A proposal for a standard terminology of anatomical notation and orientation in fossil vertebrate dentitions

There is little consistency in the notation and orientation terminology used in discussions of non-mammalian fossil vertebrate dentitions. The standardization of this terminology, as done in the medical and dental sciences, would facilitate all future research on fossil teeth. For mammals, we recommend following convention, where incisors, canines, premolars, and molars are abbreviated as In, Cn, Pn, and Mn (n = tooth number) in upper jaws and as in, cn, pn, and mn in lower jaws. Right, left, and deciduous teeth are indicated by R, L, and D (e.g., DP4, Rp2). For non-mammals, which can have dentigerous premaxillae, maxillae, and dentaries, as well as additional tooth-bearing bones (e.g., vomers, palatines, pterygoids, ectopterygoids, sphenoids, splenials, and even parasphenoids), we encourage identifying teeth using the bone abbreviation (e.g., pmn, mxn, dn, vn, paln). A number and slash (/) combination can be used to distinguish between multiple tooth rows (e.g., Pal1/n, Pal2/n), and specimen-specific maps can be created for very complicated dentitions. We suggest the use of the terms mesial and distal to designate tooth surfaces and directions facing toward and away from the mandibular symphysis. Labial is offered for those surfaces and directions facing the lips or cheeks and lingual for those facing the tongue. We offer the terms basal for the direction toward crown bases, apical for the direction toward crown tips, occlusal for views of the occlusal surfaces, and basal and root apical for views of crown bases and roots, respectively

A Role for the Kolliker-Fuse Nucleus in Cholinergic Modulation of Breathing at Night During Wakefulness and NREM Sleep

For many years, acetylcholine has been known to contribute to the control of breathing and sleep. To probe further the contributions of cholinergic rostral pontine systems in control of breathing, we designed this study to test the hypothesis that microdialysis (MD) of the muscarinic receptor antagonist atropine into the pontine respiratory group (PRG) would decrease breathing more in animals while awake than while in NREM sleep. In 16 goats, cannulas were bilaterally implanted into rostral pontine tegmental nuclei (n = 3), the lateral (n = 3) or medial (n = 4) parabrachial nuclei, or the Kölliker-Fuse nucleus (KFN; n = 6). After \u3e2 wk of recovery from surgery, the goats were studied during a 45-min period of MD with mock cerebrospinal fluid (mCSF), followed by at least 30 min of recovery and a second 45-min period of MD with atropine. Unilateral and bilateral MD studies were completed during the day and at night. MD of atropine into the KFN at night decreased pulmonary ventilation and breathing frequency and increased inspiratory and expiratory time by 12–14% during both wakefulness and NREM sleep. However, during daytime studies, MD of atropine into the KFN had no effect on these variables. Unilateral and bilateral nighttime MD of atropine into the KFN increased levels of NREM sleep by 63 and 365%, respectively. MD during the day or at night into the other three pontine sites had minimal effects on any variable studied. Finally, compared with MD of mCSF, bilateral MD of atropine decreased levels of acetylcholine and choline in the effluent dialysis fluid. Our data support the concept that the KFN is a significant contributor to cholinergically modulated control of breathing and sleep

Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning

Many animals, and an increasing number of artificial agents, display sophisticated capabilities to perceive and manipulate objects. But human beings remain distinctive in their capacity for flexible, creative tool use -- using objects in new ways to act on the world, achieve a goal, or solve a problem. To study this type of general physical problem solving, we introduce the Virtual Tools game. In this game, people solve a large range of challenging physical puzzles in just a handful of attempts. We propose that the flexibility of human physical problem solving rests on an ability to imagine the effects of hypothesized actions, while the efficiency of human search arises from rich action priors which are updated via observations of the world. We instantiate these components in the "Sample, Simulate, Update" (SSUP) model and show that it captures human performance across 30 levels of the Virtual Tools game. More broadly, this model provides a mechanism for explaining how people condense general physical knowledge into actionable, task-specific plans to achieve flexible and efficient physical problem-solving.Comment: This manuscript is in press at PNAS. It is an extended version of a paper "Rapid Trial-and-Error Learning in Physical Problem Solving" accepted for oral presentation at the 41st Annual Meeting of the Cognitive Science Society (2019). It represents ongoing work on the part of the author

Chain walking of allylrhodium species towards esters during rhodium-catalyzed nucleophilic allylations of imines

Allylrhodium species derived from δ-trifluoroboryl β,γ-unsaturated esters undergo chain walking towards the ester moiety.The resulting allylrhodium species react with imines to give products containing two new stereocenters and a Z-alkene. By using a chiral diene ligand, products can be obtained with high enantioselectivities, where a pronounced matched/mismatched effect with the chirality of the allyltrifluoroborate is evident

Are Deep Neural Networks SMARTer than Second Graders?

Recent times have witnessed an increasing number of applications of deep neural networks towards solving tasks that require superior cognitive abilities, e.g., playing Go, generating art, ChatGPT, etc. Such a dramatic progress raises the question: how generalizable are neural networks in solving problems that demand broad skills? To answer this question, we propose SMART: a Simple Multimodal Algorithmic Reasoning Task and the associated SMART-101 dataset, for evaluating the abstraction, deduction, and generalization abilities of neural networks in solving visuo-linguistic puzzles designed specifically for children in the 6--8 age group. Our dataset consists of 101 unique puzzles; each puzzle comprises a picture and a question, and their solution needs a mix of several elementary skills, including arithmetic, algebra, and spatial reasoning, among others. To scale our dataset towards training deep neural networks, we programmatically generate entirely new instances for each puzzle, while retaining their solution algorithm. To benchmark performances on SMART-101, we propose a vision and language meta-learning model using varied state-of-the-art backbones. Our experiments reveal that while powerful deep models offer reasonable performances on puzzles in a supervised setting, they are not better than random accuracy when analyzed for generalization. We also evaluate the recent ChatGPT and other large language models on a part of SMART-101 and find that while these models show convincing reasoning abilities, the answers are often incorrect.Comment: Accepted at CVPR 2023. For the SMART-101 dataset, see https://doi.org/10.5281/zenodo.776179

A two-way photonic interface for linking Sr+ transition at 422 nm to the telecommunications C-band

We report a single-stage bi-directional interface capable of linking Sr+ trapped ion qubits in a long-distance quantum network. Our interface converts photons between the Sr+ emission wavelength at 422 nm and the telecoms C-band to enable low-loss transmission over optical fiber. We have achieved both up- and down-conversion at the single photon level with efficiencies of 9.4% and 1.1% respectively. Furthermore we demonstrate noise levels that are low enough to allow for genuine quantum operation in the future.Comment: 5 pages, 4 figure