arXiv Physics @arxiv_physics@qoto.org

We are currently in an era of escalating technological complexity and profound societal transformations, where artificial intelligence (AI) technologies exemplified by large language models (LLMs) have reignited discussions on the 'Technological Singularity'. 'Technological Singularity' is a philosophical concept referring to an irreversible and profound transformation that occurs when AI capabilities surpass those of humans comprehensively. However, quantitative modeling and analysis of the historical evolution and future trends of AI technologies remain scarce, failing to substantiate the singularity hypothesis adequately. This paper hypothesizes that the development of AI technologies could be characterized by the superposition of multiple logistic growth processes. To explore this hypothesis, we propose a multi-logistic growth process model and validate it using two real-world datasets: AI Historical Statistics and Arxiv AI Papers. Our analysis of the AI Historical Statistics dataset assesses the effectiveness of the multi-logistic model and evaluates the current and future trends in AI technology development. Additionally, cross-validation experiments on the Arxiv AI Paper, GPU Transistor and Internet User dataset enhance the robustness of our conclusions derived from the AI Historical Statistics dataset. The experimental results reveal that around 2024 marks the fastest point of the current AI wave, and the deep learning-based AI technologies are projected to decline around 2035-2040 if no fundamental technological innovation emerges. Consequently, the technological singularity appears unlikely to arrive in the foreseeable future.

This paper presents a novel approach to Chinese characters through the lens of physics, network analysis, and natural systems. Computational analysis of over 6,000 characters identified 422 elemental characters as fundamental building blocks. Using a physics-inspired "Zi-Matrix" model, we analyzed character structure across eleven spatial positions, revealing systematic patterns in component relationships and semantic extension. Our research demonstrates that Chinese characters exhibit properties of natural systems: emergent complexity, self-organization, and adaptive resilience. The Fibonacci sequence provides an organizing framework for understanding character evolution, from simple pictographs to sophisticated abstractions. Case studies of character families and semantic networks show how meaning radiates from concrete to abstract domains while maintaining coherent principles. By viewing Chinese characters as a living system, this research transcends mere simplification to reveal how human cognition organizes and transmits knowledge. While the elemental character set reduces memorization burden, it also illuminates profound connections between language, thought, and natural patterns. Chinese characters emerge not just as tools for communication, but as windows into human understanding. This perspective, combined with AI-assisted learning approaches, promises to transform language education from knowledge mastery to meaning discovery, bridging traditional wisdom with modern computational methods.

Among all public transit modes, bus transit systems stand out as the most prevalent and popular. This prominence has spurred a significant body of research addressing various aspects of bus systems. In the literature, analytical approaches and mathematical programming are predominantly used to explore the Public Bus Transit Network Design Problem and Operations Planning (PBTNDP&OP). Part I of our study presented statistical analyses of literature applying these methodologies to PBTNDP&OP, along with a comprehensive review of analytical papers, highlighting the strengths and weaknesses of each approach. In Part II, we delve into the applications of mathematical programming within PBTNDP&OP, building upon the 15 major sub-categories identified in Part I. We have critically analyzed the identified papers within these sub-categories from various perspectives, including the problems investigated, modeling methods employed, decision variables, network structures, and key findings. This critical review highlights selected papers in each category. Finally, acknowledging existing research gaps, we propose potential extensions for future research. Despite the extensive array of publications, numerous topics still warrant further exploration. Notably, sustainable PBTNDP&OP and challenges associated with integrating emerging technologies are poised to dominate future research agendas.

Sequential registering of fluorescence signals in conventional Excitation-Emission Matrices (EEMs), followed by modeling based on multilinear properties of the data, requires stable fluorophore concentrations throughout the acquisition of each EEM. Rapid concentration changes, as seen in chromatography or certain kinetics, can disrupt the conventional bilinearity of EEMs. This deviation depends on the relative rates of concentration changes versus spectral scanning speeds. Although entire scans can be slow, individual data points are acquired almost instantaneously, maintaining linear dependencies. Within specific time intervals, concentrations can be assumed constant. By using time-based localization, partial EEM data can be organized into partially filled cubes that allow for the correct modeling of third-order data. Additionally, Multivariate Curve Resolution requires reshaping operations. Two third-order datasets were analyzed using a time-assisted MCR implementation, following a strategy similar to one reported for chromatographic data (LC-EEM) with Parallel Factor Analysis. The second dataset comes from Diclofenac reaction kinetics (Kin-EEM). The results suggest that time-assisted MCR resolves such data effectively. The solutions found were similar to those obtained when processing the same data as cubes. Predictions from calibration models based on MCR results were comparable to those obtained when deriving higher order models from the same data. High degrees of similarity were achieved between resolved and reference spectral profiles. Both chromatographic and kinetic profiles were accurate and physically meaningful. The proposed strategy highlights the potential of incorporating time-based localization to enhance the analysis of fluorescence data in dynamic systems.

