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How can we precisely estimate a large language model's (LLM) accuracy on questions belonging to a specific topic within a larger question-answering dataset? The standard direct estimator, which averages the model's accuracy on the questions in each subgroup, may exhibit high variance for subgroups (topics) with small sample sizes. Synthetic regression modeling, which leverages the model's accuracy on questions about other topics, may yield biased estimates that are too unreliable for large subgroups. We prescribe a simple yet effective solution: an empirical Bayes (EB) estimator that balances direct and regression estimates for each subgroup separately, improving the precision of subgroup-level estimates of model performance. Our experiments on multiple datasets show that this approach consistently provides more precise estimates of the LLM performance compared to the direct and regression approaches, achieving substantial reductions in the mean squared error. Confidence intervals for EB estimates also have near-nominal coverage and are narrower compared to those for the direct estimator. Additional experiments on tabular and vision data validate the benefits of this EB approach., Comment: To appear at EMNLP 2024

The morphology of quantum dots plays an important role in governing their photophysics. Here, we explore the photoluminescence of spheroidal CsPbBr3 quantum dots synthesized via the room-temperature trioctlyphosphine oxide/PbBr2 method. Despite photoluminescence quantum yields nearing 100%, these spheroidal quantum dots exhibit an elongated red photoluminescence tail not observed in typical cubic quantum dots synthesized via hot injection. We explore this elongated red tail through structural and optical characterization including small-angle x-ray scattering, transmission electron microscopy and time-resolved, steady-state, and single quantum dot photoluminescence. From these measurements we conclude that the red tail originates from emissive surface traps. We hypothesize that these emissive surface traps are located on the (111) surfaces and show that the traps can be passivated by adding phenethyl ammonium bromide, resulting in a more symmetric line shape

Recent advancements in large language models have demonstrated their potential in numerous medical applications, particularly in automating clinical trial matching for translational research and enhancing medical question answering for clinical decision support. However, our study shows that incorporating non decisive sociodemographic factors such as race, sex, income level, LGBT+ status, homelessness, illiteracy, disability, and unemployment into the input of LLMs can lead to incorrect and harmful outputs for these populations. These discrepancies risk exacerbating existing health disparities if LLMs are widely adopted in healthcare. To address this issue, we introduce EquityGuard, a novel framework designed to detect and mitigate the risk of health inequities in LLM based medical applications. Our evaluation demonstrates its efficacy in promoting equitable outcomes across diverse populations.

The rapid proliferation of frontier model agents promises significant societal advances but also raises concerns about systemic risks arising from unsafe interactions. Collusion to the disadvantage of others has been identified as a central form of undesirable agent cooperation. The use of information hiding (steganography) in agent communications could render collusion practically undetectable. This underscores the need for evaluation frameworks to monitor and mitigate steganographic collusion capabilities. We address a crucial gap in the literature by demonstrating, for the first time, that robust steganographic collusion in LLMs can arise indirectly from optimization pressure. To investigate this problem we design two approaches -- a gradient-based reinforcement learning (GBRL) method and an in-context reinforcement learning (ICRL) method -- for reliably eliciting sophisticated LLM-generated linguistic text steganography. Importantly, we find that emergent steganographic collusion can be robust to both passive steganalytic oversight of model outputs and active mitigation through communication paraphrasing. We contribute a novel model evaluation framework and discuss limitations and future work. Our findings imply that effective risk mitigation from steganographic collusion post-deployment requires innovation in passive and active oversight techniques.

In "Embers of Autoregression" (McCoy et al., 2023), we showed that several large language models (LLMs) have some important limitations that are attributable to their origins in next-word prediction. Here we investigate whether these issues persist with o1, a new system from OpenAI that differs from previous LLMs in that it is optimized for reasoning. We find that o1 substantially outperforms previous LLMs in many cases, with particularly large improvements on rare variants of common tasks (e.g., forming acronyms from the second letter of each word in a list, rather than the first letter). Despite these quantitative improvements, however, o1 still displays the same qualitative trends that we observed in previous systems. Specifically, o1 -- like previous LLMs -- is sensitive to the probability of examples and tasks, performing better and requiring fewer "thinking tokens" in high-probability settings than in low-probability ones. These results show that optimizing a language model for reasoning can mitigate but might not fully overcome the language model's probability sensitivity., Comment: 6 pages; updated to fix typo in Fig 4 caption

