Our Work

Galactic Civilization Network Theory has described interstellar contact with a single length, the distance light covers during a communicative lifetime L, and has assumed that contact requires&nb…

Galactic Civilization Network Theory has described interstellar contact with a single length, the distance light covers during a communicative lifetime L, and has assumed that contact requires contemporaries. This paper removes both assumptions and follows the consequences, from contact counts to large-scale connectivity. Separating the detection range dmax from the light horizon cL gives the reach ratio χ = dmax/cL, and treating a civilization as an event of finite duration splits contact into four networks: listening, audibility, mutual listening and relay. The density parameter is the contactability η = πnd2 max, the mean number of civilizations within detection range of a given one, for a surface density n. A civilization hears 2η others at every χ and for any lifetime distribution, twice the coeval value; audibility and mutual listening sum exactly to 4η; the fraction of contacts that can be returned depends on geometry alone, and is also the probability that a detected civilization is still transmitting; and relay chains settle to a branching factor κη, κ = 1.707. The counts do not determine connectivity. Monte Carlo measurement across five decades in χ finds that, as χ → 0, the non-relay networks approach a critical contactability ηc = 1.32 ± 0.01, a factor 3.4 below the coeval threshold 4.51; that relaying costs about 7% on average; and that there is no critical length of history. In a disc whose contactability falls outward the frontier moves out 3.2 kpc, while the width of the front stays near 3.7 kpc for all four networks. A non-detection bounds the single-epoch count ⟨k⟩obs = η in both frameworks, so the reference bound η(R0) ≤ 2.75 stays where it is. The connectivity threshold, however, falls below that bound, opening a consistency window between non-detection and Galactic-scale connectivity. That bound still allows 5.5 civilizations to be heard over a full communicative lifetime, but fewer than one of those contacts returns a reply once χ ≳ 1.

Ground monitoring becomes more useful when aircraft state, communication condition, time, and location are interpreted together rather than as independent streams. This paper presents an integrated gr…

Ground monitoring becomes more useful when aircraft state, communication condition, time, and location are interpreted together rather than as independent streams. This paper presents an integrated ground-monitoring workflow that combines historical flight-telemetry visualization with a causal multi-horizon machine-learning pipeline for predicting future wireless-link change. The main prediction target is future change in signal-to-interference-plus-noise ratio (ΔSINR) at horizons of 1, 2, 5, 10, 15, 20, and 30 s. The quantitative study uses three physical flight records drawn from two public AERPAW measurement sources: two Ericsson 5G NSA traces at yaw orientations of 45° and 315°, and one Lake Wheeler Android-based 4G LTE/5G NR trace. The records contain LTE/NR radio indicators, throughput, position, altitude, cell information, and motion/orientation fields, with 2376 raw observations across the three physical flights. The processing uses a causal 1-s grid, flight-isolated temporal features, chronological evaluation with a horizon-sized purge, training only mutual-information feature selection, and a persistence baseline. Three classical regressors—HistGradientBoosting (HGB), Random Forest, and Extra Trees—are evaluated. An engineering link-quality index (LQI) in the range 0–100 summarizes 12 available cellular and throughput indicators. At the predeclared 30-s operating point, HGB obtains a ΔSINR MAE of 3.110 dB versus 3.669 dB for persistence, a 15.23% reduction. A separate NASA Tail-654 ground-monitoring implementation replays a historical MATLAB flight record, presents map-based trajectory and primary telemetry, and applies phase-aware statistical anomaly monitoring using robust envelopes, eight-second persistence, and recovery hysteresis. The NASA component is kept experimentally separate from the wireless-link prediction metrics. The resulting system connects telemetry observability with forward-looking communication-state information while preserving a strict separation between information available at prediction time and future observations.

Tezisda raqamli boshqaruvning institutsional shartlari bilan natijaviy ko‘rsatkichlarini ajratib baholash yondashuvi asoslanadi. Dissertatsiyaning 3.3-paragrafidagi oltita omil va uchta natija m…

Tezisda raqamli boshqaruvning institutsional shartlari bilan natijaviy ko‘rsatkichlarini ajratib baholash yondashuvi asoslanadi. Dissertatsiyaning 3.3-paragrafidagi oltita omil va uchta natija mezoni OTM–yil paneli uchun tizimlashtiriladi. Ikki tomonlama qat’iy effektlar modeli va dinamik panel spetsifikatsiyasining vazifalari izohlanadi. To‘liq panel ma’lumotlari mavjud bo‘lmagani sababli koeffitsiyentlar baholanmaydi; natija kelgusidagi empirik tekshiruv uchun metodologik tartibdan iborat.

The article studies the form of artistic time - retrospection and its role in the composition of the work. Any example ofartistic creativity, in particular prose, widely uses various methods…

The article studies the form of artistic time - retrospection and its role in the composition of the work. Any example ofartistic creativity, in particular prose, widely uses various methods that form an integral part of the artistic time, based on the ideological and aesthetic concept of the writer. The writer's depiction of past memories through the method of retrospection is studied on the example of Shoyim Botayev's prose.

