Recent advancements in video models have shown tremendous progress, particularly in long video understanding. However, current benchmarks predominantly feature western-centric data and English as the dominant language, introducing significant biases in evaluation. To address this, we introduce MINERVA-Cultural, a challenging benchmark for multicultural and multilingual video reasoning. MINERVA-Cultural comprises high-quality, entirely human-generated annotations from diverse, region-specific cultural videos across 18 global locales. Unlike prior work that relies on automatic translations, MINERVA-Cultural provides complex questions, answers, and multi-step reasoning steps, all crafted in native languages. Making progress on MINERVA-Cultural requires a deeply situated understanding of visual cultural context. Furthermore, we leverage MINERVA-Cultural's reasoning traces to construct evidence-based graphs and propose a novel iterative strategy using these graphs to identify fine-grained errors in reasoning. Our evaluations reveal that SoTA Video-LLMs struggle significantly, performing substantially below human-level accuracy, with errors primarily stemming from the visual perception o
Three decades ago, humanity entered the Exoplanet Era, with the discovery of the first planets orbiting other stars. Today, more than 6000 exoplanets are known - a tally recently bolstered by NASA's TESS spacecraft. Whilst TESS is an exceptional planet finding machine, dedicated follow-up observations from the ground are required to confirm the existence of the planets it discovers. To achieve this, we constructed the southern hemisphere's only dedicated exoplanet detection and characterisation facility, MINERVA-Australis, at the University of Southern Queensland's Mt Kent Observatory. Funded in 2015, MINERVA-Australis saw first light in 2018, in time for the launch of TESS. MINERVA-Australis has since been scouring the skies, working to confirm and characterise the incredible harvest of planets detected by TESS. To date, the facility has contributed to the discovery of 40 new exoplanets, and continued the legacy of radial velocity data from the Anglo-Australian Planet Search program.
We present an overview of the MINERVA survey, a 259.8 hour (prime) and 127 hour (parallel) Cycle 4 treasury program on the James Webb Space Telescope (JWST). MINERVA is obtaining 8 filter NIRCam medium band imaging (F140M, F162M, F182M, F210M, F250M, F300M, F360M, F460M) and 2 filter MIRI imaging (F1280W, F1500W) in four of the five CANDELS Extragalactic fields: UDS, COSMOS, AEGIS and GOODS-N. These fields were previously observed in Cycle 1 with 7 - 9 NIRCam filters by the PRIMER, CEERS and JADES programs. MINERVA reaches a 5$σ$ depth of 28.1 mag in F300M and covers $\sim$ 542 arcmin$^2$, increasing the area of existing JWST medium-band coverage in at least 8 bands by $\sim$ 7$\times$. The MIRI imaging reaches a 5$σ$ depth of 23.9 mag in F1280W and covers $\sim$ 275 arcmin$^2$ in at least 2 MIRI filters. When combined with existing imaging, these data will provide a photometric catalog with 20-26 JWST filters (depending on field) and 26-35 filters total, including HST. This paper presents a detailed breakdown of the filter coverage, exposure times, and field layout relative to previous observations, as well as an overview of the primary science goals of the project. These include
Despite advances in AI for contact centers, customer experience (CX) continues to suffer from high average handling time (AHT), low first-call resolution, and poor customer satisfaction (CSAT). A key driver is the cognitive load on agents, who must navigate fragmented systems, troubleshoot manually, and frequently place customers on hold. Existing AI-powered agent-assist tools are often reactive driven by static rules, simple prompting, or retrieval-augmented generation (RAG) without deeper contextual reasoning. We introduce Agentic AI goal-driven, autonomous, tool-using systems that proactively support agents in real time. Unlike conventional approaches, Agentic AI identifies customer intent, triggers modular workflows, maintains evolving context, and adapts dynamically to conversation state. This paper presents a case study of Minerva CQ, a real-time Agent Assist product deployed in voice-based customer support. Minerva CQ integrates real-time transcription, intent and sentiment detection, entity recognition, contextual retrieval, dynamic customer profiling, and partial conversational summaries enabling proactive workflows and continuous context-building. Deployed in live product
We compare recent MINERvA antineutrino-hydrogen charged-current measurements to phenomenological predictions of the axial-vector form factor based on fits to all available electron scattering and deuterium bubble-chamber data and to representative lattice-QCD (LQCD) determination by the PNDME Collaboration. While there is $1$--$2σ$ agreement in the cross section with MINERvA data for each bin in $Q^2$, we identify three regions with different relevance and opportunity for LQCD predictions. For $Q^2 \lesssim 0.2~\mathrm{GeV}^2$, the phenomenological extractions have large number of data points and LQCD is competitive, while MINERvA data have large errors. For $0.2~\mathrm{GeV}^2 \lesssim Q^2 \lesssim 1~\mathrm{GeV}^2$, LQCD is competitive with the MINERvA determination, and both give values larger than from phenomenological extraction. For $Q^2 > 1~\mathrm{GeV}^2$, the MINERvA data are the most precise. Our analysis indicates that with improving precision of MINERvA-like experiments and LQCD data, the uncertainty in the nucleon axial-vector form factor will be steadily reduced.
