Reviewerly/exHarmony huggingface dataset:
ML interpretability: Simple isn't easy

Authors:
raw_author_name: Tim Räz
raw_affiliation_string: University of Bern, Institute of Philosophy, Länggassstrasse 49a, 3012 Bern, Switzerland

Abstract: 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.
AE-Qdrop: Towards Accurate and Efficient Low-Bit Post-Training Quantization for A Convolutional Neural Network

Authors:
raw_author_name: Gang Chen
raw_affiliation_string: Beijing Key Lab of Semiconductor Neural Network Intelligent Perception and Computing Technology, Beijing 100083, China; Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China; University of Chinese Academy of Sciences, Beijing 100089, China
raw_author_name: Min Jin
raw_affiliation_string: Beijing Key Lab of Semiconductor Neural Network Intelligent Perception and Computing Technology, Beijing 100083, China; Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China; University of Chinese Academy of Sciences, Beijing 100089, China
raw_author_name: Wenyu Mao
raw_affiliation_string: Beijing Key Lab of Semiconductor Neural Network Intelligent Perception and Computing Technology, Beijing 100083, China; Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China; University of Chinese Academy of Sciences, Beijing 100089, China
raw_author_name: Huaxiang Lu
raw_affiliation_string: Beijing Key Lab of Semiconductor Neural Network Intelligent Perception and Computing Technology, Beijing 100083, China; Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China; University of Chinese Academy of Sciences, Beijing 100089, China

Abstract: 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

Authors:
raw_author_name: Maria Mesimeri
raw_affiliation_string: 1Swiss Seismological Service, ETH Zürich, Zürich, Switzerland
raw_author_name: Daniel Armbruster
raw_affiliation_string: 1Swiss Seismological Service, ETH Zürich, Zürich, Switzerland
raw_author_name: Philip Kästli
raw_affiliation_string: 1Swiss Seismological Service, ETH Zürich, Zürich, Switzerland
raw_author_name: Luca Scarabello
raw_affiliation_string: 1Swiss Seismological Service, ETH Zürich, Zürich, Switzerland
raw_author_name: Tobias Diehl
raw_affiliation_string: 1Swiss Seismological Service, ETH Zürich, Zürich, Switzerland
raw_author_name: John Clinton
raw_affiliation_string: 1Swiss Seismological Service, ETH Zürich, Zürich, Switzerland
raw_author_name: Stefan Wiemer
raw_affiliation_string: 1Swiss Seismological Service, ETH Zürich, Zürich, Switzerland

Abstract: 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

Authors:
raw_author_name: Matthieu Wyart
raw_affiliation_string: Institute of Physics, Ecole Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland

Abstract: 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.
Statistical learning of motor preparation.

Authors:
raw_author_name: Jan Theeuwes
raw_affiliation_string: Cognitive Psychology, Vrije Universiteit Amsterdam.
raw_author_name: Changrun Huang
raw_affiliation_string: Cognitive Psychology, Vrije Universiteit Amsterdam.
raw_author_name: Christian Frings
raw_affiliation_string: Cognitive Psychology, University of Trier.

Abstract: Statistical learning, the process of extracting regularities from the environment, is one of the most fundamental abilities playing an essential role in almost all aspects of human cognition. Previous studies have shown that attentional selection is biased toward locations that are likely to contain a target and away from locations that are likely to contain a distractor. The current study investigated whether participants can also learn to extract that a specific motor response is more likely when the target is presented at specific locations within the visual field. To that end, the additional singleton paradigm was adapted such that when the singleton target was presented at one specific location, one response (e.g., right index finger) was more likely than the other (e.g., right middle finger) and the reverse was true for another location. The results show that participants learned to extract that a particular motor response is more likely when the singleton target (which was unrelated to the response) was presented at a specific location within the visual field. The results also suggest that it is the location of the target and not its shape that is associated with the biased response. This learning cannot be considered as being top-down or conscious as participants showed little, if any, awareness of the response biases present. The results are discussed in terms of the event coding theory. The study increases the scope of statistical learning and shows how individuals adapt automatically, without much awareness, to the regularities present in the environment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
A decentralized and security-enhanced professional title evaluation system in universities under mobile Internet of Things

Authors:
raw_author_name: Miao Miao
raw_affiliation_string: Department of human Resources, Jiangsu University of Technology, 1801 Zhongwu Road, Changzhou, Jiangsu, 213001, China
raw_author_name: Zhengjun Jing
raw_affiliation_string: College of computer engineering, Jiangsu University of Technology, 1801 Zhongwu Road, Changzhou, Jiangsu, 213001, China
raw_author_name: Xiaolong Xu
raw_affiliation_string: College of computer engineering, Jiangsu University of Technology, 1801 Zhongwu Road, Changzhou, Jiangsu, 213001, China

