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  1. Article ; Online: The well-worn route revisited: Striatal and hippocampal system contributions to familiar route navigation.

    Buckley, Matthew / McGregor, Anthony / Ihssen, Niklas / Austen, Joseph / Thurlbeck, Simon / Smith, Shamus P / Heinecke, Armin / Lew, Adina R

    Hippocampus

    2024  

    Abstract: Classic research has shown a division in the neuroanatomical structures that support flexible (e.g., short-cutting) and habitual (e.g., familiar route following) navigational behavior, with hippocampal-caudate systems associated with the former and ... ...

    Abstract Classic research has shown a division in the neuroanatomical structures that support flexible (e.g., short-cutting) and habitual (e.g., familiar route following) navigational behavior, with hippocampal-caudate systems associated with the former and putamen systems with the latter. There is, however, disagreement about whether the neural structures involved in navigation process particular forms of spatial information, such as associations between constellations of cues forming a cognitive map, versus single landmark-action associations, or alternatively, perform particular reinforcement learning algorithms that allow the use of different spatial strategies, so-called model-based (flexible) or model-free (habitual) forms of learning. We sought to test these theories by asking participants (N = 24) to navigate within a virtual environment through a previously learned, 9-junction route with distinctive landmarks at each junction while undergoing functional magnetic resonance imaging (fMRI). In a series of probe trials, we distinguished knowledge of individual landmark-action associations along the route versus knowledge of the correct sequence of landmark-action associations, either by having absent landmarks, or "out-of-sequence" landmarks. Under a map-based perspective, sequence knowledge would not require hippocampal systems, because there are no constellations of cues available for cognitive map formation. Within a learning-based model, however, responding based on knowledge of sequence would require hippocampal systems because prior context has to be utilized. We found that hippocampal-caudate systems were more active in probes requiring sequence knowledge, supporting the learning-based model. However, we also found greater putamen activation in probes where navigation based purely on sequence memory could be planned, supporting models of putamen function that emphasize its role in action sequencing.
    Language English
    Publishing date 2024-05-09
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1074352-2
    ISSN 1098-1063 ; 1050-9631
    ISSN (online) 1098-1063
    ISSN 1050-9631
    DOI 10.1002/hipo.23607
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Deviancy Aversion and Social Norms.

    Gollwitzer, Anton / Martel, Cameron / Heinecke, Anna / Bargh, John A

    Personality & social psychology bulletin

    2022  Volume 50, Issue 4, Page(s) 516–532

    Abstract: We propose that deviancy aversion-people's domain-general discomfort toward the distortion of patterns (repeated forms or models)-contributes to the strength and prevalence of social norms in society. Five studies ( ...

    Abstract We propose that deviancy aversion-people's domain-general discomfort toward the distortion of patterns (repeated forms or models)-contributes to the strength and prevalence of social norms in society. Five studies (
    MeSH term(s) Humans ; Social Norms ; Social Behavior ; Judgment
    Language English
    Publishing date 2022-12-10
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2047603-6
    ISSN 1552-7433 ; 0146-1672
    ISSN (online) 1552-7433
    ISSN 0146-1672
    DOI 10.1177/01461672221131378
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Book ; Online: Supercomputing for Molecular Dynamics Simulations

    Heinecke, Alexander

    Handling Multi-Trillion Particles in Nanofluidics

    (SpringerBriefs in Computer Science)

    2015  

    Abstract: This work presents modern implementations of relevant molecular dynamics algorithms using ls1 mardyn, a simulation program for engineering applications. The text focuses strictly on HPC-related aspects, covering implementation on HPC architectures, ... ...

    Author's details by Alexander Heinecke, Wolfgang Eckhardt, Martin Horsch, Hans-Joachim Bungartz
    Series title SpringerBriefs in Computer Science
    Abstract This work presents modern implementations of relevant molecular dynamics algorithms using ls1 mardyn, a simulation program for engineering applications. The text focuses strictly on HPC-related aspects, covering implementation on HPC architectures, taking Intel Xeon and Intel Xeon Phi clusters as representatives of current platforms. The work describes distributed and shared-memory parallelization on these platforms, including load balancing, with a particular focus on the efficient implementation of the compute kernels. The text also discusses the software-architecture of the resulting code
    Keywords Computer science ; Computer simulation ; Computer system performance ; Software engineering
    Language English
    Size Online-Ressource (X, 76 p. 35 illus., 13 illus. in color), online resource
    Publisher Springer International Publishing
    Publishing place Cham ;s.l
    Document type Book ; Online
    ISBN 9783319171470 ; 9783319171487 ; 331917147X ; 3319171488
    DOI 10.1007/978-3-319-17148-7
    Database Former special subject collection: coastal and deep sea fishing

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  4. Article ; Online: Bayesian splines versus fractional polynomials in network meta-analysis.

