{"id":13660,"date":"2023-08-25T11:58:14","date_gmt":"2023-08-25T09:58:14","guid":{"rendered":"https:\/\/sano.science\/?post_type=people&p=13660"},"modified":"2026-03-24T11:36:34","modified_gmt":"2026-03-24T10:36:34","slug":"jan-argasinski","status":"publish","type":"people","link":"https:\/\/sano.science\/people\/jan-argasinski\/","title":{"rendered":"Jan K. Argasi\u0144ski"},"excerpt":{"rendered":"

Research Team Leader of Computational Neuroscience<\/p>\n","protected":false},"featured_media":21360,"template":"","people_teams":[33],"class_list":["post-13660","people","type-people","status-publish","has-post-thumbnail","hentry","people_teams-computational-neuroscience"],"yoast_head":"\nJan K. Argasi\u0144ski - Centre for Computational Personalized Medicine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sano.science\/people\/jan-argasinski\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Jan K. Argasi\u0144ski\" \/>\n<meta property=\"og:description\" content=\"Research Team Leader of Computational Neuroscience\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/people\/jan-argasinski\/\" \/>\n<meta property=\"og:site_name\" content=\"Centre for Computational Personalized Medicine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/sano.science\/\" \/>\n<meta property=\"article:modified_time\" content=\"2026-03-24T10:36:34+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/08\/Jan_Argasinski.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"1200\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@sanoscience\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/people\\\/jan-argasinski\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/people\\\/jan-argasinski\\\/\",\"name\":\"Jan K. 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Argasinski, PhD is a Research Team Leader at Sano Centre for Computational Medicine and an Assistant Professor at the Institute of Applied Computer Science, Jagiellonian University. His interests include artificial intelligence, affective computing, computational neuroscience, and VR\/AR.<\/p>\n<p> <\/p>\n<h4 class=\"app-article-masthead--book__info--book-title\" data-test=\"book-title\"><a href=\"https:\/\/link.springer.com\/book\/10.1007\/978-3-031-80087-0\" target=\"_blank\" rel=\"noopener\">Building Personality-Driven Language Models<\/a><\/h4>\n","email":"","social_media":[{"icon":{"ID":11992,"id":11992,"title":"orcid","filename":"orcid.svg","filesize":15590,"url":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/orcid.svg","link":"https:\/\/sano.science\/people\/maciej-malawski\/orcid\/","alt":"","author":"5","description":"","caption":"","name":"orcid","status":"inherit","uploaded_to":531,"date":"2023-07-06 11:21:29","modified":"2023-07-06 11:21:29","menu_order":0,"mime_type":"image\/svg+xml","type":"image","subtype":"svg+xml","icon":"https:\/\/sano.science\/wp-includes\/images\/media\/default.png","width":1,"height":1,"sizes":{"thumbnail":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/orcid.svg","thumbnail-width":150,"thumbnail-height":150,"medium":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/orcid.svg","medium-width":300,"medium-height":300,"medium_large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/orcid.svg","medium_large-width":768,"medium_large-height":1,"large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/orcid.svg","large-width":1024,"large-height":1024,"1536x1536":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/orcid.svg","1536x1536-width":1,"1536x1536-height":1,"2048x2048":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/orcid.svg","2048x2048-width":1,"2048x2048-height":1}},"link":"https:\/\/orcid.org\/0000-0002-2992-718X","name":"ORCID"},{"icon":{"ID":11986,"id":11986,"title":"google","filename":"google.svg","filesize":14070,"url":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/google.svg","link":"https:\/\/sano.science\/people\/maciej-malawski\/google\/","alt":"","author":"5","description":"","caption":"","name":"google","status":"inherit","uploaded_to":531,"date":"2023-07-06 10:59:21","modified":"2023-07-06 