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Preface to the joint proceedings of the ComplexRec and ImpactRS workshops at ACM RecSys 2020

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Preface to the joint proceedings of the ComplexRec and Impac...

2020 Workshops on Recommendation in Complex Scenarios and the Impact of Recommender Systems, ComplexRec-ImpactRS 2020

作者： Bogers, Toine Koolen, Marijn Petersen, Casper Mobasher, Bamshad Tuzhilin, Alexander Shalom, Oren Sar Jannach, Dietmar Konstan, Joseph A. Aalborg University Department of Communication and Psychology Copenhagen Denmark Royal Netherlands Academy of Arts and Sciences Amsterdam Netherlands SamPension Copenhagen Denmark School of Computing DePaul University ChicagoIL United States New York University Stern School of Business New York CityNY United States Facebook United States University of Klagenfurt Department of Applied Informatics Austria University of Minnesota Department of Computer Science and Engineering MN United States

Recommender systems are widely used in modern online applications, from e-commerce sites over media streaming services to social networks. In academic research, we however often abstract from the specifics of these applications and rely on simplified assumptions such as the availability of past rating data. Furthermore, we mostly focus on predicting to what extent a user will like a certain item, but do not explicitly consider the long-term effects of recommendations on the users' decision-making processes or the expected impact on orgnizations. The 14th ACM Conference on Recommender Systems hosted two workshops which aim to look beyond our often too simplifying assumptions, the Fourth Workshop on Recommendation in Complex Environments and the Second Workshop on the Impact of Recommender Systems. These proceedings describe the specific goals of the workshops and contain the papers that were presented during the online events. © 2020 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Recommender systems

Artificial Intelligence and Machine Learning in 5G Network Security: Opportunities, advantages, and future research trends

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arXiv 2020年

作者： Haider, Noman Baig, Zeeshan Imran, Muhammad College of Engineering and Science Victoria University Sydney Campus Sydney2000 Australia Department of Computing Faculty of Science and Engineering Macquarie University Sydney2109 Australia College of Applied Computer Science King Saud University Riyadh Saudi Arabia

Recent technological and architectural advancements in 5G networks have proven their worth as the deployment has started over the world. Key performance elevating factor from access to core network are softwareization, cloudification and virtualization of key enabling network functions. Along with the rapid evolution comes the risks, threats and vulnerabilities in the system for those who plan to exploit it. Therefore, ensuring fool proof end-to-end (E2E) security becomes a vital concern. Artificial intelligence (AI) and machine learning (ML) can play vital role in design, modelling and automation of efficient security protocols against diverse and wide range of threats. AI and ML has already proven their effectiveness in different fields for classification, identification and automation with higher accuracy. As 5G networks’ primary selling point has been higher data rates and speed, it will be difficult to tackle wide range of threats from different points using typical/traditional protective measures. Therefore, AI and ML can play central role in protecting highly data-driven softwareized and virtualized network components. This article presents AI and ML driven applications for 5G network security, their implications and possible research directions. Also, an overview of key data collection points in 5G architecture for threat classification and anomaly detection are discussed. Copyright © 2020, The Authors. All rights reserved.

关键词： 5G mobile communication systems

Entangled subspaces and generic local state discrimination with pre-shared entanglement

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arXiv 2020年

作者： Lovitz, Benjamin Johnston, Nathaniel Institute for Quantum Computing Department of Applied Mathematics University of Waterloo 200 University Ave W WaterlooON Canada Department of Mathematics & Computer Science Mount Allison University Canada Department of Mathematics & Statistics University of Guelph Canada

Walgate and Scott have determined the maximum number of generic pure quantum states that can be unambiguously discriminated by an LOCC measurement [42]. In this work, we determine this number in a more general setting in which the local parties have access to pre-shared entanglement in the form of a resource state. We find that, for an arbitrary pure resource state, this number is equal to the Krull dimension of (the closure of) the set of pure states obtainable from the resource state by SLOCC. Surprisingly, a generic resource state maximizes this number. Local state discrimination is closely related to the topic of entangled subspaces, which we study in its own right. We introduce r-entangled subspaces, which naturally generalize previously studied spaces to higher multipartite entanglement. We use algebraic-geometric methods to determine the maximum dimension of an r-entangled subspace, and present novel explicit constructions of such spaces. We obtain similar results for symmetric and antisymmetric rentangled subspaces, which correspond to entangled subspaces of bosonic and fermionic systems, respectively. © 2020, CC BY.

