Reviewerly/exHarmony huggingface dataset:
Do We Have a Duty to Mitigate the Deterioration of Democratic Communication?

Authors:
raw_author_name: Corrado Fumagalli

Abstract: Previous articleNext article No AccessDo We Have a Duty to Mitigate the Deterioration of Democratic Communication?Corrado FumagalliCorrado FumagalliPDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmailPrint SectionsMoreDetailsFiguresReferencesCited by The Journal of Politics Just Accepted Sponsored by the Southern Political Science Association Article DOIhttps://doi.org/10.1086/729957 HistoryAccepted October 16, 2023 © 2024 Southern Political Science Association. All Rights reserved.PDF download Crossref reports no articles citing this article.
An efficient quantum circuit implementation of Shor’s algorithm for GPU accelerated simulation

Authors:
raw_author_name: Peng Gao
raw_affiliation_string: School of Integrated Circuits, Guangdong University of Technology 2 , Guangzhou 510006, China

Abstract: In this study, we introduce a novel implementation of Shor’s algorithm specifically designed for the Graphics Processing Unit (GPU) acceleration framework. Our focus lies on achieving efficient execution of the modular multiplication circuit through GPU simulation. To seamlessly integrate our design into the PyQPanda library framework, we made necessary modifications, making a deliberate trade-off by sacrificing a small number of quantum resources to leverage the advantages of GPU acceleration. Subsequently, we conducted simulations and rigorously validated the functionality of our circuit using the PyQPanda library, resulting in a significant speedup compared to a central processing unit-only mode.
Model selection over partially ordered sets

Authors:
raw_author_name: Armeen Taeb
raw_affiliation_string: Department of Statistics, University of Washington, Seattle, WA 98195
raw_author_name: Peter Bühlmann
raw_affiliation_string: Seminar for Statistics, Eidgenossische Technische Hochschule Zürich, Zurich, CH-8092, Switzerland
raw_author_name: Venkat Chandrasekaran
raw_affiliation_string: Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125

Abstract: In problems such as variable selection and graph estimation, models are characterized by Boolean logical structure such as the presence or absence of a variable or an edge. Consequently, false-positive error or false-negative error can be specified as the number of variables/edges that are incorrectly included or excluded in an estimated model. However, there are several other problems such as ranking, clustering, and causal inference in which the associated model classes do not admit transparent notions of false-positive and false-negative errors due to the lack of an underlying Boolean logical structure. In this paper, we present a generic approach to endow a collection of models with partial order structure, which leads to a hierarchical organization of model classes as well as natural analogs of false-positive and false-negative errors. We describe model selection procedures that provide false-positive error control in our general setting, and we illustrate their utility with numerical experiments.
Generative Adversarial Networks for Brain MRI Synthesis: Impact of Training Set Size on Clinical Application

Authors:
raw_author_name: BJ Erickson
raw_affiliation_string: Department of Radiology, Mayo Clinic, Rochester, MN, USA
raw_author_name: GM Conte
raw_affiliation_string: Department of Radiology, Mayo Clinic, Rochester, MN, USA

Abstract: We evaluated the impact of training set size on generative adversarial networks (GANs) to synthesize brain MRI sequences. We compared three sets of GANs trained to generate pre-contrast T1 (gT1) from post-contrast T1 and FLAIR (gFLAIR) from T2. The baseline models were trained on 135 cases; for this study, we used the same model architecture but a larger cohort of 1251 cases and two stopping rules, an early checkpoint (early models) and one after 50 epochs (late models). We tested all models on an independent dataset of 485 newly diagnosed gliomas. We compared the generated MRIs with the original ones using the structural similarity index (SSI) and mean squared error (MSE). We simulated scenarios where either the original T1, FLAIR, or both were missing and used their synthesized version as inputs for a segmentation model with the original post-contrast T1 and T2. We compared the segmentations using the dice similarity coefficient (DSC) for the contrast-enhancing area, non-enhancing area, and the whole lesion. For the baseline, early, and late models on the test set, for the gT1, median SSI was .957, .918, and .947; median MSE was .006, .014, and .008. For the gFLAIR, median SSI was .924, .908, and .915; median MSE was .016, .016, and .019. The range DSC was .625-.955, .420-.952, and .610-.954. Overall, GANs trained on a relatively small cohort performed similarly to those trained on a cohort ten times larger, making them a viable option for rare diseases or institutions with limited resources.
Multimodal Sensor Fusion Frameworks With Application to Human Action Recognition