10:59:21","menu_order":0,"mime_type":"image\/svg+xml","type":"image","subtype":"svg+xml","icon":"https:\/\/sano.science\/wp-includes\/images\/media\/default.png","width":1,"height":1,"sizes":{"thumbnail":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/google.svg","thumbnail-width":150,"thumbnail-height":150,"medium":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/google.svg","medium-width":300,"medium-height":300,"medium_large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/google.svg","medium_large-width":768,"medium_large-height":1,"large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/google.svg","large-width":1024,"large-height":1024,"1536x1536":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/google.svg","1536x1536-width":1,"1536x1536-height":1,"2048x2048":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/google.svg","2048x2048-width":1,"2048x2048-height":1}},"link":"https:\/\/scholar.google.pl\/citations?user=4-KaZPsAAAAJ&hl=pl","name":"GOOGLE SCHOLAR"}],"tabs":false,"quote":"","position_with_team":{"text_before_link":"Research Team Leader of ","link_text":"Computational Neuroscience","text_after_link":"","link":"https:\/\/sano.science\/research-teams\/computational-neuroscience\/"},"publications":[{"ID":30042,"post_author":"8","post_date":"2026-03-23 16:17:49","post_date_gmt":"2026-03-23 15:17:49","post_content":"<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-Cin69o\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">A new paper by members of the Computational Neuroscience team at Sano, published in<br>Neuro-Oncology, introduces a novel neuroimaging biomarker for glioblastoma (GBM) called<br>the Lesion-Tract Density Index (L-TDI). Moving beyond viewing GBM as a focal lesion, this<br>study treats it as a network disease that interacts with the brain\u2019s white matter scaffold. By<br>analyzing large-scale white matter pathways in two independent patient cohorts, the<br>researchers found that L-TDI robustly stratifies survival rates and predicts outcomes more<br>accurately than traditional measures like tumor volume. This work marks a significant step<br>toward connectomics-guided neuro-oncology and improved individualized patient care.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-IwNK8h\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:image {\"id\":30035,\"sizeSlug\":\"large\",\"linkDestination\":\"none\",\"epAnimationGeneratedClass\":\"edplus_anim-U0lq6Y\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<figure class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2026\/03\/A-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma-1024x821.jpeg\" alt=\"The lesion-tract density index (L\u2009-TDI): a survival marker in human GBM. (A) For every subject, the data were normalized to a common template. The T2-weighted image was chosen as the reference modality, and the GBM was inversely masked to discard distorted tissue during the optimization. (B) The normalized tumor mask is used in combination with the whole brain normative tractogram to extract the set of streamlines that intersect the tumor in the same template space as in (A). (C) The lesion-tract density map (L\u2009-TDM) provides a unique white matter density map describing the tracts in (B) that interact with the GBM. (D) From the L\u2009-TDM, we can define the L\u2009-TDI marker by averaging the tract density within the L\u2009-TDM binary mask in (C). For a given cohort, the sample can be stratified according to a given percentile. (E) Once a stratification threshold has been set, we conduct thorough tests to assess the discovery of the biomarker. Additional survival analyses on multiple stratification thresholds help determine whether the potential biomarker holds prognostic value. The L\u2009-TDI can also be used to characterize both morphological and anatomical landscapes in GBMs, which can be used to understand differences in the survival rates. Once a robust effect has been identified, the L\u2009-TDI can be incorporated into standard risk analysis and predictive frameworks to improve patient care.\" class=\"wp-image-30035\"\/><figcaption class=\"wp-element-caption\">Image source: Figure 1 from \u201cA non-local diffusion magnetic resonance imaging tract density index to capture network-level tumor\u2013brain interactions\u201d, Neuro-Oncology, Oxford University Press <a href=\"https:\/\/academic.oup.com\/view-large\/figure\/556903333\/noaf234f1.tif\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">academic.oup.com\/view-large\/figure\/556903333\/noaf234f1.tif<\/a><\/figcaption><\/figure>\n<!