Data4UrbanMobility is going to be presented at the “Dienstleistungstagung 2019 Service Systems Innovation” in Paderborn. The event takes place at April 8th and 9th 2019. Data4UrbanMobiltiy can be found  in Area 4”: “Individuell und Urban”.

• Grammatical Error Detection on Spontaneous Children’s Speech Using Iterative Pseudo Labeling. Gebauer, Lars Rumberg Christopher (2025).
• Quo Vadis CKKS: Comparison of the Realization of Basic Mathematical Functions for the Homomorphic Cryptosystem CKKS using De Bello and Polynomial Approximations. Prantl, Thomas; Horn, Lukas; Engel, Simon; Bauer, André; Kounev, Samuel (2025). 93
• Quo Vadis CKKS: Comparison of the Realization of Basic Mathematical Functions for the Homomorphic Cryptosystem CKKS using De Bello and Polynomial Approximations. Prantl, Thomas; Horn, Lukas; Engel, Simon; Bauer, André; Kounev, Samuel (2025). 93
• Quo Vadis CKKS: Comparison of the Realization of Basic Mathematical Functions for the Homomorphic Cryptosystem CKKS using De Bello and Polynomial Approximations. Prantl, Thomas; Horn, Lukas; Engel, Simon; Bauer, André; Kounev, Samuel (2025). 93
• Oratio Homomorphico: Creating a Lecture for Homomorphic Encryption by applying Design-Based Research. Prantl, Thomas; Schneider, Tobias; Horn, Lukas; Engel, Simon; Iffländer, Lukas; Krupitzer, Christian; Bonilla, Rafael; Kounev, Samuel in EAI CSECS 2025 (2025).
• Privacy-Preserving Training and Inference of CNNs: A Medical Performance Case Study. Prantl, Thomas; Schneider, Louis; Engel, Simon; Horn, Lukas; Borst, Vanessa; Krupitzer, Christian; Kounev, Samuel; Bonilla, Rafael in ICSC2025 (2025).
• Rule-Based Grammatical Error Detection on Spontaneous Children’s Speech. Gebauer, Lars Rumberg Christopher (2025). 117–124.
• Oratio Homomorphico: Creating a Lecture for Homomorphic Encryption by applying Design-Based Research. Prantl, Thomas; Schneider, Tobias; Horn, Lukas; Engel, Simon; Iffländer, Lukas; Krupitzer, Christian; Bonilla, Rafael; Kounev, Samuel in EAI CSECS 2025 (2025).
• Eliminating Majority Illusion. Fioravantes, Foivos; Lahiri, Abhiruk; Lauerbach, Antonio; Sabater, Llu’is; Sieper, Marie Diana; Wolf, Samuel in AAMAS ’25 (2025). 749–757.
• Privacy-Preserving Training and Inference of CNNs: A Medical Performance Case Study. Prantl, Thomas; Schneider, Louis; Engel, Simon; Horn, Lukas; Borst, Vanessa; Krupitzer, Christian; Kounev, Samuel; Bonilla, Rafael in ICSC2025 (2025).
• Last fifty years of integer linear programming: A focus on recent practical advances. Clautiaux, François; Ljubić, Ivana (2025). 324(3) 707–731.

  Mixed-integer linear programming (MILP) has become a cornerstone of operations research. This is driven by the enhanced efficiency of modern solvers, which can today find globally optimal solutions within seconds for problems that were out of reach a decade ago. The versatility of these solvers allowed successful applications in many areas, such as transportation, logistics, supply chain management, revenue management, finance, telecommunications, and manufacturing. Despite the impressive success already obtained, many challenges remain, and MILP is still a very active field. This article provides an overview of the most significant results achieved in advancing the MILP solution methods. Given the immense literature on this topic, we made deliberate choices to focus on computational aspects and recent practical performance improvements, emphasizing research that reports computational experiments. We organize our survey into three main parts, dedicated to branch-and-cut methods, Dantzig-Wolfe decomposition, and Benders decomposition. The paper concludes by highlighting ongoing challenges and future opportunities in MILP research.
• Exact predicates, exact constructions and combinatorics for mesh CSG. Levy, Bruno (2025).

