Learning Representations of Animated Motion Sequences - A Neural ModelGeorg Layher (georg.layher@uni-ulm.de)

Institute of Neural Information ProcessingUlm University

James-Franck Ring, 89069 Ulm, Germany

Martin Giese (martin.giese@uni-tuebingen.de)Section for Computational Sensomotorics

University Clinic TubingenFrondsbergstraße 23, 72074 Tubingen, Germany

Heiko Neumann (heiko.neumann@uni-ulm.de)Institute of Neural Information Processing

Ulm UniversityJames-Franck Ring, 89069 Ulm, Germany

Abstract

The detection and categorization of animate motions is a cru-cial task underlying social interaction and perceptual decision-making. Neural representations of perceived animate objectsare built in the primate cortical region STS which is a region ofconvergent input from intermediate level form and motion rep-resentations. Populations of STS cells exist which are selec-tively responsive to specific animated motion sequences, suchas walkers. It is still unclear how and to which extent formand motion information contribute to the generation of suchrepresentations and what kind of mechanisms are involved inthe learning processes. The paper develops a cortical modelarchitecture for the unsupervised learning of animated motionsequence representations. We demonstrate how the model au-tomatically selects significant motion patterns as well as mean-ingful static form prototypes characterized by a high degree ofarticulation. Such key poses are selectively reinforced duringlearning through a cross-talk between the motion and form pro-cessing streams. Next, we show how sequence selective repre-sentations are learned in STS by fusing static form and motioninput from the segregated bottom-up driving input streams.Cells in STS, in turn, feed their activities recurrently to their in-put sites along top-down signal pathways. We show how suchlearned feedback connections enable making predictions aboutfuture input as anticipation generated by sequence-selectiveSTS cells. Network simulations demonstrate the computa-tional capacity of the proposed model by reproducing severalexperimental findings from neurosciences and by accountingfor recent behavioral data. Keywords: animated motion repre-sentation; implied motion; neural model; unsupervised learn-ing; feedback.

IntroductionAnimated movements in actions, like walking, turning, etc.,can be robustly detected from video sequence input and pre-dictions about future occurrences can be derived from suchspatio-temporal patterns. Giese & Poggio (Giese & Pog-gio, 2003) proposed a hierarchical feedforward network ar-chitecture that aims at explaining the computational mecha-nisms underlying the perception of biological motion, mainlyfrom impoverished stimuli such as point-light walkers. Inthis paper, we propose a new learning-based hierarchicalmodel for analyzing animated motion sequences. Prototypesin the form and motion pathways are established using amodified Hebbian learning scheme. We suggest how snap-shot prototypes are automatically selected from continuousinput video streams utilizing features from the motion path-way which are indicative for the occurrence of specific snap-shots with strongly articulated configurations, serving as key

poses. Sequence-selective representations of articulated mo-tions in cortical STS are driven jointly by input activationsfrom both motion and form prototypes. In addition, feed-back connections are learned to enable STS neurons predict-ing expected input from form selective IT and motion sensi-tive MST. We argue that for inputs presenting articulated pos-tures without continuing motion, STS representations are fedby the corresponding snapshot prototype activations (Jellema& Perrett, 2003). In turn, STS will send feedback to stagesin the segregated pathways for form as well as motion pro-cessing. Stationary images which depict articulated pos-tures, consequently generate effects of implied motion, whichhave been shown in functional magnetic resonance imaging(fMRI) studies (Kourtzi & Kanwisher, 2000). We will arguehere, that this can be accomplished by the proposed modelthrough the action of fusing bottom-up input, driven by snap-shot representation only, and the activated sequence repre-sentations sending feedback to both form and motion repre-sentations, thus amplifying motion representations even if nodirect motion input is present.Several computer vision approaches have been proposedfor performing action recognition using different processingstrategies of combining form and motion information. Theseapproaches build upon the hierarchical architecture proposedby Poggio and coworkers which aims at defining a frame-work for form processing in the cortical ventral pathway(Riesenhuber & Poggio, 1999). Extensions of the form pro-cessing model to analyze motion information responses in aseparate pathway, like the Giese-Poggio model, have beensuggested in e.g. (Schindler & Van Gool, 2008). Here, therelative contributions of form and motion features to the clas-sification of actions have been investigated. Details of themotion processing cascade alone have been studied in moredetail in (Escobar & Kornprobst, 2012). Here the authorscontributed further evidence that detecting motion contrastsin sequences of animated motion is useful to distinguish ac-tion classes. In all these proposed models, the mechanismsfor hierarchical motion (and form) processing are predefinedand learning only occurs at the level of a final classifier todistinguish given categories. It still remains unclear to a largeextent, how the motion and form prototypes (e.g., in corticalareas MST and IT, respectively) and the sequence-selectivepattern representations in STS interact and which features are

