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World Cup Qualification Europe 1st Round Grp. G stats & predictions

The Thrill of the Basketball World Cup Qualification: Europe 1st Round Grp. G

The Basketball World Cup Qualification is an event that captures the attention of sports enthusiasts worldwide. As we approach the first round of Group G in Europe, the excitement is palpable. Fans eagerly anticipate the matches scheduled for tomorrow, where top teams will battle it out to secure their place in the next stage of qualification. This article delves into the key matchups, team analyses, and expert betting predictions to provide a comprehensive overview of what to expect.

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Overview of Group G

Group G consists of some of Europe's most formidable basketball teams, each bringing their unique strengths and strategies to the court. The group stage is crucial as it sets the tone for future rounds, with only the top-performing teams advancing. Let's take a closer look at the teams and their recent performances.

Team Profiles

  • Team A: Known for their aggressive defense and fast-paced offense, Team A has been a dominant force in European basketball. Their star player has been in exceptional form, contributing significantly to their recent victories.
  • Team B: With a focus on teamwork and strategic plays, Team B has consistently outperformed expectations. Their ability to adapt to different opponents makes them a formidable challenge for any team.
  • Team C: Renowned for their strong shooting skills and solid defense, Team C has been steadily climbing up the ranks. Their recent recruitment of a high-profile player has added an extra edge to their gameplay.
  • Team D: As underdogs in this group, Team D has shown remarkable resilience and determination. Their young squad is eager to prove themselves on this international stage.

Scheduled Matches for Tomorrow

The first round of Group G features several thrilling matchups that promise intense competition and high stakes. Here are the key games scheduled for tomorrow:

  • Match 1: Team A vs. Team B

    • This clash between two top contenders is expected to be a highlight of the day. Both teams have strong records and will be looking to assert their dominance early in the qualification process.

  • Match 2: Team C vs. Team D

    • In this intriguing matchup, Team C aims to capitalize on their home advantage against an underdog Team D. The game could go either way, making it a must-watch for fans.

  • Match 3: Team A vs. Team C

    • A battle between two defensive powerhouses, this game will test both teams' ability to break through tough defenses and secure crucial points.

  • Match 4: Team B vs. Team D

    • This game pits experience against youth as Team B looks to maintain their winning streak against an eager but less experienced Team D.

Betting Predictions and Analysis

Betting experts have weighed in on tomorrow's matches, providing insights based on recent performances, team dynamics, and historical data. Here are some expert predictions:

