Zeljeznicar vs Sloga Doboj

Zeljeznicar vs Sloga Doboj

This match between Zeljeznicar and Sloga Doboj is anticipated to be a tactical showdown, with both teams bringing distinct strengths and weaknesses to the field. Zeljeznicar has been displaying a strong home performance recently, with an average of 2.53 goals scored per game, indicating their offensive prowess. In contrast, Sloga Doboj has been more defensively oriented, conceding an average of 1.51 goals per game. Historically, head-to-head encounters have shown closely contested matches, often decided by narrow margins. Key factors include Zeljeznicar’s ability to capitalize on home advantage and Sloga Doboj’s resilience in away fixtures.

Match Result Analysis

The prediction for this match leans towards a competitive encounter with moderate scoring potential. The market suggests that the likelihood of the Home Team winning stands at 58.60%, reflecting confidence in Zeljeznicar‘s capability to secure victory on their turf. The prediction for Under 2.5 Goals at 60.40% indicates expectations of a tightly contested match where both teams might prioritize defensive stability over high-scoring ambitions.

Home Team To Win

Zeljeznicar’s recent form and home advantage provide a solid foundation for predicting their victory. Their average total goals per game being 2.84 suggests they have the attacking capability to break through Sloga Doboj‘s defense when needed.

Under 2.5 Goals

Given both teams’ tendencies—Zeljeznicar focusing on attacking play and Sloga Doboj emphasizing defense—the expectation of fewer than 2.5 goals seems reasonable. This aligns with statistical evidence suggesting that while Zeljeznicar can score, they may not do so excessively against a resilient opponent like Sloga Doboj.

Draw In First Half

The odds for a draw in the first half at 53.80% reflect the anticipated cautious approach from both sides early in the game as they assess each other’s strategies and adjust accordingly.

Goals Market Assessment

The analysis of goal-related markets indicates that while there is potential for goals, it is likely to be distributed across different halves rather than concentrated in one period.

Both Teams Not To Score In 1st Half

The prediction that both teams will not score in the first half (76.30%) highlights expectations of an initial period focused on strategic positioning and tactical setups rather than immediate attacking plays.

Home Team To Score In 2nd Half

Zeljeznicar is expected to find more opportunities to score in the second half (85.40%), likely due to fatigue setting in for Sloga Doboj’s defenders or adjustments made by Zeljeznicar based on first-half observations.

Avg Total Goals: 2.84

This statistic reinforces the likelihood of multiple goal-scoring opportunities throughout the match, supporting predictions related to scoring patterns across different halves.

Avg Conceded Goals: 1.51

Sloga Doboj’s average conceded goals suggest they are generally effective defensively but may struggle against sustained pressure from a team like Zeljeznicar who are known for their offensive capabilities.

Avg Goals Scored: 2.53

Zeljeznicar’s average goals scored indicate their potential impact offensively, particularly if they exploit any defensive lapses from Sloga Doboj as predicted by various markets.

Red Cards: 1.59

The predicted number of red cards (1.59) suggests that while discipline might be an issue during intense moments of play, it could also influence team dynamics significantly if such events occur.

These analyses underscore an anticipated match where strategic decisions will heavily influence outcomes, with particular emphasis on timing and tactical adjustments during play phases.
lucashenke/assistant<|file_sep|

Detailed Expert Opinion: Football Game Analysis

In this analysis of the upcoming football match between Team A and Team B scheduled for December 17th at 15:00, we delve into various betting markets using data-driven insights and expert predictions based on current form, historical performance, and key player statistics.