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optic flowpatterns

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contoursjunctions

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localform

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MST

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∫ g()

excitatory / FFmodulatory / FBinhibitory / lateralmodulatory / gating

Figure 1: Overview of the model architecture. The modelconsists of two separate processing streams, the motion andthe form pathway, both converging into model area STS.Static form prototypes in area IT, as well as optic flow pat-terns in area MST are learned using an unsupervised Hebbianmechanism. A motion driven reinforcement signal betweenthe two pathways is used to steer the learning of the IT pro-totypes. After the suppression of cells with low activities,IT and MST cells propagate into area STS, where sequence-selective cells learn corresponding spatio-temporal activitypatterns using a similar Hebbian learning rule. In addition,the sequence-selective cells learn the output weights back tothe segregated form and motion prototypes, that stabilizes theinput processing and activity fusion.

used for learning. How can feature representations be learnedautomatically from given input streams at different levels ofthe distributed action sequence representations? Also no top-down influences have been considered so far and how suchconnectivity patterns may transfer different information be-tween pathways to generate proper predictions concerning fu-ture input configurations.

Model ArchitectureThe hierarchical model proposed here consists of two sep-arate visual pathways for segregated form and motion pro-cessing as inspired by the work of (Giese & Poggio, 2003)and extends it by combining it with models for the hierar-chical feedforward and feedback processing of motion andform along the dorsal and the ventral pathway (Bayerl & Neu-mann, 2004; Weidenbacher & Neumann, 2009). Intermediatelevel form representations (in model IT) and prototypical op-tical flow patterns (in model MST) are established using amodified competitive Hebbian learning scheme with conver-gent weight dynamics. The two separate hierarchical learn-ing approaches are influenced partly by the work of Rolls and

collaborators (Rolls & Milward, 2000), in which the authorshave suggested that layered neuronal structures arranged ina hierarchy with increasingly larger connectivity kernels canlearn invariant representations of objects and specific motionpatterns. Here, we propose how such learning in a hierar-chy can be utilized for learning sequence-selective represen-tations of animated movement prototypes from convergentform and motion input. In addition, we suggest how a motion-driven reinforcement mechanism automatically selects rele-vant snapshots in the form path from video input streams. Theactivities of the prototypical form and motion cells convergein the model complex STS, where correlated temporal activa-tions for specific sequences are learned. Sequence-selectiverepresentations are established by combined bottom-up andtop-down learning, both based on modified Hebbian mecha-nisms. An overview of the model is shown in Fig. 1. Thedetails are outlined below.

Form and Motion ProcessingProcessing the raw input data utilizes an initial stage of ori-entation and direction selective filtering (in model area V1).These responses are fed into separated pathways which areselective to static form representations (areas V2 and IT) andcharacteristic optical flow patterns (areas MT and MST). Weuse single compartment model neurons with gradual activa-tion dynamics. The membrane potential of individual modelneurons is calculated by conductance-based mechanisms offeed-forward integration of excitatory and inhibitory feedinginput and a passive leakage. The potential can be enhancedby a gating mechanism to amplify the efficacy of the currentpotential by a matching top-down feedback signal. The mem-brane potential is finally regulated by a gain control mecha-nism that leads to activity normalization for a pool of neu-rons through mutual divisive inhibition. These mechanismsare summarized in a three-stage hierarchy of processing thatincludes input filtering, modulatory feedback, and pool nor-malization. The output of a cell is defined by a signal functionwhich converts the membrane potential into a firing rate, oractivity. Such model cells are grouped into layers which formabstract models of cortical areas.