This repository provides access to a collection of evapotranspiration datasets, enabling researchers, scientists, and practitioners to conveniently access and utilize these valuable resources. Th…

This repository provides access to a collection of evapotranspiration datasets, enabling researchers, scientists, and practitioners to conveniently access and utilize these valuable resources. The repository ensures easy retrieval of evapotranspiration data by providing clear documentation and instructions for downloading the datasets. With a diverse range of evapotranspiration data available, users can explore various spatial and temporal resolutions tailored to their specific research needs. This repository aims to facilitate the integration of evapotranspiration data into diverse applications, such as hydrological modeling, climate studies, and agricultural research, fostering scientific advancements and data-driven decision-making.

First versioned journal-submission release for: Learning When Black Hole Hair Is Observable: Physical Identifiability, Generalization, and Observable Complementarity This release contains the publicat…

First versioned journal-submission release for: Learning When Black Hole Hair Is Observable: Physical Identifiability, Generalization, and Observable Complementarity This release contains the publication-facing Kiselev black hole identifiability benchmark, including: validated timelike-emitter and 3D direct null-geodesic shooting; Jacobian identifiability diagnostics; leakage-aware random, grouped, and directional inverse-learning protocols; non-learned nearest-model and local-Jacobian baselines; 81-, 161-, and targeted 321-phase numerical-resolution audits; observation-facing redshift and signed-sky sensitivity analysis; manuscript source and compiled manuscript PDF; publication figures, machine-readable derived results, and reproducibility tests. This is a theoretical identifiability benchmark and does not constitute an observational constraint on black hole hair.

Spoken language carries far more information than is preserved in its orthographic representation. Pitch movements signal whether an utterance is a question, assertion,or exclamation; syllable duratio…

Spoken language carries far more information than is preserved in its orthographic representation. Pitch movements signal whether an utterance is a question, assertion,or exclamation; syllable duration marks prominence and phrase boundaries; amplitude variations highlight the communicatively most important words. These prosodic dimensions are the subject of linguistic research, yet remain underrepresented in the output of automated speech recognition systems and are difficult to annotate consistently without specialist training. Writing and speech have different virtues and purposes. Orthography (standard spelling and punctuation) captures words and structure, but loses prosody: the emotional and musical dimensions of spoken language. Yet some situations demand richer written representation. Movie subtitles could convey more of the spoken original to deaf viewers. Text-to-speech systems require prosodic guidance to produce natural output and achieve an optimal representation in the acoustic domain. Without explicit prosodic marking in writing, ambiguity persists; with prosody in speech, the intended meaning is immediately clear.For this thesis, we present SpeechPrint, a tool that converts continuous prosodic features into a symbolic, discrete representation. SpeechPrint takes a .wav recording and outputs a structured TextGrid with time-aligned phonemes, syllables, and symbolic prosody, supporting both linguistic research (Praat, ELAN) and creative applications. The prosody symbols encode pitch movements, prominence, and accentuation: Symbol Meaning/ Rising pitch within syllable\ Falling pitch within syllablē High level (vs. neighbors)_ Low level (vs. neighbors)* Prominent accent* ̄// Accented, high, strongly rising_\\ Low, strongly falling These ASCII symbols are intuitive, printable in any environment, and designed to support both linguistic frameworks (ToBI) and creative annotation. SpeechPrint integrates existing speech technologies (Whisper, WhisperX, Montreal Forced Aligner, pYIN) into a coherent pipeline. The key technical contribution is identifying and correcting octave errors in pitch detection: a widespread problem that skews prosodic analysis by full octaves. Vowel nuclei serve as the most acoustically stable carriers of prosodic information, making them ideal targets for reliable pitch and intensity extraction. Systematic evaluation of five pitch trackers revealed that probabilistic methods (pYIN) outperform traditional autocorrelation approaches, addressing a key bottleneck in syllable-level prosody annotation. The evaluation corpus consists of English (305.5 s), German GToBI, and three underresourced languages Daakie (Oceanic, Vanuatu), Cabécar (Chibchan, Costa Rica), and Kakabe (Mande, Guinea). The evaluation demonstrates that the system can recover major prosodic contours and assign symbolic annotations that broadly correspond to human expert judgments. Expert linguist feedback was collected throughout the research process, which identified limitations in handling non-modal phonation and informed refinements to the system architecture. This work belongs in Sound and Music Computation because it bridges technical tool development and artistic practice. On the technical side, it addresses an establishedproblem in acoustic analysis: reliable pitch tracking and prosody extraction. On the expressive side, it treats prosody as a musical dimension: pitch, timing, and dynamic control enabling new forms of artistic representation, interactive speech editing, and accessible multimedia. These two angles: tool engineering and creative application, define Sound and Music Computation research.

