Reviewerly/exHarmony huggingface dataset:
Understanding via Exploration: Discovery of Interpretable Features With Deep Reinforcement Learning

Understanding the environments through interactions has been one of the most important human intellectual activities in mastering unknown systems. Deep reinforcement learning (DRL) has already been known to achieve effective control through human-like exploration and exploitation in many applications. However, the opaque nature of deep neural network (DNN) often hides critical information about feature relevance to control, which is essential for understanding the target systems. In this article, a novel online feature selection framework, namely, the dual-world-based attentive feature selection (D-AFS), is first proposed to identify the contribution of the inputs over the whole control process. Rather than the one world used in most DRL, D-AFS has both the real world and its virtual peer with twisted features. The newly introduced attention-based evaluation (AR) module performs the dynamic mapping from the real world to the virtual world. The existing DRL algorithms, with slight modification, can learn in the dual world. By analyzing the DRL's response in the two worlds, D-AFS can quantitatively identify respective features' importance toward control. A set of experiments is performed on four classical control systems in OpenAI Gym. Results show that D-AFS can generate the same or even better feature combinations than the solutions provided by human experts and seven recent feature selection baselines. In all cases, the selected feature representations are closely correlated with the ones used by underlying system dynamic models.
BASS: Broad Network Based on Localized Stochastic Sensitivity

The training of the standard broad learning system (BLS) concerns the optimization of its output weights via the minimization of both training mean square error (MSE) and a penalty term. However, it degrades the generalization capability and robustness of BLS when facing complex and noisy environments, especially when small perturbations or noise appear in input data. Therefore, this work proposes a broad network based on localized stochastic sensitivity (BASS) algorithm to tackle the issue of noise or input perturbations from a local perturbation perspective. The localized stochastic sensitivity (LSS) prompts an increase in the network's noise robustness by considering unseen samples located within a Q -neighborhood of training samples, which enhances the generalization capability of BASS with respect to noisy and perturbed data. Then, three incremental learning algorithms are derived to update BASS quickly when new samples arrive or the network is deemed to be expanded, without retraining the entire model. Due to the inherent superiorities of the LSS, extensive experimental results on 13 benchmark datasets show that BASS yields better accuracies on various regression and classification problems. For instance, BASS uses fewer parameters (12.6 million) to yield 1% higher Top-1 accuracy in comparison to AlexNet (60 million) on the large-scale ImageNet (ILSVRC2012) dataset.
Confusion-Based Metric Learning for Regularizing Zero-Shot Image Retrieval and Clustering

Deep metric learning turns to be attractive in zero-shot image retrieval and clustering (ZSRC) task in which a good embedding/metric is requested such that the unseen classes can be distinguished well. Most existing works deem this "good" embedding just to be the discriminative one and race to devise the powerful metric objectives or the hard-sample mining strategies for learning discriminative deep metrics. However, in this article, we first emphasize that the generalization ability is also a core ingredient of this "good" metric and it largely affects the metric performance in zero-shot settings as a matter of fact. Then, we propose the confusion-based metric learning (CML) framework to explicitly optimize a robust metric. It is mainly achieved by introducing two interesting regularization terms, i.e., the energy confusion (EC) and diversity confusion (DC) terms. These terms daringly break away from the traditional deep metric learning idea of designing discriminative objectives and instead seek to "confuse" the learned model. These two confusion terms focus on local and global feature distribution confusions, respectively. We train these confusion terms together with the conventional deep metric objective in an adversarial manner. Although it seems weird to "confuse" the model learning, we show that our CML indeed serves as an efficient regularization framework for deep metric learning and it is applicable to various conventional metric methods. This article empirically and experimentally demonstrates the importance of learning an embedding/metric with good generalization, achieving the state-of-the-art performances on the popular CUB, CARS, Stanford Online Products, and In-Shop datasets for ZSRC tasks.
Flying in Dynamic Scenes With Multitarget Velocimetry and Perception-Enhanced Planning

Micro drones have been widely applied in applications, such as aerial cinematography and environment exploration. Due to the presence of numerous demanding scenes, such as crowded obstacles and dynamic objects, remotely controlling a drone poses a significant challenge for humans. As a result, there is an urgent need for highly autonomous flight capabilities. This article presents a fully autonomous flight system in a complex dynamic environment, showing satisfactory performance in real-world tests, and outperforms the state-of-the-art works in both dynamic object perception, and flight safety and efficiency. A lightweight but effective multiobject velocimetry based on a cross-correlation algorithm and local points feature is proposed, with a robust image-based object classifier as the front end. Also, we plan the flight trajectory considering the camera's field of view and the uncertainty in the dynamic object's constant velocity model. At last, we further explore the benefits of vehicle's active yaw control for improving perception quality and flight safety.
Data, language and graph-based reasoning methods for identification of human malicious behaviors in nuclear security

