搜索结果：Imperiling

We consider debiased inference on finite-dimensional functionals of infinite-dimensional least-squares solutions to inverse problems as a way to avoid having to assume exact solutions exist. Such assumptions are substantive and not innocuous, and their failure may imperil inference when we impose them on the statistical model. Our approach instead allows us to conduct inference on a quantity that is defined regardless of solutions existing and coincides with the usual estimands when they do. For the case of instrumental variables, this means we can motivate the analysis with structural models but these do not need to hold exactly for the semiparametric inferential procedure to remain valid.

We study Gaussian-copula models with discrete margins, with primary emphasis on low-count (Poisson) data. Our goal is exact yet computationally efficient maximum likelihood (ML) estimation in regimes where many observations contain small counts, which imperils both identifiability and numerical stability. We develop three novel Kendall's tau-based approaches for initialization tailored to discrete margins in the low-count regime and embed it within an inference functions for margins (IFM) inspired start. We present three practical initializers (exact, low-intensity approximation, and a transformation-based approach) that substantially reduce the number of ML iterations and improve convergence. For the ML stage, we use an unconstrained reparameterization of the model's parameters using the log and spherical-Cholesky and compute exact rectangle probabilities. Analytical score functions are supplied throughout to stabilize Newton-type optimization. A simulation study across dimensions, dependence levels, and intensity regimes shows that the proposed initialization combined with exact ML achieves lower root-mean-squared error, lower bias and faster computation times than the alternativ

The global crisis of language endangerment meets a technological turning point as Generative AI (GenAI) and Large Language Models (LLMs) unlock new frontiers in automating corpus creation, transcription, translation, and tutoring. However, this promise is imperiled by fragmented practices and the critical lack of a methodology to navigate the fraught balance between LLM capabilities and the profound risks of data scarcity, cultural misappropriation, and ethical missteps. This paper introduces a novel analytical framework that systematically evaluates GenAI applications against language-specific needs, embedding community governance and ethical safeguards as foundational pillars. We demonstrate its efficacy through the Te Reo Māori revitalization, where it illuminates successes, such as community-led Automatic Speech Recognition achieving 92% accuracy, while critically surfacing persistent challenges in data sovereignty and model bias for digital archives and educational tools. Our findings underscore that GenAI can indeed revolutionize language preservation, but only when interventions are rigorously anchored in community-centric data stewardship, continuous evaluation, and transpa

In circuit-based quantum state preparation, qubit loss and coherent errors are circuit imperfections that imperil the formation of long-range entanglement beyond a certain threshold. The critical theory at the threshold is a continuous entanglement transition known to be described by a (2+0)-dimensional non-unitary conformal field theory which, for the two types of imperfections of certain circuits, is described by either percolation or Nishimori criticality, respectively. Here we study the threshold behavior when the two types of errors simultaneously occur and show that, when moving away from the Clifford-regime of projective stabilizer measurements, the percolation critical point becomes unstable and the critical theory flows to Nishimori universality. We track this critical renormalization group (RG) crossover flow by mapping out the entanglement phase diagrams, parametrized by the probability and strength of random weak measurements, of two dual protocols preparing surface code or GHZ-class cat states from a parent cluster state via constant-depth circuits. Extensive numerical simulations, using hybrid Gaussian fermion and tensor network / Monte Carlo sampling techniques on sy

In the final paper of this series, we discuss new perspectives and challenges in the study of interstellar medium (ISM), leveraging comprehensive catalogs and physical insights presented in our previous papers. We focus on key questions of far-infrared (FIR) fine-structure lines (FSLs): their origins, diagnostic value, and implications of correlations. Our analysis reveals a strong dependence on elemental abundance, so that FSL/H$α$ traces metallicity, [N II]/[C II] traces N/O, and $\sim$80% of [C II] emission arises from neutral gas without systematic variations. We conclude a coherence exists between the emissions from ionized and neutral gases regarding energy sources and distribution. We argue that [C II] is physically a metallicity-dependent star formation rate (SFR) tracer, while its correlations with atomic or molecular gas masses are secondary. Crucially, the [C II] ``deficit'' is only part of a universal ``deficit'' problem that shows in all neutral and ionized gas lines including extinction-corrected H$α$, caused by infrared (IR) luminosities and characterized by a dichotomy in gas and dust behaviors. This universal ``deficit'' marks a breakdown of the obscuration-correct