Authors:
raw_author_name: Zeeshan Ahmad
raw_affiliation_string: (Electrical Engineering), NED University of Engineering and Technology, Karachi, Pakistan, 2001; (Electrical Engineering), Ryerson University, Toronto, Canada, 2017; Electrical and Computer Engineering Toronto, Ontario, Canada, 2021; MSc (Electrical Engineering), National University of Sciences and Technology, Karachi, Pakistan, 2005

Abstract: &lt;p&gt;Human Action Recognition (HAR) is a progressive research area in the field of computer vision and machine learning. Earlier methods on HAR were based on single sensor modality, either vision-based sensor or wearable inertial sensor. Both of these modalities have some limitations that prevent widespread adoption of HAR; e.g. visual sensors typically require elaborate hardware setup and are limited to a small operating area, where inertial sensors are prone to drift. The solution is to fuse the information from different modalities. In this dissertation, we present novel multimodal sensor fusion frameworks that overcome the limitations of single sensor modality. In these frameworks, we convert all data streams to images through innovative signal to image conversion schemes and feed them to Convolutional Neural Networks (CNN), thus enabling extraction of higher-level features that CNNs are proven to be capable of especially from images. Moreover, we propose fast and robust multilevel fusion schemes by extracting features from multiple layers of the CNNs and employing statistical methods such as Canonical Correlation Analysis and gated fusion, instead of the more popular single stage fusion. We applied these fusion frameworks for HAR using depth and inertial sensors. At the input of each fusion framework, we transform depth and inertial sensor data into images called Sequential Front view Images (SFI) and Signal Images (SI). The SFI and SI images are then fused through our proposed multilevel frameworks for more accurate HAR while maintaining computational speed. We evaluate the proposed frameworks on three public multimodal HAR datasets, namely, UTD Multimodal Human Action Dataset (MHAD), Berkeley MHAD, and UTDMHAD Kinect V2 and achieved accuracies of 99.3%, 99.85% and 99.8% respectively.&lt;/p&gt; &lt;p&gt;While the proposed frameworks were developed with HAR as a target application area, they can be applied to other fusion problems as well. We show the generalizability of frameworks by applying them to a different domain, where ECG (1D time series) data is converted to multimodal images and fed through our fusion frameworks for arrythmia classification and stress assessment. Preliminary results in these applications are encouraging, further strengthening the significance of the proposed frameworks.&lt;/p&gt;
Floquet analysis of frequency collisions

Authors:
raw_author_name: Kentaro Heya
raw_affiliation_string: IBM Quantum, IBM T.J. Watson Research Center, Yorktown Heights, New York 10598, USA
raw_author_name: Moein Malekakhlagh
raw_affiliation_string: IBM Quantum, IBM T.J. Watson Research Center, Yorktown Heights, New York 10598, USA
raw_author_name: Seth Merkel
raw_affiliation_string: IBM Quantum, IBM T.J. Watson Research Center, Yorktown Heights, New York 10598, USA
raw_author_name: Naoki Kanazawa
raw_affiliation_string: IBM Quantum, IBM Research Tokyo, 19-21 Nihonbashi Hakozaki-cho, Chuo-ku, Tokyo 103-8510, Japan
raw_author_name: Emily Pritchett
raw_affiliation_string: IBM Quantum, IBM T.J. Watson Research Center, Yorktown Heights, New York 10598, USA