    Heinecke, Andreas / Tallarita, Marta / De Iorio, Maria

    BMC medical research methodology

    2020  Volume 20, Issue 1, Page(s) 261

    Abstract: Background: Network meta-analysis (NMA) provides a powerful tool for the simultaneous evaluation of multiple treatments by combining evidence from different studies, allowing for direct and indirect comparisons between treatments. In recent years, NMA ... ...

    Abstract Background: Network meta-analysis (NMA) provides a powerful tool for the simultaneous evaluation of multiple treatments by combining evidence from different studies, allowing for direct and indirect comparisons between treatments. In recent years, NMA is becoming increasingly popular in the medical literature and underlying statistical methodologies are evolving both in the frequentist and Bayesian framework. Traditional NMA models are often based on the comparison of two treatment arms per study. These individual studies may measure outcomes at multiple time points that are not necessarily homogeneous across studies.
    Methods: In this article we present a Bayesian model based on B-splines for the simultaneous analysis of outcomes across time points, that allows for indirect comparison of treatments across different longitudinal studies.
    Results: We illustrate the proposed approach in simulations as well as on real data examples available in the literature and compare it with a model based on P-splines and one based on fractional polynomials, showing that our approach is flexible and overcomes the limitations of the latter.
    Conclusions: The proposed approach is computationally efficient and able to accommodate a large class of temporal treatment effect patterns, allowing for direct and indirect comparisons of widely varying shapes of longitudinal profiles.
    MeSH term(s) Algorithms ; Bayes Theorem ; Humans ; Longitudinal Studies ; Network Meta-Analysis
    Language English
    Publishing date 2020-10-20
    Publishing country England
    Document type Journal Article
    ZDB-ID 2041362-2
    ISSN 1471-2288 ; 1471-2288
    ISSN (online) 1471-2288
    ISSN 1471-2288
    DOI 10.1186/s12874-020-01113-9
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Book ; Online: Deep Representation with ReLU Neural Networks

    Heinecke, Andreas / Hwang, Wen-Liang

    2019  

    Abstract: We consider deep feedforward neural networks with rectified linear units from a signal processing perspective. In this view, such representations mark the transition from using a single (data-driven) linear representation to utilizing a large collection ... ...

    Abstract We consider deep feedforward neural networks with rectified linear units from a signal processing perspective. In this view, such representations mark the transition from using a single (data-driven) linear representation to utilizing a large collection of affine linear representations tailored to particular regions of the signal space. This paper provides a precise description of the individual affine linear representations and corresponding domain regions that the (data-driven) neural network associates to each signal of the input space. In particular, we describe atomic decompositions of the representations and, based on estimating their Lipschitz regularity, suggest some conditions that can stabilize learning independent of the network depth. Such an analysis may promote further theoretical insight from both the signal processing and machine learning communities.
    Keywords Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Signal Processing ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2019-03-29
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Training Neural Machine Translation (NMT) Models using Tensor Train Decomposition on TensorFlow (T3F)

    Drew, Amelia / Heinecke, Alexander

    2019  

    Abstract: We implement a Tensor Train layer in the TensorFlow Neural Machine Translation (NMT) model using the t3f library. We perform training runs on the IWSLT English-Vietnamese '15 and WMT German-English '16 datasets with learning rates $\in \{0.0004,0.0008,0 ... ...