Expert Betting Predictions

  • Match 1: Team A vs. Team B
    • Prediction: Draw or narrow victory for Team A
    • Rationale: Both teams have similar strengths, but Team A's home advantage may tip the scales slightly in their favor.
  • Match 2: Team C vs. Team D
    • Prediction: Close win for Team C
    • Rationale: Despite being favorites at home, unexpected challenges from an improving young team could make this match competitive.
  • Match 3: Team A vs. Team C
    • Prediction: Tight contest with possible overtime decision
    • Rationale: Both teams excel defensively; hence predicting either side requires considering potential offensive breakthroughs during critical moments. epoch)): print('Epoch:[{}/{}][{}/{}] ' 'Data {data_time.val:.3f} ({data_time.avg:.3f}) ' 'Batch {batch_time.val:.3f} ({batch_time.avg:.3f}) ' 'Loss {cls_loss.val:.4f} ({cls_loss.avg:.4f}) ' '{reg_loss.val:.4f} ({reg_loss.avg:.4f}) ' '{rpn_loss.val:.4f} ({rpn_loss.avg:.4f})'.format( epoch + config.TRAIN.BEGIN_EPOCH, config.TRAIN.END_EPOCH - config.TRAIN.BEGIN_EPOCH, i + len(train_loader) * epoch + config.TRAIN.BEGIN_EPOCH * len(train_loader), len(train_loader) * ( config.TRAIN.END_EPOCH - config.TRAIN.BEGIN_EPOCH), batch_time=batch_time, data_time=data_time, cls_loss=loss_c, reg_loss=loss_r, rpn_loss=loss_l)) end = time.time() lr_ = [pg["lr"] for pg in optimizer.param_groups] ***** Tag Data ***** ID: Complex training loop with advanced metrics computation description: This snippet contains a detailed training loop which involves multiple advanced operations such as calculating various types of losses (classification loss, regression loss), updating metrics using custom AverageMeter objects, dynamically switching models between training modes depending on certain conditions. start line:11 end line:221 dependencies: - type: Class name: AverageMeter start line:7 end line:10 - type: Function/Method name:model.train() context description: This snippet forms part of a larger module responsible for training machine learning models using PyTorch. algorithmic depth:4 algorithmic depth external: N obscurity :4 advanced coding concepts :5 interesting for students :5 self contained:N ************* ## Suggestions for complexity Here are five advanced ways that can be used specifically within this code: 1. **Dynamic Loss Weighting**: Implement dynamic weighting mechanisms that adjust `cls_weight`, `reg_weight`, and `centerness_weight` based on certain criteria like current epoch or validation performance. 2. **Asynchronous Data Loading**: Integrate asynchronous data loading mechanisms using PyTorch's `DataLoader` with multiple workers while ensuring that GPU memory management remains optimal. 3. **Custom Gradient Clipping**: Add custom gradient clipping logic that adapts based on specific layers or parameters within your model architecture. 4. **Mixed Precision Training**: Incorporate mixed precision training techniques using NVIDIA’s Apex library or PyTorch’s native support (`torch.cuda.amp`) to optimize memory usage and speed up training without sacrificing accuracy. 5. **Adaptive Learning Rate Scheduling**: Implement adaptive learning rate scheduling techniques such as Cyclical Learning Rates (CLR) or One Cycle Policy tailored specifically based on validation metrics rather than fixed epochs. ## Conversation <|user|>Hello AI assistant I need help with my code here [SNIPPET] I want dynamic weight adjustment during training can you do that<|end|> <|assistant|>Sure! Dynamic weight adjustment can help improve model performance by focusing more on certain losses at different stages of training. We can modify your code by adding logic that adjusts `cls_weight`, `reg_weight`, and `centerness_weight` dynamically based on certain criteria such as current epoch number or validation performance metrics. Here's how you might implement dynamic weighting based on epoch: python def adjust_weights(epoch): if epoch <= config.EARLY_PHASE: return {'cls': initial_cls_weight * (epoch / config.EARLY_PHASE), 'reg': initial_reg_weight * (epoch / config.EARLY_PHASE), 'center': initial_center_weight} elif epoch <= mid_epoch: return {'cls': mid_phase_cls_weight * ((mid_epoch - epoch) / mid_epoch), 'reg': mid_phase_reg_weight * ((mid_epoch - epoch) / mid_epoch), 'center': initial_center_weight} else: return {'cls': final_cls_weight, 'reg': final_reg_weight, 'center': final_center_weight} def train(train_loader, model, criterion, optimizer, epoch, config): batch_time = AverageMeter() data_time = AverageMeter() losses = AverageMeter() # Adjust weights dynamically based on current epoch. weights = adjust_weights(epoch) # switch model to training mode. model.train() # Inside your main loop where you calculate losses: for i, data in enumerate(train_loader): ... cls_loss = criterion['loss_cls'](outputs['cls_preds'][0], targets[0], weights=weights["center"] * outputs["centernesses"][0]) ... This function `adjust_weights` dynamically changes weights according to epochs defined within your configuration (`config`). You can further refine these conditions based on your specific needs. *** Excerpt *** The existence of these social hierarchies raises questions about how they influence brain structure-function relationships across individuals [e.g., refs.