-- \/wp:image -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-Cin69o\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><br>See the full text:<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-SkBTix\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_69c1598464b0b\",\"name\":\"acf\/button\",\"data\":{\"title\":\"Read the article\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/academic.oup.com\/neuro-oncology\/article\/28\/2\/564\/8287534\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-SkBTix\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-Plfl6U\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Autors: <a href=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/Sano-Joan-Falco-Roget.png\" type=\"attachment\" id=\"18504\">Joan Falc\u00f3-Roget<\/a>, Gianpaolo Antonio Basile, <a href=\"https:\/\/sano.science\/people\/anna-janus\/\" type=\"people\" id=\"22468\">Anna Janus<\/a>, Sara Lillo, Letterio S Politi, <a href=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/08\/Jan_Argasinski.webp\" type=\"attachment\" id=\"21360\">Jan K Argasinski<\/a>, Alberto Cacciola<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-l9wZem\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Keywords: glioblastoma, brain connectome, diffusion tractography, glioblastoma, survival prediction<\/p>\n<!-- \/wp:paragraph -->","post_title":"A non-local diffusion magnetic resonance imaging tract density biomarker to stratify, predict, and interpret survival rates in human glioblastoma","post_excerpt":"Published in Neuro-Oncology Journals, 2026","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"a-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma","to_ping":"","pinged":"","post_modified":"2026-03-24 11:34:47","post_modified_gmt":"2026-03-24 10:34:47","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=30042","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":25669,"post_author":"8","post_date":"2025-08-18 13:37:45","post_date_gmt":"2025-08-18 11:37:45","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-fpdlUo\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-karol-nbsp-przystalski-nbsp-jan-k-nbsp-argasinski-nbsp-iwona-nbsp-grabska-gradzinska-nbsp-jeremi-k-nbsp-ochab\">Karol Przystalski, Jan K. Argasi\u0144ski, Iwona Grabska-Gradzi\u0144ska, Jeremi K. Ochab<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-keL0bT\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-sG0pIN\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">A recent study co-authored by <a href=\"https:\/\/sano.science\/people\/jan-argasinski\/\">Jan K. Argasi\u0144ski <\/a>from the Computational Neuroscience group at the Sano Centre for Computational Medicine investigates the potential of stylometry \u2014 a technique traditionally applied in authorship attribution \u2014 to differentiate between human-written content and texts produced by Large Language Models (LLMs).<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-dj0jA9\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">The researchers developed a benchmark dataset sourced from Wikipedia, featuring:<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:list 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-->\n<ul class=\"wp-block-list eplus-wrapper\"><!-- wp:list-item {\"epAnimationGeneratedClass\":\"edplus_anim-lofqvS\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<li class=\" eplus-wrapper\">Summaries authored by humans<\/li>\n<!-- \/wp:list-item -->\n\n<!-- wp:list-item {\"epAnimationGeneratedClass\":\"edplus_anim-5lm2GJ\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<li class=\" eplus-wrapper\">Summaries generated by various LLMs (GPT-3.5\/4, LLaMa 2\/3, Orca, Falcon)<\/li>\n<!-- \/wp:list-item -->\n\n<!-- wp:list-item {\"epAnimationGeneratedClass\":\"edplus_anim-k7Ifax\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<li class=\" eplus-wrapper\">Summaries altered by automatic summarisation tools (T5, BART, Gensim, Sumy)<\/li>\n<!-- \/wp:list-item -->\n\n<!-- wp:list-item {\"epAnimationGeneratedClass\":\"edplus_anim-bmjz9I\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<li class=\" eplus-wrapper\">Texts reworded using paraphrasing systems (Dipper, T5)<\/li>\n<!-- \/wp:list-item --><\/ul>\n<!-- \/wp:list -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-pI3S0u\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">By applying decision trees and LightGBM classifiers, and incorporating both manually crafted features (via the StyloMetrix toolkit) and n-gram-based stylometric indicators, the models achieved notable results:<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:list {\"epStylingOptions\":{\"columnsResponsiveEnabled\":false,\"columnsHoverEnabled\":false,\"itemsSpacingResponsiveEnabled\":false,\"itemsSpacingHoverEnabled\":false,\"listStyleResponsiveEnabled\":false,\"listStyleHoverEnabled\":false,\"listIconResponsiveEnabled\":false,\"listIconHoverEnabled\":false,\"columns\":{\"target\":\"\",\"responsive\":true,\"hover\":true,\"options\":[{\"custom\":true,\"control\":\"ToggleOptions\"},{\"label\":\"Columns\",\"control\":\"Range\",\"attribute\":\"columns\",\"css\":\"grid-template-columns\",\"customValue\":\"repeat({{value}}, 