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Welcome and Keynote: Mobilizing to Defend Research Against U.S. Government Censorship: An Origin Story and An Invitation
No full textThe Second Annual Blockchain Tax Conference on January 24, 2025: What Happens When You’re Paid in Crypto?
Get PDFA comprehensive look at PUDG-R21: stellar population and kinematics of a globular cluster-rich ultra-diffuse galaxy in the Perseus Cluster
Get PDFWe present the analysis of the stellar populations and kinematics of the globular cluster (GC)-rich ultra-diffuse galaxy (UDG), PUDG-R21, using spectroscopic observations obtained with the Keck Cosmic Web Imager. The recessional velocity is measured to be 5536 ± 10 km s−1, confirming its association with the Perseus Cluster. The galaxy exhibits mild rotation of 15.6 ± 10 km s−1 and a stellar velocity dispersion of 19.4 ± 3.5 km s−1 within the galaxy effective radius. From this, we infer a dynamical mass of Mdyn = 9.3 ± 3.3 × 108 M☉. Based on a halo mass derived from PUDG-R21 GC counts, we find our dynamical mass is consistent with a cored dark matter profile. The integrated stellar population analysis reveals a predominantly old stellar population of 10.4 ± 1.2 Gyr, with intermediate–low metallicity ([M/H] = −0.64 ± 0.12 dex) and elevated α abundances ([Mg/Fe] = 0.38 ± 0.25 dex). The inferred star formation history suggests rapid stellar assembly, likely truncating prior to or during the galaxy’s infall into the cluster at an early epoch (∼10 Gyr ago). The analysis of stellar population gradients (age and metallicity) indicates a flat profile out to one effective radius. Here, we consider the involvement of two star formation events, initially forming a large population of metal-poor GCs, and then the latter contributing to the more metal-enriched diffuse stellar body. The evidence of subsequent star formation suggests this galaxy is more like an extension of the classical dwarf population than the much-discussed failed galaxy UDGs
Explainable Use of Foundation Models for Job Hiring
Get PDFAutomating candidate shortlisting is a non-trivial task that stands to benefit substantially from advances in artificial intelligence. We evaluate a suite of foundation models such as Llama 2, Llama 3, Mixtral, Gemma-2b, Gemma-7b, Phi-3 Small, Phi-3 Mini, Zephyr, and Mistral-7b for their ability to predict hiring outcomes in both zero-shot and few-shot settings. Using only features extracted from applicants’ submissions, these models, on average, achieved an AUC above 0.5 in zero-shot settings. Providing a few examples similar to the job applicants based on a nearest neighbor search improved the prediction rate marginally, indicating that the models perform competently even without task-specific fine-tuning. For Phi-3 Small and Mixtral, all reported performance metrics fell within the 95% confidence interval across evaluation strategies. Model outputs were interpreted quantitatively via post hoc explainability techniques and qualitatively through prompt engineering, revealing that decisions are largely attributable to knowledge acquired during pre-training. A task-specific MLP classifier trained solely on the provided dataset only outperformed the strongest foundation model (Zephyr in 5-shot setting) by approximately 3 percentage points on accuracy, but all the foundational models outperformed the baseline model by more than 15 percentage points on f1 and recall, underscoring the competitive strength of general-purpose language models in the hiring domain
Finite-Temperature kinetic ferromagnetism in the square-lattice Hubbard model
Get PDFWhile the exact phase diagram of the Fermi-Hubbard model remains poorly understood despite decades of progress, nearly 60 years ago Nagaoka proved that a single dopant in an otherwise half-filled Hubbard system can bring about ferromagnetism through kinetic means. The phenomenon was recently observed with ultracold atoms in triangular optical lattices. Here, we explore the kinetic ferromagnetism within the square lattice Hubbard model and its strong-coupling counterpart, the t-J model, at finite temperatures in the thermodynamic limit via numerical linked-cluster expansions. We find evidence of ferromagnetic Nagaoka polarons at dopings up to ∼30% away from half filling for a variety of interaction strengths and at temperatures as low as 0.2 of the hopping energy. We map out the boundaries of this phase through analyzing various correlation functions
Influence of plasma screening on high-density inverse bremsstrahlung absorption
Get PDFA spherical-implosion platform diagnosed with the beamlets scattered-light detector provides high sensitivity to the impact of plasma screening on inverse bremsstrahlung absorption. Contrary to the more restrictive screening length suggested previously [D. Turnbull et al., Phys. Rev. Lett. 130, 145103 (2023)0031-900710.1103/PhysRevLett.130.145103; D. Turnbull et al., Phys. Plasmas 31, 063304 (2024)1070-664X10.1063/5.0203446], the beamlets data indicate that the electron-only Debye length is the relevant screening length for high-density inverse bremsstrahlung absorption. Using the updated absorption model, we simulate the OMEGA direct-drive inertial confinement fusion implosion database and show that bang times are well reproduced without any ad hoc multipliers
Avatars and the Real World: A Study of the Personal and Organizational Impact of Virtual Identities
Get PDFThis research is proposed according to the main goal of analyzing the impact of avatars in the building of a digital identity and social interactions in the metaverse. In order to achieve it, a mix of methods has been applied (interviews, survey and content analysis), from an exploratory approach and from a triple perspective: perceptions of brands, users and psychology experts. Findings reflect a concordance between avatars and personal identities, a less marked trend to try new identities, and the persistence of physical stereotypes. From brands perspective, avatars allow them to connect with their audiences and simplify commercial transactions. For its part, from a psychological point of view, avatars can strengthen self-image and make socialization easier, but also generate a social pressure about appearance and encourage virtual bullying. The study concludes that the metaverse is still in an early stage of development. It has the potential to transform social and commercial interactions, but needs accessibility, inclusion and security improvements in order to allow a safe and equity experience for users
Renewable Electricity Management Cloud System for Smart Communities Using Advanced Machine Learning
Get PDFBased on the renewable energy assessment in 2023, it was found that only 21% of total electricity is generated using renewable sources. As the global demand for electricity rises in the AI world, the need for electricity management will increase and must be optimized. Based on research, many companies are working on green AI electricity management, but few companies are working on predicting shortages. To identify the rising electricity demand, predict the shortage, and to bring attention to consumption, this study focuses on the optimization of solar electricity generation, tracking its consumption, and forecasting the electricity shortages well in advance. This system demonstrates a novel approach using advanced machine learning, deep learning, and reinforcement learning to maximize solar energy utilization. This paper proposes and develops a community-based model that manages and analyzes multiple buildings’ energy usage, allowing the model to perform both distributed and aggregated decision-making, achieving an accuracy of 98.2% using stacking results of models with reinforcement learning. Concerning the real-world problem, this paper provides a sustainable solution by combining data-driven models with reinforcement learning, contributing to the current market need
