Unknown Material Research Articles

Virtual home staging and relighting from a single panorama under natural illumination

Virtual staging technique can digitally showcase a variety of real-world scenes. However, relighting indoor scenes from a single image is challenging due to unknown scene geometry, material properties, and outdoor spatially-varying lighting. In this study, we use the High Dynamic Range (HDR) technique to capture an indoor panorama and its paired outdoor hemispherical photograph, and we develop a novel inverse rendering approach for scene relighting and editing. Our method consists of four key components: (1) panoramic furniture detection and removal, (2) automatic floor layout design, (3) global rendering with scene geometry, new furniture objects, and the real-time outdoor photograph, and (4) virtual staging with new camera position, outdoor illumination, scene texture, and electrical light. The results demonstrate that a single indoor panorama can be used to generate high-quality virtual scenes under new environmental conditions. Additionally, we contribute a new calibrated HDR (Cali-HDR) dataset that consists of 137 paired indoor and outdoor photographs. The animation for virtual rendered scenes is available here.

Учитель, ученики, эпоха: жизнь и педагогическая деятельность Федора Васильевича Андерсона

Исследование нацелено на реконструкцию основных вех биографии Ф. В. Андерсона, ученика В. И. Вернадского, талантливого педагога и краеведа, в первой трети ХХ в. преподававшего естествознание и географию в средних учебных заведениях Екатеринодара (Краснодара). Источниковую базу составили материалы личного происхождения (дневники, письма), неопубликованные делопроизводственные документы из фондов государственных архивов, а также материалы личных архивов родственников Ф. В. Андерсона. Выявлены факты, связанные с его происхождением, дана характеристика периода обучения Ф. В. Андерсона в Московском университете. Основной вектор исследовательского внимания направлен на анализ профессиональной деятельности Ф. В. Андерсона в 1-й мужской Екатеринодарской гимназии и отражение его достижений в этой области. Освещены важнейшие события биографии педагога в период Гражданской войны. Приводится периодизация жизненного и профессионального пути Ф. В. Андерсона. The study is aimed at reconstructing the milestones in the biography of Fyodor Vasilyevich Anderson, Vladimir Vernadsky’s student, a talented teacher and local historian, who taught in secondary schools in Ekaterinodar (Krasnodar) in the first third of the twentieth century. The authors focus on identifying and analyzing events related to the pedagogical and local history work he carried out. The source base consisted of materials of personal origin (diaries, letters), unpublished office documents from the collections of Russian state archives, as well as previously unknown materials (including photographs) from the personal archives of Anderson’s descendants. The methodology is based on the biographical method, which is understood by the authors in the spirit of the “genetic personalistics” concept by Boris Ananyev. Historical-anthropological and hermeneutic methods, as well as the methodology of historical source study, were also used in the work. The main facts related to the family of Anderson’s parents and his immediate relatives are revealed, biographical events related to the period of his studies at Moscow University are highlighted. The authors paid the closest attention to the years of his work as a teacher in the 1st Ekaterinodar Men’s Gymnasium: the innovative methods Anderson used are highlighted’ his activities in organizing an educational natural science museum and student clubs, conducting research excursions and other experimental and practical forms of work with gymnasium students are reflected. Anderson's successes in local history work are described. The influence that the teacher had on his students is shown – Anderson guided some of them to choose the natural science as a field of future professional scientific activities. The authors emphasize that among the outstanding students of Fyodor Anderson were the most famous Soviet scientists: entomologist Nikolai Bogdanov-Katkov, embryologist and ichthyologist Sergei Kryzhanovsky, ornithologist Evgeniy Ptushenko. The stages of Anderson's biography are reconstructed, covering the revolutions of 1917 and the Civil War, as well as the period that followed, associated with the establishment and strengthening of Soviet power. The authors’ periodization of the life and professional path of Fyodor Anderson is given. The authors conclude that he was a talented natural scientist, but did not have the opportunity to devote himself entirely to scientific pursuits and found self-realization in pedagogical work, training students who in the future became highly qualified researchers.

Usefulness of architectural drawing in urban planning decision-making for façade interventions: the ensanche of San Sebastián

The renovation of façades to improve energy efficiency has led to rapid and unregulated change in the landscape of cities. A loss of identity is occurring in urban centres, with previously unknown materials, textures and colours distorting the composition and style of buildings and their respective façade surfacing. This article considers the graphic research conducted to create a pilot system that encourages consistency in façade interventions. Modelling and rendering techniques can generate scenarios to test the consequences of certain regulations. Those graphic techniques were accordingly used to view different regulatory proposals applied to façades. More comparative decision-making based on realistic images is hence achieved, using graphic expression as a decision-making mechanism in urban planning.