In a series of two papers, we provide a comprehensive agenda of actions the American Astronomical Society (AAS) can take to create a more diverse and inclusive professional system for astronomers, with a focus on women astronomers. This first paper of the series outlines the background and methods, while the recommendations are treated in the second companion paper (Paper II). We take the stance that since the 2020 Decadal Survey (Astro2020) was delivered in 2021, with its first-ever set of recommendations on the State of the Profession, now is the time for the AAS to take decisive action to transform astronomy into a diverse and inclusive profession. In the spring of 2019, the CSWA surveyed the astronomical community to assess the popularity and feasibility of actions that the AAS can take to reduce harassment and advance career development for women in astronomy. Here we present the quantitative results of that survey and a synopsis of the free response sections, which are publicly accessible. By combining the results of our survey, peer-reviewed academic literature, and findings from many of the white papers submitted to Astro2020, the CSWA has developed 26 specific actions that the AAS can take to help end harassment in astronomy, to advance career development for astronomers who are women and who are other members of historically marginalized groups, and intersections of these populations, and to improve the climate and culture of AAS and AAS-sponsored meetings. This paper presents the data we used to make these recommendations, and the recommendations themselves will be presented in Paper II.

This paper, the second in a series of two, provides a set of recommendations that the American Astronomical Society (AAS) can take to create a more diverse and inclusive professional society for astronomers, with a focus on women astronomers. As noted in Paper I, now is the time for the AAS to take decisive action to transform astronomy into a diverse and inclusive profession. By combining the results of our 2019 survey, which is described in Paper I, peer-reviewed academic literature, and findings from many of the white papers submitted to Astro2020, the CSWA has developed 26 specific actions the AAS can take to help end harassment and bullying in astronomy; advance career development for astronomers who are women, members of other underrepresented groups, and intersections of these populations; and improve the climate and culture of AAS meetings. Actions to reduce rates of harassment and bullying include improvements to the AAS's anti-harassment policies and procedures and the development of astronomy-specific anti-harassment training resources. Actions to advance career development include creating a compensation database, improving how jobs are posted in the AAS Job Register, and supporting/enhancing a distance mentorship program. Finally, we call on the AAS to continue improving the accessibility of AAS meetings and to continue to support meeting sessions whose focus is to discuss issues of equity, diversity, and inclusion.

In this paper, we report on a study of the road networks of the provinces of Canada as complex networks. A number of statistical features have been analyzed and compared with two random models. In addition, we have also studied the resilience of these road networks under random failures and targeted attacks.

Arrival flow profiles enable precise assessment of urban arterial dynamics, aiding signal control optimization. License Plate Recognition (LPR) data, with its comprehensive coverage and event-based detection, is promising for reconstructing arrival flow profiles. This paper introduces an arrival flow profile estimation and prediction method for urban arterials using LPR data. Unlike conventional methods that assume traffic homogeneity and overlook detailed traffic wave features and signal timing impacts, our approach employs a time partition algorithm and platoon dispersion model to calculate arrival flow, considering traffic variations and driving behaviors using only boundary data. Shockwave theory quantifies the piecewise function between arrival flow and profile. We derive the relationship between arrival flow profiles and traffic dissipation at downstream intersections, enabling recursive calculations for all intersections. This approach allows prediction of arrival flow profiles under any signal timing schemes. Validation through simulation and empirical cases demonstrates promising performance and robustness under various conditions.

In this work we show the quaternionic quantum descriptions of physical processes from the Planck to macro scale. The results presented here are based on the concepts of the Cauchy continuum and the elementary cell at the Planck scale. The symmetrization of quaternion relations and the postulate of quaternion velocity have been crucial in the present development. The momentum of the expansion and compression is the consequence of the scalar term in the quaternionic deformation potential.