Fix two words over the binary alphabet $\{0,1\}$, and generate iid Bernoulli$(p)$ bits until one of the words occurs in sequence. This setup, commonly known as Penney's ante, was popularized by Conway, who found (in unpublished work) a simple formula for the probability that a given word occurs first. We study win probabilities in Penney's ante from an analytic and combinatorial perspective, building on previous results for the case $p = \frac{1}{2}$ and words of the same length. For words of arbitrary lengths, our results bound how large the win probability can be for the longer word. When $p = \frac{1}{2}$ we characterize when a longer word can be statistically favorable, and for $p \neq \frac{1}{2}$ we present a conjecture describing the optimal pairs, which is supported by computer computations. Additionally, we find that Penney's ante often exhibits symmetry under the transformation $p \to 1-p$. We construct new explicit bijections that account for these symmetries, under conditions that can be easily verified by examining auto- and cross-correlations of the words., Comment: 20 pages, 2 figures

While many astrophysical plasmas can be modelled successfully assuming ionisation and thermal equilibrium, in some cases this is not appropriate and a non-equilibrium approach is required. In nebulae around evolved stars the local elemental abundances may also strongly vary in space and time. Here we present a non-equilibrium multi-ion module developed for the fluid-dynamics code \textsc{pion}, describing the physical processes included and demonstrating its capabilities with some test calculations. A non-equilbrium ionisation solver is developed that allows arbitrary elemental abundances for neutral and ionised (but not molecular) gas, for the elements H, He, C, N, O, Ne, Si, S and Fe. Collisional ionisation and recombination, photoionisation and charge-exchange reactions are included, and ion-by-ion non-equilibrium radiative cooling is calculated based on the instantaneous ion fractions of each element. Element and ion mass-fractions are advected using passive scalars, operator-split from the microphysical processes. The module is validated by comparing with equilibrium and non-equilibrium calculations in the literature. Effects of charge exchange on ion abundances in cooling plasmas are discussed. Application to modelling shocks and photoionised H~\textsc{ii} regions is demonstrated. The time-dependent expansion of a Wolf-Rayet nebula is studied, including photoionisation and collisional processes, and spectral-line luminosities calculated for non-equilibrium and equilibrium plasma states. The multi-ion module enables simulation of ionised plasmas with spatially varying elemental abundances using self-consistent ion abundances and thermal evolution. This allows prediction of spectral lines in UV, optical, IR and X-ray even in cases where the plasma is out of ionisation equilibrium.

We present near-simultaneous X-ray and optical polarization measurements in the high synchrotron peaked (HSP) blazar Mrk 421. The X-ray polarimetric observations were carried out using {\it Imaging X-ray Polarimetry Explorer} ({\it IXPE}) on 06 December 2023. During {\it IXPE} observations, we also carried out optical polarimetric observations using 104cm Sampurnanand telescope at Nainital and multi-band optical imaging observations using 2m Himalayan Chandra Telescope at Hanle. From model-independent analysis of {\it IXPE} data, we detected X-ray polarization with degree of polarization ($\Pi_X$) of 8.5$\pm$0.5\% and an electric vector position angle ($\Psi_X$) of 10.6$\pm$1.7 degrees in the 2$-$8 keV band. From optical polarimetry on 06 December 2023, in B, V, and R bands, we found values of $\Pi_B$ = 4.27$\pm$0.32\%, $\Pi_V$= 3.57$\pm$0.31\%, and $\Pi_R$= 3.13$\pm$0.25\%. The value of $\Pi_B$ is greater than that observed at longer optical wavelengths, with the degree of polarization suggesting an energy-dependent trend, gradually decreasing from higher to lower energies. This is consistent with that seen in other HSP blazars and favour a stratified emission region encompassing a shock front. The emission happening in the vicinity of the shock front will be more polarized due to the ordered magnetic field resulting from shock compression. The X-ray emission, involving high-energy electrons, originates closer to the shock front than the optical emission. The difference in the spatial extension could plausibly account for the observed variation in polarization between X-ray and optical wavelengths. This hypothesis is further supported by the broadband spectral energy distribution modeling of the X-ray and optical data., Comment: 9 pages, 3 figures. Accepted for publication in ApJ

