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ABSTRACT AimThe goals of this study were to (1) identify how climate change impacts the distribution of amphibian species and high‐priority conservation areas (HPCA) in the drylands of the Southwest United States and Northern Mexico, (2) describe the relationship between environmental variables and spatial configurations of HPCA and (3) explore how climate change will impact the distribution of HPCA and investigate the relationship between HPCA and protected area (PA) network. LocationSouthwest United States and Northern Mexico. TaxonAmphibians. MethodsWe used distribution maps for 209 amphibian species to estimate surrogates of amphibian diversity, assessed by rarity‐weighted richness (RWR), site importance (Zonation) and species richness. Then, we used species accumulation curves to assess their efficiency in representing amphibians in the least number of sites. Next, we used the most effective surrogate to identify HPCA for amphibians. We used environmental variables, usually related to amphibian distribution, and random forest models to assess the impact of climate on the spatial configuration of HPCA in the current and future times. We also used PA networks to assess their representation. ResultsRWR produced a similar spatial configuration of HPCA as Zonation but could not depict the same level of connectivity. HPCAs were observed mainly across California, central Texas and western Mexico. The spatial distribution of HPCA was mostly influenced by precipitation, temperature and solar radiation. Climate change will influence the future distribution of HPCA. The overlay between HPCA and PA is weak. Main ConclusionClimate change is becoming an ever‐increasing issue for conservation efforts, especially in dryland ecosystems where natural resources are already scarce for native species. Results show an alteration in the spatial configuration of amphibian HPCA, and much is still needed to protect and manage them. 

We study the problem of synthesizing a core fragment of relational queries called select-project-join (SPJ) queries from input-output examples. Search-based synthesis techniques are suited to synthesizing projections and joins by navigating the network of relational tables but require additional supervision for synthesizing comparison predicates. On the other hand, decision tree learning techniques are suited to synthesizing comparison predicates when the input database can be summarized as a single labelled relational table. In this paper, we adapt and interleave methods from the domains of relational query synthesis and decision tree learning, and present an end-to-end framework for synthesizing relational queries with categorical and numerical comparison predicates. Our technique guarantees the completeness of the synthesis procedure and strongly encourages minimality of the synthesized program. We present Libra, an implementation of this technique and evaluate it on a benchmark suite of 1,475 instances of queries over 159 databases with multiple tables. Libra solves 1,361 of these instances in an average of 59 seconds per instance. It outperforms state-of-the-art program synthesis tools Scythe and PatSQL in terms of both the running time and the quality of the synthesized programs. 

While Chain-of-Thought (CoT) prompting boosts Language Models’ (LM) performance on a gamut of complex reasoning tasks, the generated reasoning chain does not necessarily reflect how the model arrives at the answer (aka. faithfulness). We propose Faithful CoT, a reasoning framework involving two stages: Translation (Natural Language query → symbolic reasoning chain) and Problem Solving (reasoning chain → answer), using an LM and a deterministic solver respectively. This guarantees that the reasoning chain provides a faithful explanation of the final answer. Aside from interpretability, Faithful CoT also improves empirical performance: it outperforms standard CoT on 9 of 10 benchmarks from 4 diverse domains, with a relative accuracy gain of 6.3% on Math Word Problems (MWP), 3.4% on Planning, 5.5% on Multi-hop Question Answering (QA), and 21.4% on Relational Inference. Furthermore, with GPT-4 and Codex, it sets the new state-of-the-art few-shot performance on 7 datasets (with 95.0+ accuracy on 6 of them), showing a strong synergy between faithfulness and accuracy.