Abstract: <h2>Abstract</h2> Digital online application is a commonly used method for professional title evaluation in colleges and universities. With the rapid development of the mobile Internet of Things, the fastest way for users to access the evaluation system is through mobile devices. However, this also poses security challenges to the traditional professional title evaluation system. Therefore, this paper proposes a decentralized and security-enhanced digital title evaluation system in mobile IoT. Since the system uses blockchain to implement a decentralized review process, the transparency and fairness of the review process are guaranteed. At the same time, the review data and reviewee information in the system are encrypted and stored in the distributed network to ensure the security and non-tamperability of the data. All review records are recorded on the blockchain, and anyone can view and verify the legitimacy of the review results. Additionally, the system incorporates security enhancement mechanisms such as identity verification, smart contracts, and auditing functions to improve the credibility of the evaluation process and prevent cheating under mobile IoT. Experiments have shown that this system has important application value in the field of professional title review in universities, which can improve review efficiency, reduce human intervention, and provide strong support for the credibility of review results.
Artificial intelligence’s impact on breast cancer pathology: a literature review

Authors:
raw_author_name: Amr Soliman
raw_affiliation_string: Department of Pathology, Ohio State University, Columbus, OH, USA.
raw_author_name: Zaibo Li
raw_affiliation_string: Department of Pathology, Ohio State University, Columbus, OH, USA.
raw_author_name: Anil V. Parwani
raw_affiliation_string: Department of Pathology, Ohio State University, Columbus, OH, USA. anil.parwani@osumc.edu.

Abstract: Abstract This review discusses the profound impact of artificial intelligence (AI) on breast cancer (BC) diagnosis and management within the field of pathology. It examines the various applications of AI across diverse aspects of BC pathology, highlighting key findings from multiple studies. Integrating AI into routine pathology practice stands to improve diagnostic accuracy, thereby contributing to reducing avoidable errors. Additionally, AI has excelled in identifying invasive breast tumors and lymph node metastasis through its capacity to process large whole-slide images adeptly. Adaptive sampling techniques and powerful convolutional neural networks mark these achievements. The evaluation of hormonal status, which is imperative for BC treatment choices, has also been enhanced by AI quantitative analysis, aiding interobserver concordance and reliability. Breast cancer grading and mitotic count evaluation also benefit from AI intervention. AI-based frameworks effectively classify breast carcinomas, even for moderately graded cases that traditional methods struggle with. Moreover, AI-assisted mitotic figures quantification surpasses manual counting in precision and sensitivity, fostering improved prognosis. The assessment of tumor-infiltrating lymphocytes in triple-negative breast cancer using AI yields insights into patient survival prognosis. Furthermore, AI-powered predictions of neoadjuvant chemotherapy response demonstrate potential for streamlining treatment strategies. Addressing limitations, such as preanalytical variables, annotation demands, and differentiation challenges, is pivotal for realizing AI’s full potential in BC pathology. Despite the existing hurdles, AI’s multifaceted contributions to BC pathology hold great promise, providing enhanced accuracy, efficiency, and standardization. Continued research and innovation are crucial for overcoming obstacles and fully harnessing AI’s transformative capabilities in breast cancer diagnosis and assessment. Graphical Abstract
Multi-Label Contrastive Learning for Abstract Visual Reasoning

Authors:
raw_author_name: Mikolaj Malkinski
raw_affiliation_string: Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
raw_author_name: Jacek Mandziuk
raw_affiliation_string: Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland

Abstract: For a long time, the ability to solve abstract reasoning tasks was considered one of the hallmarks of human intelligence. Recent advances in the application of deep learning (DL) methods led to surpassing human abstract reasoning performance, specifically in the most popular type of such problems-Raven's progressive matrices (RPMs). While the efficacy of DL systems is indeed impressive, the way they approach the RPMs is very different from that of humans. State-of-the-art systems solving RPMs rely on massive pattern-based training and sometimes on exploiting biases in the dataset, whereas humans concentrate on the identification of the rules/concepts underlying the RPM to be solved. Motivated by this cognitive difference, this work aims at combining DL with the human way of solving RPMs. Specifically, we cast the problem of solving RPMs into a multilabel classification framework where each RPM is viewed as a multilabel data point, with labels determined by the set of abstract rules underlying the RPM. For efficient training of the system, we present a generalization of the noise contrastive estimation algorithm to the case of multilabel samples and a new sparse rule encoding scheme for RPMs. The proposed approach is evaluated on the two most popular benchmark datasets [I-RAVEN and procedurally generated matrices (PGM)] and on both of them demonstrate an advantage over the state-of-the-art results.
Unsupervised Feature Selection With Flexible Optimal Graph

