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FPGAs are now ubiquitous in cloud computing infrastructures and reconfigurable system-on-chip, particularly for AI acceleration. Major cloud service providers such as Amazon and Microsoft are increasingly incorporating FPGAs for specialized compute-intensive tasks within their data centers. The availability of FPGAs in cloud data centers has opened up new opportunities for users to improve application performance by implementing customizable hardware accelerators directly on the FPGA fabric. However, the virtualization and sharing of FPGA resources among multiple users open up new security risks and threats. We present a novel fault attack methodology capable of causing persistent fault injections in partial bitstreams during the process of FPGA reconfiguration. This attack leverages power-wasters and is timed to inject faults into bitstreams as they are being loaded onto the FPGA through the reconfiguration manager, without needing to remain active throughout the entire reconfiguration process. Our experiments, conducted on a Pynq FPGA setup, demonstrate the feasibility of this attack on various partial application bitstreams, such as a neural network accelerator unit and a signal processing accelerator unit., Comment: Accepted for presentation in the 2024 IEEE Asian Test Symposium (ATS)

Mobile robots frequently navigate on roadmaps, i.e., graphs where edges represent safe motions, in applications such as healthcare, hospitality, and warehouse automation. Often the environment is quasi-static, i.e., it is sufficient to construct a roadmap once and then use it for any future planning queries. Roadmaps are typically used with graph search algorithm to find feasible paths for the robots. Therefore, the roadmap should be well-connected, and graph searches should produce near-optimal solutions with short solution paths while simultaneously be computationally efficient to execute queries quickly. We propose a new method to construct roadmaps based on the Gray-Scott reaction diffusion system and Delaunay triangulation. Our approach, GSRM, produces roadmaps with evenly distributed vertices and edges that are well-connected even in environments with challenging narrow passages. Empirically, we compare to classical roadmaps generated by 8-connected grids, probabilistic roadmaps (PRM, SPARS2), and optimized roadmap graphs (ORM). Our results show that GSRM consistently produces superior roadmaps that are well-connected, have high query efficiency, and result in short solution paths., Comment: Presented at IROS 2024

This paper introduces AMMORE, a new dataset of 53,000 math open-response question-answer pairs from Rori, a learning platform used by students in several African countries and conducts two experiments to evaluate the use of large language models (LLM) for grading particularly challenging student answers. The AMMORE dataset enables various potential analyses and provides an important resource for researching student math acquisition in understudied, real-world, educational contexts. In experiment 1 we use a variety of LLM-driven approaches, including zero-shot, few-shot, and chain-of-thought prompting, to grade the 1% of student answers that a rule-based classifier fails to grade accurately. We find that the best-performing approach -- chain-of-thought prompting -- accurately scored 92% of these edge cases, effectively boosting the overall accuracy of the grading from 98.7% to 99.9%. In experiment 2, we aim to better understand the consequential validity of the improved grading accuracy, by passing grades generated by the best-performing LLM-based approach to a Bayesian Knowledge Tracing (BKT) model, which estimated student mastery of specific lessons. We find that relatively modest improvements in model accuracy at the individual question level can lead to significant changes in the estimation of student mastery. Where the rules-based classifier currently used to grade student, answers misclassified the mastery status of 6.9% of students across their completed lessons, using the LLM chain-of-thought approach this misclassification rate was reduced to 2.6% of students. Taken together, these findings suggest that LLMs could be a valuable tool for grading open-response questions in K-12 mathematics education, potentially enabling encouraging wider adoption of open-ended questions in formative assessment.

We use a neural network model and ALMA observations of HCN and HNC to constrain the physical conditions, most notably the cosmic-ray ionization rate (CRIR, zeta), in the Central Molecular Zone (CMZ) of the starburst galaxy NGC 253. Using output from the chemical code UCLCHEM, we train a neural network model to emulate UCLCHEM and derive HCN and HNC molecular abundances from a given set of physical conditions. We combine the neural network with radiative transfer modeling to generate modeled integrated intensities, which we compare to measurements of HCN and HNC from the ALMA Large Program ALCHEMI. Using a Bayesian nested sampling framework, we constrain the CRIR, molecular gas volume and column densities, kinetic temperature, and beam-filling factor across NGC 253's CMZ. The neural network model successfully recovers UCLCHEM molecular abundances with about 3 percent error and, when used with our Bayesian inference algorithm, increases the parameter inference speed tenfold. We create images of these physical parameters across NGC 253's CMZ at 50 pc resolution and find that the CRIR, in addition to the other gas parameters, is spatially variable with zeta a few times 10^{14} s^{-1} at greater than 100 pc from the nucleus, increasing to zeta greater than 10^{-13} s^{-1} at its center. These inferred CRIRs are consistent within 1 dex with theoretical predictions based on non-thermal emission. Additionally, the high CRIRs estimated in NGC 253's CMZ can be explained by the large number of cosmic-ray-producing sources as well as a potential suppression of cosmic-ray diffusion near their injection sites.

Covert channel attacks have been continuously studied as severe threats to modern computing systems. Software-based covert channels are a typically hard-to-detect branch of these attacks, since they leverage virtual resources to establish illegitimate communication between malicious actors. In this work, we present MeMoir: a novel software-driven covert channel that, for the first time, utilizes memory usage as the medium for the channel. We implemented the new covert channel on two real-world platforms with different architectures: a general-purpose Intel x86-64-based desktop computer and an ARM64-based embedded system. Our results show that our new architecture- and hardware-agnostic covert channel is effective and achieves moderate transmission rates with very low error. Moreover, we present a real use-case for our attack where we were able to communicate information from a Hyper-V virtualized enviroment to a Windows 11 host system. In addition, we implement a machine learning-based detector that can predict whether an attack is present in the system with an accuracy of more than 95% with low false positive and false negative rates by monitoring the use of system memory. Finally, we introduce a noise-based countermeasure that effectively mitigates the attack while inducing a low power overhead in the system compared to other normal applications.

