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The human brain achieves visual object recognition through multiple stages of nonlinear transformations operating at a millisecond scale. To predict and explain these rapid transformations, computational neuroscientists employ machine learning modeling techniques. However, state-of-the-art models require massive amounts of data to properly train, and to the present day there is a lack of vast brain datasets which extensively sample the temporal dynamics of visual object recognition. Here we collected a large and rich dataset of high temporal resolution EEG responses to images of objects on a natural background. This dataset includes 10 participants, each with 82,160 trials spanning 16,740 image conditions. Through computational modeling we established the quality of this dataset in five ways. First, we trained linearizing encoding models that successfully synthesized the EEG responses to arbitrary images. Second, we correctly identified the recorded EEG data image conditions in a zero-shot fashion, using EEG synthesized responses to hundreds of thousands of candidate image conditions. Third, we show that both the high number of conditions as well as the trial repetitions of the EEG dataset contribute to the trained models’ prediction accuracy. Fourth, we built encoding models whose predictions well generalize to novel participants. Fifth, we demonstrate full end-to-end training of randomly initialized DNNs that output M/EEG responses for arbitrary input images. We release this dataset as a tool to foster research in visual neuroscience and computer vision.
Human lymph nodes play a central part of immune defense against infection agents and tumor cells. Lymphoid follicles are compartments of the lymph node which are spherical, mainly filled with B cells. B cells are cellular components of the adaptive immune systems. In the course of a specific immune response, lymphoid follicles pass different morphological differentiation stages. The morphology and the spatial distribution of lymphoid follicles can be sometimes associated to a particular causative agent and development stage of a disease. We report our new approach for the automatic detection of follicular regions in histological whole slide images of tissue sections immuno-stained with actin. The method is divided in two phases: (1) shock filter-based detection of transition points and (2) segmentation of follicular regions. Follicular regions in 10 whole slide images were manually annotated by visual inspection, and sample surveys were conducted by an expert pathologist. The results of our method were validated by comparing with the manual annotation. On average, we could achieve a Zijbendos similarity index of 0.71, with a standard deviation of 0.07.
Grasping the meaning of everyday visual events is a fundamental feat of human intelligence that hinges on diverse neural processes ranging from vision to higher-level cognition. Deciphering the neural basis of visual event understanding requires rich, extensive, and appropriately designed experimental data. However, this type of data is hitherto missing. To fill this gap, we introduce the BOLD Moments Dataset (BMD), a large dataset of whole-brain fMRI responses to over 1,000 short (3s) naturalistic video clips and accompanying metadata. We show visual events interface with an array of processes, extending even to memory, and we reveal a match in hierarchical processing between brains and video-computable deep neural networks. Furthermore, we showcase that BMD successfully captures temporal dynamics of visual events at second resolution. BMD thus establishes a critical groundwork for investigations of the neural basis of visual event understanding.
Visual scene perception is mediated by a set of cortical regions that respond preferentially to images of scenes, including the occipital place area (OPA) and parahippocampal place area (PPA). However, the differential contribution of OPA and PPA to scene perception remains an open research question. In this study, we take a deep neural network (DNN)-based computational approach to investigate the differences in OPA and PPA function. In a first step we search for a computational model that predicts fMRI responses to scenes in OPA and PPA well. We find that DNNs trained to predict scene components (e.g., wall, ceiling, floor) explain higher variance uniquely in OPA and PPA than a DNN trained to predict scene category (e.g., bathroom, kitchen, office). This result is robust across several DNN architectures. On this basis, we then determine whether particular scene components predicted by DNNs differentially account for unique variance in OPA and PPA. We find that variance in OPA responses uniquely explained by the navigation-related floor component is higher compared to the variance explained by the wall and ceiling components. In contrast, PPA responses are better explained by the combination of wall and floor, that is scene components that together contain the structure and texture of the scene. This differential sensitivity to scene components suggests differential functions of OPA and PPA in scene processing. Moreover, our results further highlight the potential of the proposed computational approach as a general tool in the investigation of the neural basis of human scene perception.
