Dark matter capture in celestial objects: light mediators, self-interactions, and complementarity with direct detection

We generalize the formalism for DM capture in celestial bodies to account for arbitrary mediator mass, and update the existing and projected astrophysical constraints on DM-nucleon scattering cross section from observations of neutron stars. We show that the astrophysical constraints on the DM-nucleon interaction strength, that were thought to be the most stringent, drastically weaken for light mediators and can be completely voided. For asymmetric DM, existing astrophysical constraints are completely washed out for mediators lighter than 5 MeV, and for annihilating DM the projected constraints are washed out for mediators lighter than 0.25 MeV. Related terrestrial direct detection bounds also weaken, but in a complementary fashion; they supersede the astrophysical capture bounds for small or large DM mass, respectively for asymmetric or annihilating DM. Repulsive self-interactions of DM have an insignificant impact on the total capture rate, but a significant impact on the black hole formation criterion. This further weakens the constraints on DM-nucleon interaction strength for asymmetric self-repelling DM, whereas constraints remain unaltered for annihilating self-repelling DM. We use the correct Hawking evaporation rate of the newly formed black hole, that was approximated as a blackbody in previous studies, and show that, despite a more extensive alleviation of collapse as a result, the observation of a neutron star collapse can probe a wide range of DM self-interaction strengths.Comment: v1: 28 pages, 9 figures, Comments welcom

DIVAS: An LLM-based End-to-End Framework for SoC Security Analysis and Policy-based Protection

Securing critical assets in a bus-based System-On-Chip (SoC) is imperative to mitigate potential vulnerabilities and prevent unauthorized access, ensuring the integrity, availability, and confidentiality of the system. Ensuring security throughout the SoC design process is a formidable task owing to the inherent intricacies in SoC designs and the dispersion of assets across diverse IPs. Large Language Models (LLMs), exemplified by ChatGPT (OpenAI) and BARD (Google), have showcased remarkable proficiency across various domains, including security vulnerability detection and prevention in SoC designs. In this work, we propose DIVAS, a novel framework that leverages the knowledge base of LLMs to identify security vulnerabilities from user-defined SoC specifications, map them to the relevant Common Weakness Enumerations (CWEs), followed by the generation of equivalent assertions, and employ security measures through enforcement of security policies. The proposed framework is implemented using multiple ChatGPT and BARD models, and their performance was analyzed while generating relevant CWEs from the SoC specifications provided. The experimental results obtained from open-source SoC benchmarks demonstrate the efficacy of our proposed framework.Comment: 15 pages, 7 figures, 8 table

Rankers, Rankees, & Rankings: Peeking into the Pandora's Box from a Socio-Technical Perspective

Algorithmic rankers have a profound impact on our increasingly data-driven society. From leisurely activities like the movies that we watch, the restaurants that we patronize; to highly consequential decisions, like making educational and occupational choices or getting hired by companies -- these are all driven by sophisticated yet mostly inaccessible rankers. A small change to how these algorithms process the rankees (i.e., the data items that are ranked) can have profound consequences. For example, a change in rankings can lead to deterioration of the prestige of a university or have drastic consequences on a job candidate who missed out being in the list of the preferred top-k for an organization. This paper is a call to action to the human-centered data science research community to develop principled methods, measures, and metrics for studying the interactions among the socio-technical context of use, technological innovations, and the resulting consequences of algorithmic rankings on multiple stakeholders. Given the spate of new legislations on algorithmic accountability, it is imperative that researchers from social science, human-computer interaction, and data science work in unison for demystifying how rankings are produced, who has agency to change them, and what metrics of socio-technical impact one must use for informing the context of use.Comment: Accepted for Interrogating Human-Centered Data Science workshop at CHI'2