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The sharp Dirac cone of the electronic dispersion confers to graphene a remarkable sensitivity to strain. It is usually encoded in scalar and pseudo-vector potentials, induced by the modification of hopping parameters, which have given rise to new phenomena at the nanoscale such as giant pseudomagnetic fields and valley polarization. Here, we unveil the effect of these potentials on the quantum transport across a succession of strain-induced barriers. We use high-mobility, hBN-encapsulated graphene, transferred over a large (10x10 $\mu$m$^{2}$) crenellated hBN substrate. We show the emergence of a broad resistance ancillary peak at positive energy that arises from Klein tunneling barriers induced by the tensile strain at the trench edges. Our theoretical study, in quantitative agreement with experiment, highlights the balanced contributions of strain-induced scalar and pseudo-vector potentials on ballistic transport. Our results establish crenellated van der Waals heterostructures as a promising platform for strain engineering in view of applications and basic physics.

Recommendation algorithms play a pivotal role in shaping our media choices, which makes it crucial to comprehend their long-term impact on user behavior. These algorithms are often linked to two critical outcomes: homogenization, wherein users consume similar content despite disparate underlying preferences, and the filter bubble effect, wherein individuals with differing preferences only consume content aligned with their preferences (without much overlap with other users). Prior research assumes a trade-off between homogenization and filter bubble effects and then shows that personalized recommendations mitigate filter bubbles by fostering homogenization. However, because of this assumption of a tradeoff between these two effects, prior work cannot develop a more nuanced view of how recommendation systems may independently impact homogenization and filter bubble effects. We develop a more refined definition of homogenization and the filter bubble effect by decomposing them into two key metrics: how different the average consumption is between users (inter-user diversity) and how varied an individual's consumption is (intra-user diversity). We then use a novel agent-based simulation framework that enables a holistic view of the impact of recommendation systems on homogenization and filter bubble effects. Our simulations show that traditional recommendation algorithms (based on past behavior) mainly reduce filter bubbles by affecting inter-user diversity without significantly impacting intra-user diversity. Building on these findings, we introduce two new recommendation algorithms that take a more nuanced approach by accounting for both types of diversity., Comment: This paper was accepted at the ACM Web Conference 2024 (WWW '24)

Advances in language modeling have paved the way for novel human-AI co-writing experiences. This paper explores how varying levels of scaffolding from large language models (LLMs) shape the co-writing process. Employing a within-subjects field experiment with a Latin square design, we asked participants (N=131) to respond to argumentative writing prompts under three randomly sequenced conditions: no AI assistance (control), next-sentence suggestions (low scaffolding), and next-paragraph suggestions (high scaffolding). Our findings reveal a U-shaped impact of scaffolding on writing quality and productivity (words/time). While low scaffolding did not significantly improve writing quality or productivity, high scaffolding led to significant improvements, especially benefiting non-regular writers and less tech-savvy users. No significant cognitive burden was observed while using the scaffolded writing tools, but a moderate decrease in text ownership and satisfaction was noted. Our results have broad implications for the design of AI-powered writing tools, including the need for personalized scaffolding mechanisms., Comment: Appearing at CHI 2024 (Honolulu, HI)

Radio emission has been detected from tens of white dwarfs, in particular in accreting systems. Additionally, radio emission has been predicted as a possible outcome of a planetary system around a white dwarf. We searched for 3 GHz radio continuum emission in 846,000 candidate white dwarfs previously identified in Gaia using the Very Large Array Sky Survey (VLASS) Epoch 1 Quick Look Catalogue. We identified 13 candidate white dwarfs with a counterpart in VLASS within 2". Five of those were found not to be white dwarfs in follow-up or archival spectroscopy, whereas seven others were found to be chance alignments with a background source in higher-resolution optical or radio images. The remaining source, WDJ204259.71+152108.06, is found to be a white dwarf and M-dwarf binary with an orbital period of 4.1 days and long-term stochastic optical variability, as well as luminous radio and X-ray emission. For this binary, we find no direct evidence of a background contaminant, and a chance alignment probability of only ~2 per cent. However, other evidence points to the possibility of an unfortunate chance alignment with a background radio and X-ray emitting quasar, including an unusually poor Gaia DR3 astrometric solution for this source. With at most one possible radio emitting white dwarf found, we conclude that strong (> 1-3 mJy) radio emission from white dwarfs in the 3 GHz band is virtually nonexistent outside of interacting binaries., Comment: 18 pages, 17 figures. Updated to match version accepted for publication in MNRAS