Identification of human malicious behaviors is a demanding problem in nuclear security due to its potential to cause huge personnel and economic casualties. With the visibly rapid development of deep learning technology especially computer vision, the automatic identification of human malicious behaviors become feasible, offering significant savings in human resources. However, there is few research focusing on the explainable reasoning process based on the calculation results of computer vision. In this paper, an innovative framework is proposed, which contains two modules called computer vision module and reasoning module. Among them, the computer vision module could extract and detect human actions by some computer vision technologies, while these actions would be comprehensively utilized in reasoning module to derive the reasoning results. Furthermore, within the reasoning module, three novel reasoning methods are developed which are data-based reasoning method, language-based reasoning method and graph-based reasoning method. For evaluating the practicality and effectiveness of these methods, experiments are conducted on four typical scenarios in nuclear security, which are normal status, fence climbing, wire cutting and weapon holding. The results show that the proposed language-based reasoning method turns out to be the best one, which obtain a higher precision value of 0.7917 and a perfect recall value of 1.0000.
Diagnostic Performance of Artificial Intelligence–Based Computer-Aided Detection Software for Automated Breast Ultrasound

This study aimed to evaluate the diagnostic performance of radiologists following the utilization of artificial intelligence (AI)-based computer-aided detection software (CAD) in detecting suspicious lesions in automated breast ultrasounds (ABUS).ABUS-detected 262 breast lesions (histopathological verification; January 2020 to December 2022) were included. Two radiologists reviewed the images and assigned a Breast Imaging Reporting and Data System (BI-RADS) category. ABUS images were classified as positive or negative using AI-CAD. The BI-RADS category was readjusted in four ways: the radiologists modified the BI-RADS category using the AI results (AI-aided 1), upgraded or downgraded based on AI results (AI-aided 2), only upgraded for positive results (AI-aided 3), or only downgraded for negative results (AI-aided 4). The AI-aided diagnostic performances were compared to radiologists. The AI-CAD-positive and AI-CAD-negative cancer characteristics were compared.For 262 lesions (145 malignant and 117 benign) in 231 women (mean age, 52.2 years), the area under the receiver operator characteristic curve (AUC) of radiologists was 0.870 (95% confidence interval [CI], 0.832-0.908). The AUC significantly improved to 0.919 (95% CI, 0.890-0.947; P = 0.001) using AI-aided 1, whereas it improved without significance to 0.884 (95% CI, 0.844-0.923), 0.890 (95% CI, 0.852-0.929), and 0.890 (95% CI, 0.853-0.928) using AI-aided 2, 3, and 4, respectively. AI-CAD-negative cancers were smaller, less frequently exhibited retraction phenomenon, and had lower BI-RADS category. Among nonmass lesions, AI-CAD-negative cancers showed no posterior shadowing.AI-CAD implementation significantly improved the radiologists' diagnostic performance and may serve as a valuable diagnostic tool.
An Implicit-Explicit Prototypical Alignment Framework for Semi-Supervised Medical Image Segmentation

Semi-supervised learning methods have been explored to mitigate the scarcity of pixel-level annotation in medical image segmentation tasks. Consistency learning, serving as a mainstream method in semi-supervised training, suffers from low efficiency and poor stability due to inaccurate supervision and insufficient feature representation. Prototypical learning is one potential and plausible way to handle this problem due to the nature of feature aggregation in prototype calculation. However, the previous works have not fully studied how to enhance the supervision quality and feature representation using prototypical learning under the semi-supervised condition. To address this issue, we propose an implicit-explicit alignment (IEPAlign) framework to foster semi-supervised consistency training. In specific, we develop an implicit prototype alignment method based on dynamic multiple prototypes on-the-fly. And then, we design a multiple prediction voting strategy for reliable unlabeled mask generation and prototype calculation to improve the supervision quality. Afterward, to boost the intra-class consistency and inter-class separability of pixel-wise features in semi-supervised segmentation, we construct a region-aware hierarchical prototype alignment, which transmits information from labeled to unlabeled and from certain regions to uncertain regions. We evaluate IEPAlign on three medical image segmentation tasks. The extensive experimental results demonstrate that the proposed method outperforms other popular semi-supervised segmentation methods and achieves comparable performance with fully-supervised training methods.
Applying artificial intelligence and computational modeling to nanomedicine