Development of organic aggregation-induced emission fluorescent materials based on machine learning models and experimental validation

Organic fluorescent materials have attracted significant attention due to their unique chemical and optoelectronic properties. Unfortunately, traditional organic fluorescent materials are limited by aggregation-caused quenching (ACQ), which restricts their application potential in various fields. The discovery of aggregation-induced emission (AIE) materials has revolutionized the field by providing a solution to this issue. Despite considerable efforts to design and synthesize novel AIE materials, the development process still relies on tedious trial-and-error methods. Therefore, advanced cross-disciplinary techniques must be introduced to establish AIE property prediction models and enhance the development efficiency of organic AIE fluorescent materials. Machine learning (ML) is a powerful tool that has been widely applied across diverse fields to accelerate material development through complex relationship mapping of large data sets. This study presents an ML-based approach aimed at accelerating the development of organic AIE fluorescent materials. We assembled a dataset of 3,074 molecules with AIE/ACQ properties, developed a prediction model using the LightGBM ensemble learning algorithm, and used combined molecular fingerprints as input. The model achieved a remarkable independent test-set accuracy of up to 0.974 and its extrapolation ability was validated by out-of-sample validation (accuracy = 0.963). In addition, experimental validation was performed to assess the reliability of the ML prediction model in an unknown molecular space, showing a satisfactory agreement between the model predictions and the experimental results. Our work highlights the potential of ML for rapidly and accurately predicting the AIE property of unknown molecules and screening AIE materials, thereby accelerating the development of organic AIE fluorescent materials.

Enhancing decanter centrifuge process design with data‐driven material parameters in multi‐compartment modeling

AbstractPredicting the separation performance of decanter centrifuges is challenging due to dynamic events within the apparatus. Current methods for designing decanter centrifuges rely on simplified models, often leading to inaccuracies. Consequently, manufacturers must perform time‐intensive pilot scale experiments to derive their own correction factors. Growing computing power sparks interest in alternative modeling strategies. Grey box models (GBM) combine mechanistic white box models (WBM) and data‐driven black box models (BBM), with the optimal structure (parallel or serial) varying by application. For modeling decanter centrifuges, we propose a serial GBM that comprises an artificial neural network that outputs unknown material parameters into a first‐principle multi‐compartment model. Comparing this approach to alternative data‐driven modeling strategies (pure BBM, parallel GBM), we conclude that the serial GBM excels in terms of extrapolation, prediction ability, and transparency while also enabling a better comprehension of the separation process.

Obsidian in the Caribbean islands? Mysterious Ceramic Age glass artefacts in the Lesser Antilles

AbstractThe Caribbean islands witnessed a population expansion of ceramic‐using horticulturalists during the Early Ceramic Age (ca. 500 BC to 750 AD) from the Orinoco Valley to Puerto Rico. We examined 18 lithic artefacts from Guadeloupe and Dominica initially thought to be obsidian, a material believed to be absent from those islands. We investigated the volcanic or meteoritic origin of this unique and yet unknown material through observation (binocular, SEM, microtomography) and geochemical analyses (PIXE, SEM‐EDS, ED‐XRF, EPMA, LA‐ICP‐MS). Elemental analyses rule out the hypothesis of an origin from a meteoritic impact (i.e. identification as tektites). Most of the artefacts have an andesitic composition (<63% SiO2), which appears to be unique among ‘massive’ glasses. The only artefact with a rhyolitic composition has been traced back to the Guadeloupe's Volcan du Tuf, where glassy fragments have been collected and analysed. The geological source of the other vitreous artefacts that exhibit an andesitic composition could be from a sublocal subduction‐arc volcanism (maybe from Martinique), although no volcanic vitreous material of this kind has ever been reported worldwide. These results once again highlight the regional mobility of Early Ceramic populations and the production of standard lithic products using a highly original, albeit low‐quality, local lithic resource, and provide valuable references for future identification of similar materials.