Interaction between galaxies play a pivotal role in their evolution. Ongoing star formation in spiral galaxies can be affected by these processes. We select a sample of interacting galaxies in field environments at various interaction stages and are nearly face-on and chose galaxy pairs NGC 2207/IC 2163, NGC 4017/4016 (ARP 305) and NGC 7753/7752 (ARP 86). We use the UltraViolet Imaging Telescope (UVIT) onboard AstroSat to characterize the star-forming regions in the galaxy with a superior resolution of ~1.4". We identified and characterized star-forming regions in the UVIT images of the sample and correlated them with the neutral hydrogen (HI) distribution. We detected localized regions of enhancement in star formation surface density and distortions in the sample of galaxies. We found this consistent with the distribution of HI in the galaxy. These are possible evidence of past and ongoing interactions affecting the star formation properties in the galaxies. We then conducted a study to understand whether the observed enhancements hold true for a wider sample of interacting galaxies. We observe a moderate enhancement in the star formation rate (SFR) with the interaction class, with the maximum of 1.8 being in the merger class of galaxies. We studied the SFR enhancement for the main galaxies in our sample as a function of pair mass ratio and pair separation. We observe a strong anti-correlation between the SFR enhancement and pair mass ratio and no linear correlation between the enhancement and pair separation. This suggests that the enhancement in interaction-induced star formation may be more strongly influenced by the pair mass ratios, rather than the pair separation. We also infer that the pair separation can possibly act as a limiting parameter for the SFR enhancement., Comment: 11 pages, seven figures. Accepted for publication in MNRAS

We study the spectral stability of smooth, small-amplitude periodic traveling wave solutions of the Novikov equation, which is a Camassa-Holm type equation with cubic nonlinearities. Specifically, we investigate the $L^2(\mathbb{R})$-spectrum of the associated linearized operator, which in this case is an integro-differential operator with periodic coefficients, in a neighborhood of the origin in the spectral plane. Our analysis shows that such small-amplitude periodic solutions are spectrally unstable to long-wavelength perturbations if the wave number if greater than a critical value, bearing out the famous Benmajin-Feir instability for the Novikov equation. On the other hand, such waves with wave number less than the critical value are shown to be spectrally stable. Our methods are based on applying spectral perturbation theory to the associated linearization., Comment: 21 pages, 1 figure

We give a new definition of a Frobenius structure on an algebra object in a monoidal category, generalising Frobenius algebras in the category of vector spaces. Our definition allows Frobenius forms valued in objects other than the unit object, and can be seen as a categorical version of Frobenius extensions of the second kind. When the monoidal category is pivotal we define a Nakayama morphism for the Frobenius structure and explain what it means for this morphism to have finite order. Our main example is a well-studied algebra object in the (additive and idempotent completion of the) Temperley-Lieb category at a root of unity. We show that this algebra has a Frobenius structure and that its Nakayama morphism has order 2. As a consequence, we obtain information about Nakayama morphisms of preprojective algebras of Dynkin type, considered as algebras over the semisimple algebras on their vertices., Comment: 22 pages. v2 is PDF upload with diagrams fixed

Motivated by its application in the frequency assignment problem for cellular networks, conflict-free coloring was first studied by Even et al. in [Conflict-free colorings of simple geometric regions with applications to frequency assignment in cellular networks, SIAM Journal on Computing, 2004]. A \emph{conflict-free coloring} of a hypergraph $\mathcal{H}$ is an assignment of colors to the vertex set of $\mathcal{H}$ such that every hyperedge in $\mathcal{H}$ has a vertex whose color is distinct from every other vertex in that hyperedge. The minimum number of colors required for such a coloring is known as the \emph{conflict-free chromatic number} of $\mathcal{H}$. Conflict-free coloring has also been studied on open/closed neighborhood hypergraphs of a given graph. In this paper, we study the list variant of conflict-free coloring where, for every vertex $v$, we are given a list of admissible colors $L_v$ such that $v$ is allowed to be colored only from $L_v$. We prove upper bounds for the list conflict-free chromatic number of general hypergraphs and graphs., Comment: 17 pages

