Reviewerly/exHarmony huggingface dataset:
Permanent Displacement Distribution From Strong Ground Motion Records of the 2023 Mw7.7 Earthquake.

One of the most damaging earthquake effects occurring in the vicinity of the fault trace is fling step, also known as permanent displacement. However, due to the fact that the standard filtering techniques eliminate the low frequency portions of the motion, the permanent displacements are not seen on the displacement time histories derived from the accelerogram records. Thus, fling step is neglected in many engineering practices. To reveal the permanent displacements, special data processing schemes based on removal of the baseline shifts in separated time windows were proposed. In this study, the most recently proposed data processing scheme eBASCO (Schiappapietra et al., 2021) is improved and the effectiveness of the new scheme is tested by comparing the obtained displacements on the processed records with those derived from nearby GPS data for 25 records from worldwide earthquakes.&amp;#160;Preliminary site screening efforts and geodetic observations demonstrated that the earthquake sequence of February 6, 2023 in Turkey caused remarkable permanent displacements, which might be one of the reasons for severe damage and collapse of the structures, especially those which have long fundamental periods such as pipelines, roadways and high-rise buildings. In this study, near fault records of the earthquake sequence are processed with the proposed scheme and the obtained permanent displacements are evaluated with those predicted by existing models.
Inconsistency and violation of causality in Bayesian inversion paradigms

Probabilistic formulations of inverse problems are most often based on Bayes Rule, which is considered a powerful tool for integration of data information and prior information about potential solutions. However, since its introduction it has become apparent that the Bayesian inference paradigm presents a number of difficulties, especially in the phase where the problem is mathematically formulated.&amp;#160;Perhaps the most notable difficulty arises because Bayes Theorem is usually formulated as a relation between probability densities on continuous manifolds. This creates an acute crisis because of a problem described by the French mathematician Joseph Bertrand (1889), and later investigated by Kolmogorov and Borel. According to Kolmogorov's (1933/1956) investigations, conditioning of a probability density is underdetermined: In different parameterizations (reference frames), conditional probability densities express different probability distributions. Surprisingly, this problem is persistently neglected in the scientific literature, not least in applications of Bayesian inversion. We will explore this problem and show that it is a serious threat to the objectivity and quality of Bayesian computations including Bayesian inversion, computation of Bayes Factors, and trans-dimensional inversion.&amp;#160;Another difficulty in Bayesian Inference methods derives from the fact that data uncertainties, and prior information on the unknown parameters, are often unknown or poorly known. Because they are required in the calculations, statisticians have inventedhierarchical methods to compute parameters (known as hyper-parameters) controlling these uncertainties. However, since both the data uncertainties and the prior information on the unknowns are supposed to be known 'a priori', but are calculated 'a posteriori', this creates another crisis, namely a violation of causality. We will take a close look at the consequences of this mixing of 'prior' and 'posterior', and show how it potentially jeopardizes the validity of Bayesian computations.&amp;#160;
Shared communication for coordinated large-scale reinforcement learning control

Deep Reinforcement Learning (DRL) recently emerged as a possibility to control complex systems without the need to model them mathematically. In contrast to classical controllers, DRL alleviates the need for constant parameter tuning, tedious design of control laws, and re-identification procedures in the event of performance degradation. However, the application of DRL algorithms remains fairly modest, and they have not yet established a significant position in process industries. One major obstacle has been their sample inefficiency when facing tasks featuring large state-action spaces. In this work, we show that it is possible to use DRL for plant-wide control by decentralizing and coordinating reinforcement learning. Namely, we express the global policy as a collection of local policies. Every local policy receives local observations and is responsible for controlling a different region of the environment. To enable coordination among local policies, we present a mechanism based on message passing. Messages are encoded by a shared communication channel, which is equipped with a model-based stream to capture the dynamics of the system and enable effective pre-training. The proposed method is evaluated on a set of robotic tasks and a large-scale vinyl acetate monomer (VAM) plant. Experimental results highlight that the proposed model exhibits drastic improvements over baselines in terms of mean scores and sample efficiency.
Investigating interoperable event corpora: limitations of reusability of resources and portability of models

