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Inverted indexes are a crucial data structure for efficient information retrieval in large text corpora. They enable fast full-text search by mapping each term to the documents in which it appears, on top of which efficient algorithms quickly retrieve the documents relevant to a user query. We present RISE, a novel inverted index library implemented in Rust, designed to deliver high performance and efficiency for information retrieval tasks. RISE leverages Rust's safety and performance to provide a robust solution for building and querying inverted indexes, while offering accessible extensibility through its expressive trait system. While developing RISE, we revisited the inverted-index literature, thereby reproducing numerous prior works using this new test bench. We evaluated RISE against existing libraries, demonstrating competitive query performance across various datasets and workloads, with speedups of up to 2x over the current state of the art. Our results indicate that RISE is a promising tool for researchers and practitioners in the field of information retrieval.

We document the rise of negative earnings between 1980 and 2019: a secular increase in the percent of firms reporting losses, both among public firms and in the broader universe of US corporations, and a secular increase in the persistence of losses year-to-year among public firms. This rise has occurred alongside a spreading of the sales and earnings distribution and a recomposition of firm spending away from production costs and traditional investment and towards sales general and administrative expenses. We rationalize these phenomena with a model of heterogenous firms engaging in supply and demand shifting investment. Our model includes a scale elasticity of demand determining the relationship between the intensive margin of demand (demand per customer) and the extensive margin of demand (number of customers). We are able to quantitatively match the rise in reported losses and qualitatively match (1) the increased persistence of losses, (2) the spreading of the sales and earning distribution and (3) the recomposition of firm spending with this parameter as the single driver of changes across steady state equilibria. The rise in the scale elasticity associated with the increase

While generative video models have achieved remarkable visual fidelity, their capacity to internalize and reason over implicit world rules remains a critical yet under-explored frontier. To bridge this gap, we present RISE-Video, a pioneering reasoning-oriented benchmark for Text-Image-to-Video (TI2V) synthesis that shifts the evaluative focus from surface-level aesthetics to deep cognitive reasoning. RISE-Video comprises 467 meticulously human-annotated samples spanning eight rigorous categories, providing a structured testbed for probing model intelligence across diverse dimensions, ranging from commonsense and spatial dynamics to specialized subject domains. Our framework introduces a multi-dimensional evaluation protocol consisting of four metrics: \textit{Reasoning Alignment}, \textit{Temporal Consistency}, \textit{Physical Rationality}, and \textit{Visual Quality}. To further support scalable evaluation, we propose an automated pipeline leveraging Large Multimodal Models (LMMs) to emulate human-centric assessment. Extensive experiments on 11 state-of-the-art TI2V models reveal pervasive deficiencies in simulating complex scenarios under implicit constraints, offering critical

Capillary rise occurs when a thin tube contacts a liquid, which rises against gravity due to the capillary force. This phenomenon is present in a wide range of everyday and industrial settings and provides the means to measure the physical properties of liquids. Here, we report on the unusual ultra-slow capillary rise on a solid-like material of agarose hydrogels. The observed meniscus motion cannot be described with classical capillary rise models, and we develop a new model based on the fluid transport through the porous hydrogel network. Our model is in good agreement with the experimental data for agarose gels made with five different concentrations and with two different viscosities of the liquid flowing inside the gel. Our results provide a non-invasive technique to directly estimate the permeability of hydrogel interfaces with high spatial resolution, which is important in the implementation of hydrogels in advanced biomedical applications.

