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Book ; Online: Learning with Local Gradients at the Edge

Lomnitz, Michael / Daniels, Zachary / Zhang, David / Piacentino, Michael

2022

Abstract: To enable learning on edge devices with fast convergence and low memory, we present a novel backpropagation-free optimization algorithm dubbed Target Projection Stochastic Gradient Descent (tpSGD). tpSGD generalizes direct random target projection to ... ...

Abstract	To enable learning on edge devices with fast convergence and low memory, we present a novel backpropagation-free optimization algorithm dubbed Target Projection Stochastic Gradient Descent (tpSGD). tpSGD generalizes direct random target projection to work with arbitrary loss functions and extends target projection for training recurrent neural networks (RNNs) in addition to feedforward networks. tpSGD uses layer-wise stochastic gradient descent (SGD) and local targets generated via random projections of the labels to train the network layer-by-layer with only forward passes. tpSGD doesn't require retaining gradients during optimization, greatly reducing memory allocation compared to SGD backpropagation (BP) methods that require multiple instances of the entire neural network weights, input/output, and intermediate results. Our method performs comparably to BP gradient-descent within 5% accuracy on relatively shallow networks of fully connected layers, convolutional layers, and recurrent layers. tpSGD also outperforms other state-of-the-art gradient-free algorithms in shallow models consisting of multi-layer perceptrons, convolutional neural networks (CNNs), and RNNs with competitive accuracy and less memory and time. We evaluate the performance of tpSGD in training deep neural networks (e.g. VGG) and extend the approach to multi-layer RNNs. These experiments highlight new research directions related to optimized layer-based adaptor training for domain-shift using tpSGD at the edge.
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
Subject code	006
Publishing date	2022-08-17
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Efficient Model Adaptation for Continual Learning at the Edge

Daniels, Zachary A. / Hu, Jun / Lomnitz, Michael / Miller, Phil / Raghavan, Aswin / Zhang, Joe / Piacentino, Michael / Zhang, David

2023

Abstract: Most machine learning (ML) systems assume stationary and matching data distributions during training and deployment. This is often a false assumption. When ML models are deployed on real devices, data distributions often shift over time due to changes in ...

Abstract	Most machine learning (ML) systems assume stationary and matching data distributions during training and deployment. This is often a false assumption. When ML models are deployed on real devices, data distributions often shift over time due to changes in environmental factors, sensor characteristics, and task-of-interest. While it is possible to have a human-in-the-loop to monitor for distribution shifts and engineer new architectures in response to these shifts, such a setup is not cost-effective. Instead, non-stationary automated ML (AutoML) models are needed. This paper presents the Encoder-Adaptor-Reconfigurator (EAR) framework for efficient continual learning under domain shifts. The EAR framework uses a fixed deep neural network (DNN) feature encoder and trains shallow networks on top of the encoder to handle novel data. The EAR framework is capable of 1) detecting when new data is out-of-distribution (OOD) by combining DNNs with hyperdimensional computing (HDC), 2) identifying low-parameter neural adaptors to adapt the model to the OOD data using zero-shot neural architecture search (ZS-NAS), and 3) minimizing catastrophic forgetting on previous tasks by progressively growing the neural architecture as needed and dynamically routing data through the appropriate adaptors and reconfigurators for handling domain-incremental and class-incremental continual learning. We systematically evaluate our approach on several benchmark datasets for domain adaptation and demonstrate strong performance compared to state-of-the-art algorithms for OOD detection and few-/zero-shot NAS. Comment: Unpublished White Paper
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2023-08-03
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: A general approach to bridge the reality-gap

Lomnitz, Michael / Hampel-Arias, Zigfried / Lopatina, Nina / Mejia, Felipe A.

2020

Abstract: Employing machine learning models in the real world requires collecting large amounts of data, which is both time consuming and costly to collect. A common approach to circumvent this is to leverage existing, similar data-sets with large amounts of ... ...

Abstract	Employing machine learning models in the real world requires collecting large amounts of data, which is both time consuming and costly to collect. A common approach to circumvent this is to leverage existing, similar data-sets with large amounts of labelled data. However, models trained on these canonical distributions do not readily transfer to real-world ones. Domain adaptation and transfer learning are often used to breach this "reality gap", though both require a substantial amount of real-world data. In this paper we discuss a more general approach: we propose learning a general transformation to bring arbitrary images towards a canonical distribution where we can naively apply the trained machine learning models. This transformation is trained in an unsupervised regime, leveraging data augmentation to generate off-canonical examples of images and training a Deep Learning model to recover their original counterpart. We quantify the performance of this transformation using pre-trained ImageNet classifiers, demonstrating that this procedure can recover half of the loss in performance on the distorted data-set. We then validate the effectiveness of this approach on a series of pre-trained ImageNet models on a real world data set collected by printing and photographing images in different lighting conditions. Comment: 8 pages, 4 figures, 2 tables
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
Subject code	006
Publishing date	2020-09-03
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators

Kandaswamy, Indhumathi / Farkya, Saurabh / Daniels, Zachary / van der Wal, Gooitzen / Raghavan, Aswin / Zhang, Yuzheng / Hu, Jun / Lomnitz, Michael / Isnardi, Michael / Zhang, David / Piacentino, Michael

2022

Abstract: In this paper we present Hyper-Dimensional Reconfigurable Analytics at the Tactical Edge (HyDRATE) using low-SWaP embedded hardware that can perform real-time reconfiguration at the edge leveraging non-MAC (free of floating-point MultiplyACcumulate ... ...