Abstract Studies on the applicability of heterogeneous semantically interoperable corpora are rare. We investigate to what extent reusability (both of systems and of annotations) is entailed by corpora whose interoperability is based on compliance to standards. In particular, we look at event detection in English texts, supported by the ISO-TimeML annotation scheme. We run two sets of experiments using a common neural network architecture and extensively evaluate our results on both in-distribution and out-of-distribution settings. In all experimental settings, systems obtain state-of-the-art results on the in-distribution data and underperform out-of-distribution ones, setting limits to the benefits of semantically interoperable corpora. By means of a detailed error analysis, we show that while being compliant to a standard guarantees semantic interoperability, this becomes only a necessary condition for reusability, with factors such as differences in the quality of the annotations having a much stronger impact.
Physics news on the Internet (based on electronic preprints)

Yurii Nikolaevich Kul'chin (on his 70th birthday), Bagaev S.N., Garnov S.V., Deyev S.M., Kovalchuk M.V., Kolachevsky N.N., Konov V.I., Krasnikov G.Ya., Latyshev A.V., Panchenko V.Ya., Popov V.O., Khomich V.Yu., Shalagin A.M.
CIFair: Constructing continuous domains of invariant features for image fair classifications

With learning models widely deployed in daily life, researchers are discovering that many of them generate discriminatory predictions towards sensitive attributes such as gender or race. A common way of tackling this problem is to learn fair features without sensitive information by removing features. This method requires extra heads for attribute predictions and eliminates the information via adversarial settings. Although the processes can impose fairness, they reduce the accuracy of the models relative to the originals. In this research, we generate continuous domains containing information from different subgroups with mixup operations. We then learn domain invariant features with similarity constraints. Different from previous methods, the proposed method, CIFair, can learn fair features without feature removal operations or task-irrelevant learning objectives. Finally, we evaluated our approach on the CelebA dataset with different sensitive attributes under multiple settings. All experimental results demonstrate that CIFair is able to impose enforce better fair prediction results than previous methods while maintaining model accuracy performance.
A novel, flexible circuit used to implement selected mathematical operations for AI algorithms optimized for hardware applications

The paper presents a novel circuit that enables an implementation of various mathematical operations with automatic conversion of the resulting multi-bit digital signals into voltage. The circuit consists of a number of parallel branches consisting of a combination of switches and resistors. Depending on the value of the resistive elements used, it enables the implementation of such operations as adding, subtracting and multiplying numbers stored as digital signals. Additionally, it can be used to obtain an approximation of selected nonlinear functions that include logarithmic, exponential, tangent functions. For binary weighted resistances (1, 2, 4, 8, …) in subsequent branches, the circuit works as a conventional voltage digital-to-analog converter. The functions that can be implemented with this circuit enable its application in various AI algorithms implemented as specialized integrated circuits. In this work, we focus in particular on its application to the implementation of basic operators of fuzzy logic systems. The circuit was implemented in CMOS 0.13μm technology.
Gaussian Mixture Filtering with Nonlinear Measurements Minimizing Forward Kullback-Leibler Divergence

A Gaussian mixture filter is proposed for the state estimation of dynamical systems with nonlinear measurements. The filter is derived by solving an assumed density filtering problem where Kullback-Leibler (KL) divergence from the assumed posterior, which is a Gaussian mixture, to the true posterior (which we call forward KL divergence) is minimized. The approximate solution to this problem gives an iterative measurement update by which the weights, means and covariances of the assumed posterior are optimized. The resulting Gaussian mixture filter is shown to be a generalization of the (damped) posterior linearization filter to Gaussian mixture posteriors. The performance of the proposed filter is illustrated and compared to alternatives on a challenging example of target tracking in a sensor network and on an example of tracking with a radar. The results show that the proposed filter can outperform Gaussian filters as well as the Gaussian sum filter and the Gaussian sum particle filter yielding results very close to a bootstrap particle filter when the number of components in the assumed posterior is sufficiently large.
Cloud service provisioning strategy using data center consortium clustering

The rapid growth of data centers provides cloud users with better choices and services. An increasing number of companies are involved in the construction of data centers; at the same time, the original cloud service providers are constantly expanding new data centers to meet the needs of users. Therefore, the joint scheduling of multiple data centers has become a difficult problem that cloud service providers must face. Joint scheduling refers to the resource allocation among multiple data centers of a cloud service provider. That is, how can the virtual machine application of cloud users be deployed on the physical machine in multiple data centers. A single data center provisioning strategy can solve the scheduling problem within the data center. However, multiple data centers can make the previous provisioning strategy invalid due to geographical location and other differences. In this paper, the characteristics of different data centers are analyzed and clustered to form a data center consortium. Then, the virtual machine (VM) is classified according to the deadline. As cloud users purchase virtual machine resources from cloud service providers, they are billed by time in most cases. The deadline of the virtual machine refers to the time when the virtual machine ends its service. Then, we analyze the power consumption of the data center and deploy VMs based on the rule of avoiding hotspot deployment of VMs with priority energy consumption. Finally, a cloud service provisioning strategy using data center consortium clustering (PSDC3) is proposed and verified. It can be seen from the results that the algorithm can significantly reduce the number of hotspots and energy consumption of cloud data centers.
Exploring Changes in Controllability According to Width of Selection Items for Control: Gender Difference Effect on Website Interactivity

