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Measurements of B-mode polarization in the CMB sourced from primordial gravitational waves would provide information on the energy scale of inflation and its potential form. To achieve these goals, one must carefully characterize the Galactic foregrounds, which can be distinguished from the CMB by conducting measurements at multiple frequencies. BICEP Array is the latest-generation multi-frequency instrument of the BICEP/Keck program, which specifically targets degree-scale primordial B-modes in the CMB. In its final configuration, this telescope will consist of four small-aperture receivers, spanning frequency bands from 30 to 270 GHz. The 220/270 GHz receiver designed to characterize Galactic dust is currently undergoing commissioning at Stanford University and is scheduled to deploy to the South Pole during the 2024--2025 austral summer. Here, we will provide an overview of this high-frequency receiver and discuss the integration status and test results as it is being commissioned.

Numerous stars exhibit surprisingly large variations in their refractory element abundances, often interpreted as signatures of planetary ingestion events. In this study, we propose that differences in the dust-to-gas ratio near stars during their formation can produce similar observational signals. We investigate this hypothesis using a suite of radiation-dust-magnetohydrodynamic STARFORGE simulations of star formation. Our results show that the distribution of refractory abundance variations ($\rm \Delta [X/H]$) has extended tails, with about 10-30% of all stars displaying variations around $\sim$0.1 dex. These variations are comparable to the accretion of $2-5 \rm M_\oplus$ of planetary material into the convective zones of Sun-like stars. The width of the distributions increases with the incorporation of more detailed dust physics, such as radiation pressure and back-reaction forces, as well as with larger dust grain sizes and finer resolutions. Furthermore, our simulations reveal no correlation between $\rm \Delta [X/H]$ and stellar separations, suggesting that dust-to-gas fluctuations likely occur on scales smaller than those of wide binaries. These findings highlight the importance of considering dust dynamics as a potential source of the observed chemical enrichment in stars., Comment: 10 pages, 3 figures. Submitted to ApJL

Anonymity is an important principle online. However, malicious actors have long used misleading identities to conduct fraud, spread disinformation, and carry out other deceptive schemes. With the advent of increasingly capable AI, bad actors can amplify the potential scale and effectiveness of their operations, intensifying the challenge of balancing anonymity and trustworthiness online. In this paper, we analyze the value of a new tool to address this challenge: "personhood credentials" (PHCs), digital credentials that empower users to demonstrate that they are real people -- not AIs -- to online services, without disclosing any personal information. Such credentials can be issued by a range of trusted institutions -- governments or otherwise. A PHC system, according to our definition, could be local or global, and does not need to be biometrics-based. Two trends in AI contribute to the urgency of the challenge: AI's increasing indistinguishability from people online (i.e., lifelike content and avatars, agentic activity), and AI's increasing scalability (i.e., cost-effectiveness, accessibility). Drawing on a long history of research into anonymous credentials and "proof-of-personhood" systems, personhood credentials give people a way to signal their trustworthiness on online platforms, and offer service providers new tools for reducing misuse by bad actors. In contrast, existing countermeasures to automated deception -- such as CAPTCHAs -- are inadequate against sophisticated AI, while stringent identity verification solutions are insufficiently private for many use-cases. After surveying the benefits of personhood credentials, we also examine deployment risks and design challenges. We conclude with actionable next steps for policymakers, technologists, and standards bodies to consider in consultation with the public., Comment: 63 pages, 7 figures, 5 tables; minor additions to acknowledgments and wording changes for clarity; corrected typo

To address the rural broadband challenge and to leverage the unique opportunities that rural regions provide for piloting advanced wireless applications, we design and implement the ARA wireless living lab for research and innovation in rural wireless systems and their applications in precision agriculture, community services, and so on. ARA focuses on the unique community, application, and economic context of rural regions, and it features the first-of-its-kind, real-world deployment of long-distance, high-capacity wireless x-haul and access platforms across a rural area of diameter over 30 km. With both software-defined radios and programmable COTS systems and through effective orchestration of these wireless resources with fiber as well as compute resources embedded end-to-end across user equipment, base stations, edge, and cloud, ARA offers programmability, performance, robustness, and heterogeneity at the same time, thus enabling rural-focused co-evolution of wireless and applications while helping advance the frontiers of wireless systems in domains such as O-RAN, NextG, and agriculture applications. Here we present the design principles and implementation strategies of ARA, characterize its performance and heterogeneity, and highlight example wireless and application experiments uniquely enabled by ARA., Comment: 17 pages, 18 figures

