A Higgs Mechanism for Vector Galileons

Vector theories with non-linear derivative self-interactions that break gauge symmetries have been shown to have interesting cosmological applications. In this paper we introduce a way to spontaneously break the gauge symmetry and construct these theories via a Higgs mechanism. In addition to the purely gauge field interactions, our method generates new ghost-free scalar-vector interactions between the Higgs field and the gauge boson. We show how these additional terms are found to reduce, in a suitable decoupling limit, to scalar bi-Galileon interactions between the Higgs field and Goldstone bosons. Our formalism is first developed in the context of abelian symmetry, which allows us to connect with earlier work on the extension of the Proca action. We then show how this formalism is straightforwardly generalised to generate theories with non-abelian symmetry.Comment: 15 pages, no figures. V2: published versio

Alexandria: Extensible Framework for Rapid Exploration of Social Media

The Alexandria system under development at IBM Research provides an extensible framework and platform for supporting a variety of big-data analytics and visualizations. The system is currently focused on enabling rapid exploration of text-based social media data. The system provides tools to help with constructing "domain models" (i.e., families of keywords and extractors to enable focus on tweets and other social media documents relevant to a project), to rapidly extract and segment the relevant social media and its authors, to apply further analytics (such as finding trends and anomalous terms), and visualizing the results. The system architecture is centered around a variety of REST-based service APIs to enable flexible orchestration of the system capabilities; these are especially useful to support knowledge-worker driven iterative exploration of social phenomena. The architecture also enables rapid integration of Alexandria capabilities with other social media analytics system, as has been demonstrated through an integration with IBM Research's SystemG. This paper describes a prototypical usage scenario for Alexandria, along with the architecture and key underlying analytics.Comment: 8 page

Feed and Host Genetics Drive Microbiome Diversity with Resultant Consequences for Production Traits in Mass-Reared Black Soldier Fly (Hermetia illucens) Larvae

Mass rearing the black soldier fly, Hermetia illucens, for waste bioremediation and valorisation is gaining traction on a global scale. While the health and productivity of this species are underpinned by associations with microbial taxa, little is known about the factors that govern gut microbiome assembly, function, and contributions towards host phenotypic development in actively feeding larvae. In the present study, a 16S rDNA gene sequencing approach applied to a study system incorporating both feed substrate and genetic variation is used to address this knowledge gap. It is determined that the alpha diversity of larval gut bacterial communities is driven primarily by features of the larval feed substrate, including the diversity of exogenous bacterial populations. Microbiome beta diversity, however, demonstrated patterns of differentiation consistent with an influence of diet, larval genetic background, and a potential interaction between these factors. Moreover, evidence for an association between microbiome structure and the rate of larval fat accumulation was uncovered. Taxonomic enrichment analysis and clustering of putative functional gut profiles further suggested that feed-dependent turnover in microbiome communities is most likely to impact larval characteristics. Taken together, these findings indicate that host–microbiome interactions in this species are complex yet relevant to larval trait emergence

REVAMP: Automated Simulations of Adversarial Attacks on Arbitrary Objects in Realistic Scenes

Deep Learning models, such as those used in an autonomous vehicle are vulnerable to adversarial attacks where an attacker could place an adversarial object in the environment, leading to mis-classification. Generating these adversarial objects in the digital space has been extensively studied, however successfully transferring these attacks from the digital realm to the physical realm has proven challenging when controlling for real-world environmental factors. In response to these limitations, we introduce REVAMP, an easy-to-use Python library that is the first-of-its-kind tool for creating attack scenarios with arbitrary objects and simulating realistic environmental factors, lighting, reflection, and refraction. REVAMP enables researchers and practitioners to swiftly explore various scenarios within the digital realm by offering a wide range of configurable options for designing experiments and using differentiable rendering to reproduce physically plausible adversarial objects. We will demonstrate and invite the audience to try REVAMP to produce an adversarial texture on a chosen object while having control over various scene parameters. The audience will choose a scene, an object to attack, the desired attack class, and the number of camera positions to use. Then, in real time, we show how this altered texture causes the chosen object to be mis-classified, showcasing the potential of REVAMP in real-world scenarios. REVAMP is open-source and available at https://github.com/poloclub/revamp

Documents and Bureaucracy

Abstract This review surveys anthropological and other social research on bureaucratic documents. The fundamental insight of this literature is that documents are not simply instruments of bureaucratic organizations, but rather are constitutive of bureaucratic rules, ideologies, knowledge, practices, subjectivities, objects, outcomes, even the organizations themselves. It explores the reasons why documents have been late to come under ethnographic scrutiny and the implications for our theoretical understandings of organizations and methods for studying them

LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked

Large language models (LLMs) have skyrocketed in popularity in recent years due to their ability to generate high-quality text in response to human prompting. However, these models have been shown to have the potential to generate harmful content in response to user prompting (e.g., giving users instructions on how to commit crimes). There has been a focus in the literature on mitigating these risks, through methods like aligning models with human values through reinforcement learning. However, it has been shown that even aligned language models are susceptible to adversarial attacks that bypass their restrictions on generating harmful text. We propose a simple approach to defending against these attacks by having a large language model filter its own responses. Our current results show that even if a model is not fine-tuned to be aligned with human values, it is possible to stop it from presenting harmful content to users by validating the content using a language model

Alcohol Control by Referendum in Northern Native Communities: The Alaska Local Option Law

When Alaska became a state in 1959, state laws removed control of alcohol regulation from the federal government and Native communities. In 1981, however, the state legislature changed alcohol laws to give residents broad powers to regulate how alcohol comes into their communities via a local option referendum. By mid-1999, 112 small communities had held 197 alcohol control elections under the state law. Sixty-nine percent of these elections added new restrictions on alcohol, while 13% removed restrictions previously imposed. The remaining 18% of elections did not receive a majority vote needed to change the existing status. Most communities passing local option restrictions chose to ban sale and importation. Although most of these elections occurred during the first eight years after the law was passed, elections continue to occur as the law evolves and as communities debate the merits of alcohol control. Although growing evidence suggests that the local option law may reduce adverse effects of alcohol abuse in Alaska Native communities, its most important contributioncmay be to restore to these communities a limited form of self-government