This study investigated the controllability that can change depending on the user interface design (UID) of the website. Controllability was analyzed in the same way as the cognitive process of recognizing and interpreting letters, assuming that controllability is affected by an individual's visual span, and significant results were found. Controllability was measured by presenting UIDs with different widths for male and female groups with different visual spans. The sample comprised of 36 undergraduate and graduate students from a university in South Korea (age range: 18–38 years, M = 22.61, and SD = 4.12). A between-subjects experimental design was employed with two groups (before and after UID modification). Each group was recruited with similar male to female ratios, and the changes of controllability were experimented. The results revealed that participants’ perception of controllability was affected by UID and moderated by participant’s gender. As a result of the experiments, gender differences were observed in the visual span for interpreting the perceived objects for controllability, and the interpretation of these results was discussed.
Reducing Time for Performing ASPICE Assessments

The development of embedded systems requires the use of development processes. These processes for development are defined in ASPICE for assessment of their capability. The processes ASPICE covers are about software, system, support, project management, supplier monitoring. It defines a scheme to assess the processes according to the rating scheme NPLF (Not, Partially, Largely, Fully). HW Spice is not part of the current version of ASPICE yet. In order to fulfill the requirements of cybersecurity HW Spice is considered as important part. VDA defines additional processes for cybersecurity to fulfill the requirements of standard ISO/SAE 21434. These additional processes are about Supplier Request and Selection, Risk Management and Engineering. They increase the effort performing an assessment. This paper finds common work products and dependencies to reduce the time for an assessment covering processes of both ASPICE and Cybersecurity.
A Healthcare Delivery System Powered by Semantic Data Description and Blockchain

Continuous technological progress has been supporting health providers with powerful tools to help them improve the delivery of health services and ensure patients’ safety. These adopted technologies focus on data collection. However, patients’ data are distributed and fragmented, which highly affects the quality of care. In this paper, we propose a blockchain-based approach to improve the management of patients’ data by addressing: the adverse drug interactions or reactions due to inappropriate medication intake. Although the initial target of our contribution is the healthcare system in Lebanon, our solution can be applied to improve any healthcare system. It combines the usage of a blockchain with Semantic Web technologies, where the blockchain provides a secure infrastructure for data sharing and prevents data tampering through a tamper-proof audit log. As for the semantic description of data, it brings a common shared understanding of the data at hand, boosting knowledge discovery, and automating decision making.
Combining Primary and Secondary Skills of Team Members in Demanding Work Conditions

Various aspects of life may require an additional workforce that has not been planned in advance. This is especially demanding when skillful employees are hard to find or nearly impossible to bring to remote places when they are needed. In such cases, one should count on those that are present to handle what a situation demands. One of the important questions is where can one find information about additional skills employees may have and how to reshuffle both work tasks and people in a way that the situation is handled properly. We propose an application of directed graphs for visualizing of dependencies between skills people have for performing their work tasks and additional skills they may possess due to previous education, training, or job.
Nonlinear Merge and Split Based Image Clustering Method Through Changing the Number of Clusters

Nonlinear merge and split based image clustering method through changing the number of clusters is proposed. A nonlinear merge and split method for image clustering is proposed to overcome a complicated situation for the cluster’s distributions in a feature space which is relatively difficult to cluster with the linear approach like a k-means and ISODATA clustering algorithms. Through experiments, it is found that the proposed clustering method with the merge and split method by considering the within and between cluster variances, allows to overcome the complicated situation of the probability density function of clusters. It is also found that the proposed method is superior to the existing clustering method in terms of the similarity between the clustered resultant image and the topographic map of the corresponding map. Furthermore, the effectiveness of the proposed clustering method is shown with the real remote sensing satellite imagery data.
Visual Named Entity Validation for Short Names in Financial Domain with Fine-Tuned BERT and Data Augmentation

