Reviewerly/exHarmony huggingface dataset:
A Real-Time and Data-Driven Ground-Motion Prediction Framework for Earthquake Early Warning

ABSTRACT The ShakeAlert earthquake early warning system in the western United States characterizes earthquake source locations and magnitudes in real time, issuing public alerts for areas where predicted ground-motion intensities exceed a threshold value. Although rapid source characterization methods have attracted significant scientific attention in recent years, the ground-motion models used by ShakeAlert have received notably less. This study develops a data-driven framework for earthquake early warning-specific ground-motion models by precomputing and incorporating site-specific corrections, while using a Bayesian approach to estimate event-specific corrections in real time. The study involves analyzing a quality-controlled set of more than 420,000 seismic recordings from 1389 M 3–7 events in the state of California, from 2011 to 2022. We first compare the observed ground motions to predictions from existing ground-motion models, namely the modified Boore and Atkinson (2008) and active crustal Next Generation Attenuation (NGA)-West2 ground-motion prediction equations, before implementing a new Bayesian model optimized for a real-time setting. Residual analysis of peak ground acceleration and peak ground velocity metrics across a host of earthquake rupture scenarios from the two ground-motion models show that the active crustal NGA-West2 model is better suited for ShakeAlert in California. In addition, the event-terms calculated using our Bayesian approach rapidly converge such that errors from earthquake magnitude estimation can be corrected for when forecasting shaking intensity in real time. Equipped with these improved ground-shaking predictions, we show that refined ShakeAlert warnings could be issued to the public within as soon as 5 s following ShakeAlert’s initial warning. This approach could be used both to reduce prediction uncertainties and thus improve ShakeAlert’s alerting decision.
MATEE

Confidential computing services enable users to run their workloads in Trusted Execution Environments (TEEs) leveraging secure hardware like Intel SGX, and verify them by performing remote attestation. This process offers necessary proof for the integrity of users' software and the authenticity of the hardware, signed by a hardware-specific attestation key. Recent side-channel attacks have successfully retrieved such keys, enabling attackers to forge the attestation data and thereby undermining users' trust in their TEE. If the attestation proof is bound to a second hardware root of trust impervious to side-channel attacks, then the remote attestation process can maintain its security guarantees.
DCert

Light clients have been widely used in blockchain systems to support lightweight nodes by synchronizing and verifying block headers only. However, there are two major limitations with the current light client design. First, with the ever increasing blockchain size, the cost for light clients to process and store all the block headers would soon become prohibitively high. Second, only simple queries can be supported by light clients due to the limited functionality of block headers. To address these issues, in this paper, we propose DCert, a novel decentralized certification framework, to enable superlight clients with constant storage and state validation costs. The main idea is to leverage a trusted enclave (e.g., Intel SGX) to recursively certify the entire history of the blockchain. With DCert, the blockchain integrity can be easily validated by superlight clients with a secure certificate. Furthermore, to support rich verifiable queries on light clients, DCert can be extended to certify authenticated indexes for different types of queries on an as-needed basis. While DCert is compatible with existing blockchain systems, its security is guaranteed by the trusted enclave. Our benchmark-based empirical study shows that DCert incurs a small certification overhead, yet it is capable of supporting efficient verifiable queries with a constant storage size of 2.97 KB and a constant bootstrapping time of 0.14 ms.
Optimizing communication in deep reinforcement learning with <i>XingTian</i>

Deep Reinforcement Learning (DRL) achieves great success in various domains. Communication in today's DRL algorithms takes non-negligible time compared to the computation. However, prior DRL frameworks usually focus on computation management while paying little attention to communication optimization, and fail to utilize the opportunity of the communication-computation overlap that hides the communication from the critical path of DRL algorithms. Consequently, communication can take more time than the computation in prior DRL frameworks. In this paper, we present XingTian, a novel DRL framework that co-designs the management of communication and computation in DRL algorithms. XingTian organizes the computation in DRL algorithms in a decentralized way and provides an asynchronous communication channel. XingTian makes the communication execute asynchronously and aggressively and takes advantage of the communication-computation overlapping opportunity from DRL algorithms. Experimental results show that XingTian improves data transmission efficiency and can transmit at least twice as much data per second as the state-of-the-art DRL framework RLLib. DRL algorithms based on XingTian achieve up to 70.71% more throughput than RLLib-based ones with better or similar convergent performance. XingTian maintains high communication efficiency under different scale deployments and the XingTian-based DRL algorithm achieves 91.12% higher throughput than the RLLib-based one when deployed in four machines. XingTian is open-sourced and publicly available at https://github.com/huawei-noah/xingtian.
Groundwaters and deep-seated fluid circulation around Aso Volcano, Southwest Japan, revealed by multivariate statistical analysis of the geochemical data