  This article introduces a general mesh intersection algorithm that exactly computes the so-called Weiler model (also called a 3D arrangement) and that uses it to implement boolean operations with arbitrary multi-operand expressions, CSG (constructive solid geometry) and some mesh repair operations. From an input polygon soup, the algorithm first computes the co-refinement, with an exact representation of the intersection points. Then, the decomposition of 3D space into volumetric regions (Weiler model) is constructed, by sorting the facets around the non-manifold intersection edges (radial sort), using specialized exact predicates. Finally, based on the input boolean expression, the triangular facets that belong to the boundary of the result are classified. The main contribution is a 2D Constrained Delaunay Triangulation with exact coordinates that represent the intersections, thanks to two geometric kernels that are proposed, tested and discussed (arithmetic expansions and multi-precision floating-point). As a guiding principle, the combinatorial information shared between each step is kept as simple as possible. It is made possible by treating all the particular cases in the kernel. In particular, triangles with intersections are remeshed using the (uniquely defined) Constrained Delaunay Triangulation, with symbolic perturbations to disambiguate configurations with co-cyclic points. It makes it easy to discard the duplicated triangles that appear when remeshing overlapping facets. The method is tested and compared with previous work, on the existing “thingi10K” dataset (to test co-refinement and mesh repair) and on a new “thingiCSG” dataset made publicly available (to test the full CSG pipeline) on a variety of interesting examples featuring different types of “pathologies”.
• Momentum microscopy with combined hemispherical and time-of-flight electron analyzers at the soft X-ray beamline I09 of the diamond light source. Schmitt, Matthias; Biswas, Deepnarayan; Tkach, Olena; Fedchenko, Olena; Liu, Jieyi; Elmers, Hans-Joachim; Sing, Michael; Claessen, Ralph; Lee, Tien-Lin; Schönhense, Gerd (2025). 276 114169.

  The three-dimensional recording scheme of time-of-flight momentum microscopes (ToF-MMs) is advantageous for fast mapping of the photoelectron distribution in (E,k) parameter space over the entire Brillouin zone. However, the 2 ns pulse period of most synchrotrons is too short for pure ToF photoelectron spectroscopy. The use of a hemispherical analyzer (HSA) as a pre-filter allows ToF-MM at such high pulse rates. The first HSA & ToF hybrid MM is operated at the soft X-ray branch of beamline I09 at the Diamond Light Source, UK. The photon energy ranges from 105 eV to 2 keV, with circular polarization available for hν ≥ 145 eV. The HSA reduces the transmitted energy band to typically 0.5 eV, which is then further analyzed by ToF recording. In initial experiments, the overall efficiency gain when switching from the standard 2D (kx,ky) mode to the 3D (kx,ky,Ekin) hybrid mode was about 24. This value is determined by the number of resolved kinetic energies (here 12) and the transmission gain of the electron optics due to the high pass energy of the HSA in hybrid mode (Epass up to 500 eV). The transmission gain depends on the size of the photon footprint on the sample. Under k-imaging conditions, the energy and momentum resolution are 10.2 meV (FWHM) (4.2 meV with 200 μm slits and Epass = 8 eV) and 0.010 Å-1. The energy filtered X-PEEM mode showed a spatial resolution of 250 nm. As examples, we show 2D band mapping of bilayer graphene, 3D mapping of the Fermi surface of Cu, circular dichroic ARPES for intercalated indenene layers, and the sp valence band of Au. Full-field photoelectron diffraction patterns of Ge show rich structure in k-field diameters of up to 6 Å-1.
• Bismuthene under cover: Graphene intercalation of a large gap quantum spin Hall insulator. Gehrig, Lukas; Schmitt, Cedric; Erhardt, Jonas; Liu, Bing; Wagner, Tim; Kamp, Martin; Moser, Simon; Claessen, Ralph (2025). n/a(n/a) 2502412.

  The quantum spin Hall insulator bismuthene, a two-third monolayer of bismuth on SiC(0001), is distinguished by helical metallic edge states that are protected by a groundbreaking 800 meV topological gap, making it ideal for room temperature applications. This massive gap inversion arises from a unique synergy between flat honeycomb structure, strong spin orbit coupling, and an orbital filtering effect that is mediated by the substrate. However, the rapid oxidation of bismuthene in air has severely hindered the development of applications, so far confining experiments to ultra-high vacuum conditions. Here, we successfully overcome this barrier, intercalating bismuthene between SiC and a protective sheet of graphene. As we demonstrate through scanning tunneling microscopy and photoemission spectroscopy, graphene intercalation preserves the structural and topological integrity of bismuthene, while effectively shielding it from oxidation in air. We identify hydrogen as the critical component that was missing in previous bismuth intercalation attempts. Our findings facilitate ex-situ experiments and pave the way for the development of bismuthene based devices, signaling a significant step forward in the development of next-generation technologies.