Identifying infall motions is crucial for our understanding of accretion processes in regions of star formation. The NH3 (1,1) hyperfine intensity anomaly (HIA) has been proposed to be a readily usable tracer for such infall motions in star-forming regions harboring young stellar objects at very early evolutionary stages. In this paper, we seek to study the HIA toward fifteen infall candidate regions to assess its reliability as an infall tracer. By using deep observations of the NH3 (1,1) transition with the Effelsberg 100 m telescope, HIAs have been identified toward all the targets. Fourteen out of fifteen sources exhibit anomalous intensities either in the inner or outer satellite lines. All the derived HIAs conform to the framework of the existing two models, namely, hyperfine selective trapping (HST) and systematic contraction or expansion motion (CE) models. In our sample of infall candidates, a majority of the HIAs remain consistent with the HST model. Only in three targets, the HIAs are consistent with infall motions under the CE model. Thus HIAs could be used as an infall tracer but seem not highly sensitive to infall motions in our single-dish data. Nevertheless, the emission could be blended with emission from outflow activities. HIAs consistent with the HST model show stronger anomalies with increasing kinetic temperatures (Tk), which is expected by the HST model. On the other hand, HIAs consistent with infall motions show little dependence on Tk. Therefore, HIAs may preferably trace infall of cold gas., Comment: 8 pages, 5 figures, accepted for publication in A&A

We present results on simultaneous observations of Class~I methanol masers at 25, 36, and 44 GHz towards 22 Galactic targets carried out with the Effelsberg 100-m telescope. The study investigates relations between the hyperfine (HF) structure of the torsion-rotation transitions in CH3OH and maser activity. By analyzing the radial velocity shifts between different maser lines together with the patterns of the HF structure based on laboratory measurements and quantum-chemical calculations, we find that in any source only one specific HF transition forms the maser emission and that this transition changes from source to source. The physical conditions leading to this selective behavior are still unclear. Using accurate laboratory rest frequencies for the 25 GHz transitions, we have refined the centre frequencies for the HF multiplets at 36, 44, and 95 GHz: f_36 = (36169.2488 +/- 0.0002_stat +/- 0.0004_sys) MHz. f_44 = (44069.4176 +/- 0.0002_stat +/- 0.0004_sys) MHz, and f_95 = (95169.4414 +/- 0.0003_stat +/- 0.0004_sys) MHz. Comparison with previous observations of 44 GHz masers performed 6-10 years ago with a Korean 21-m KVN telescope towards the same targets confirms the kinematic stability of Class~I maser line profiles during this time interval and reveals a systematic radial velocity shift of 0.013 +/- 0.005 km/s between the two telescopes., Comment: 24 pages, 8 figures, 9 tables; accepted for publication in MNRAS

When moist air meets a cold surface, it creates a breath figure characterized by numerous small droplets. The central question is how the vapor flux is distributed between the growth of previously condensed drops and the nucleation of new ones. Here, we investigate the nucleation, growth, and coalescence of droplets on soft crosslinked polymer networks. The number of droplets initially remains constant, until drops start to coarsen according to a universal law; both phenomena are explained via the formation of a saturated boundary layer. Although nucleation occurs at a scale where the polymer network resembles a melt, we quantitatively unveil an algebraic sensitivity of the number of droplets on the substrate elasticity. Our findings suggest that nucleation follows a surprisingly low-energy pathway, influenced by the degree of crosslinking. Consequently, breath figures offer a macroscopic approach to probe the molecular characteristics of the polymer interface., Comment: 5 pages, 5 figures, 44 references

Large language models (LLMs) are flexible, personalizable, and available, which makes their use within Intelligent Tutoring Systems (ITSs) appealing. However, that flexibility creates risks: inaccuracies, harmful content, and non-curricular material. Ethically deploying LLM-backed ITS systems requires designing safeguards that ensure positive experiences for students. We describe the design of a conversational system integrated into an ITS, and our experience evaluating its safety with red-teaming, an in-classroom usability test, and field deployment. We present empirical data from more than 8,000 student conversations with this system, finding that GPT-3.5 rarely generates inappropriate messages. Comparatively more common is inappropriate messages from students, which prompts us to reason about safeguarding as a content moderation and classroom management problem. The student interaction behaviors we observe provide implications for designers - to focus on student inputs as a content moderation problem - and implications for researchers - to focus on subtle forms of bad content., Comment: EDM 2024 LLM Workshop