To understand the fluid circulation under volcanoes and their surrounding area, geochemical compositions of 590 groundwater and spring/river water samples from the central Kyusyu in SW Japan, including hot and mineral spring waters around Aso and Kuju Volcanoes, have been analyzed for 12 major solutes as well as oxygen‑hydrogen and helium isotopic ratios. To extract independent processes and sources involved in formation of the groundwaters, whitened data-based k-means cluster analysis (KCA) has been applied to the data set of 12 major solutes. Eight clusters have been identified by KCA, which were mapped geographically and were compared with the isotopic compositions, temperature, and pH that were not included in KCA. By the geographical mapping, a concentric zonal structure of the clusters emerges in the Aso caldera, which resulted broadly from two fluid cycling systems from caldera rim and central cones. In the entire study area including Aso and Kuju Volcanoes, the mapping also shows geographical provenance, and the distribution of each cluster shows characteristics related to geological features, such as upwelling of deep-seated fluids along the prominent tectonic lines. On the other hand, the Piper diagram-based classification is significantly different from the KCA results and exhibits neither a concentric zonal structure within the Aso caldera nor a regional geographical provenance. These results suggest that multivariate statistical analysis such as KCA is potentially useful for investigating the origins of groundwaters and shows the advantages over classical Piper diagram-based evaluations.
A deep learning based steganography integration framework for ad-hoc cloud computing data security augmentation using the V-BOINC system

In the early days of digital transformation, the automation, scalability, and availability of cloud computing made a big difference for business. Nonetheless, significant concerns have been raised regarding the security and privacy levels that cloud systems can provide, as enterprises have accelerated their cloud migration journeys in an effort to provide a remote working environment for their employees, primarily in light of the COVID-19 outbreak. The goal of this study is to come up with a way to improve steganography in ad hoc cloud systems by using deep learning. This research implementation is separated into two sections. In Phase 1, the "Ad-hoc Cloud System" idea and deployment plan were set up with the help of V-BOINC. In Phase 2, a modified form of steganography and deep learning were used to study the security of data transmission in ad-hoc cloud networks. In the majority of prior studies, attempts to employ deep learning models to augment or replace data-hiding systems did not achieve a high success rate. The implemented model inserts data images through colored images in the developed ad hoc cloud system. A systematic steganography model conceals from statistics lower message detection rates. Additionally, it may be necessary to incorporate small images beneath huge cover images. The implemented ad-hoc system outperformed Amazon AC2 in terms of performance, while the execution of the proposed deep steganography approach gave a high rate of evaluation for concealing both data and images when evaluated against several attacks in an ad-hoc cloud system environment.
Application of Edutainment Technology in the Development of Cognitive Abilities of Students

The article discusses creative technologies that not only develop cognitive abilities, but also become non-standard tools in enhancing creative activity, including educational activities. By increasing the role of creative approaches to the educational process, it becomes possible to maintain feedback with students, which in modern conditions allows solving a common problem with a decrease in students’ interest and attention to educational subjects, without changing their content. The purpose of the study is to determine the effectiveness of the application of edutainment technology. Methods used in the study: literature review, pedagogical experiment, comparative analysis. The experiment involved two control groups of students, each of which performed tasks using the Six Thinking Hats and Brainstorming methods. As a result of the study, it is confirmed that the possibilities of using edutainment technology in the educational process should be considered effective in teaching and developing students’ cognitive abilities.
RESTORATION OF DISCONTINUOUS FUNCTIONS BY DISCONTINUOUS INTERLINATION SPLINES

Context. The problem of development and research of methods for approximation of discontinuous functions by discontinuous interlination splines and its further application to problems of computed tomography. The object of the study was the modeling of objects with a discontinuous internal structure.&#x0D; Objective. The aim of this study is to develop a general method for constructing discontinuous interlining polynomial splines, which, as a special case, include discontinuous and continuously differentiated splines.&#x0D; Method. Modern methods of restoring functions are characterized by new approaches to obtaining, processing and analyzing information. There is a need to build mathematical models in which information can be represented not only by function values at points, but also in the form of a set of function traces on planes or straight lines.&#x0D; At the same time, practice shows that among the multidimensional objects that need to be investigated, more problems are described by a discontinuous functions.&#x0D; The paper develops a general method for constructing discontinuous interlining polynomial splines, which, as a special case, include discontinuous and continuously differentiable splines. It is considered that the domain of the definition of the required twodimensional function is divided into rectangular elements. Theorems on interlination and approximation properties of such discontinuous constructions are formulated and proved. The method is developed for approximating discontinuous functions of two variables based on the constructed discontinuous splines. The input data are the traces of an unknown function along a given system of mutually perpendicular straight lines. The proposed method has not only theoretical significance but also practical application in the IT domain, especially in computing tomography, allowing more accurately restore the internal structure of the body.&#x0D; Results. The discontinuous interlination operator from known traces of the function of two variables on a system of mutually perpendicular straight lines is researched.&#x0D; Conclusions. The functions of two variables that are discontinuous at some points or on some lines are better approximated by discontinuous spline interlinants. At the same time, equally high approximation estimates can be obtained. The results obtained have significant advantages over existing methods of interpolation and approximation of discontinuous functions. In further research, the authors plan to develop a theory of discontinuous splines on areas of complex shape bounded by arcs of known curves.
Artificial Reality: Immersive but Factually Dishonest AR Experience