We investigate MeV-scale electron neutrino charged current interactions in a liquid argon time projection chamber equipped with an enhanced photon detection system. Using simulations of deposited energy in charge and light calorimetry, we explore the potential for dual calorimetric neutrino energy reconstruction. We found energy reconstruction based on light-only calorimetry has a better resolution than combined charge and light calorimetry when hadrons are produced in these events. Meanwhile, enhanced light detection offers improved nanosecond timing resolution and broad optical coverage, enabling neutron tagging and identification of delayed low-energy gamma emissions. These advancements open new avenues in low-energy neutrino physics in next-generation LArTPCs.

Recent developments demonstrate that General Relativity (GR) is as an effective field theory (EFT) with a limited domain of validity, undergoing a breakdown of its fundamental symmetry in strong fields and exhibiting only one-loop finiteness in a linearized, weak-field expansion. In this paper, we introduce the \emph{Principle of Spatial Energy Potentiality}, wherein both time and gravity emerge from a purely spatial, high-energy configuration. This framework reinterprets the Big Bang as a phase transition from 3D space to 4D spacetime, thereby avoiding traditional singularities and offering alternative early-universe dynamics. We illustrate these ideas with toy-model derivations and discuss potential observational consequences, arguing that new principles beyond fundamental covariance are needed to address gravity in both weak- and strong-field regimes.

In this study, we innovatively modeled photon-enhanced thermionic emission (PETE) devices, incorporating positive ion injection and bidirectional discharge's effects on the space charge barrier simultaneously. Compared to previous models, our model allows the positive ion distribution function to be compatible with scenarios in which the anode motive is either higher or lower than the cathode motive, and also adapts to significant anode discharge. Through numerical simulations and parametric analyses, we found that: (1) As the ratio of the positive ion increases, the capability for space charge neutralization becomes stronger. (2) The lower the electron affinity is, the smaller the ratio of positive ions are required. (3) When the anode temperature is higher or the anode work function is lower, the impact of reverse discharge on the net current density is more pronounced. Conversely, when the anode temperature is higher or the anode work function is greater, the ratio of positive ions required to achieve complete space charge neutralization increases. This study further elucidates the mechanisms and characteristics of space charge neutralization effects in PETE devices, providing a theoretical foundation for optimizing their design. Additionally, the accompanying theory and algorithm possess the potential to spark innovative research across diverse fields.

Quantum Physics is a cornerstone of modern science and technology, yet a comprehensive approach to integrating it into school curricula and communicating its foundations to policymakers, industrial stakeholders, and the general public has yet to be established. In this paper, we discuss the rationale for introducing entanglement and Bell's inequalities to a non-expert audience, and how these topics have been presented in the exhibit "Dire l'indicibile" ("Speaking the Unspeakable"), as a part of the Italian Quantum Weeks project. This initiative aims to make quantum mechanics accessible to all, bridging the gap between complex scientific principles and public understanding. Our approach meets the challenge of simplifying quantum concepts without sacrificing their core meaning, specifically avoiding the risks of oversimplification and inaccuracy. Through interactive activities, including a card game demonstration and the staging of CHSH experiments, participants explore the fundamental differences between classical and quantum probabilistic predictions. They gain insights into the significance of Bell inequality verification experiments and the implications of the 2022 Nobel Prize in Physics. Preliminary results from both informal and formal assessment sessions are encouraging, suggesting the effectiveness of this approach.

Mode division multiplexing (MDM) systems leveraging spatial modes carrying orbital angular momentum (OAM) present a promising approach to enhance communication capacity in free-space and fiber-optic networks. Efficient detection of OAM modes is critical for their practical implementation. Spiral phase plates (SPPs) are low-cost optical elements with simple structures, capable of generating OAM waves with high conversion efficiency and precision. This makes the inverse-SPP a promising candidate for the detection of OAM modes. In this study, the performance of SPPs as OAM detectors is thoroughly analyzed. Key parameters, including efficiency, crosstalk, and signal-to-interference ratio, are examined through analytical and numerical methods. Results demonstrate that an inverse-SPP, combined with an optimized propagation length and aperture size, enables effective and precise OAM detection operation.

