Wabash College is a private liberal arts men's college in Crawfordsville, Indiana. Founded in 1832 by several Dartmouth College graduates and Midwestern leaders, it enrolls nearly 900 students. The college offers an undergraduate liberal arts curriculum in three academic divisions with 39 majors.
Background: The low-lying level structure of 13Be is still not fully understood; specifically, it has not been ruled out that the first 1/2- state could be located above the isomeric 0+ 2 state of 12Be and decay via that state. Purpose: Search for a possible decay path of 13Be excited states through the isomeric 0+ 2 state in 12Be. Method: The invariant mass technique was used to reconstruct (12Be + n) decay energies from neutron removal reactions of 14Be at 78 MeV/u on a 9Be target in coincidence with delayed gamma rays. Charged particles and neutrons were detected and identified with a telescope and a subset of the Modular Neutron Array (MoNA), respectively, around zero degrees. The CAESium-iodide scintillator ARray (CAESAR) was located around the telescope measuring the gamma rays. Results: No evidence for the population and decay of the 0+ 2 state in 12Be was observed. From the nonobservation of 511 keV gamma rays an upper limit of 6% of the p-wave contribution was extracted. Conclusions: The previously observed p-wave resonance at about 500 keV does not decay via the isomeric state and thus corresponds to the ground state of 13Be decaying to the 12Be ground state.
A single embedding space that covers text, images, video, and audio lets one index serve every query a user can pose. Embedding models built on vision-language backbones now lead text/image/video retrieval benchmarks but lack audio entirely, while audio-text retrieval is led by specialist systems that serve no other modality. We present the Fusion Embedding family, which adds audio to a frozen vision-language embedding base whose parameters are never updated: generation 1 (fusion-embedding-1) trains only a 16.4M-parameter connector between a frozen audio tower and the frozen base, and generation 2 (fusion-embedding-2) adds modality-gated deep adapters (44.2M parameters) whose branch never executes on text, image, or video inputs: their outputs are bit-for-bit those of the released base, verified after every training run. Because the base already binds text, images, and video, aligning audio to text alone makes audio-image retrieval emerge, with zero paired audio-visual training data. Alongside the recipe we map its design space with controlled negative results (rewriting training captions with an LLM, substituting a leaderboard-stronger audio tower, and widening the connector each reduce retrieval) and with training-protocol findings that we expect to transfer to any frozen decoder-LM embedding backbone. Both generations train in hours on a single GPU. Weights, code, and the evaluation harness are openly released.
Multiagent AI systems require consistent communication, but we lack methods to verify that agents share the same understanding of the terms used. Natural language is interpretable but vulnerable to semantic drift, while learned protocols are efficient but opaque. We propose a certification protocol based on the stimulus-meaning model, where agents are tested on shared observable events and terms are certified if empirical disagreement falls below a statistical threshold. In this protocol, agents restricting their reasoning to certified terms ("core-guarded reasoning") achieve provably bounded disagreement. We also outline mechanisms for detecting drift (recertification) and recovering shared vocabulary (renegotiation). In simulations with varying degrees of semantic divergence, core-guarding reduces disagreement by 72-96
Scaling the corpus is the default remedy when a contrastive representation lacks an attribute. We report a case where it does nothing, and identify what does: adding a lexical-speech round to a frozen-base multimodal embedding model raises zero-shot keyword spotting by 76 points while reducing speech-emotion recognition by 14. The loss is not a capacity limit: fine-tuning on 7,442 clips from a prosody-controlled corpus recovers emotion past its pre-speech level at a five-point keyword cost. Nor is it data volume: 29,428 mined clips whose captions explicitly name emotions, at matched exposure, move emotion by -0.0007. The difference is structural: a contrastive objective encodes an attribute only when the in-batch negatives cannot be separated without it; the controlled corpus holds sentence content fixed, so prosody is the only separating signal, whereas mined captions name emotion yet remain separable by scene content. Intervention on the same audio confirms causality: raising caption similarity does not recover emotion, but collapsing caption diversity so that emotion becomes the only separating axis recovers it by 8.9 points across three seeds, with a smaller, same-signed gain on a non-acted corpus, while keyword accuracy trades back. Corpus structure, not size or caption vocabulary, controls what a contrastive audio embedding encodes.
Though sanctions on family members of primary targets have become increasingly popular coercive instruments, we know very little about their use, effectiveness, and major legal and human rights consequences. Drawing insight from several targeted sanctions directed at family members of the Russian elite in recent years, this article offers a detailed analysis of (1) why sanctioning states have increasingly opted for family member sanctions, (2) whether targeting family members increases the efficacy of sanctions regimes, (3) to what extent such targeted sanctions are legally and ethically defensible, and (4) whether they undermine human rights of targeted individuals. Our analysis suggests that family member sanctions have limited utility as they are mostly ineffective tools. They could also cause major human rights issues and legal challenges for the sanctioned individuals. Hence, family member sanctions appear to be ethically problematic tools that warrant careful reconsideration.