• A Spatial Decision Support System for climate-adapted agriculture designed with and for stakeholders in West Africa. Ziegler, Katrin; Abel, Daniel; König, Lorenz; Weber, Torsten; Otte, Insa; Teucher, Mike; Conrad, Christopher; Thiel, Michael; Gbode, Imoleayo Ezekiel; Ajayi, Vincent Olanrewaju; Coulibaly, Amadou; Traoré, Seydou B.; Zoungrana, Benewinde Jean-Bosco; Paeth, Heiko (2025). 39 16.

  This paper presents a Spatial Decision Support System (SDSS) designed to assist stakeholders in West Africa in analysing critical climate and land use indicators for risk management in agriculture and further sectors being affected by extreme precipitation and temperature events. Developed as part of the WASCAL WRAP 2.0 project LANDSURF, the SDSS makes scientific data accessible and comprehensible to non-scientific audiences, facilitating informed decision-making among communities affected by climate change. From the beginning of the development process, the web portal was co-designed with relevant West African stakeholders. Due to the challenging conditions during the COVID-19 pandemic, alternative online communication tools, e.g. ZOOM, online surveys and email, successfully were utilized to interact with stakeholders instead of on-site activities. The co-design process carried out with stakeholders includes several steps such as stakeholder analysis, identification of their information needs using specific climate, crop and remote sensing indicators, and the evaluation of the SDSS in a dedicated workshop. In total, the co-design process involved nine different steps, recorded and described in a stakeholder interaction protocol. The SDSS integrates observational data, including CHIRPS and ERA5-Land datasets, and state-of-the-art high- resolution climate model outputs under two greenhouse gas concentration scenarios (RCP2.6 and RCP8.5) and remote sensing data. It enables the comparison of model outputs with observations and facilitates the assessment of regional climate variability and trends. Two concept studies illustrate the SDSS’s functionality: one focusing on a farmer in Burkina Faso assessing irrigation needs for millet cultivation, and another involving a regional planner analysing drought and heat wave impacts in coastal West Africa. These examples highlight the SDSS’s usability in supporting adaptive strategies and enhancing resilience to climate-related challenges, underscoring the importance of integrating local knowledge with scientific data for effective climate adaptation and mitigation.
• Die Unverzichtbaren: Menschen in Basisarbeit. Erkenntnisse für eine politische Auseinandersetzung mit ihren Perspektiven und Forderungen in der Transformationsgesellschaft Siebert, Johanna; Buchstab, Mara (2025).

  Sie reinigen Büros, liefern Essen aus, verbauen Autoteile oder räumen Supermarktregale ein. Menschen, deren berufliche Tätigkeiten keine formale Qualifikation erfordern, bilden mit ihrer Arbeit die Basis unserer Gesellschaft. Gleichzeitig werden diese Beschäftigten in un- und angelernten Berufen (Basisarbeiter:innen) in zentralen Reformdebatten weitestgehend übersehen. Unsere Studie möchte das ändern. Sie unternimmt den Versuch einer Vermessung von Basisarbeit im Vergleich zu qualifizierten Tätigkeiten und geht dabei der Frage nach: Was sind die Arbeits- und Lebensrealitäten von Basisarbeiter:innen und wie blicken sie auf ihre eigene Tätigkeit und die Gesellschaft? Angesichts einer sich wandelnden Arbeitswelt, angetrieben von der ökologischen und digitalen Transformation, nimmt die Studie darüber hinaus die demokratiepolitische Relevanz von Arbeit unter die Lupe. Sie fragt: Wie wirken sich unsichere Arbeitsbedingungen in der Basisarbeit, aber auch auf dem gesamten Arbeitsmarkt, auf das Vertrauen in die Demokratie und die Offenheit gegenüber Transformationsprozessen aus? Zur Beantwortung dieser Fragen haben wir ein mehrdimensionales Forschungsdesign, bestehend aus Einzelinterviews, Fokusgruppen und einer quantitativen Meinungsumfrage, gewählt. Die Ergebnisse zeigen: Basisarbeiter:innen sind in ihrer Position auf dem Arbeitsmarkt benachteiligt. Im Vergleich zu Menschen in qualifizierten Berufen verfügen sie über weniger formelle Anpassungs- und Gestaltungsressourcen, um sich innerhalb einer sich wandelnden Arbeitswelt zurechtzufinden. Gleichzeitig finden ihre Anliegen in Politik und Gesellschaft seltener Gehör. Entsprechend gering ist das Vertrauen in politische Akteure und demokratische Institutionen. Für unsere Demokratie sind Basisarbeiter:innen jedoch in dreierlei Hinsicht unverzichtbar: gesellschaftlich, wirtschaftlich und politisch.