Achieving specific and targeted delivery of nanomedicines to diseased tissues is a major challenge. This is because the process of designing, formulating, testing, and selecting a nanoparticle delivery vehicle for a specific disease target is governed by complex multivariate interactions. Computational modeling and artificial intelligence are well-suited for analyzing and modeling large multivariate datasets in short periods of time. Computational approaches can be applied to help design nanomedicine formulations, interpret nanoparticle–biological interactions, and create models from high-throughput screening techniques to improve the selection of the ideal nanoparticle carrier. In the future, many steps in the nanomedicine development process will be done computationally, reducing the number of experiments and time needed to select the ideal nanomedicine formulation.
Study of the seismic activity of the Almalik-Angren industrial zone based on lineament analysis

In this work, an automated lineament analysis was carried out to search for earthquake precursors for the territory of the Almalyk-Angren industrial zone in Uzbekistan. The seven events with a magnitude of about 3 were selected for analysis. The Landsat 8 satellite images were processed using the automated lineament detection method in the LEFA software. The processing steps included detecting line elements in raster images, calculating the characteristics of the spatial distribution of line elements, and combining collinear linear elements into lineaments. The analyses of the cyclicality of precursors before and after earthquakes were based on the study of the distribution of the lineament trend in the study area using rose diagrams and lineament density maps. The results showed a change in the dynamics of the lineament structure. The statistics of the number of lineaments showed that their increase begins almost 20 days before the event, reaches its maximum about 1 – 2 days before the earthquake, decreases starting from 14 days after the earthquake, and has a minimum value of 1 – 2 months. The main trends observed in the lineament map showed the dominant trend in NS, WE, NW-SE directions.
ML interpretability: Simple isn't easy

The interpretability of ML models is important, but it is not clear what it amounts to. So far, most philosophers have discussed the lack of interpretability of black-box models such as neural networks, and methods such as explainable AI that aim to make these models more transparent. The goal of this paper is to clarify the nature of interpretability by focussing on the other end of the "interpretability spectrum". The reasons why some models, linear models and decision trees, are highly interpretable will be examined, and also how more general models, MARS and GAM, retain some degree of interpretability. It is found that while there is heterogeneity in how we gain interpretability, what interpretability is in particular cases can be explicated in a clear manner.
Susceptibility of AutoML mortality prediction algorithms to model drift caused by the COVID pandemic

Abstract Background Concept drift and covariate shift lead to a degradation of machine learning (ML) models. The objective of our study was to characterize sudden data drift as caused by the COVID pandemic. Furthermore, we investigated the suitability of certain methods in model training to prevent model degradation caused by data drift. Methods We trained different ML models with the H2O AutoML method on a dataset comprising 102,666 cases of surgical patients collected in the years 2014–2019 to predict postoperative mortality using preoperatively available data. Models applied were Generalized Linear Model with regularization, Default Random Forest, Gradient Boosting Machine, eXtreme Gradient Boosting, Deep Learning and Stacked Ensembles comprising all base models. Further, we modified the original models by applying three different methods when training on the original pre-pandemic dataset: (1) we weighted older data weaker, (2) used only the most recent data for model training and (3) performed a z-transformation of the numerical input parameters. Afterwards, we tested model performance on a pre-pandemic and an in-pandemic data set not used in the training process, and analysed common features. Results The models produced showed excellent areas under receiver-operating characteristic and acceptable precision-recall curves when tested on a dataset from January-March 2020, but significant degradation when tested on a dataset collected in the first wave of the COVID pandemic from April-May 2020. When comparing the probability distributions of the input parameters, significant differences between pre-pandemic and in-pandemic data were found. The endpoint of our models, in-hospital mortality after surgery, did not differ significantly between pre- and in-pandemic data and was about 1% in each case. However, the models varied considerably in the composition of their input parameters. None of our applied modifications prevented a loss of performance, although very different models emerged from it, using a large variety of parameters. Conclusions Our results show that none of our tested easy-to-implement measures in model training can prevent deterioration in the case of sudden external events. Therefore, we conclude that, in the presence of concept drift and covariate shift, close monitoring and critical review of model predictions are necessary.
AE-Qdrop: Towards Accurate and Efficient Low-Bit Post-Training Quantization for A Convolutional Neural Network

