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An extremely compact object (ECO) is defined as a quantum object without horizon, whose radius is just a small distance $s$ outside its Schwarzschild radius. We show that any ECO of mass $M$ in $d+1$ dimensions with $s\ll (M/m_p)^{2/(d-2)(d+1)}l_p$ must have (at leading order) the same thermodynamic properties -- temperature, entropy and radiation rates -- as the corresponding semiclassical black hole of mass $M$. An essential aspect of the argument involves showing that the Tolman-Oppenheimer-Volkoff equation has no consistent solution in the region just outside the ECO surface, unless this region is filled with radiation at the (appropriately blueshifted) Hawking temperature. In string theory it has been found that black hole microstates are fuzzballs -- objects with no horizon -- which are expected to have a radius that is only a little larger than the horizon radius. Thus the arguments of this paper provide a nice closure to the fuzzball paradigm: the absence of a horizon removes the information paradox, and the thermodynamic properties of the semiclassical hole are nonetheless recovered to an excellent approximation., Comment: 51 pages, 4 figures v4: more references added

We search for indirect signals of $\mathscr{O}$(keV) dark matter annihilating or decaying into $\mathscr{O}$(eV) dark photons. These dark photons will be highly boosted and have decay lengths larger than the Milky Way, and can be absorbed by neutrino or dark matter experiments at a rate dependent on the photon-dark photon kinetic mixing parameter and the optical properties of the experiment. We show that current experiments can not probe new parameter space, but future large-scale gaseous detectors with low backgrounds (i.e. CYGNUS, NEXT, PANDAX-III) may be sensitive to this signal when the annihilation cross section is especially large., Comment: 10 pages

We study young massive clusters (YMCs) in their embedded "infant" phase with $\sim0.\!^{\prime\prime}1$ ALMA, HST, and JWST observations targeting the central starburst ring in NGC 3351, a nearby Milky Way analog galaxy. Our new ALMA data reveal 18 bright and compact (sub-)millimeter continuum sources, of which 8 have counterparts in JWST images and only 6 have counterparts in HST images. Based on the ALMA continuum and molecular line data, as well as ancillary measurements for the HST and JWST counterparts, we identify 14 sources as infant star clusters with high stellar and/or gas masses (${\sim}10^5\;\mathrm{M_\odot}$), small radii (${\lesssim}\,5\;\mathrm{pc}$), large escape velocities ($6{-}10\;\mathrm{km/s}$), and short free-fall times ($0.5{-}1\;\mathrm{Myr}$). Their multiwavelength properties motivate us to divide them into four categories, likely corresponding to four evolutionary stages from starless clumps to exposed HII region-cluster complexes. Leveraging age estimates for HST-identified clusters in the same region, we infer an evolutionary timeline going from $\sim$1-2 Myr before cluster formation as starless clumps, to $\sim$4-6 Myr after as exposed HII region-cluster complexes. Finally, we show that the YMCs make up a substantial fraction of recent star formation across the ring, exhibit an non-uniform azimuthal distribution without a very coherent evolutionary trend along the ring, and are capable of driving large-scale gas outflows., Comment: 27 pages, 12 figures; ApJ accepted

Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences. In this White Paper, we take a first step at presenting some of the most common use cases as well as critical/emerging use cases of (including the use of artificial intelligence for) biological and medical image data, which would benefit tremendously from better frameworks for sharing (including technical, resourcing, legal, and ethical aspects). In the second half of this paper, we paint an ideal world scenario for how global image data sharing could work and benefit all life sciences and beyond. As this is still a long way off, we conclude by suggesting several concrete measures directed toward our institutions, existing imaging communities and data initiatives, and national funders, as well as publishers. Our vision is that within the next ten years, most researchers in the world will be able to make their datasets openly available and use quality image data of interest to them for their research and benefit. This paper is published in parallel with a companion White Paper entitled Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data, which addresses challenges and opportunities related to producing well-documented and high-quality image data that is ready to be shared. The driving goal is to address remaining challenges and democratize access to everyday practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location., Comment: This manuscript (arXiv:2401.13023) is published with a closely related companion entitled, Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data, which can be found at the following link, arXiv:2401.13022

Dynamic data race detection has emerged as a key technique for ensuring reliability of concurrent software in practice. However, dynamic approaches can often miss data races owing to nondeterminism in the thread scheduler. Predictive race detection techniques cater to this shortcoming by inferring alternate executions that may expose data races without re-executing the underlying program. More formally, the dynamic data race prediction problem asks, given a trace \sigma of an execution of a concurrent program, can \sigma be correctly reordered to expose a data race? Existing state-of-the art techniques for data race prediction either do not scale to executions arising from real world concurrent software, or only expose a limited class of data races, such as those that can be exposed without reversing the order of synchronization operations. In general, exposing data races by reasoning about synchronization reversals is an intractable problem. In this work, we identify a class of data races, called Optimistic Sync(hronization)-Reversal races that can be detected in a tractable manner and often include non-trivial data races that cannot be exposed by prior tractable techniques. We also propose a sound algorithm OSR for detecting all optimistic sync-reversal data races in overall quadratic time, and show that the algorithm is optimal by establishing a matching lower bound. Our experiments demonstrate the effectiveness of OSR on our extensive suite of benchmarks, OSR reports the largest number of data races, and scales well to large execution traces., Comment: ICSE'24