General Match Overview

This encounter features two formidable opponents with contrasting styles; Team A has been performing consistently well at home this season while Team B excels away from home with solid defensive records but less prolific attack output recently. Key factors influencing this matchup include recent injuries within both squads affecting starting line-ups as well as weather conditions potentially impacting playing style adaptations required by either side depending upon pitch conditions come match day itself – all crucial considerations when making informed betting decisions ahead timeframes specified earlier hereinabove text body sections herein described below further detailed analysis breakdowns provided hereunder subsequently following general overview summary above stated previously before moving onto next section content details mentioned below subsequently thereafter following initial introduction segment described earlier above hereinafter outlined subsequently below hereafter continuing further henceforth explained below:

Team Form & Head-to-Head Statistics

Team A boasts an impressive home record this season which bodes well considering its upcoming fixture against Team B who historically struggle against top-tier opposition especially when playing away games; however last five encounters between these two sides ended evenly split with three wins apiece plus one draw suggesting tight competition lies ahead should either side manage maintain momentum going forward into forthcoming fixture currently under review herein detailed analysis breakdown provided hereunder subsequently following general overview summary above stated previously before moving onto next section content details mentioned below subsequently thereafter following initial introduction segment described earlier above hereinafter outlined subsequently below hereafter continuing further henceforth explained below:

Key Players & Potential Impact Factors

The presence or absence of key players due to injuries or suspensions can significantly sway match outcomes; thus evaluating these variables forms part essential groundwork needed prior engaging deeper analytical exploration pertaining specific betting markets discussed shortly thereafter within subsequent sections outlined hereinbelow:

Betting Market Analysis: Match Result Predictions

Analyzing various betting markets provides insights into probable outcomes based on statistical probabilities derived from past performances alongside expert predictions factoring current form trends observed recently among competing teams:

Betting Market – Home Team To Win (58%) vs Draw (32%) vs Away Team To Win (10%)

• Rationale:

• The higher probability assigned towards Home Team winning reflects their superior track record at home coupled with recent victories which instill confidence among bettors favoring this outcome; conversely lower percentages attributed towards Draw or Away Win suggest skepticism about either team securing outright victory given balanced nature competitive landscape present within league standings overall thus far noted earlier hereinabove text body sections herein described briefly summarized succinctly concisely compactly encapsulated effectively efficiently economically expeditiously expediently forthrightly forthcoming fully furnished fully furnished forthwith henceforward henceforth henceupon henceunto hereby heretofore herewith hitherto holistically holistically holistically holistically holistically holistically holisticall encompassing encapsulating comprehensively conclusively definitively decisively determinatively definitively conclusively decisively determinatively definitively conclusively decisively determinatively conclusively decisively determinatively conclusively decisively determinatively conclusively decisively determinatively conclusively decisively determinatively conclusively decisively determinatively conclusiveness:

• Prediction Confidence Level:

• Moderate-High confidence level given consistent pattern favoring Home Side victories although unforeseen variables such as key player absences or tactical changes could alter anticipated results unexpectedly thereby necessitating vigilant monitoring leading up event date itself;

• Data-Driven Insights:

• Past five matches reveal Home Side secured four victories out five occasions indicating strong trend supportive betting proposition favoring them;

• Expert Prediction:

• I predict Home Side winning by narrow margin considering current form trends along historical head-to-head data supporting similar conclusion albeit acknowledging inherent uncertainties surrounding unpredictable nature sport events themselves;

Betting Market – Over/Under Total Goals (Over 1 Goal: 65%, Under:35%)

• Rationale:

• Analyzing goal-scoring tendencies reveals both teams averaging approximately two goals per game collectively thus justifying higher probability placed upon Over scenario given statistical evidence supporting expectation multiple-goal outcome;

• Prediction Confidence Level:

• Moderate confidence level owing largely towards historical data aligning closely current performance metrics albeit slight variability inherent future fixtures still present;

• Data-Driven Insights:</l#include “inference.h”
  #include “utils.h”

  #include “tensorflow/core/framework/tensor_shape.h”
  #include “tensorflow/core/framework/tensor.h”

  using namespace tensorflow;

  // Tensorflow inference wrapper
  Inference::Inference(const std::string& model_path)
  {
  // Load graph
  GraphDef graph_def;
  ReadBinaryProto(Env::Default(), model_path.c_str(), &graph_def);
  graph_.reset(new Session(GraphDef(), Env::Default()));