Notes on the Nesting Behavior of Four Solitary Wasps (Insecta: Hymenoptera) in India

India is one of countries that has the highest biodiversity level of Hymenoptera in the Oriental region, as well as in the world. Hence, studies of its biology have received a large consideration of entomologists. In a course of study on Hymenoptera in Odisha state of the country in 2015, we observed the nesting behavior of several wasps and recorded their new nesting sites. Therefore, this study presents notes on the nesting behavior of the four Indian wasps, namely, Chalybion bengalense (Dahlbom) (Sphecidae), Trypoxylon petiolatum F. Smith (Crabronidae), Delta esuriens (Fabricius) and Xenorhynchium nitidulum (Fabricius) (both, Vespidae: Eumeninae). The first two wasps used man-made holes on the walls of a big temple for their nesting sites, fully stored paralyzed spiders in the nest cells for offspring and plugged them with mud, in a case of Chalybion bengalense, females added a white layer of unknown material on the outside surface of covers. Delta esuriens chose its nesting site beneath the thatched roof of a human house, built a linear series of pot-shaped mud cells that were firmly attached to some rice blades hung loosely beneath the roof and provisioned caterpillars in these nest cells. The latter built a mud nest on a wall of a human house. Xenorhynchium nitidulum made a nest cell oblique a 30° angle from the vertical surface of the wall, plastered the outside surface of the cell with tree resin of a certain plant species after completion of building and then stored prey of an unknown caterpillar in the cell. The female of Xenorhynchium nitidulum sit in the nest cell after provisioning of the first prey to the following morning suggesting that the wasp progressively provisions its nest and the egg is laid prior to provisioning.

Thin film sensing near exceptional point utilizing terahertz plasmonic metasurfaces

Non-Hermitian quantum systems along with engineered metasurfaces enable a versatile podium for sensor designs from industrial to medical sectors. The singularity points known as exceptional points (EPs) can be realized in such non-Hermitian systems. EP demonstrates a square root topology on minute perturbations, hence promising to be a potential candidate to sense external parameters, such as temperature, thermal fluctuations, refractive index, and biomolecules. Hence, in this work, through numerical and analytical investigations, we explore the sensing capabilities in the vicinity of EP utilizing suitably designed terahertz metasurfaces. Here, we propose a non-Hermitian metasystem comprising two orthogonally twisted square split ring resonators coupled by near-field Electromagnetic interactions that can exhibit dark-bright modes. In such a system, the presence of an active (photo-doped) material in the split gap of one of the resonators opens up an effective avenue to introduce controllable asymmetric losses, ultimately leading to the emergence of EPs in the polarization space. Hence, thin film sensing at the proximity of the emerged EP is investigated for different refractive indices by coating with an overlayer atop the metasurface. In such a configuration, the sensitivities of the eigenstates are calculated in terms of the refractive index unit, which turns out to be −0.044 THz RIU−1 and −0.063 THz RIU−1 when the system is perturbed near EP. Our proposed metasurface-inspired EP-based sensing strategy can open up novel ways to sense the refractive index of unknown materials besides other physical parameters.

Accelerating the discovery of direct bandgap doped-spinel photovoltaic materials: A target-driven approach using interpretable machine learning

The spinel structure, unifying tetrahedral and octahedral coordination within a single crystal lattice, emerges as a promising alternative to perovskite and traditional semiconductors, particularly in the context of photovoltaic applications. However, the majority of surveyed spinels exhibit an indirect bandgap, imposing substantial limitations on their utility. Consequently, doping is required to tailor indirect bandgap spinels into direct ones. In this study, leveraging machine learning (ML) algorithms, we design a target-driven framework to accelerate the ab initio predictions of unknown doped-spinels using elements from the periodic table. Utilizing this approach, we have introduced a novel machine learning classification model designed for the prediction of the direct-indirect bandgap nature in spinel materials. This pioneering model shows promising potential for achieving rapid and precise predictions, particularly in scenarios involving small dataset. Especially, a full list of potential 3449 (AxA′1-x)B2X4-type doped-spinels and 3809 A(BxB′2-x)X4-type doped-spinels with direct bandgaps is identified from the vast pool of unknown doped-spinel materials. Further, the application of interpretable ML extracts the first ionization energy of the B-site ion as the most important feature for the nature of bandgap, offering insightful design rules. This research provides a novel perspective on unraveling spinel materials, and the proposed design framework proves effective in identifying high-performance materials within a vast chemical space, all while minimizing computational costs.

