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Collaborative inference among multiple wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications, particularly for sensing and computer vision. This approach typically involves a three-stage process: a) data acquisition through sensing, b) feature extraction, and c) feature encoding for transmission. However, transmitting the extracted features poses a significant privacy risk, as sensitive personal data can be exposed during the process. To address this challenge, we propose a novel privacy-preserving collaborative inference mechanism, wherein each edge device in the network secures the privacy of extracted features before transmitting them to a central server for inference. Our approach is designed to achieve two primary objectives: 1) reducing communication overhead and 2) ensuring strict privacy guarantees during feature transmission, while maintaining effective inference performance. Additionally, we introduce an over-the-air pooling scheme specifically designed for classification tasks, which provides formal guarantees on the privacy of transmitted features and establishes a lower bound on classification accuracy., Comment: This work is under review for possible IEEE publication. arXiv admin note: substantial text overlap with arXiv:2406.00256

Fragmentation and evolution for the molecular shells of the compact HII regions are less explored compared to their evolved counterparts. We map nine compact HII regions with a typical diameter of 0.4 pc that are surrounded by molecular shells traced by CCH. Several to a dozen dense gas fragments probed by H13CO+ are embedded in these molecular shells. These gas fragments, strongly affected by the HII region, have a higher surface density, mass, and turbulence than those outside the shells but within the same pc-scale natal clump. These features suggest that the shells swept up by the early HII regions can enhance the formation of massive dense structures that may host the birth of higher-mass stars. We examine the formation of fragments and find that fragmentation of the swept-up shell is unlikely to occur in these early HII regions, by comparing the expected time scale of shell fragmentation with the age of HII region. We propose that the appearance of gas fragments in these shells is probably the result of sweeping up pre-existing fragments into the molecular shell that has not yet fragmented. Taken together, this work provides a basis for understanding the interplay of star-forming sites with an intricate environment containing ionization feedback such as those observed in starburst regions., Comment: Accepted by MNRAS, 24 pages, 14 figures

In this paper, we present a detailed analysis of the IRS 17 filament within the intermediate-mass protocluster IRAS 08448-4343 (of $\sim\,10^3\,\rm L_{\odot}$), using ALMA data from the ATOMS 3-mm and QUARKS 1.3-mm surveys. The IRS 17 filament, which spans $\sim$54000 au ($0.26\,\rm pc$) in length and $\sim$4000 au ($0.02\,\rm pc$) in width, exhibits a complex, multi-component velocity field, and harbours hierarchical substructures. These substructures include three bundles of seven velocity-coherent fibers, and 29 dense ($n\sim 10^8\,\rm cm^{-3}$) condensations. The fibers have a median length of $\sim 4500\,\rm au$ and a median width of $\sim 1400\,\rm au$. Among these fibers, four are identified as ``fertile", each hosting at least three dense condensations, which are regarded as the ``seeds" of star formation. While the detected cores are randomly spaced within the IRS\,17 filament based on the 3-mm dust continuum image, periodic spacing ($\sim1600\,\rm au$) of condensations is observed in the fertile fibers according to the 1.3-mm dust map, consistent with the predictions of linear isothermal cylinder fragmentation models. These findings underscore the crucial role of fibers in star formation and suggest a hierarchical fragmentation process that extends from the filament to the fibers, and ultimately, to the smallest-scale condensations., Comment: 19 pages, 10 figures, 4 tables, accepted by ApJ

We develop a framework for describing the effects of systematic state preparation error in quantum-enhanced atom interferometry on sensing performance. We do this in the context of both spin-squeezed and non-Gaussian states for the two-axis-twisting (TAT), one-axis-twisting (OAT), and twist-and-turn (TNT) state preparation schemes, and derive general conditions for robustness and susceptibility of quantum states to state preparation error. In the spin-squeezing regime, we find that OAT is more susceptible to state preparation error than TAT due to its parameter-dependent phase space rotation. In the non-Gaussian regime, we find that OAT is robust to state preparation errors, which can be explained by a small ratio of off-diagonal to diagonal elements in its Fisher-covariance matrix. In contrast, TNT does not exhibit this robustness. We find that the single parameter unbiased estimators that are habitually used in quantum-enhanced atom interferometry are not always optimal, and that there may be occasions where biased estimators, or two-parameter unbiased estimators, lead to lower net error., Comment: 15 pages, 10 figures

Language professionals spend long hours at the computer, which may have an impact on their short- and long-term physical health. In 2023, I ran a survey to investigate workstation ergonomics, eye and upper-body problems, and self-reported strategies that alleviate those problems among language professionals who work sitting or standing at a desk. Of the 791 respondents, about one third reported eye problems and over two-thirds reported upper-body aches or pains in the past 12 months, with significantly higher upper-body pain prevalence among females than males, and also among younger respondents than older ones. While the pain prevalence rate in the survey was similar to figures published in the literature, as was the sex risk factor, the association of higher pain prevalence among younger people contrasted with other studies that have found increasing age to be a risk factor for pain. In this article I share the survey results in detail and discuss possible explanations for the findings., Comment: 9 pages, 1 table, 5 figures. Correction to fig. 4