What's Changed Fix weighted inverse covariance calculation in sWeights by @TingzhenXiao in https://github.com/scikit-hep/hepstats/pull/205 Be more explicit about minimum requirements on zfit for test…

What's Changed Fix weighted inverse covariance calculation in sWeights by @TingzhenXiao in https://github.com/scikit-hep/hepstats/pull/205 Be more explicit about minimum requirements on zfit for tests by @eduardo-rodrigues in https://github.com/scikit-hep/hepstats/pull/207 Add license statements to files by @eduardo-rodrigues in https://github.com/scikit-hep/hepstats/pull/210 CI updates Remove sondrelg/pep585-upgrade from pre-commit hooks by @eduardo-rodrigues in https://github.com/scikit-hep/hepstats/pull/206 chore(deps): bump codecov/codecov-action from 6 to 7 by @dependabot[bot] in https://github.com/scikit-hep/hepstats/pull/208 chore(deps): bump actions/checkout from 6 to 7 by @dependabot[bot] in https://github.com/scikit-hep/hepstats/pull/209 [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci[bot] in https://github.com/scikit-hep/hepstats/pull/212 chore(deps): bump actions/setup-python from 6 to 7 by @dependabot[bot] in https://github.com/scikit-hep/hepstats/pull/214 New Contributors @TingzhenXiao made their first contribution in https://github.com/scikit-hep/hepstats/pull/205 Full Changelog: https://github.com/scikit-hep/hepstats/compare/v0.10.0...v0.10.1

Ground monitoring becomes more useful when aircraft state, communication condition, time, and location are interpreted together rather than as independent streams. This paper presents an integrated gr…

Ground monitoring becomes more useful when aircraft state, communication condition, time, and location are interpreted together rather than as independent streams. This paper presents an integrated ground-monitoring workflow that combines historical flight-telemetry visualization with a causal multi-horizon machine-learning pipeline for predicting future wireless-link change. The main prediction target is future change in signal-to-interference-plus-noise ratio (ΔSINR) at horizons of 1, 2, 5, 10, 15, 20, and 30 s. The quantitative study uses three physical flight records drawn from two public AERPAW measurement sources: two Ericsson 5G NSA traces at yaw orientations of 45° and 315°, and one Lake Wheeler Android-based 4G LTE/5G NR trace. The records contain LTE/NR radio indicators, throughput, position, altitude, cell information, and motion/orientation fields, with 2376 raw observations across the three physical flights. The processing uses a causal 1-s grid, flight-isolated temporal features, chronological evaluation with a horizon-sized purge, training only mutual-information feature selection, and a persistence baseline. Three classical regressors—HistGradientBoosting (HGB), Random Forest, and Extra Trees—are evaluated. An engineering link-quality index (LQI) in the range 0–100 summarizes 12 available cellular and throughput indicators. At the predeclared 30-s operating point, HGB obtains a ΔSINR MAE of 3.110 dB versus 3.669 dB for persistence, a 15.23% reduction. A separate NASA Tail-654 ground-monitoring implementation replays a historical MATLAB flight record, presents map-based trajectory and primary telemetry, and applies phase-aware statistical anomaly monitoring using robust envelopes, eight-second persistence, and recovery hysteresis. The NASA component is kept experimentally separate from the wireless-link prediction metrics. The resulting system connects telemetry observability with forward-looking communication-state information while preserving a strict separation between information available at prediction time and future observations.

Abstract The topic of pronunciation has historically received limited support within second language (L2) education. One reason for the lack of support for pronunciation within L2 education is that t…

Abstract The topic of pronunciation has historically received limited support within second language (L2) education. One reason for the lack of support for pronunciation within L2 education is that the traditional setting of L2 classrooms is not conducive to the type of feedback that pronunciation instructors require from students. Although there are a variety of AI technologies that have been introduced to L2 education that provide benefits to language learners, the integration of these technologies into pronunciation education has been lacking. The AI-Mediated Pronunciation Learning Loop (AIM-Loop) model was created to incorporate three essential components of pronunciation education: perception, AI feedback, and learner autonomy into a framework that can be utilized to continuously facilitate the pronunciation learning of L2 students. Through reviewing studies on AI and pronunciation published between 2015 and 2025, it becomes possible to identify the mechanisms that contribute to the success of pronunciation education that utilizes AI technologies and frameworks. The results of these studies reveal that the best results occur when these AI technologies are incorporated into pronunciation education in a way that ensures that each of these three components is addressed in each pronunciation learning cycle for L2 students. By proposing the AIM-Loop, researchers can utilize AI technologies to better inform pronunciation education frameworks, and pronunciation educators can utilize this model to better incorporate artificial intelligence into their classrooms and methods of pronunciation education for their L2 students.