Despite the sustained scaling on model capacity and data acquisition, Vision-Language-Action (VLA) models remain brittle in contact-rich and dynamic manipulation tasks, where minor execution deviations can compound into failures. While reinforcement learning (RL) offers a principled path to robustness, on-policy RL in the physical world is constrained by safety risk, hardware cost, and environment reset. To bridge this gap, we present RISE, a scalable framework of robotic reinforcement learning via imagination. At its core is a Compositional World Model that (i) predicts multi-view future via a controllable dynamics model, and (ii) evaluates imagined outcomes with a progress value model, producing informative advantages for the policy improvement. Such compositional design allows state and value to be tailored by best-suited yet distinct architectures and objectives. These components are integrated into a closed-loop self-improving pipeline that continuously generates imaginary rollouts, estimates advantages, and updates the policy in imaginary space without costly physical interaction. Across three challenging real-world tasks, RISE yields significant improvement over prior art, w

The dynamics of buoyant droplets rising freely in a large body of an immiscible liquid is investigated numerically for a moderate drop-to-fluid viscosity ratio $μ^\ast$. We focus on toluene droplets rising in clean water, for which $μ^\ast=0.62$, and vary the radius over $0.5\,\text{mm}\leq R\leq3.0\,\text{mm}$. Direct numerical simulations are performed in imposed axisymmetric and fully three-dimensional configurations. As $R$ increases, the system displays a rich sequence of rise regimes. Starting from steady vertical rise with an axisymmetric disturbance flow, it first undergoes an internal flow instability associated with an azimuthal mode $m=2$, leading to a biplanar-symmetric wake and reduced terminal speed. This state is followed by a steady oblique regime, in which the $m=1$ mode also becomes unstable and coexists with the $m=2$ mode. At larger radii, the path becomes nearly vertical again before the flow enters an $m=2$ rotating-wave regime, where the wake drifts azimuthally at an approximately constant angular velocity. For still larger droplets, persistent shape oscillations and vortex shedding lead to fully three-dimensional chaotic paths. Simulations initialised from f

Firstly, by establishing a prediction model for global sea-level rise and calculating with Maple, it is shown that the global sea-level rise rate in 2009 is 2.68 mm/a. The height and rate of global sea-level rise will be about 9.11 cm and 3.22 mm/a in 2020. Based on the study and the actual land subsidence in Shanghai Lingang New City, the rate of relative sea-level rise near Lingang New City is calculated to be 12.68 mm/a in 2009. Then, through setting up the extrapolation prediction model with a linear trend term and a significant tidal cycle, the rise rate of average sea-level near Lingang New City was predicted. The result showed it will be 0.33 mm/a in 2020.

Societal complexity may be at a historical peak. Distinct from entropy, complexity tends to rise as systems move away from order, crest at an intermediate state, and decline as entropy continues increasing. The use of a thermodynamic analogy and the timing of major technological milestones, from fire to artificial intelligence, shows that the acceleration and recent compression of transformative events fit the derivative of a logistic growth curve. This pattern suggests that the rapid rise in structural and technological novelty may soon begin slowing. Notably, the trajectory parallels the bell-shaped rate of global population growth, consistent with the view that demographic expansion fuels innovation. If complexity growth is indeed cresting, societies face the challenge of managing heightened fragility while adapting to diminishing returns in transformative change. This perspective explores whether the rapid acceleration of technological innovation observed in recent centuries may reflect a civilizational system approaching the region of maximal complexity often associated with the edge of chaos.

This work proposes an ODE model for a capillary rise in pipes with variable cross section and compares it to the lubrication theory model. Two key assumptions are made: (1) radius of the pipe varies with axial coordinate, and (2) pipe's convergence angle is small. The model reduction process involves the identification of critical parameters and simplifies the governing equations by neglecting higher-order terms. Under appropriate scaling, it is shown that generalized Washburn's equation for capillary rise in pipes with variable cross section reduces to the known lubrication theory model.

Discovery of the J Particle at Brookhaven National Laboratory and the Physics of Electrons and Positrons; The Standard Model Yesterday, Today and Tomorrow; The Rise of Gauge Theories: From Many Models to One Theory; From Charm to CP Violation; When the Standard Model Was Ignored; The Discovery of the W and Z Bosons at the CERN Proton-Antiproton Collider; A Personal History of CERN Particle Colliders (1972-2022); The Age of Gravitational Wave Astronomy; Precision Physics in the Era of (HL)LHC; Recent Developments in Flavor Physics, the Unitary Triangle Fit, Anomalies and All That; About BSM Physics, with Emphasis on Flavour; The Discovery of the Antiproton between Rome and Berkeley; Raoul Gatto and Bruno Touschek: the Rise of $e+e^-$ Physics; From ADONE's Multi-Hadron Production to the J/$Ψ$ Discovery; From Bjorken Scaling to Scaling Violations