  // Get input/output tensors
  std::vector input_shapes;
  std::vector output_shapes;
  Status status = graph_->ListGraphInputs(&input_shapes);
  CHECK(status.ok()) <ListGraphOutputs(&output_shapes);
  CHECK(status.ok()) << status.ToString();

  // Create placeholders
  input_tensors_.resize(input_shapes.size());
  output_tensors_.resize(output_shapes.size());

  Tensor* tensor = nullptr;
  TensorShape shape;

  for(size_t i =0; i<input_shapes.size(); ++i)
  {
  shape = TensorShapeFromProto(*input_shapes[i]);
  status = NewPlaceholder(tensor_, shape.dims(), DataTypeToEnum("float"), &input_tensors_[i]);
  CHECK(status.ok()) << status.ToString();
  }

  for(size_t i=0; i<output_shapes.size(); ++i)
  {
  shape = TensorShapeFromProto(*output_shapes[i]);
  status = NewPlaceholder(tensor_, shape.dims(), DataTypeToEnum("float"), &output_tensors_[i]);
  CHECK(status.ok()) <Run({ { input_tensors_[0], input_data[0] } }, { { output_tensors_[0], output_data[0] } });
  }
  johanngeorg/super-resolution<|file_sep#pragma once

  #include "tensorflow/c/c_api.h"

  class Inference
  {
  public:
  Inference(const std::string& model_path);
  void run(float* input_data[], float* output_data[]);

  private:
  std::unique_ptr graph_;
  std::vector<std::unique_ptr> input_tensors_;
  std::vector<std::unique_ptr> output_tensors_;
  };johanngeorg/super-resolution<|file_sep `Super Resolution using Deep Learning`

  The task was performed using TensorFlow C++ API.

  The main function can be found [here](https://github.com/johanngeorg/super-resolution/blob/master/main.cpp).

  The network used was taken from [this repo](https://github.com/alexjc/neural-enhance) which is written using Keras.

  It consists out of:

  – ResNet block
  – Upsampling block

  It uses sub-pixel convolutional layers instead of transposed convolutional layers.

  I tried implementing transposed convolutional layers but it didn't work out because I couldn't figure out how to set weights properly.

  Here are some example results:

  

  

  johanngeorg/super-resolution<|file_sep #include "utils.h"
  #include "inference.h"

  #define STB_IMAGE_IMPLEMENTATION
  #include "stb_image.h"

  #define STB_IMAGE_WRITE_IMPLEMENTATION
  #include "stb_image_write.h"

  int main(int argc , char** argv)
  {
  if(argc !=5)
  {
  printf("Usage:n");
  printf("%s path_to_model path_to_input_image path_to_output_image upscale_factorn", argv[0]);
  return EXIT_FAILURE;
  }

  const char *model_path = argv[1];
  const char *input_image_path = argv[2];
  const char *output_image_path = argv[3];
  int upscale_factor = atoi(argv[4]);

  Inference inference(model_path);

  int width_in , height_in , channels_in ;
  unsigned char* image_in = stbi_load(input_image_path , &width_in , &height_in , &channels_in , STBI_rgb_alpha);
  if(!image_in)
  {
  printf("Could not load image %sn", input_image_path);
  return EXIT_FAILURE;
  }

  int width_out = width_in * upscale_factor;
  int height_out = height_in * upscale_factor;

  float *data_input_tensor = new float[(width_in / upscale_factor) * (height_in / upscale_factor) * channels_in];
  float *data_output_tensor = new float[(width_out) * (height_out) * channels_in];

  for(int y=0 ; y<(int)(height_out / upscale_factor); ++y)
  for(int x=0 ; x<(int)(width_out / upscale_factor); ++x)
  for(int c=0 ; c<(int)channels_in ; ++c)
  data_input_tensor[y*(width_out / upscale_factor)+x+c*((height_out / upscale_factor)*(width_out / upscale_factor))] =
  image_in[y*upscale_factor+x*upscale_factor+c*(width_in*height_in)];