We investigate the impact of the new measurement of the antineutrino-proton scattering cross-section from the MINERvA Collaboration on generalized parton distributions (GPDs), particularly the polarized GPDs denoted as $\widetilde{H}^q$. To achieve this, we perform some QCD analyses of the MINERvA data, in addition to all available data of the proton's axial form factors. We demonstrate that MINERvA data lead to consistent results with other related experimental data, confirming the universality of GPDs. Our results indicate that MINERvA data can impose new constraints on GPDs, particularly on $\widetilde{H}^q$. Our predictions for the proton's axial charge radius, WACS cross-section, and axial form factor show good consistency with those of other studies and measurements. This leads us to conclude that the result of a more comprehensive analysis, considering all related experimental data, is not only reasonable but also more reliable, even in light of existing tensions among the data. The present study can be considered as a guideline for performing a new and comprehensive QCD global analysis of GPDs including the MINERvA measurements like that presented in Phys. Rev. D \textbf{10
Multimodal LLMs are turning their focus to video benchmarks, however most video benchmarks only provide outcome supervision, with no intermediate or interpretable reasoning steps. This makes it challenging to assess if models are truly able to combine perceptual and temporal information to reason about videos, or simply get the correct answer by chance or by exploiting linguistic biases. To remedy this, we provide a new video reasoning dataset called MINERVA for modern multimodal models. Each question in the dataset comes with 5 answer choices, as well as detailed, hand-crafted reasoning traces. Our dataset is multimodal, diverse in terms of video domain and length, and consists of complex multi-step questions. Extensive benchmarking shows that our dataset provides a challenge for frontier open-source and proprietary models. We perform fine-grained error analysis to identify common failure modes across various models, and create a taxonomy of reasoning errors. We use this to explore both human and LLM-as-a-judge methods for scoring video reasoning traces, and find that failure modes are primarily related to temporal localization, followed by visual perception errors, as opposed to
Data silos create barriers in accessing and utilizing data dispersed over networks. Directly sharing data easily suffers from the long downloading time, the single point failure and the untraceable data usage. In this paper, we present Minerva, a peer-to-peer cross-cluster data query system based on InterPlanetary File System (IPFS). Minerva makes use of the distributed Hash table (DHT) lookup to pinpoint the locations that store content chunks. We theoretically model the DHT query delay and introduce the fat Merkle tree structure as well as the DHT caching to reduce it. We design the query plan for read and write operations on top of Apache Drill that enables the collaborative query with decentralized workers. We conduct comprehensive experiments on Minerva, and the results show that Minerva achieves up to $2.08 \times$ query performance acceleration compared to the original IPFS data query, and could complete data analysis queries on the Internet-like environments within an average latency of $0.615$ second. With collaborative query, Minerva could perform up to $1.39 \times$ performance acceleration than centralized query with raw data shipment.
