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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. 

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. 

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. 

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. 

An investigation of high-transverse-momentum (high- $p_T$) photon-triggered jets in proton-proton ( $p\text{−}p$) and ion-ion ( $A\text{−}A$) collisions at $\sqrt{s_{NN}}=0.2$and $5.02\mathrm{TeV}$is carried out, using the multistage description of in-medium jet evolution. Monte Carlo simulations of hard scattering and energy loss in heavy-ion collisions are performed using parameters tuned in a previous study of the nuclear modification factor ( $R_{AA}$) for inclusive jets and high- $p_T$hadrons. We obtain a good reproduction of the experimental data for photon-triggered jet $R_{AA}$, as measured by the ATLAS detector, the distribution of the ratio of jet to photon $p_T$( $X_{J\gamma }$), measured by both CMS and ATLAS, and the photon-jet azimuthal correlation as measured by CMS. We obtain a moderate description of the photon-triggered jet $I_{AA}$, as measured by STAR. A noticeable improvement in the comparison is observed when one goes beyond prompt photons and includes bremsstrahlung and decay photons, revealing their significance in certain kinematic regions, particularly at $X_{J\gamma }>1$. Moreover, azimuthal angle correlations demonstrate a notable impact of bremsstrahlung photons on the distribution, emphasizing their role in accurately describing experimental results. This work highlights the success of the multistage model of jet modification to straightforwardly predict (this set of) photon-triggered jet observables. This comparison, along with the role played by bremsstrahlung photons, has important consequences on the inclusion of such observables in a future Bayesian analysis. Published by the American Physical Society2025 

The Collaboration reports a new determination of the jet transport parameter $\hat{q}$in the quark-gluon plasma (QGP) using Bayesian inference, incorporating all available inclusive hadron and jet yield suppression data measured in heavy-ion collisions at the BNL Relativistic Heavy Ion Collider (RHIC) and the CERN Large Hadron Collider (LHC). This multi-observable analysis extends the previously published Bayesian inference determination of $\hat{q}$, which was based solely on a selection of inclusive hadron suppression data. is a modular framework incorporating detailed dynamical models of QGP formation and evolution, and jet propagation and interaction in the QGP. Virtuality-dependent partonic energy loss in the QGP is modeled as a thermalized weakly coupled plasma, with parameters determined from Bayesian calibration using soft-sector observables. This Bayesian calibration of $\hat{q}$utilizes active learning, a machine-learning approach, for efficient exploitation of computing resources. The experimental data included in this analysis span a broad range in collision energy and centrality, and in transverse momentum. In order to explore the systematic dependence of the extracted parameter posterior distributions, several different calibrations are reported, based on combined jet and hadron data; on jet or hadron data separately; and on restricted kinematic or centrality ranges of the jet and hadron data. Tension is observed in comparison of these variations, providing new insights into the physics of jet transport in the QGP and its theoretical formulation. Published by the American Physical Society2025 

A combination of spin–orbit coupling and electron–electron interaction gives rise to a new type of collective spin modes, which correspond to oscillations of magnetization even in the absence of the external magnetic field. We review recent progress in theoretical understanding and experimental observation of such modes, focusing on three examples of real-life systems: a two-dimensional electron gas with Rashba and/or Dresselhaus spin–orbit coupling, graphene with proximity-induced spin–orbit coupling, and the Dirac state on the surface of a three-dimensional topological insulator. This paper is dedicated to the 95th birthday of Professor Emmanuel I. Rashba.