  stbi_image_free(image_in);

  int count_x_steps =(int)((float)width_out/(float)(upscale_factor));
  int count_y_steps =(int)((float)height_out/(float)(upscale_factor));

  for(int y_step=0 ; y_step<count_y_steps;++y_step)
  for(int x_step=0 ; x_step<count_x_steps;++x_step)
  inference.run(&data_input_tensor[x_step+(y_step*(count_x_steps))],&data_output_tensor[x_step+upscale_factor+y_step*(count_x_steps)*upscale_factor]);

  stbi_write_png(output_image_path,width_out,height_out,(channels_in),data_output_tensor,(channels_in)*width_out);

  delete [] data_input_tensor;
  delete [] data_output_tensor;

  return EXIT_SUCCESS;
  }johanngeorg/super-resolution<|file_sep ".PHONY : build clean "

  build :
  g++ -o sr *.cpp tensorflow/libtensorflow_cc.so -ltensorflow_framework -lpthread -lm -lz -ldl

  clean :
  rm sr johanngeorg/super-resolution<|file_sepating that we don't want any padding,
  // so we need to make sure our kernel size is odd.
  //
  // This function creates an upsampling layer that doubles resolution.
  //
  // Input tensor dimensions are assumed as follows:
  // [batch_size][rows][cols][channels]
  //
  // Output tensor dimensions are assumed as follows:
  // [batch_size][rows*factor][cols*factor][channels]
  //
  static void UpsampleLayer(TF_OperationDescription **op_desc,
  TF_Output inputs[],
  TF_Output outputs[],
  const int factor,
  const int batch_size,
  const int rows,
  const int cols,
  const int channels,
  TF_Status *status)
  {

  TF_DataType dtypes[] ={TF_FLOAT};
  TF_TensorShape dims[] ={TF_TENSOR_TYPE_INT64,{batch_size}, {rows},{cols},{channels}};
  TF_TensorShape douts[]={TF_TENSOR_TYPE_INT64,{batch_size},{rows*factor},{cols*factor},{channels}};

  inputs[0] ={op_desc[1],{NULL}};
  outputs[0] ={op_desc[6],{NULL}};

  TF_SetAttrType(op_desc[6], "T", TF_FLOAT,status);
  TF_SetAttrInt(op_desc[6],"Tshape",{dims,sizeof(dims)/sizeof(dims[0])},status);

  TF_SetAttrType(op_desc[7],"T",TF_INT64,status);
  TF_SetAttrInt(op_desc[7],"Tshape",{douts,sizeof(douts)/sizeof(dims)},status);

  TF_SetAttrString(op_desc[8],"padding","VALID",status);

  TF_SetAttrInt(op_desc[9],"stride",{(int64_t []){1,factor,factor,1}},status);
  TF_SetAttrInt(op_desc[9],"rates",{(int64_t []){1,factor,factor,1}},status);

  TF_SetAttrString(op_desc[10],"padding","SAME",status);

  size_t ksize[]={(size_t []){1,(size_t)(factor+factor+1),factor+factor+1,(size_t )channels}};
  size_t stride[]={(size_t []){1,(size_t)fator,(size_t)fator,(size_t )channels}};
  size_t rate[]={(size_t []){1,(size_t)fator,(size_t)fator,(size_t )channels}};

  size_t kernel_dims[]={(size_t){batch_size,factor,factor,batch_size};
  size_t kernel_shape[]={(size_t){kernel_dims,sizeof(kernel_dims)/sizeof(kernel_dims)[0]}};

  float (*kernel)[kernel_dims]={NULL};

  kernel=new float[kernel_shape];

  kernel[(k)*kernel_shape+(r)*kernel_shape[factor]+c+(b)*kernel_shape[factor]*kernel_shape[factor]]=((r==c)&&((k-b)%fator==r/fator))?((k-b)==fator-1)?sqrtf(factor):(-sqrtf(factor)/(factor-1)):0;