Ransomware attacks have caused billions of dollars in damages in recent years, and are expected to cause billions more in the future. Consequently, significant effort has been devoted to ransomware detection and mitigation. Behavioral-based ransomware detection approaches have garnered considerable attention recently. These behavioral detectors typically rely on process-based behavioral profiles to identify malicious behaviors. However, with an increasing body of literature highlighting the vulnerability of such approaches to evasion attacks, a comprehensive solution to the ransomware problem remains elusive. This paper presents Minerva, a novel, robust approach to ransomware detection. Minerva is engineered to be robust by design against evasion attacks, with architectural and feature selection choices informed by their resilience to adversarial manipulation. We conduct a comprehensive analysis of Minerva across a diverse spectrum of ransomware types, encompassing unseen ransomware as well as variants designed specifically to evade Minerva. Our evaluation showcases the ability of Minerva to accurately identify ransomware, generalize to unseen threats, and withstand evasion attacks
Recent neutrino-nucleus cross-section measurements of observables characterising kinematic imbalance from the T2K, MicroBooNE and MINERvA experiments are used to benchmark predictions from widely used neutrino interaction event generators. Given the different neutrino energy spectra and nuclear targets employed by the three experiments, comparisons of model predictions to their measurements breaks degeneracies that would be present in any single measurement. In particular, the comparison of T2K and MINERvA measurements offers a probe of energy dependence, whilst a comparison of T2K and MicroBooNE investigates scaling with nuclear target. In order to isolate the impact of individual nuclear effects, model comparisons are made following systematic alterations to: the nuclear ground state; final state interactions and multi-nucleon interaction strength. The measurements are further compared to the generators used as an input to DUNE/SBN and T2K/Hyper-K analyses. Whilst no model is able to quantitatively describe all the measurements, evidence is found for mis-modelling of A-scaling in multi-nucleon interactions and it is found that tight control over how energy is distributed among ha
We revisit models of heavy neutral leptons (neutrissimos) with transition magnetic moments as explanations of the $4.8σ$ excess of electron-like events at MiniBooNE. We perform a detailed Monte Carlo-based analysis to re-examine the preferred regions in the model parameter space to explain MiniBooNE, considering also potential contributions from oscillations due to an eV-scale sterile neutrino. We then derive robust constraints on the model using neutrino-electron elastic scattering data from MINERvA. We find that MINERvA rules out a large region of parameter space, but allowed solutions exist at the $2σ$ confidence level. A dedicated MINERvA analysis would likely be able to probe the entire region of preference of MiniBooNE in this model.
The MINiature Exoplanet Radial Velocity Array (MINERVA) is a dedicated observatory of four 0.7m robotic telescopes fiber-fed to a KiwiSpec spectrograph. The MINERVA mission is to discover super-Earths in the habitable zones of nearby stars. This can be accomplished with MINERVA's unique combination of high precision and high cadence over long time periods. In this work, we detail changes to the MINERVA facility that have occurred since our previous paper. We then describe MINERVA's robotic control software, the process by which we perform 1D spectral extraction, and our forward modeling Doppler pipeline. In the process of improving our forward modeling procedure, we found that our spectrograph's intrinsic instrumental profile is stable for at least nine months. Because of that, we characterized our instrumental profile with a time-independent, cubic spline function based on the profile in the cross dispersion direction, with which we achieved a radial velocity precision similar to using a conventional "sum-of-Gaussians" instrumental profile: 1.8 m s$^{-1}$ over 1.5 months on the RV standard star HD 122064. Therefore, we conclude that the instrumental profile need not be perfectly a
From a set of adaptive optics (AO) observations collected with the W.M. Keck telescope between August and September 2009, we derived the orbital parameters of the most recently discovered satellites of the large C-type asteroid (93) Minerva. The satellites of Minerva, which are approximately 3 and 4 km in diameter, orbit very close to the primary $\sim$5 & $\sim$8 $\times$ Rp and $\sim$1% & $\sim$2% $\times$ RHill) in a circular manner, sharing common characteristics with most of the triple asteroid systems in the main-belt. Combining these AO observations with lightcurve data collected since 1980 and two stellar occultations in 2010 & 2011, we removed the ambiguity of the pole solution of Minerva's primary and showed that it has an almost regular shape with an equivalent diameter Deq = 154 $\pm$ 6 km in agreement with IRAS observations. The surprisingly high bulk density of 1.75 $\pm$ 0.30 g/cm$\^3$ for this C-type asteroid, suggests that this taxonomic class is composed of asteroids with different compositions, For instance, Minerva could be made of the same material as dry CR, CO, and CV meteorites. We discuss possible scenarios on the origin of the system and conclu
The Minerva-Australis telescope array is a facility dedicated to the follow-up, confirmation, characterisation, and mass measurement of bright transiting planets discovered by the Transiting Exoplanet Survey Satellite (TESS) -- a category in which it is almost unique in the southern hemisphere. It is located at the University of Southern Queensland's Mount Kent Observatory near Toowoomba, Australia. Its flexible design enables multiple 0.7m robotic telescopes to be used both in combination, and independently, for high-resolution spectroscopy and precision photometry of TESS transit planet candidates. Minerva-Australis also enables complementary studies of exoplanet spin-orbit alignments via Doppler observations of the Rossiter-McLaughlin effect, radial velocity searches for non-transiting planets, planet searches using transit timing variations, and ephemeris refinement for TESS planets. In this first paper, we describe the design, photometric instrumentation, software, and science goals of Minerva-Australis, and note key differences from its Northern hemisphere counterpart -- the Minerva array. We use recent transit observations of four planets--WASP-2b, WASP-44b, WASP-45b, and HD
The MINERvA collaboration operated a scaled-down replica of the solid scintillator tracking and sampling calorimeter regions of the MINERvA detector in a hadron test beam at the Fermilab Test Beam Facility. This article reports measurements with samples of protons, pions, and electrons from 0.35 to 2.0 GeV/c momentum. The calorimetric response to protons, pions, and electrons are obtained from these data. A measurement of the parameter in Birks' law and an estimate of the tracking efficiency are extracted from the proton sample. Overall the data are well described by a Geant4-based Monte Carlo simulation of the detector and particle interactions with agreements better than 4%, though some features of the data are not precisely modeled. These measurements are used to tune the MINERvA detector simulation and evaluate systematic uncertainties in support of the MINERvA neutrino cross section measurement program.