Blockwise reconstruction with adaptive rounding helps achieve acceptable 4-bit post-training quantization accuracy. However, adaptive rounding is time intensive, and the optimization space of weight elements is constrained to a binary set, thus limiting the performance of quantized models. The optimality of block-wise reconstruction requires that subsequent network blocks remain unquantized. To address this, we propose a two-stage post-training quantization scheme, AE-Qdrop, encompassing block-wise reconstruction and global fine-tuning. In the block-wise reconstruction stage, a progressive optimization strategy is introduced as a replacement for adaptive rounding, enhancing both quantization accuracy and efficiency. Additionally, the integration of randomly weighted quantized activation helps mitigate the risk of overfitting. In the global fine-tuning stage, the weights of each quantized network block are corrected simultaneously through logit matching and feature matching. Experiments in image classification and object detection tasks validate that AE-Qdrop achieves high precision and efficient quantization. For the 2-bit MobileNetV2, AE-Qdrop outperforms Qdrop in quantization accuracy by 6.26%, and its quantization efficiency is fivefold higher.
SCDetect: A SeisComP Module for Real-Time Waveform Cross-Correlation-Based Earthquake Detection

Abstract Enhanced earthquake catalogs based on waveform cross correlation (template matching) have become routine when studying regional or sequence specific seismicity. Currently, there is no existing open-source cross-correlation software that is designed to be fully integrated in real-time operations of seismic networks. To fill this gap, we introduce SCDetect, a software that implements real-time earthquake detection based on waveform cross correlation in the time domain. SCDetect with the extension module scdetect-cc is an open-source SeisComP package written in C++. scdetect-cc can be used to process both archived waveform data (playback mode), and real-time data. In the real-time application, waveforms are fetched through one of the SeisComP RecordStream interfaces, and its output (picks, origin times, amplitudes, and magnitudes) are sent to the SeisComP messaging system. The new origins are associated either with existing events detected by other pick-based SeisComP modules, or create new events. Optionally, the hypocenter location can be refined by downstream application of existing SeisComP modules. scdetect-cc offers two magnitude estimation methods that are based on the amplitudes of the template earthquakes and the new detections. In the real-time application, scdetect-cc can be scaled to handle thousands of templates without overloading the application or becoming too latent, unable to keep up with the data flow. In the playback mode, we applied scdetect-cc to three recent earthquake sequences occurring in Switzerland and surrounding regions between 2019 and 2020. Two scenarios are tested to simulate its performance in real time. The first scenario cross correlates the signals of the nearest station, whereas the second requires four stations. In both cases, we successfully detected most of the cataloged events and added hundreds of new detections. Overall, scdetect-cc is a computationally efficient and highly customizable tool to detect earthquakes for regional networks that implement the SeisComP system for earthquake monitoring.
On the different regimes of stochastic gradient descent

Modern deep networks are trained with stochastic gradient descent (SGD) whose key hyperparameters are the number of data considered at each step or batch size <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>B</mml:mi> </mml:math> , and the step size or learning rate <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>η</mml:mi> </mml:math> . For small <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>B</mml:mi> </mml:math> and large <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>η</mml:mi> </mml:math> , SGD corresponds to a stochastic evolution of the parameters, whose noise amplitude is governed by the “temperature” <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:mi>T</mml:mi> <mml:mo>≡</mml:mo> <mml:mi>η</mml:mi> <mml:mo stretchy="false">/</mml:mo> <mml:mi>B</mml:mi> </mml:mrow> </mml:math> . Yet this description is observed to break down for sufficiently large batches <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:mi>B</mml:mi> <mml:mo>≥</mml:mo> <mml:msup> <mml:mi>B</mml:mi> <mml:mo>∗</mml:mo> </mml:msup> </mml:mrow> </mml:math> , or simplifies to gradient descent (GD) when the temperature is sufficiently small. Understanding where these cross-overs take place remains a central challenge. Here, we resolve these questions for a teacher-student perceptron classification model and show empirically that our key predictions still apply to deep networks. Specifically, we obtain a phase diagram in the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>B</mml:mi> </mml:math> - <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>η</mml:mi> </mml:math> plane that separates three dynamical phases: i) a noise-dominated SGD governed by temperature, ii) a large-first-step-dominated SGD and iii) GD. These different phases also correspond to different regimes of generalization error. Remarkably, our analysis reveals that the batch size <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msup> <mml:mi>B</mml:mi> <mml:mo>∗</mml:mo> </mml:msup> </mml:math> separating regimes (i) and (ii) scale with the size <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>P</mml:mi> </mml:math> of the training set, with an exponent that characterizes the hardness of the classification problem.