The dust grain size distribution (GSD) likely varies significantly across different star-forming environments in the Universe, but the overall impact of this variation on star formation remains unclear. This ambiguity arises because the GSD interacts non-linearly with processes like heating/cooling, radiation, and chemistry, which have competing effects and different environmental dependencies. In this study, we investigate the effects of GSD variation on the thermochemistry and evolution of giant molecular clouds (GMCs). To achieve this, we conducted radiation-dust-magnetohydrodynamic simulations spanning a range of cloud masses and grain sizes, which explicitly incorporate the dynamics of dust grains within the full-physics framework of the STARFORGE project. We find that differences in grain size significantly alter the thermochemistry of GMCs. Specifically, we show that the leading-order effect is that larger grains, under fixed dust mass and dust-to-gas ratio conditions, result in lower dust opacities. This reduced opacity permits ISRF photons to penetrate more deeply and allows internal radiation field photons to permeate more extensively into the cloud, resulting in rapid gas heating and the inhibition of star formation. We find that star formation efficiency is highly sensitive to grain size, with an order of magnitude reduction in efficiency when grain size increases from 0.1 $\rm\mu m$ to 10 $\rm\mu m$. Additionally, we note that warmer gas suppresses the formation of low-mass stars. Moreover, as a consequence of the decreased opacities, we observe a greater proportion of gas residing in diffuse ionized structures., Comment: 13 pages, 5 figures, submitted to ApJ

In task-oriented conversational AI evaluation, unsupervised methods poorly correlate with human judgments, and supervised approaches lack generalization. Recent advances in large language models (LLMs) show robust zeroshot and few-shot capabilities across NLP tasks. This paper explores using LLMs for automated dialogue quality evaluation, experimenting with various configurations on public and proprietary datasets. Manipulating factors such as model size, in-context examples, and selection techniques, we examine "chain-of-thought" (CoT) reasoning and label extraction procedures. Our results show that (1) larger models yield more accurate dialogue labels; (2) algorithmic selection of in-context examples outperforms random selection; (3) CoT reasoning where an LLM is asked to provide justifications before outputting final labels improves performance; and (4) fine-tuned LLMs outperform out-of-the-box ones. Our results indicate that LLMs that are suitably fine-tuned and have sufficient reasoning capabilities can be leveraged for automated dialogue evaluation.

Stars form within dense cores composed of both gas and dust within molecular clouds. However, despite the crucial role that dust plays in the star formation process, its dynamics is frequently overlooked, with the common assumption being a constant, spatially uniform dust-to-gas ratio and grain size spectrum. In this study, we introduce a set of radiation-dust-magnetohydrodynamic simulations of star forming molecular clouds from the {\small STARFORGE} project. These simulations expand upon the earlier radiation MHD models, which included cooling, individual star formation, and feedback. Notably, they explicitly address the dynamics of dust grains, considering radiation, drag, and Lorentz forces acting on a diverse size spectrum of live dust grains. We find that interactions between radiation and dust significantly influence the properties of gas surrounding and accreting onto massive stars. Specifically, we find that once stars exceed a certain mass threshold ($\sim 2 M_{\odot}$), their emitted radiation can evacuate dust grains from their vicinity, giving rise to a dust-suppressed zone of size $\sim 100$ AU. Commencing during the early accretion stages and preceding the Main-sequence phase, this process results in a mass-dependent depletion in the accreted dust-to-gas (ADG) mass ratio within both the circumstellar disc and the star. We predict massive stars ($\gtrsim 10 M_{\odot}$) would exhibit ADG ratios that are approximately one order of magnitude lower than that of their parent clouds. Consequently, stars, their discs, and circumstellar environments would display notable deviations in the abundances of elements commonly associated with dust grains, such as carbon and oxygen., Comment: 14 pages, 9 figures, submitted to ApJ

The BICEP/$\textit{Keck}$ (BK) series of cosmic microwave background (CMB) polarization experiments has, over the past decade and a half, produced a series of field-leading constraints on cosmic inflation via measurements of the "B-mode" polarization of the CMB. Primordial B modes are directly tied to the amplitude of primordial gravitational waves (PGW), their strength parameterized by the tensor-to-scalar ratio, $r$, and thus the energy scale of inflation. Having set the most sensitive constraints to-date on $r$, $\sigma(r)=0.009$ ($r_{0.05}<0.036, 95\%$ C.L.) using data through the 2018 observing season ("BK18"), the BICEP/$\textit{Keck}$ program has continued to improve its dataset in the years since. We give a brief overview of the BK program and the "BK18" result before discussing the program's ongoing efforts, including the deployment and performance of the $\textit{Keck Array}$'s successor instrument, BICEP Array, improvements to data processing and internal consistency testing, new techniques such as delensing, and how those will ultimately serve to allow BK reach $\sigma(r) \lesssim 0.003$ using data through the 2027 observing season., Comment: 9 pages, 5 figures. Contribution to the 2024 Cosmology session of the 58th Rencontres de Moriond