Using the upGREAT instrument on SOFIA, we have imaged the [C II] 158 {\mu}m fine structure line emission in bright-rimmed pillars located at the southern edge of the IC1848 H II region, and carried out pointed observations of the [O I] 63 and 145 {\mu}m fine structure lines toward selected positions. The observations are used to characterize the morphology, velocity field, and the physical conditions in the G1 - G3 filaments. The velocity-resolved [C II] spectra show evidence of a velocity shift at the head of the brightest G1 filament, possibly caused by radiation pressure from the impinging UV photons or the rocket effect of the evaporating gas. Archival Herschel PACS and SPIRE data imply H2 column densities in the range 10^{21} - 10^{22} cm^{-2}, corresponding to maximum visual extinction AV = 10 mag, and average H2 volume density of about 4500 cm^{-3} in the filaments. The [C II] emission traces ~ 17% of the total H2 column density, as derived from dust SED fits. PDR models are unable to explain the observed line intensities of the two [O I] fine structure lines in IC1848, with the observed [O I] 145 {\mu}m line being too strong compared to the model predictions. The [O I] lines in IC1848 are overall weak and the signal-to-noise ratio is limited. However, our observations suggest that the [O I] 63/145 {\mu}m intensity ratio is a sensitive probe of the physical conditions in photon dominated regions such as IC1848. These lines are thus excellent targets for future high-altitude balloon instruments, less affected by telluric absorption., Comment: Accepted for publication in Astronomy and Astrophysics

Economics Job Market Rumors (EJMR) is an online forum and clearinghouse for information on the academic job market for economists. It also includes content that is abusive, defamatory, racist, misogynistic, or otherwise "toxic." Almost all of this content is created anonymously by contributors who receive a four-character username when posting on EJMR. Using only publicly available data we show that the statistical properties of the scheme by which these usernames were generated allows the IP addresses from which most posts were made to be determined with high probability. We recover 47,630 distinct IP addresses of EJMR posters and attribute them to 66.1% of the roughly 7 million posts made over the past 12 years. We geolocate posts and describe aggregated cross-sectional variation -- particularly regarding toxic, misogynistic, and hate speech -- across sub-forums, geographies, institutions, and IP addresses. Our analysis suggests that content on EJMR comes from all echelons of the economics profession, including, but not limited to, its elite institutions.

We study the problem of finding fair allocations -- EF1 and EFX -- of indivisible goods with orientations. In an orientation, every agent gets items from their own predetermined set. For EF1, we show that EF1 orientations always exist when agents have monotone valuations, via a pseudopolynomial-time algorithm. This surprisingly positive result is the main contribution of our paper. We complement this result with a comprehensive set of scenarios where our algorithm, or a slight modification of it, finds an EF1 orientation in polynomial time. For EFX, we focus on the recently proposed graph instances, where every agent corresponds to a vertex on a graph and their allowed set of items consists of the edges incident to their vertex. It was shown that finding an EFX orientation is NP-complete in general. We prove that it remains intractable even when the graph has a vertex cover of size 8, or when we have a multigraph with only 10 vertices. We essentially match these strong negative results with a fixed-parameter tractable algorithm that is virtually the best someone could hope for., Comment: 21 pages, 3 figures

The petaling effect, induced by pupil fragmentation from the telescope spider, drastically affects the performance of high contrast instruments by inducing core splitting on the PSF. Differential piston/tip/tilt aberrations within each optically separated fragment of the pupil are poorly measured by commonly used Adaptive Optics (AO) systems. We here pursue a design of dedicated low-order wavefront sensor -- or petalometers -- to complement the main AO. Interferometric devices sense differential aberrations between fragments with optimal sensitivity; their weakness though is their limitation to wrapped phase measurements. We show that by combining multiple spectral channels, we increase the capture range for petaling aberrations beyond several microns, enough to disambiguate one-wave wrapping errors made by the main AO system. We propose here to implement a petalometer from the multi-wavelength imaging mode of the VAMPIRES visible-light instrument, deployed on SCExAO at the Subaru Telescope. The interferometric measurements obtained in four spectral channels through a 7 hole non-redundant mask allow us to effiiently reconstruct diffierential piston between pupil petals., Comment: Paper 13097-89 from SPIE Astronomical Telescopes + Instrumentation, 2024, Yokohama, Japan

Logistics and transportation networks require a large amount of resources to realize necessary connections between locations and minimizing these resources is a vital aspect of planning research. Since such networks have dynamic connections that are only available at specific times, intricate models are needed to portray them accurately. In this paper, we study the problem of minimizing the number of resources needed to realize a dynamic network, using the temporal graphs model. In a temporal graph, edges appear at specific points in time. Given a temporal graph and a natural number k, we ask whether we can cover every temporal edge exactly once using at most k temporal journeys; in a temporal journey consecutive edges have to adhere to the order of time. We conduct a thorough investigation of the complexity of the problem with respect to four dimensions: (a) whether the type of the temporal journey is a walk, a trail, or a path; (b) whether the chronological order of edges in the journey is strict or non-strict; (c) whether the temporal graph is directed or undirected; (d) whether the start and end points of each journey are given or not. We almost completely resolve the complexity of all these problems and provide dichotomies for each one of them with respect to k.