Scholarly journals rely on peer review to identify the science most worthy of publication. Yet finding willing and qualified reviewers to evaluate manuscripts has become an increasingly challenging task, possibly even threatening the long-term viability of peer review as an institution. What can or should be done to salvage it? Here, we develop mathematical models to reveal the intricate interactions among incentives faced by authors, reviewers, and readers in their endeavors to identify the best science. Two facets are particularly salient. First, peer review partially reveals authors' private sense of their work's quality through their decisions of where to send their manuscripts. Second, journals' reliance on traditionally unpaid and largely unrewarded review labor deprives them of a standard market mechanism -- wages -- to recruit additional reviewers when review labor is in short supply. We highlight a resulting feedback loop that threatens to overwhelm the peer review system: (1) an increase in submissions overtaxes the pool of suitable peer reviewers; (2) the accuracy of review drops because journals either must either solicit assistance from less qualified reviewers or ask

Adaptive experiments such as multi-arm bandits adapt the treatment-allocation policy and/or the decision to stop the experiment to the data observed so far. This has the potential to improve outcomes for study participants within the experiment, to improve the chance of identifying best treatments after the experiment, and to avoid wasting data. Seen as an experiment (rather than just a continually optimizing system) it is still desirable to draw statistical inferences with frequentist guarantees. The concentration inequalities and union bounds that generally underlie adaptive experimentation algorithms can yield overly conservative inferences, but at the same time the asymptotic normality we would usually appeal to in non-adaptive settings can be imperiled by adaptivity. In this article we aim to explain why, how, and when adaptivity is in fact an issue for inference and, when it is, understand the various ways to fix it: reweighting to stabilize variances and recover asymptotic normality, always-valid inference based on joint normality of an asymptotic limiting sequence, and characterizing and inverting the non-normal distributions induced by adaptivity.

Context: Students often misunderstand programming problem descriptions. This can lead them to solve the wrong problem, which creates frustration, obstructs learning, and imperils grades. Researchers have found that students can be made to better understand the problem by writing examples before they start programming. These examples are checked against correct and wrong implementations -- analogous to mutation testing -- provided by course staff. Doing so results in better student understanding of the problem as well as better test suites to accompany the program, both of which are desirable educational outcomes. Inquiry: Producing mutant implementations requires care. If there are too many, or they are too obscure, students will end up spending a lot of time on an unproductive task and also become frustrated. Instead, we want a small number of mutants that each correspond to common problem misconceptions. This paper presents a workflow with partial automation to produce mutants of this form which, notably, are not those produced by mutation-testing tools. Approach: We comb through student tests that fail a correct implementation. The student misconceptions are embedded in these fa

In this study we are concerned with the general problem of choosing from a set of endangered species T a subset S of k species to protect as a priority. Here, the interest to protect the species of S is assessed by the resulting expected phylogenetic diversity (ePD) of the set T, a widely used criterion for measuring the expected amount of evolutionary history associated with T. We consider that the survival of the protected species is assured and, on the contrary, that there is a risk of extinction for the unprotected species. The problem is easy to solve by a greedy type method if the extinction probabilities of the unprotected species are known but these probabilities are generally not easy to quantify. We show in this note that the choice of the precise values attributed to the extinction probabilities-provided it respects the rank of imperilment of each species-is not as decisive as might be feared for the considered problem. The values of these probabilities have a clear impact on the selection of the species to be protected but a little impact on the resulting ePD. More precisely, if T1 and T2 are the two optimal subsets of species corresponding to two scenarios (two differe

Decentralised Finance (DeFi) applications constitute an entire financial ecosystem deployed on blockchains. Such applications are based on complex protocols and incentive mechanisms whose financial safety is hard to determine. Besides, their adoption is rapidly growing, hence imperilling an increasingly higher amount of assets. Therefore, accurate formalisation and verification of DeFi applications is essential to assess their safety. We have developed a tool for the formal analysis of one of the most widespread DeFi applications: Lending Pools (LP). This was achieved by leveraging an existing formal model for LPs, the Maude verification environment and the MultiVeStA statistical analyser. The tool supports several analyses including reachability analysis, LTL model checking and statistical model checking. In this paper we show how the tool can be used to analyse several parameters of LPs that are fundamental to assess and predict their behaviour. In particular, we use statistical analysis to search for threshold and reward parameters that minimize the risk of unrecoverable loans.