Class\,I methanol masers provide sensitive information about the shocked environment around star-forming regions. Among the brightest Class~I methanol masers, we have those in the $J_{-1}\rightarrow(J-$ 1)$_{0}-E$ line series, currently reported for the $J=4-9$ transitions, with the only exception being the $J=7$ one at 181.295~GHz, and never expanded to higher $J$ transitions. We aim to search for population inversion in the $7_{-1}\rightarrow6_{0}-E$ and $10_{-1}\rightarrow9_{0}-E$ methanol transition lines at 181.295 and 326.961\,GHz, respectively, and also extend the number of known low-mass star-forming sources harboring Class\,I methanol masers. We employed the Atacama Pathfinder Experiment (APEX) 12\,m telescope to survey low-mass Galactic sources, focusing on methanol emission lines. We conducted rotation diagrams for all sources with detected $J=7$ methanol line transitions, while employing radiative transfer modeling (both in and out of local thermodynamic equilibrium) to characterize methanol excitation conditions in detail for one specific source with detected masers. We have detected the $7_{-1}\rightarrow6_{0}-E$ and $10_{-1}\rightarrow9_{0}-E$ methanol transitions in six out of nineteen sources. Among them, we firmly determined the $10_{-1}\rightarrow9_{0}-E$ maser nature in CARMA\,7, L1641N, NGC\,2024, and Serpens FIRS, and we claim for the presence of inverted population emission in the $7_{-1}\rightarrow6_{0}-E$ line toward CARMA\,7 and L1641N. This represents the first report of methanol maser emission in these particular transitions. Our study supports previous works indicating that conditions for Class\,I methanol maser emission are satisfied in low-mass star-forming regions and expands the range of detectable frequencies toward higher values.

Deep metric learning (DML) has shown to be effective for content-based image retrieval (CBIR) in remote sensing (RS). Most of DML methods for CBIR rely on a high number of annotated images to accurately learn model parameters of deep neural networks (DNNs). However, gathering such data is time-consuming and costly. To address this, we propose an annotation cost-efficient active learning (ANNEAL) method tailored to DML-driven CBIR in RS. ANNEAL aims to create a small but informative training set made up of similar and dissimilar image pairs to be utilized for accurately learning a metric space. The informativeness of image pairs is evaluated by combining uncertainty and diversity criteria. To assess the uncertainty of image pairs, we introduce two algorithms: 1) metric-guided uncertainty estimation (MGUE); and 2) binary classifier guided uncertainty estimation (BCGUE). MGUE algorithm automatically estimates a threshold value that acts as a boundary between similar and dissimilar image pairs based on the distances in the metric space. The closer the similarity between image pairs is to the estimated threshold value the higher their uncertainty. BCGUE algorithm estimates the uncertainty of the image pairs based on the confidence of the classifier in assigning correct similarity labels. The diversity criterion is assessed through a clustering-based strategy. ANNEAL combines either MGUE or BCGUE algorithm with the clustering-based strategy to select the most informative image pairs, which are then labelled by expert annotators as similar or dissimilar. This way of annotating images significantly reduces the annotation cost compared to annotating images with land-use land-cover class labels. Experimental results on two RS benchmark datasets demonstrate the effectiveness of our method. The code of this work is publicly available at \url{https://git.tu-berlin.de/rsim/anneal_tgrs}., Comment: Accepted for publication in the IEEE Transactions on Geoscience and Remote Sensing (TGRS)

The nitrogen isotope ratio 14N/15N is a powerful tool to trace Galactic stellar nucleosynthesis and constraining Galactic chemical evolution. Previous observations have found lower 14N/15N ratios in the Galactic center and higher values in the Galactic disk. This is consistent with the inside-out formation scenario of our Milky Way. However, previous studies mostly utilized double isotope ratios also including 12C/13C, which introduces additional uncertainties. Here we therefore present observations of C14N and its rare isotopologue, C15N, toward a sample of star forming regions, measured by the IRAM 30 m and/or the ARO 12 m telescope at $\lambda$ ~3 mm wavelength. For those 35 sources detected in both isotopologues, physical parameters are determined. Furthermore we have obtained nitrogen isotope ratios using the strongest hyperfine components of CN and C15N. For those sources showing small deviations from Local Thermodynamical Equilibrium and/or self-absorption, the weakest hyperfine component, likely free of the latter effect, was used to obtain reliable 14N/15N values. Our measured 14N/15N isotope ratios from C14N and C15N measurements are compatible with those from our earlier measurements of NH3 and 15NH3 (Paper I), i.e., increasing ratios to a Galacticentric distance of ~9 kpc. The unweighted second order polynomial fit yields $\frac{{\rm C^{14}N}}{{\rm C^{15}N}} = (-4.85 \pm 1.89)\;{\rm kpc^{-2}} \times R_{\rm GC}^{2} + (82.11 \pm 31.93) \;{\rm kpc^{-1}} \times R_{\rm GC} - (28.12 \pm 126.62)$. Toward the outer galaxy, the isotope ratio tends to decrease, supporting an earlier finding by H13CN/HC15N. Galactic chemical evolution models are consistent with our measurements of the 14N/15N isotope ratio, i.e. a rising trend from the Galactic center region to approximately 9 kpc, followed by a decreasing trend with increasing $R_{\rm GC}$ toward the outer Galaxy., Comment: 34 pages, 9 figures, 6 tables

High resolution (0."037-0."13 [10-35 pc]) e-MERLIN ($\lambda6-18$ cm) and (0."024 [6.5 pc]) ALMA ($\lambda 1.1$ mm) observations have been used to image OH (hydroxyl) and H$_2$CO (formaldehyde) megamaser emission, and HCN 3->2 emission towards the nuclear (<100 pc) region of the luminous infrared galaxy Zw049.057. Zw049.057 hosts a compact obscured nucleus (CON), thus representing a class of galaxies that are often associated with inflow and outflow motions. Formaldehyde megamaser emission is detected towards the nuclear region, <30 pc (<0."1), and traces a structure along the disk major axis. OH megamaser (OHM) emission is detected along the minor axis of the disk, ~30 pc (0."1) from the nucleus, where it exhibits a velocity gradient with extrema of -20 km/s south-east (SE) of the disk and -110 km/s north-west (NW) of the disk. HCN 3->2 emission reveals extended emission, along the disk minor axis out to ~60 pc (0."2). Analysis of the minor axis HCN emission reveals high-velocity features, extending out to 600 km/s, redshifted on the SE side and blueshifted on the NW side. We propose that the high-velocity HCN emission traces a fast >250 km/s and collimated outflow, that is enveloped by a wide-angle and slow ~50 km/s outflow that is traced by the OHM emission. Analysis of the outflow kinematics suggests that the slow wide-angle outflow will not reach escape velocity and instead will fall back to the galaxy disk, evolving as a so-called fountain flow, while the fast collimated outflow traced by HCN emission will likely escape the nuclear region. We suggest that the absence of OHM emission in the nuclear region is due to high densities there. Even though OHMs associated with outflows are an exception to conventional OHM emission, we expect them to be common in CON sources that host both OHM and H$_2$CO megamasers., Comment: 13 pages, 3 figures. Accepted to A&A