Investigation on grinding temperature characteristics of CF/PEEK and material removal mechanism under temperature treatments

Carbon fiber reinforced thermoplastic (CFRTP) composites grinding temperatures could even exceed the glass transition temperature (Tg) of the material due to heat accumulation, combined with the unknown material removal mechanism, resulting in machining defects. To investigate the grinding temperature distribution and maximum temperatures of carbon fiber-reinforced poly-ether-ether-ketone (CF/PEEK) composites, as a typical kind of CFRTP, an analytical temperature model considering the anisotropy was proposed based on the moving heat source theory, verified by the experimental results measured by the infrared camera. Further, the material removal mechanism under temperature treatments (below and above Tg) was also studied. In high temperatures (above Tg), there were many defects on surfaces after grinding, including resin smearing, severe deflection, fiber distortion, and grinding wheel clogging. Therefore, to reduce the grinding temperature below Tg, the vortex tube cooling-assisted grinding method was proposed in the research. The proposed method could reduce the grinding temperature in the grinding area, suppress resin smearing, improve the surface quality, enhance the elastic modulus and nano-hardness of the workpiece, and reduce the clogging of grinding wheels. This study provided a better understanding of the CF/PEEK grinding temperature characteristics and suggested a method to improve the grinding quality.

Tomography of nonlinear materials via the monotonicity principle

In this paper we present a first non-iterative imaging method for nonlinear materials, based on Monotonicity Principle. Specifically, we deal with the inverse obstacle problem, where the aim is to retrieve a nonlinear anomaly embedded in linear known background. The Monotonicity Principle (MP) is a general property for various class of PDEs, that has recently generalized to nonlinear elliptic PDEs. Basically, it states a monotone relation between the point-wise value of the unknown material property and the boundary measurements. It is at the foundation of a class of non-iterative imaging methods, characterized by a very low execution time that makes them ideal candidates for real-time applications. In this work, we develop an inversion method that overcomes some of the peculiar difficulties in practical application of MP to imaging of nonlinear materials, preserving the feasibility for real-time applications. For the sake of clarity, we focus on a specific application, i.e. the Magnetostatic Permeability Tomography where the goal is retrieving the unknown (nonlinear) permeability by boundary measurements in DC operations. This choice is motivated by applications in the inspection of boxes and containers for security. Reconstructions from simulated data prove the effectiveness of the presented method.

Multimodal zero-shot learning for tactile texture recognition

Tactile sensing plays an irreplaceable role in robotic material recognition. It enables robots to distinguish material properties such as their local geometry and textures, especially for materials like textiles. However, most tactile recognition methods can only classify known materials that have been touched and trained with tactile data, yet cannot classify unknown materials that are not trained with tactile data. To solve this problem, we propose a tactile Zero-Shot Learning framework to recognise materials when they are touched for the first time, using their visual and semantic information, without requiring tactile training samples. The biggest challenge in tactile Zero-Shot Learning is to recognise disjoint classes between training and test materials, i.e., the test materials that are not among the training ones. To bridge this gap, the visual modality, providing tactile cues from sight, and semantic attributes, giving high-level characteristics, are combined together and act as a link to expose the model to these disjoint classes. Specifically, a generative model is learnt to synthesise tactile features according to corresponding visual images and semantic embeddings, and then a classifier can be trained using the synthesised tactile features for zero-shot recognition. Extensive experiments demonstrate that our proposed multimodal generative model can achieve a high recognition accuracy of 83.06% in classifying materials that were not touched before. The robotic experiment demo and the FabricVST dataset are available at https://sites.google.com/view/multimodalzsl.

Application of ultrasonic waves to evaluate the rheological properties of non-Newtonian slurries: a preliminary study

An ultrasonic-wave system was developed to measure the rheological properties of non-Newtonian materials based on their responses to shear and pressure waves. This system was composed of a commercial shear wave transducer and various homemade devices, including a signal generator, a signal conditioner, and a transmission/receiving switch. The shear wave reflections at the interface between the delay line and the unknown test materials can be interpreted by the signals measured with calibration materials (e.g. air, water, olive oil and honey) that have well-known rheological properties. If the Voigt viscoelastic model is used to describe the dynamic behaviour of these test materials, the interpreted data can be used to determine the acoustic impedances, and thus solve for their shear moduli (G), kinematic viscosities (ν) and bulk densities (ρ). After the calibration procedures, the proposed system was used to test a consolidating kaolinite slurry with zero salinity and an initial suspended sediment concentration of 420 g l–1. The measurements showed that the shear modulus G is developed until consolidation for approximately 100 h. Afterward, the G gradually approached a value on the order of 500 N m–2 after approximately 200 h. This corresponded to an initial viscosity of approximately 7 × 10−5 m2 s–1, which decreased slowly over time to approximately 4 × 10−6 m2 s–1 after 300 h. With minor modification to capture pressure waves from the shear wave transducer, the bulk density can also be measured. The integrated system offers a non-invasive and efficient method for characterizing non-Newtonian fluids, providing valuable insights into their rheological properties, which are crucial for a wide range of industrial and scientific applications.

