We present the confirmation of HD 190360 d, a warm ( P=88.690-0.049+0.051days ), low-mass ( msini=10.23-0.80+0.81M circle plus ) planet orbiting the nearby (d = 16.0 pc), Sun-like (G7) star HD 190360. We detect HD 190360 d at high statistical significance even though its radial velocity (RV) semiamplitude is only K = 1.48 +/- 0.11 m s-1. Such low-amplitude signals are often challenging to confirm due to potential confusion with low-amplitude stellar signals. The HD 190360 system previously had two known planets: the 1.7 MJ (true mass) HD 190360 b on a 7.9 yr orbit and the 21 M circle plus(minimum mass) HD 190360 c on a 17.1 days orbit. Here, we present an in-depth analysis of the HD 190360 planetary system that comprises more than 30 yr of RV measurements and absolute astrometry from the Hipparcos and Gaia spacecraft. Our analysis uses more than 1400 RVs, including nearly 100 from NEID. The proper motion anomaly as measured by these two astrometric missions solves for the dynamical mass of HD 190360 b and contributes to our understanding of the overall system architecture, while the long baseline of RVs enables the robust characterization of HD 190360 c and confirms the discovery of HD 190360 d.
We study how international migrant income prospects affect long-run development in origin areas. We leverage the 1997 Asian Financial Crisis exchange rate shocks in a shift-share identification strategy across Philippine provinces. Initial migrant income shocks are magnified six-fold over time, increasing domestic income, education levels, migrant skills, and high-skilled migration. Remarkably, 74.9 percent of long-run income gains come from domestic rather than migrant income. Trade driven impacts of exchange rate shocks are orthogonal to effects via migrant income. A structural model reveals that 19.7 percent of long-run income gains stem from educational investments. International migration fosters broad economic development in origin communities. (JEL F22, F31, G01, J24, J82, O15, O16)
We address the global-in-time existence and pathwise uniqueness of solutions for the three-dimensional incompressible stochastic Navier-Stokes equations with multiplicative noise. Under natural smallness conditions on the noise, we prove an almost sure global existence result for small initial data in L3. Specifically, we show that sufficiently small L3 data yields a pathwise unique strong solution that remains small in L3 and is global-in-time on a set of probability close to 1, with this probability increasing as the initial L3 norm decreases. Moreover, the solution decays exponentially with large probability. (c) 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Community-driven Text-to-SQL evaluation platforms play a pivotal role in tracking the state of the art of Text-to-SQL performance. The reliability of the evaluation process is critical for driving progress in the field. Current evaluation methods are largely test-based, which involves comparing the execution results of a generated SQL query and a human-labeled ground-truth on a static test database. Such an evaluation is optimistic, as two queries can coincidentally produce the same output on the test database while actually being different. In this work, we propose a new alternative evaluation pipeline, called SpotIt, where a formal bounded equivalence verification engine actively searches for a database that differentiates the generated and ground-truth SQL queries. We develop techniques to extend existing verifiers to support a richer SQL subset relevant to Text-to-SQL. A performance evaluation of ten Text-to-SQL methods on the high-profile BIRD dataset suggests that test-based methods can often overlook differences between the generated query and the ground-truth. Further analysis of the verification results reveals a more complex picture of the current Text-to-SQL evaluation.
AI evaluation results are produced at scale but reported inconsistently across leaderboards, model cards, benchmark papers, and company blogs. This fragmentation is borne at the level of interpretation: readers cannot reliably compare results across sources, identify what is missing from a given report, or trace an aggregate claim to the evidence behind it. Recent efforts address isolated components of this problem but leave three gaps unresolved: They cover only narrow slices of the evaluation lifecycle and do not compose into a single interpretable record. They specify static representations that do not differentiate between the questions technical and policy readers bring to the same evidence. And they remain proposals on paper, without the extraction infrastructure required for adoption at scale. We present EVALUATION CARDS, an operational reporting layer that composes benchmark metadata, evaluation run data, and model metadata into a unified record. We (1) derive a reporting schema from a structured literature review of 52 papers and 10 stakeholder interviews, (2) implement four interpretive signals (reproducibility, documentation completeness, provenance and risk, and score comparability), rendered through reader modes calibrated to research and policy audiences, and (3) provide a deployed monitoring tool that applies EVALUATION CARDS across 5,498 models, 635 benchmarks, and 101,843 results, surfacing systematic gaps in current reporting practice.