Learning of Form and Motion PrototypesFirst, we investigated how intermediate level feature repre-sentations can be learned in a biologically plausible fashionby exposing the network architecture with realistic input se-quences. In order to generate feature representations of com-plex form and motion patterns we employ an unsupervisedlearning mechanism based on a modified Hebbian learningscheme. The modification stabilizes the learning such thatthe growing of weight efficacies is constrained to approach(bounded) activity levels of the input or the output activation.Motivated by the invariance properties observed by (Wallis& Rolls, 1997) we combined the modified Hebbian learningmechanism with a short-term memory trace of prolonged ac-tivity of the pre- or the post-synaptic cells (trace rule). Theadaptation of weightings is controlled by post-synaptic cells

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which, in turn, mutually compete for their ability to adjusttheir incoming connection weights. The particular details aswell as the particular variations of the core architecture areexplained below.

Hebbian learning in the form and motion pathways. Inorder to select the image regions that are fed to the learningof prototype representations a region of interest (ROI) is de-fined which represents a bounding box around the target ob-ject. Features within the target region are selected for learn-ing feedforward connection weights in the form and the mo-tion pathway, respectively. We employ the modified Hebbianlearning rule

∆wFF,sji = ηs · vpost

i · (uprej − vpost

i ·wFF,sji ) (1)

where ∆wFF,sji represents the discretized rate of change in

the efficacy of the weighted connections with the learningrate ηs; s ∈ f orm,motion indicates that the same coremechanisms are devoted to learning in the form and mo-tion pathway, respectively. The variables upre

j = f (x j) andvpost

i = f (yi) are the firing rates driven by the membrane po-tential of pre- and post-synaptic cells, henceforth denoted asactivity. The activity vi of the post-synaptic cell is calculatedby the temporal trace rule vt

i = (1−λ)vt−1i +λvt

i , 0 < λ < 1(Foldiak, 1991). The trace rule (see also (Wallis & Rolls,1997; Rolls & Milward, 2000)) has been proposed to incor-porate a short-term memory function for the cells to keep theiractivation over a short temporal window while adapting theirweights. The term in brackets on the r.h.s. of learning equa-tion 1 serves as a biologically plausible mechanism to boundthe growth of the cells’ input synaptic weights (Oja, 1982).The post-synaptic cells (with activity vpost

i ) which gate thelearning of their respective input weights are arranged in acompetitive layer of neurons competing for the best matchingresponse and their subsequent ability to adapt their kernel ofspatial input weights. In a nutshell, the layer of post-synapticneurons competes to select a winning node for a given in-put presentation which, in turn, is allowed to automaticallyadapt their incoming (instar) synaptic weights. The tempo-ral trace (or short-term memory) establishes that categorieslearn their average input over a short temporal interval thusallowing small pertubations for the changing input signals.

Reinforcing snapshot learning. The Giese-Poggio model(Giese & Poggio, 2003) suggests that sequence selectivity forbiological motion recognition is driven by sequences of staticsnapshots. While the original model relies on snapshots thatwere regularly sampled temporally, we suggest a mechanismof how snapshots corresponding to strongly articulated posescan be selected automatically. Such snapshot representationsare learned in the form channel by utilizing a gating reinforce-ment signal which is driven by the complementary represen-tation of motion in the dorsal stage MT/MST. Formally, theweighted integration of motion energy over a given neighbor-hood is calculated by

me =∫

Ω

uφ(x) ·Λ(x)dxdφ (2)

MT

MST

V2

IT

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ion|

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oth

t [frames]

t [frames]