Abstract: Implementation of high-fidelity gate operations on integrated-qubit systems is vital for fault-tolerant quantum computation. Qubit-frequency allocation is an essential part of improving control fidelity. A metric for qubit-frequency allocation, frequency collision, has been proposed on simple systems of only a few qubits driven by a monomodal microwave drive. However, frequency allocation for quantum processors for more advanced purposes, such as quantum error correction, needs further investigation. In this study, we propose a Floquet analysis of frequency collisions. The key to our proposed method is a reinterpretation of frequency collisions as an unintended degeneracy of Floquet states, which allows a collision analysis on more complex systems with many qubits driven by multimodal microwave drives. Although the Floquet state is defined in an infinite-dimensional Hilbert space, we develop algorithms, based on operation perturbation theory, to truncate the Hilbert space down to the optimal computational complexity. In particular, we show that the computational complexity of the collision analysis for a sparse qubit lattice is linear with the number of qubits. Finally, we demonstrate our proposed method on cross-resonance-based experimental protocols. We first study the cross-resonance gate in an isolated three-qubit system, where the effectiveness of our method is verified by comparing it with previous studies. We next consider the more complex problem of syndrome extraction in the heavy-hexagon code [Christopher et al., Phys. Rev. X 10, 011022 (2020). doi:10.1103/PhysRevX.10.011022]. Our proposed method advances our understanding of quantum control for quantum processors and contributes to their improved design and control.
Study on cluster centers optimization of max-min distance k-means clustering algorithm based on inter-cluster separation measure

Authors:
raw_author_name: Wenhao Xie
raw_affiliation_string: School of Science, Xi’an Shiyou University, Xi’an, Shaanxi, China
raw_author_name: Lin Lei
raw_affiliation_string: School of Computing, Xi’an Shiyou University, Xi’an, Shaanxi, China
raw_author_name: Xiangyi Liu
raw_affiliation_string: School of Science, Xi’an Shiyou University, Xi’an, Shaanxi, China
raw_author_name: Yuan Liu
raw_affiliation_string: College of Petroleum Engineering, Xi’an Shiyou University, Xi’an, Shaanxi, China; E-mail:; Fax: +86 02988382421

Abstract: Clustering is an essential unsupervised technique when category information is not available. Although K-means and Max-min distance K-means clustering algorithms are widely used, they have some disadvantages such as dependence on the initial centers, sensitivity to outliers caused by using only distance as the clustering criterion. To overcome the problems, this paper proposes SMM-K-means algorithm which overcomes the dependence on the initial cluster centers and the initial number of clusters and the sensitivity to the outliers. First, the initial value K of the optimal cluster number is determined by the elbow method, and K-means is used for initial clustering. A new inter-cluster separation measure is then constructed based on the idea of q-nearest neighbors, which is constructed by comprehensive considering the separation between clusters and the distribution compactness of clusters themselves. Finally, the two sample points with highest degree of separation are brought into Max-min distance K-means algorithm as new initial centers for clustering. The definite determining method of cluster centers eliminates the complicated iterative calculation, and the construction of inter-cluster separation measure overcomes the sensitivity of clustering results to noise points and isolated points, and has good applicability and generalization. In addition, this algorithm is not limited by the shape and size of the clusters and has better flexibility. The experimental results show that the SMM-K-means algorithm has higher CH values, resulting in a better clustering effect and stability.
Profiling with trust: system monitoring from trusted execution environments

Authors:
raw_author_name: Christian Eichler
raw_affiliation_string: Ruhr University Bochum, Bochum, Germany
raw_author_name: Jonas Röckl
raw_affiliation_string: FAU Erlangen-Nürnberg, Erlangen, Germany
raw_author_name: Benedikt Jung
raw_affiliation_string: Nokia Solutions and Networks GmbH & Co. KG, Nürnberg, Germany
raw_author_name: Ralph Schlenk
raw_affiliation_string: Nokia Solutions and Networks GmbH & Co. KG, Nürnberg, Germany
raw_author_name: Tilo Müller
raw_affiliation_string: Hof University of Applied Sciences, Hof, Germany
raw_author_name: Timo Hönig
raw_affiliation_string: Ruhr University Bochum, Bochum, Germany