1fr)\",\"props\":{\"max\":5,\"min\":1,\"supportedUnits\":[]},\"defaults\":{\"desktop\":\"1\"},\"show_if\":{\"className\":[\"ep-custom-list\"]}}]},\"itemsSpacing\":{\"target\":\"\",\"responsive\":true,\"hover\":true,\"options\":[{\"label\":\"Items Spacing\",\"control\":\"Range\",\"attribute\":\"itemsSpacing\",\"css\":\"grid-gap\",\"props\":{\"max\":100,\"min\":0,\"supportedUnits\":[\"px\"]},\"show_if\":{\"className\":[\"ep-custom-list\"]}}]},\"listStyle\":{\"target\":\"\",\"responsive\":true,\"hover\":true,\"options\":[{\"custom\":true,\"control\":\"IconClassToggle\",\"condition\":{\"relation\":\"AND\",\"query\":[{\"field\":\"attributes.className\",\"compare\":\"IN\",\"value\":\"ep-custom-list\"}]}},{\"label\":\"Choose List 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78.604-35.542 111.2z'\/\\u003e\\u003c\/svg\\u003e\",\"value\":\"none\"},{\"customIcon\":\"\\u003csvg enable-background='new 0 0 29.107 29.107' version='1.1' viewBox='0 0 29.107 29.107' xml:space='preserve' xmlns='http:\/\/www.w3.org\/2000\/svg'\\u003e\\u003cpath d='M14.554,0C6.561,0,0,6.562,0,14.552c0,7.996,6.561,14.555,14.554,14.555c7.996,0,14.553-6.559,14.553-14.555 C29.106,6.562,22.55,0,14.554,0z'\/\\u003e\\u003c\/svg\\u003e\",\"value\":\"disc\"},{\"customIcon\":\"\\u003csvg enable-background='new 0 0 341.333 341.333' version='1.1' viewBox='0 0 341.33 341.33' xml:space='preserve' xmlns='http:\/\/www.w3.org\/2000\/svg'\\u003e\\u003cpath d='m170.67 0c-94.257 0-170.67 76.41-170.67 170.67s76.41 170.67 170.67 170.67 170.67-76.41 170.67-170.67-76.411-170.67-170.67-170.67zm0 298.67c-70.692 0-128-57.308-128-128s57.308-128 128-128 128 57.308 128 128-57.308 128-128 128z'\/\\u003e\\u003c\/svg\\u003e\",\"value\":\"circle\"},{\"customIcon\":\"\\u003csvg enable-background='new 0 0 60 60' 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0-192-86.135-192-192 0-40.406 12.25-78.604 35.542-111.2l267.66 267.66c-32.594 23.292-70.792 35.542-111.2 35.542zm156.46-80.802-267.66-267.66c32.594-23.292 70.792-35.542 111.2-35.542 105.86 0 192 86.135 192 192 0 40.406-12.25 78.604-35.542 111.2z'\/\\u003e\\u003c\/svg\\u003e\",\"value\":\"none\"},{\"customIcon\":\"\\u003csvg id='Capa_1' enable-background='new 0 0 512 512' viewBox='0 0 512 512' xmlns='http:\/\/www.w3.org\/2000\/svg'\\u003e\\u003cg id='_x31__1_'\\u003e\\u003cpath d='m376 512v-512h-99.902l-3.56 9.932c-9.419 26.294-27.202 49.746-52.837 69.697-26.528 20.684-51.211 34.849-73.359 42.1l-10.342 3.383v114.771l19.702-6.519c36.387-12.012 69.448-29.268 98.672-51.445v330.081z'\/\\u003e\\u003c\/g\\u003e\\u003c\/svg\\u003e\",\"value\":\"decimal\"},{\"customIcon\":\"\u2160\",\"value\":\"upper-roman\"},{\"customIcon\":\"\u2170\",\"value\":\"lower-roman\"}]}}]},\"listIcon\":{\"target\":\"li:before\",\"includeDotSuffix\":false,\"responsive\":true,\"hover\":true,\"hoverTarget\":\"{{class}} 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-->\n<ul class=\"wp-block-list eplus-wrapper\"><!-- wp:list-item {\"epAnimationGeneratedClass\":\"edplus_anim-Xp75O2\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<li class=\" eplus-wrapper\">Up to 0.87 Matthews correlation coefficient (MCC) in a multi-class (7-way) classification task<\/li>\n<!-- \/wp:list-item -->\n\n<!-- wp:list-item {\"epAnimationGeneratedClass\":\"edplus_anim-RwUbiW\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<li class=\" eplus-wrapper\">Between 0.79 and 1.0 accuracy in binary classification, with detection of GPT-4 texts reaching 98% accuracy on balanced datasets<\/li>\n<!-- \/wp:list-item --><\/ul>\n<!-- \/wp:list -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-sG0pIN\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Interpretability analysis showed that LLM-generated texts tend to be more grammatically uniform and exhibit distinctive lexical patterns compared to human writing. These findings demonstrate that, despite the growing sophistication of AI-generated text, distinguishing between machine and human authorship remains feasible \u2014 especially for structured formats like encyclopaedia entries.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-3CwJPj\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:image {\"id\":25671,\"sizeSlug\":\"large\",\"linkDestination\":\"none\",\"epAnimationGeneratedClass\":\"edplus_anim-D7P1CU\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<figure class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2025\/08\/Fig_1-1024x260.jpg\" alt=\"\" class=\"wp-image-25671\"\/><\/figure>\n<!