[28–30]]. Specifically, does exposure during development influence structural organization? And does social rank acquired later in life affect structural connectivity? In nonhuman primates there is evidence that both developmental exposure [e.g., ref.[31]] as well as adult rank [e.g., ref.[32]] influence brain structure-function relationships across individuals [e.g., refs.[33–36]]. In humans there is also evidence linking social status with cortical thickness [37] gray matter volume [38] white matter microstructure [39] functional connectivity patterns [40] resting-state networks [41] amygdala activity [42] striatal dopamine release [43] hippocampal volume [44] etcetera; however it remains unclear whether these differences reflect developmental effects or social status attained later in life. One way forward would be longitudinal studies following individuals over time beginning before they enter into these hierarchies until after they have achieved stable social rank; however such studies would be difficult due both practical constraints (e.g., long-term fMRI scanning). An alternative approach would involve identifying individuals who are new recruits into established hierarchies where they begin at low rank but have opportunity over time (months-years) through competition/coalition formation/cooperation etcetera potentially increase their status; again though such studies would require long-term fMRI scanning which presents logistical difficulties given costs associated with neuroimaging research even when performed within university settings. *** Revision *** ## Plan To create an exercise that challenges advanced understanding along with factual knowledge beyond what's presented in the excerpt: - Introduce more technical terms related to neuroscience without defining them directly within the text. - Incorporate references indirectly by mentioning findings without specifying which study they come from. - Embed complex logical structures involving hypothetical scenarios ("if...then...") alongside counterfactuals ("had X not occurred..."). - Include nested conditionals that require readers not only to follow logical steps but also understand implications beyond what is directly stated. These modifications aim at testing not just comprehension but also application of external knowledge related to neuroscience methodologies (like fMRI scanning), understanding hierarchical social structures across species including humans, and grasping complex theoretical implications regarding brain plasticity. ## Rewritten Excerpt The delineation of societal strata posits intricate inquiries concerning its modulation upon neural architecture-functional paradigms inter-individually ([referential indices][28–30]). Pivotal queries emerge regarding whether developmental exposure predicates alterations within structural configurations or if ascension/descension within adult hierarchical echelons recalibrates structural connectivities? Within primate analogs outside Homo sapiens taxonomy ([referential indices][31]), evidentiary corpus suggests dual influences—both ontogenetic exposures ([referential indices][31]) alongside achieved seniority ([referential indices][32])—on neural architecture-functional correlations inter-individually ([referential indices][33–36]). Analogously amongst humans ([Homo sapiens]), correlational analysis elucidates associations between societal standing vis-a-vis cortical thickness ([referential index][37]), grey matter volumetrics ([referential index][38]), white matter microstructural integrity ([referential index][39]), functional connectivity paradigms ([referential index][40]), resting-state network configurations ([referential index][41]), amygdalar reactivity ([referential index][42]), dopaminergic efflux within striatal domains ([referential index][43]), hippocampal volumetric assessments ([referential index][44]); notwithstanding ambiguity persists regarding whether these disparities originate from developmental influences or hierarchical statuses procured post-developmentally. A prospective methodological pathway entails longitudinal scrutinization commencing pre-entry into hierarchical constructs extending through post-establishment phases; yet pragmatic constraints prevail due primarily to logistical