  float scale=(sqrtf(factor)-((sqrtf(factor)-floor(sqrtf(factor))<=ceil(sqrtf(factor))-sqrt(factior))?ceil(sqrt(factior)):floor(sqrt(factior))));

  kernel[(k)*kernel_shape+(r)*kernel_shape[factor]+c+(b)*kernel_shape[factior]*kernel_shape[factior]]=scale;

  inputs[k]=op_desc[k+11],{NULL};
  outputs[k]=op_desc[k+16],{NULL};

  TF_SetAttrType(op_desc[k+11],"T",TF_FLOAT,status);
  TF_SetAttrInt(op_desc[k+11],"Tshape",{dims,sizeof(dims)/sizeof(dims)[0]},status);

  TF_SetAttrType(op_desc[k+12],"T",TF_FLOAT,status);
  TF_SetAttrInt(opdesc[k+12],"Tshape",{douts,sizeof(douts)/sizeof(douts)[0]},status);

  TF_SetattrString(opdesc[k+13],"padding","VALID",status);

  kernel=(float *)malloc(sizeof(float )*product(kernel_dims));

  kernel=(float *)malloc(sizeof(float )*product(kernel_dims));

  memset(kernel,float );

  double val=((double )(((double )k-(double )b)/(double )(factor-1)));

  val=val-floor(val)<=ceil(val)-val?ceil(val):floor(val);

  val=sqrt((double )factior)-(val)?sqrt((double )(factior)-(val)*(val)):val;

  kernel[((k-kernel_dims)+(r-r_kernel_dims)+(c-c_kernel_dims)+(b-b_kernel_dims))*product(kernel_dims)]=(float )(val);

  free(kernel);

  for(k=14;k<=17;k+=4)

  inputs[k]=opdesc[k+20],{NULL};

  outputs[k]=opdesc[k+25],{NULL};

  }

  static void ResidualBlock(TFOperationDescription **opdesc,

  TFOperationInput inputs[],
  TFOperationOutput outputs[],
  const int filter_dim,

  const int batch_size,

  const int rows,

  const int cols,

  const int channels,

  TFStatus *statu

  void ConvolutionLayer(TFOperationDescription **opdesc,TFOperationInput inputs[], TFOperationOutput outputs[],const size filters,const size filter_dim,const size batch_size,const size rows,const size cols,const size channels,TFSatus*s)

  {

  inputs[o]={opdesc[o]{NULL}};

  outputs[o]={opdesc[o]{NULL}};

  Setattrtype opdesc[o]"T"TF_FLOAT,s;

  Setattrtype opdesc[o]"T"TF_INT64,s;

  Setattrstring opdesco"padding"VALID,s;

  Setattrtype opdesc[o]"T"TF_FLOAT,s;

  Setattrtype opdesc[o]"T"TF_INT64,s;

  const sizesize kernelsize[]{filterdimfilterdimfilters};

  const sizesize kernelshape[]{kernelsizesizesize(kernelsizesize)/sizesize(kernelsizesize)[sizesize]};

  Float(* kernelsizesizesize)=new Float(kernelshape);

  for(size k=00;k<kernelsizesize;k+=filters)

  for(size r=kernelsizesizemiddlefilters;r<kernelsizesizer;k+=filters)

  for(size c=kernelsizesizemiddlefilters;c<kernelsizersizemiddlefilters;c+=filters)

  if(r==c&&r%middlefilters==middlesquare)

  else

  kernelsizesizesizekernelsizersizer+kernelsizersizermiddlefilters+kernelsizersizemiddlefilters+kernelsizersizemiddlefilters=

  zeros;

  free(kernals);

  inputs[o]={opdesco}{NULL};

  outputs[o]={opdesco}{NULL};

  Setattrtype opdesco"T"TF_FLOAT,s;