Authors:
raw_author_name: Hong Chen
raw_affiliation_string: School of Computer Science, the School of Artificial Intelligence, Optics and Electronics (iOPEN), and the Key Laboratory of Intelligent Interaction and Applications, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi&#x2019;an, Shaanxi, China
raw_author_name: Feiping Nie
raw_affiliation_string: School of Computer Science, the School of Artificial Intelligence, Optics and Electronics (iOPEN), and the Key Laboratory of Intelligent Interaction and Applications, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi&#x2019;an, Shaanxi, China
raw_author_name: Rong Wang
raw_affiliation_string: School of Artificial Intelligence, Optics and Electronics (iOPEN), and the Key Laboratory of Intelligent Interaction and Applications, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi&#x2019;an, Shaanxi, China
raw_author_name: Xuelong Li
raw_affiliation_string: School of Artificial Intelligence, Optics and Electronics (iOPEN), and the Key Laboratory of Intelligent Interaction and Applications, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi&#x2019;an, Shaanxi, China

Abstract: In the unsupervised feature selection method based on spectral analysis, constructing a similarity matrix is a very important part. In existing methods, the linear low-dimensional projection used in the process of constructing the similarity matrix is too hard, it is very challenging to construct a reliable similarity matrix. To this end, we propose a method to construct a flexible optimal graph. Based on this, we propose an unsupervised feature selection method named unsupervised feature selection with flexible optimal graph and l2,1 -norm regularization (FOG-R). Unlike other methods that use linear projection to approximate the low-dimensional manifold of the original data when constructing a similarity matrix, FOG-R can learn a flexible optimal graph, and by combining flexible optimal graph learning and feature selection into a unified framework to get an adaptive similarity matrix. In addition, an iterative algorithm with a strict convergence proof is proposed to solve FOG-R. l2,1 -norm regularization will introduce an additional regularization parameter, which will cause parameter-tuning trouble. Therefore, we propose another unsupervised feature selection method, that is, unsupervised feature selection with a flexible optimal graph and l2,0 -norm constraint (FOG-C), which can avoid tuning additional parameters and obtain a more sparse projection matrix. Most critically, we propose an effective iterative algorithm that can solve FOG-C globally with strict convergence proof. Comparative experiments conducted on 12 public datasets show that FOG-R and FOG-C perform better than the other nine state-of-the-art unsupervised feature selection algorithms.
Refining Graph Structure for Incomplete Multi-View Clustering

Authors:
raw_author_name: Xiang-Long Li
raw_affiliation_string: School of Computer Science and Engineering, Guangdong Province Key Laboratory of Computational Science, Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Sun Yat-sen University, Guangzhou, China
raw_author_name: Man-Sheng Chen
raw_affiliation_string: School of Computer Science and Engineering, Guangdong Province Key Laboratory of Computational Science, Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Sun Yat-sen University, Guangzhou, China
raw_author_name: Chang-Dong Wang
raw_affiliation_string: School of Computer Science and Engineering, Guangdong Province Key Laboratory of Computational Science, Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Sun Yat-sen University, Guangzhou, China
raw_author_name: Jian-Huang Lai
raw_affiliation_string: School of Computer Science and Engineering, Guangdong Key Laboratory of Information Security Technology, Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Sun Yat-sen University, Guangzhou, China

Abstract: As a challenging problem, incomplete multi-view clustering (MVC) has drawn much attention in recent years. Most of the existing methods contain the feature recovering step inevitably to obtain the clustering result of incomplete multi-view datasets. The extra target of recovering the missing feature in the original data space or common subspace is difficult for unsupervised clustering tasks and could accumulate mistakes during the optimization. Moreover, the biased error is not taken into consideration in the previous graph-based methods. The biased error represents the unexpected change of incomplete graph structure, such as the increase in the intra-class relation density and the missing local graph structure of boundary instances. It would mislead those graph-based methods and degrade their final performance. In order to overcome these drawbacks, we propose a new graph-based method named Graph Structure Refining for Incomplete MVC (GSRIMC). GSRIMC avoids recovering feature steps and just fully explores the existing subgraphs of each view to produce superior clustering results. To handle the biased error, the biased error separation is the core step of GSRIMC. In detail, GSRIMC first extracts basic information from the precomputed subgraph of each view and then separates refined graph structure from biased error with the help of tensor nuclear norm. Besides, cross-view graph learning is proposed to capture the missing local graph structure and complete the refined graph structure based on the complementary principle. Extensive experiments show that our method achieves better performance than other state-of-the-art baselines.