Abstract: Abstract Large-scale attacks on IoT and edge computing devices pose a significant threat. As a prominent example, Mirai is an IoT botnet with 600,000 infected devices around the globe, capable of conducting effective and targeted DDoS attacks on (critical) infrastructure. Driven by the substantial impacts of attacks, manufacturers and system integrators propose Trusted Execution Environments (TEEs) that have gained significant importance recently. TEEs offer an execution environment to run small portions of code isolated from the rest of the system, even if the operating system is compromised. In this publication, we examine TEEs in the context of system monitoring and introduce the Trusted Monitor (TM), a novel anomaly detection system that runs within a TEE. The TM continuously profiles the system using hardware performance counters and utilizes an application-specific machine-learning model for anomaly detection. In our evaluation, we demonstrate that the TM accurately classifies 86% of 183 tested workloads, with an overhead of less than 2%. Notably, we show that a real-world kernel-level rootkit has observable effects on performance counters, allowing the TM to detect it. Major parts of the TM are implemented in the Rust programming language, eliminating common security-critical programming errors.
Fundamental limits on quantum cloning from the no-signaling principle

Authors:
raw_author_name: Yanglin Hu
raw_affiliation_string: Centre for Quantum Technologies, National University of Singapore, Singapore 117543, Singapore
raw_author_name: Marco Tomamichel
raw_affiliation_string: Centre for Quantum Technologies, National University of Singapore, Singapore 117543, Singapore and Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore

Abstract: The no-cloning theorem is a cornerstone of quantum cryptography. Here we generalize and rederive in a unified framework various upper bounds on the maximum achievable fidelity of probabilistic and deterministic cloning machines. Building on ideas by Gisin [Phys. Lett. A 242, 1 (1998)], our result follows from the fact that remote state preparation is possible and the no-signaling principle holds. We apply our general theorem to several subsets of states that are of interest in quantum cryptography.
A planar tracking strategy based on multiple-interpretable improved PPO algorithm with few-shot technique

Authors:
raw_author_name: Xiao Wang
raw_affiliation_string: College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China
raw_author_name: Zhe Ma
raw_affiliation_string: Intelligent Science & Technology, Academy Limited of CASIC, Beijing, 100043, China
raw_author_name: Lu Cao
raw_affiliation_string: National Innovation Institute of Defense Technology, Academy of Military Sciences, Beijing, 100071, China
raw_author_name: Dechao Ran
raw_affiliation_string: National Innovation Institute of Defense Technology, Academy of Military Sciences, Beijing, 100071, China
raw_author_name: Mingjiang Ji
raw_affiliation_string: National Innovation Institute of Defense Technology, Academy of Military Sciences, Beijing, 100071, China
raw_author_name: Kewu Sun
raw_affiliation_string: Key Lab of Aerospace Defense Intelligent System and Technology, Beijing, 100043, China
raw_author_name: Jiake Li
raw_affiliation_string: Key Lab of Aerospace Defense Intelligent System and Technology, Beijing, 100043, China

Abstract: Abstract Facing to a planar tracking problem, a multiple-interpretable improved Proximal Policy Optimization (PPO) algorithm with few-shot technique is proposed, namely F-GBQ-PPO. Compared with the normal PPO, the main improvements of F-GBQ-PPO are to increase the interpretability, and reduce the consumption for real interaction samples. Considering to increase incomprehensibility of a tracking policy, three levels of interpretabilities has been studied, including the perceptual, logical and mathematical interpretabilities. Detailly speaking, it is realized through introducing a guided policy based on Apollonius circle, a hybrid exploration policy based on biological motions, and the update of external parameters based on quantum genetic algorithm. Besides, to deal with the potential lack of real interaction samples in real applications, a few-shot technique is contained in the algorithm, which mainly generate fake samples through a multi-dimension Gaussian process. By mixing fake samples with real ones in a certain proportion, the demand for real samples can be reduced.
Entwicklung eines neuen Ansatzes für eine digitale, kartographische Fels- und Reliefdarstellung