The kinetic temperature structure of the massive filament DR21 has been mapped using the IRAM 30 m telescope. This mapping employed the para-H$_2$CO triplet ($J_{\rm K_aK_c}$ = 3$_{03}$--2$_{02}$, 3$_{22}$--2$_{21}$, and 3$_{21}$--2$_{20}$) on a scale of $\sim$0.1 pc. By modeling the averaged line ratios of para-H$_{2}$CO with RADEX under non-LTE assumptions, the kinetic temperature of the dense gas was derived at a density of $n$(H$_{2}$) = 10$^{5}$ cm$^{-3}$. The para-H$_2$CO lines reveal significantly higher temperatures than NH$_3$ (1,1)/(2,2) and FIR wavelengths. The dense clumps appear to correlate with the notable kinetic temperature. Among the four dense cores (N44, N46, N48, and N54), temperature gradients are observed on a scale of $\sim$0.1-0.3 pc. This suggests that the warm dense gas is influenced by internal star formation activity. With the exception of N54, the temperature profiles of these cores were fitted with power-law indices ranging from $-$0.3 to $-$0.5. This indicates that the warm dense gas is heated by radiation emitted from internally embedded protostar(s) and/or clusters. While there is no direct evidence supporting the idea that the dense gas is heated by shocks resulting from a past explosive event in the DR21 region, our measurements toward the DR21W1 region provide compelling evidence that the dense gas is indeed heated by shocks originating from the western DR21 flow. Higher temperatures appear to be associated with turbulence. The physical parameters of the dense gas in the DR21 filament exhibit a remarkable similarity to the results obtained in OMC-1 and N113. This may imply that the physical mechanisms governing the dynamics and thermodynamics of dense gas traced by H$_{2}$CO in diverse star formation regions may be dominated by common underlying principles despite variations in specific environmental conditions. (abbreviated), Comment: 16 pages, 8 figures, 3 tabels. Accepted for publication by Astronomy & Astrophysics

Sulphur-bearing species are detected in various environments within Galactic star-forming regions and are particularly abundant in the gas phase of outflows and shocks, and photo-dissociation regions. In this work, we aim to investigate the nature of the emission from the most common sulphur-bearing species observable at millimetre wavelengths towards the nuclear starburst of the galaxy NGC 253. We intend to understand which type of regions are probed by sulphur-bearing species and which process(es) dominate(s) the release of sulphur into the gas phase. We used the high-angular resolution (1.6" or 27 pc) observations from the ALCHEMI ALMA Large Program to image several sulphur-bearing species towards the central molecular zone (CMZ) of NGC 253. We performed local thermodynamic equilibrium (LTE) and non-LTE large velocity gradient (LVG) analyses to derive the physical conditions of the gas in which S-bearing species are emitted, and their abundance ratios across the CMZ. Finally, we compared our results with previous ALCHEMI studies and a few selected Galactic environments. We found that not all sulphur-bearing species trace the same type of gas: strong evidence indicates that H2S and part of the emission of OCS, H2CS, and SO, are tracing shocks whilst part of SO and CS emission rather trace the dense molecular gas. For some species, such as CCS and SO2, we could not firmly conclude on their origin of emission. The present analysis indicates that the emission from most sulphur-bearing species throughout the CMZ is likely dominated by shocks associated with ongoing star formation. In the inner part of the CMZ where the presence of super star clusters was previously indicated, we could not distinguish between shocks or thermal evaporation as the main process releasing the S-bearing species., Comment: 44 pages, 20 figures, Accepted for publication in A&A

CF$^{+}$ has been established as a valuable diagnostic tool for investigating photo-dissociation regions (PDRs) and fluorine abundances in the Milky Way. However, its role in extragalactic environments remains largely uncharted. Our objective is to explore the significance of CF$^{+}$ in the Large Magellanic Cloud (LMC) and assess its utility as a valuable probe for examining C$^{+}$ and fluorine abundances in external galaxies. We performed pointed CF$^{+}$ observations toward an active star-forming region, N113 in the LMC, using the Atacama Pathfinder EXperiment 12~m sub-millimeter telescope. We report the first discovery of CF$^{+}$ in the LMC through the successful detection of the CF$^{+}$ (2$\to$1) and (3$\to$2) lines. The excitation models indicate that CF$^{+}$ emission originates from dense PDRs characterized by an H$_{2}$ number density of $(0.5-7.9)\times 10^{4}$~cm$^{-3}$ in N113. Our observations provide the first constraint on the fluorine abundance in molecular clouds in the LMC, disclosing a value of $\lesssim 1.7\times 10^{-9}$. This value is about an order of magnitude lower than those previously measured toward red giants in the LMC, indicative of fluorine deficiency in the molecular gas. The estimated column density ratio between C$^{+}$ and CF$^{+}$ appears to be lower than the anticipated equilibrium ratio derived from the fluorine abundance in red giants. Both phenomena can be explained by the deficiency of CF$^{+}$ caused by the freeze-out of its primary chemical precursor, HF, onto dust grains. The deficiency of CF$^{+}$ within molecular clouds suggests that the measurements presented in this work serve exclusively as conservative estimates, establishing lower bounds for both the fluorine abundance and C$^{+}$ column densities in external galaxies., Comment: 9 pages, 6 figures, 1 table, accepted for publication in A&A