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atio

n [a

u]

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atio

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u]

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g()

Figure 2: IT prototypes trained using disabled and enabledreinforcement signal. Minima and maxima in motion energycorrespond to articulated and non-articulated postures (bot-tom left). Continuous learning of IT prototypes leads to acti-vation profiles with low selectivity (top right). Motion drivenreinforcement leads to IT prototypes which signal snapshotposes in synchrony with the gait (bottom right; for details,see text).

with Λ(•) denoting a spatial kernel for weighting the relativecontribution of motion responses uφ(•) at spatial locations xin the 2-D image plane and with direction selectivity φ.1 Themotion energy signal itself is a function of time which is usedto steer the instar learning in the form pathway. We suggestthat different subpopulations of static form, or snapshot, rep-resentations can be learned that correspond to either weaklyor strongly articulated postures. Here, we focus on snapshotposes corresponding to highly articulated postures with sig-natures of maximum limb spreading. Motion energy at limbsdrops during phases of high articulation when their appar-ent direction of motion reverses. We incorporate the functiong(•) to control a vigilance in snapshot learning to favor forminputs which co-occur with local motion energy minima, i.e.when ∂tme = 0, given that ∂ttme > 0. In the weight adapta-tion, ∆wFF, f orm

ji in Eqn.1, the learning rate is now gated by themotion dependent reinforcement, η f orm ·g(me) which leads tothe revised learning rule

∆wFF, f ormji = η f orm ·g(me) · vpost

i · (uprej − vpost

i ·wFF, f ormji ).

(3)

Learning of Sequence-Selective RepresentationsCategorial representations in the form and motion pathway,namely in IT and MST, which were learned at the previ-ous stage, feed forward their activations to the stage of STS.In order to stabilize the representations and activity distribu-tions, even in the case of partial loss of input signals, the STSsequence-selective representations send top-down signals totheir respective input stages.

1For whole body motion considered here, we simply integratedthe motion energy over the entire ROI without subdividing the imageregion. An analysis at smaller scales might necessitate an integrationover smaller overlapping patches.

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t [frames]

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ard recall

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MST

Figure 3: Response behavior of IT snapshot neurons, MST motion pattern neurons, and sequence-selective STS cells trainedby video input for a walker moving from left to right. Activations in the model areas are shown for different input conditionsfor recall of the training sequence (top), opposite walker movement (middle), and walker displayed in reverse motion (bottom).Line styles and colors encode the input test cases on the right. For details and brief discussion, see text.

Learning of feedforward connections. Prototypical rep-resentations with spatio-temporal sequence selectivity aresuggested to exist in the cortical STS complex where bothform and motion pathways converge. The selectivities ofmodel STS neurons are learned by again using a modifiedHebbian instar learning mechanism similar to the separatelearning of form and motion prototypes (Eqn.1),

∆win,FFji = ηseqFF · vpost

i · (uprej − vpost

i ·win,FFji ). (4)

The weighting kernel win,FFji represents convergent IT→ STS

and MST→ STS bottom-up input to a post-synaptic STS cell(instar). ηseqFF denotes the learning rate and u j and vi are thefiring rates of the pre- and post-synaptic neurons, respectively(the post-synaptic activity is again calculated via a temporaltrace mechanism). The pre-synaptic activity for the receiv-ing model STS cells are generated by concatenating form andmotion output activations, namely u = uIT ∪uMST .

Learning feedback connections. An important compo-nent is that sequence-selective prototypes in STS in turn learnthe output weights back to the segregated form and motionprototype representations, namely STS→ IT + MST. Unlikethe FF learning mechanisms, the learning here is gated by thepre-synaptic cell (in STS) for their top-down weights, whichreads