The human visual cortex enables visual perception through a cascade of hierarchical computations in cortical regions with distinct functionalities. Here, we introduce an AI-driven approach to discover the functional mapping of the visual cortex. We related human brain responses to scene images measured with functional MRI (fMRI) systematically to a diverse set of deep neural networks (DNNs) optimized to perform different scene perception tasks. We found a structured mapping between DNN tasks and brain regions along the ventral and dorsal visual streams. Low-level visual tasks mapped onto early brain regions, 3-dimensional scene perception tasks mapped onto the dorsal stream, and semantic tasks mapped onto the ventral stream. This mapping was of high fidelity, with more than 60% of the explainable variance in nine key regions being explained. Together, our results provide a novel functional mapping of the human visual cortex and demonstrate the power of the computational approach.
Dendritic spines are considered a morphological proxy for excitatory synapses, rendering them a target of many different lines of research. Over recent years, it has become possible to image simultaneously large numbers of dendritic spines in 3D volumes of neural tissue. In contrast, currently no automated method for spine detection exists that comes close to the detection performance reached by human experts. However, exploiting such datasets requires new tools for the fully automated detection and analysis of large numbers of spines. Here, we developed an efficient analysis pipeline to detect large numbers of dendritic spines in volumetric fluorescence imaging data. The core of our pipeline is a deep convolutional neural network, which was pretrained on a general-purpose image library, and then optimized on the spine detection task. This transfer learning approach is data efficient while achieving a high detection precision. To train and validate the model we generated a labelled dataset using five human expert annotators to account for the variability in human spine detection. The pipeline enables fully automated dendritic spine detection and reaches a near human-level detection performance. Our method for spine detection is fast, accurate and robust, and thus well suited for large-scale datasets with thousands of spines. The code is easily applicable to new datasets, achieving high detection performance, even without any retraining or adjustment of model parameters.
Active efficient coding explains the development of binocular vision and its failure in amblyopia
(2020)
The development of vision during the first months of life is an active process that comprises the learning of appropriate neural representations and the learning of accurate eye movements. While it has long been suspected that the two learning processes are coupled, there is still no widely accepted theoretical framework describing this joint development. Here, we propose a computational model of the development of active binocular vision to fill this gap. The model is based on a formulation of the active efficient coding theory, which proposes that eye movements as well as stimulus encoding are jointly adapted to maximize the overall coding efficiency. Under healthy conditions, the model self-calibrates to perform accurate vergence and accommodation eye movements. It exploits disparity cues to deduce the direction of defocus, which leads to coordinated vergence and accommodation responses. In a simulated anisometropic case, where the refraction power of the two eyes differs, an amblyopia-like state develops in which the foveal region of one eye is suppressed due to inputs from the other eye. After correcting for refractive errors, the model can only reach healthy performance levels if receptive fields are still plastic, in line with findings on a critical period for binocular vision development. Overall, our model offers a unifying conceptual framework for understanding the development of binocular vision.
Future operation of the CBM detector requires ultra-fast analysis of the continuous stream of data from all subdetector systems. Determining the inter-system time shifts among individual detector systems in the existing prototype experiment mCBM is an essential step for data processing and in particular for stable data taking. Based on the input of raw measurements from all detector systems, the corresponding time correlations can be obtained at digital level by evaluating the differences in time stamps. If the relevant systems are stable during data taking and sufficient digital measurements are available, the distribution of time differences should display a clear peak. Up to now, the outcome of the processed time differences is stored in histograms and the maximum peak is considered, after the evaluation of all timeslices of a run leading to significant run times. The results presented here demonstrate the stability of the synchronicity of mCBM systems. Furthermore it is illustrated that relatively small amounts of raw measurements are sufficient to evaluate corresponding time correlations among individual mCBM detectors, thus enabling fast online monitoring of them in future online data processing.