  Setattrtype opdesco"T"TF_INT64,s;

  Setattrstring opdesco"padding"SAME,s;

  const sizesize strides[]{sizesizessquare,squaresquaresquare,channels};

  const sizesze rates[]{sizesizessquare,squaresquaresquare,channels};

  inputs[o]={opdesco}{NULL};

  outputs[o]={opdesco}{NULL};

  Setattrtype opdesco"T"TF_FLOAT,s;

  Setattrtype opdesco"T"TF_INT64,s;

  inputs[o]={opdesco}{NULL};

  outputs[o]={opdesco}{NULL};

  }
  HartmutSchoen/DocDataParserSampleAppForWpfDotNetCoreMVVMAndMVVMLightLibrariesOnWindowsDesktopPlatformWithCSharpLanguageCodeSamplesAndDemosInVisualStudio2019AndVisualStudio20222EditionsWithDotNetCoreFrameworkV47TargetRuntimeVersionAndXamlMarkupLanguageFilesForUserInterfaceLayoutDesignAndStyleSpecificationOfUserInterfaceLookAndFeelForApplicationAppearanceThemeSettingsWithCSharpSourceCodeBehindFilesForBusinessLogicImplementationOfApplicationFeaturesUsingModernArchitectureWithMvvmPatternAndLinqQueriesWithEntityFrameworkCoreDatabaseAccessTechnologyAsDatabaseAccessAbstractionLayerWithDependencyInjectionSupportForIoCContainersBuiltInDependencyInjectionContainerSupportUsingMicrosoftExtensionsDependencyInjectionNugetPackageAsIoCContainerLibraryModuleForIoCContainerUseCasesInTheProjectRepositoryManagementSystemApplicationSoftwareSolutionProjectDevelopmentScenarioBasedSampleAppDemoExampleApplicationSampleProgramCaseStudyProjectTutorialTrainingExerciseProblemSolvingSolutionAnswerQuestionAnsweredChallengeTaskToDoItemWorkItemTicketBugReportIssueFeatureRequestEnhancementImprovementProposalRecommendationSuggestionIdeaConceptThoughtOpinionViewpointPerspectiveStandpointPositionStanceBeliefConvictionFaithDoctrineCreedPrincipleTenetMaximRuleLawRegulationPolicyGuidelineStandardProtocolProcedureMethodTechniqueApproachStrategyPlanSchemeBlueprintDesignLayoutStructureOrganizationArrangementFormationConfigurationSettingAdjustmentCalibrationAlignmentSyncronizationHarmonizationCoordinationIntegrationUnificationAmalgamationMeldingBlendingMixingCombinationJuxtapositionComparisonContrastDifferentiationDistinctionDivergenceVariationDeviationDisparityIncongruityDiscrepancyMismatchConflictClashCollisionImpactEncounterInteractionExchangeCommunicationDialogueConversationDiscourseDebateArgumentationRationalizationJustificationRationaleExplanationClarificationElucidationIllustrationDemonstrationExpositionPresentationDisplayShowExhibitionManifestationRevelationDisclosureUnveilingUnmaskingUncoveringDiscoveryFindingDetectionIdentificationRecognitionAcknowledgmentAdmissionConfessionDeclarationAssertionAffirmationConfirmationVerificationValidationCorroborationAuthenticationAuthorizationAccreditationCertificationEndorsementApprovalSanctionPermissionLicenseGrantAllowanceEntitlementRightClaimDemandRequestPetitionApplicationProposalOfferSuggestionRecommendationAdviceDirectionGuidanceInstructionManualHandbookTextbookReferenceBookSourceMaterialDocumentPaperArticleEssayReportStudyAnalysisResearchInvestigationExplorationSurveyExaminationAssessmentEvaluationJudgmentCritiqueReviewCommentaryInterpretationExegesisExplicationElaborationExpansionAmplificationAugmentationEnlargementIncreaseGrowthExpansionExtensionBroadeningWideningStretchLengtheningElongationDilationSwellingInflationBloatingDistensionPuffinessFluffinessPlumpnessChubbinessFatnessObesityCorpulenceHeavinessWeightinessBulkinessMassivenessGirthinessThicknessWidthinessDepthinessHeightinessLengthinessLongitudinalDimensionalityHorizontalVerticalDiagonalTransverseAnterioPosteriorMedialLateralDorsalVentralProximalDistalRostralCaudalDorsolateralVentralmedialPosteriolateralAnteromedialAnterodorsalPosterodorsalCranialCaudalRostralCaudalLateralMedialDorsoventralVentraldorsalProximalDistalAnteriorPosteriorSuperiorInferiorSuperomedialInferolateralSuperolateralSuperotemporalInfranasol 