Superconducting qubit lifetimes must be both long and stable to provide an adequate foundation for quantum computing. This stability is imperiled by two-level systems (TLSs), currently a dominant loss mechanism, which exhibit slow spectral dynamics that destabilize qubit lifetimes on hour timescales. Stability is also threatened at millisecond timescales, where ionizing radiation has recently been found to cause bursts of correlated multi-qubit decays, complicating quantum error correction. Here we study both ionizing radiation and TLS dynamics on a 27-qubit processor, repurposing the standard transmon qubits as sensors of both radiation impacts and TLS dynamics. Unlike prior literature, we observe resilience of the qubit lifetimes to the transient quasiparticles generated by the impact of radiation. However, we also observe a new interaction between these two processes, "TLS scrambling," in which a radiation impact causes multiple TLSs to jump in frequency, which we suggest is due to the same charge rearrangement sensed by qubits near a radiation impact. As TLS scrambling brings TLSs out of or in to resonance with the qubit, the lifetime of the qubit increases or decreases. Our fi

Fighting wildfires is a precarious task, imperiling the lives of engaging firefighters and those who reside in the fire's path. Firefighters need online and dynamic observation of the firefront to anticipate a wildfire's unknown characteristics, such as size, scale, and propagation velocity, and to plan accordingly. In this paper, we propose a distributed control framework to coordinate a team of unmanned aerial vehicles (UAVs) for a human-centered active sensing of wildfires. We develop a dual-criterion objective function based on Kalman uncertainty residual propagation and weighted multi-agent consensus protocol, which enables the UAVs to actively infer the wildfire dynamics and parameters, track and monitor the fire transition, and safely manage human firefighters on the ground using acquired information. We evaluate our approach relative to prior work, showing significant improvements by reducing the environment's cumulative uncertainty residual by more than $ 10^2 $ and $ 10^5 $ times in firefront coverage performance to support human-robot teaming for firefighting. We also demonstrate our method on physical robots in a mock firefighting exercise.

Like all natural systems, great societies and their cultures emerge by a growth process from their environments, developing, organized, and behaving as wholes with their internal designs linked with their external worlds. So the general patterns of how growth produces both successful and failing new designs for new systems can be a great teacher of what does and does not successfully work. Our own world culture imperils its survival by being unresponsive to change as our world economy has; driven to endlessly maximize its compound growth and so growing conflict with internal and external systems. New methods now let us compare different kinds of growth systems in their natural contexts to expose their different ways of coupling with their contexts and steering in response to their futures. Some work out fine by respecting internal limits and finding a fit with external parts, and so working adaptively for survival as a whole. For living systems, steering is much improved by rich experience of external contexts, allowing more prompt attention and response to new risks and opportunities. Recognizing emerging systems starts with noticing change associated with a growing center of rela

The Trump administration is asking the Space Force to do a lot。 This will require more launches

K2-18b is one of the most promising worlds for the search for extraterrestrial life, so astronomers conducted an unusually powerful radio survey using both the VLA and MeerKAT telescopes。 Advanced software analyzed millions of signals, filtering out Earth-based interference and other false positives。 No convincing artificial radio transmissions wer

A new review highlights exciting progress in atomically thin quantum materials where light and magnetism work together in ways never before possible。 In these materials, light-generated excitons can interact directly with magnetic behavior, creating opportunities to control magnetic states using light alone。 Scientists believe this could pave the w

NatGeo's Pompeii: Out of Time fuses historical fact and imagination to bring city's last 24 hours to life

California and XPRIZE competition tests whether drones can stop wildfires early

A catastrophic asteroid breakup may have triggered a huge wave of impacts across the inner solar system about 800 million years ago。 The debris was launched from near a gravitational gateway controlled by Jupiter, sending fragments toward Earth, the Moon, and Mars。 The bombardment may explain ancient lunar craters and could have contributed to majo