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First Direct Detection Constraints on Planck-Scale Mass Dark Matter with Multiple-Scatter Signatures Using the DEAP-3600 Detector.
Adhikari P, Ajaj R, Alpízar-Venegas M, Auty DJ, Benmansour H, Bina CE, Bonivento W, Boulay MG, Cadeddu M, Cai B, Cárdenas-Montes M, Cavuoti S, Chen Y, Cleveland BT, Corning JM, Daugherty S, DelGobbo P, Di Stefano P, Doria L, Dunford M, Ellingwood E, Erlandson A, Farahani SS, Fatemighomi N, Fiorillo G, Gallacher D, García Abia P, Garg S, Giampa P, Goeldi D, Gorel P, Graham K, Grobov A, Hallin AL, Hamstra M, Hugues T, Ilyasov A, Joy A, Jigmeddorj B, Jillings CJ, Kamaev O, Kaur G, Kemp A, Kochanek I, Kuźniak M, Lai M, Langrock S, Lehnert B, Leonhardt A, Levashko N, Li X, Lissia M, Litvinov O, Lock J, Longo G, Machulin I, McDonald AB, McElroy T, McLaughlin JB, Mielnichuk C, Mirasola L, Monroe J, Oliviéro G, Pal S, Peeters SJM, Perry M, Pesudo V, Picciau E, Piro MC, Pollmann TR, Raj N, Rand ET, Rethmeier C, Retière F, Rodríguez-García I, Roszkowski L, Ruhland JB, Sanchez García E, Sánchez-Pastor T, Santorelli R, Seth S, Sinclair D, Skensved P, Smith B, Smith NJT, Sonley T, Stainforth R, Stringer M, Sur B, Vázquez-Jáuregui E, Viel S, Walding J, Waqar M, Ward M, Westerdale S, Willis J, Zuñiga-Reyes A; DEAP Collaboration. Adhikari P, et al. Phys Rev Lett. 2022 Jan 7;128(1):011801. doi: 10.1103/PhysRevLett.128.011801. Phys Rev Lett. 2022. PMID: 35061499
A search for multiscatter signals from supermassive dark matter was performed with a blind analysis of data collected over a 813 d live time with DEAP-3600, a 3.3 t single-phase liquid argon-based detector at SNOLAB. ...
A search for multiscatter signals from supermassive dark matter was performed with a blind analysis of data collected over a 813 d live time …
First Results from the DEAP-3600 Dark Matter Search with Argon at SNOLAB.
Amaudruz PA, Baldwin M, Batygov M, Beltran B, Bina CE, Bishop D, Bonatt J, Boorman G, Boulay MG, Broerman B, Bromwich T, Bueno JF, Burghardt PM, Butcher A, Cai B, Chan S, Chen M, Chouinard R, Cleveland BT, Cranshaw D, Dering K, DiGioseffo J, Dittmeier S, Duncan FA, Dunford M, Erlandson A, Fatemighomi N, Florian S, Flower A, Ford RJ, Gagnon R, Giampa P, Golovko VV, Gorel P, Gornea R, Grace E, Graham K, Gulyev E, Hakobyan R, Hall A, Hallin AL, Hamstra M, Harvey PJ, Hearns C, Jillings CJ, Kamaev O, Kemp A, Kuźniak M, Langrock S, La Zia F, Lehnert B, Lidgard JJ, Lim C, Lindner T, Linn Y, Liu S, Majewski P, Mathew R, McDonald AB, McElroy T, McGinn T, McLaughlin JB, Mead S, Mehdiyev R, Mielnichuk C, Monroe J, Muir A, Nadeau P, Nantais C, Ng C, Noble AJ, O'Dwyer E, Ohlmann C, Olchanski K, Olsen KS, Ouellet C, Pasuthip P, Peeters SJM, Pollmann TR, Rand ET, Rau W, Rethmeier C, Retière F, Seeburn N, Shaw B, Singhrao K, Skensved P, Smith B, Smith NJT, Sonley T, Soukup J, Stainforth R, Stone C, Strickland V, Sur B, Tang J, Taylor J, Veloce L, Vázquez-Jáuregui E, Walding J, Ward M, Westerdale S, Woolsey E, Zielinski J; DEAP-3600 Collaboration. Amaudruz PA, et al. Phys Rev Lett. 2018 Aug 17;121(7):071801. doi: 10.1103/PhysRevLett.121.071801. Phys Rev Lett. 2018. PMID: 30169081 Free article.
