迄今为止，迄今为止，众所周知，对广泛的互补临床相关任务进行了全面比较了医学图像登记方法。这限制了采用研究进展，以防止竞争方法的公平基准。在过去五年内已经探讨了许多新的学习方法，但优化，建筑或度量战略的问题非常适合仍然是开放的。 Learn2reg涵盖了广泛的解剖学：脑，腹部和胸部，方式：超声波，CT，MRI，群体：患者内部和患者内部和监督水平。我们为3D注册的培训和验证建立了较低的入境障碍，这帮助我们从20多个独特的团队中汇编了65多个单独的方法提交的结果。我们的互补度量集，包括稳健性，准确性，合理性和速度，使得能够独特地位了解当前的医学图像登记现状。进一步分析监督问题的转移性，偏见和重要性，主要是基于深度学习的方法的优越性，并将新的研究方向开放到利用GPU加速的常规优化的混合方法。

在集成感测和通信（ISAC）系统中表征传感和通信性能权衡，在基于学习的人类运动识别的应用中具有挑战性。这是因为大型实验数据集和深神经网络的黑盒性质。本文介绍了SDP3，这是一种模拟驱动的性能预测指标和优化器，由SDP3数据模拟器，SDP3性能预测器和SDP3性能优化器组成。具体而言，SDP3数据模拟器在虚拟环境中生成生动的无线传感数据集，SDP3性能预测器预测基于函数回归方法的传感性能，而SDP3性能优化器会在分析上研究传感和通信性能。结果表明，模拟传感数据集在运动识别精度中非常匹配实验数据集。通过利用SDP3，发现可实现的识别准确性和通信吞吐量由通信饱和区组成，感应饱和区和通讯感应的对抗区域，ISAC系统的所需平衡性能位于第三个一。

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

The geographically weighted regression (GWR) is an essential tool for estimating the spatial variation of relationships between dependent and independent variables in geographical contexts. However, GWR suffers from the problem that classical linear regressions, which compose the GWR model, are more prone to be underfitting, especially for significant volume and complex nonlinear data, causing inferior comparative performance. Nevertheless, some advanced models, such as the decision tree and the support vector machine, can learn features from complex data more effectively while they cannot provide explainable quantification for the spatial variation of localized relationships. To address the above issues, we propose a geographically gradient boosting weighted regression model, GWRBoost, that applies the localized additive model and gradient boosting optimization method to alleviate underfitting problems and retains explainable quantification capability for spatially-varying relationships between geographically located variables. Furthermore, we formulate the computation method of the Akaike information score for the proposed model to conduct the comparative analysis with the classic GWR algorithm. Simulation experiments and the empirical case study are applied to prove the efficient performance and practical value of GWRBoost. The results show that our proposed model can reduce the RMSE by 18.3\% in parameter estimation accuracy and AICc by 67.3\% in the goodness of fit.

Natural Language Processing (NLP) is one of the core techniques in AI software. As AI is being applied to more and more domains, how to efficiently develop high-quality domain-specific language models becomes a critical question in AI software engineering. Existing domain-specific language model development processes mostly focus on learning a domain-specific pre-trained language model (PLM); when training the domain task-specific language model based on PLM, only a direct (and often unsatisfactory) fine-tuning strategy is adopted commonly. By enhancing the task-specific training procedure with domain knowledge graphs, we propose KnowledgeDA, a unified and low-code domain language model development service. Given domain-specific task texts input by a user, KnowledgeDA can automatically generate a domain-specific language model following three steps: (i) localize domain knowledge entities in texts via an embedding-similarity approach; (ii) generate augmented samples by retrieving replaceable domain entity pairs from two views of both knowledge graph and training data; (iii) select high-quality augmented samples for fine-tuning via confidence-based assessment. We implement a prototype of KnowledgeDA to learn language models for two domains, healthcare and software development. Experiments on five domain-specific NLP tasks verify the effectiveness and generalizability of KnowledgeDA. (Code is publicly available at https://github.com/RuiqingDing/KnowledgeDA.)

As many deep anomaly detection models have been deployed in the real-world, interpretable anomaly detection becomes an emerging task. Recent studies focus on identifying features of samples leading to abnormal outcomes but cannot recommend a set of actions to flip the abnormal outcomes. In this work, we focus on interpretations via algorithmic recourse that shows how to act to revert abnormal predictions by suggesting actions on features. The key challenge is that algorithmic recourse involves interventions in the physical world, which is fundamentally a causal problem. To tackle this challenge, we propose an interpretable Anomaly Detection framework using Causal Algorithmic Recourse (ADCAR), which recommends recourse actions and infers counterfactual of abnormal samples guided by the causal mechanism. Experiments on three datasets show that ADCAR can flip the abnormal labels with minimal interventions.

