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Uncertainty persists over how and why some countries become democratic and others do not, or why some countries remain democratic and others 'backslide' toward autocracy. Furthermore, while scholars generally agree on the nature of 'democracy' and 'autocracy', the nature of regimes in-between, and changes between them, are much less clear. By applying the spectral dimensionality-reduction technique Diffusion Map to political-science data from the V-Dem project for the period 1900 to 2021, we identify a low-dimensional non-linear manifold on which all electoral regimes move. Using the diffusion equation from statistical physics, we measure the time scale on which countries change their degree of electoral quality, freedom of association, and freedom of expression depending on their position on the manifold. By quantifying the coefficients of the diffusion equation for each country and over time, we show that democracies behave like sub-diffusive (i.e. slow spreading) particles and that autocracies on the verge of collapse behave like super-diffusive (i.e. fast spreading) particles. We show that regimes in-between exhibit diffusion dynamics distinct from autocracies and democracies, and an overall higher instability. Furthermore, we show that a country's position on the manifold and its dynamics are linked to its propensity for civil conflict. Our study pioneers the use of statistical physics in the analysis of political regimes. Our results provide a quantitative foundation for developing theories about what changes during democratization and democratic backsliding, as well as a new framework for regime-transformation and risk-of-conflict assessment., Comment: 15 pages, 5 figures

The slow microstructural evolution of materials often plays a key role in determining material properties. When the unit steps of the evolution process are slow, direct simulation approaches such as molecular dynamics become prohibitive and Kinetic Monte-Carlo (kMC) algorithms, where the state-to-state evolution of the system is represented in terms of a continuous-time Markov chain, are instead frequently relied upon to efficiently predict long-time evolution. The accuracy of kMC simulations however relies on the complete and accurate knowledge of reaction pathways and corresponding kinetics. This requirement becomes extremely stringent in complex systems such as concentrated alloys where the astronomical number of local atomic configurations makes the a priori tabulation of all possible transitions impractical. Machine learning models of transition kinetics have been used to mitigate this problem by enabling the efficient on-the-fly prediction of kinetic parameters. In this study, we show how physics-informed ML architectures can exactly enforce the detailed balance condition, by construction. Using the diffusion of a vacancy in a concentrated alloy as an example, we show that such ML architectures also exhibit superior performance in terms of prediction accuracy, demonstrating that the imposition of physical constraints can facilitate the accurate learning of barriers at no increase in computational cost., Comment: 20 pages, 11 figures

Very different processes characterize the decoupling of neutrinos to form the cosmic neutrino background (C$\nu$B) and the much later decoupling of photons from thermal equilibrium to form the cosmic microwave background (CMB). The C$\nu$B emerges from the fuzzy, energy-dependent neutrinosphere and encodes the physics operating in the early universe in the temperature range $T\sim 10\,{\rm MeV}$ to $T\sim10\,{\rm keV}$. This is the epoch where beyond Standard Model (BSM) physics may be influential in setting the light element abundances and the necessarily distorted fossil neutrino energy spectra. Here we use techniques honed in extensive CMB studies to analyze the C$\nu$B as calculated in detailed neutrino energy transport and nuclear reaction simulations. Our moment method, relative entropy, and differential visibility approach can leverage future high precision CMB and primordial abundance measurements to provide new insights into the C$\nu$B and any BSM physics it encodes. We demonstrate that the evolution of the energy spectrum of the C$\nu$B throughout the weak decoupling epoch is accurately captured in the Standard Model by only three parameters per species, a non-trivial conclusion given the deviation from thermal equilibrium. Furthermore, we can interpret each of the three parameters as physical characteristics of a non-equilibrium system. The success of our compact description within the Standard Model motivates its use also in BSM scenarios. We demonstrate how observations of primordial light element abundances can be used to place constraints on the C$\nu$B energy spectrum, deriving response functions that can be applied for general C$\nu$B spectral distortions. Combined with the description of those deviations that we develop here, our methods provide a convenient and powerful framework to constrain the impact of BSM physics on the C$\nu$B., Comment: v2: Added Figure 9, Appendix B, and references; Updated to match published version; v1: 50 pages, 14 figures

A critical issue for society today is the emergence and decline of democracy worldwide. It is unclear, however, how democratic features, such as elections and civil liberties, influence this change. Democracy indices, which are the standard tool to study this question, are based on the a priori assumption that improvement in any individual feature strengthens democracy overall. We show that this assumption does not always hold. We use the V-Dem dataset for a quantitative study of electoral regimes worldwide during the 20th century. We find a so-far overlooked trade-off between election quality and civil liberties. In particular, we identify a threshold in the democratisation process at which the correlation between election quality and civil liberties flips from negative to positive. Below this threshold we can thus clearly separate two kinds of non-democratic regimes: autocracies that govern through tightly controlled elections and regimes in which citizens are free but under less certainty -- a distinction that existing democracy indices cannot make. We discuss the stabilising role of election quality uncovered here in the context of the recently observed decline in democracy score of long-standing democracies, so-called `democratic backsliding' or `democratic recession'.