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Unmasking Uncovering Discovery Finding Detection Identification Recognition Acknowledgment Admission Confession Declaration Assertion Affirmation Confirmation Verification Validation Corroboration Authentication Authorization Accreditation Certification Endorsement Approval Sanction Permission License Grant Allowance Entitlement Right Claim Demand Request Petition Application Proposal Offer Suggestion Recommendation Advice Direction Guidance Instruction Manual Handbook Textbook Reference Book Source Material Document Paper Article Essay Report Study Analysis Research Investigation Exploration Survey Examination Assessment Evaluation Judgment Critique Review Commentary Interpretation Exegesis Explication Elaboration Expansion Amplification Augmentation Enlargement Increase Growth Expansion Extension Broadening Widening Stretch Lengthening Elongation Dilation Swelling Inflation Bloating Distension Puffiness Fluffiness Plumpness Chubbiness Fatness Obesity Corpulence Heaviness Weightiness Bulkiness Massiveness Girth Thickness Width Depth Height Length Longitudinal Dimensionality Horizontal Vertical Diagonal Transverse Anterio Posterior Medial Lateral Dorsal Ventral Proximal Distal Rostral Caudal Dorsolateral Ventralmedial Posteriolateral Anteromedial Anterodorsal Posterodorsal Cranial Caudal Rostral Caudal Lateral Medial Dorsoventral Ventraldorsal Proximal Distal Anterior Posterior Superior Inferior Superomedian Inferolateral Superolateral Superotemporal Inferotemporal Supratentorial Infraoccipital Basifrontal Basitemporal Basilobular Basilabiar Baselablellar Basel chord Basel cervical Basel sternal Basel humeral Basel scapular Basel lamin Basel stragal basinlar Basepterygoid basinlar Baseoccipital Basinotic process basinlar Basin basis Basin tubera Basis posterior process basis Basis anterior process basis Basis anterior inferior tubera basis Basis anterior superior tubera basis Basi cranium Basis nasion Basi coron al b asi cranium Basi orbital e b asi cranium Basi cerebell ar i b asi cranium Basi cerebr al e b asi cranium Basi sph en oid al e b asi cranium Basi sph en oid al e su ture b asi cranium Basi sph en oid ale su ture reg ion b asi cranium Basi tempora le b asi cranium Basi tempora le su ture b asi cranium Ba si prom ont ori um Ba si prom ont ori um su ture Ba si hyp ophy se ali Ba si norm a ba si cra ni um Ba si hyp ophy se ali Ba si norm aba si hyp ophy sis area Ba si hyp ophy se ali Ba si norm aba si hy po phy sis area Ba si hyp ophy se ali Ba si norm ab ai ch hy po phy sis area Ba si hyp ophy se ali Ba si norm ab ai ch hy po phy sis area area su ture ba si cra ni um Fa ci al re g ion ba sicra ni um Fa ci al re g ion ba sicra ni um re g io nal su ture ba sicra ni um Fa ci al re g io nal su ture ba sicra ni um Fa ci al re g io nal su tu res ba sic ra ni um Fa ci al sub na sa les su tu res re g io nal su tu res ba sic ra ni um Fa ci al sub na sa les su tu res re g io nal su tu res ba sic ra ni um fa ci als ph en oi dal pl an ea ba sic ra ni um fa ci al sub na sa les su tu res re g io nal su tu res fa sci als ph en oi dal pl an ea ba sic ra ni um fa ci al sub na sa les su tu res re g io nal su tu res fa sci als ph en oi dal pl an ea fa sci als ph en oi dal pl an ea re g io nal su ture ba sic ra ni um fa sci als ph en oi dal pl an ea re g io nal su ture ba sic ra ni um Bs ai lamb doi desBa si lamb doi desBa Si norm aba sicra ni um Bs ai lamb doi desBa Si norm aba sic ra ni um Bs ai lamb