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.
Drawing on theories of affective intelligence and intergroup emotions, we develop a theory of affective representation in Congress in which lawmakers vary the emotional tone of their communications to align with the emotional experiences of rank-and-file co-partisans, thereby strengthening their popularity with constituents. In a nationalized political environment where control of the presidency is crucial, belonging to the presidential party influences both the emotional experiences of rank-and-file partisans and the emotions lawmakers express, even more than legislative events do. We test our theory by analyzing discrete emotions in congressional e-newsletters and find that the affective content of these newsletters depends on whether members of Congress are part of the president’s party rather than whether they are in the majority or have legislative success. These findings reflect the emotions that rank-and-file co-partisans also feel toward the President and politics more broadly. Finally, using a survey experiment that keeps the newsletter’s informational content constant, we demonstrate that partisans evaluate lawmakers more positively when lawmakers express congruent negative emotions, such as anger and disgust, but mirroring positive emotions does not produce the same benefit.
Objective We evaluated the dimensionality of an Anti-Asian racism (AAR) measure, informed the development of a brief measure, and validated a brief AAR measure. Method We conducted a national survey of 748 Asian American adults in 2023 (64.6% female). Bi-factor analysis was used to evaluate the dimensionality of the AAR measure, and Graded Response Models (GRM) informed the development of the brief measure of AAR. We also evaluated concurrent validity. Results The AAR scale is unidimensional. GRM was conducted to derive a brief (6-items), reliable, and valid measure of AAR. Our brief measure of AAR yielded valid and reliable scores, and was associated with firearm purchasing, depressive symptoms, anxiety symptoms, and appraisals of safety, demonstrating concurrent validity. Conclusions The brief measure of AAR includes items related to subtle, blatant, and cultural racism. The brief scale offers opportunities for real-time, momentary assessments and opportunities to discuss AAR in the community health setting.
Scientists' limited observations of giant squid have fueled a centuries-long fascination with these elusive creatures. Technological developments have recently allowed researchers to study them in greater detail. This paper investigates several instances where technology brought giant squid to the attention of naturalists and the public. In 1873, a photograph of a giant squid captured near Newfoundland provided tangible proof of its existence. In 2000, a giant squid caught off New Zealand's coast was taxidermied and displayed in a Paris museum. And in 2012, a film crew used a submersible to capture video footage of a living giant squid in its natural environment. By examining how visual evidence of giant squid and the treatment of their physical remains has shifted since the nineteenth century, this paper informs our understanding of the visual epistemology of marine science and the ways in which new technologies reshaped both scientific and public perceptions of ocean life. It provides a fresh perspective on the role of animals in forging human's understanding of the ocean's depths, and on the interplay between science, technology, and myth in defining the human relationship to the natural world.
Large Language Models (LLMs) have exhibited remarkable reasoning capabilities, achieving impressive results across a wide range of tasks. Despite these advances, significant reasoning failures persist, occurring even in seemingly simple scenarios. To systematically understand and address these shortcomings, we present the first comprehensive survey dedicated to reasoning failures in LLMs. We introduce a novel categorization framework that distinguishes reasoning into embodied and non-embodied types, with the latter further subdivided into informal (intuitive) and formal (logical) reasoning. In parallel, we classify reasoning failures along a complementary axis into three types: fundamental failures intrinsic to LLM architectures that broadly affect downstream tasks; application-specific limitations that manifest in particular domains; and robustness issues characterized by inconsistent performance across minor variations. For each reasoning failure, we provide a clear definition, analyze existing studies, explore root causes, and present mitigation strategies. By unifying fragmented research efforts, our survey provides a structured perspective on systemic weaknesses in LLM reasoning, offering valuable insights and guiding future research towards building stronger, more reliable, and robust reasoning capabilities. We additionally release a comprehensive collection of research works on LLM reasoning failures, as a GitHub repository at https://github.com/Peiyang-Song/Awesome-LLM-Reasoning-Failures, to provide an easy entry point to this area.