Protein dynamics play a crucial role in many biological processes and drug interactions. However, measuring, and simulating protein dynamics is challenging and time-consuming. While machine learning holds promise in deciphering the determinants of protein dynamics from structural information, most existing methods for protein representation learning operate at the residue level, ignoring the finer details of atomic interactions. In this work, we propose for the first time to use graph neural networks (GNNs) to learn protein representations at the atomic level and predict B-factors from protein 3D structures. The B-factor reflects the atomic displacement of atoms in proteins, and can serve as a surrogate for protein flexibility. We compared different GNN architectures to assess their performance. The Meta-GNN model achieves a correlation coefficient of 0.71 on a large and diverse test set of over 4k proteins (17M atoms) from the Protein Data Bank (PDB), outperforming previous methods by a large margin. Our work demonstrates the potential of representations learned by GNNs for protein flexibility prediction and other related tasks.

Thermophotovoltaics, devices that convert thermal infrared photons to electricity, offer a key pathway for a variety of critical renewable energy technologies including thermal energy storage, waste heat recovery, and direct solar-thermal power generation. However, conventional far-field devices struggle to generate reasonable powers at lower temperatures. Near-field thermophotovoltaics provide a pathway to substantially higher powers by leveraging photon tunneling effects. Here we present a large area near-field thermophotovoltaic device, created with an epitaxial co-fabrication approach, that consists of a self-supported 0.28 cm2 emitter-cell pair with a 150 nm gap. The device generates 1.22 mW at 460 C, a twenty-five-fold increase over the same cell measured in a far-field configuration. Furthermore, the near-field device demonstrates short circuit current densities greater than the far-field photocurrent limit at all the temperatures tested, confirming the role of photon tunneling effects in the performance enhancement. Modeling suggests several practical directions for cell improvements and further increases in power density. These results highlight the promise of near-field thermophotovoltaics, especially for low temperature applications.

This paper targets to generalize the notion of Hopfian groups in the commutative case by defining the so-called {\bf relatively Hopfian groups} and {\bf weakly Hopfian groups}, and establishing some their crucial properties and characterizations. Specifically, we prove that for a reduced Abelian $p$-group $G$ such that $p^{\omega}G$ is Hopfian (in particular, is finite), the notions of relative Hopficity and ordinary Hopficity do coincide. We also show that if $G$ is a reduced Abelian $p$-group such that $p^{\omega}G$ is bounded and $G/p^{\omega}G$ is Hopfian, then $G$ is relatively Hopfian. This allows us to construct a reduced relatively Hopfian Abelian $p$-group $G$ with $p^{\omega}G$ an infinite elementary group such that $G$ is {\bf not} Hopfian. In contrast, for reduced torsion-free groups, we establish that the relative and ordinary Hopficity are equivalent. Moreover, the mixed case is explored as well, showing that the structure of both relatively and weakly Hopfian groups can be quite complicated., Comment: 26 pages

A Photonic Lantern (PL) is a novel device that efficiently converts a multi-mode fiber into several single-mode fibers. When coupled with an extreme adaptive optics (ExAO) system and a spectrograph, PLs enable high throughput spectroscopy at high angular resolution. The Subaru Coronagraphic Extreme Adaptive Optics (SCExAO) system of the Subaru Telescope recently acquired a PL that converts its multi-mode input into 19 single-mode outputs. The single mode outputs feed a R~4,000 spectrograph optimized for the 600 to 760 nm wavelength range. We present here the integration of the PL on SCExAO, and study the device performance in terms of throughput, field of view, and spectral reconstruction. We also present the first on-sky demonstration of a Visible PL coupled with an ExAO system, showing a significant improvement of x12 in throughput compared to the use of a sole single-mode fiber. This work paves the way towards future high throughput photonics instrumentation at small angular resolution., Comment: Proceeding published in SPIE Astronomical Telescopes + Instrumentation (2024) - Paper number 13096-25