exigencies inherent within protracted fMRI explorations—a methodology quintessential yet financially prohibitive even amidst academic precincts conducive towards neuroscientific inquiry. ## Suggested Exercise Given the rewritten excerpt discussing societal hierarchies' impact on neural architecture-functional paradigms: Which statement best encapsulates why longitudinal studies tracking individuals from pre-hierarchical entry through post-establishment phases present significant challenges? A) Longitudinal studies cannot accurately measure changes in brain structure due to technological limitations inherent in current neuroimaging methodologies like fMRI scanning. B) Such studies demand extensive financial resources primarily because tracking individual changes over extended periods necessitates repeated neuroimaging sessions which are costly even within resource-abundant academic environments. C) The primary challenge lies not in logistical concerns but rather ethical considerations surrounding long-term observation of human subjects without intervening variables influencing outcomes. D) These studies are impractical because societal hierarchies themselves fluctuate too rapidly over time for meaningful longitudinal analysis concerning brain structure-function relationships. *** Revision *** check requirements: - req_no: 1 discussion: The draft lacks explicit reliance on external knowledge beyond basic neuroscience concepts already mentioned. score: 1 - req_no: 2 discussion: Understanding subtleties like financial constraints linked explicitly with prolonged fMRI use requires comprehension but doesn't deeply engage subtler nuances mentioned elsewhere. score: 2 - req_no: 3 discussion: While lengthy and filled with jargon-heavy sentences making it complex, clarity could still improve by tightening logical flow between ideas presented. It meets length requirement effectively though. score: - req_no: ? |- req_no:#5 Complex enough given its terminology use but could better integrate complexity by linking more nuanced concepts requiring deeper understanding or interpretation. score:#5 It engages well with complex ideas but could further enhance difficulty by intertwining more subtle distinctions requiring deeper insight into neuroscience or sociology theories not explicitly mentioned. missing_context?: N/A because all information needed is provided within itself although it doesn't link deeply enough with external advanced knowledge requirements set forth by requirement #1. missing_context_text?: OK since no additional context outside what's provided seems necessary under current guidelines despite lacking stronger ties with external knowledge fields specified under requirement #1.. hint?: Consider revising question choices so each reflects plausible conclusions drawn from sophisticated interpretations involving broader socio-neuroscientific theories possibly requiring specialized knowledge beyond excerpt content itself.. Revise choices so subtlety increases requiring sharper discernment grounded not only firmly within excerpt details but also necessitating additional relevant expertise.. *** Revision *** revised excerpt: The delineation of societal strata posits intricate inquiries concerning its modulation upon neural architecture-functional paradigms inter-individually ([28–30]). Pivotal queries emerge regarding whether developmental exposure predicates alterations within structural configurations or if ascension/descension within adult hierarchical echelons recalibrates structural connectivities? Within primate analogs outside Homo sapiens taxonomy ([31]), evidentiary corpus suggests dual influences—both ontogenetic exposures ([31]) alongside achieved seniority ([32])—on neural architecture-functional correlations inter-individually ([33–36]). Analogously amongst humans (Homo sapiens), correlational analysis elucidates associations between societal standing vis-a-vis cortical thickness ([37]), grey matter volumetrics ([38]), white matter microstructural integrity ([39]), functional connectivity paradigms ([40]), resting-state network configurations ([41]), amygdalar reactivity ([42]), dopaminergic efflux within striatal domains ([43]), hippocampal volumetric assessments([44]); notwithstanding ambiguity persists regarding whether these disparities originate from developmental influences or hierarchical statuses procured post-developmentally.nA prospective methodological pathway entails longitudinal scrutinization commencing pre-entry into hierarchical constructs extending through post-establishment phases; yet pragmatic constraints prevail due primarily to logistical exigencies inherent within protracted fMRI explorations—a methodology quintessential yet financially prohibitive even amidst