    Abstract We implement a Tensor Train layer in the TensorFlow Neural Machine Translation (NMT) model using the t3f library. We perform training runs on the IWSLT English-Vietnamese '15 and WMT German-English '16 datasets with learning rates $\in \{0.0004,0.0008,0.0012\}$, maximum ranks $\in \{2,4,8,16\}$ and a range of core dimensions. We compare against a target BLEU test score of 24.0, obtained by our benchmark run. For the IWSLT English-Vietnamese training, we obtain BLEU test/dev scores of 24.0/21.9 and 24.2/21.9 using core dimensions $(2, 2, 256) \times (2, 2, 512)$ with learning rate 0.0012 and rank distributions $(1,4,4,1)$ and $(1,4,16,1)$ respectively. These runs use 113\% and 397\% of the flops of the benchmark run respectively. We find that, of the parameters surveyed, a higher learning rate and more `rectangular' core dimensions generally produce higher BLEU scores. For the WMT German-English dataset, we obtain BLEU scores of 24.0/23.8 using core dimensions $(4, 4, 128) \times (4, 4, 256)$ with learning rate 0.0012 and rank distribution $(1,2,2,1)$. We discuss the potential for future optimization and application of Tensor Train decomposition to other NMT models.

    Comment: 10 pages, 2 tables
    Keywords Computer Science - Machine Learning ; Computer Science - Computation and Language ; Statistics - Machine Learning
    Subject code 670
    Publishing date 2019-11-05
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: FPGA-based AI Smart NICs for Scalable Distributed AI Training Systems

    Ma, Rui / Georganas, Evangelos / Heinecke, Alexander / Boutros, Andrew / Nurvitadhi, Eriko

    2022  

    Abstract: Rapid advances in artificial intelligence (AI) technology have led to significant accuracy improvements in a myriad of application domains at the cost of larger and more compute-intensive models. Training such models on massive amounts of data typically ... ...

    Abstract Rapid advances in artificial intelligence (AI) technology have led to significant accuracy improvements in a myriad of application domains at the cost of larger and more compute-intensive models. Training such models on massive amounts of data typically requires scaling to many compute nodes and relies heavily on collective communication algorithms, such as all-reduce, to exchange the weight gradients between different nodes. The overhead of these collective communication operations in a distributed AI training system can bottleneck its performance, with more pronounced effects as the number of nodes increases. In this paper, we first characterize the all-reduce operation overhead by profiling distributed AI training. Then, we propose a new smart network interface card (NIC) for distributed AI training systems using field-programmable gate arrays (FPGAs) to accelerate all-reduce operations and optimize network bandwidth utilization via data compression. The AI smart NIC frees up the system's compute resources to perform the more compute-intensive tensor operations and increases the overall node-to-node communication efficiency. We perform real measurements on a prototype distributed AI training system comprised of 6 compute nodes to evaluate the performance gains of our proposed FPGA-based AI smart NIC compared to a baseline system with regular NICs. We also use these measurements to validate an analytical model that we formulate to predict performance when scaling to larger systems. Our proposed FPGA-based AI smart NIC enhances overall training performance by 1.6x at 6 nodes, with an estimated 2.5x performance improvement at 32 nodes, compared to the baseline system using conventional NICs.

    Comment: 5 pages, 4 figures
    Keywords Computer Science - Distributed ; Parallel ; and Cluster Computing ; Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2022-04-22
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures

    Georganas, Evangelos / Kalamkar, Dhiraj / Voronin, Kirill / Noack, Antonio / Pabst, Hans / Breuer, Alexander / Heinecke, Alexander

    2023  

    Abstract: During the past decade, Deep Learning (DL) algorithms, programming systems and hardware have converged with the High Performance Computing (HPC) counterparts. Nevertheless, the programming methodology of DL and HPC systems is stagnant, relying on highly- ... ...

    Abstract During the past decade, Deep Learning (DL) algorithms, programming systems and hardware have converged with the High Performance Computing (HPC) counterparts. Nevertheless, the programming methodology of DL and HPC systems is stagnant, relying on highly-optimized, yet platform-specific and inflexible vendor-optimized libraries. Such libraries provide close-to-peak performance on specific platforms, kernels and shapes thereof that vendors have dedicated optimizations efforts, while they underperform in the remaining use-cases, yielding non-portable codes with performance glass-jaws. This work introduces a framework to develop efficient, portable DL and HPC kernels for modern CPU architectures. We decompose the kernel development in two steps: 1) Expressing the computational core using Tensor Processing Primitives (TPPs): a compact, versatile set of 2D-tensor operators, 2) Expressing the logical loops around TPPs in a high-level, declarative fashion whereas the exact instantiation (ordering, tiling, parallelization) is determined via simple knobs. We demonstrate the efficacy of our approach using standalone kernels and end-to-end workloads that outperform state-of-the-art implementations on diverse CPU platforms.
    Keywords Computer Science - Distributed ; Parallel ; and Cluster Computing ; Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2023-04-25
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Assessing combinatorial effects of HIV infection and former cocaine dependence on cognitive control processes: A functional neuroimaging study of response inhibition.