This Letter reports the first results of a direct dark matter search with the DEAP-3600 single-phase liquid argon (LAr) detector. The experiment was performed 2 km underground at SNOLAB (Sudbury, Canada) utilizing a large target mass, with the LAr target contained in a sph …
This Letter reports the first results of a direct dark matter search with the DEAP-3600 single-phase liquid argon (LAr) detector. The …
Pulse-shape discrimination against low-energy Ar-39 beta decays in liquid argon with 4.5 tonne-years of DEAP-3600 data.
Adhikari P, Ajaj R, Alpízar-Venegas M, Amaudruz PA, Auty DJ, Batygov M, Beltran B, Benmansour H, Bina CE, Bonatt J, Bonivento W, Boulay MG, Broerman B, Bueno JF, Burghardt PM, Butcher A, Cadeddu M, Cai B, Cárdenas-Montes M, Cavuoti S, Chen M, Chen Y, Cleveland BT, Corning JM, Cranshaw D, Daugherty S, DelGobbo P, Dering K, DiGioseffo J, Di Stefano P, Doria L, Duncan FA, Dunford M, Ellingwood E, Erlandson A, Farahani SS, Fatemighomi N, Fiorillo G, Florian S, Flower T, Ford RJ, Gagnon R, Gallacher D, García Abia P, Garg S, Giampa P, Goeldi D, Golovko V, Gorel P, Graham K, Grant DR, Grobov A, Hallin AL, Hamstra M, Harvey PJ, Hearns C, Hugues T, Ilyasov A, Joy A, Jigmeddorj B, Jillings CJ, Kamaev O, Kaur G, Kemp A, Kochanek I, Kuźniak M, Lai M, Langrock S, Lehnert B, Leonhardt A, Levashko N, Li X, Lidgard J, Lindner T, Lissia M, Lock J, Longo G, Machulin I, McDonald AB, McElroy T, McGinn T, McLaughlin JB, Mehdiyev R, Mielnichuk C, Monroe J, Nadeau P, Nantais C, Ng C, Noble AJ, O'Dwyer E, Oliviéro G, Ouellet C, Pal S, Pasuthip P, Peeters SJM, Perry M, Pesudo V, Picciau E, Piro MC, Pollmann TR, Rand ET, Rethmeier C, Retière F, Rodríguez-García I, Roszkowski L, Ruhland JB, Sánchez-García … See abstract for full author list ➔ Adhikari P, et al. Eur Phys J C Part Fields. 2021;81(9):823. doi: 10.1140/epjc/s10052-021-09514-w. Epub 2021 Sep 16. Eur Phys J C Part Fields. 2021. PMID: 34720726 Free PMC article.
The DEAP-3600 detector searches for the scintillation signal from dark matter particles scattering on a 3.3 tonne liquid argon target. ...
The DEAP-3600 detector searches for the scintillation signal from dark matter particles scattering on a 3.3 tonne liquid argon target …
Emotional State Recognition from Peripheral Physiological Signals Using Fused Nonlinear Features and Team-Collaboration Identification Strategy.
Pan L, Yin Z, She S, Song A. Pan L, et al. Entropy (Basel). 2020 Apr 30;22(5):511. doi: 10.3390/e22050511. Entropy (Basel). 2020. PMID: 33286283 Free PMC article.
In order to make full use of the advantages of other classifiers and avoid the limitation of single classifier, the team-collaboration model is built and the team-collaboration decision-making mechanism is designed according to the proposed team-collaboration …
In order to make full use of the advantages of other classifiers and avoid the limitation of single classifier, the team-collaboration …
A pHe sensitive nanodrug for collaborative penetration and inhibition of metastatic tumors.