Wearable-based Human Activity Recognition (HAR) is a key task in human-centric machine learning due to its fundamental understanding of human behaviours. Due to the dynamic nature of human behaviours, continual learning promises HAR systems that are tailored to users' needs. However, because of the difficulty in collecting labelled data with wearable sensors, existing approaches that focus on supervised continual learning have limited applicability, while unsupervised continual learning methods only handle representation learning while delaying classifier training to a later stage. This work explores the adoption and adaptation of CaSSLe, a continual self-supervised learning model, and Kaizen, a semi-supervised continual learning model that balances representation learning and down-stream classification, for the task of wearable-based HAR. These schemes re-purpose contrastive learning for knowledge retention and, Kaizen combines that with self-training in a unified scheme that can leverage unlabelled and labelled data for continual learning. In addition to comparing state-of-the-art self-supervised continual learning schemes, we further investigated the importance of different loss terms and explored the trade-off between knowledge retention and learning from new tasks. In particular, our extensive evaluation demonstrated that the use of a weighting factor that reflects the ratio between learned and new classes achieves the best overall trade-off in continual learning., Comment: AAAI 2024 HCRL (Human-Centric Representation Learning) Workshop

Symbiotic binaries exhibit a wide range of photometric variability spanning different timescales attributed to orbital motion, intrinsic variability of individual components, or the interaction between the two stars. In the range from minutes to hours, variability induced by accretion processes, likely originating from the accretion disks, denoted as flickering, is detected. This variability could mimic solar-like oscillations exhibited by luminous red giants. We aim to investigate whether it is possible to utilize the precise observations of the NASA TESS mission to detect flickering in symbiotic stars despite such studies being usually performed at shorter wavelengths. Additionally, our goal is to develop a quantitative method for the detection of accretion-induced flickering that does not rely solely on subjective assessment of the light curves. We obtain the light curves of known symbiotic stars and a comprehensive control sample of assumed single red giants from the TESS FFIs. From the processed light curves and their PSD, we measure the amplitudes of the variability and other parameters. We introduce a method that enables the differentiation between flickering sources and stars that do not exhibit this variability. We detect flickering-like variability in 20 symbiotic stars utilizing TESS data, with 13 of them being previously unidentified as flickering sources. Moreover, the TESS observations facilitate the detection of related variations occurring over timescales of a few days, as well as changes in the flickering behavior across multiple sectors. The flickering has now been likely detected in a total of 35 known symbiotic stars. When focusing solely on accreting-only symbiotic stars where the detection of flickering is presumably more straightforward, the fraction could reach as high as ~80%. This suggests that accretion disks may be rather prevalent in these binaries., Comment: 16 pages, 21 figures, 3 tables; accepted in Astronomy & Astrophysics

Given a non-deterministic finite automaton (NFA) A with m states, and a natural number n (presented in unary), the #NFA problem asks to determine the size of the set L(A_n) of words of length n accepted by A. While the corresponding decision problem of checking the emptiness of L(A_n) is solvable in polynomial time, the #NFA problem is known to be #P-hard. Recently, the long-standing open question -- whether there is an FPRAS (fully polynomial time randomized approximation scheme) for #NFA -- was resolved in \cite{ACJR19}. The FPRAS due to \cite{ACJR19} relies on the interreducibility of counting and sampling, and computes, for each pair of state q and natural number i <= n, a set of O(\frac{m^7 n^7}{epsilon^7}) many uniformly chosen samples from the set of words of length i that have a run ending at q (\epsilon is the error tolerance parameter of the FPRAS). This informative measure -- the number of samples maintained per state and length -- also affects the overall time complexity with a quadratic dependence. Given the prohibitively high time complexity, in terms of each of the input parameters, of the FPRAS due to \cite{ACJR19}, and considering the widespread application of approximate counting (and sampling) in various tasks in Computer Science, a natural question arises: Is there a faster FPRAS for #NFA that can pave the way for the practical implementation of approximate #NFA tools? In this work, we demonstrate that significant improvements in time complexity are achievable. Specifically, we have reduced the number of samples required for each state to be independent of m, with significantly less dependence on $n$ and $\epsilon$, maintaining only \widetilde{O}(\frac{n^4}{epsilon^2}) samples per state., Comment: To appear in the proceedings of PODS 2024