We combine a new Galactic plane survey of Hydrogen Radio Recombination Lines (RRLs) with far-infrared (FIR) surveys of ionized Nitrogen, N+, to determine Nitrogen abundance across Galactic radius. RRLs were observed with NASA DSS-43 70m antenna and the Green Bank Telescope in 108 lines-of-sight spanning -135 degrees < l < 60 degrees, at b=0 degrees. These positions were also observed in [N II] 122 um and 205 um lines with the Herschel Space Observatory. Combining RRL and [N II] 122 um and 205 um observations in 41 of 108 samples with high signal-to-noise ratio, we studied ionized Nitrogen abundance distribution across Galactocentric distances of 0-8 kpc. Combined with existing Solar neighborhood and Outer galaxy N/H abundance determinations, we studied this quantity's distribution within the Milky Way's inner 17 kpc for the first time. We found a Nitrogen abundance gradient extending from Galactocentric radii of 4-17 kpc in the Galactic plane, while within 0-4 kpc, the N/H distribution remained flat. The gradient observed at large Galactocentric distances supports inside-out galaxy growth with the additional steepening resulting from variable star formation efficiency and/or radial flows in the Galactic disk, while the inner 4 kpc flattening, coinciding with the Galactic bar's onset, may be linked to radial flows induced by the bar potential. Using SOFIA/FIFI-LS and Herschel/PACS, we observed the [N III] 57 um line to trace doubly ionized gas contribution in a sub-sample of sightlines. We found negligible N++ contributions along these sightlines, suggesting mostly singly ionized Nitrogen originating from low ionization H II region outskirts., Comment: Accepted for publication at the Astrophysical Journal. 25 pages, 13 figures

Using new continuum and molecular line data from the ALMA Three-millimeter Observations of Massive Star-forming Regions (ATOMS) survey and archival VLA, 4.86 GHz data, we present direct observational evidence of hierarchical triggering relating three epochs of massive star formation in a ring-like H II region, G24.47+0.49. We find from radio flux analysis that it is excited by a massive star(s) of spectral type O8.5V-O8V from the first epoch of star formation. The swept-up ionized ring structure shows evidence of secondary collapse, and within this ring a burst of massive star formation is observed in different evolutionary phases, which constitutes the second epoch. ATOMS spectral line (e.g., HCO$^+$(1-0)) observations reveal an outer concentric molecular gas ring expanding at a velocity of $\sim$ 9 $\rm km\,s^{-1}$, constituting the direct and unambiguous detection of an expanding molecular ring. It harbors twelve dense molecular cores with surface mass density greater than 0.05 $\rm g\,cm^{-2}$, a threshold typical of massive star formation. Half of them are found to be subvirial, and thus in gravitational collapse, making them third epoch of potential massive star-forming sites., Comment: 18 pages, 7 figures, accepted for publication in The Astrophysical Journal Letters

We are motivated by a study that seeks to better understand the dynamic relationship between muscle activation and paw position during locomotion. For each gait cycle in this experiment, activation in the biceps and triceps is measured continuously and in parallel with paw position as a mouse trotted on a treadmill. We propose an innovative general regression method that draws from both ordinary differential equations and functional data analysis to model the relationship between these functional inputs and responses as a dynamical system that evolves over time. Specifically, our model addresses gaps in both literatures and borrows strength across curves estimating ODE parameters across all curves simultaneously rather than separately modeling each functional observation. Our approach compares favorably to related functional data methods in simulations and in cross-validated predictive accuracy of paw position in the gait data. In the analysis of the gait cycles, we find that paw speed and position are dynamically influenced by inputs from the biceps and triceps muscles, and that the effect of muscle activation persists beyond the activation itself.

The Travelling Salesman Problem (TSP) is an important combinatorial optimisation problem, and is usually solved on a quantum computer using a Quadratic Unconstrained Binary Optimisation (QUBO) formulation or a Higher Order Binary Optimisation(HOBO) formulation. In these formulations, penalty terms are added to the objective function for outputs that don't map to valid routes. We present a novel formulation which needs fewer binary variables, and where, by design, there are no penalty terms because all outputs from the quantum device are mapped to valid routes. Simulations of a quantum boson sampler were carried out which demonstrate that larger networks can be solved with this penalty-free formulation than with formulations with penalties. Simulations were successfully translated to hardware by running a non-QUBO formulation with penalties on an early experimental prototype ORCA PT-1 boson sampler. Although we worked with a boson sampler, we believe that this novel formulation is relevant to other quantum devices. This work shows that a good embedding for combinatorial optimisation problems can solve larger problems with the same quantum computing resource. The flexibility of boson sampling quantum devices is a powerful asset in solving combinatorial optimisation problem, because it enables formulations where the output string is always mapped to a valid solution, avoiding the need for penalties., Comment: 16 pages and 4 figures