Abstract	In this paper we present Hyper-Dimensional Reconfigurable Analytics at the Tactical Edge (HyDRATE) using low-SWaP embedded hardware that can perform real-time reconfiguration at the edge leveraging non-MAC (free of floating-point MultiplyACcumulate operations) deep neural nets (DNN) combined with hyperdimensional (HD) computing accelerators. We describe the algorithm, trained quantized model generation, and simulated performance of a feature extractor free of multiply-accumulates feeding a hyperdimensional logic-based classifier. Then we show how performance increases with the number of hyperdimensions. We describe the realized low-SWaP FPGA hardware and embedded software system compared to traditional DNNs and detail the implemented hardware accelerators. We discuss the measured system latency and power, noise robustness due to use of learnable quantization and HD computing, actual versus simulated system performance for a video activity classification task and demonstration of reconfiguration on this same dataset. We show that reconfigurability in the field is achieved by retraining only the feed-forward HD classifier without gradient descent backpropagation (gradient-free), using few-shot learning of new classes at the edge. Initial work performed used LRCN DNN and is currently extended to use Two-stream DNN with improved performance. Comment: 9 pages, 15 figures. Will be presented in Embedded Vision Workshop at CVPR2022
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Hardware Architecture
Subject code	006
Publishing date	2022-06-10
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Reducing audio membership inference attack accuracy to chance

Lomnitz, Michael / Lopatina, Nina / Gamble, Paul / Hampel-Arias, Zigfried / Tindall, Lucas / Mejia, Felipe A. / Barrios, Maria Alejandra

4 defenses

2019

Abstract: It is critical to understand the privacy and robustness vulnerabilities of machine learning models, as their implementation expands in scope. In membership inference attacks, adversaries can determine whether a particular set of data was used in training, ...

Abstract	It is critical to understand the privacy and robustness vulnerabilities of machine learning models, as their implementation expands in scope. In membership inference attacks, adversaries can determine whether a particular set of data was used in training, putting the privacy of the data at risk. Existing work has mostly focused on image related tasks; we generalize this type of attack to speaker identification on audio samples. We demonstrate attack precision of 85.9\% and recall of 90.8\% for LibriSpeech, and 78.3\% precision and 90.7\% recall for VOiCES (Voices Obscured in Complex Environmental Settings). We find that implementing defenses such as prediction obfuscation, defensive distillation or adversarial training, can reduce attack accuracy to chance. Comment: 7 pages, 2 figures, 7 tables
Keywords	Computer Science - Cryptography and Security ; Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Audio and Speech Processing
Subject code	006
Publishing date	2019-10-31
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Thesis: Ein Fall von ausgetragener Extrauterinschwangerschaft

Lomnitz, Martin

1903

Author's details	Martin Lomnitz
Language	German
Size	28 Seiten, 2 ungezählte Blätter, 8°
Publisher	Th. Schatzky
Publishing place	Breslau
Publishing country	XA-DXDE
Document type	Book ; Thesis
Thesis / German Habilitation thesis	Dissertation, Universität Breslau, 1903
HBZ-ID	HT021874428
Database	Catalogue ZB MED Medicine, Health

In stock of ZB MED Cologne/Königswinter

Shelf mark	Loan status	Location
Eu. Diss. Breslau 1903: Lomnitz	order to use in reading room	Magazin

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Article ; Online: Azimuthal Harmonics in Small and Large Collision Systems at RHIC Top Energies.

Adam, J / Adamczyk, L / Adams, J R / Adkins, J K / Agakishiev, G / Aggarwal, M M / Ahammed, Z / Alekseev, I / Anderson, D M / Aoyama, R / Aparin, A / Arkhipkin, D / Aschenauer, E C / Ashraf, M U / Atetalla, F / Attri, A / Averichev, G S / Bai, X / Bairathi, V /