Iterative multi-objective design of hydrogen embrittlement resistant high-strength steels using Bayesian optimization

The infiltration of hydrogen can weaken the strength and toughness of materials, especially in high-strength steel. Considering the inherent trade-off between strength and toughness in steel design, balancing these two factors with the material's hydrogen embrittlement sensitivity has become a huge challenge in steel design. The long cycle nature of hydrogen embrittlement testing makes the iteration of traditional trial and error methods infeasible for steel development. This work adopts active learning algorithms based on Bayesian and Pareto optimization to iteratively optimize the chemical composition and processing technology of high-strength steel, in order to simultaneously improve its hydrogen filled tensile strength (UTS_H) and hydrogen filled total elongation (TEL_H). After two rounds of iterative optimization, the iterative optimization algorithm significantly improved the strength prediction of materials after hydrogen charging. The machine learning model found 16 types of steel from over 15 million unknown materials. Eight materials showed better comprehensive performance than known data, with one material having a tensile strength exceeding 1800 MPa and an elongation of 12.6%. Subsequent experiments showed that the optimization algorithm demonstrated an understanding of the impact of austenite content on hydrogen embrittlement sensitivity in multiple experiments, and designed high-strength steel resistant to hydrogen embrittlement through the austenite content of martensitic steel.

Signed Fingermark Liveness Detection Method Based on Deep Residual Networks and Multimodal Decision Fusion

Fingermarks play an important role in document identification. At the same time, fingermarks on paper documents are often accompanied by signatures and background text, which introduce noise to the original fingermark textures and increase the difficulty of detection. A signed fingermark detection method based on deep residual networks and a decision-level fusion strategy was proposed to defend against spoofing attacks from fake fingermarks. Firstly, the multi-scale structure was introduced in the residual module, which improved the network’s depth and breadth without increasing the parameters. Then, the multi-probability label strategy was refined and employed to enhance the local encoding ability of the feature extraction. A score fusion strategy was designed, with weights allocated based on the difference in signed interference levels of local image blocks. Finally, a model fusion strategy based on evidence theory was suggested, which improved detection accuracy by leveraging complementarity between models. A large-scale fingermark database was established, which included real fingermarks made from real fingers and fake fingermarks made from various materials, and this was divided into two sub databases: signed and unsigned. The experimental results show that the proposed method achieves 96.16% accuracy based on the fingerprint dataset of the global liveness detection competition called LivDet2017 and achieves 99.30% accuracy based on the signed fingermark database, while it has good resistance to spoofing attacks from unknown materials.

Reference materials: A review

Several factors have increased the use of reference materials in laboratories. This can be explained by the fact that the reference materials have several roles, namely: the confirmation of the identity of unknown materials and/or the determination of their properties; the calibration of measuring equipment; the validation of methods; the realization of proficiency tests; etc. To be able to produce and use them, a set of standards and guidelines concerning the subject of reference materials has been established. There are several producers of reference materials in many fields, but finding the right choice is sometimes considered difficult given the multitude of materials to be analyzed that do not correspond perfectly to the reference material, especially in the case of matrices. This makes the market always seek new materials. To develop them, five steps are essential: material preparation, homogeneity study, stability study, characterization, and evaluation of measurement uncertainties. These steps are equally important; the fact of highlighting less than one of them will imply a significant decrease in the quality of the reference material developed. This review seeks to furnish the scientific community with a paper elucidating the functions of these materials in research laboratories, the normative references devised to standardize their production and utilization, the factors influencing their production, and the essential steps for their development.