This paper presents reports on a series of experiments with a novel dataset evaluating how well Large Language Models (LLMs) can mark (i.e. grade) open text responses to short answer questions, Specifically, we explore how well different combinations of GPT version and prompt engineering strategies performed at marking real student answers to short answer across different domain areas (Science and History) and grade-levels (spanning ages 5-16) using a new, never-used-before dataset from Carousel, a quizzing platform. We found that GPT-4, with basic few-shot prompting performed well (Kappa, 0.70) and, importantly, very close to human-level performance (0.75). This research builds on prior findings that GPT-4 could reliably score short answer reading comprehension questions at a performance-level very close to that of expert human raters. The proximity to human-level performance, across a variety of subjects and grade levels suggests that LLMs could be a valuable tool for supporting low-stakes formative assessment tasks in K-12 education and has important implications for real-world education delivery.

Electromagneto-quasistatic (EMQS) field formulations are often dubbed as Darwin-type field formulations which approximate the Maxwell equations by neglecting radiation effects while modelling resistive, capacitive, and inductive effects. A common feature of EMQS field models is the Darwin-Amp\'ere equation formulated with the magnetic vector potential and the electric scalar potential. EMQS field formulations yield different approximations to the Maxwell equations by choice of additional gauge equations. These EMQS formulations are analyzed within the port-Hamiltonian system (PHS) framework. It is shown via the PHS compatibility equation that formulations based on the combination of the Darwin-Amp\'ere equation and the full Maxwell continuity equation yield port-Hamiltonian systems implying numerical stability and specific EMQS energy conservation., Comment: 8 pages, 0 figures, pre-submission version (preprint), presented at and submitted to the proceedings of "The 15th International Conference on Scientific Computing in Electrical Engineering" (SCEE 2024), March 4-8, 2024, Darmstadt, Germany

The rapid expansion of renewable energy sources has introduced significant volatility and unpredictability in the energy supply chain, necessitating advanced control strategies to ensure grid stability and reliability. Green hydrogen production via electrolysis offers a viable solution for converting and storing this volatile renewable energy. However, the inherent fluctuations of renewable energy sources present challenges for consistent utilization and integration of green hydrogen. This work proposes a two-stage optimization approach, combining site-wide optimization and real-time optimization for managing systems of electrolyzers. By adapting an existing static optimization model, dual use is achieved in both site-wide optimization and real-time optimization. The hierarchical optimization structure, characterized by distinct temporal resolutions, enables effective responses to both dynamic changes and long-term trends. The side-wide optimization layer generates long-term plans based on forecast data, while the real-time optimization layer refines these plans in real-time, accommodating immediate fluctuations and ensuring efficient operation. The results from the case study on a system of electrolyzers demonstrate the method's effectiveness in aligning electrolyzer operation with actual availability of renewable energy. This approach offers a robust framework for optimizing the operation of electrolyzers but also other types of flexible energy resources, contributing to sustainable and economically viable energy management., Comment: Updated version

The physical properties of galactic molecular outflows are important as they could constrain outflow formation mechanisms. We study the properties of the southwest (SW) outflow streamer including gas kinematics, optical depth, dense gas fraction, and shock strength in the central molecular zone of the starburst galaxy NGC 253. We image the molecular emission at a spatial resolution of $\sim$27 pc based on data from the ALCHEMI program. We trace the kinematics of molecular gas with CO(1-0) line. We constrain the optical depth of CO emission with CO/$^{13}$CO(1-0) ratio, the dense gas fraction with HCN/CO(1-0) ratio, as well as the shock strength with SiO(2-1)/$^{13}$CO(1-0) ratio. The CO/$^{13}$CO(1-0) integrated intensity ratio is $\sim$21 in the SW streamer region, which approximates the C/$^{13}$C isotopic abundance ratio. The higher integrated intensity ratio compared to the disk can be attributed to the optically thinner environment for CO(1-0) emission inside the SW streamer. The HCN/CO(1-0) and SiO(2-1)/$^{13}$CO(1-0) integrated intensity ratios both approach $\sim$0.2 in three giant molecular clouds (GMCs) at the base of the outflow streamers, which implies the higher dense gas fraction and enhanced strength of fast shocks in those GMCs than in the disk. The contours of those two integrated intensity ratios are extended towards the directions of outflow streamers, which connects the enhanced dense gas fraction and shock strength with molecular outflow. Moreover, the molecular gas with enhanced dense gas fraction and shock strength located at the base of the SW streamer shares the same velocity with the outflow. These phenomena suggest that the star formation inside the GMCs can trigger the shocks and further drive the molecular outflow., Comment: Accepted for publication in A&A, 14 pages, 11 figures