We consider the problem of privately estimating the mean of vectors distributed across different nodes of an unreliable wireless network, where communications between nodes can fail intermittently. We adopt a semi-decentralized setup, wherein to mitigate the impact of intermittently connected links, nodes can collaborate with their neighbors to compute a local consensus, which they relay to a central server. In such a setting, the communications between any pair of nodes must ensure that the privacy of the nodes is rigorously maintained to prevent unauthorized information leakage. We study the tradeoff between collaborative relaying and privacy leakage due to the data sharing among nodes and, subsequently, propose PriCER: Private Collaborative Estimation via Relaying -- a differentially private collaborative algorithm for mean estimation to optimize this tradeoff. The privacy guarantees of PriCER arise (i) implicitly, by exploiting the inherent stochasticity of the flaky network connections, and (ii) explicitly, by adding Gaussian perturbations to the estimates exchanged by the nodes. Local and central privacy guarantees are provided against eavesdroppers who can observe different signals, such as the communications amongst nodes during local consensus and (possibly multiple) transmissions from the relays to the central server. We substantiate our theoretical findings with numerical simulations. Our implementation is available at https://github.com/rajarshisaha95/private-collaborative-relaying., Comment: 14 pages, 6 figures. arXiv admin note: text overlap with arXiv:2303.00035

Collaborative inference in next-generation networks can enhance Artificial Intelligence (AI) applications, including autonomous driving, personal identification, and activity classification. This method involves a three-stage process: a) data acquisition through sensing, b) feature extraction, and c) feature encoding for transmission. Transmission of the extracted features entails the potential risk of exposing sensitive personal data. To address this issue, in this work a new privacy-protecting collaborative inference mechanism is developed. Under this mechanism, each edge device in the network protects the privacy of extracted features before transmitting them to a central server for inference. This mechanism aims to achieve two main objectives while ensuring effective inference performance: 1) reducing communication overhead, and 2) maintaining strict privacy guarantees during features transmission.

This paper updates Currie, Kleven, and Zwiers (2020) by examining the credibility revolution across fields, including finance and macroeconomics, using NBER working papers up to May 2024. While the growth in terms related to identification and research designs have continued, finance and macroeconomics have lagged behind applied micro. Difference-in-differences and regression discontinuity designs have risen since 2002, but the growth in difference-in-difference has been larger, more persistent, and more ubiquitous. In contrast, instrumental variables have stayed flat over this period. Finance and macro, particularly corporate finance, has experienced significant growth in mentions of experimental and quasi-experimental methods and identification over this time period, but a large component of the credibility revolution in finance is due to difference-in-differences. Bartik and shift-share instruments have grown across all fields, with the most pronounced growth in international trade and investment, economic history, and labor studies. Synthetic control has not seen continued growth, and has fallen since 2020., Comment: Update with minor tweaks + full cite of Currie kleven and Zwiers (instead of et al.)

The prohibitive sizes of Large Language Models (LLMs) today make it difficult to deploy them on memory-constrained edge devices. This work introduces $\rm CALDERA$ -- a new post-training LLM compression algorithm that harnesses the inherent low-rank structure of a weight matrix $\mathbf{W}$ by approximating it via a low-rank, low-precision decomposition as $\mathbf{W} \approx \mathbf{Q} + \mathbf{L}\mathbf{R}$. Here, $\mathbf{L}$ and $\mathbf{R}$ are low rank factors, and the entries of $\mathbf{Q}$, $\mathbf{L}$ and $\mathbf{R}$ are quantized. The model is compressed by substituting each layer with its $\mathbf{Q} + \mathbf{L}\mathbf{R}$ decomposition, and the zero-shot performance of the compressed model is evaluated. Additionally, $\mathbf{L}$ and $\mathbf{R}$ are readily amenable to low-rank adaptation, consequently enhancing the zero-shot performance. $\rm CALDERA$ obtains this decomposition by formulating it as an optimization problem $\min_{\mathbf{Q},\mathbf{L},\mathbf{R}}\lVert(\mathbf{Q} + \mathbf{L}\mathbf{R} - \mathbf{W})\mathbf{X}^\top\rVert_{\rm F}^2$, where $\mathbf{X}$ is the calibration data, and $\mathbf{Q}, \mathbf{L}, \mathbf{R}$ are constrained to be representable using low-precision formats. Theoretical upper bounds on the approximation error of $\rm CALDERA$ are established using a rank-constrained regression framework, and the tradeoff between compression ratio and model performance is studied by analyzing the impact of target rank and quantization bit budget. Results illustrate that compressing LlaMa-$2$ $7$B/$13B$/$70$B and LlaMa-$3$ $8$B models using $\rm CALDERA$ outperforms existing post-training LLM compression techniques in the regime of less than $2.5$ bits per parameter. The implementation is available at: https://github.com/pilancilab/caldera., Comment: Accepted to The 38th Conference on Neural Information Processing Systems (NeurIPS 2024). [31 pages, 10 figures, 9 tables]