Validating OCR-extracted financial entities against a financial organization’s codified data is a very challenging semantic textual similarity task because of limited context, short text, presence of abbreviations/acronyms, and OCR errors. To study this problem, we built a synthetic dataset with images that contain pseudo financial entities for OCR; model-generated short names with abbreviations/acronyms for validation. With fine-tuned BERT and data augmentation, we achieved top notch performance on this task and compared with several baseline systems.
Non-digital Travellers – Five Need-Based Personas to Understand Their Drivers and Needs

Abstract Many people believe that low digital skills are only a problem of the elderly. However, the group of analogue or non-digital travellers is much larger and much more diverse than that. In the Netherlands alone, it is estimated that a group of 3–4 million people is not digitally able enough to make use of digital services. This is due to several reasons. In order to make use of digital mobility services, users need to be able and willing to use digital services. In transport, especially for demand responsive transport (DRT) services, the lack of digital skills can create a barrier for people to make use of the service. Based on insights from literature and interviews about digital skills, we have categorized the different groups of non-digital travellers, and created five need-based personas. On the basis of this, we formulated user requirements and design recommendations for mobility services, and for DRT services specifically.
Do we need early exit networks in human activity recognition?

Deep learning is nowadays considered state-of-the-art technology in many applications thanks to huge performance capabilities. However, the accuracy levels that can be obtained with these models entail computationally demanding resources. This results in a challenging task when such systems have to be deployed on edge devices with tight computing, memory, and communication requirements and when energy expenditure and inference delays have to be kept under control. Early exit is a design methodology aimed at reducing the burden of neural networks on computational resources, trading off accuracy for latency. In this work, we aim at exploring the use of early exit for human activity recognition tasks. In particular, we propose an experimental assessment of the accuracy–latency trade-off on different deep network architectures across various publicly available datasets. We also evaluate the impact of early exiting in distributed environments by taking into account communication technologies. Experimental results provide evidence of the significant gain provided by early exits in terms of latency (up to 35×), without a reduction in accuracy (in most cases), confirming the viability of an adaptive approach. In a distributed environment, early exit results are not beneficial in all situations. In particular, it is not convenient for models that are very fast (with inference latency lower than, or as equal as, that of communication) and for models that are forced to make extensive use of far exit points to satisfy the accuracy requirements. Therefore, communication delays in a distributed environment shape performance in an architecture-dependent way.
RsALUNet: A reinforcement supervision U-Net-based framework for multi-ROI segmentation of medical images

A new multiple region of interest (multi-ROI) segmentation framework, RsALUNet, is proposed in this paper, whose backbone was U-Net. Rs represented the reinforcement-supervision strategy by utilizing adversarial learning (AL) between U-Net’s decoders and an additional discriminator, which was based on the differences among the segmentation results (diffSegs) and labels of multi-ROI (diffLabels). As the AL progressed, diffSegs was increasingly similar to diffLabels, and this further contributed to a more accurate segmentation of each member in the multi-ROI. In addition to the Rs strategy, three blocks were proposed to enhance RsALUNet, namely, a dilated convolution chain providing diverse and large receptive fields to accommodate different target sizes, a fusion block integrating features of large targets to small ones to optimize the segmentation of the latter, and a location-encoder block extracting multi-scale positional information to enhance the model's attention to the ROI. RsALUNet was evaluated through three multi-ROI segmentation tasks using different imaging modalities, including X-ray, ultrasound, and magnetic-resonance (MR) imaging. The mean Dice coefficient (Dice) increased from 1.1% to 8.5% compared to the other frameworks. The results demonstrate the promising adaptability and extendibility of our strategy and RsALUNet for multi-ROI segmentation in X-ray, ultrasound, and MR images.
Statistical Mechanical Analysis of Phase Unwrapping —One-Dimensional Model—

We study a phase unwrapping model in the one-dimensional space on the basis of Bayesian inference using the maximizer of posterior marginals (MPM) estimate by the statistical mechanical methods. We propose a model in which the recursion relations to obtain statistical quantities such as MPM estimates are derived. We introduce the three state Potts model to handle the discontinuities in observed data, and propose two methods, the step and direct methods. We derive the recursion relations for MPM estimates of hyperparameters and phase differences in both methods, and investigate the random and regular phase differences, and previously studied other type of random phase differences. We find that the phase differences are inferred fairly well in rather wide ranges of noise amplitudes. The ranges depend on samples and the system sizes. Furthermore, we find that the step method has performance in phase unwrapping comparable to the direct method, and that it is much faster in numerical computation and applicable to much larger system sizes than the direct method.