Accurately capturing dynamic scenes with wide-ranging motion and light intensity is crucial for many vision applications. However, acquiring high-speed high dynamic range (HDR) video is challenging because the camera's frame rate restricts its dynamic range. Existing methods sacrifice speed to acquire multi-exposure frames. Yet, misaligned motion in these frames can still pose complications for HDR fusion algorithms, resulting in artifacts. Instead of frame-based exposures, we sample the videos using individual pixels at varying exposures and phase offsets. Implemented on a monochrome pixel-wise programmable image sensor, our sampling pattern simultaneously captures fast motion at a high dynamic range. We then transform pixel-wise outputs into an HDR video using end-to-end learned weights from deep neural networks, achieving high spatiotemporal resolution with minimized motion blurring. We demonstrate aliasing-free HDR video acquisition at 1000 FPS, resolving fast motion under low-light conditions and against bright backgrounds - both challenging conditions for conventional cameras. By combining the versatility of pixel-wise sampling patterns with the strength of deep neural networks at decoding complex scenes, our method greatly enhances the vision system's adaptability and performance in dynamic conditions., Comment: 17 pages, 18 figures

We introduce SANPO, a large-scale egocentric video dataset focused on dense prediction in outdoor environments. It contains stereo video sessions collected across diverse outdoor environments, as well as rendered synthetic video sessions. (Synthetic data was provided by Parallel Domain.) All sessions have (dense) depth and odometry labels. All synthetic sessions and a subset of real sessions have temporally consistent dense panoptic segmentation labels. To our knowledge, this is the first human egocentric video dataset with both large scale dense panoptic segmentation and depth annotations. In addition to the dataset we also provide zero-shot baselines and SANPO benchmarks for future research. We hope that the challenging nature of SANPO will help advance the state-of-the-art in video segmentation, depth estimation, multi-task visual modeling, and synthetic-to-real domain adaptation, while enabling human navigation systems. SANPO is available here: https://google-research-datasets.github.io/sanpo_dataset/, Comment: 10 pages plus additional references. 13 figures

Interlayer excitons, or bound electron-hole pairs whose constituent quasiparticles are located in distinct stacked semiconducting layers, are being intensively studied in heterobilayers of two dimensional semiconductors. They owe their existence to an intrinsic type-II band alignment between both layers that convert these into p-n junctions. Here, we unveil a pronounced interlayer exciton (IX) in heterobilayers of metal monochalcogenides, namely gamma-InSe on epsilon-GaSe, whose pronounced emission is adjustable just by varying their thicknesses given their number of layers dependent direct bandgaps. Time-dependent photoluminescense spectroscopy unveils considerably longer interlayer exciton lifetimes with respect to intralayer ones, thus confirming their nature. The linear Stark effect yields a bound electron-hole pair whose separation d is just (3.6 \pm 0.1) {\AA} with d being very close to dSe = 3.4 {\AA} which is the calculated interfacial Se separation. The envelope of IX is twist angle dependent and describable by superimposed emissions that are nearly equally spaced in energy, as if quantized due to localization induced by the small moir\'e periodicity. These heterostacks are characterized by extremely flat interfacial valence bands making them prime candidates for the observation of magnetism or other correlated electronic phases upon carrier doping., Comment: ACS Nano, in press 42 pages plus, 4 figures, plus supplementary information file, containing 21 supplementary figures