Barish, K / Bassill, A J / Behera, A / Bellwied, R / Bhasin, A / Bhati, A K / Bielcik, J / Bielcikova, J / Bland, L C / Bordyuzhin, I G / Brandenburg, J D / Brandin, A V / Brown, D / Bryslawskyj, J / Bunzarov, I / Butterworth, J / Caines, H / Calderón de la Barca Sánchez, M / Cebra, D / Chakaberia, I / Chaloupka, P / Chan, B K / Chang, F-H / Chang, Z / Chankova-Bunzarova, N / Chatterjee, A / Chattopadhyay, S / Chen, J H / Chen, X / Cheng, J / Cherney, M / Christie, W / Contin, G / Crawford, H J / Csanad, M / Das, S / Dedovich, T G / Deppner, I M / Derevschikov, A A / Didenko, L / Dilks, C / Dong, X / Drachenberg, J L / Dunlop, J C / Efimov, L G / Elsey, N / Engelage, J / Eppley, G / Esha, R / Esumi, S / Evdokimov, O / Ewigleben, J / Eyser, O / Fatemi, R / Fazio, S / Federic, P / Federicova, P / Fedorisin, J / Filip, P / Finch, E / Fisyak, Y / Flores, C E / Fulek, L / Gagliardi, C A / Galatyuk, T / Geurts, F / Gibson, A / Grosnick, D / Gunarathne, D S / Guo, Y / Gupta, A / Guryn, W / Hamad, A I / Hamed, A / Harlenderova, A / Harris, J W / He, L / Heppelmann, S / Herrmann, N / Hirsch, A / Holub, L / Hong, Y / Horvat, S / Huang, B / Huang, H Z / Huang, S L / Huang, T / Huang, X / Humanic, T J / Huo, P / Igo, G / Jacobs, W W / Jentsch, A / Jia, J / Jiang, K / Jowzaee, S / Ju, X / Judd, E G / Kabana, S / Kagamaster, S / Kalinkin, D / Kang, K / Kapukchyan, D / Kauder, K / Ke, H W / Keane, D / Kechechyan, A / Kikoła, D P / Kim, C / Kinghorn, T A / Kisel, I / Kisiel, A / Kochenda, L / Kosarzewski, L K / Kraishan, A F / Kramarik, L / Krauth, L / Kravtsov, P / Krueger, K / Kulathunga, N / Kumar, L / Kunnawalkam Elayavalli, R / Kvapil, J / Kwasizur, J H / Lacey, R / Landgraf, J M / Lauret, J / Lebedev, A / Lednicky, R / Lee, J H / Li, C / Li, W / Li, X / Li, Y / Liang, Y / Lidrych, J / Lin, T / Lipiec, A / Lisa, M A / Liu, F / Liu, H / Liu, P / Liu, Y / Liu, Z / Ljubicic, T / Llope, W J / Lomnitz, M / Longacre, R S / Luo, S / Luo, X / Ma, G L / Ma, L / Ma, R / Ma, Y G / Magdy, N / Majka, R / Mallick, D / Margetis, S / Markert, C / Matis, H S / Matonoha, O / Mazer, J A / Meehan, K / Mei, J C / Minaev, N G / Mioduszewski, S / Mishra, D / Mohanty, B / Mondal, M M / Mooney, I / Morozov, D A / Nasim, Md / Negrete, J D / Nelson, J M / Nemes, D B / Nie, M / Nigmatkulov, G / Niida, T / Nogach, L V / Nonaka, T / Odyniec, G / Ogawa, A / Oh, K / Oh, S / Okorokov, V A / Olvitt, D / Page, B S / Pak, R / Panebratsev, Y / Pawlik, B / Pei, H / Perkins, C / Pinter, R L / Pluta, J / Porter, J / Posik, M / Pruthi, N K / Przybycien, M / Putschke, J / Quintero, A / Radhakrishnan, S K / Ramachandran, S / Ray, R L / Reed, R / Ritter, H G / Roberts, J B / Rogachevskiy, O V / Romero, J L / Ruan, L / Rusnak, J / Rusnakova, O / Sahoo, N R / Sahu, P K / Salur, S / Sandweiss, J / Schambach, J / Schmah, A M / Schmidke, W B / Schmitz, N / Schweid, B R / Seck, F / Seger, J / Sergeeva, M / Seto, R / Seyboth, P / Shah, N / Shahaliev, E / Shanmuganathan, P V / Shao, M / Shen, F / Shen, W Q / Shi, S S / Shou, Q Y / Sichtermann, E P / Siejka, S / Sikora, R / Simko, M / Singh, J / Singha, S / Smirnov, D / Smirnov, N / Solyst, W / Sorensen, P / Spinka, H M / Srivastava, B / Stanislaus, T D S / Stewart, D J / Strikhanov, M / Stringfellow, B / Suaide, A A P / Sugiura, T / Sumbera, M / Summa, B / Sun, X M / Sun, X / Sun, Y / Surrow, B / Svirida, D N / Szymanski, P / Tang, A H / Tang, Z / Taranenko, A / Tarnowsky, T / Thomas, J H / Timmins, A R / Tlusty, D / Todoroki, T / Tokarev, M / Tomkiel, C A / Trentalange, S / Tribble, R E / Tribedy, P / Tripathy, S K / Tsai, O D / Tu, B / Ullrich, T / Underwood, D G / Upsal, I / Van Buren, G / Vanek, J / Vasiliev, A N / Vassiliev, I / Videbæk, F / Vokal, S / Voloshin, S A / Vossen, A / Wang, F / Wang, G / Wang, P / Wang, Y / Webb, J C / Wen, L / Westfall, G D / Wieman, H / Wissink, S W / Witt, R / Wu, Y / Xiao, Z G / Xie, G / Xie, W / Xu, J / Xu, N / Xu, Q H / Xu, Y F / Xu, Z / Yang, C / Yang, Q / Yang, S / Yang, Y / Ye, Z / Yi, L / Yip, K / Yoo, I-K / Yu, N / Zbroszczyk, H / Zha, W / Zhang, J / Zhang, L / Zhang, S / Zhang, X P / Zhang, Y / Zhang, Z / Zhao, J / Zhong, C / Zhou, C / Zhu, X / Zhu, Z / Zyzak, M

Physical review letters

2019 Volume 122, Issue 17, Page(s) 172301

Abstract: The first (v_{1}^{fluc}), second (v_{2}), and third (v_{3}) harmonic coefficients of the azimuthal particle distribution at midrapidity are extracted for charged hadrons and studied as a function of transverse momentum (p_{T}) and mean charged particle ... ...