关键词： Algebra

The Case of Performance Variability on Dragonfly-based Systems

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The Case of Performance Variability on Dragonfly-based Syste...

International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Abhinav Bhatele Jayaraman J. Thiagarajan Taylor Groves Rushil Anirudh Staci A. Smith Brandon Cook David K. Lowenthal Department of Computer Science University of Maryland College Park Maryland USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore California USA NERSC Lawrence Berkeley National Laboratory Berkeley California USA Department of Computer Science The University of Arizona Tucson Arizona USA

ISBN: (数字)9781728168760

ISBN: (纸本)9781728168777

Performance of a parallel code running on a large supercomputer can vary significantly from one run to another even when the executable and its input parameters are left unchanged. Such variability can occur due to perturbation of the computation and/or communication in the code. In this paper, we investigate the case of performance variability arising due to network effects on supercomputers that use a dragonfly topology - specifically, Cray XC systems equipped with the Aries interconnect. We perform post-mortem analysis of network hardware counters, profiling output, job queue logs, and placement information, all gathered from periodic representative application runs. We investigate the causes of performance variability using deviation prediction and recursive feature elimination. Additionally, using time-stepped performance data of individual applications, we train machine learning models that can forecast the execution time of future time steps.

关键词： Network topology 3G mobile communication Topology Predictive models Supercomputers Data models Production

Assessing the Symbiotic Organism Search Variants using Standard Benchmark Functions

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Journal of Physics: Conference Series 2021年第1期1830卷

作者： Nurul Asyikin Zainal Fakhrud Din Kamal Z. Zamli Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang 26600 Pekan Malaysia Department of Computer Science & IT University of Malakand KPK Pakistan

Symbiotic Organism Search (SOS) is one of the latest meta-heuristic algorithms created to solve optimization problems. Combining the fact that this new algorithm is parameter-less (no need for tuning) and having a superior performance compared with other meta-heuristic algorithms, the interest to investigate and enhanced this algorithm had emerged. In this paper, we present a new version of SOS by looping the current algorithm rather than doing it one after the other. The target of this paper is to find the effect of changing the structure of algorithm from the original SOS using selected benchmark functions. Our findings indicate that using a loop structure can improve the solution in some of the benchmarks functions as compared to the original SOS.

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Diagnosis of Pulmonary Hypertension by Integrating Multimodal Data with a Hybrid Graph Convolutional and Transformer Network

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arXiv 2025年

作者： Zhu, Fubao Zhang, Yang Liang, Gengmin Nan, Jiaofen Li, Yanting Han, Chuang Sun, Danyang Wang, Zhiguo Zhao, Chen Zhou, Wenxuan He, Jian Xu, Yi Cheang, Iokfai Zhu, Xu Zhou, Yanli Zhou, Weihua School of Computer Science and Technology Zhengzhou University of Light Industry Henan Zhengzhou450001 China State Key Laboratory for Innovation and Transformation of Luobing Theory Department of Cardiology The First Affiliated Hospital of Nanjing Medical University Jiangsu Nanjing210029 China Department of Computer Science Kennesaw State University MariettaGA United States Department of Integrated Traditional Chinese and Western Clinical Medicine Hebei Medical University Hebei China Department of Radiology The First Affiliated Hospital of Nanjing Medical University Nanjing210029 China Department of Applied Computing Michigan Technological University HoughtonMI United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI United States

Early and accurate diagnosis of pulmonary hypertension (PH) is essential for optimal patient management. Differentiating between pre-capillary and post-capillary PH is critical for guiding treatment decisions. This study develops and validates a deep learning-based diagnostic model for PH, designed to classify patients as non-PH, pre-capillary PH, or post-capillary PH. This retrospective study analyzed data from 204 patients (112 with pre-capillary PH, 32 with post-capillary PH, and 60 non-PH controls) at the First Affiliated Hospital of Nanjing Medical University. Diagnoses were confirmed through right heart catheterization. We selected 6 samples from each category for the test set (18 samples, 10%), with the remaining 186 samples used for the training set. This process was repeated 35 times for testing. This paper proposes a deep learning model that combines Graph convolutional networks (GCN), Convolutional neural networks (CNN), and Transformers. The model was developed to process multimodal data, including short-axis (SAX) sequences, four-chamber (4CH) sequences, and clinical parameters. Our model achieved a performance of Area under the receiver operating characteristic curve (AUC) = 0.81±0.06(standard deviation) and Accuracy (ACC) = 0.73±0.06 on the test set. The discriminative abilities were as follows: non-PH subjects (AUC = 0.74±0.11), pre-capillary PH (AUC = 0.86±0.06), and post-capillary PH (AUC = 0.83±0.10). It has the potential to support clinical decision-making by effectively integrating multimodal data to assist physicians in making accurate and timely diagnoses. © 2025, CC BY-NC-ND.