Authors:
raw_author_name: Alexander Klaus
raw_affiliation_string: Department of Geoinformatics, Munich University of Applied Sciences HM, Munich, Germany

Abstract: Abstract This paper presents a new approach to digital rock and relief representation. A prerequisite for the method was its applicability for different scales and thus different resolutions of the underlying terrain models. A further requirement was its suitability for different rock types. It must also take into account the rock surfaces that have become ice-free due to glacier retreat and be easy to integrate into the map in the event of an update. The relief representation follows the principle that the terrain should be reproduced as naturally as possible to provide the map user with the best possible orientation in the terrain. The approach requires a very good knowledge of the terrain as well as a certain artistic talent to achieve an appealing, natural color scheme. Through the process of color manipulation from the four-color set, it is also possible, in addition to the summer representation from the data collected with remote sensing methods, to generate winter representations for e.g. ski tourers. The use of established, automatic procedures considerably reduces the workload for the cartographer and also enables simple integration of corrections and changes to the landscape, e.g. due to glacial retreat and landslides, and can be applied to large map series due to its reproducibility.
Deep photonic network platform enabling arbitrary and broadband optical functionality

Authors:
raw_author_name: Ali Najjar Amiri
raw_affiliation_string: Department of Electrical and Electronics Engineering, Koç University, Sariyer, Istanbul, 34450, Turkey
raw_author_name: Aycan Deniz Vit
raw_affiliation_string: Department of Electrical and Electronics Engineering, Koç University, Sariyer, Istanbul, 34450, Turkey
raw_author_name: Kazim Gorgulu
raw_affiliation_string: Department of Electrical and Electronics Engineering, Koç University, Sariyer, Istanbul, 34450, Turkey
raw_author_name: Emir Salih Magden
raw_affiliation_string: Department of Electrical and Electronics Engineering, Koç University, Sariyer, Istanbul, 34450, Turkey

Abstract: Expanding applications in optical communications, computing, and sensing continue to drive the need for high-performance integrated photonic components. Designing these on-chip systems with arbitrary functionality requires beyond what is possible with physical intuition, for which machine learning-based methods have recently become popular. However, computational demands for physically accurate device simulations present critical challenges, significantly limiting scalability and design flexibility of these methods. Here, we present a highly-scalable, physics-informed design platform for on-chip optical systems with arbitrary functionality, based on deep photonic networks of custom-designed Mach-Zehnder interferometers. Leveraging this platform, we demonstrate ultra-broadband power splitters and a spectral duplexer, each designed within two minutes. The devices exhibit state-of-the-art experimental performance with insertion losses below 0.66 dB, and 1-dB bandwidths exceeding 120 nm. This platform provides a tractable path towards systematic, large-scale photonic system design, enabling custom power, phase, and dispersion profiles for high-throughput communications, quantum information processing, and medical/biological sensing applications.
Deep Learning based Effective Watermarking Technique for IoT Systems Signal Authentication

Authors:
raw_author_name: Dr. Vinit Gupta
raw_affiliation_string: Assistant Professor, Department of Electronics and Communication Engineering, Medi-Caps University, Indore, Madhya Pradesh, India
raw_author_name: Dr. Aditya Mandloi
raw_affiliation_string: Assistant Professor, Department of Electronics and Communication Engineering, Medi-Caps University, Indore, Madhya Pradesh, India
raw_author_name: Dr. Sachin Puntambekar
raw_affiliation_string: Assistant Professor, Department of Electronics and Communication Engineering, Medi-Caps University, Indore, Madhya Pradesh, India

Abstract: In order to identify cyber-attacks, this research suggests a special watermarking technique for dynamic IoT System signal validation. IoT Systems (IoTSs) can extract a group of randomly generated characteristics from their produced signal and then periodically watermark these attributes into the transmission owing to the proposed efficient watermarking technique. Using dynamic watermarking for IoT signal authentication, a potent deep learning technique is used to detect cyber-attacks. Based on an LSTM structure, the proposed learning system enables IoT devices to extract a set of random features from the signal they release, hence enabling dynamic watermarking of the signal.