Uncertainty quantification in multiaxial fatigue life prediction using Bayesian neural networks

In practical engineering scenarios, unforeseen failures may result in significant costs and risks. Assessing the uncertainty related to multiaxial fatigue life prediction is crucial for engineering applications. However, establishing a reliable multiaxial fatigue life prediction model remains challenging due to uncertainties in physical models, material properties, and measurement data. This paper proposes an effective Bayesian Neural Network (BNN) model for quantifying the uncertainty in multiaxial fatigue life prediction. Fatigue parameters in the multiaxial fatigue life prediction model are used as inputs for the BNN. The BNN method utilizes No-U-turn Sampler (NUTS) and Automatic Differentiation Variational Inference (ADVI) to address the inference problem in the model and perform life predictions for unknown non-proportional loading paths and different materials. Validation using experimental data from six different materials confirms the effectiveness of the BNN model. Experiments show that the performance of BNN models is beneficial in most cases compared to classical ML models, assessed through different error standards, statistical tests, Taylor diagrams, uncertainty analysis, and scatter index analysis. The proposed method provides a clear understanding of the uncertainty in multiaxial fatigue life prediction, offering a promising approach for simulating fatigue life under multiaxial loading in engineering applications.

Stability of ternary interfaces and its effects on ideal switching characteristics in inverted coplanar organic transistors

Inverted coplanar or bottom-gate bottom-contact (BGBC)-type thin-film transistors (TFTs) present several advantages for the manufacture and application of organic TFTs, although serious difficulties are encountered when trying to achieve sufficiently high performance. Recently, it was demonstrated that both high mobility and ideal switching are attainable in BGBC-type printed organic TFTs with highly clean semiconductor-gate dielectric interfaces. However, an unknown channel material dependence in the device performance is found. Here, we show that the stability of semiconductor/metal/dielectric ternary interfaces is a crucial factor in the operation of BGBC-type organic TFTs. We fabricate single-crystal organic semiconductor (OSC) films with various numbers of layers using two different materials (phenyl/alkyl-substituted benzothieno[3,2-]benzothiophene and phenyl/alkyl-substituted benzothieno[3,2-]naphtho[2,3-]thiophene) on highly lyophobic Cytop gate dielectric surfaces. The transfer characteristics exhibit notable time-dependent degradation, which clearly depends on the material, layer number, and encapsulation. Kelvin-probe force microscopy measurements reveal that the degradation is ascribed to contact resistance at the source electrodes, while it can be more suppressed in multilayer (two or more layers) OSCs. Atomic force microscopy and in-plane x-ray diffraction profiles present signs of the transformation in single molecular bilayer OSCs laid on the electrodes. The results suggest the importance of the quality of the OSC layer at ternary interfaces, providing a clue for improving the performance of BGBC-type organic TFTs. Published by the American Physical Society 2024

Государственная академия художественных наук vs. петроградский формализм: теория стиха. 2. О «Рифме» В.М. Жирмунского

The article presents a historical and scientific analysis of the oral presentations and other works that criticized Boris Eikhenbaum’s “Melodics of Verse” and Viktor Zhirmunsky’s “Rhyme, Its History and Theory” from the perspective of Moscow formalism. The overview relies on unknown materials, which can thus be introduced into scholarly discourse. It refers to the presentations made by the philologist and philosopher Maksim Kœnigsberg and the literary scholar Mikhail Shtokmar, a student of Boris Yarkho at the State Academy of Artistic Sciences. In addition, the data from the newly discovered article by Maxim Kœnigsberg, “Compound Rhymes in the Lyrics of Innokentii Annenskii” (1924), is explored. The theses of Kœnigsberg’s exposé are published for the first time, with notes. The aim of the article is to reveal the polemical layer in the works of Moscow formalists. This is achieved by identifying key positions in the analysed texts, by their historical commentary, mutual comparison, and the discovery of authors’ logic. The results of the research include the publication of archival materials and their interpretation in the context of internal formalistic polemics. New materi­als reveal that Zhirmunsky allowed Kœnigsberg to develop his own theory of verse with an orientation towards semantics; whereas Shtokmar, on the contrary, rejected an important part in Zhirmunsky’s rhyme theory. The interrelation between different branches of the Russian formalism become clearer.

Poufne spotkania w środowisku oficerskim przed przewrotem majowym, czyli kilka spostrzeżeń i hipotez wokół dochodzenia wojskowej prokuratury (listopad 1925 – maj 1926)

The May Coup of 1926 was one of the most important events in the history of the Second Polish Republic. The several-day armed coup led to significant changes in the positions of the chief state authorities. This article, based mostly on previously unknown archival materials, describes the several-month period preceding the so-called May events. This is possible thanks to the investigation conducted by the military prosecutor’s office in Warsaw into Col. Bolesław Wieniawa- -Długoszowski and Lt. Col. Roman Abraham, who discussed various issues in confidential conversations with other officers, including the takeover of power in the state.