The radio transient ASKAP J173608.2-321735, at the position (l,b)= (356.0872,-0.0390), was serendipitously observed by The HI/OH/Recombination Line Survey of the Galactic Center (THOR-GC) at three epochs in March 2020, April 2020 and February 2021. The source was detected only on 2020 April 11 with flux density 20.6 +/- 1.1 mJy at 1.23 GHz and in-band spectral index alpha = -3.1 +/- 0.2. The commensal VLA Low-band Ionsophere and Transient Experiment (VLITE) simultaneously detected the source at 339 MHz with a flux density 122.6 +/- 20.4 mJy, indicating a spectral break below 1 GHz. The rotation measure in April 2020 was 63.9 +/- 0.3rad/m2, which almost triples the range of the variable rotation measure observed by Wang et al. (2021) to ~130 rad/m2. The polarization angle, corrected for Faraday rotation, was 97 +/- 6 degrees. The 1.23 GHz linear polarization was 76.7% +/- 3.9% with wavelength-dependent depolarization indicating Faraday depth dispersion sigma_phi = 4.8^{+0.5}_{-0.7} rad/m2. We find an upper limit to circular polarization |V|/I < 10.1%. Interpretation of the data in terms of diffractive scattering of radio waves by a plasma near the source indicates electron density and line-of-sight magnetic field strength within a factor 3 of n_e ~2 cm^{-3} and B_par ~2 x 10^5 microgauss. Combined with causality limits to the size of the source, these parameters are consistent with the low-frequency spectral break resulting from synchrotron self-absorption, not free-free absorption. A possible interpretation of the source is a highly supersonic neutron star interacting with a changing environment., Comment: Accepted for publication in the Astrophysical Journal

We study the private online change detection problem for dynamic communities, using a censored block model (CBM). Focusing on the notion of edge differential privacy (DP), we seek to understand the fundamental tradeoffs between the privacy budget, detection delay, and exact community recovery of community labels. We establish the theoretical lower bound on the delay in detecting changes privately and propose an algorithm capable of identifying changes in the community structure, while maintaining user privacy. Further, we provide theoretical guarantees for the effectiveness of our proposed method by showing necessary and sufficient conditions on change detection and exact recovery under edge DP. Simulation and real data examples are provided to validate the proposed method.

We have investigated the physical properties of Planck Galactic Cold Clumps (PGCCs) located in the Galactic Plane, using the JCMT Plane Survey (JPS) and the SCUBA-2 Continuum Observations of Pre-protostellar Evolution (SCOPE) survey. By utilising a suite of molecular-line surveys, velocities and distances were assigned to the compact sources within the PGCCs, placing them in a Galactic context. The properties of these compact sources show no large-scale variations with Galactic environment. Investigating the star-forming content of the sample, we find that the luminosity-to-mass ratio (L/M) is an order of magnitude lower than in other Galactic studies, indicating that these objects are hosting lower levels of star formation. Finally, by comparing ATLASGAL sources that are associated or are not associated with PGCCs, we find that those associated with PGCCs are typically colder, denser, and have a lower L/M ratio, hinting that PGCCs are a distinct population of Galactic Plane sources., Comment: 18 pages, 14 figures, 7 tables. Accepted for publication in MNRAS

Quantum machine learning (QML) is a promising early use case for quantum computing. There has been progress in the last five years from theoretical studies and numerical simulations to proof of concepts. Use cases demonstrated on contemporary quantum devices include classifying medical images and items from the Iris dataset, classifying and generating handwritten images, toxicity screening, and learning a probability distribution. Potential benefits of QML include faster training and identification of feature maps not found classically. Although, these examples lack the scale for commercial exploitation, and it may be several years before QML algorithms replace the classical solutions, QML is an exciting area. This article is written for those who already have a sound knowledge of quantum computing and now wish to gain a basic overview of the terminology and some applications of classical machine learning ready to study quantum machine learning. The reader will already understand the relevant relevant linear algebra, including Hilbert spaces, a vector space with an inner product., Comment: 32 pages, 21 figures, technical report

Context. Recent studies of the optical depth comparing [12CII] and [13CII] line profiles in Galactic star-forming regions revealed strong self-absorption in [12CII] by low excitation foreground material, implying a large column density of C+ corresponding to an equivalent AV of a few, up to about 10 mag. Aims. As the nature and origin of such a large column of cold C+ foreground gas are difficult to explain, it is essential to constrain the physical conditions of this material. Methods. We conducted high-resolution observations of [OI] 63 um and [OI] 145 um lines in M17 SW and Mon R2. The [OI] 145 um transition traces warm PDR-material, while the [OI] 63 um line traces foreground material as manifested by absorption dips. Results. Comparison of both [OI] line profiles with [CII] isotopic lines confirms warm PDR-origin background emission and a significant column of cold foreground material causing self-absorption visible in [12CII] and [OI] 63 um profiles. In M17 SW, the C+ and O column densities are comparable for both layers. Mon R2 exhibits larger O columns compared to C+, indicating additional material where the carbon is neutral or in molecular form. Small-scale spatial variation of the foreground absorption profiles and the large column density (around 1E18 cm-2 ) of the foreground material suggest emission from high-density regions associated with the cloud complex, not a uniform diffuse foreground cloud. Conclusions. The analysis confirms that the previously detected intense [CII] foreground absorption is attributable to a large column of low excitation dense atomic material, where carbon is ionized, and oxygen is in neutral atomic form., Comment: 30 pages, 24 figures