Feeding the growing human population sustainably amidst climate change is one of the most important challenges in the 21st century. Current practices often lead to the overuse of agronomic inputs, such as synthetic fertilizers and water, resulting in environmental contamination and diminishing returns on crop productivity. The complexity of agricultural systems, involving plant-environment interactions and human management, presents significant scientific and technical challenges for developing sustainable practices. Addressing these challenges necessitates transdisciplinary research, involving intense collaboration among fields such as plant science, engineering, computer science, and social sciences. Here, we present five case studies from two research centers demonstrating successful transdisciplinary approaches toward more sustainable water and fertilizer use. These case studies span multiple scales. Starting from whole-plant signaling, we explore how reporter plants can transform our understanding of plant communication and enable efficient application of water and fertilizers. We then show how new fertilizer technologies could increase the availability of phosphorus in the soil. To accelerate advancements in breeding new cultivars, we discuss robotic technologies for high-throughput plant screening in different environments at a population scale. At the ecosystem scale, we investigate phosphorus recovery from aquatic systems and methods to minimize phosphorus leaching. Finally, as agricultural outputs affect all people, we show how to integrate stakeholder perspectives and needs into the research. With these case studies, we hope to encourage the scientific community to adopt transdisciplinary research and promote cross-training among biologists, engineers, and social scientists to drive discovery and innovation in advancing sustainable agricultural systems.

Extending the standard $\Lambda$CDM model by considering dissipative effects within a causal viscous framework, and obtaining an analytical solution for the Hubble parameter remains a challenge in the literature. In this work, we resolve this dilemma by deriving a complete and original solution for the Hubble parameter by introducing a novel form for the bulk viscous coefficient associated with bulk viscous dark matter (vDM). A thorough analysis of the model is conducted by deriving theoretical constraints on the parameters and comparing the model with the latest observational data sets. Intriguingly, we find that the model predicts a sign-switching bulk viscous pressure, which facilitates both the early decelerated expansion and the late accelerated expansion of the universe. Also, the redshift at which the viscous pressure switches sign is found to be strongly correlated with the relaxation time parameter of the viscous fluid. Thermodynamic analysis revealed that, the model satisfies both the covariant and generalized second law of thermodynamics as well as the convexity condition for entropy. Additionally, we reconstructed the model by unifying viscous dark matter and dark energy into a single unified dark matter (UDM) component, and found that this unified model predicts identical dynamical evolution for the Universe, while satisfying the necessary near-equilibrium condition throughout that evolution (both in early and late phases)., Comment: 20 pages, 12 figures

Over the past decade, wearable computing devices (``smart glasses'') have undergone remarkable advancements in sensor technology, design, and processing power, ushering in a new era of opportunity for high-density human behavior data. Equipped with wearable cameras, these glasses offer a unique opportunity to analyze non-verbal behavior in natural settings as individuals interact. Our focus lies in predicting engagement in dyadic interactions by scrutinizing verbal and non-verbal cues, aiming to detect signs of disinterest or confusion. Leveraging such analyses may revolutionize our understanding of human communication, foster more effective collaboration in professional environments, provide better mental health support through empathetic virtual interactions, and enhance accessibility for those with communication barriers. In this work, we collect a dataset featuring 34 participants engaged in casual dyadic conversations, each providing self-reported engagement ratings at the end of each conversation. We introduce a novel fusion strategy using Large Language Models (LLMs) to integrate multiple behavior modalities into a ``multimodal transcript'' that can be processed by an LLM for behavioral reasoning tasks. Remarkably, this method achieves performance comparable to established fusion techniques even in its preliminary implementation, indicating strong potential for further research and optimization. This fusion method is one of the first to approach ``reasoning'' about real-world human behavior through a language model. Smart glasses provide us the ability to unobtrusively gather high-density multimodal data on human behavior, paving the way for new approaches to understanding and improving human communication with the potential for important societal benefits. The features and data collected during the studies will be made publicly available to promote further research., Comment: 22 pages, first three authors equal contribution