With the continuous evolution of computational devices, more and more applications are being executed remotely. The applications operate on a wide spectrum of devices, ranging from IoT nodes with low computational capabilities to large cloud providers with high capabilities. Remote execution often deals with sensitive data or executes proprietary software. Hence, the challenge of ensuring that the code execution will not be compromised rises. Remote Attestation deals with this challenge. It ensures the code is executed in a non-compromised environment by calculating a potentially large sequence of cryptographic hash values. Each hash calculation is computationally intensive and over a large sequence the overhead becomes extremely high. In this work, we propose LightFAt: a Lightweight Control Flow Attestation scheme. Instead of relying on the expensive cryptographic hash calculation, LightFAt leverages the readings from the processor's Performance Monitor Unit (PMU) in conjunction with a lightweight unsupervised machine learning (ML) classifier to detect whether a target application's control flow is compromised, hence improving the system's security. On the verifier's side, LightFAt reaches a detection accuracy of over 95%, with low false-negative and false-positive rates., Comment: This official version of this paper will appear in the 2024 IEEE International Symposium on Hardware Oriented Security and Trust (HOST)

This paper reviews the history of the conformal extension of Galilean symmetry, now called Schr\"odinger symmetry. In the physics literature, its discovery is commonly attributed to Jackiw, Niederer and Hagen (1972). However, Schr\"odinger symmetry has a much older ancestry: the associated conserved quantities were known to Jacobi in 1842/43 and its euclidean counterpart was discovered by Sophus Lie in 1881 in his studies of the heat equation. A convenient way to study Schr\"odinger symmetry is provided by a non-relativistic Kaluza-Klein-type "Bargmann" framework, first proposed by Eisenhart (1929), but then forgotten and re-discovered by Duval {\it et al.} only in 1984. Representations of Schr\"odinger symmetry differ by the value $z=2$ of the dynamical exponent from the value $z=1$ found in representations of relativistic conformal invariance. For generic values of $z$, whole families of new algebras exist, which for $z=2/\ell$ include the $\ell$-conformal galilean algebras. We also review the non-relativistic limit of conformal algebras and that this limit leads to the $1$-conformal galilean algebra and not to the Schr\"odinger algebra. The latter can be recovered in the Bargmann framework through reduction. A distinctive feature of Galilean and Schr\"odinger symmetries are the Bargmann super-selection rules, algebraically related to a central extension. An empirical consequence of this was known as "mass conservation" already to Lavoisier. As an illustration of these concepts, some applications to physical ageing in simple model systems are reviewed., Comment: Latex2e, 62 pages, 5 figures included

We present the results of a search for ammonia maser emission in 119 Galactic high-mass star-forming regions (HMSFRs) known to host 22 GHz H$_2$O maser emission. Our survey has led to the discovery of non-metastable NH$_3$ inversion line masers toward 14 of these sources. This doubles the number of known non-metastable ammonia masers in our Galaxy, including nine new very high excitation ($J,K$)~=~(9,6) maser sources. These maser lines, including NH$_3$ (5,4), (6,4), (6,5), (7,6), (8,6), (9,6), (9,8), (10,8), and (11,9), arise from energy levels of 342 K, 513 K, 465 K, 606 K, 834 K, 1090 K, 942 K, 1226 K, and 1449 K above the ground state. Additionally, we tentatively report a new metastable NH$_3$ (3,3) maser in G048.49 and an NH$_3$ (7,7) maser in G029.95. Our observations reveal that all of the newly detected NH$_3$ maser lines exhibit either blueshifted or redshifted velocities with respect to the source systemic velocities. Among the non-metastable ammonia maser lines, larger velocity distributions, offset from the source systemic velocities, are found in the ortho-NH$_3$ ($K=3n$) than in the para-NH$_3$ ($K\neq3n$) transitions., Comment: 14 pages, 4 tables, 9 figures. Accepted for publication in A&A

How galaxies regulate nuclear growth through gas accretion by supermassive black holes (SMBHs) is one of the most fundamental questions in galaxy evolution. One potential way to regulate nuclear growth is through a galactic wind that removes gas from the nucleus. It is unclear whether galactic winds are powered by jets, mechanical winds, radiation, or via magnetohydrodynamic (MHD) processes. Compact obscured nuclei (CONs) represent a significant phase of galactic nuclear growth. These galaxies hide growing SMBHs or unusual starbursts in their very opaque, extremely compact (r $<$ 100 pc) centres. They are found in approximately 30 % of the luminous and ultra-luminous infrared galaxy (LIRG and ULIRG) population. Here, we present high-resolution ALMA observations ($\sim$30 mas, $\sim$5 pc) of ground-state and vibrationally excited HCN towards ESO 320-G030 (IRAS 11506-3851). ESO 320-G030 is an isolated luminous infrared galaxy known to host a compact obscured nucleus and a kiloparsec-scale molecular wind. Our analysis of these high-resolution observations excludes the possibility of a starburst-driven wind, a mechanically or energy driven active galactic nucleus (AGN) wind, and exposes a molecular MDH wind. These results imply that the nuclear evolution of galaxies and the growth of SMBHs are similar to the growth of hot cores or protostars where gravitational collapse of the nuclear torus drives a MHD wind. These results mean galaxies are capable, in part, of regulating the evolution of their nuclei without feedback., Comment: 9 pages, 10 figures