• Oratio Homomorphico: Creating a Lecture for Homomorphic Encryption by applying Design-Based Research. Prantl, Thomas; Schneider, Tobias; Horn, Lukas; Engel, Simon; Iffländer, Lukas; Krupitzer, Christian; Bonilla, Rafael; Kounev, Samuel in EAI CSECS 2025 (2025).
• Privacy-Preserving Training and Inference of CNNs: A Medical Performance Case Study. Prantl, Thomas; Schneider, Louis; Engel, Simon; Horn, Lukas; Borst, Vanessa; Krupitzer, Christian; Kounev, Samuel; Bonilla, Rafael in ICSC2025 (2025).
• Quo Vadis CKKS: Comparison of the Realization of Basic Mathematical Functions for the Homomorphic Cryptosystem CKKS using De Bello and Polynomial Approximations. Prantl, Thomas; Horn, Lukas; Engel, Simon; Bauer, André; Kounev, Samuel (2025). 93
• Oratio Homomorphico: Creating a Lecture for Homomorphic Encryption by applying Design-Based Research. Prantl, Thomas; Schneider, Tobias; Horn, Lukas; Engel, Simon; Iffländer, Lukas; Krupitzer, Christian; Bonilla, Rafael; Kounev, Samuel in EAI CSECS 2025 (2025).
• Towards Automatic Bias Analysis in Multimedia Journalism. Hinrichs, Reemt; Steffen, Hauke; Avetisyan, Hayastan; Broneske, David; Ostermann, Jörn (2025).
• UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model. Kaiser, Timo; Norrenbrock, Thomas; Rosenhahn, Bodo (2025).
• Privacy-Preserving Training and Inference of CNNs: A Medical Performance Case Study. Prantl, Thomas; Schneider, Louis; Engel, Simon; Horn, Lukas; Borst, Vanessa; Krupitzer, Christian; Kounev, Samuel; Bonilla, Rafael in ICSC2025 (2025).
• Oratio Homomorphico: Creating a Lecture for Homomorphic Encryption by applying Design-Based Research. Prantl, Thomas; Schneider, Tobias; Horn, Lukas; Engel, Simon; Iffländer, Lukas; Krupitzer, Christian; Bonilla, Rafael; Kounev, Samuel in EAI CSECS 2025 (2025).
• Quo Vadis CKKS: Comparison of the Realization of Basic Mathematical Functions for the Homomorphic Cryptosystem CKKS using De Bello and Polynomial Approximations. Prantl, Thomas; Horn, Lukas; Engel, Simon; Bauer, André; Kounev, Samuel (2025). 93
• Oratio Homomorphico: Creating a Lecture for Homomorphic Encryption by applying Design-Based Research. Prantl, Thomas; Schneider, Tobias; Horn, Lukas; Engel, Simon; Iffländer, Lukas; Krupitzer, Christian; Bonilla, Rafael; Kounev, Samuel in EAI CSECS 2025 (2025).
• Privacy-Preserving Training and Inference of CNNs: A Medical Performance Case Study. Prantl, Thomas; Schneider, Louis; Engel, Simon; Horn, Lukas; Borst, Vanessa; Krupitzer, Christian; Kounev, Samuel; Bonilla, Rafael in ICSC2025 (2025).
• User Preferences for Interaction Timing in Smartwatch Sleep Hygiene Games. Liang, Zilu; Hwang, Daeun; Chen, Samantha; Nhung, Huyen Hoang; Khotchasing, Kingkarn; Melcer, Edward F. N. Yamashita, V. Evers, K. Yatani, S. X. Ding, B. Lee, M. Chetty, P. O. T. Dugas (eds.) (2025). 470:1–470:17.
• Quo Vadis CKKS: Comparison of the Realization of Basic Mathematical Functions for the Homomorphic Cryptosystem CKKS using De Bello and Polynomial Approximations. Prantl, Thomas; Horn, Lukas; Engel, Simon; Bauer, André; Kounev, Samuel (2025). 93
• Quo Vadis CKKS: Comparison of the Realization of Basic Mathematical Functions for the Homomorphic Cryptosystem CKKS using De Bello and Polynomial Approximations. Prantl, Thomas; Horn, Lukas; Engel, Simon; Bauer, André; Kounev, Samuel (2025). 93
• Privacy-Preserving Training and Inference of CNNs: A Medical Performance Case Study. Prantl, Thomas; Schneider, Louis; Engel, Simon; Horn, Lukas; Borst, Vanessa; Krupitzer, Christian; Kounev, Samuel; Bonilla, Rafael in ICSC2025 (2025).