-- \/wp:image -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-FrkgCl\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><em>Fig. 1. Explanations for binary classification between the Wikipedia and GPT-4.<\/em><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-FrkgCl\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Source: <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417425026181#fig0002\" target=\"_blank\" rel=\"noreferrer noopener\">www.sciencedirect.com\/science\/article\/pii\/S0957417425026181#fig0002<\/a><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-PeMgAz\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_68a43236c6f42\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417425026181?via%3Dihub\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-PeMgAz\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-GAnn1Y\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Stylometry recognizes human and LLM-generated texts in short samples<\/strong><br><strong>Authors<\/strong>: Karol Przystalski, Jan K. Argasi\u0144ski, Iwona Grabska-Gradzi\u0144ska, Jeremi K. Ochab<br><strong>DOI<\/strong>: <a href=\"https:\/\/doi.org\/10.1016\/j.eswa.2025.129001\" target=\"_blank\" rel=\"noreferrer noopener\">10.1016\/j.eswa.2025.129001<\/a><\/p>\n<!-- \/wp:paragraph -->","post_title":"Stylometry recognizes human and LLM-generated texts in short samples","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"stylometry-recognizes-human-and-llm-generated-texts-in-short-samples","to_ping":"","pinged":"","post_modified":"2025-08-19 10:22:24","post_modified_gmt":"2025-08-19 08:22:24","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=25669","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":14958,"post_author":"8","post_date":"2024-01-16 13:49:12","post_date_gmt":"2024-01-16 12:49:12","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-5dqj2O\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Jan K. Argasi\u0144ski, Natalia Lipp, Szymon Mazurek<\/h2>\n<!-- \/wp:heading -->","post_title":"Electroencephalographic (EEG) correlates of Visually Induced Motion Sickness (VIMS) in the Virtual Reality (VR) based simulations","post_excerpt":"In: INTERACT 2023 (19th International Conference of Technical Committee 13 (Human- Computer Interaction) of IFIP (International Federation for Information Processing, 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"electroencephalographic-eeg-correlates-of-visually-induced-motion-sickness-vims-in-the-virtual-reality-vr-based-simulations","to_ping":"","pinged":"","post_modified":"2026-01-31 19:23:11","post_modified_gmt":"2026-01-31 18:23:11","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=14958","menu_order":15,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":15030,"post_author":"5","post_date":"2024-01-18 10:17:58","post_date_gmt":"2024-01-18 09:17:58","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-szoDDG\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Natalia Lipp, Pawe\u0142 Strojny, Agnieszka Strojny, S\u0142awomir \u015apiewak, Jan Argasi\u0144ski, Przemys\u0142aw Korzeniowski<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-pOH2qF\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-UpwHJ3\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Simulation realism is a crucial factor for task performance in virtual reality. The issue is that it does not simply result from a simulation's graphical quality and is often connected to users' expectations and the capacity of the cognitive system. It is assumed that perceived realism may be affected by physiological arousal which is the basis of emotional reactions and of attention prioritizing. The main aim of the presented study is to verify whether perceptual characteristics of a virtual scene \u2013 complexity and vividness of virtual objects \u2013 affect perceived realism. The secondary aim is to test whether realism assessment changes because of arousal. An experimental study was conducted with 100 participants in total. Four experimental groups differ in terms of the complexity of a virtual scene (i.e., number of objects in the field of view) and vividness of virtual objects (i.e., figure-ground contrast). Participants were asked to perform a task, that was taking on the role of a marshaller and positioning the plane on the airport apron in the virtual reality simulation. During the task, electrodermal activity was measured. After the virtual session, participants assessed perceived realism. Results indicate that the complexity and vividness of virtual scene do not affect perceived realism directly. Physiological arousal (i.e., skin conductance level) is a moderator of the relationship between the vividness of the virtual scene and perceived realism. A high level of arousal increases realism assessment in vivid simulations.