As commonly known, technology is a double-edged sword, and augmented reality (AR) is no exception. This article raises concerns and promotes awareness of the use of AR in mass media, in particular in those industries, such as news reporting, that aspire to report facts. Our main message is that the standard workflow for creating AR content, albeit having no mala fide intent, might lead to artificial reality. This titular term was introduced by the first author during a talk organized by a technical committee of IEEE Consumer Technology Society (CTSoc), where we decided that we should urge more ethics and standard discussions on the issue through this article.
HI-Net: Boosting Self-Supervised Indoor Depth Estimation via Pose Optimization

Pose estimation plays a critical role in self-supervised monocular depth estimation for indoor scenes, especially those involving complex ego-motion. In this letter, we leverage the two-view geometry constraints into pose estimation to boost the accuracy of pose estimation, which ultimately improves the performance of self-supervised depth estimation. Specifically, we decompose pose estimation into two steps: initial homography estimation and iterative residual refinement. We first introduce a Homography Estimation Module (HEM) to estimate large 3-DoF rotations. Then, we refine the 6-DoF residual pose estimation with an Iterative Residual Refinement Module (IRM). Finally, the supervision signal is generated with the refined pose and used for the training of DepthNet. Experiments on the NYU depth V2 dataset show that our pose estimation approach significantly improves the performance of DepthNet, and the proposed method achieves state-of-the-art depth estimation results. Furthermore, experiments on the ScanNet dataset demonstrate the generalization ability of our method for both pose estimation and depth estimation.
Reliable Extraction of Semantic Information and Rate of Innovation Estimation for Graph Signals

Semantic signal processing and communications are poised to play a central part in developing the next generation of sensor devices and networks. A crucial component of a semantic system is the extraction of semantic signals from the raw input signals, which has become increasingly tractable with the recent advances in machine learning (ML) and artificial intelligence (AI) techniques. The accurate extraction of semantic signals using the aforementioned ML and AI methods, and the detection of semantic innovation for scheduling transmission and/or storage events are critical tasks for reliable semantic signal processing and communications. In this work, we propose a reliable semantic information extraction framework based on our previous work on semantic signal representations in a hierarchical graph-based structure. The proposed framework includes a time integration method to increase fidelity of ML outputs in a class-aware manner, a graph-edit-distance based metric to detect innovation events at the graph-level and filter out sporadic errors, and a Hidden Markov Model (HMM) to produce smooth and reliable graph signals. The proposed methods within the framework are demonstrated individually and collectively through simulations and case studies based on real-world computer vision examples.
CareDx, Inc. v. Natera, Inc. <i>United States Court of Appeals of the Federal Circuit, 2022 40 F.4th 1371</i>

Biotechnology Law ReportVol. 41, No. 6 Case in PointCareDx, Inc. v. Natera, Inc. United States Court of Appeals of the Federal Circuit, 2022 40 F.4th 1371Edited for Biotechnology Law Report by Christopher M. HolmanEdited for Biotechnology Law Report by Christopher M. HolmanChristopher M. Holman is a Professor of Law at the University of Missouri–Kansas City School of Law; a Senior Scholar at the Center for the Protection of Intellectual Property at the Antonin Scalia Law School, George Mason University; and the Executive Editor of Biotechnology Law Report.*The synopsis is original content and the decision itself has been condensed for ease of readability.Search for more papers by this authorPublished Online:15 Dec 2022https://doi.org/10.1089/blr.2022.29289.cipAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 41Issue 6Dec 2022 InformationCopyright 2022, Mary Ann Liebert, Inc, publishersTo cite this article:Edited for Biotechnology Law Report by Christopher M. Holman.CareDx, Inc. v. Natera, Inc. United States Court of Appeals of the Federal Circuit, 2022 40 F.4th 1371.Biotechnology Law Report.Dec 2022.298-304.http://doi.org/10.1089/blr.2022.29289.cipPublished in Volume: 41 Issue 6: December 15, 2022Online Ahead of Print:November 30, 2022PDF download
Online numerical association rule miner

