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Joint T1 and T2 Mapping with Tiny Dictionaries and Subspace-Constrained Reconstruction [article]

Volkert Roeloffs, Martin Uecker, Jens Frahm
2018 arXiv   pre-print
Based on simulation results, tiny dictionaries were used for T1-T2 mapping in phantom and in vivo studies. Reconstruction and parameter mapping were performed entirely in subspace.  ...  Purpose: To develop a method that adaptively generates tiny dictionaries for joint T1-T2 mapping.  ...  The demonstrated ability to perform reconstruction and parameter mapping entirely in subspace justifies the coined term "tiny dictionaries".  ... 
arXiv:1812.09560v1 fatcat:mfr2nvwxl5c6vgjl7dw6zg2oia

Physics-based Reconstruction Methods for Magnetic Resonance Imaging [article]

Xiaoqing Wang, Zhengguo Tan, Nick Scholand, Volkert Roeloffs, Martin Uecker
2021 arXiv   pre-print
complete with data and code.  ...  By formulating image reconstruction as an inverse problem, quantitative maps of the underlying physical parameters can then be extracted directly from efficiently acquired k-space signals without intermediate  ...  Tobias Block for the radial spin-echo sequence, and Dr. Christian Holme for help with the scripts.  ... 
arXiv:2010.01403v3 fatcat:dwd2abbjgvhpvh4c63lmmyaouy

An overview of deep learning in medical imaging focusing on MRI

Alexander Selvikvåg Lundervold, Arvid Lundervold
2018 Zeitschrift für Medizinische Physik  
Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition  ...  We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis.  ...  Our work was financially supported by the Bergen Research Foundation through the project "Computational medical imaging and machine learning -methods, infrastructure and applications".  ... 
doi:10.1016/j.zemedi.2018.11.002 fatcat:kkimovnwcrhmth7mg6h6cpomjm

Visual detection of vehicles using a bag-of-features approach

Pedro Pinto, Ana Tome, Vitor Santos
2013 2013 13th International Conference on Autonomous Robot Systems  
Chauhan, and L. Lopes for the technical support and help. ACKNOWLEDGEMENTS The authors acknowledge the major support given by the ISEP-IPP Institution and by the INESC TEC, to this project.  ...  e Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP -01-0124-FEDER-022701.  ...  Accordingly, the training-sequence can be described as a i = (v t1 , v t2 , ..., v m ), under the causal ordering v t1 v t2 ... v m and a i ∈ A.  ... 
doi:10.1109/robotica.2013.6623539 fatcat:ialsxj53yzfkfe5f766krtkkrq

Automatic Face Understanding: Recognizing Families in Photos [article]

Joseph P Robinson
2021 arXiv   pre-print
We also trained CNNs on FIW and deployed the model on the renowned KinWild I and II to gain SOTA. Most recently, we further augmented FIW with MM.  ...  Furthermore, our model is robust with a reduced size: 1/8 the number of channels is comparable to SOTA in real-time on a CPU.  ...  A unique feature was the construction of a Table 5 .10, 5.12, and 5.14 for T1, T2, and T3, respectfully.  ... 
arXiv:2102.08941v1 fatcat:eqje3jh23nb6do7crz7rw6342a

Message from the general chair

Benjamin C. Lee
2015 2015 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)  
We propose a joint learning model which combines pairwise classification and mention clustering with Markov logic.  ...  Joint Learning for Coreference Resolution with Markov Logic Resolving "This-issue" Anaphora Varada Kolhatkar and Graeme Hirst Saturday 12:00pm-12:30pm -202 A (ICC) We annotate and resolve a particular  ...  : Lotus 1 (Shilla) 3:30pm -4:00pm Coffee Break 6:00pm -9:00pm Welcome Reception Halla Hall (Shilla) T1: Qualitative Modeling of Spatial Prepositions and Motion Expressions p. 34 T2: Systematic  ... 
doi:10.1109/ispass.2015.7095776 dblp:conf/ispass/Lee15 fatcat:ehbed6nl6barfgs6pzwcvwxria

29th Annual Computational Neuroscience Meeting: CNS*2020

2020 BMC Neuroscience  
This includes (i) how growing axons navigate to their targets by detecting and responding to molecular cues in their environment, (ii) the formation of maps in the visual cortex and how these are influenced  ...  Selection starts in the primary visual cortex (V1), which creates a bottom-up saliency map to guide the fovea to selected visual locations via gaze shifts.  ...  Institute (Challenge grants to SJ), the Research Corporation for Science Advancement (a Cottrell SEED Award to TV), and the German Research Foundation (DFG grant #ME 1535/7-1 to RM), and the Foundation  ... 
doi:10.1186/s12868-020-00593-1 pmid:33342424 fatcat:edosycf35zfifm552a2aogis7a

The Modern Mathematics of Deep Learning [article]

Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen
2021 arXiv   pre-print
surprisingly successful optimization performance despite the non-convexity of the problem, understanding what features are learned, why deep architectures perform exceptionally well in physical problems, and  ...  More precisely, one transforms unlabeled training data z (i) into features x(i) = T1 (z (i) ) ∈ X and labels y (i) = T2 (z (i) ) ∈ Y using suitable transformations T1 : Z → X , T2 : Z → Y.  ...  In doing so, one asks for a model fs approximating the transformation T2T1−1 : X → Y which is, e.g., done in order to learn feature representations or invariances.  ... 
arXiv:2105.04026v1 fatcat:lxnfyzr6qfasneo433inpgseia

27th Annual Computational Neuroscience Meeting (CNS*2018): Part One

2018 BMC Neuroscience  
All network simulations carried out with NEST (http:// www.nest-simul  ...  Acknowledgements This research was supported by NIH grant NS086082 and a GSU Brains and Behavior Seed Grant (DNC), N.H. is a Brains and Behavior and Honeycutt Fellow; A.A.P. is a 2CI Neurogenomics and  ...  Using a publicly available dataset, we demonstrate that this algorithm accurately transforms between T1-and T2-weighted images, proton density images, time-of-flight angiograms, and diffusion MRI images  ... 
doi:10.1186/s12868-018-0452-x pmid:30373544 pmcid:PMC6205781 fatcat:xv7pgbp76zbdfksl545xof2vzy

Kuo_columbia_0054D_16530.pdf [article]

Anna Elisabeth Dorfi and Prof. Glen O'Neil for the construction of the microscope, execution of data collections and many helpful advices for reconstruction algorithm designs.  ...  It has been nothing short of amazement to work with a truly intellectual inspiring advisor, as well as a man with unquenchable Acknowledgment For the SECM work, I want to thank Prof.  ...  (B.93) Write (B.92) as two summation of independent random variables with t = j − i by separating sum into two sets J t1 , J t2 defined in (B.4) with both |J t1 | , |J t2 | < nθ 2 with probability at least  ... 
doi:10.7916/d8-je9b-wz48 fatcat:5dejwa4c5rhajcn3tjqglgitii

Solving underdetermined inverse problems [article]

Maximilian Arthus März, Technische Universität Berlin, Martin Genzel, Pierre Weiss
This cumulative dissertation investigates and designs methods for the reconstruction of unknown signals from severely underdetermined linear measurements.  ...  Such an assumption leads to a synthesis- and an analysis-based sparsity model.  ...  Acknowledgments The authors thank Claire Boyer, Peter Jung, Jackie Ma, and Pierre Weiss for fruitful discussions.  ... 
doi:10.14279/depositonce-12206 fatcat:bzdkctclcfaq5cfr756kw5efe4

Color, consciousness, and the isomorphism constraint

S E Palmer
1999 Behavioral and Brain Sciences  
The relations among consciousness, brain behavior, and scientific explanation are explored in the domain of color perception.  ...  Current scientific knowledge about color similarity, color composition, dimensional structure, unique colors, and color categories is used to assess Locke's "inverted spectrum argument" about the undetectability  ...  Comment on T2.  ... 
pmid:11301573 fatcat:iqudwwya4ne4jlax66cgqr6jf4

Forum Bildverarbeitung 2014

Michael Heizmann, Fernando Puente León
2015 TM. Technisches Messen  
Binder+Co AG supplied us with a batch of shards of glass waste that we used as sample.  ...  Acknowledgments The first author is funded by the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB). We thank Prof. Beyerer for his valuable input.  ...  For the bagging we apply training data sampling with replacement, and for the decisions based on a random feature subspace we use a linear discrimination of 2D subspaces with thresholding of the distance  ... 
doi:10.1515/teme-2015-0033 fatcat:nzkj6o5l2zcydezksxkcjnpf7a

Introduction [chapter]

2016 Music Data Analysis  
This series aims to foster the integration between the computer sciences and statistical, numerical, and probabilistic methods by publishing a broad range of reference works, textbooks, and handbooks.  ...  The interface between the computer and statistical sciences is increasing, as each discipline seeks to harness the power and resources of the other.  ...  Perceptual tempo estimation algorithms estimate two tempo values in BPM (T1 and T2, where T1 is the slower of the two tempo values).  ... 
doi:10.1201/9781315370996-5 fatcat:avooqogcpnbjngqmzuonil3exq

Network resilience [article]

Xueming Liu, Daqing Li, Manqing Ma, Boleslaw K. Szymanski, H Eugene Stanley, Jianxi Gao
2022 arXiv   pre-print
failures in infrastructure systems, and social convention changes in human and animal networks.  ...  their resilience function and early warning indicators.  ...  The term −aMC means that macroalgae can overgrow corals, and γMT captures the phenomenon that macroalgae col- Control Parameter State T2 T1 Figure 6 : The hysteresis in an ecosystem.  ... 
arXiv:2007.14464v2 fatcat:vyas2dqb4ngkpazcfvwkth7maa
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