We review the level of welcomeness that members of the lattice field theory community feel based on the results of a survey performed in May and June 2023. While respondents reported generally high levels of feeling welcome at the lattice conference, women and people with diverse gender identities, sexual orientations, ethnic backgrounds and religious affiliations feel less included and have more negative experiences at the lattice conference than their peers. Respondents report that they are actively informing themselves about inequities in the community, however a large fraction of survey participants underestimate the severity of the problem, as was found in previous surveys. The survey data indicate that this situation can be most effectively improved by organizing talks and events about issues of diversity and inclusion within the lattice community. Respondents also reported that individual readings of scientific papers on equity and inclusion are effective in giving people agency in making a change and hence it may be helpful to collate a collection of important articles on these topics., Comment: Proceedings contribution to "The 40th International Symposium on Lattice Field Theory (Lattice 2023)"

Deep learning models have become increasingly computationally intensive, requiring extensive computational resources and time for both training and inference. A significant contributing factor to this challenge is the uniform computational effort expended on each input example, regardless of its complexity. We introduce \textbf{DynaLay}, an alternative architecture that features a decision-making agent to adaptively select the most suitable layers for processing each input, thereby endowing the model with a remarkable level of introspection. DynaLay reevaluates more complex inputs during inference, adjusting the computational effort to optimize both performance and efficiency. The core of the system is a main model equipped with Fixed-Point Iterative (FPI) layers, capable of accurately approximating complex functions, paired with an agent that chooses these layers or a direct action based on the introspection of the models inner state. The model invests more time in processing harder examples, while minimal computation is required for easier ones. This introspective approach is a step toward developing deep learning models that "think" and "ponder", rather than "ballistically'' produce answers. Our experiments demonstrate that DynaLay achieves accuracy comparable to conventional deep models while significantly reducing computational demands.

Photometric time series gathered by space telescopes such as CoRoT and Kepler allow to detect solar-like oscillations in red-giant stars and to measure their global seismic constraints, which can be used to infer global stellar properties (e.g. masses, radii, evolutionary states). Combining such precise constraints with photospheric abundances provides a means of testing mixing processes that occur inside red-giant stars. In this work, we conduct a detailed spectroscopic and seismic analysis of nine nearby (d < 200 pc) red-giant stars observed by Kepler. Both seismic constraints and grid-based modelling approaches are used to determine precise fundamental parameters for those evolved stars. We compare distances and radii derived from Gaia Data Release 3 parallaxes with those inferred by a combination of seismic, spectroscopic and photometric constraints. We find no deviations within errorsbars, however the small sample size and the associated uncertainties are a limiting factor for such comparison. We use the period spacing of mixed modes to distinguish between ascending red-giants and red-clump stars. Based on the evolutionary status, we apply corrections to the values of $\Delta\nu$ for some stars, resulting in a slight improvement to the agreement between seismic and photometric distances. Finally, we couple constraints on detailed chemical abundances with the inferred masses, radii and evolutionary states. Our results corroborate previous studies that show that observed abundances of lithium and carbon isotopic ratio are in contrast with predictions from standard models, giving robust evidence for the occurrence of additional mixing during the red-giant phase., Comment: 18 pages, 19 figures

Online mental health support communities have grown in recent years for providing accessible mental and emotional health support through volunteer counselors. Despite millions of people participating in chat support on these platforms, the clinical effectiveness of these communities on mental health symptoms remains unknown. Furthermore, although volunteers receive some training based on established therapeutic skills studied in face-to-face environments such as active listening and motivational interviewing, it remains understudied how the usage of these skills in this online context affects people's mental health status. In our work, we collaborate with one of the largest online peer support platforms and use both natural language processing and machine learning techniques to measure how one-on-one support chats affect depression and anxiety symptoms. We measure how the techniques and characteristics of support providers, such as using affirmation, empathy, and past experience on the platform, affect support-seekers' mental health changes. We find that online peer support chats improve both depression and anxiety symptoms with a statistically significant but relatively small effect size. Additionally, support providers' techniques such as emphasizing the autonomy of the client lead to better mental health outcomes. However, we also found that some behaviors (e.g. persuading) are actually harmful to depression and anxiety outcomes. Our work provides key understanding for mental health care in the online setting and designing training systems for online support providers.