We present the first homogeneous release of several thousand Type Ia supernovae (SNe Ia), all having spectroscopic classification, and spectroscopic redshifts for half the sample. This release, named the "DR2", contains 3628 nearby (z < 0.3) SNe Ia discovered, followed and classified by the Zwicky Transient Facility survey between March 2018 and December 2020. Of these, 3000 have good-to-excellent sampling and 2667 pass standard cosmology light-curve quality cuts. This release is thus the largest SN Ia release to date, increasing by an order of magnitude the number of well characterized low-redshift objects. With the "DR2", we also provide a volume-limited (z < 0.06) sample of nearly a thousand SNe Ia. With such a large, homogeneous and well controlled dataset, we are studying key current questions on SN cosmology, such as the linearity SNe Ia standardization, the SN and host dependencies, the diversity of the SN Ia population, and the accuracy of the current light-curve modeling. These, and more, are studied in detail in a series of articles associated with this release. Alongside the SN Ia parameters, we publish our force-photometry gri-band light curves, 5138 spectra, local and global host properties, observing logs, and a python tool to ease use and access of these data. The photometric accuracy of the "DR2" is not yet suited for cosmological parameter inference, which will follow as "DR2.5" release. We nonetheless demonstrate that the multi-thousand SN Ia Hubble Diagram has a typical 0.15 mag scatter., Comment: ZTF SN Ia DR2 release paper. Accepted for publication (Astronomy and Astrophysics). Reference paper for the ZTF SN Ia DR2 A&A Special Issue

We present a joint analysis of the CMB lensing power spectra measured from the Data Release 6 of the Atacama Cosmology Telescope and Planck PR4, cross-correlations between the ACT and Planck lensing reconstruction and galaxy clustering from unWISE, and the unWISE clustering auto-spectrum. We obtain 1.5% constraints on the matter density fluctuations at late times parametrised by the best constrained parameter combination $S_8^{\rm 3x2pt}\equiv\sigma_8 (\Omega_m/0.3)^{0.4}=0.815\pm0.012$. The commonly used $S_8\equiv\sigma_8 (\Omega_m/0.3)^{0.5}$ parameter is constrained to $S_8=0.816\pm0.015$. In combination with baryon acoustic oscillation (BAO) measurements we find $\sigma_8=0.815\pm 0.012$. We also present sound-horizon-independent estimates of the present day Hubble rate of $H_0=66.4^{+3.2}_{-3.7} \,\mathrm{km}\,\mathrm{s}^{-1}\mathrm{Mpc}^{-1}$ from our large scale structure data alone and $H_0=64.3^{+2.1}_{-2.4}\,\mathrm{km}\,\mathrm{s}^{-1}\mathrm{Mpc}^{-1}$ in combination with uncalibrated supernovae from Pantheon+. Using parametric estimates of the evolution of matter density fluctuations, we place constraints on cosmic structure in a range of high redshifts typically inaccessible with cross-correlation analyses. Combining lensing cross- and auto-correlations, we derive a 3.3% constraint on the integrated matter density fluctuations above $z=2.4$, one of the tightest constraints in this redshift range and fully consistent with a $\Lambda$CDM model fit to the primary CMB from Planck. Combining with primary CMB observations and using the extended low redshift coverage of these combined data sets we derive constraints on a variety of extensions to the $\Lambda$CDM model including massive neutrinos, spatial curvature, and dark energy. We find in flat $\Lambda$CDM $\sum m_\nu<0.12$ eV at 95% confidence using the LSS data, BAO measurements from SDSS and primary CMB observations., Comment: 30 pages, 15 figures, to be submitted to PRD, comments welcome

The sum of cosmic neutrino masses can be measured cosmologically, as the sub-eV particles behave as `hot' dark matter whose main effect is to suppress the clustering of matter compared to a universe with the same amount of purely cold dark matter. Current astronomical data provide an upper limit on $\Sigma m_{\nu}$ between 0.07 - 0.12 eV at 95% confidence, depending on the choice of data. This bound assumes that the cosmological model is $\Lambda$CDM, where dark energy is a cosmological constant, the spatial geometry is flat, and the primordial fluctuations follow a pure power-law. Here, we update studies on how the mass limit degrades if we relax these assumptions. To existing data from the Planck satellite we add new gravitational lensing data from the Atacama Cosmology Telescope, the new Type Ia Supernova sample from the Pantheon+ survey, and baryonic acoustic oscillation (BAO) measurements from the Sloan Digital Sky Survey and the Dark Energy Spectrosopic Instrument. We find the neutrino mass limit is stable to most model extensions, with such extensions degrading the limit by less than 10%. We find a broadest bound of $\Sigma m_{\nu} < 0.19 ~\rm{eV}$ at 95% confidence for a model with dynamical dark energy, although this scenario is not statistically preferred over the simpler $\Lambda$CDM model., Comment: 8 pages

The acceleration of Large Language Models (LLMs) with speculative decoding provides a significant runtime improvement without any loss of accuracy. Currently, EAGLE-2 is the state-of-the-art speculative decoding method, improving on EAGLE with a dynamic draft tree. We introduce Dynamic Depth Decoding (DDD), which optimises EAGLE-2's tree drafting method using a dynamic depth. This extends the average speedup that EAGLE-2 achieves over EAGLE by $44\%$, giving DDD an average speedup of $3.16$x.