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-pOH2qF\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_65a8ecb3de7de\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/ieeexplore.ieee.org\/document\/10322268\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Are You Aroused Enough to See the Difference? The Role of Physiological Arousal in Perceiving Realism of Virtual Scene","post_excerpt":"In: IEEE ISMAR, 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"are-you-aroused-enough-to-see-the-difference-the-role-of-physiological-arousal-in-perceiving-realism-of-virtual-scene","to_ping":"","pinged":"","post_modified":"2024-01-18 10:17:58","post_modified_gmt":"2024-01-18 09:17:58","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=15030","menu_order":8,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":15080,"post_author":"5","post_date":"2024-01-18 20:46:29","post_date_gmt":"2024-01-18 19:46:29","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-v86eth\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Natalia Lipp, Pawe\u0142 Strojny, Agnieszka Strojny, S\u0142awomir \u015apiewak, Jan Argasi\u0144ski, Przemys\u0142aw Korzeniowski<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-mw314Z\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-Wmf9PZ\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">The main aims of the presented study are to verify whether the amount of textures in a virtual scene affects task performance and to test whether visual imagery changes the relationship between realism and task performance. An experimental study with three groups differed in visual realism was conducted (n=100). Participants were asked to perform a task: taking on the role of a marshaller and positioning the plane on the airport apron. Results indicate that texturing does not affect task performance. Visual imagery is a moderator of the relationship between perceived realism and task performance. A high level of imagery interferes with a high realism assessment decreasing task performance.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-mw314Z\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_65a97ff331c86\",\"name\":\"acf\/button\",\"data\":{\"title\":\"\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Performing Tasks in Virtual Reality. Interplay between Realism and Visual Imagery","post_excerpt":"In: ACM VRST (The 29th ACM Symposium on Virtual Reality Software and Technology), 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"performing-tasks-in-virtual-reality-interplay-between-realism-and-visual-imagery","to_ping":"","pinged":"","post_modified":"2024-01-18 20:46:29","post_modified_gmt":"2024-01-18 19:46:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=15080","menu_order":11,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":17163,"post_author":"8","post_date":"2024-06-12 18:14:22","post_date_gmt":"2024-06-12 16:14:22","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-tNFDKI\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Authors: Agus Hartoyo, Jan K. Argasi\u0144ski, Aleksandra Trenk, Kinga Przybylska, Anna B\u0142asiak, Alessandro Crimi<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"20px\",\"epAnimationGeneratedClass\":\"edplus_anim-5WDO6d\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-Vsv85h\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Covariance and Hessian matrices have been analyzed separately in the literature for classification problems. However, integrating these matrices has the potential to enhance their combined power in improving classification performance. We present a novel approach that combines the eigenanalysis of a covariance matrix evaluated on a training set with a Hessian matrix evaluated on a deep learning model to achieve optimal class separability in binary classification tasks. Our approach is substantiated by formal proofs that establish its capability to maximize between-class mean distance and minimize within-class variances. By projecting data into the combined space of the most relevant eigendirections from both matrices, we achieve optimal class separability as per the linear discriminant analysis (LDA) criteria. Empirical