Huo M, Zhou J, Wang H, Zheng Y, Tong Y, Zhou J, Liu J, Yin T. Huo M, et al. J Control Release. 2022 Dec;352:893-908. doi: 10.1016/j.jconrel.2022.11.012. Epub 2022 Nov 14. J Control Release. 2022. PMID: 36370879
Herein, we constructed a tumor extracellular pH (pH(e)) sensitive methotrexate-chitosan conjugate (MTX-GC-DEAP) and co-assembled it with quercetin (QUE) to achieve co-delivered nanodrugs (MTX-GC-DEAP/QUE). The pH(e) sensitive protonation and disassembly enabled MTX- …
Herein, we constructed a tumor extracellular pH (pH(e)) sensitive methotrexate-chitosan conjugate (MTX-GC-DEAP) and co-assembled it w …
Robust Latent Multi-Source Adaptation for Encephalogram-Based Emotion Recognition.
Tao J, Dan Y, Zhou D, He S. Tao J, et al. Front Neurosci. 2022 Apr 27;16:850906. doi: 10.3389/fnins.2022.850906. eCollection 2022. Front Neurosci. 2022. PMID: 35573289 Free PMC article.
Specifically, by jointly aligning the statistical and semantic distribution discrepancies between each source and target pair, multiple domain-invariant classifiers can be trained collaboratively in a unified framework. This framework can fully utilize the correlated knowl …
Specifically, by jointly aligning the statistical and semantic distribution discrepancies between each source and target pair, multiple doma …
Cross-subject emotion recognition using hierarchical feature optimization and support vector machine with multi-kernel collaboration.
Pan L, Tang Z, Wang S, Song A. Pan L, et al. Physiol Meas. 2023 Dec 18;44(12). doi: 10.1088/1361-6579/ad10c6. Physiol Meas. 2023. PMID: 38029444
The proposed model with hierarchical feature optimization and SVM with multi-kernel function collaboration demonstrates superior emotion recognition accuracy compared to state-of-the-art techniques. In addition, the analysis based on DEAP dataset composition charact …
The proposed model with hierarchical feature optimization and SVM with multi-kernel function collaboration demonstrates superior emot …
Local domain generalization with low-rank constraint for EEG-based emotion recognition.
Tao J, Dan Y, Zhou D. Tao J, et al. Front Neurosci. 2023 Nov 7;17:1213099. doi: 10.3389/fnins.2023.1213099. eCollection 2023. Front Neurosci. 2023. PMID: 38027525 Free PMC article.
Since the ignorance of the fine-grained distribution information in the source may still bind the DG expectation on EEG datasets with multimodal structures, multiple patches (or subdomains) should be reconstructed from the source domain, on which multi-classifiers could be learne …
Since the ignorance of the fine-grained distribution information in the source may still bind the DG expectation on EEG datasets with multim …
EEG-based emotion recognition using 4D convolutional recurrent neural network.
Shen F, Dai G, Lin G, Zhang J, Kong W, Zeng H. Shen F, et al. Cogn Neurodyn. 2020 Dec;14(6):815-828. doi: 10.1007/s11571-020-09634-1. Epub 2020 Sep 14. Cogn Neurodyn. 2020. PMID: 33101533 Free PMC article.
Our model achieves state-of-the-art performance both on SEED and DEAP datasets under intra-subject splitting. The experimental results demonstrate the effectiveness of integrating frequency, spatial and temporal information of EEG for emotion recognition....
Our model achieves state-of-the-art performance both on SEED and DEAP datasets under intra-subject splitting. The experimental result …
Emotion recognition from multichannel EEG signals using K-nearest neighbor classification.
Li M, Xu H, Liu X, Lu S. Li M, et al. Technol Health Care. 2018;26(S1):509-519. doi: 10.3233/THC-174836. Technol Health Care. 2018. PMID: 29758974 Free PMC article.
METHODS: We classified the emotional states in the valence and arousal dimensions using different combinations of EEG channels. Firstly, DEAP default preprocessed data were normalized. Next, EEG signals were divided into four frequency bands using discrete wavelet transfor …
METHODS: We classified the emotional states in the valence and arousal dimensions using different combinations of EEG channels. Firstly, …
14 results