关键词： Hyperthermia therapy

Selective Excitation of Superconducting Qubits with a Shared Control Line through Pulse Shaping

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arXiv 2025年

作者： Matsuda, R. Ohira, R. Sumida, T. Shiomi, H. Machino, A. Morisaka, S. Koike, K. Miyoshi, T. Kurimoto, Y. Sugita, Y. Ito, Y. Suzuki, Y. Spring, P.A. Wang, S. Tamate, S. Tabuchi, Y. Nakamura, Y. Ogawa, K. Negoro, M. Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka560-8531 Japan QuEL Inc. Daiwaunyu Building 3F 2-9-2 Owadamachi Hachioji Tokyo192-0045 Japan Center for Quantum Information and Quantum Biology Osaka University 1-2 Machikaneyama Toyonaka Osaka560-0043 Japan e-trees.Japan Inc. Daiwaunyu Building 2F 2-9-2 Owadamachi Hachioji Tokyo192-0045 Japan NTT Computer and Data Science Laboratories NTT Corporation Musashino180-8585 Japan RIKEN Center for Quantum Computing Saitama Wako351-0198 Japan Department of Applied Physics Graduate School of Engineering University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan

In conventional architectures of superconducting quantum computers, each qubit is connected to its own control line, leading to a commensurate increase in the number of microwave lines as the system scales. Frequency-multiplexed qubit-control addresses this problem by enabling multiple qubits to share a single microwave line. However, it can cause unwanted excitation of non-target qubits, especially when the detuning between qubits is smaller than the pulse bandwidth. Here, we propose a selective-excitation-pulse (SEP) technique that suppresses unwanted excitations by shaping a drive pulse to create null points at non-target qubit frequencies. In a proof-of-concept experiment with three fixed-frequency transmon qubits, we demonstrate that the SEP technique achieves single-qubit gate fidelities comparable to those obtained with conventional Gaussian pulses while effectively suppressing unwanted excitations in non-target qubits. These results highlight the SEP technique as a promising tool for enhancing frequency-multiplexed qubit-control. © 2025, CC0.

关键词： Qubits

Speed-Robust Scheduling: Sand, Bricks, and Rocks

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arXiv 2020年

作者： Eberle, Franziska Hoeksma, Ruben Megow, Nicole Nölke, Lukas Schewior, Kevin Simon, Bertrand London School of Economics and Political Sciences Department of Mathematics United Kingdom University of Twente Department of Applied Mathematics Netherlands University of Bremen Department of Mathematics and Computer Science Germany University of Southern Denmark Department of Mathematics and Computer Science Odense Denmark IN2P3 Computing Center CNRS Villeurbanne France

The speed-robust scheduling problem is a two-stage problem where, given m machines, jobs must be grouped into at most m bags while the processing speeds of the machines are unknown. After the speeds are revealed, the grouped jobs must be assigned to the machines without being separated. To evaluate the performance of algorithms, we determine upper bounds on the worst-case ratio of the algorithm's makespan and the optimal makespan given full information. We refer to this ratio as the robustness factor. We give an algorithm with a robustness factor 2− 1/m for the most general setting and improve this to 1.8 for equal-size jobs. For the special case of infinitesimal jobs, we give an algorithm with an optimal robustness factor equal to e/e−1 ≈ 1.58. The particular machine environment in which all machines have either speed 0 or 1 was studied before by Stein and Zhong (ACM Trans. Alg. 2020). For this setting, we provide an algorithm for scheduling infinitesimal jobs with an optimal robustness factor of (Equation presented). It lays the foundation for an algorithm matching the lower bound of 4/3 for equal-size jobs. Copyright © 2020, The Authors. All rights reserved.

关键词： Speed

Why is the Winner the Best?

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Why is the Winner the Best?

Conference on computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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