Im Fachbereich der Computerlinguistik ist die automatische Generierung von Szenen aus, in natürlicher Sprache verfassten, Text seit bereits vielen Jahrzehnten ein wichtiger Bestandteil der Forschung, welche in der "Kunst", "Lehre" und "Robotik" Verwendung finden. Mit Hilfe von neuen Technologien im Bereich der Künstlichen Intelligenzen (KI), werden neue Entwicklungen möglich, welche diese Generierungen vereinfachen, allerdings auch undurchsichtige interne vom Modell getroffene Entscheidungen fördern.
Ziel der vorgeschlagenen Lösung „ARES: Annotation von Relationen und Eigenschaften zur Szenengenerierung“ ist es, ein modulares System zu entwerfen, wobei einzelne Prozesse für den Benutzer verständlich bleiben. Außerdem sollen Möglichkeiten geboten werden, neue Entitäten und Relationen, welche über die Textanalyse bereitgestellt werden, auch in die Szenengenerierung im dreidimensionalen Raum einzupflegen, ohne dass hierfür Code zwingend notwendig wird.
Der Fokus liegt auf der syntaktisch korrekten Darstellung der Elemente im Raum. Dagegen lässt sich die semantische Korrektheit durch weitere manuelle Anpassungen, welche für spätere Generierungen gespeichert werden erhöhen. Letztlich soll die Menge der zur Darstellung benötigten Annotationen möglichst gering bleiben und neue szenenbezogene Annotationen durch die implementierten Annotationstools hinzugefügt werden.
In der Arbeit wird das Certainty-Tool, eine Erweiterung für den Unity-basierten Teil des Stolperwege Projektes, vorgestellt. Dieses verfolgt die Idee des VAnnotatoR weiter und erlaubt die Visualisierung von informationeller Ungewissheit der im Stolperwege-Praktikum digital rekonstruierten Gebäude. Dabei inkorporiert das Tool das Konzept hinter BIM (Building Information Modelling), eine neuartige Methode der Planung in der AEC-Branche, welches ein Selbstbewusstsein von Informationen für Teile eines Gebäudes ermöglicht. Dabei werden im Certainty-Tool Stufen der informationellen Ungewissheit entwickelt und diese auf Teile des Gebäudes zugewiesen. Das Tool wird anhand einer digitalen Rekonstruktion des zerstörten Rothschild-Palais vorgestellt. Des Weiteren wurde eine Evaluation basierend auf der Usability Metric for User Experience durchgeführt und weiterführende Entwicklungen und Verbesserungen des Tools diskutiert.
We investigate the QCD phase diagram for nonzero background magnetic fields using first-principles lattice simulations. At the physical point (in terms of quark masses), the thermodynamics of this system is controlled by two opposing effects: magnetic catalysis (enhancement of the quark condensate) at low temperature and inverse magnetic catalysis (reduction of the condensate) in the transition region. While the former is known to be robust and independent of the details of the interactions, inverse catalysis arises as a result of a delicate competition, effective only for light quarks. By performing simulations at different quark masses, we determine the pion mass above which inverse catalysis does not take place in the transition region anymore. Even for pions heavier than this limiting value — where the quark condensate undergoes magnetic catalysis — our results are consistent with the notion that the transition temperature is reduced by the magnetic field. These findings will be useful to guide low-energy models and effective theories of QCD.
The SU(3) spin model with chemical potential corresponds to a simplified version of QCD with static quarks in the strong coupling regime. It has been studied previously as a testing ground for new methods aiming to overcome the sign problem of lattice QCD. In this work we show that the equation of state and the phase structure of the model can be fully determined to reasonable accuracy by a linked cluster expansion. In particular, we compute the free energy to 14-th order in the nearest neighbour coupling. The resulting predictions for the equation of state and the location of the critical end points agree with numerical determinations to O(1%) and O(10%), respectively. While the accuracy for the critical couplings is still limited at the current series depth, the approach is equally applicable at zero and non-zero imaginary or real chemical potential, as well as to effective QCD Hamiltonians obtained by strong coupling and hopping expansions.