• Privacy-Preserving Training and Inference of CNNs: A Medical Performance Case Study. Prantl, Thomas; Schneider, Louis; Engel, Simon; Horn, Lukas; Borst, Vanessa; Krupitzer, Christian; Kounev, Samuel; Bonilla, Rafael in ICSC2025 (2025).
• Oratio Homomorphico: Creating a Lecture for Homomorphic Encryption by applying Design-Based Research. Prantl, Thomas; Schneider, Tobias; Horn, Lukas; Engel, Simon; Iffländer, Lukas; Krupitzer, Christian; Bonilla, Rafael; Kounev, Samuel in EAI CSECS 2025 (2025).
• Oratio Homomorphico: Creating a Lecture for Homomorphic Encryption by applying Design-Based Research. Prantl, Thomas; Schneider, Tobias; Horn, Lukas; Engel, Simon; Iffländer, Lukas; Krupitzer, Christian; Bonilla, Rafael; Kounev, Samuel in EAI CSECS 2025 (2025).
• Privacy-Preserving Training and Inference of CNNs: A Medical Performance Case Study. Prantl, Thomas; Schneider, Louis; Engel, Simon; Horn, Lukas; Borst, Vanessa; Krupitzer, Christian; Kounev, Samuel; Bonilla, Rafael in ICSC2025 (2025).
• Identification and Localization COVID-19 Abnormalities on Chest Radiographs. Pham, Van Tien; Nguyen, Thanh Phuong in Lecture Notes on Data Engineering and Communications Technologies, A. E. Hassanien, A. Haqiq, A. T. Azar, K. Santosh, M. A. Jabbar, A. Slowik, P. Subashini (eds.) (2023). (Vol. 164) 251–261.
• CoRAL: a Context-aware Croatian Abusive Language Dataset. Shekhar, Ravi; Karan, Mladen; Purver, Matthew Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 217–225.
• Automating Interlingual Homograph Recognition with Parallel Sentences. Han, Yi; Sasano, Ryohei; Takeda, Koichi Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 211–216.
• Understanding the Use of Quantifiers in Mandarin. Chen, Guanyi; van Deemter, Kees Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 73–80.
• LEATHER: A Framework for Learning to Generate Human-like Text in Dialogue. Sicilia, Anthony; Alikhani, Malihe Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 30–53.
• A Multilingual Multiway Evaluation Data Set for Structured Document Translation of Asian Languages. Buschbeck, Bianka; Dabre, Raj; Exel, Miriam; Huck, Matthias; Huy, Patrick; Rubino, Raphael; Tanaka, Hideki Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 237–245.
• Cross-domain Analysis on Japanese Legal Pretrained Language Models. Miyazaki, Keisuke; Yamada, Hiroaki; Tokunaga, Takenobu Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 274–281.
• Open-Domain Conversational Question Answering with Historical Answers. Fang, Hung-Chieh; Hung, Kuo-Han; Huang, Chao-Wei; Chen, Yun-Nung Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 319–326.
• Meta-Learning Adaptive Knowledge Distillation for Efficient Biomedical Natural Language Processing. Obamuyide, Abiola; Johnston, Blair Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 131–137.
• Efficient Entity Embedding Construction from Type Knowledge for BERT. Feng, Yukun; Fayazi, Amir; Rastogi, Abhinav; Okumura, Manabu Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 1–10.
• Chop and Change: Anaphora Resolution in Instructional Cooking Videos. Oguz, Cennet; Kruijff-Korbayová, Ivana; Vincent, Emmanuel; Denis, Pascal; van Genabith, Josef Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 364–374.
• Assessing How Users Display Self-Disclosure and Authenticity in Conversation with Human-Like Agents: A Case Study of Luda Lee. Cho, Won-Ik; Kim, Soomin; Choi, Eujeong; Jeong, Younghoon Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 145–152.
• Modeling Referential Gaze in Task-oriented Settings of Varying Referential Complexity. Alaçam, Özge; Ruppert, Eugen; Zarrieß, Sina; Malhotra, Ganeshan; Biemann, Chris Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 197–210.
• A Hybrid Architecture for Labelling Bilingual Māori-English Tweets. Trye, David; Yogarajan, Vithya; König, Jemma L.; Keegan, Te Taka; Bainbridge, David; Apperley, Mark D. Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 119–130.
• Robustness Evaluation of Text Classification Models Using Mathematical Optimization and Its Application to Adversarial Training. Tomonari, Hikaru; Nishino, Masaaki; Yamamoto, Akihiro Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 327–333.
• Spa: On the Sparsity of Virtual Adversarial Training for Dependency Parsing. Lou, Chao; Han, Wenjuan; Tu, Kewei Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 11–21.
