{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/gesture-recognition/papers/ran/1","list_of":"/task/gesture-recognition","task":"Gesture Recognition","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,13],"of":13,"counts":{"archive_papers_tagged":572,"with_a_code_link":149,"where_syntology_ran_a_sample":13,"not_listed_spam_title":0,"listed":572,"listed_where_code_ran":13,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":10,"every_run_a_failure_of_syntologys_instrument":3,"listed_with_a_run_with_no_instrument_failure":10,"listed_every_run_a_failure_of_syntologys_instrument":3,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/gesture-recognition/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/scalable-event-by-event-processing-of","slug":"scalable-event-by-event-processing-of","title":"Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Models","date":"2024-04-29","arxiv_id":"2404.18508","repositories_listed":1,"syntology":{"n":18,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/scalable-event-by-event-processing-of#ran","syntology_url":"https://syntology.ai/paper/2404.18508","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.18508"}},"official":{"repos":["Efficient-Scalable-Machine-Learning/event-ssm"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/a-simple-baseline-for-efficient-hand-mesh","slug":"a-simple-baseline-for-efficient-hand-mesh","title":"A Simple Baseline for Efficient Hand Mesh Reconstruction","date":"2024-03-04","arxiv_id":"2403.01813","repositories_listed":1,"syntology":{"n":15,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/a-simple-baseline-for-efficient-hand-mesh#ran","syntology_url":"https://syntology.ai/paper/2403.01813","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.01813"}},"official":null}},{"url":"/paper/milliflow-scene-flow-estimation-on-mmwave","slug":"milliflow-scene-flow-estimation-on-mmwave","title":"milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing","date":"2023-06-29","arxiv_id":"2306.17010","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/milliflow-scene-flow-estimation-on-mmwave#ran","syntology_url":"https://syntology.ai/paper/2306.17010","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.17010"}},"official":{"repos":["toytiny/milliflow"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/online-training-through-time-for-spiking","slug":"online-training-through-time-for-spiking","title":"Online Training Through Time for Spiking Neural Networks","date":"2022-10-09","arxiv_id":"2210.04195","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/online-training-through-time-for-spiking#ran","syntology_url":"https://syntology.ai/paper/2210.04195","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.04195"}},"official":{"repos":["pkuxmq/ottt-snn"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-learning-and-its-applications-to-wifi","slug":"deep-learning-and-its-applications-to-wifi","title":"SenseFi: A Library and Benchmark on Deep-Learning-Empowered WiFi Human Sensing","date":"2022-07-16","arxiv_id":"2207.07859","repositories_listed":2,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":7,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/deep-learning-and-its-applications-to-wifi#ran","syntology_url":"https://syntology.ai/paper/2207.07859","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.07859"}},"official":{"repos":["chenxinyan-sg/wifi-csi-sensing-benchmark","xyanchen/wifi-csi-sensing-benchmark"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/neuro2vec-masked-fourier-spectrum-prediction","slug":"neuro2vec-masked-fourier-spectrum-prediction","title":"Neuro-BERT: Rethinking Masked Autoencoding for Self-supervised Neurological Pretraining","date":"2022-04-20","arxiv_id":"2204.12440","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/neuro2vec-masked-fourier-spectrum-prediction#ran","syntology_url":"https://syntology.ai/paper/2204.12440","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.12440"}},"official":{"repos":["Westlake-AI/OpenBioSeq"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/direcformer-a-directed-attention-in","slug":"direcformer-a-directed-attention-in","title":"DirecFormer: A Directed Attention in Transformer Approach to Robust Action Recognition","date":"2022-03-19","arxiv_id":"2203.10233","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":8,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/direcformer-a-directed-attention-in#ran","syntology_url":"https://syntology.ai/paper/2203.10233","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.10233"}},"official":{"repos":["uark-cviu/direcformer"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/beat-a-large-scale-semantic-and-emotional","slug":"beat-a-large-scale-semantic-and-emotional","title":"BEAT: A Large-Scale Semantic and Emotional Multi-Modal Dataset for Conversational Gestures Synthesis","date":"2022-03-10","arxiv_id":"2203.05297","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/beat-a-large-scale-semantic-and-emotional#ran","syntology_url":"https://syntology.ai/paper/2203.05297","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.05297"}},"official":{"repos":["PantoMatrix/PantoMatrix"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/semg-gesture-recognition-with-a-simple-model","slug":"semg-gesture-recognition-with-a-simple-model","title":"sEMG Gesture Recognition with a Simple Model of Attention","date":"2020-06-05","arxiv_id":"2006.03645","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/semg-gesture-recognition-with-a-simple-model#ran","syntology_url":"https://syntology.ai/paper/2006.03645","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.03645"}},"official":{"repos":["josephsdavid/semg_repro"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["named_in_paper"]}}},{"url":"/paper/comparing-snns-and-rnns-on-neuromorphic","slug":"comparing-snns-and-rnns-on-neuromorphic","title":"Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differences","date":"2020-05-02","arxiv_id":"2005.02183","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/comparing-snns-and-rnns-on-neuromorphic#ran","syntology_url":"https://syntology.ai/paper/2005.02183","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.02183"}},"official":null}},{"url":"/paper/mmtm-multimodal-transfer-module-for-cnn","slug":"mmtm-multimodal-transfer-module-for-cnn","title":"MMTM: Multimodal Transfer Module for CNN Fusion","date":"2019-11-20","arxiv_id":"1911.08670","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/mmtm-multimodal-transfer-module-for-cnn#ran","syntology_url":"https://syntology.ai/paper/1911.08670","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.08670"}},"official":null}},{"url":"/paper/word-level-deep-sign-language-recognition","slug":"word-level-deep-sign-language-recognition","title":"Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison","date":"2019-10-24","arxiv_id":"1910.11006","repositories_listed":3,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/word-level-deep-sign-language-recognition#ran","syntology_url":"https://syntology.ai/paper/1910.11006","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.11006"}},"official":{"repos":["dxli94/WLASL"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/deep-learning-for-electromyographic-hand","slug":"deep-learning-for-electromyographic-hand","title":"Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning","date":"2018-01-10","arxiv_id":"1801.07756","repositories_listed":4,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/deep-learning-for-electromyographic-hand#ran","syntology_url":"https://syntology.ai/paper/1801.07756","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1801.07756"}},"official":{"repos":["Giguelingueling/MyoArmbandDataset"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}}],"record_sha256":"981dc91e93edf5ccdd7f7914396a8a226ffec247dad12c9fbf49457d2c8bb54e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}