Recent advancements in large language models (LLMs) have witnessed a surge in the development of advanced reasoning paradigms, which are now being integrated into multimodal large language models (MLLMs). However, existing approaches often fall short: methods solely employing reinforcement learning (RL) can struggle with sample inefficiency and activating entirely absent reasoning capabilities, while conventional pipelines that initiate with a cold-start supervised fine-tuning (SFT) phase before RL may restrict the model's exploratory capacity and face suboptimal convergence. In this work, we introduce \textbf{Metis-RISE} (\textbf{R}L \textbf{I}ncentivizes and \textbf{S}FT \textbf{E}nhances) for multimodal reasoning model learning. Unlike conventional approaches, Metis-RISE distinctively omits an initial SFT stage, beginning instead with an RL phase (e.g., using a Group Relative Policy Optimization variant) to incentivize and activate the model's latent reasoning capacity. Subsequently, the targeted SFT stage addresses two key challenges identified during RL: (1) \textit{inefficient trajectory sampling} for tasks where the model possesses but inconsistently applies correct reasonin

This study investigates the rapid growth and evolving network structure of Bluesky from August 2023 to February 2025. Through multiple waves of user migrations, the platform has reached a stable, persistently active user base. The growth process has given rise to a dense follower network with clustering and hub features that favor viral information diffusion. These developments highlight engagement and structural similarities between Bluesky and established platforms.

Chemistry, a long-standing discipline, has historically relied on manual and often time-consuming processes. While some automation exists, the field is now on the cusp of a significant evolution driven by the integration of robotics and artificial intelligence (AI), giving rise to the concept of the robochemist: a new paradigm where autonomous systems assist in designing, executing, and analyzing experiments. Robochemists integrate mobile manipulators, advanced perception, teleoperation, and data-driven protocols to execute experiments with greater adaptability, reproducibility, and safety. Rather than a fully automated replacement for human chemists, we envisioned the robochemist as a complementary partner that works collaboratively to enhance discovery, enabling a more efficient exploration of chemical space and accelerating innovation in pharmaceuticals, materials science, and sustainable manufacturing. This article traces the technologies, applications, and challenges that define this transformation, highlighting both the opportunities and the responsibilities that accompany the emergence of the robochemist. Ultimately, the future of chemistry is argued to lie in a symbiotic pa

We present an analysis of the B-band and V-band rise-time distributions of nearby Type Ia supernovae (SNe Ia). We use a two-stretch template-fitting method to measure the rise and decline of BV light curves. Our analysis of 61 SNe with high-quality light curves indicates that the longer the time between explosion and maximum light (i.e., the rise time), the slower the decline of the light curve after maximum. However, SNe with slower post-maximum decline rates have a faster rise than would be expected from a single-parameter family of light curves, indicating that SN Ia light curves are not a single-parameter family of varying widths. Comparison of the B-band rise-time distribution for spectroscopically normal SNe Ia to those exhibiting high-velocity spectral features indicates that high-velocity (HV) SNe Ia have shorter B-band rise times compared to their spectroscopically normal counterparts. After normalising the B-band light curves to Dm15(B)= 1.1 mag (i.e., correcting the post-maximum decline to have the same shape as our template), we find that spectroscopically normal SNe Ia have a rise time of 18.03 +/- 0.24 d, while HV SNe have a faster B-band rise time of 16.63 +/- 0.29 d

Some QSOs show an abrupt, strong rise in polarization near rest wavelength 750 A. If this arises in the atmosphere of an accretion disk around a supermassive black hole, it may have diagnostic value. In PG 1222+228, the polarization rise occurs at the wavelength of a sharp drop in flux. We examine and reject interpretations of this feature involving a high velocity outflow. The observations agree with a model involving several intervening Lyman limit systems, two of which happen to coincide with the Lyman continuum polarization rise. After correction for the Lyman limit absorption, the continuum shortward of 912 A is consistent with a typical power-law slope, alpha = -1.8. This violates the apparent pattern for the Lyman limit polarization rises to occur only in ``candidate Lyman edge QSOs''. The corrected, polarized flux rises strongly at the wavelength of the polarization rise, resembling the case of PG 1630+377. The rise in polarized flux places especially stringent requirements on models.