• PLATO-XL: Exploring the Large-scale Pre-training of Dialogue Generation. Bao, Siqi; He, Huang; Wang, Fan; Wu, Hua; Wang, Haifeng; Wu, Wenquan; Wu, Zhihua; Guo, Zhen; Lu, Hua; Huang, Xinxian; Tian, Xin; Xu, Xinchao; Lin, Yingzhan; Niu, Zheng-Yu Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 107–118.
• Conceptual Similarity for Subjective Tags. Gaci, Yacine; Benatallah, Boualem; Casati, Fabio; Benabdeslem, Khalid Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 54–66.
• Does Representational Fairness Imply Empirical Fairness?. Shen, Aili; Han, Xudong; Cohn, Trevor; Baldwin, Timothy; Frermann, Lea Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 81–95.
• "#DisabledOnIndianTwitter" : A Dataset towards Understanding the Expression of People with Disabilities on Indian Twitter. Mondal, Ishani; Kaur, Sukhnidh; Bali, Kalika; Vashistha, Aditya; Swaminathan, Manohar Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 375–386.
• The Effects of Surprisal across Languages: Results from Native and Non-native Reading. de Varda, Andrea Gregor; Marelli, Marco Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 138–144.
• Multilingual Auxiliary Tasks Training: Bridging the Gap between Languages for Zero-Shot Transfer of Hate Speech Detection Models. Montariol, Syrielle; Riabi, Arij; Seddah, Djamé Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 347–363.
• Block Diagram-to-Text: Understanding Block Diagram Images by Generating Natural Language Descriptors. Bhushan, Shreyanshu; Lee, Minho Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 153–168.
• Differential Bias: On the Perceptibility of Stance Imbalance in Argumentation. Palomino, Alonso; Khatib, Khalid Al; Potthast, Martin; Stein, Benno Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 411–421.
• HERB: Measuring Hierarchical Regional Bias in Pre-trained Language Models. Li, Yizhi; Zhang, Ge; Yang, Bohao; Lin, Chenghua; Ragni, Anton; Wang, Shi; Fu, Jie Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 334–346.
• BeamR: Beam Reweighing with Attribute Discriminators for Controllable Text Generation. Landsman, David; Chen, Jerry Zikun; Zaidi, Hussain Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 422–437.
• A Simple yet Effective Learnable Positional Encoding Method for Improving Document Transformer Model. Wang, Guoxin; Lu, Yijuan; Cui, Lei; Lv, Tengchao; Florêncio, Dinei A. F.; Zhang, Cha Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 453–463.
• GCWSNet: Generalized Consistent Weighted Sampling for Scalable and Accurate Training of Neural Networks. Li, Ping; Zhao, Weijie M. A. Hasan, L. Xiong (eds.) (2022). 1188–1198.
• MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization. Niu, Hui; Li, Siyuan; Li, Jian M. A. Hasan, L. Xiong (eds.) (2022). 1573–1583.
• MMM: An Emotion and Novelty-aware Approach for Multilingual Multimodal Misinformation Detection. Gupta, Vipin; Kumari, Rina; Ashok, Nischal; Ghosal, Tirthankar; Ekbal, Asif Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 464–477.
• A Copy Mechanism for Handling Knowledge Base Elements in SPARQL Neural Machine Translation. Hirigoyen, Rose; Zouaq, Amal; Reyd, Samuel Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 226–236.
• Hierarchical Processing of Visual and Language Information in the Brain. Kawasaki, Haruka; Nishida, Satoshi; Kobayashi, Ichiro Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 405–410.
• Multilingual CheckList: Generation and Evaluation. K, Karthikeyan; Bhatt, Shaily; Singh, Pankaj; Aditya, Somak; Dandapat, Sandipan; Sitaram, Sunayana; Choudhury, Monojit Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 282–295.
• On Measures of Biases and Harms in NLP. Dev, Sunipa; Sheng, Emily; Zhao, Jieyu; Amstutz, Aubrie; Sun, Jiao; Hou, Yu; Sanseverino, Mattie; Kim, Jiin; Nishi, Akihiro; Peng, Nanyun; Chang, Kai-Wei Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 246–267.
• ArgGen: Prompting Text Generation Models for Document-Level Event-Argument Aggregation. Kar, Debanjana; Sarkar, Sudeshna; Goyal, Pawan Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 399–404.
• Part Represents Whole: Improving the Evaluation of Machine Translation System Using Entropy Enhanced Metrics. Liu, Yilun; Tao, Shimin; Su, Chang; Zhang, Min; Zhao, Yanqing; Yang, Hao Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 296–307.