The magnetohydrodynamics (MHD) equations plus 'non-ideal' (Ohmic, Hall, ambipolar) resistivities are widely used to model weakly-ionized astrophysical systems. We show that if gradients in the magnetic field become too steep, the implied charge drift speeds become much faster than microphysical signal speeds, invalidating the assumptions used to derive both the resistivities and MHD equations themselves. Generically this situation will excite microscale instabilities that suppress the drift and current. We show this could be relevant at low ionization fractions especially if Hall terms appear significant, external forces induce supersonic motions, or dust grains become a dominant charge carrier. Considering well-established treatments of super-thermal drifts in laboratory, terrestrial, and Solar plasmas as well as conduction and viscosity models, we generalize a simple prescription to rectify these issues, where the resistivities are multiplied by a correction factor that depends only on already-known macroscopic quantities. This is generalized for multi-species and weakly-ionized systems, and leaves the equations unchanged in the drift limits for which they are derived, but restores physical behavior (driving the system back towards slow drift by diffusing away small-scale gradients in the magnetic field) if the limits are violated. This has important consequences: restoring intuitive behaviors such as the system becoming hydrodynamic in the limit of zero ionization; suppressing magnetic structure on scales below a critical length which can comparable to circumstellar disk sizes; limiting the maximum magnetic amplification; and suppressing the effects of the Hall term in particular. This likely implies that the Hall term does not become dynamically important under most conditions of interest in these systems., Comment: 16 pages, 4 figures. Submitted to the Open Journal of Astrophysics. Comments welcome

We demonstrate a wide-band diplexed focal plane suitable for observing low-frequency foregrounds that are important for cosmic microwave background polarimetry. The antenna elements are composed of slotted bowtie antennas with 60% bandwidth that can be partitioned into two bands. Each pixel is composed of two interleaved 12$\times$12 pairs of linearly polarized antenna elements forming a phased array, designed to synthesize a symmetric beam with no need for focusing optics. The signal from each antenna element is captured in-phase and uniformly weighted by a microstrip summing tree. The antenna signal is diplexed into two bands through the use of two complementary, six-pole Butterworth filters. This filter architecture ensures a contiguous impedance match at all frequencies, and thereby achieves minimal reflection loss between both bands. Subsequently, out-of-band rejection is increased with a bandpass filter and the signal is then deposited on a transition-edge sensor bolometer island. We demonstrate the performance of this focal plane with two distinct bands, 30 and 40 GHz, each with a bandwidth of $\sim$20 and 15 GHz, respectively. The unequal bandwidths between the two bands are caused by an unintentional shift in diplexer frequency from its design values. The end-to-end optical efficiency of these detectors are relatively modest, at 20-30%, with an efficiency loss due to an unknown impedance mismatch in the summing tree. Far-field beam maps show good optical characteristics with edge pixels having no more than $\sim$ 5% ellipticity and $\sim$10-15% peak-to-peak differences for A-B polarization pairs., Comment: 17 pages, 13 figures. Accepted for publication in ApJS

Clinical trials are pivotal in the drug discovery process to determine the safety and efficacy of a drug candidate. The high failure rates of these trials are attributed to deficiencies in clinical model development and protocol design. Improvements in the clinical drug design process could therefore yield significant benefits for all stakeholders involved. This paper examines the current challenges faced in clinical trial design and optimization, reviews established classical computational approaches, and introduces quantum algorithms aimed at enhancing these processes. Specifically, the focus is on three critical aspects: clinical trial simulations, site selection, and cohort identification. This study aims to provide a comprehensive framework that leverages quantum computing to innovate and refine the efficiency and effectiveness of clinical trials.

In-Memory Computing (IMC) introduces a new paradigm of computation that offers high efficiency in terms of latency and power consumption for AI accelerators. However, the non-idealities and defects of emerging technologies used in advanced IMC can severely degrade the accuracy of inferred Neural Networks (NN) and lead to malfunctions in safety-critical applications. In this paper, we investigate an architectural-level mitigation technique based on the coordinated action of multiple checksum codes, to detect and correct errors at run-time. This implementation demonstrates higher efficiency in recovering accuracy across different AI algorithms and technologies compared to more traditional methods such as Triple Modular Redundancy (TMR). The results show that several configurations of our implementation recover more than 91% of the original accuracy with less than half of the area required by TMR and less than 40% of latency overhead., Comment: This paper will be presented at 29th IEEE European Test Symposium 2024 (ETS) 2024