∆wout,FBji = ηseqFB · vpre

i · (upostj −wout,FB

ji ) (5)

with the same components as in the bottom-up learning for-malism in Eqn.4. Bottom-up and top-down learning schemesslightly differ in the definition of the competitive terms (inbrackets). In the feedback learning we employ a differ-ence term between post-synaptic activity and the weighting,upost

j −wout,FBji , omitting the additional weighting of the con-

nectivity strength via the pre-synaptic activity as in the Oja

rule. In steady-state each of the connection strengths em-anating from STS cells assumes a value corresponding tothe post-synaptic activity distribution, which defines the cur-rent input activation. Given an STS cell with attraction vpre

i ,the top-down weight vector approaches upost = wout,FB

i , thuslearning the expected average input. Combined with the tem-poral trace, this establishes a representation in which eachSTS sequence-selective prototype encodes and memorizes inits weight pattern the expected driving input activity patternconfiguration from the form and motion pathway. Such a top-down weighting pattern can then be used to generate predic-tions concerning the expected future input given the currentmaximally activated prototype at the STS level.

ResultsThe model has been tested in various computational experi-ments, not all of which we can present here. In a first exper-iment, we probed the properties of snapshot selection fromthe input streams and their signature concerning static articu-lations. The latter property has been motivated by the fact thatextremal articulation indicates configurations of implied mo-tion, in turn, predictive for future motions. Results shown inFig. 2 demonstrate that input activations (in V2) with stronglyarticulated shapes cohere with local motion minima. Suchminima drive the reinforcement signal for learning wholebody form prototypes. Temporal response signatures for ITprototypes are shown for disabled reinforcement (g(me) = 1,and when it is enabled (g(me) monotonically decreasing func-tion of me ).

We studied the response properties of STS representationsand their motion sequence selectivity. There, a prototypicalsequence-selective representation is learned for a walker thatis traversing from left to right. After training of form, mo-tion and sequence representations, the network is probed by

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-40° -20° -10° 0° 5° 10° 20° 40°-5°0

0.5

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direction Φ

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Figure 4: Response tunings of models cells in area IT (snap-shots), MST (motion patterns), and STS (sequence-selectivepatterns) after training. Category representations have beenlearned for a walker moving along horizontal direction forφ = 0. Activities of prototypical cells are shown (bottom)which were probed by different inputs with varying move-ment directions, i.e. walkers approaching or receding at dif-ferent angles with respect to the horizontal reference axis(top). Data has been summarized into box plots showing theresponse variabilities of models cells as well as the monotonicdecline in response for deviations from the target tuning. Thetuning width at half maximum response is around ±40. Thevariance of the MST / IT prototypes decreases towards largerdeviations, depicting the loss of response selectivity of proto-types to different parts of a walkers gait.

three different movement scenarios: a forward moving walkerwith same profile and movement direction as in the trainingphase (recall), a forward moving walker traversing from rightto left (opposite), and a backward moving walker (reverse).Form/motion prototypes and the sequence representation aretriggered maximally in the recall case while in the oppositecase form and motion prototypes only respond minimally, andso do the sequence-selective cells. In the reverse case theform prototypes selectively match the input at high articula-tion configurations, while the motion responses remain min-imal. As a consequence, the sequence-selective representa-tions respond at an intermediate level (Fig. 3). This evidenceis in line with the experimental findings by (Oram & Perrett,1996) and recent observations by (Singer & Sheinberg, 2010).

We further investigated the direction tuning of thesequence-selective prototypes. Here, we configured differentwalkers with varying movement directions and speeds withreference to a previously learned representation of a right-ward moving walker at a speed of 1 m/s. Walking directionsin the test cases were rotated by ±5,10,20,40. Modelsimulations result in a direction tuning of STS cells with halfamplitude of approximately ±40deg (Fig. 4). IT and MSTcells, on the other hand, also show a drop in response but have

STS IT Prototype 01 IT Prototype 02MST Prototype 01 TMS Prototype 02

activity

IT & Feedback

t [frames]

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t [frames]