Abstract	The first (v_{1}^{fluc}), second (v_{2}), and third (v_{3}) harmonic coefficients of the azimuthal particle distribution at midrapidity are extracted for charged hadrons and studied as a function of transverse momentum (p_{T}) and mean charged particle multiplicity density ⟨N_{ch}⟩ in U+U (sqrt[s_{NN}]=193 GeV), Au+Au, Cu+Au, Cu+Cu, d+Au, and p+Au collisions at sqrt[s_{NN}]=200 GeV with the STAR detector. For the same ⟨N_{ch}⟩, the v_{1}^{fluc} and v_{3} coefficients are observed to be independent of the collision system, while v_{2} exhibits such a scaling only when normalized by the initial-state eccentricity (ϵ_{2}). The data also show that ln(v_{2}/ϵ_{2}) scales linearly with ⟨N_{ch}⟩^{-1/3}. These measurements provide insight into initial-geometry fluctuations and the role of viscous hydrodynamic attenuation on v_{n} from small to large collision systems.
Language	English
Publishing date	2019-05-20
Publishing country	United States
Document type	Journal Article
ZDB-ID	208853-8
ISSN	1079-7114 ; 0031-9007
ISSN (online)	1079-7114
ISSN	0031-9007
DOI	10.1103/PhysRevLett.122.172301
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Polarization of Λ (Λ[over ¯]) Hyperons along the Beam Direction in Au+Au Collisions at sqrt[s_{NN}]=200 GeV.

Bassill, A J / Behera, A / Bellwied, R / Bhasin, A / Bhati, A K / Bielcik, J / Bielcikova, J / Bland, L C / Bordyuzhin, I G / Brandenburg, J D / Brandin, A V / Bryslawskyj, J / Bunzarov, I / Butterworth, J / Caines, H / Calderón de la Barca Sánchez, M / Cebra, D / Chakaberia, I / Chaloupka, P / Chan, B K / Chang, F-H / Chang, Z / Chankova-Bunzarova, N / Chatterjee, A / Chattopadhyay, S / Chen, J H / Chen, X / Cheng, J / Cherney, M / Christie, W / Crawford, H J / Csanád, M / Das, S / Dedovich, T G / Deppner, I M / Derevschikov, A A / Didenko, L / Dilks, C / Dong, X / Drachenberg, J L / Dunlop, J C / Edmonds, T / Elsey, N / Engelage, J / Eppley, G / Esha, R / Esumi, S / Evdokimov, O / Ewigleben, J / Eyser, O / Fatemi, R / Fazio, S / Federic, P / Fedorisin, J / Feng, Y / Filip, P / Finch, E / Fisyak, Y / Fulek, L / Gagliardi, C A / Galatyuk, T / Geurts, F / Gibson, A / Gopal, K / Grosnick, D / Gupta, A / Guryn, W / Hamad, A I / Hamed, A / Harris, J W / He, L / Heppelmann, S / Herrmann, N / Holub, L / Hong, Y / Horvat, S / Huang, B / Huang, H Z / Huang, S L / Huang, T / Huang, X / Humanic, T J / Huo, P / Igo, G / Jacobs, W W / Jena, C / Jentsch, A / Ji, Y / Jia, J / Jiang, K / Jowzaee, S / Ju, X / Judd, E G / Kabana, S / Kagamaster, S / Kalinkin, D / Kang, K / Kapukchyan, D / Kauder, K / Ke, H W / Keane, D / Kechechyan, A / Kelsey, M / Khyzhniak, Y V / Kikoła, D P / Kim, C / Kinghorn, T A / Kisel, I / Kisiel, A / Kocan, M / Kochenda, L / Kosarzewski, L K / Kramarik, L / Kravtsov, P / Krueger, K / Kulathunga Mudiyanselage, N / Kumar, L / Kunnawalkam Elayavalli, R / Kwasizur, J H / Lacey, R / Landgraf, J M / Lauret, J / Lebedev, A / Lednicky, R / Lee, J H / Li, C / Li, W / Li, X / Li, Y / Liang, Y / Licenik, R / Lin, T / Lipiec, A / Lisa, M A / Liu, F / Liu, H / Liu, P / Liu, T / Liu, X / Liu, Y / Liu, Z / Ljubicic, T / Llope, W J / Lomnitz, M / Longacre, R S / Luo, S / Luo, X / Ma, G L / Ma, L / Ma, R / Ma, Y G / Magdy Abdelwahab Abdelrahman, N / Majka, R / Mallick, D / Margetis, S / Markert, C / Matis, H S / Matonoha, O / Mazer, J A / Meehan, K / Mei, J C / Minaev, N G / Mioduszewski, S / Mishra, D / Mohanty, B / Mondal, M M / Mooney, I / Moravcova, Z / Morozov, D A / Nasim, Md / Nayak, K / Nelson, J M / Nemes, D B / Nie, M / Nigmatkulov, G / Niida, T / Nogach, L V / Nonaka, T / Odyniec, G / Ogawa, A / Oh, K / Oh, S / Okorokov, V A / Page, B S / Pak, R / Panebratsev, Y / Pawlik, B / Pawlowska, D / Pei, H / Perkins, C / Pintér, R L / Pluta, J / Porter, J / Posik, M / Pruthi, N K / Przybycien, M / Putschke, J / Quintero, A / Radhakrishnan, S K / Ramachandran, S / Ray, R L / Reed, R / Ritter, H G / Roberts, J B / Rogachevskiy, O V / Romero, J L / Ruan, L / Rusnak, J / Rusnakova, O / Sahoo, N R / Sahu, P K / Salur, S / Sandweiss, J / Schambach, J / Schmidke, W B / Schmitz, N / Schweid, B R / Seck, F / Seger, J / Sergeeva, M / Seto, R / Seyboth, P / Shah, N / Shahaliev, E / Shanmuganathan, P V / Shao, M / Shen, F / Shen, W Q / Shi, S S / Shou, Q Y / Sichtermann, E P / Siejka, S / Sikora, R / Simko, M / Singh, J / Singha, S / Smirnov, D / Smirnov, N / Solyst, W / Sorensen, P / Spinka, H M / Srivastava, B / Stanislaus, T D S / Stefaniak, M / Stewart, D J / Strikhanov, M / Stringfellow, B / Suaide, A A P / Sugiura, T / Sumbera, M / Summa, B / Sun, X M / Sun, Y / Surrow, B / Svirida, D N / Szymanski, P / Tang, A H / Tang, Z / Taranenko, A / Tarnowsky, T / Thomas, J H / Timmins, A R / Tlusty, D / Todoroki, T / Tokarev, M / Tomkiel, C A / Trentalange, S / Tribble, R E / Tribedy, P / Tripathy, S K / Tsai, O D / Tu, B / Tu, Z / Ullrich, T / Underwood, D G / Upsal, I / Van Buren, G / Vanek, J / Vasiliev, A N / Vassiliev, I / Videbæk, F / Vokal, S / Voloshin, S A / Wang, F / Wang, G / Wang, P / Wang, Y / Webb, J C / Wen, L / Westfall, G D / Wieman, H / Wissink, S W / Witt, R / Wu, Y / Xiao, Z G / Xie, G / Xie, W / Xu, H / Xu, N / Xu, Q H / Xu, Y F / Xu, Z / Yang, C / Yang, Q / Yang, S / Yang, Y / Yang, Z / Ye, Z / Yi, L / Yip, K / Yoo, I-K / Zbroszczyk, H / Zha, W / Zhang, D / Zhang, L / Zhang, S / Zhang, X P / Zhang, Y / Zhang, Z / Zhao, J / Zhong, C / Zhou, C / Zhu, X / Zhu, Z / Zurek, M / Zyzak, M