Understanding evolution of vocal communication in social animals is an important research problem. In that context, beyond humans, there is an interest in analyzing vocalizations of other social animals such as, meerkats, marmosets, apes. While existing approaches address vocalizations of certain species, a reliable method tailored for meerkat calls is lacking. To that extent, this paper investigates feature representations for automatic meerkat vocalization analysis. Both traditional signal processing-based representations and data-driven representations facilitated by advances in deep learning are explored. Call type classification studies conducted on two data sets reveal that feature extraction methods developed for human speech processing can be effectively employed for automatic meerkat call analysis., Comment: Accepted at Interspeech 2024 satellite event (VIHAR 2024)

Empirical likelihood (EL) and its extension via the jackknife empirical likelihood (JEL) method provide robust alternatives to parametric approaches, in the contexts with uncertain data distributions. This paper explores the theoretical foundations and practical applications of JEL in the context of multivariate sample-based U-statistics. In this study we develop the JEL method for multivariate U-statistics with three (or more) samples. This study enhance the JEL methods capability to handle complex data structures while preserving the computation efficiency of the empirical likelihood method. To demonstrate the applications of the JEL method, we compute confidence intervals for differences in VUS measurements which have potential applications in classification problems. Monte Carlo simulation studies are conducted to evaluate the efficiency of the JEL, Normal approximation and Kernel based confidence intervals. These studies validate the superior performance of the JEL approach in terms of coverage probability and computational efficiency compared to other two methods. Additionally, a real data application illustrates the practical utility of the approach. The JEL method developed here has potential applications in dealing with complex data structures.

We investigate the most general gauging operations in 2+1 dimensional oriented field theories with finite symmetry groups, which correspond to gapped boundary conditions in 3+1 dimensional Dijkgraaf-Witten theory. The classification is achieved by enumerating 2+1 dimensional oriented topological quantum field theories that cancel the 't Hooft anomaly associated with the symmetry. This framework is rigorously formulated using twisted crossed extensions of modular fusion categories and projective 3-representations. Additionally, we explore the resulting fusion 2-category symmetries and argue that this framework captures all possible categorical symmetries in 2+1 dimensional oriented field theories., Comment: 22 pages, 7 figures, v2: minor revisions

We measure the ionised gas velocity dispersions of star-forming galaxies in the MAGPI survey ($z\sim0.3$) and compare them with galaxies in the SAMI ($z\sim0.05$) and KROSS ($z\sim1$) surveys to investigate how the ionised gas velocity dispersion evolves. For the first time, we use a consistent method that forward models galaxy kinematics from $z=0$ to $z=1$. This method accounts for spatial substructure in emission line flux and beam smearing. We investigate the correlation between gas velocity dispersion and galaxy properties to understand the mechanisms that drive gas turbulence. We find that in both MAGPI and SAMI galaxies, the gas velocity dispersion more strongly correlates with the star-formation rate surface density ($\Sigma_{\rm SFR}$) than with a variety of other physical properties, and the average gas velocity dispersion is similar, at the same $\Sigma_{\rm SFR}$, for SAMI, MAGPI and KROSS galaxies. The results indicate that mechanisms related to $\Sigma_{\rm SFR}$ could be the dominant driver of gas turbulence from $z\sim1$ to $z\sim0$, for example, stellar feedback and/or gravitational instability. The gas velocity dispersion of MAGPI galaxies is also correlated with the non-rotational motion of the gas, illustrating that in addition to star-formation feedback, gas transportation and accretion may also contribute to the gas velocity dispersion for galaxies at $z\sim 0.3$. KROSS galaxies only have a moderate correlation between gas velocity dispersion and $\Sigma_{\rm SFR}$ and a higher scatter of gas velocity dispersion with respect to $\Sigma_{\rm SFR}$, in agreement with the suggestion that other mechanisms, such as gas transportation and accretion, are relatively more important at higher redshift galaxies., Comment: 15 pages, 10 figures, accepted for publication in MNRAS