Stroke segmentation plays a crucial role in the diagnosis and treatment of stroke patients by providing spatial information about affected brain regions and the extent of damage. Segmenting stroke lesions accurately is a challenging task, given that conventional manual techniques are time consuming and prone to errors. Recently, advanced deep models have been introduced for general medical image segmentation, demonstrating promising results that surpass many state of the art networks when evaluated on specific datasets. With the advent of the vision Transformers, several models have been introduced based on them, while others have aimed to design better modules based on traditional convolutional layers to extract long-range dependencies like Transformers. The question of whether such high-level designs are necessary for all segmentation cases to achieve the best results remains unanswered. In this study, we selected four types of deep models that were recently proposed and evaluated their performance for stroke segmentation: a pure Transformer-based architecture (DAE-Former), two advanced CNN-based models (LKA and DLKA) with attention mechanisms in their design, an advanced hybrid model that incorporates CNNs with Transformers (FCT), and the well-known self-adaptive nnUNet framework with its configuration based on given data. We examined their performance on two publicly available datasets, and found that the nnUNet achieved the best results with the simplest design among all. Revealing the robustness issue of Transformers to such variabilities serves as a potential reason for their weaker performance. Furthermore, nnUNet's success underscores the significant impact of preprocessing and postprocessing techniques in enhancing segmentation results, surpassing the focus solely on architectural designs

Online social platforms allow users to filter out content they do not like. According to selective exposure theory, people tend to view content they agree with more to get more self-assurance. This causes people to live in ideological filter bubbles. We report on a user study that encourages users to break the political filter bubble of their Twitter feed by reading more diverse viewpoints through social comparison. The user study is conducted using political-bias analyzing and Twitter-mirroring tools to compare the political slant of what a user reads and what other Twitter users read about a topic, and in general. The results show that social comparison can have a great impact on users' reading behavior by motivating them to read viewpoints from the opposing political party., Comment: * Both of the first two authors contributed equally to this work

Skysails Power GmbH is the leading manufacturer of light and efficient power kites that harness the wind's untapped supplies at high altitudes, aiming at profoundly altering wind energy's impact in achieving the global energy transition. Novel, variable trim kites have been developed that allow to modulate the aerodynamic coefficients of the airborne system, significantly improving the overall system efficiency. The flight control of variable trim kites is much more complex than that of previous kite generations and its mastering is a challenge and one of the keys to a successful operation. Numerical optimization is applied to find a set of flight trajectories in order to maximize the energy production while satisfying several constraints on the system operating in a wide range of conditions. This industry abstract provides a general introduction of the trajectory optimization problem with variable trim kites. We also briefly introduce the state-of-the-art optimization setup. This is followed by demonstration of high-quality example results of the optimization. Finally, we discuss the results and their applications., Comment: Industrial Abstract accepted to ECC 2024

In communities with social hierarchies, fear of judgment can discourage communication. While anonymity may alleviate some social pressure, fully anonymous spaces enable toxic behavior and hide the social context that motivates people to participate and helps them tailor their communication. We explore a design space of meronymous communication, where people can reveal carefully chosen aspects of their identity and also leverage trusted endorsers to gain credibility. We implemented these ideas in a system for scholars to meronymously seek and receive paper recommendations on Twitter and Mastodon. A formative study with 20 scholars confirmed that scholars see benefits to participating but are deterred due to social anxiety. From a month-long public deployment, we found that with meronymity, junior scholars could comfortably ask ``newbie'' questions and get responses from senior scholars who they normally found intimidating. Responses were also tailored to the aspects about themselves that junior scholars chose to reveal., Comment: Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI '24), May 11--16, 2024, Honolulu, HI, USA

Diabetic retinopathy (DR) is a significant cause of vision impairment, emphasizing the critical need for early detection and timely intervention to avert visual deterioration. Diagnosing DR is inherently complex, as it necessitates the meticulous examination of intricate retinal images by experienced specialists. This makes the early diagnosis of DR essential for effective treatment and the prevention of eventual blindness. Traditional diagnostic methods, relying on human interpretation of these medical images, face challenges in terms of accuracy and efficiency. In the present research, we introduce a novel method that offers superior precision in DR diagnosis, compared to these traditional methods, by employing advanced deep learning techniques. Central to this approach is the concept of transfer learning. This entails using pre-existing, well-established models, specifically InceptionResNetv2 and Inceptionv3, to extract features and fine-tune select layers to cater to the unique requirements of this specific diagnostic task. Concurrently, we also present a newly devised model, DiaCNN, which is tailored for the classification of eye diseases. To validate the efficacy of the proposed methodology, we leveraged the Ocular Disease Intelligent Recognition (ODIR) dataset, which comprises eight different eye disease categories. The results were promising. The InceptionResNetv2 model, incorporating transfer learning, registered an impressive 97.5% accuracy in both the training and testing phases. Its counterpart, the Inceptionv3 model, achieved an even more commendable 99.7% accuracy during training, and 97.5% during testing. Remarkably, the DiaCNN model showcased unparalleled precision, achieving 100% accuracy in training and 98.3\% in testing.