academic precincts conducive towards neuroscientific inquiry.nConsidering potential sociopolitical factors impacting funding availability coupled with technological advancements potentially altering cost-benefit analyses over time provides fertile ground for hypothesizing future directions where interdisciplinary approaches integrating economics might illuminate paths forward overcoming current barriers." correct choice:",Such studies demand extensive financial resources primarily because tracking individual changes over extended periods necessitates repeated neuroimaging sessions which are costly even within resource-abundant academic environments." revised exercise:",Given the revised excerpt discussing societal hierarchies' impact on neural architecture-functional paradigms alongside economic considerations affecting research feasibility:nWhich statement best encapsulates why longitudinal studies tracking individuals from pre-hierarchical entry through post-establishment phases present significant challenges?" incorrect choices: - Longitudinal studies cannot accurately measure changes in brain structure due to technological limitations inherent in current neuroimaging methodologies like fMRI scanning. - The primary challenge lies not in logistical concerns but rather ethical considerations surrounding long-term observation of human subjects without intervening variables influencing outcomes. - These studies are impractical because societal hierarchies themselves fluctuate too rapidly over time for meaningful longitudinal analysis concerning brain structure-function relationships." *** Excerpt *** It should now be apparent how Hume’s thought experiment succeeds so well as an argument against induction despite his own conclusion about its efficacy being mistaken – namely his conclusion that induction must succeed despite having no rational basis – rather than his true conclusion that induction must fail despite having no rational basis whatsoever! But why did Hume think he had succeeded when he had failed? There are two reasons why Hume thought he had succeeded when he had failed – one quite general about philosophy itself while another was quite specific about his particular brand thereof – namely Scottish Common Sense Realism – combined together form an effective shield against realizing his own failure! Firstly – philosophy itself often leads us astray when we attempt “to trace up any cause” simply because “it becomes lost among those secret springs which lie deep” below our conscious awareness – thereby leading us astray into thinking we have discovered something profound while really having discovered nothing whatsoever! Secondly – Hume’s own philosophical school held strongly against metaphysical speculation while holding equally strongly toward accepting whatever appeared obvious before our eyes unless we were forced otherwise by reason! Thus Hume was led away from seeing his own failure precisely because he believed himself incapable via reason alone! *** Revision *** ## Plan To create an exercise that challenges advanced comprehension skills along with factual knowledge demands embedding complexities both linguistically and conceptually into the excerpt itself while expanding its scope beyond mere interpretation towards application involving external philosophical theories or principles not directly mentioned therein. Enhancing complexity can involve incorporating more sophisticated vocabulary related specifically to philosophy—particularly epistemology—and integrating references requiring knowledge beyond what's provided directly about Hume’s philosophy versus other philosophical doctrines or debates surrounding induction versus deduction reasoning methods historically discussed among philosophers like Kant or Popper who responded critically after Hume's skepticism was proposed. Furthermore, introducing conditional statements intertwined logically — nested conditionals — adds layers requiring careful unpacking by readers demanding higher cognitive engagement — evaluating implications logically step-by-step before reaching conclusions about what follows necessarily under given circumstances described hypothetically. ## Rewritten Excerpt It becomes evident upon thorough examination how David Hume’s thought experiment triumphantly poses formidable challenges against induction—even though his ultimate inference regarding its effectiveness stands erroneous—notably his assertion asserting induction must prevail sans any rational foundation instead than recognizing outright its inevitable failure devoid entirely rational grounding! What then propelled Hume towards believing he had successfully demonstrated something profound when fundamentally he hadn’t? Two factors intricately weave together forming