We are living in an age of protest. Although we have an excellent understanding of the factors that predict participation in protest, we understand little about the conditions that foster a sustained (versus transient) movement. How do interactions between supporters and authorities combine to influence whether and how people engage (i.e., using conventional or radical tactics)? This paper introduces a novel, theoretically-founded and empirically-informed agent-based model (DIMESim) to address these questions. We model the complex interactions between the psychological attributes of the protester (agents), the authority to whom the protests are targeted, and the environment that allows protesters to coordinate with each other -- over time, and at a population scale. Where an authority is responsive and failure is contested, a modest sized conventional movement endured. Where authorities repeatedly and incontrovertibly fail the movement, the population disengaged from action but evidenced an ongoing commitment to radicalism (latent radicalism)., Comment: Initial submission version of journal paper

We construct the phase space of a spherically symmetric causal diamond in $(d+2)$-dimensional Minkowski spacetime. Utilizing the covariant phase space formalism, we identify the relevant degrees of freedom that localize to the $d$-dimensional bifurcate horizon and, upon canonical quantization, determine their commutators. On this phase space, we find two Iyer-Wald charges. The first of these charges, proportional to the area of the causal diamond, is responsible for shifting the null time along the horizon and has been well-documented in the literature. The second charge is much less understood, being integrable for $d \geq 2$ only if we allow for field-dependent diffeomorphisms and is responsible for changing the size of the causal diamond., Comment: 9 pages, 2 figures

While recent zero-shot multispeaker text-to-speech (TTS) models achieve impressive results, they typically rely on extensive transcribed speech datasets from numerous speakers and intricate training pipelines. Meanwhile, self-supervised learning (SSL) speech features have emerged as effective intermediate representations for TTS. It was also observed that SSL features from different speakers that are linearly close share phonetic information while maintaining individual speaker identity, which enables straight-forward and robust voice cloning. In this study, we introduce SSL-TTS, a lightweight and efficient zero-shot TTS framework trained on transcribed speech from a single speaker. SSL-TTS leverages SSL features and retrieval methods for simple and robust zero-shot multi-speaker synthesis. Objective and subjective evaluations show that our approach achieves performance comparable to state-of-the-art models that require significantly larger training datasets. The low training data requirements mean that SSL-TTS is well suited for the development of multi-speaker TTS systems for low-resource domains and languages. We also introduce an interpolation parameter which enables fine control over the output speech by blending voices. Demo samples are available at https://idiap.github.io/ssl-tts

This study examines the statistical fluctuations in energy on the Hubble horizon and the Komar energy, which represents the total gravitational energy within the horizon, as the universe expands. Our analysis reveals that these fluctuations generally decrease over time but experience a pronounced spike during the universe's transition into its later accelerated expansion phase. In the framework of the standard $\Lambda$CDM model, the evolution of these fluctuations shows similar behaviour, eventually stabilising at a constant minimum value. We also find that these fluctuations are holographically connected, provided the universe ends in a de Sitter phase. This holographic connection suggests that in the final de Sitter epoch, the relative fluctuations of the horizon energy and the Komar energy are equivalent, and satisfies, $\sigma_H^2/\left^2=1/N_{surf}=1/N_{bulk} = \sigma_V^2/\left^2.$, Comment: 20 pages, 3 figures

Convolutional Neural Networks (CNNs) combine large amounts of parallelizable computation with frequent memory access. Field Programmable Gate Arrays (FPGAs) can achieve low latency and high throughput CNN inference by implementing dataflow accelerators that pipeline layer-specific hardware to implement an entire network. By implementing a different processing element for each CNN layer, these layer-pipelined accelerators can achieve high compute density, but having all layers processing in parallel requires high memory bandwidth. Traditionally this has been satisfied by storing all weights on chip, but this is infeasible for the largest CNNs, which are often those most in need of acceleration. In this work we augment a state-of-the-art dataflow accelerator (HPIPE) to leverage both High-Bandwidth Memory (HBM) and on-chip storage, enabling high performance layer-pipelined dataflow acceleration of large CNNs. Based on profiling results of HBM's latency and throughput against expected address patterns, we develop an algorithm to choose which weight buffers should be moved off chip and how deep the on-chip FIFOs to HBM should be to minimize compute unit stalling. We integrate the new hardware generation within the HPIPE domain-specific CNN compiler and demonstrate good bandwidth efficiency against theoretical limits. Compared to the best prior work we obtain speed-ups of at least 19.4x, 5.1x and 10.5x on ResNet-18, ResNet-50 and VGG-16 respectively.