The interstellar medium in the Milky Way's Central Molecular Zone (CMZ) is known to be strongly magnetised, but its large-scale morphology and impact on the gas dynamics are not well understood. We explore the impact and properties of magnetic fields in the CMZ using three-dimensional non-self gravitating magnetohydrodynamical simulations of gas flow in an external Milky Way barred potential. We find that: (1) The magnetic field is conveniently decomposed into a regular time-averaged component and an irregular turbulent component. The regular component aligns well with the velocity vectors of the gas everywhere, including within the bar lanes. (2) The field geometry transitions from parallel to the Galactic plane near $z=0$ to poloidal away from the plane. (3) The magneto-rotational instability (MRI) causes an in-plane inflow of matter from the CMZ gas ring towards the central few parsecs of $0.01-0.1$ M$_\odot$ yr$^{-1}$ that is absent in the unmagnetised simulations. However, the magnetic fields have no significant effect on the larger-scale bar-driven inflow that brings the gas from the Galactic disc into the CMZ. (4) A combination of bar inflow and MRI-driven turbulence can sustain a turbulent vertical velocity dispersion of $\sigma_z \simeq 5$ km s$^{-1}$ on scales of $20$ pc in the CMZ ring. The MRI alone sustains a velocity dispersion of $\sigma_z \simeq 3$ km s$^{-1}$. Both these numbers are lower than the observed velocity dispersion of gas in the CMZ, suggesting that other processes such as stellar feedback are necessary to explain the observations. (5) Dynamo action driven by differential rotation and the MRI amplifies the magnetic fields in the CMZ ring until they saturate at a value that scales with the average local density as $B \simeq 102 (n/10^3 cm^{-3})^{0.33}$ $\mu$G. Finally, we discuss the implications of our results within the observational context in the CMZ.

The behaviour of the solutions of the time-fractional diffusion equation, based on the Caputo derivative, is studied and its dependence on the fractional exponent is analysed. The time-fractional convection-diffusion equation is also solved and an application to Pennes bioheat model is presented. Generically, a wave-like transport at short times passes over to a diffusion-like behaviour at later times.

The security of cloud field-programmable gate arrays (FPGAs) faces challenges from untrusted users attempting fault and side-channel attacks through malicious circuit configurations. Fault injection attacks can result in denial of service, disrupting functionality or leaking secret information. This threat is further amplified in multi-tenancy scenarios. Detecting such threats before loading onto the FPGA is crucial, but existing methods face difficulty identifying sophisticated attacks. We present MaliGNNoma, a machine learning-based solution that accurately identifies malicious FPGA configurations. Serving as a netlist scanning mechanism, it can be employed by cloud service providers as an initial security layer within a necessary multi-tiered security system. By leveraging the inherent graph representation of FPGA netlists, MaliGNNoma employs a graph neural network (GNN) to learn distinctive malicious features, surpassing current approaches. To enhance transparency, MaliGNNoma utilizes a parameterized explainer for the GNN, labeling the FPGA configuration and pinpointing the sub-circuit responsible for the malicious classification. Through extensive experimentation on the ZCU102 board with a Xilinx UltraScale+ FPGA, we validate the effectiveness of MaliGNNoma in detecting malicious configurations, including sophisticated attacks, such as those based on benign modules, like cryptography accelerators. MaliGNNoma achieves a classification accuracy and precision of 98.24% and 97.88%, respectively, surpassing state-of-the-art. We compare MaliGNNoma with five state-of-the-art scanning methods, revealing that not all attack vectors detected by MaliGNNoma are recognized by existing solutions, further emphasizing its effectiveness. Additionally, we make MaliGNNoma and its associated dataset publicly available., Comment: Will appear in the 2024 IEEE International Symposium on Hardware Oriented Security and Trust (HOST)

In Federated Learning (FL), devices that participate in the training usually have heterogeneous resources, i.e., energy availability. In current deployments of FL, devices that do not fulfill certain hardware requirements are often dropped from the collaborative training. However, dropping devices in FL can degrade training accuracy and introduce bias or unfairness. Several works have tacked this problem on an algorithmic level, e.g., by letting constrained devices train a subset of the server neural network (NN) model. However, it has been observed that these techniques are not effective w.r.t. accuracy. Importantly, they make simplistic assumptions about devices' resources via indirect metrics such as multiply accumulate (MAC) operations or peak memory requirements. In this work, for the first time, we consider on-device accelerator design for FL with heterogeneous devices. We utilize compressed arithmetic formats and approximate computing, targeting to satisfy limited energy budgets. Using a hardware-aware energy model, we observe that, contrary to the state of the art's moderate energy reduction, our technique allows for lowering the energy requirements (by 4x) while maintaining higher accuracy.

Compact obscured nuclei (CONs) are an extremely obscured (N$_{H2}$ >10$^{25}$ cm$^{-2}$) class of galaxy nuclei thought to exist in 20-40 per cent of nearby (ultra-)luminous infrared galaxies. While they have been proposed to represent a key phase of the active galactic nucleus (AGN) feedback cycle, the nature of these CONs - what powers them, their dynamics, and their impact on the host galaxy - remains unknown. This work analyses the large-scale optical properties of the local CON, NGC4418 (z=0.00727). We present new, targeted integral field unit observations of the galaxy with the Multi-Unit Spectroscopic Explorer (MUSE). For the first time, we map the ionised and neutral gas components of the galaxy, along with their dynamical structure, to reveal several previously unknown features of the galaxy. We confirm the presence of a previously postulated blueshifted outflow along the minor axis of NGC4418. We find this outflow to be decelerating and, for the first time, show it to extend bilaterally from the nucleus. We report the discovery of two further outflow structures: a redshifted southern outflow connected to a tail of ionised gas surrounding the galaxy and a blueshifted bubble to the north. In addition to these features, we find the [OIII] emission reveals the presence of knots across the galaxy, which are consistent with regions of the galaxy that have been photoionised by an AGN. Based on the properties of these features, we conclude that the CON in NGC4418 is most likely powered by AGN activity., Comment: 17 pages, 15 figures, accepted to A&A