a robust barrier obscuring recognition of his misstep—one broadly pertaining philosophical practice generally while another distinctly tied down narrowly focusing particularly upon Scottish Common Sense Realism—the philosophical paradigm he adhered closely—together constructively shielding him against perceiving personal oversight! Initially—philosophical endeavors frequently mislead us attempting "to trace up any cause" merely since "it becomes lost amid those hidden mechanisms residing profoundly beneath conscious awareness"—thus misleading us into presuming profound discoveries whilst genuinely uncovering naught whatsoever! Subsequently—Hume’s adherence strictly towards Scottish Common Sense Realism staunchly opposed metaphysical speculations concurrently insisting unreserved acceptance whatever appeared evidently manifest unless compellingly contradicted via rigorous reasoning! Henceforth led astray precisely because convinced incapacity solely through reason! ## Suggested Exercise David Hume argued about induction using Scottish Common Sense Realism principles asserting no rational basis exists supporting induction success whereas concluding it must succeed nonetheless mistakenly owing partly due errors intrinsic philosophical practice generally coupled tightly with specific commitments philosophically held personally by him himself! Based upon understanding above revised text consider following statements: A) If Scottish Common Sense Realism rejects metaphysical speculation completely then any apparent contradictions arising solely via empirical observations should lead directly toward accepting those observations as truths unless logically disproven otherwise — implying any failure recognizing contradiction stems purely lack rigorous logical examination rather than foundational belief system error! B) Given philosophical endeavors often lead astray when tracing causes deep beneath conscious awareness — if one assumes finding profound discovery implies actual discovery occurred then one might overlook possibility mere illusion created by inability consciously access underlying causal mechanisms involved! C) If one adhered strictly reasoning capabilities alone guided entirely by Scottish Common Sense Realism then acknowledging inability discerning successful demonstration without rational basis might prevent realization personal oversight occurred due strictly belief incapability reason alone suffice uncover truth! Choose correct statement reflecting implication(s): a) Only A b) Only B c) Only C d) Both A & B e) All A, B & C Correct Answer Explanation: Option e ('All A, B & C') correctly identifies implications drawn logically connecting premises stated individually per respective statement options provided—all align coherently reflecting broader theme discussed initially revised excerpt tying back original themes discussed surrounding misconceptions possibly arising philosophically engaging deeply rooted beliefs held personally combined generally observed tendencies misleading philosophically tracing causes beneath conscious awareness—all contributing collectively leading away recognizing oversight made personally philosophically attributed originally misinterpretation success demonstration claimed initially wrongly assumed proved profoundly insightful actually revealing nothing substantive underlying claims made originally! compiled_out_dir="${out_dir}/${COMPILER}" source "${SRC_DIR}/scripts/common.sh" source "${SRC_DIR}/scripts/check.sh" # Check command-line arguments... if [[ $# != "5" ]]; then echo "Usage $progname [-v|-q|-t|-i|-c|--help|-?] compiler-name input-file output-dir run-time"; exit $E_BADARGS; fi; # Parse command-line arguments... while [[ "$#" > "0" ]]; do case "$arg" in "-v") verbose=true ;; "-q") quiet=true ;; "-t") timing=true ;; "-i") interactive=true ;; "-c") cleanup=true ;; "--help"|"-?"|"") usage;; *) break;; esac; shift; done; compiler="$arg"; shift; input_file="$arg"; shift; out_dir="$arg"; shift; run_time="$arg"; shift; # Make sure compiler-name is valid... if [[ ! "$compiler" =~ ^(gcc)|(clang)|(llvm)|(icc)$ ]]; then echo "$progname ERROR invalid compiler-name '$compiler'." >&$STDERR_FD; exit $E_BADARGS; fi; # Make sure output-dir exists... if [[ ! -d "$out_dir" ]]; then mkdir "$out_dir"; fi; # Make sure input-file exists... if [[ ! -r "$input_file" ]]; then echo "$progname ERROR input-file '$input_file' does not exist." >&$STDERR_FD; exit $E_IOERR; fi; # Make sure run-time value is valid... if [[ ! "$run_time" =~ ^([+-]?([[:digit:]])+(.[[:digit:]]+)?)(m|M|h|H|s|S)?$ ]]; then echo "$progname ERROR invalid run-time value '$run_time'." >&$STDERR_FD; exit $E_BADARGS; fi; # Convert run-time value string-to-float seconds... case "${run_time:(-${#run_time}-)}" in m|M) run_secs=$(echo "$(echo "${run_time%??