This report evaluates the applicability of the ideal gas law in the pressurized air-bearing of hard disk drives when calculating the relative humidity. We employ a semi-analytical numerical method that solves vapor-liquid equilibrium using the Redlich-Kwong and Peng-Robinson equation of state to calculate the saturation pressure of water. The deviation from the ideal gas law is quantified and examined through saturation pressure isotherms. We find that at low temperatures, lighter gases such as helium show little deviation from the ideal gas law, whereas heavier gases such as nitrogen deviate significantly. As temperature increases, the difference between the gases decreases. The results suggest that in areas of low temperature, the non-ideal nature of gases must be taken into account.

Spicules are the thin hair/grass-like structures that are prominently observed at the chromospheric solar limb. It is believed that fibrils and rapid blueshifted and redshifted excursions (RBEs and RREs; collectively referred to as REs) correspond to on-disk counterparts of type I spicules and type II spicules, respectively. Our investigation focuses on observing the response of these REs alongside similar spectral features in the chromosphere, transition Region (TR), and corona, utilizing space-time plots derived from coordinated observations from Swedish 1 m Solar Telescope/H{\alpha}, Interface Region Imaging Spectrograph (IRIS), and Solar Dynamics Observatory. Our analysis reveals upflowing REs, promptly reaching temperatures characteristic of the TR and corona, indicating a multi-thermal nature. Similarly, downflowing features exhibiting similar spectral signatures over the disk display plasma motion from the corona to chromospheric temperatures, demonstrating a multithermal nature. In addition to distinct upflows and downflows, we observe sequential upflow and downflow along the same path, depicting a distinctive parabolic trajectory in space-time plots of observations sampling TR and various coronal passbands. Similar to isolated upflows and downflows, these REs also exhibit a multi-thermal nature throughout their trajectory. Furthermore, our results reveal a more intricate motion of the REs in which both upflow and downflow coexist at the same spatial location. On a different note, our analysis, utilizing coordinated IRIS spectral observations, shows spatio-temporal redshifts/downflows in both the TR and chromosphere, suggesting that at least subsets of the strong redshifts/downflows observed in TR temperature spectra result from the return from the upper atmosphere flow of plasma in the form of bundles of spicules or features exhibiting similar spectra.

In this study, we demonstrate the efficacy of the Ultraviolet Imaging Telescope (UVIT) in identifying and characterizing white dwarfs (WDs) within the Milky Way Galaxy. Leveraging the UVIT point source catalogue towards SMC and crossmatching with Gaia DR3 data, we identified 43 single WDs (37 new detections), 13 new WD+main sequence (MS) candidates and 161 UV bright MS stars by analysing their Spectral Energy Distributions (SED). Using the WD evolutionary models, we determine the masses, effective temperatures, and cooling ages of these identified WDs. Masses of these WDs range from 0.2 to 1.3 M$_{\odot}$ and effective temperatures (T$_{eff}$) between 10000 K to 15000 K, with cooling ages spanning 0.1 to 2 Gyr. Notably, we detect hotter WDs compared to literature values, which is attributed to the sensitivity of UVIT. Furthermore, we report the detection of 20 new extremely low-mass (ELM) candidates from our analysis. Future spectroscopic studies of the ELM candidates will help us understand the formation scenarios of these exotic objects. Despite limitations in Gaia DR3 distance measurements for optically faint WDs, we provide a crude estimate of the WD space density within 1kpc as 1.3 $\times$ 10$^{-3}$ pc$^{-3}$, which is higher than previous estimates in the literature. Our results underscore the instrumental capabilities of UVIT and anticipate the forthcoming UV missions like INSIST for systematic WD discovery. Our methodology sets a precedent for future analyses in other UVIT fields to find more WDs and perform spectroscopic studies to verify their candidacy., Comment: Accepted for publication in Astronomy & Astrophysics