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We present a novel methodology for crafting effective public messages by combining large language models (LLMs) and conjoint analysis. Our approach personalizes messages for diverse personas – context-specific archetypes representing distinct attitudes and behaviors – while reducing the costs and time associated with traditional surveys. We tested this method in public health contexts (e.g., COVID-19 mandates) and civic engagement initiatives (e.g., voting). A total of 153 distinct messages were generated, each composed of components with varying levels, and evaluated across five personas tailored to each context. Conjoint analysis identified the most effective message components for each persona, validated through a study with 2,040 human participants. This research highlights LLMs’ potential to enhance public communication, providing a scalable, cost-effective alternative to surveys, and offers new directions for HCI, particularly for the design of adaptive, user-centered, persona-driven interfaces and systems. 

We present the analysis of the luminous Type II Supernova (SN) 2021tsz, which exploded in a low-luminosity galaxy. It reached a peak magnitude of −18.88 ± 0.13 mag in therband and exhibited an initial rapid decline of 4.05 ± 0.14 mag (100 d)⁻¹from peak luminosity till ∼30 d. The photospheric phase is short, with the SN displaying bluer colours and a weak Hαabsorption component–features consistent with other luminous, short-photospheric phase Type II SNe. A distinct transition from the photospheric to the radioactive tail phase in theVband–as is common in hydrogen-rich Type II SNe–is not visible in SN 2021tsz, although a modest ∼1 mag drop is apparent in the redder filters. Hydrodynamic modelling suggests the luminosity is powered by ejecta-circumstellar material (CSM) interaction during the early phases (< 30 days). Interaction with 0.6 M_⊙of dense CSM extending to 3100 R_⊙reproduces the observed luminosity, with an explosion energy of 1.3 × 10⁵¹erg. The modelling indicates a pre-SN mass of 9 M_⊙, which includes a hydrogen envelope of 4 M_⊙, and a radius of ∼1000 R_⊙. Spectral energy distribution analysis and strong-line diagnostics revealed that the host galaxy of SN 2021tsz is a low-metallicity, dwarf galaxy. The low-metallicity environment and the derived high mass loss from the hydrodynamical modelling strongly support a binary progenitor system for SN 2021tsz. 

Growing interconnect bandwidth demand in large datacenters requires energy-efficient optical transceivers that operate with four-level pulse amplitude modulation (PAM4) to enable high per-wavelength data rates. Further increases in bandwidth density is possible by leveraging wavelength-division multiplexing (WDM), which optical link architectures based on silicon photonic microring modulators (MRMs) and drop filters inherently enable. This paper presents high-speed PAM4 transmitter and receiver front-ends implemented in a 28nm CMOS process that are co-designed with these silicon photonic optical devices to enable energy-efficient operation. The transmitter utilizes an optical digital-to-analog converter (DAC) approach with two PAM2 AC-coupled pulsed-cascode high-swing voltage-mode output stages to drive the MRM MSB/LSB segments. A 3.42Vppd output swing is achieved when operating at 80Gb/s PAM4 with an energy efficiency of 3.66pJ/bit. The receiver front-end interfaces with a silicon-germanium avalanche photodiode (APD) and utilizes a low-bandwidth input transimpedance amplifier followed by continuous-time linear equalizer and variable-gain amplifier stages. Biasing the APD to realize a gain of 2 allows for -7dBm optical modulation amplitude (OMA) sensitivity at 56Gb/s PAM4 with a BER=10-4 and an energy efficiency of 1.61pJ/bit. Experimental verification of the full PAM4 transceiver at 50Gb/s operation shows -4.66dBm OMA sensitivity at a BER~4x10-4. 

We investigate medium-induced modifications to jet substructure observables that characterize hard components in central Pb-Pb collisions at $\sqrt{s_{NN}}=5.02$TeV. Using a multistage Monte Carlo simulation of in-medium jet shower evolution, we explore flavor-dependent medium effects through simulations of inclusive and $\gamma$-tagged jets. The results show that quark jets undergo a nonmonotonic modification compared with gluon jets in observables such as the Pb-Pb to $p\text{−}p$ratio of the soft drop prong angle $r_g$, the relative prong transverse momentum $k_{T,g}$, and the groomed mass $m_g$distributions. Due to this nonmonotonic modification, $\gamma$-tagged jets, enriched in quark jets, provide surprisingly clear signals of medium-induced structural modifications, distinct from effects dominated by selection bias. Further systematic studies demonstrate that these effects are dominated by recoil medium response. This work highlights the potential of hard substructures in $\gamma$-tagged jets as powerful tools for probing the jet-medium interactions in high-energy heavy-ion collisions, in particular by enabling detailed investigations of jet-medium parton scatterings via their associated medium response. All simulations for $\gamma$-tagged jet analyses carried out in this paper used triggered events containing at least one hard photon, which highlights the utility of these observables for future Bayesian analysis. 

This paper describes an NSF (National Science Foundation) S-STEM-funded scholarship program, representing a collaborative five-year grant project among three prominent universities in the Southeast region of the United States. Its primary objective is to support dedicated scholars in graduating and finding a professional pathway. Each institution recruited a cohort of 15-20 scholars annually for three years. The project offers scholarships and provides curricular and cocurricular support to academically talented but financially challenged students in the computing disciplines, including Computer Science, Computer Engineering, Cybersecurity, and Information Technology majors, starting from their junior years. The program aims to impact 150 scholars, most of whom are underrepresented in computing. Scholars receive support throughout their graduation and beyond should they pursue graduate studies in a STEM (Science, Technology, Engineering, and Math) discipline at any of the three participating institutions. Besides funds, the program provides an expansive career pathway opportunity to each of its students, accompanied by various supporting services, a dedicated advising team, experiential learning offices, career services offices, and graduate schools. Supporting services include internship fairs, panel discussions with alumni, resume workshops, graduate school application workshops, and career fairs. The project brings together the unique collaboration of three institutions for each of its supported activities to significantly enhance the support and opportunities offered to its scholars and to conduct meaningful research studies that include significant-sized intersectional populations. 

Domain experts play an important role in data science, as their knowledge can unlock valuable insights from data. As they often lack technical skills required to analyze data, they need collaborations with technical experts. In these joint efforts, productive collaborations are critical not only in the phase of constructing a data science task, but more importantly, during the execution of a task. This need stems from the inherent complexity of data science, which often involves user-defined functions or machine-learning operations. Consequently, collaborators want various interactions during runtime, such as pausing/resuming the execution, inspecting an operator's state, and modifying an operator's logic. To achieve the goal, in the past few years we have been developing an open-source system called Texera to support collaborative data analytics using GUI-based workflows as cloud services. In this paper, we present a holistic view of several important design principles we followed in the design and implementation of the system. We focus on different methods of sending messages to running workers, how these methods are adopted to support various runtime interactions from users, and their trade-offs on both performance and consistency. These principles enable Texera to provide powerful user interactions during a workflow execution to facilitate efficient collaborations in data analytics.