Whole-slide images (WSI) in computational pathology have high resolution with gigapixel size, but are generally with sparse regions of interest, which leads to weak diagnostic relevance and data inefficiency for each area in the slide. Most of the existing methods rely on a multiple instance learning framework that requires densely sampling local patches at high magnification. The limitation is evident in the application stage as the heavy computation for extracting patch-level features is inevitable. In this paper, we develop RLogist, a benchmarking deep reinforcement learning (DRL) method for fast observation strategy on WSIs. Imitating the diagnostic logic of human pathologists, our RL agent learns how to find regions of observation value and obtain representative features across multiple resolution levels, without having to analyze each part of the WSI at the high magnification. We benchmark our method on two whole-slide level classification tasks, including detection of metastases in WSIs of lymph node sections, and subtyping of lung cancer. Experimental results demonstrate that RLogist achieves competitive classification performance compared to typical multiple instance learning algorithms, while having a significantly short observation path. In addition, the observation path given by RLogist provides good decision-making interpretability, and its ability of reading path navigation can potentially be used by pathologists for educational/assistive purposes. Our code is available at: \url{https://github.com/tencent-ailab/RLogist}.

Federated learning seeks to address the issue of isolated data islands by making clients disclose only their local training models. However, it was demonstrated that private information could still be inferred by analyzing local model parameters, such as deep neural network model weights. Recently, differential privacy has been applied to federated learning to protect data privacy, but the noise added may degrade the learning performance much. Typically, in previous work, training parameters were clipped equally and noises were added uniformly. The heterogeneity and convergence of training parameters were simply not considered. In this paper, we propose a differentially private scheme for federated learning with adaptive noise (Adap DP-FL). Specifically, due to the gradient heterogeneity, we conduct adaptive gradient clipping for different clients and different rounds; due to the gradient convergence, we add decreasing noises accordingly. Extensive experiments on real-world datasets demonstrate that our Adap DP-FL outperforms previous methods significantly.

Large language models (LLMs) show excellent performance but are compute- and memory-intensive. Quantization can reduce memory and accelerate inference. However, for LLMs beyond 100 billion parameters, existing methods cannot maintain accuracy or do not run efficiently on hardware. We propose SmoothQuant, a training-free, accuracy-preserving, and general-purpose post-training quantization (PTQ) solution to enable 8-bit weight, 8-bit activation (W8A8) quantization for LLMs that can be implemented efficiently. We observe that systematic outliers appear at fixed activation channels. Based on the fact that weights are easy to quantize while activations are not, SmoothQuant smooths the activation outliers by offline migrating the quantization difficulty from activations to weights with a mathematically equivalent transformation. SmoothQuant enables an INT8 quantization of both weights and activations for all the GEMMs in LLMs, including OPT-175B, BLOOM-176B, and GLM-130B. SmoothQuant has better hardware efficiency than existing techniques using mixed-precision activation quantization or weight-only quantization. We demonstrate up to 1.56x speedup and 2x memory reduction for LLMs with negligible loss in accuracy. Thanks to the hardware-friendly design, we integrate SmoothQuant into FasterTransformer, a state-of-the-art LLM serving framework, and achieve faster inference speed with half the number of GPUs compared to FP16. Our work offers a turn-key solution that reduces hardware costs and democratizes LLMs. Code is available at: https://github.com/mit-han-lab/smoothquant.

Data mixing strategies (e.g., CutMix) have shown the ability to greatly improve the performance of convolutional neural networks (CNNs). They mix two images as inputs for training and assign them with a mixed label with the same ratio. While they are shown effective for vision transformers (ViTs), we identify a token fluctuation phenomenon that has suppressed the potential of data mixing strategies. We empirically observe that the contributions of input tokens fluctuate as forward propagating, which might induce a different mixing ratio in the output tokens. The training target computed by the original data mixing strategy can thus be inaccurate, resulting in less effective training. To address this, we propose a token-label alignment (TL-Align) method to trace the correspondence between transformed tokens and the original tokens to maintain a label for each token. We reuse the computed attention at each layer for efficient token-label alignment, introducing only negligible additional training costs. Extensive experiments demonstrate that our method improves the performance of ViTs on image classification, semantic segmentation, objective detection, and transfer learning tasks. Code is available at: https://github.com/Euphoria16/TL-Align.