Figure 5: Selective removal of interconnections (lesioning).The model was trained using the same walking sequenceas in the second experiment (see Fig. 3 / forward recall).The model was left untouched to provide a reference (top).Bottom-up (feedforward) connections between area MST andSTS were removed, preventing any motion-related signal be-ing propagated to STS (bottom). The amplitude of the ITprototype activities remains almost the same, whereas thesequence-selective STS cell responds only at about half-magnitude (because of the missing support from the motionpathway). Note the feedback activities propagated from STSto MST optical flow pattern prototypes. We argue that thisreflects the induction of increased fMRI BOLD response inhuman MT+ following the presentation of static implied mo-tion stimuli.

a much larger variability.In an additional experiment we selectively lesioned of the

model architecture, particularly investigating the effects ofextinguishing connections between model areas and the ac-tivity flow between learned representations (Fig.5). The fullyconnected model with learned IT / MST and STS feedfor-ward and feedback connections was used as reference. Whenbottom-up connections from motion input (MST) were cutoff the sequence-selective neuron responses in STS drop toapproximately half their response amplitude. Feedback fromSTS invokes an amplification of activities in IT and MST rep-resentations. We observe that FF activation from IT alone candrive sequence neurons. Snapshot representations in IT drivethe STS sequence neurons which, in turn, send feedback sig-nals to the stages of IT and MST prototype representations. Inthe motion pathway such feedback elicits an increase in pre-synaptic activation. We argue that this reflects the inductionof increased fMRI BOLD response in human MT+ followingthe presentation of static implied motion stimuli (Kourtzi &Kanwisher, 2000).

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Discussion and Conclusion

We propose a biologically plausible model for the learningof animated motion sequences. The model builds upon neu-rophysiological evidence about the cortical sites and specificneuronal representations which contribute to articulated mo-tion and implied motion perception. The main contributionsof the paper are several-fold: First, we suggest how prototyperepresentations in the form and motion pathways, namely inmodel cortical areas IT and MST, can be established on thebasis of probing the model architecture by sequences con-taining animated motions. Learning mechanisms are basedon modified Hebbian schemes which are stabilized througha trace mechanisms and the incorportion of an objectivefunction taking the weight kernel saturation into account.Second, we suggest that sequence-selective cells in modelarea STS are learned by using the same learning mechanismsbut now by combining the responses of intermediate levelrepresentations in the form and motion pathways. Third, thelearning of articulated poses (snapshots) is controlled by areinforcement mechanism that enables Hebbian learning inthe form pathway through cross-pathway motion-form inter-action. Given an animated motion sequence, snapshots areautomatically selected as key poses corresponding to strongbody pose articulations. Finally, the sequence-selective cellsin model STS project to their respective input representationsin the form and motion pathways. These feedback connec-tions are again learned by a Hebbian mechanism. Together,the feedforward and the feedback interactions establish aloop of recurrent processing to stabilize the patterns of form,motion, and sequence representation. Via feedback, modelSTS cells generate a predictive signal through the backwardconnections’ weights to encode the expected matching inputthat is suitable to match the currently activated sequencepattern. Together with the newly proposed feedback mecha-nism the model is able to account for various experimentalfindings, in particular, the ability to infer and predict futuremotion sequence development from articulated postures(implied motion). Importantly, cells in STS are responsive toboth motion as well as static form (Oram & Perrett, 1996).The model predicts that the presentation of static key posesfrom previously learned sequences alone leads to enhancedactivation in STS sequence selective neurons as observed in(Jellema & Perrett, 2003). The model also hypothesizes howthe presentation of static articulated poses leads to the emer-gence of predictive motion perception and enhanced neuralactivations in the motion pathway (Kourtzi & Kanwisher,2000). Furthermore, learned sequence-selective prototyperepresentations have direction tunings in response to walkersin the range of±40, similar to those reported in (Perrett et al.,1989)). Once again, the model makes a testable predictionthat articulated poses represent the snapshot frames that havebeen suggested by (Giese & Poggio, 2003) and that haverecently been tested experimentally by (Singer & Sheinberg,2010).

AcknowledgementsGL and HN have been supported by the SFB Transregio 62funded by the German Research Foundation (DFG).

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