We analyze 330 ks of {\it Chandra} X-ray imaging and spectra of the nearby, edge-on starburst and Seyfert Type 2 galaxy NGC 4945 to measure the hot gas properties along the galactic outflows. We extract and model spectra from 15 regions extending from $-$0.55 kpc to $+$0.85 kpc above and below the galactic disk to determine the best-fit parameters and metal abundances. We find that the hot gas temperatures and number densities peak in the central regions and decrease along the outflows. These profiles are inconsistent with a spherical, adiabatically-expanding wind model, suggesting the need to include mass loading and/or a non-spherical outflow geometry. We estimate the mass outflow rate of the hot wind to be $1.6\:M_{\odot}~\rm{yr}^{-1}$. Emission from charge exchange is detected in the northern outflow, and we estimate it contributes 12\% to the emitted, broad-band ($0.5-7$ keV) X-ray flux., Comment: 11 pages, 7 figures; submitted (13 December 2023), accepted ApJ (11 April 2023)

We probe the neutral circumgalactic medium (CGM) along the major axes of NGC891 and NGC4565 in 21-cm emission out to $\gtrsim 100$kpc using the Green Bank Telescope (GBT), extending our previous minor axes observations. We achieve an unprecedented $5\sigma$ sensitivity of $6.1\times 10^{16}$ cm$^{-2}$ per 20 km s$^{-1}$ velocity channel. We detect HI with diverse spectral shapes, velocity widths, and column densities. We compare our detections to the interferometric maps from the Westerbork Synthesis Radio Telescope (WSRT) obtained as part of the HALOGAS survey. At small impact parameters, $> 31-43\%$ of the emission detected by the GBT cannot be explained by emission seen in the WSRT maps, and it increases to $> 64-73\%$ at large impact parameters. This implies the presence of diffuse circumgalactic HI. The mass ratio between HI in the CGM and HI in the disk is an order of magnitude larger than previous estimates based on shallow GBT mapping. The diffuse HI along the major axes pointings is corotating with the HI disk. The velocity along the minor axes pointings is consistent with an inflow and/or fountain in NGC891 and an inflow/outflow in NGC4565. Including the circumgalactic HI, the depletion time and the accretion rate of NGC4565 are sufficient to sustain its star formation. In NGC891, most of the required accreting material is still missing., Comment: 18 pages, 12 figures, accepted for publication in MNRAS

3D generation has rapidly accelerated in the past decade owing to the progress in the field of generative modeling. Score Distillation Sampling (SDS) based rendering has improved 3D asset generation to a great extent. Further, the recent work of Denoising Diffusion Policy Optimization (DDPO) demonstrates that the diffusion process is compatible with policy gradient methods and has been demonstrated to improve the 2D diffusion models using an aesthetic scoring function. We first show that this aesthetic scorer acts as a strong guide for a variety of SDS-based methods and demonstrates its effectiveness in text-to-3D synthesis. Further, we leverage the DDPO approach to improve the quality of the 3D rendering obtained from 2D diffusion models. Our approach, DDPO3D, employs the policy gradient method in tandem with aesthetic scoring. To the best of our knowledge, this is the first method that extends policy gradient methods to 3D score-based rendering and shows improvement across SDS-based methods such as DreamGaussian, which are currently driving research in text-to-3D synthesis. Our approach is compatible with score distillation-based methods, which would facilitate the integration of diverse reward functions into the generative process. Our project page can be accessed via https://ddpo3d.github.io.

With the goal of understanding the visual concepts that CLIP associates with text prompts, we show that the latent space of CLIP can be visualized solely in terms of linear transformations on simple geometric primitives like circles and straight lines. Although existing approaches achieve this by sketch-synthesis-through-optimization, they do so on the space of B\'ezier curves, which exhibit a wastefully large set of structures that they can evolve into, as most of them are non-essential for generating meaningful sketches. We present CLIPDrawX, an algorithm that provides significantly better visualizations for CLIP text embeddings, using only simple primitive shapes like straight lines and circles. This constrains the set of possible outputs to linear transformations on these primitives, thereby exhibiting an inherently simpler mathematical form. The synthesis process of CLIPDrawX can be tracked end-to-end, with each visual concept being explained exclusively in terms of primitives. Implementation will be released upon acceptance. Project Page: $\href{https://clipdrawx.github.io/}{\text{https://clipdrawx.github.io/}}$.

The bound state of one NS5 brane (wrapped on a $\mathbb{T}^4$) and $N$ NS1-branes has two dual descriptions: its low-energy dynamics is described by the symmetric orbifold of $\mathbb{T}^4$, while the near horizon geometry is captured by string theory on ${\rm AdS}_3 \times {\rm S}^3\times \mathbb{T}^4$ with one unit of NS flux. The latter theory is exactly solvable in the hybrid formalism, and this allows one to prove the equivalence of the two descriptions. In this paper we extend this duality to $\mathbb{Z}_k$ orbifolds of this ${\rm AdS}_3 \times {\rm S}^3$ background. In particular, we show that the corresponding worldsheet spectrum reproduces exactly the perturbative excitations on top of a certain non-perturbative state in the dual symmetric orbifold theory. Since the ${\rm AdS}/{\rm CFT}$ duality map is exact for these models, we obtain an interesting picture of how the duality relates boundary and bulk descriptions., Comment: 37 pages, 1 figure