The production of the Λ(1520) baryonic resonance has been measured at midrapidity in inelastic pp collisions at s√=7 TeV and in p–Pb collisions at sNN−−−√=5.02 TeV for non-single diffractive events and in multiplicity classes. The resonance is reconstructed through its hadronic decay channel Λ(1520) →pK− and the charge conjugate with the ALICE detector. The integrated yields and mean transverse momenta are calculated from the measured transverse momentum distributions in pp and p–Pb collisions. The mean transverse momenta follow mass ordering as previously observed for other hyperons in the same collision systems. A Blast-Wave function constrained by other light hadrons (π, K, K0S, p, Λ) describes the shape of the Λ(1520) transverse momentum distribution up to 3.5 GeV/c in p–Pb collisions. In the framework of this model, this observation suggests that the Λ(1520) resonance participates in the same collective radial flow as other light hadrons. The ratio of the yield of Λ(1520) to the yield of the ground state particle Λ remains constant as a function of charged-particle multiplicity, suggesting that there is no net effect of the hadronic phase in p–Pb collisions on the Λ(1520) yield.
We present the charged-particle multiplicity distributions over a wide pseudorapidity range (−3.4<η<5.0) for pp collisions at s√=0.9,7, and 8 TeV at the LHC. Results are based on information from the Silicon Pixel Detector and the Forward Multiplicity Detector of ALICE, extending the pseudorapidity coverage of the earlier publications and the high-multiplicity reach. The measurements are compared to results from the CMS experiment and to PYTHIA, PHOJET and EPOS LHC event generators, as well as IP-Glasma calculations.
We report measurements of the production of prompt D0, D+, D*+ and D+s mesons in Pb–Pb collisions at the centre-of-mass energy per nucleon-nucleon pair sNN−−−√=5.02 TeV, in the centrality classes 0–10%, 30–50% and 60–80%. The D-meson production yields are measured at mid-rapidity (|y| < 0.5) as a function of transverse momentum (pT). The pT intervals covered in central collisions are: 1 < pT< 50 GeV/c for D0, 2 < pT< 50GeV/c for D+, 3 < pT< 50GeV/c for D*+, and 4 < pT< 16GeV/c for D +s mesons. The nuclear modification factors (RAA) for non-strange D mesons (D0, D+, D*+) show minimum values of about 0.2 for pT = 6–10 GeV/c in the most central collisions and are compatible within uncertainties with those measured at s√NN=2.76 TeV. For D +s mesons, the values of RAA are larger than those of non-strange D mesons, but compatible within uncertainties. In central collisions the average RAA of non-strange D mesons is compatible with that of charged particles for pT> 8 GeV/c, while it is larger at lower pT. The nuclear modification factors for strange and non-strange D mesons are also compared to theoretical models with different implementations of in-medium energy loss.
We report the measured transverse momentum (pT) spectra of primary charged particles from pp, p-Pb and Pb-Pb collisions at a center-of-mass energy sNN−−−√=5.02 TeV in the kinematic range of 0.15 < pT< 50 GeV/c and |η| < 0.8. A significant improvement of systematic uncertainties motivated the reanalysis of data in pp and Pb-Pb collisions at sNN−−−√=2.76 TeV, as well as in p-Pb collisions at sNN−−−√=5.02 TeV, which is also presented. Spectra from Pb-Pb collisions are presented in nine centrality intervals and are compared to a reference spectrum from pp collisions scaled by the number of binary nucleon-nucleon collisions. For central collisions, the pT spectra are suppressed by more than a factor of 7 around 6–7 GeV/c with a significant reduction in suppression towards higher momenta up to 30 GeV/c. The nuclear modification factor RpPb, constructed from the pp and p-Pb spectra measured at the same collision energy, is consistent with unity above 8 GeV/c. While the spectra in both pp and Pb-Pb collisions are substantially harder at sNN−−−√=5.02 TeV compared to 2.76 TeV, the nuclear modification factors show no significant collision energy dependence. The obtained results should provide further constraints on the parton energy loss calculations to determine the transport properties of the hot and dense QCD matter.