Physical review letters

2019 Volume 123, Issue 13, Page(s) 132301

Abstract: The Λ (Λ[over ¯]) hyperon polarization along the beam direction has been measured in Au+Au collisions at sqrt[s_{NN}]=200 GeV, for the first time in heavy-ion collisions. The polarization dependence on the hyperons' emission angle relative to the ... ...

Abstract	The Λ (Λ[over ¯]) hyperon polarization along the beam direction has been measured in Au+Au collisions at sqrt[s_{NN}]=200 GeV, for the first time in heavy-ion collisions. The polarization dependence on the hyperons' emission angle relative to the elliptic flow plane exhibits a second harmonic sine modulation, indicating a quadrupole pattern of the vorticity component along the beam direction, expected due to elliptic flow. The polarization is found to increase in more peripheral collisions, and shows no strong transverse momentum (p_{T}) dependence at p_{T} greater than 1 GeV/c. The magnitude of the signal is about 5 times smaller than those predicted by hydrodynamic and multiphase transport models; the observed phase of the emission angle dependence is also opposite to these model predictions. In contrast, the kinematic vorticity calculations in the blast-wave model tuned to reproduce particle spectra, elliptic flow, and the azimuthal dependence of the Gaussian source radii measured with the Hanbury Brown-Twiss intensity interferometry technique reproduce well the modulation phase measured in the data and capture the centrality and transverse momentum dependence of the polarization signal.
Language	English
Publishing date	2019-11-13
Publishing country	United States
Document type	Journal Article
ZDB-ID	208853-8
ISSN	1079-7114 ; 0031-9007
ISSN (online)	1079-7114
ISSN	0031-9007
DOI	10.1103/PhysRevLett.123.132301
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Observation of Excess J/ψ Yield at Very Low Transverse Momenta in Au+Au Collisions at sqrt[s_{NN}]=200 GeV and U+U Collisions at sqrt[s_{NN}]=193 GeV.