We use the APOSTLE cosmological simulations to examine the role of the cosmic UV background in regulating star formation (SF) in low-mass LCDM halos. In agreement with earlier work, we find that after reionization SF proceeds mainly in halos whose mass exceeds a redshift-dependent ``critical'' mass, Mcrit, set by the structure of the halos and by the thermal pressure of UV-heated gas. Mcrit increases from ~10^8 Msun at z~10 to Mcrit ~10^9.7 Msun at z=0, roughly following the average mass growth of halos in that mass range. This implies that halos well above or below critical at present have remained so since early times. Halos of luminous dwarfs today were already above-critical and star-forming at high redshift, explaining naturally the ubiquitous presence of ancient stellar populations in dwarfs, regardless of luminosity. The SF history of systems close to the critical boundary is more complex. SF may cease or reignite in dwarfs whose host halo falls below or climbs above the critical boundary, suggesting an attractive explanation for the episodic nature of SF in some dwarfs. Also, some subcritical halos today may have been above critical in the past; these systems should at present make up a sizable population of faint field dwarfs lacking ongoing star formation. Although few such galaxies are currently known, the discovery of this population would provide strong support for our results. Our work indicates that, rather than stellar feedback, it is the ionizing UV background and mass accretion history what regulates SF in the faintest dwarfs., Comment: 12 pages, 12 figures. Accepted by MNRAS

Getting up from an arbitrary fallen state is a basic human skill. Existing methods for learning this skill often generate highly dynamic and erratic get-up motions, which do not resemble human get-up strategies, or are based on tracking recorded human get-up motions. In this paper, we present a staged approach using reinforcement learning, without recourse to motion capture data. The method first takes advantage of a strong character model, which facilitates the discovery of solution modes. A second stage then learns to adapt the control policy to work with progressively weaker versions of the character. Finally, a third stage learns control policies that can reproduce the weaker get-up motions at much slower speeds. We show that across multiple runs, the method can discover a diverse variety of get-up strategies, and execute them at a variety of speeds. The results usually produce policies that use a final stand-up strategy that is common to the recovery motions seen from all initial states. However, we also find policies for which different strategies are seen for prone and supine initial fallen states. The learned get-up control strategies often have significant static stability, i.e., they can be paused at a variety of points during the get-up motion. We further test our method on novel constrained scenarios, such as having a leg and an arm in a cast., Comment: SIGGRAPH 2022. Project page: https://tianxintao.github.io/get_up_control/

In this paper, we present a re-analysis of SuperCDMS data using a profile likelihood approach to search for sub-GeV dark matter particles (DM) through two inelastic scattering channels: bremsstrahlung radiation and the Migdal effect. By considering possible inelastic scattering channels, experimental sensitivity can be extended to DM masses that would otherwise be undetectable through the DM-nucleon elastic scattering channel, given the energy threshold of current experiments. We exclude DM masses down to $220~\textrm{MeV}/c^2$ at $2.7 \times 10^{-30}~\textrm{cm}^2$ via the bremsstrahlung channel. The Migdal channel search excludes DM masses down to $30~\textrm{MeV}/c^2$ at $5.0 \times 10^{-30}~\textrm{cm}^2$., Comment: This paper is being withdrawn due to an error in data selection during the analysis. Although incorrect, the limits are roughly representative of the sensitivity. The new corrected version of the result will be uploaded once ready

We leverage state-of-the-art machine learning methods and a decade's worth of archival data from CFHT to predict observatory image quality (IQ) from environmental conditions and observatory operating parameters. Specifically, we develop accurate and interpretable models of the complex dependence between data features and observed IQ for CFHT's wide-field camera, MegaCam. Our contributions are several-fold. First, we collect, collate and reprocess several disparate data sets gathered by CFHT scientists. Second, we predict probability distribution functions (PDFs) of IQ and achieve a mean absolute error of $\sim0.07''$ for the predicted medians. Third, we explore the data-driven actuation of the 12 dome "vents" installed in 2013-14 to accelerate the flushing of hot air from the dome. We leverage epistemic and aleatoric uncertainties in conjunction with probabilistic generative modeling to identify candidate vent adjustments that are in-distribution (ID); for the optimal configuration for each ID sample, we predict the reduction in required observing time to achieve a fixed SNR. On average, the reduction is $\sim12\%$. Finally, we rank input features by their Shapley values to identify the most predictive variables for each observation. Our long-term goal is to construct reliable and real-time models that can forecast optimal observatory operating parameters to optimize IQ. We can then feed such forecasts into scheduling protocols and predictive maintenance routines. We anticipate that such approaches will become standard in automating observatory operations and maintenance by the time CFHT's successor, the Maunakea Spectroscopic Explorer, is installed in the next decade., Comment: 25 pages and 12 figures (main) + 7 pages and 8 figures (2 appendices). Accepted for publication in MNRAS