doi desre gi on alsu tureba sicra ni um Bs ai lamb doi desre gi on alsu tureba sic ra ni um Bs ai lam bo id ei ps alterei gi nai pl ain bs ai lam bo id ei ps alterei gi nai pl ain bs ai lam bo id ei ps alterei gi nai pl ain reg ion bs ai lam bo id ei ps alterei gi nai pl ain reg ion bs ai lam bo id ei ps alterei gi nai pl ain reg ion bs ai lam bo id ei ps alterei gi nai pl ain reg ion bs ai lam bo id ei ps alterei gi nai pl ain reg ion bilaterally symmetrical asymmetrical symmetrical asymmetrical balance equilibrium imbalance disequilibrium instability instability unbalance instability unbalance unsteadyness unre li ability unpredictability uncontrollability unmanageability unregulated undirected unfocused unfocused focus direction orientation alignment adjustment calibration tuning syncronization harmonization coordination integration unification amalgamation melding blending mix combination juxtaposition comparison contrast differentiation distinction divergence variation deviation disparity incongruity discrepancy mismatch conflict clash collision impact encounter interaction exchange communication dialogue conversation discourse debate argument rationalization justification rationale explanation clarification elucidation illustration demonstration exposition presentation display show exhibition manifestation revelation disclosure unveiling unmasking uncovering discovery finding detection identification recognition acknowledgment admission confession declaration assertion affirmation confirmation verification validation corroboration authentication authorization accreditation certification endorsement approval sanction permission license grant allowance entitlement right claim demand request petition application proposal offer suggestion recommendation advice direction guidance instruction manual handbook textbook reference book source material document paper article essay report study analysis research investigation exploration survey examination assessment evaluation judgment critique review commentary interpretation exegesis explication elaboration expansion amplification augmentation enlargement increase growth expansion extension broadening widening stretch lengthening elongaton dilation swelling inflation bloating distension puffines fluffines plum pines chubbines fatnes obesity corpulence heavines weightines bulkines massivesness girtnes thickness widtnes depthnes heightnes lengthnes longitudinal dimensionality horizontal vertical diagonal transverse anteri posteri medial lateral dorsal ventral proximal distali rostr caud dorsolat ventralmed posteriol antermid anterdors posterodorsi cra nil caud rostra laud lateral medila dorsoventral ventrad dors proxi distali anteri posteri superi inferio superomed inferolater superolater superotempo inferoto supratent infroocciptibasisfronftibasiltemporibasillobula bilabiari blablarrillarrillarrillarrillarrillarrillarrillarrilla rchord cervic sternal humeral scapular lamin stragal basinlar pterygoid occipital otic bassinosis tubera posterior processus anterior processus anterior inferior tubera anterior superior tubera coronala orbitalia cerebellaria cerebralia sphenoidea sphenoidea planum planum planum planum temporalia promontoria promontoria hypophysealia normae craunia hypophysealia normae craunia hypophysealia normae hypophysisarea hypophysealia normae hypo phy sisarea hypo phy sisarea hypo phy sisarearegionale craunia facialis region craunia facialis region region alle sue tra craunia facialis region subnasa les sue tra region alle sue tra craunia facialis region subnasa les sue tra region alle sue tra craunia facialis