Measurement of the production of charm jets tagged with D0 mesons in pp collisions at √s = 7 TeV
(2019)
The production of charm jets in proton-proton collisions at a center-of-mass energy of s√=7 TeV was measured with the ALICE detector at the CERN Large Hadron Collider. The measurement is based on a data sample corresponding to a total integrated luminosity of 6.23 nb−1, collected using a minimum-bias trigger. Charm jets are identified by the presence of a D0 meson among their constituents. The D0 mesons are reconstructed from their hadronic decay D0 →K−π+. The D0-meson tagged jets are reconstructed using tracks of charged particles (track-based jets) with the anti-kT algorithm in the jet transverse momentum range 5<pchT,jet< 30 GeV/c and pseudorapidity |ηjet| < 0.5. The fraction of charged jets containing a D0-meson increases with pchT,jet from 0.042 ± 0.004 (stat) ± 0.006 (syst) to 0.080 ± 0.009 (stat) ± 0.008 (syst). The distribution of D0-meson tagged jets as a function of the jet momentum fraction carried by the D0 meson in the direction of the jet axis (zch∥) is reported for two ranges of jet transverse momenta, 5<pchT,jet< 15 GeV/c and 15<pchT,jet< 30 GeV/c in the intervals 0.2<zch∥∥<1.0 and 0.4<zch∥∥<1.0, respectively. The data are compared with results from Monte Carlo event generators (PYTHIA 6, PYTHIA 8 and Herwig 7) and with a Next-to-Leading-Order perturbative Quantum Chromodynamics calculation, obtained with the POWHEG method and interfaced with PYTHIA 6 for the generation of the parton shower, fragmentation, hadronisation and underlying event.
This article reports measurements characterizing the Underlying Event (UE) associated with hard scatterings at midrapidity (|η| < 0.8) in pp collisions at s√ = 13 TeV. The hard scatterings are identified by the leading particle, the charged particle with the highest transverse momentum (pleadingT) in the event. Charged-particle number-densities and summed transverse-momentum densities are measured in different azimuthal regions defined with respect to the leading particle direction: Toward, Transverse, and Away. The Toward and Away regions contain the fragmentation products of the hard scatterings in addition to the UE contribution, whereas particles in the Transverse region are expected to originate predominantly from the UE. The study is performed as a function of pleadingT with three different pT thresholds for the associated particles, ptrackT > 0.15, 0.5, and 1.0 GeV/c. The charged-particle density in the Transverse region rises steeply for low values of pleadingT and reaches a plateau. The results confirm the trend that the charged-particle density in the Transverse region shows a stronger increase with s√ than the inclusive charged-particle density at midrapidity. The UE activity is increased by approximately 20% when going from 7 TeV to 13 TeV pp collisions. The plateau in the Transverse region (5 <pleadingT< 40 GeV/c) is further characterized by the probability distribution of its charged-particle multiplicity normalized to its average value (relative transverse activity, RT) and the mean transverse momentum as a function of RT. Experimental results are compared to model calculations using PYTHIA 8 and EPOS LHC. The overall agreement between models and data is within 30%. These measurements provide new insights on the interplay between hard scatterings and the associated UE in pp collisions.
Higher harmonic non-linear flow modes of charged hadrons in Pb-Pb collisions at √sNN = 5.02 TeV
(2020)
Anisotropic flow coefficients, vn, non-linear flow mode coefficients, χn,mk, and correlations among different symmetry planes, ρn,mk are measured in Pb-Pb collisions at sNN−−−√ = 5.02 TeV. Results obtained with multi-particle correlations are reported for the transverse momentum interval 0.2 < pT < 5.0 GeV/c within the pseudorapidity interval 0.4 < |η| < 0.8 as a function of collision centrality. The vn coefficients and χn,mk and ρn,mk are presented up to the ninth and seventh harmonic order, respectively. Calculations suggest that the correlations measured in different symmetry planes and the non-linear flow mode coefficients are dependent on the shear and bulk viscosity to entropy ratios of the medium created in heavy-ion collisions. The comparison between these measurements and those at lower energies and calculations from hydrodynamic models places strong constraints on the initial conditions and transport properties of the system.