Leveraging the high resolution, high sensitivity, and wide frequency coverage of the Atacama Large Millimeter/submillimeter Array (ALMA), the QUARKS survey, standing for "Querying Underlying mechanisms of massive star formation with ALMA-Resolved gas Kinematics and Structures", is observing 139 massive star-forming clumps at ALMA Band 6 ($\lambda\sim$ 1.3 mm). This paper introduces the Atacama Compact Array (ACA) 7-m data. Combining multi-wavelength data, we provide the first edition of QUARKS atlas, offering insights into the multiscale and multiphase interstellar medium in high-mass star formation. The ACA 1.3 mm catalog includes 207 continuum sources that are called ACA sources. Their gas kinetic temperatures are estimated using three formaldehyde (H$_2$CO) transitions with a non-LTE radiation transfer model, and the mass and density are derived from a dust emission model. The ACA sources are massive (16-84 percentile values of 6-160 $M_{\odot}$), gravity-dominated ($M\propto R^{1.1}$) fragments within massive clumps, with supersonic turbulence ($\mathcal{M}>1$) and embedded star-forming protoclusters. We find a linear correlation between the masses of the fragments and the massive clumps, with a ratio of 6% between the two. When considering the fragments as representative of dense gas, the ratio indicates a dense gas fraction (DGF) of 6%, although with a wide scatter ranging from 1% to 10%. If we consider the QUARKS massive clumps to be what is observed at various scales, then the size-independent DGF indicates a self-similar fragmentation or collapsing mode in protocluster formation. With the ACA data over four orders of magnitude of luminosity-to-mass ratio ($L/M$), we find that the DGF increases significantly with $L/M$, which indicates clump evolutionary stage. We observed a limited fragmentation at the subclump scale, which can be explained by dynamic global collapse process., Comment: 24 pages, 7 figures. Accepted for publication in Research in Astronomy and Astrophysics. QUARKS atlas link: https://drive.google.com/file/d/1KTqXxCDduYepvLd9kIvZVSSytK48OmfL/view?usp=sharing

The electrical double layer (EDL) at aqueous solution-metal oxide interfaces critically affects many fundamental processes in electrochemistry, geology and biology, yet understanding its microscopic structure is challenging for both theory and experiments. Here we employ ab initio-based machine learning potentials including long-range electrostatics in large-scale atomistic simulations of the EDL at the TiO2-electrolyte interface. Our simulations provide a molecular-scale picture of the EDL that demonstrates the limitations of standard mean-field models. We further develop a method to accurately calculate the electrostatic potential drop at the interface. The computed capacitance originating from the adsorbed charges and the potential drop agrees with experiments, supporting the reliability of our description of the EDL. The larger interfacial capacitance of basic relative to acidic solutions originates from the higher affinity of the cations for the oxide surface and gives rise to distinct charging mechanisms on negative and positive surfaces.

Network diffusion models are used to study things like disease transmission, information spread, and technology adoption. However, small amounts of mismeasurement are extremely likely in the networks constructed to operationalize these models. We show that estimates of diffusions are highly non-robust to this measurement error. First, we show that even when measurement error is vanishingly small, such that the share of missed links is close to zero, forecasts about the extent of diffusion will greatly underestimate the truth. Second, a small mismeasurement in the identity of the initial seed generates a large shift in the locations of expected diffusion path. We show that both of these results still hold when the vanishing measurement error is only local in nature. Such non-robustness in forecasting exists even under conditions where the basic reproductive number is consistently estimable. Possible solutions, such as estimating the measurement error or implementing widespread detection efforts, still face difficulties because the number of missed links are so small. Finally, we conduct Monte Carlo simulations on simulated networks, and real networks from three settings: travel data from the COVID-19 pandemic in the western US, a mobile phone marketing campaign in rural India, and in an insurance experiment in China.

Asgard/NOTT (previously Hi-5) is a European Research Council (ERC)-funded project hosted at KU Leuven and a new visitor instrument for the Very Large Telescope Interferometer (VLTI). Its primary goal is to image the snow line region around young stars using nulling interferometry in the L-band (3.5 to 4.0)$\mu$m, where the contrast between exoplanets and their host stars is advantageous. The breakthrough is the use of a photonic beam combiner, which only recently allowed the required theoretical raw contrast of $10^{-3}$ in this spectral range. Nulling interferometry observations of exoplanets also require a high degree of balancing between the four pupils of the VLTI in terms of intensity, phase, and polarization. The injection into the beam combiner and the requirements of nulling interferometry are driving the design of the warm optics and the injection system. The optical design up to the beam combiner is presented. It offers a technical solution to efficiently couple the light from the VLTI into the beam combiner. During the coupling, the objective is to limit throughput losses to 5% of the best expected efficiency for the injection. To achieve this, a list of different loss sources is considered with their respective impact on the injection efficiency. Solutions are also proposed to meet the requirements on beam balancing for intensity, phase, and polarization. The different properties of the design are listed, including the optics used, their alignment and tolerances, and their impact on the instrumental performances in terms of throughput and null depth. The performance evaluation gives an expected throughput loss of less than <6.4% of the best efficiency for the injection and a null depth of $\sim2.10^{-3}$, mainly from optical path delay errors outside the scope of this work., Comment: Accepted for publication in JATIS. 23 pages, 11 figures, 8 tables