validation across neural and health datasets consistently supports our theoretical framework and demonstrates that our method outperforms established methods. Our method stands out by addressing both LDA criteria, unlike PCA and the Hessian method, which predominantly emphasize one criterion each. This comprehensive approach captures intricate patterns and relationships, enhancing classification performance. Furthermore, through the utilization of both LDA criteria, our method outperforms LDA itself by leveraging higher-dimensional feature spaces, in accordance with Cover's theorem, which favors linear separability in higher dimensions. Our method also surpasses kernel-based methods and manifold learning techniques in performance. Additionally, our approach sheds light on complex DNN decision-making, rendering them comprehensible within a 2D space.<\/p>\n<!-- \/wp:paragraph -->","post_title":"Synergistic eigenanalysis of covariance and Hessian matrices for enhanced binary classification on health datasets","post_excerpt":"In: https:\/\/arxiv.org\/abs\/2402.09281, 2024","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"synergistic-eigenanalysis-of-covariance-and-hessian-matrices-for-enhanced-binary-classification-on-health-datasets","to_ping":"","pinged":"","post_modified":"2024-09-23 12:57:38","post_modified_gmt":"2024-09-23 10:57:38","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=17163","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":19058,"post_author":"5","post_date":"2024-10-03 13:32:55","post_date_gmt":"2024-10-03 11:32:55","post_content":"<!-- wp:heading {\"level\":3,\"epAnimationGeneratedClass\":\"edplus_anim-FyogrK\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h3 class=\"wp-block-heading eplus-wrapper\" id=\"h-jan-k-nbsp-argasinski-nbsp-katarzyna-nbsp-baliga-nicholson-nbsp-anna-nbsp-partyka-nbsp-kamil-nbsp-pilch-nbsp-joan-nbsp-falco-roget-nbsp\">Jan K. Argasinski, Katarzyna Baliga-Nicholson, Anna Partyka, Kamil Pilch, Joan Falco-Roget <\/h3>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-xgdCqo\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-bxXdXJ\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Departing from the confines of conventional linear podcasts, our groundbreaking method introduces a dynamic, interactive framework designed to enrich user involvement and deepen educational insights. Envision a podcast that allows you to navigate through a network of video clips, each seamlessly connected to pertinent scientific publications, interactive charts, and learning materials. This format fosters a customized, investigative experience in computational neuroscience, enhancing both comprehension and intrigue. Utilizing principles from game design and dynamic hyperlinking, our goal is to construct an engaging, immersive educational atmosphere that captivates and educates listeners.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-xgdCqo\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-bxXdXJ\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Authors<\/strong>: Jan K. Argasinski, Katarzyna Baliga-Nicholson, Anna Partyka, Kamil Pilch, Joan Falco-Roget <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-9XJw5w\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: <a href=\"https:\/\/dl.acm.org\/doi\/10.1145\/3648188.3678217\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">https:\/\/dl.acm.org\/doi\/10.1145\/3648188.3678217<\/a>\u00a0<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-oizXwr\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Link to article<\/strong>: <a href=\"https:\/\/dl.acm.org\/doi\/10.1145\/3648188.3678217\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">https:\/\/dl.acm.org\/doi\/10.1145\/3648188.3678217<\/a><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-7myS8u\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Link to article<\/strong>: <a href=\"https:\/\/www.growkudos.com\/publications\/10.1145%25252F3648188.3678217\/reader\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">https:\/\/www.growkudos.com\/publications\/10.1145%25252F3648188.3678217\/reader<\/a><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-suS7dT\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: scientific podcasts<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-xgdCqo\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-OnUHbe\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><\/p>\n<!-- \/wp:paragraph -->","post_title":"Disrupting scientific podcasts. 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