Approximately 150 low-mass white dwarfs, with masses below 0.4Msun, have been discovered. The majority of these low-mass WDs are observed in binary systems as they cannot be formed through single-star evolution within the Hubble time. In this study, we present a comprehensive analysis of the double low-mass WD eclipsing binary system J2102-4145. Our investigation involved an extensive observational campaign, resulting in the acquisition of approximately 28 hours of high-speed photometric data across multiple nights using NTT/ULTRACAM, SOAR/Goodman, and SMARTS-1m telescopes. These observations have provided critical insights into the orbital characteristics of this system, including parameters such as inclination and orbital period. To disentangle the binary components of J2102-4145, we employed the XT GRID spectral fitting method with GMOS/Gemini-South and X-Shooter data. Additionally, we used the PHOEBE package for light curve analysis on NTT/ULTRACAM high-speed time-series photometry data to constrain the binary star properties. Our analysis reveals remarkable similarities between the two components of this binary system. For the primary star, we determined Teff1 = 13688 +- 65 K, log g1 = 7.36 +- 0.01, R1 = 0.0211 +- 0.0002 Rsun, and M1 = 0.375 +- 0.003 Msun, while the secondary star is characterized by Teff2 = 12952 +- 53 K, log g2 = 7.32 +- 0.01, R2 = 0.0203 +- 0.0002 Rsun, and M2 = 0.31 +- 0.003 Msun. Furthermore, we observe a notable discrepancy between Teff and R of the less massive WD compared to evolutionary sequences for WDs from the literature, which has significant implications for our understanding of WD evolution. We discuss a potential formation scenario for this system that might explain this discrepancy and explore its future evolution. We predict that this system will merge in about 800 Myr, evolving into a helium-rich hot subdwarf star and later into a hybrid He/CO WD.

There is a notable dearth of results characterizing the preconditioning effect of Adam and showing how it may alleviate the curse of ill-conditioning -- an issue plaguing gradient descent (GD). In this work, we perform a detailed analysis of Adam's preconditioning effect for quadratic functions and quantify to what extent Adam can mitigate the dependence on the condition number of the Hessian. Our key finding is that Adam can suffer less from the condition number but at the expense of suffering a dimension-dependent quantity. Specifically, for a $d$-dimensional quadratic with a diagonal Hessian having condition number $\kappa$, we show that the effective condition number-like quantity controlling the iteration complexity of Adam without momentum is $\mathcal{O}(\min(d, \kappa))$. For a diagonally dominant Hessian, we obtain a bound of $\mathcal{O}(\min(d \sqrt{d \kappa}, \kappa))$ for the corresponding quantity. Thus, when $d < \mathcal{O}(\kappa^p)$ where $p = 1$ for a diagonal Hessian and $p = 1/3$ for a diagonally dominant Hessian, Adam can outperform GD (which has an $\mathcal{O}(\kappa)$ dependence). On the negative side, our results suggest that Adam can be worse than GD for a sufficiently non-diagonal Hessian even if $d \ll \mathcal{O}(\kappa^{1/3})$; we corroborate this with empirical evidence. Finally, we extend our analysis to functions satisfying per-coordinate Lipschitz smoothness and a modified version of the Polyak-\L ojasiewicz condition.

Efficient electric heat pumps that replace fossil-fueled heating systems could significantly reduce greenhouse gas emissions. However, electric heat pumps can sharply increase electricity demand, causing high utility bills and stressing the power grid. Residential neighborhoods could see particularly high electricity demand during cold weather, when heat demand rises and heat pump efficiencies fall. This paper presents the development and field demonstration of a predictive control system for an air-to-air heat pump with backup electric resistance heat. The control system adjusts indoor temperature set-points based on weather forecasts, occupancy conditions, and data-driven models of the building and heating equipment. Field tests from January to March of 2023 in an occupied, all-electric, 208 m^2 detached single-family house in Indiana, USA, included outdoor temperatures as low as -15 C. On average over these tests, the control system reduced daily heating energy use by 19% (95% confidence interval: 13--24%), energy used for backup heat by 38%, and the frequency of using the highest stage (19 kW) of backup heat by 83%. Concurrent surveys of residents showed that the control system maintained satisfactory thermal comfort. The control system could reduce the house's total annual heating costs by about $300 (95% confidence interval: 23--34%). These real-world results could strengthen the case for deploying predictive home heating control, bringing the technology one step closer to reducing emissions, utility bills, and power grid impacts at scale.

The JWST North Ecliptic Pole (NEP) Time Domain Field (TDF) is a $>$14 arcmin diameter field optimized for multi-wavelength time-domain science with JWST. It has been observed across the electromagnetic spectrum both from the ground and from space, including with the Hubble Space Telescope (HST). As part of HST observations over 3 cycles (the "TREASUREHUNT" program), deep images were obtained with ACS/WFC in F435W and F606W that cover almost the entire JWST NEP TDF. Many of the individual pointings of these programs partially overlap, allowing an initial assessment of the potential of this field for time-domain science with HST and JWST. The cumulative area of overlapping pointings is ~88 arcmin$^2$, with time intervals between individual epochs that range between 1 day and 4$+$ years. To a depth of $m_{AB}$ $\simeq$ 29.5 mag (F606W), we present the discovery of 12 transients and 190 variable candidates. For the variable candidates, we demonstrate that Gaussian statistics are applicable, and estimate that ~80 are false positives. The majority of the transients will be supernovae, although at least two are likely quasars. Most variable candidates are AGN, where we find 0.42% of the general $z$ $<$ 6 field galaxy population to vary at the $~3\sigma$ level. Based on a 5-year timeframe, this translates into a random supernova areal density of up to ~0.07 transients per arcmin$^2$ (~245 deg$^{-2}$) per epoch, and a variable AGN areal density of ~1.25 variables per arcmin$^2$ (~4500 deg$^{-2}$) to these depths., Comment: 32 pages, 11 figures, 5 tables, 1 Appendix