Evaporation rates from porous evaporators under sunlight have been reported to exceed the solar-thermal limit, determined by relating the incoming solar energy to the latent and sensible heat of water, for applications in desalination and brine pond drying. Although flat two-dimensional (2D) evaporators exceeding the solar limit implies a non-thermal process, tall three-dimensional (3D) solar evaporators can exceed it by absorbing additional environmental heat into its cold sidewalls. Through modeling, we explain the physics and identify the critical heights in which a fin transitions from 2D to 3D evaporation and exceeds the solar-thermal limit. Our analyses illustrate that environmental heat absorption in 3D evaporators is determined by the ambient relative humidity and the airflow velocity. The model is then coarse-grained into a large-scale fin array device on the meters scale to analyze their scalability. We identify that these devices are unlikely to scale favorably in closed environment settings such as solar stills. Our modeling clearly illustrates the benefits and limitations of 3D evaporating arrays and pinpoints design choices in previous works that hinder the device's overall performance. This work illustrates the importance in distinguishing 2D from 3D evaporation for mechanisms underlying interfacial evaporation exceeding the solar-thermal limit.

We present high-performance implementations of the two-dimensional Ising and Blume-Capel models for large-scale, multi-GPU simulations. Our approach takes full advantage of the NVIDIA GB200 NVL72 system, which features up to $72$ GPUs interconnected via high-bandwidth NVLink, enabling direct GPU-to-GPU memory access across multiple nodes. By utilizing Fabric Memory and an optimized Monte Carlo kernel for the Ising model, our implementation supports simulations of systems with linear sizes up to $L=2^{23}$, corresponding to approximately $70$ trillion spins. This allows for a peak processing rate of nearly $1.15 \times 10^5$ lattice updates per nanosecond-setting a new performance benchmark for Ising model simulations. Additionally, we introduce a custom protocol for computing correlation functions, which strikes an optimal balance between computational efficiency and statistical accuracy. This protocol enables large-scale simulations without incurring prohibitive runtime costs. Benchmark results show near-perfect strong and weak scaling up to $64$ GPUs, demonstrating the effectiveness of our approach for large-scale statistical physics simulations.

Super-resolution imaging has revolutionized the study of systems ranging from molecular structures to distant galaxies. However, existing deep-learning-based methods require extensive calibration and retraining for each imaging setup, limiting their practical deployment. We introduce a device-agnostic deep-learning framework for super-resolution imaging of point-like emitters that eliminates the need for calibration data or explicit knowledge of optical system parameters. Our model is trained on a diverse, numerically simulated dataset encompassing a broad range of imaging conditions, enabling robust generalization across different optical setups. Once trained, it reconstructs super-resolved images directly from a single resolution-limited camera frame with superior accuracy and computational efficiency compared to conventional methods. We experimentally validate our approach using a custom-built microscopy setup with ground truth emitter positions and demonstrate its versatility on astronomical and single-molecule localization microscopy datasets, achieving unprecedented resolution without prior information. Our findings establish a pathway toward universal, calibration-free super-resolution imaging, expanding its applicability across scientific disciplines.

I study the conditions under which a democratic dynamics of a public debate drives a Minority-to-Majority transition. A landscape of the opinion dynamics is thus built using the Galam Majority Model (GMM) in a 3-dimensional parameter space for three different sizes r=2, 3, 4 of local discussing groups. The related parameters are (p_0, k, x), the respective proportions of initial agents supporting opinion A, unavowed tie prejudices breaking in favor of opinion A, and contrarians. Combining k and x yields unexpected and counterintuitive results. In most part of the landscape the final outcome is predetermined with a single attractor dynamics independently of the initial supports for the competing opinions. Large domains of (k, x) values are found to lead an initial minority to turn majority democratically without any external influence. A new alternating regime is also unveiled in narrow ranges of extreme proportions of contrarians. The findings indicate that the expected democratic character of free opinion dynamics is indeed rarely satisfied. The actual values of (k, x) are found to be instrumental to predetermine the final winning opinion. Therefore, the conflicting challenge for the predetermined opinion to loose, is to modify these values appropriately to become the winner. However, developing a model which could help manipulating public opinion rises ethical questions. The issue is discussed in the conclusion.

Computing molecular vibrational energies and wavefunctions using variational approaches is a computationally demanding task, with the primary bottleneck being the diagonalization of the Hamiltonian matrix. The achievable accuracy depends on the dimensionality and complexity of the vibrational problem, the number and quality of the utilized basis functions and on the choice of vibrational coordinates. Composing basis sets with normalizing flows is a newly emerged idea to improve their expressivity and subsequently reduce the computational effort of variational methods. This approach generates coordinates tailored to a given vibrational problem for a selected truncated basis. We illustrate that the optimized coordinates results in more accurate predictions and faster convergence when transferred across different basis-set truncations, without the need for re-optimization. In addition, we show that the optimized coordinates are transferable across isotopologues and across molecules with similar structural motifs.