• SEHY: A Simple yet Effective Hybrid Model for Summarization of Long Scientific Documents. Jiang, Zhihua; Yang, Junzhan; Rao, Dongning Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 96–106.
• Topic-aware Multimodal Summarization. Mukherjee, Sourajit; Jangra, Anubhav; Saha, Sriparna; Jatowt, Adam Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 387–398.
• Revisiting Checkpoint Averaging for Neural Machine Translation. Gao, Yingbo; Herold, Christian; Yang, Zijian; Ney, Hermann Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 188–196.
• Memformer: A Memory-Augmented Transformer for Sequence Modeling. Wu, Qingyang; Lan, Zhenzhong; Qian, Kun; Gu, Jing; Geramifard, Alborz; Yu, Zhou Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 308–318.
• Adversarial Sample Generation for Aspect based Sentiment Classification. Mamta; Ekbal, Asif Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 478–492.
• Multi-Domain Dialogue State Tracking By Neural-Retrieval Augmentation. Ravuru, Lohith; Ryu, Seonghan; Choi, Hyungtak; Yang, Haehun; Ko, Hyeonmok Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 169–175.
• KreolMorisienMT: A Dataset for Mauritian Creole Machine Translation. Dabre, Raj; Sukhoo, Aneerav Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 22–29.
• TaKG: A New Dataset for Paragraph-level Table-to-Text Generation Enhanced with Knowledge Graphs. Qi, Qianqian; Deng, Zhenyun; Zhu, Yonghua; Lee, Lia Jisoo; Witbrock, Michael; Liu, Jiamou Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 176–187.
• Logographic Information Aids Learning Better Representations for Natural Language Inference. Jin, Zijian; Ataman, Duygu Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 268–273.
• R&R: Metric-guided Adversarial Sentence Generation. Xu, Lei; Cuesta-Infante, Alfredo; Berti-Équille, Laure; Veeramachaneni, Kalyan Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 438–452.
• TaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding. Sahu, Surya Kant Y. He, H. Ji, Y. Liu, S. Li, C.-H. Chang, S. Poria, C. Lin, W. L. Buntine, M. Liakata, H. Yan, Z. Yan, S. Ruder, X. Wan, M. Arana-Catania, Z. Wei, H.-H. Huang, J.-L. Wu, M.-Y. Day, P. Liu, R. Xu (eds.) (2022). 67–72.
• A Survivability-Aware Cyber-Physical Systems Design Methodology. Rashid, Nafiul; Araya, Gustavo Quiros; Faruque, Mohammad Abdullah Al (2019). 848–853.
• Simethicone use during gastrointestinal endoscopy: Position statement of the Gastroenterological Society of Australia. Devereaux, Benedict M; Taylor, Andrew C F; Athan, Eugene; Wallis, David J; Brown, Robyn R; Greig, Sue M; Bailey, Fiona K; Vickery, Karen; Wardle, Elizabeth; Jones, Dianne M (2019). 34(12) 2086–2089.

  Abstract Concern has been raised regarding the use of simethicone, a de-foaming agent, during endoscopic procedures. Following reports of simethicone residue in endoscope channels despite high level disinfection, an endoscope manufacturer recommended that it not be used due to concerns of biofilm formation and a possible increased risk of microorganism transmission. However, a detailed mucosal assessment is essential in performing high-standard endoscopic procedures. This is impaired by bubbles within the gastrointestinal lumen. The Gastroenterological Society of Australia's Infection Control in Endoscopy Guidelines (ICEG) Committee conducted a literature search utilizing the MEDLINE database. Further references were sourced from published paper bibliographies. Following a review of the available evidence, and drawing on extensive clinical experience, the multidisciplinary ICEG committee considered the risks and benefits of simethicone use in formulating four recommendations. Published reports have documented residual liquid or crystalline simethicone in endoscope channels after high level disinfection. There are no data confirming that simethicone can be cleared from channels by brushing. Multiple series report benefits of simethicone use during gastroscopy and colonoscopy in improving mucosal assessment, adenoma detection rate, and reducing procedure time. There are no published reports of adverse events related specifically to the use of simethicone, delivered either orally or via any endoscope channel. An assessment of the risks and benefits supports the continued use of simethicone during endoscopic procedures. Strict adherence to instrument reprocessing protocols is essential.
• Automatic Skill Matching for Production Machines. Araya, Gustavo Quiros; Varró, András; Eisen, Patrick; Roelofs, Frank; Kob, Peter (2018). 534–539.
• Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising. Wu, Di; Chen, Xiujun; Yang, Xun; Wang, Hao; Tan, Qing; Zhang, Xiaoxun; Xu, Jian; Gai, Kun A. Cuzzocrea, J. Allan, N. W. Paton, D. Srivastava, R. Agrawal, A. Z. Broder, M. J. Zaki, K. S. Candan, A. Labrinidis, A. Schuster, H. Wang (eds.) (2018). 1443–1451.
• Response Time Analysis for Fixed Priority Servers. Hamann, Arne; Dasari, Dakshina; Martinez, Jorge; Ziegenbein, Dirk Y. Ouhammou, F. Ridouard, E. Grolleau, M. Jan, M. Behnam (eds.) (2018). 254–264.
• An Overview of Multi-Task Learning in Deep Neural Networks. Ruder, Sebastian (2017).
• Modeling and simulation of cyberattacks for resilient cyber-physical systems. Rashid, Nafiul; Wan, Jiang; Araya, Gustavo Quiros; Canedo, Arquimedes; Faruque, Mohammad Abdullah Al (2017). 988–993.
• Dispersed automation for industrial Internet of Things - An enabler for advanced manufacturing. Araya, Gustavo Quiros; Cao, Diansong; Canedo, Arquimedes (2017). 269–274.
• Riječ je spomenik: o orijentalizmima u bosanskom jeziku, o bosanici - begovici - arebici, o bosansko-turskom rječniku iz 1631. godine, o Poturima kao narodu Šantić, Ferko (2014). 899-. Štamparija "Fojnica", Fojnica.
• Graph Kernel-Based Learning for Gene Function Prediction from Gene Interaction Network. Li, Xin; Zhang, Zhu; Chen, Hsinchun; Li, Jiexun (2007). 368–373.
• Evaluation of Protein Hydropathy Scales. Jääskeläinen, Satu; Riikonen, Pentti; Salakoski, Tapio; Vihinen, Mauno (2007). 245–251.
• Predicting Markov Chain Order in Genomic Sequences. Heath, Lenwood S.; Pati, Amrita (2007). 159–164.
• A Haplotyping Algorithm for Non-recombinant Pedigree Data Containing Missing Members. Duong, Doan Dai; Evans, Patricia A. (2007). 275–281.
• Erik Satie’s Vexations—An Exercise in Immobility. Dawson, Christopher (2001). 21(2) 29–40.

  Erik Satie's piano piece Vexations contains a Note de l’auteur in which the composer apparently instructs his interpreters to repeat the piece 840 times. Opinion among Satie's biographers is divided as to whether Satie intended a "full" performance of the piece. In this article I assess how a literary interpretation of the Note can throw light on Satie's intentions. My conclusion is that Vexations is not so much a performance piece as an exercise piece: a musical passage designed to pull his interpreters away from conventional Western notions of linear development and cumulative reception and towards his own non-developmental style of music, in preparation for a performance of a piece such as a Gymnopédie or a Gnossienne., La pièce pour piano Vexations d’Erik Satie comporte une « Note de l’auteur » où le compositeur demande apparemment aux interprètes de répéter le morceau 840 fois. Les opinions des biographes de Satie divergent quant à savoir si Satie voulait ou non une exécution « complète » de la pièce. Dans cet article, l’auteur évalue comment une interprétation littéraire de la Note peut jeter un éclairage sur les intentions de Satie. Il conclut que Vexations n’est pas tant un morceau de bravoure qu’un exercice : un moment musical conçu pour dégager les interprètes des notions occidentales conventionnelles de développement linéaire et de réception cumulative, en faveur d’un style musical personnel d’où est absent le développement, et pour les préparer à jouer d’autres pièces, telles une Gymnopédie ou une Gnossienne.
• Introduction to MCMC and Bayesian Hierarchical Models Knorr-Held, Leonhard; Rasser, Günter (2001).

  1. Introduction to Bayesian Inference 2. Markov chain Monte Carlo 3. Bayesian Hierarchical Models
• Akira. Book six OÌ„tomo, Katsuhiro; Umezawa, Yoko; Duffy, Jo (1993). (Vol. 6) Kodansha Comics, New York.
• Mathematical expression for drop size distribution in sprays. Technical Report (NASA-CR-72272), Hiroyasu, H. (1967).
• An invariant form for the prior probability in estimation problems. Jeffreys, Harold (1946). 186(1007) 453–461.

  It is shown that a certain differential form depending on the values of the parameters in a law of chance is invariant for all transformations of the parameters when the law is differentiable with regard to all parameters. For laws containing a location and a scale parameter a form with a somewhat restricted type of invariance is found even when the law is not everywhere differentiable with regard to the parameters. This form has the properties required to give a general rule for stating the prior probability in a large class of estimation problems.