Processes with precisely known cross sections, like neutrino electron elastic scattering ($νe^{-} \!\rightarrow νe^{-}$) and inverse muon decay ($ν_μe^{-} \!\rightarrow μ^{-} ν_e$) have been used by MINERvA to constrain the uncertainty on the NuMI neutrino beam flux. This work presents a new measurement of neutrino elastic scattering with electrons using the medium energy umubar enhanced NuMI beam. A sample of 578 events after background subtraction is used in combination with the previous measurement on the umu beam and the inverse muon decay measurement to reduce the uncertainty on the umu flux in the umu-enhanced beam from 7.6\% to 3.3\% and the umubar flux in the umubar-enhanced beam from 7.8\% to 4.7\%.
Between 2013 and 2019 MINERvA collected an accelerator neutrino interaction dataset that is uniquely relevant to the energy range of DUNE. These are the only currently available data at intermediate and high momentum transfers for multiple nuclear targets in the same beam. MINERvA is undertaking a campaign to preserve these data and make them publicly available so that they may be analyzed beyond the end of the MINERvA collaboration. We encourage the community to consider the development of centralized resources to enable long-term access to these data and analysis tools for the entire HEP community.
Neutrino-induced charged-current single $π^+$ production in the $Δ(1232)$ resonance region is of considerable interest to accelerator-based neutrino oscillation experiments. In this work, high statistics differential cross sections are reported for the semi-exclusive reaction $ν_μA \to μ^- π^+ +$ nucleon(s) on scintillator, carbon, water, iron, and lead targets recorded by MINERvA using a wide-band $ν_μ$ beam with $\left< E_ν\right> \approx 6$~GeV. Suppression of the cross section at low $Q^2$ and enhancement of low $T_π$ are observed in both light and heavy nuclear targets compared to phenomenological models used in current neutrino interaction generators. The cross-section ratios for iron and lead compared to CH across the kinematic variables probed are 0.8 and 0.5 respectively, a scaling which is also not predicted by current generators.
Determination of the quasi-elastic scattering cross-section over a broad range of neutrino energies, nuclear targets and Q^2 is a primary goal of the MINERvA experiment. We present preliminary comparisons of data and simulation in a sample rich in anti-ν_μ+p\rightarrowμ+n events from approximately one eighth of the total anti-ν events collected by MINERvA to date. We discuss future plans for quasi-elastic analyses in MINERvA.
This technical note describes the application of the Valencia RPA multi-nucleon effect and its uncertainty to QE reactions from the GENIE neutrino event generator. The analysis of MINERvA neutrino data in Rodrigues et al. PRL 116 071802 (2016) paper makes clear the need for an RPA suppression, especially at very low momentum and energy transfer. That published analysis does not constrain the magnitude of the effect; it only tests models with and without the effect against the data. Other MINERvA analyses need an expression of the model uncertainty in the RPA effect. A well-described uncertainty can be used for systematics for unfolding, for model errors in the analysis of non-QE samples, and as input for fitting exercises for model testing or constraining backgrounds. This prescription takes uncertainties on the parameters in the Valencia RPA model and adds a (not-as-tight) constraint from muon capture data. For MINERvA we apply it as a 2D ($q_0$,$q_3$) weight to GENIE events, in lieu of generating a full beyond-Fermi-gas quasielastic events. Because it is a weight, it can be applied to the generated and fully Geant4 simulated events used in analysis without a special GENIE sample.