Bassill, A J / Behera, A / Bellwied, R / Bhasin, A / Bhati, A K / Bielcik, J / Bielcikova, J / Bland, L C / Bordyuzhin, I G / Brandenburg, J D / Brandin, A V / Bryslawskyj, J / Bunzarov, I / Butterworth, J / Caines, H / Sánchez, M Calderón de la Barca / Cebra, D / Chakaberia, I / Chaloupka, P / Chan, B K / Chang, F-H / Chang, Z / Chankova-Bunzarova, N / Chatterjee, A / Chattopadhyay, S / Chen, J H / Chen, X / Cheng, J / Cherney, M / Christie, W / Crawford, H J / Csanad, M / Das, S / Dedovich, T G / Deppner, I M / Derevschikov, A A / Didenko, L / Dilks, C / Dong, X / Drachenberg, J L / Dunlop, J C / Edmonds, T / Elsey, N / Engelage, J / Eppley, G / Esha, R / Esumi, S / Evdokimov, O / Ewigleben, J / Eyser, O / Fatemi, R / Fazio, S / Federic, P / Fedorisin, J / Feng, Y / Filip, P / Finch, E / Fisyak, Y / Fulek, L / Gagliardi, C A / Galatyuk, T / Geurts, F / Gibson, A / Grosnick, D / Gupta, A / Guryn, W / Hamad, A I / Hamed, A / Harris, J W / He, L / Heppelmann, S / Herrmann, N / Holub, L / Hong, Y / Horvat, S / Huang, B / Huang, H Z / Huang, S L / Huang, T / Huang, X / Humanic, T J / Huo, P / Igo, G / Jacobs, W W / Jentsch, A / Jia, J / Jiang, K / Jowzaee, S / Ju, X / Judd, E G / Kabana, S / Kagamaster, S / Kalinkin, D / Kang, K / Kapukchyan, D / Kauder, K / Ke, H W / Keane, D / Kechechyan, A / Kelsey, M / Kikoła, D P / Kim, C / Kinghorn, T A / Kisel, I / Kisiel, A / Kocan, M / Kochenda, L / Kosarzewski, L K / Kramarik, L / Kravtsov, P / Krueger, K / Mudiyanselage, N Kulathunga / Kumar, L / Elayavalli, R Kunnawalkam / Kwasizur, J H / Lacey, R / Landgraf, J M / Lauret, J / Lebedev, A / Lednicky, R / Lee, J H / Li, C / Li, W / Li, X / Li, Y / Liang, Y / Licenik, R / Lin, T / Lipiec, A / Lisa, M A / Liu, F / Liu, H / Liu, P / Liu, X / Liu, Y / Liu, Z / Ljubicic, T / Llope, W J / Lomnitz, M / Longacre, R S / Luo, S / Luo, X / Ma, G L / Ma, L / Ma, R / Ma, Y G / Magdy, N / Majka, R / Mallick, D / Margetis, S / Markert, C / Matis, H S / Matonoha, O / Mazer, J A / Meehan, K / Mei, J C / Minaev, N G / Mioduszewski, S / Mishra, D / Mohanty, B / Mondal, M M / Mooney, I / Moravcova, Z / Morozov, D A / Nasim, Md / Nayak, K / Nelson, J M / Nemes, D B / Nie, M / Nigmatkulov, G / Niida, T / Nogach, L V / Nonaka, T / Odyniec, G / Ogawa, A / Oh, K / Oh, S / Okorokov, V A / Page, B S / Pak, R / Panebratsev, Y / Pawlik, B / Pei, H / Perkins, C / Pinter, R L / Pluta, J / Porter, J / Posik, M / Pruthi, N K / Przybycien, M / Putschke, J / Quintero, A / Radhakrishnan, S K / Ramachandran, S / Ray, R L / Reed, R / Ritter, H G / Roberts, J B / Rogachevskiy, O V / Romero, J L / Ruan, L / Rusnak, J / Rusnakova, O / Sahoo, N R / Sahu, P K / Salur, S / Sandweiss, J / Schambach, J / Schmidke, W B / Schmitz, N / Schweid, B R / Seck, F / Seger, J / Sergeeva, M / Seto, R / Seyboth, P / Shah, N / Shahaliev, E / Shanmuganathan, P V / Shao, M / Shen, F / Shen, W Q / Shi, S S / Shou, Q Y / Sichtermann, E P / Siejka, S / Sikora, R / Simko, M / JSingh / Singha, S / Smirnov, D / Smirnov, N / Solyst, W / Sorensen, P / Spinka, H M / Srivastava, B / Stanislaus, T D S / Stewart, D J / Strikhanov, M / Stringfellow, B / Suaide, A A P / Sugiura, T / Sumbera, M / Summa, B / Sun, X M / Sun, Y / Surrow, B / Svirida, D N / Szymanski, P / Tang, A H / Tang, Z / Taranenko, A / Tarnowsky, T / Thomas, J H / Timmins, A R / Todoroki, T / Tokarev, M / Tomkiel, C A / Trentalange, S / Tribble, R E / Tribedy, P / Tripathy, S K / Tsai, O D / Tu, B / Ullrich, T / Underwood, D G / Upsal, I / Van Buren, G / Vanek, J / Vasiliev, A N / Vassiliev, I / Videbæk, F / Vokal, S / Voloshin, S A / Wang, F / Wang, G / Wang, P / Wang, Y / Webb, J C / Wen, L / Westfall, G D / Wieman, H / Wissink, S W / Witt, R / Wu, Y / Xiao, Z G / Xie, G / Xie, W / Xu, H / Xu, N / Xu, Q H / Xu, Y F / Xu, Z / Yang, C / Yang, Q / Yang, S / Yang, Y / Ye, Z / Yi, L / Yip, K / Yoo, I-K / Zbroszczyk, H / Zha, W / Zhang, D / Zhang, L / Zhang, S / Zhang, X P / Zhang, Y / Zhang, Z / Zhao, J / Zhong, C / Zhou, C / Zhu, X / Zhu, Z / Zurek, M K / Zyzak, M