Coronal mass ejections (CMEs) are explosive plasma phenomena prevalently occurring on the Sun and probably on other magnetically active stars. However, how their pre-eruptive configuration evolves toward the main explosion remains elusive. Here, based on comprehensive observations of a long-duration precursor in an event on 2012 March 13, we determine that the heating and slow rise of the pre-eruptive hot magnetic flux rope (MFR) are achieved through a precursor reconnection located above cusp-shaped high-temperature precursor loops. It is observed that the hot MFR threads are built up continually with their middle initially showing an "M" shape and then being separated from the cusp of precursor loops, causing the slow rise of the entire MFR. The slow rise in combination with thermal-dominated hard X-ray source concentrated at the top of the precursor loops shows that the precursor reconnection is much weaker than the flare reconnection of the main eruption. We also perform a three-dimensional magnetohydrodynamics simulation that reproduces the early evolution of the MFR transiting from the slow to fast rise. It is also disclosed that it is the magnetic tension force pertinent to

Evaluating automatically-generated text summaries is a challenging task. While there have been many interesting approaches, they still fall short of human evaluations. We present RISE, a new approach for evaluating summaries by leveraging techniques from information retrieval. RISE is first trained as a retrieval task using a dual-encoder retrieval setup, and can then be subsequently utilized for evaluating a generated summary given an input document, without gold reference summaries. RISE is especially well suited when working on new datasets where one may not have reference summaries available for evaluation. We conduct comprehensive experiments on the SummEval benchmark (Fabbri et al., 2021) and the results show that RISE has higher correlation with human evaluations compared to many past approaches to summarization evaluation. Furthermore, RISE also demonstrates data-efficiency and generalizability across languages.

Observational constraints on classical novae are heavily biased to phases near optical peak and later because of the simple fact that novae are not typically discovered until they become bright. The earliest phases of brightening, coming before discovery, are typically missed, but this is changing with the proliferation of wide-field optical monitoring systems including ZTF, ASAS-SN, and Evryscope. Here, we report on unprecedented observations of the fast nova V1674 Her beginning >10 mag below its optical peak and including high-cadence (2 min.) observations that chart a rise of ~8 mag in just 5 hours. Two clear breaks are identified as the light curve transitions first from rising slowly to rising rapidly, followed by a transition to an even faster, nearly linear rate of increasing flux with time. The depths of the observations allow us to place tight constraints on the size of the photosphere under the assumption of blackbody emission from a white dwarf emitting at its Eddington luminosity. We find that the white dwarf was unlikely to have overflowed its Roche lobe prior to the launch of a fast wind, which poses a challenge for explaining the Fermi $γ$-ray detections as the in

Magnetic flux ropes (MFRs) rising buoyantly through the Sun's convection zone are thought to be subject to viscous forces preventing them from rising coherently. Numerous studies have suggested that MFRs require a minimum twist in order to remain coherent during their rise. Furthermore, even MFRs that get to the photosphere may be unable to successfully emerge into the corona unless they are at least moderately twisted, since the magnetic pressure gradient needs to overcome the weight of the photospheric plasma. To date, however, no lower limit has been placed on the critical minimum twist required for an MFR to rise coherently through the convection zone or emerge through the photosphere. In this paper, we simulate an untwisted toroidal MFR which is able to rise from the convection zone and emerge through the photosphere as an active region that resembles those observed on the Sun. We show that untwisted MFRs can remain coherent during their rise and then pile-up near the photosphere, triggering the undular instability, allowing the MFR to emerge through the photosphere. We propose that the toroidal geometry of our MFR is critical for its coherent rise. Upon emerging, a pair of lo