    Wakim, Kathryn-Mary / Freedman, Edward G / Tivarus, Madalina E / Heinecke, Armin / Foxe, John J

    Neuropharmacology

    2021  Volume 203, Page(s) 108815

    Abstract: Individuals with a diagnosis of co-morbid HIV infection and cocaine use disorder are at higher risk of poor health outcomes. Active cocaine users, both with and without HIV infection, show clear deficits in response inhibition and other measures of ... ...

    Abstract Individuals with a diagnosis of co-morbid HIV infection and cocaine use disorder are at higher risk of poor health outcomes. Active cocaine users, both with and without HIV infection, show clear deficits in response inhibition and other measures of executive function that are instrumental in maintaining drug abstinence, factors that may complicate treatment. Neuroimaging and behavioral evidence indicate normalization of executive control processes in former cocaine users as a function of the duration of drug abstinence, but it is unknown to what extent co-morbid diagnosis of HIV affects this process. To this end, we investigate the combinatorial effects of HIV and cocaine dependence on the neural substrates of cognitive control in cocaine-abstinent individuals with a history of cocaine dependence. Blood-oxygen level dependent signal changes were measured as 86 participants performed a Go/NoGo response inhibition task while undergoing functional magnetic resonance imaging (fMRI). Four groups of participants were selected based on HIV and cocaine-dependence status. Participants affected by both conditions demonstrated the lowest response accuracy of all participant groups. In a region of interest analysis, hyperactivation in the left putamen and midline-cingulate hyperactivation was observed in individuals with both HIV and cocaine dependence relative to individuals with only one condition. Results of a whole-brain analysis indicate response inhibition-related hyperactivation in the bilateral supplementary motor area, bilateral hippocampi, bilateral primary somatosensory areas, right dorsal anterior cingulate, and left insula in the CD+/HIV+ group relative to all other groups. These results indicate complex and interactive alterations in neural activation during response inhibition and highlight the importance of examining the neurocognitive effects of co-morbid conditions.
    MeSH term(s) Adult ; Cocaine-Related Disorders/diagnostic imaging ; Cocaine-Related Disorders/epidemiology ; Cocaine-Related Disorders/psychology ; Cognition/physiology ; Female ; Functional Neuroimaging/methods ; HIV Infections/diagnostic imaging ; HIV Infections/epidemiology ; HIV Infections/psychology ; Humans ; Inhibition, Psychological ; Male ; Mental Status and Dementia Tests ; Middle Aged ; Psychomotor Performance/physiology ; Reaction Time/physiology
    Language English
    Publishing date 2021-10-22
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 218272-5
    ISSN 1873-7064 ; 0028-3908
    ISSN (online) 1873-7064
    ISSN 0028-3908
    DOI 10.1016/j.neuropharm.2021.108815
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Book: Medizinische Biometrie

    Heinecke, Achim / Hultsch, Ekhard / Repges, Rudolf

    Biomathematik und Statistik ; mit 63 Tabellen

    (Springer-Lehrbuch)

    1992  

    Author's details Achim Heinecke ; Ekhard Hultsch ; Rudolf Repges
    Series title Springer-Lehrbuch
    Keywords Biometry ; Medizin ; Biometrie
    Subject Biometrics (Identification) ; Biometrik ; Biometric person authentication ; Biometric Technology ; Biometrische Identifikation ; Humanmedizin ; Heilkunst ; Medicine
    Language German
    Size XVI, 288 S. : graph. Darst.
    Publisher Springer
    Publishing place Berlin u.a.
    Document type Book
    HBZ-ID HT004324116
    ISBN 3-540-52010-4 ; 978-3-540-52010-8
    Database Catalogue ZB MED Medicine, Health

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