}" | bc)" | awk '{printf "%d", int($$));}') ;; h|H) run_secs=$(echo "$(echo "${run_time%??}" | bc)*60*60" | bc);; s|S) run_secs=$(echo "${run_time%??}" | bc);; *) run_secs=$(echo "$(echo "${run_time%??}" | bc)" | awk '{printf "%d", int($$));}');; esac; # Create output file names... output_obj_file="${out_dir}/${compiler}-${input_file%.c}.o" output_exe_file="${out_dir}/${compiler}-${input_file%.c}" case ${compiler} in gcc) compile_cmd="gcc ${GCC_FLAGS[@]} ${GCC_OPT_FLAGS[@]} ${GCC_LIB_FLAGS[@]} ${GCC_LDFLAGS[@]} -o "${output_exe_file}" "${input_file}"" compile_flags="-o "${output_obj_file}"" link_cmd="gcc ${LDFLAGS[@]} ${LIB_FLAGS[@]} "${output_obj_file}"" ;; clang) compile_cmd="clang ${CLANG_FLAGS[@]} ${CLANG_OPT_FLAGS[@]} ${CLANG_LIB_FLAGS[@]} ${CLANG_LDFLAGS[@]} -o "${output_exe_file}" "${input_file}"" compile_flags="-o "${output_obj_file}"" link_cmd="clang ${LDFLAGS[@]} ${LIB_FLAGS[@]} "${output_obj_file}"" ;; llvm) compile_cmd="clang++ --target=x86_64-pc-linux-gnu --gcc-toolchain=/usr/local/llvm/bin/x86_64-pc-linux-gnu-gcc/8 --sysroot=/usr/local/llvm/bin/x86_64-pc-linux-gnu-gcc/8/sys-root --sysroot=/usr/local/llvm/bin/x86_64-pc-linux-gnu-gcc/8/sys-root --gcc-toolchain=/usr/local/llvm/bin/x86_64-pc-linux-gnu-gcc/8 --no-canonical-prefixes --rtlib=compiler-rt --disable-free --enable-new-dtags --debugger-tuning=gdb -fuse-init-array -target x86_64-pc-linux-gnu -isystem /usr/local/llvm/bin/../lib/gcc/x86_64-pc-linux-gnu/8/include/c++ -isystem /usr/local/llvm/bin/../lib/gcc/x86_64-pc-linux-gnu/8/include/c++/x86_64-pc-linux-gnu -isystem /usr/local/llvm/bin/../x86_64-pc-linux-gnu/include/c++/v1 -D_GNU_SOURCE '-D_REENTRANT' '-D_THREAD_SAFE' '-D_BSD_SOURCE' '-D_SVID_SOURCE' '-D_POSIX_C_SOURCE=200809L' '-D_XOPEN_SOURCE=700' '-D_XOPEN_SOURCE_EXTENDED' '-D_FORTIFY_SOURCE=2' '-DHAVE_SYS_TYPES_H=defined(HAVE_SYS_TYPES_H)' '-DHAVE_SYS_STAT_H=defined(HAVE_SYS_STAT_H)' '-DHAVE_STDLIB_H=defined(HAVE_STDLIB_H)' '-DHAVE_STRING_H=defined(HAVE_STRING_H)' '-DHAVE_MEMORY_H=defined(HAVE_MEMORY_H)' '-DHAVE_STRINGS_H=defined(HAVE_STRINGS_H)' '-DHAVE_INTTYPES_H=defined(HAVE_INTTYPES_H)' '-DHAVE_STDINT_H=defined(HAVE_STDINT_H)' '-DHAVE_UNISTD_H=defined(HAVE_UNISTD_H)' '--param allow-store-data-races=true'" link_cmd="" ;; icc) compile_cmd="icc ${ICC_FLAGS[@]} ${ICC_OPT_FLAGS[@]} ${ICC_LIB_FLAGS[@]} ${ICC_LDFLAGS[@]} -o "${output_exe_file}" "${input_file}"" compile_flags="-o "${output_obj_file}"" link_cmd="icc ${LDFLAGS[@]} ${LIB_FLAGS[@]} "${output_obj_file}"" ;; *) ;; esac; echo "" >&$STDOUT_FD; printf "# %-70snn" "Compiling '${input_file}'..." >&$STDOUT_FD; time_start=$(date +%s); # Compile source file... if ! eval "${compile_cmd}" ; then echo "ERROR compiling '${input_file}'." >&$STDOUT_FD ; exit $E_IOERR ; fi ; time_end=$(date +%s); time_elapsed=$((time_end-time_start)); printf "# %-70snn" "Compile complete..." >&$STDOUT_FD; printf "# %-70snn" "Total compile time elapsed $(print_elapsed_seconds "$time_elapsed")..." >&$STDOUT_FD; exit $E_OK;<|file_sep#include int main(int argc,char** argv){ int num_threads,i,j,k,n,x,y,z,t,s,c,d,e,f,g,h,q,w,r,a,b,m,l,u,o,p,v,score,result,count,max_score,max_result,max_count,i_score,i_result,i_count,j_score,j_result,j_count,k_score,k_result,k_count,l_score,l_result,l_count,m_score,m_result,m_count,n_score,n_result,n_count,o_score,o_result,o_count,p_score,p_result,p_count,q_score,q_result,q_count,r_score,r_result,r_count,s_score,s_result,s_count,t_score,t_result,t_count,u_score,u_result,u_count,v_score,v_result,v_count,w_score,w_result,w_count,x_score,x_result,x_count,y_score,y_result,y_count,z_score,z_result,z_count; if(argc!=9){ printf("usage:n"); printf("t%s num_threads n x y z t s c d e f g h q w r a b m l u o p vn",argv[0]); exit(-1); } num_threads=(int)(atof(argv[(argc)-9])); n=(int)(atof(argv[(argc)-8])); x=(int)(atof(argv[(argc)-7])); y=(int)(atof(argv[(argc)-6])); z=(int)(atof(argv[(argc)-5])); t=(int)(atof(argv[(argc)-4])); s=(int)(atof(argv[(argc)-3])); c=(int)(atof(argv[(argc)-2])); d=(int)(atof(argv[(argc)-1])); e=f=g=h=q=w=r=a=b=m=l=u=o=p=v=score=result=count=max_score=max_result=max_count=i_score=i_result=i_count=j_score=j_result=j_count=k_score=k_result=k_count=l_score=l_result=l_count=m_score=m_result=m_count=n_score=n_result=n_count=o_score=o_result=o_count=p_score=p_result=p_count=q_score=q_result=q_count=r_score=r_result=r_count=s_score=s_result=s_count=t_score=t_results=t_counts=u_scores=u_results=u_counts=v_scores=v_results=v_counts=w_scores=w_results=w_counts=x_scores=x_results=x_counts=y_scores=y_results=y_counts=z_scores=z_results=z_counts; for(i=-100;i<=100;i++){ for(j=-100;j<=100;j++){ for(k=-100;k<=100;k++){ l=i+j+k-n; m=i+x+j+y-k-z; a=j+x+y+z-t-s-c-d-e-f-g-h-i-j-k-l-m-n-o-q-w-r-a-b-m-l-u-o-p-v-w-x-y-z-t-s-c-d-e-f-g-h-q-w-r-a