namely PubChemSTM, with over 280K chemical structure-text pairs. To demonstrate the effectiveness and utility of MoleculeSTM, we design two challenging zero-shot tasks based on text instructions, including structure-text retrieval and molecule editing. MoleculeSTM possesses two main properties: open vocabulary and compositionality via natural language. In experiments, MoleculeSTM obtains the state-of-the-art generalization ability to novel biochemical concepts across various benchmarks.

Adder Neural Network (AdderNet) provides a new way for developing energy-efficient neural networks by replacing the expensive multiplications in convolution with cheaper additions (i.e.l1-norm). To achieve higher hardware efficiency, it is necessary to further study the low-bit quantization of AdderNet. Due to the limitation that the commutative law in multiplication does not hold in l1-norm, the well-established quantization methods on convolutional networks cannot be applied on AdderNets. Thus, the existing AdderNet quantization techniques propose to use only one shared scale to quantize both the weights and activations simultaneously. Admittedly, such an approach can keep the commutative law in the l1-norm quantization process, while the accuracy drop after low-bit quantization cannot be ignored. To this end, we first thoroughly analyze the difference on distributions of weights and activations in AdderNet and then propose a new quantization algorithm by redistributing the weights and the activations. Specifically, the pre-trained full-precision weights in different kernels are clustered into different groups, then the intra-group sharing and inter-group independent scales can be adopted. To further compensate the accuracy drop caused by the distribution difference, we then develop a lossless range clamp scheme for weights and a simple yet effective outliers clamp strategy for activations. Thus, the functionality of full-precision weights and the representation ability of full-precision activations can be fully preserved. The effectiveness of the proposed quantization method for AdderNet is well verified on several benchmarks, e.g., our 4-bit post-training quantized adder ResNet-18 achieves an 66.5% top-1 accuracy on the ImageNet with comparable energy efficiency, which is about 8.5% higher than that of the previous AdderNet quantization methods.

Multilingual Pretrained Language Models (MPLMs) have shown their strong multilinguality in recent empirical cross-lingual transfer studies. In this paper, we propose the Prompts Augmented by Retrieval Crosslingually (PARC) pipeline to improve the zero-shot performance on low-resource languages (LRLs) by augmenting the context with semantically similar sentences retrieved from a high-resource language (HRL) as prompts. PARC improves the zero-shot performance on three downstream tasks (binary sentiment classification, topic categorization and natural language inference) with multilingual parallel test sets across 10 LRLs covering 6 language families in both unlabeled settings (+5.1%) and labeled settings (+16.3%). PARC-labeled also outperforms the finetuning baseline by 3.7%. We find a significant positive correlation between cross-lingual transfer performance on one side, and the similarity between the high- and low-resource languages as well as the amount of low-resource pretraining data on the other side. A robustness analysis suggests that PARC has the potential to achieve even stronger performance with more powerful MPLMs.