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Large language models (LLMs) have achieved widespread success on a variety of in-context few shot tasks, but this success is typically evaluated via correctness rather than consistency. We argue that self-consistency is an important criteria for valid multi-step reasoning in tasks where the solution is composed of the answers to multiple sub-steps. We propose two types of self consistency that are particularly important for multi-step reasoning – hypothetical consistency (a model’s ability to predict what its output would be in a hypothetical other context) and compositional consistency (consistency of a model’s final outputs when intermediate sub-steps are replaced with the model’s outputs for those steps). We demonstrate that multiple variants of the GPT-3/-4 models exhibit poor consistency rates across both types of consistency on a variety of tasks. 

The game is intended for students who do not necessarily have any prior background in computer science. Assuming the role of agents, two players exchange messages over a network to try to agree on a meeting time and location, while an adversary interferes with their plan. Following the Dolev-Yao model, the adversary has full control of the network: they can see all messages and modify, block, or forward them. We designed the game as a web application, where groups of three students play the game, taking turns being the adversary. The adversary is a legitimate communicant on the network, and the agents do not know who is the other agent and who is the adversary. Through gameplay, we expect students to be able to (1) identify the dangers of communicating through a computer network, (2) describe the capabilities of a Dolev-Yao adversary, and (3) apply three cryptographic primitives: symmetric encryption, asymmetric encryption, and digital signatures. We conducted surveys, focus groups, and interviews to evaluate the effectiveness of the game in achieving the learning objectives. The game helped students achieve the first two learning objectives, as well as using symmetric encryption. We found that students enjoyed playing MeetingMayhem. We are revising MeetingMayhem to improve its user interface and to better support students to learn about asymmetric encryption and digital signatures. 

The Dark Energy Spectroscopic Instrument (DESI) completed its 5 month Survey Validation in 2021 May. Spectra of stellar and extragalactic targets from Survey Validation constitute the first major data sample from the DESI survey. This paper describes the public release of those spectra, the catalogs of derived properties, and the intermediate data products. In total, the public release includes good-quality spectral information from 466,447 objects targeted as part of the Milky Way Survey, 428,758 as part of the Bright Galaxy Survey, 227,318 as part of the Luminous Red Galaxy sample, 437,664 as part of the Emission Line Galaxy sample, and 76,079 as part of the Quasar sample. In addition, the release includes spectral information from 137,148 objects that expand the scope beyond the primary samples as part of a series of secondary programs. Here, we describe the spectral data, data quality, data products, Large-Scale Structure science catalogs, access to the data, and references that provide relevant background to using these spectra.

 

Introduction: The plasma membrane protects a cell from the extracellular environment. As such it presents an obstacle that therapeutics needs to traverse in order to achieve efficacy. For example, small interfering RNAs (siRNAs) need to be delivered to the cytoplasm, where they can interact with the RNA interference machinery and initiate gene silencing. However, these macromolecules have poor membrane permeability, largely limiting their therapeutic potential. To address this challenge, current strategies involve encapsulating siRNAs into nanoparticles. However, upon cellular uptake, these nanoparticles are trapped in endosomes, which lack access to the cytoplasm. Towards developing an alternative strategy that provides direct access to the cytoplasm, we have been inspired by the unique capabilities of gap junctions to establish passageways between the cytoplasm of neighboring cells. Specifically, six connexins hexamerize to form a connexon hemichannel. Two hemichannels from neighboring cells dock to each other to form a complete gap junction channel, facilitating the exchange of molecular cargoes such as ions and siRNA. Therefore, incorporating the gap junction network into therapeutic delivery materials has the potential to enhance the delivery efficiency of siRNAs by directly depositing siRNAs into the cytoplasm.