Physical review letters

2019 Volume 123, Issue 13, Page(s) 132302

Abstract: We report on the first measurements of J/ψ production at very low transverse momentum (p_{T}<0.2 GeV/c) in hadronic Au+Au collisions at sqrt[s_{NN}]=200 GeV and U+U collisions at sqrt[s_{NN}]=193 GeV. Remarkably, the inferred nuclear modification ... ...

Abstract	We report on the first measurements of J/ψ production at very low transverse momentum (p_{T}<0.2 GeV/c) in hadronic Au+Au collisions at sqrt[s_{NN}]=200 GeV and U+U collisions at sqrt[s_{NN}]=193 GeV. Remarkably, the inferred nuclear modification factor of J/ψ at midrapidity in Au+Au (U+U) collisions reaches about 24 (52) for p_{T}<0.05 GeV/c in the 60%-80% collision centrality class. This noteworthy enhancement cannot be explained by hadronic production accompanied by cold and hot medium effects. In addition, the dN/dt distribution of J/ψ for the very low p_{T} range is presented for the first time. The distribution is consistent with that expected from the Au nucleus and shows a hint of interference. Comparison of the measurements to theoretical calculations of coherent production shows that the excess yield can be described reasonably well and reveals a partial disruption of coherent production in semicentral collisions, perhaps due to the violent hadronic interactions. Incorporating theoretical calculations, the results strongly suggest that the dramatic enhancement of J/ψ yield observed at extremely low p_{T} originates from coherent photon-nucleus interactions. In particular, coherently produced J/ψ's in violent hadronic collisions may provide a novel probe of the quark-gluon plasma.
Language	English
Publishing date	2019-11-13
Publishing country	United States
Document type	Journal Article
ZDB-ID	208853-8
ISSN	1079-7114 ; 0031-9007
ISSN (online)	1079-7114
ISSN	0031-9007
DOI	10.1103/PhysRevLett.123.132302
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Erratum: Observation of D^{0} Meson Nuclear Modifications in Au+Au Collisions at sqrt[s_{NN}]=200 GeV [Phys. Rev. Lett. 113, 142301 (2014)].

Adamczyk, L / Adkins, J K / Agakishiev, G / Aggarwal, M M / Ahammed, Z / Alekseev, I / Alford, J / Anson, C D / Aparin, A / Arkhipkin, D / Aschenauer, E C / Averichev, G S / Banerjee, A / Beavis, D R / Bellwied, R / Bhasin, A / Bhati, A K / Bhattarai, P / Bichsel, H /