Star formation is essential in galaxy evolution and the cycling of matter. The support of interstellar clouds against gravitational collapse by magnetic (B-) fields has been proposed to explain the low observed star formation efficiency in galaxies and the Milky Way. Despite the Planck satellite providing a 5-15' all-sky map of the B-field geometry in the diffuse interstellar medium, higher spatial resolution observations are required to understand the transition from diffuse gas to gravitationally unstable filaments. NGC 2024, the Flame Nebula, in the nearby Orion B molecular cloud, contains a young, expanding HII region and a dense filament that harbors embedded protostellar objects. Therefore, NGC 2024 is an excellent opportunity to study the role of B-fields in the formation, evolution, and collapse of filaments, as well as the dynamics and effects of young HII regions on the surrounding molecular gas. We combine new 154 and 216 micron dust polarization measurements carried out using the HAWC+ instrument aboard SOFIA with molecular line observations of 12CN(1-0) and HCO+(1-0) from the IRAM 30-meter telescope to determine the B-field geometry and to estimate the plane of the sky magnetic field strength across the NGC 2024. The HAWC+ observations show an ordered B-field geometry in NGC 2024 that follows the morphology of the expanding HII region and the direction of the main filament. The derived plane of the sky B-field strength is moderate, ranging from 30 to 80 micro G. The strongest B-field is found at the northern-west edge of the HII region, characterized by the highest gas densities and molecular line widths. In contrast, the weakest field is found toward the filament in NGC 2024. The B-field has a non-negligible influence on the gas stability at the edges of the expanding HII shell (gas impacted by the stellar feedback) and the filament (site of the current star formation)., Comment: 36 pages, 26 figures Accepted for publication in Astronomy & Astrophysics

High-latitude ($|b|>30^{\circ}$) molecular clouds have virial parameters that exceed 1, but whether these clouds can form stars has not been studied systematically. Using JCMT SCUBA-2 archival data, we surveyed 70 fields that target high-latitude Planck galactic cold clumps (HLPCs) to find dense cores with density of $10^{5}$-$10^{6}$ cm$^{-3}$ and size of $<0.1$ pc. The sample benefits from both the representativeness of the parent sample and covering densest clumps at the high column density end ($>1\times10^{21}$ cm$^{-2}$). At an average noise rms of 15 mJy/beam, we detected Galactic dense cores in only one field, G6.04+36.77 (L183), while also identifying 12 extragalactic objects and two young stellar objects. Compared to the low-latitude clumps, dense cores are scarce in HLPCs. With synthetic observations, the densities of cores are constrained to be $n_c\lesssim10^5$ cm$^{-3}$, should they exist in HLPCs. Low-latitude clumps, Taurus clumps, and HLPCs form a sequence where a higher virial parameter corresponds to a lower dense core detection rate. If HLPCs were affected by the Local Bubble, the scarcity should favor turbulence-inhibited rather than supernova-driven star formation. Studies of the formation mechanism of the L183 molecular cloud are warranted., Comment: 9 pages for the main text. 4 figures, 1 table. Published in Astrophysical Journal Letter

Only a handful of massive starless core candidates have been discovered so far, but none of them have been fully confirmed. Within the MM1 clump in the filamentary infrared dark cloud G34.43+0.24 that was covered by the ALMA-ATOMS survey at Band 3 ($\sim2\arcsec$, 6000\,au) and the ALMA-QUARKS survey at Band 6 ($\sim 0.3\arcsec$, 900\,au), two prestellar core candidates MM1-C and E1 with masses of 71 and 20 \solarmass~and radii of 2100--4400\,au were discovered. The two cores show no obvious sign of star-formation activities. In particular, MM1-C is a very promising massive prestellar core candidate with a total gas mass of 71\,\solarmass. Within MM1-C, we detected two extremely dense substructures, C1 and C2, as characterized by their high densities of $\rm n_{H_2}\sim 10^{8-9} cm^{-3}$. Moreover, evidence of further fragmentation in C2 was also revealed. We have detected the primordial fragmentation in the earliest stage of massive star formation, and we speculate that MM1-C would be the birthplace of a massive multiple system. However, we cannot fully rule out the possibility that the massive prestellar core MM1-C will just form a cluster of low-mass stars if it undergoes further fragmentation., Comment: 12 pages, 6 figures