Green AI refers to those AI methods that are friendly to the environment, i.e., are capable to keep the consumption of electrical energy at a minimum. In this sense, a new numerical association rule miner is proposed that presents a combination of the already existing offline uARMSolver, belonging to a Red AI class, and a newly developed onlineNARM miner representing the new Green AI. The former is devoted to exhaustive search of the evolutionary solution space, while the latter for faster exploiting of already explored search space. The experimental results on four transaction databases showed that, by sacrificing the quality of the results by 0.7∼%, by the onlineNARM we can obtain the results almost 85.0∼% faster than with the uARMSolver in the best test scenario.
<i>Amgen v. Sanofi</i>: The Supreme Court Takes up the Enablement Requirement in the Context of Therapeutic Monoclonal Antibodies

Biotechnology Law ReportVol. 41, No. 6 The Holman ReportAmgen v. Sanofi: The Supreme Court Takes up the Enablement Requirement in the Context of Therapeutic Monoclonal AntibodiesBy Christopher M. HolmanBy Christopher M. HolmanChristopher M. Holman is a Professor of Law at the University of Missouri–Kansas City School of Law; a Senior Scholar at the Center for the Protection of Intellectual Property at the Antonin Scalia Law School, George Mason University; and the Executive Editor of Biotechnology Law Report.Search for more papers by this authorPublished Online:15 Dec 2022https://doi.org/10.1089/blr.2022.29293.cmhAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 41Issue 6Dec 2022 InformationCopyright 2022, Mary Ann Liebert, Inc., publishersTo cite this article:By Christopher M. Holman.Amgen v. Sanofi: The Supreme Court Takes up the Enablement Requirement in the Context of Therapeutic Monoclonal Antibodies.Biotechnology Law Report.Dec 2022.269-282.http://doi.org/10.1089/blr.2022.29293.cmhPublished in Volume: 41 Issue 6: December 15, 2022Online Ahead of Print:December 7, 2022PDF download
An imbalanced binary classification method via space mapping using normalizing flows with class discrepancy constraints

For imbalanced binary classification, most methods classify data in the original space by balancing data or modifying classification algorithms. However, for datasets with severe overlaps and complex class boundaries, the classification performance is limited by the ability of the classifier to fit the decision boundary. A practical alternative is to map the data to a latent space with lower classification difficulty. In this paper, we propose an imbalanced binary classification method via space mapping using normalizing flows with class discrepancy constraints. The flow-based model is employed to map the original data to a latent space, ensuring the accuracy of the mapping and the authenticity of the mapped distribution. To solve the problem of overlaps and within-class imbalance, class discrepancy constraints, including global and local constraints, are proposed to modify the flow-based model. The former maps different subclusters to the same cluster of the latent space, and the latter increases the separability of different classes. Classification thus can be carried out in the latent space with simpler distribution and high separability to achieve better classification performance. Experimental results on 35 KEEL and UCI public datasets indicate that the proposed method outperforms other state-of-the-art methods on F1-measure and G-mean.
Voicing the story behind the cover

In this selection, we celebrate the art of science by highlighting some of the submitted cover images from the past year. In this collection, our authors share the stories behind their inspiration for how to portray their science to captivate a broader audience. In this selection, we celebrate the art of science by highlighting some of the submitted cover images from the past year. In this collection, our authors share the stories behind their inspiration for how to portray their science to captivate a broader audience.
Deep feature based cross-slide registration

Registration of multiple sections in a tissue block is an important pre-requisite task before any cross-slide image analysis. Non-rigid registration methods are capable of finding correspondence by locally transforming a moving image. These methods often rely on an initial guess to roughly align an image pair linearly and globally. This is essential to prevent convergence to a non-optimal minimum. We explore a deep feature based registration (DFBR) method which utilises data-driven descriptors to estimate the global transformation. A multi-stage strategy is adopted for improving the quality of registration. A visualisation tool is developed to view registered pairs of WSIs at different magnifications. With the help of this tool, one can apply a transformation on the fly without the need to generate a transformed moving WSI in a pyramidal form. We compare the performance on our dataset of data-driven descriptors with that of hand-crafted descriptors. Our approach can align the images with only small registration errors. The efficacy of our proposed method is evaluated for a subsequent non-rigid registration step. To this end, the first two steps of the ANHIR winner's framework are replaced with DFBR to register image pairs provided by the challenge. The modified framework produce comparable results to those of the challenge winning team.