Bielcik, J / Bielcikova, J / Bland, L C / Bordyuzhin, I G / Borowski, W / Bouchet, J / Brandin, A V / Brovko, S G / Bültmann, S / Bunzarov, I / Burton, T P / Butterworth, J / Caines, H / Calderón de la Barca Sánchez, M / Cebra, D / Cendejas, R / Cervantes, M C / Chaloupka, P / Chang, Z / Chattopadhyay, S / Chen, H F / Chen, J H / Chen, L / Cheng, J / Cherney, M / Chikanian, A / Christie, W / Chwastowski, J / Codrington, M J M / Contin, G / Cramer, J G / Crawford, H J / Cui, X / Das, S / Davila Leyva, A / De Silva, L C / Debbe, R R / Dedovich, T G / Deng, J / Derevschikov, A A / Derradi de Souza, R / Dhamija, S / di Ruzza, B / Didenko, L / Dilks, C / Ding, F / Djawotho, P / Dong, X / Drachenberg, J L / Draper, J E / Du, C M / Dunkelberger, L E / Dunlop, J C / Efimov, L G / Engelage, J / Engle, K S / Eppley, G / Eun, L / Evdokimov, O / Eyser, O / Fatemi, R / Fazio, S / Fedorisin, J / Filip, P / Finch, E / Fisyak, Y / Flores, C E / Gagliardi, C A / Gangadharan, D R / Garand, D / Geurts, F / Gibson, A / Girard, M / Gliske, S / Greiner, L / Grosnick, D / Gunarathne, D S / Guo, Y / Gupta, A / Gupta, S / Guryn, W / Haag, B / Hamed, A / Han, L-X / Haque, R / Harris, J W / Heppelmann, S / Hirsch, A / Hoffmann, G W / Hofman, D J / Horvat, S / Huang, B / Huang, H Z / Huang, X / Huck, P / Humanic, T J / Igo, G / Jacobs, W W / Jang, H / Judd, E G / Kabana, S / Kalinkin, D / Kang, K / Kauder, K / Ke, H W / Keane, D / Kechechyan, A / Kesich, A / Khan, Z H / Kikola, D P / Kisel, I / Kisiel, A / Koetke, D D / Kollegger, T / Konzer, J / Koralt, I / Kotchenda, L / Kraishan, A F / Kravtsov, P / Krueger, K / Kulakov, I / Kumar, L / Kycia, R A / Lamont, M A C / Landgraf, J M / Landry, K D / Lauret, J / Lebedev, A / Lednicky, R / Lee, J H / LeVine, M J / Li, C / Li, W / Li, X / Li, Y / Li, Z M / Lisa, M A / Liu, F / Ljubicic, T / Llope, W J / Lomnitz, M / Longacre, R S / Luo, X / Ma, G L / Ma, Y G / Madagodagettige Don, D M M D / Mahapatra, D P / Majka, R / Margetis, S / Markert, C / Masui, H / Matis, H S / McDonald, D / McShane, T S / Minaev, N G / Mioduszewski, S / Mohanty, B / Mondal, M M / Morozov, D A / Mustafa, M K / Nandi, B K / Nasim, Md / Nayak, T K / Nelson, J M / Nigmatkulov, G / Nogach, L V / Noh, S Y / Novak, J / Nurushev, S B / Odyniec, G / Ogawa, A / Oh, K / Ohlson, A / Okorokov, V / Oldag, E W / Olvitt, D L / Pachr, M / Page, B S / Pal, S K / Pan, Y X / Pandit, Y / Panebratsev, Y / Pawlak, T / Pawlik, B / Pei, H / Perkins, C / Peryt, W / Pile, P / Planinic, M / Pluta, J / Poljak, N / Porter, J / Poskanzer, A M / Pruthi, N K / Przybycien, M / Pujahari, P R / Putschke, J / Qiu, H / Quintero, A / Ramachandran, S / Raniwala, R / Raniwala, S / Ray, R L / Riley, C K / Ritter, H G / Roberts, J B / Rogachevskiy, O V / Romero, J L / Ross, J F / Roy, A / Ruan, L / Rusnak, J / Rusnakova, O / Sahoo, N R / Sahu, P K / Sakrejda, I / Salur, S / Sandweiss, J / Sangaline, E / Sarkar, A / Schambach, J / Scharenberg, R P / Schmah, A M / Schmidke, W B / Schmitz, N / Seger, J / Seyboth, P / Shah, N / Shahaliev, E / Shanmuganathan, P V / Shao, M / Sharma, B / Shen, W Q / Shi, S S / Shou, Q Y / Sichtermann, E P / Singaraju, R N / Skoby, M J / Smirnov, D / Smirnov, N / Solanki, D / Sorensen, P / Spinka, H M / Srivastava, B / Stanislaus, T D S / Stevens, J R / Stock, R / Strikhanov, M / Stringfellow, B / Sumbera, M / Sun, X / Sun, X M / Sun, Y / Sun, Z / Surrow, B / Svirida, D N / Symons, T J M / Szelezniak, M A / Takahashi, J / Tang, A H / Tang, Z / Tarnowsky, T / Thomas, J H / Timmins, A R / Tlusty, D / Tokarev, M / Trentalange, S / Tribble, R E / Tribedy, P / Trzeciak, B A / Tsai, O D / Turnau, J / Ullrich, T / Underwood, D G / Van Buren, G / van Nieuwenhuizen, G / Vandenbroucke, M / Vanfossen, J A / Varma, R / Vasconcelos, G M S / Vasiliev, A N / Vertesi, R / Videbæk, F / Viyogi, Y P / Vokal, S / Vossen, A / Wada, M / Wang, F / Wang, G / Wang, H / Wang, J S / Wang, X L / Wang, Y / Webb, G / Webb, J C / Westfall, G D / Wieman, H / Wissink, S W / Witt, R / Wu, Y F / Xiao, Z / Xie, W / Xin, K / Xu, H / Xu, J / Xu, N / Xu, Q H / Xu, Y / Xu, Z / Yan, W / Yang, C / Yang, Y / Ye, Z / Yepes, P / Yi, L / Yip, K / Yoo, I-K / Yu, N / Zawisza, Y / Zbroszczyk, H / Zha, W / Zhang, J B / Zhang, J L / Zhang, S / Zhang, X P / Zhang, Y / Zhang, Z P / Zhao, F / Zhao, J / Zhong, C / Zhu, X / Zhu, Y H / Zoulkarneeva, Y / Zyzak, M

Physical review letters

2019 Volume 121, Issue 22, Page(s) 229901

Abstract: This corrects the article DOI: 10.1103/PhysRevLett.113.142301. ...

Abstract	This corrects the article DOI: 10.1103/PhysRevLett.113.142301.
Language	English
Publishing date	2019-01-11
Publishing country	United States
Document type	Journal Article ; Published Erratum
ZDB-ID	208853-8
ISSN	1079-7114 ; 0031-9007
ISSN (online)	1079-7114
ISSN	0031-9007
DOI	10.1103/PhysRevLett.121.229901
Database	MEDical Literature Analysis and Retrieval System OnLINE

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