In today's digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning (DL), are widely used for various computer vision tasks such as image classification, object detection, and image segmentation. There are numerous types of CNNs designed to meet specific needs and requirements, including 1D, 2D, and 3D CNNs, as well as dilated, grouped, attention, depthwise convolutions, and NAS, among others. Each type of CNN has its unique structure and characteristics, making it suitable for specific tasks. It's crucial to gain a thorough understanding and perform a comparative analysis of these different CNN types to understand their strengths and weaknesses. Furthermore, studying the performance, limitations, and practical applications of each type of CNN can aid in the development of new and improved architectures in the future. We also dive into the platforms and frameworks that researchers utilize for their research or development from various perspectives. Additionally, we explore the main research fields of CNN like 6D vision, generative models, and meta-learning. This survey paper provides a comprehensive examination and comparison of various CNN architectures, highlighting their architectural differences and emphasizing their respective advantages, disadvantages, applications, challenges, and future trends.

Nuclear regions of ultraluminous and luminous infrared galaxies (U/LIRGs) are powered by starbursts and/or active galactic nuclei (AGNs). These regions are often obscured by extremely high columns of gas and dust. Molecular lines in the submillimeter windows have the potential to determine the physical conditions of these compact obscured nuclei (CONs). We aim to reveal the distributions of HCN and HCO$^+$ emission in local U/LIRGs and investigate whether and how they are related to galaxy properties. Using ALMA, we have conducted sensitive observations of the HCN J=3--2 and HCO$^+$ J=3--2 lines toward 23 U/LIRGs in the local Universe (z < 0.07) with a spatial resolution of ~0.3" (~50--400 pc). We detected both HCN and HCO$^+$ in 21 galaxies, only HCN in one galaxy, and neither in one galaxy. The global HCN/HCO$^+$ line ratios, averaged over scales of ~0.5--4 kpc, range from 0.4 to 2.3, with an unweighted mean of 1.1. These line ratios appear to have no systematic trend with bolometric AGN luminosity or star formation rate. The line ratio varies with position and velocity within each galaxy, with an average interquartile range of 0.38 on a spaxel-by-spaxel basis. In eight out of ten galaxies known to have outflows and/or inflows, we found spatially and kinematically symmetric structures of high line ratios. These structures appear as a collimated bicone in two galaxies and as a thin spherical shell in six galaxies. Non-LTE analysis suggests that the high HCN/HCO$^+$ line ratio in outflows is predominantly influenced by the abundance ratio. Chemical model calculations indicate that the enhancement of HCN abundance in outflows is likely due to high-temperature chemistry triggered by shock heating. These results imply that the HCN/HCO$^+$ line ratio can aid in identifying the outflow geometry when the shock velocity of the outflows is sufficiently high to heat the gas., Comment: 52 pages, 35 figures, accepted for publication in Astronomy & Astrophysics

Context and motivation: Requirements engineering of complex IT systems needs to manage the many, and often vague and conflicting, organisational rules that exist in the context of a modern enterprise. At the same time, IT systems affect the organisation, essentially setting new rules on how the organisation should work. Question/problem: Gathering requirements for an IT system involves understanding the complex rules that govern an organisation. The research question is: How can the holistic properties of organisational rules be conceptualised? Principal ideas/results: This paper introduces the concept of organisational rule systems that may be used to describe complex organisational rules. The concept and its components are presented as a conceptual framework, which in turn is condensed into a conceptual framework diagram. The framework is grounded in a critical literature review. Contribution: The conceptual framework will, as a first step of a wider research agenda, help requirements engineers understand the influence of organisational rules., Comment: 9 pages, 1 figure

Determining the evolution of the CNO isotopes in the interstellar medium (ISM) of starburst galaxies can yield important constraints on the ages of superstar clusters (SSCs), or on other aspects and contributing factors of their evolution. Due to the time-dependent nature of the abundances of isotopes within the ISM as they are supplied from processes such as nucleosynthesis or chemical fractionation, this provides the possible opportunity to probe the ability of isotopes ratios to trace the ages of high star forming regions, such as SSCs. The goal of this study is to investigate whether the isotopic variations in SSC regions within NGC253 are correlated with their different ages as derived from stellar population modelling. We have measured abundance ratios of CO, HCN and HCO$^+$ isotopologues in six regions containing SSCs within NGC253 using high spatial resolution (1.6",$\sim 28$pc) data from the ALCHEMI (ALma Comprehensive High-resolution Extragalactic Molecular Inventory) ALMA Large program. We have then analysed these ratios using RADEX radiative transfer modelling, with the parameter space sampled using the nested sampling Monte Carlo algorithm MLFriends. These abundance ratios were then compared to ages predicted in each region via the fitting of observed star formation tracers (such as Br$\gamma$) to starburst stellar population evolution models. We do not find any significant trend with age for the CO and HCN isotopologue ratios on the timescales for the ages of the SSC* regions observed. The driving factors of these ratios within SSCs could be the Initial Mass Function as well as possibly fractionation effects. To further probe these effects in SSCs over time a larger sample of SSCs must be observed spanning a larger age range., Comment: 44 pages, 43 Figures, Accepted for Publication to A&A

This study evaluates the impact of Rori, an AI powered conversational math tutor accessible via WhatsApp, on the math performance of approximately 1,000 students in grades 3-9 across 11 schools in Ghana. Each school was assigned to a treatment group or control group; the students in the control group continued their regular math instruction, while students in the treatment group engaged with Rori, for two 30-minute sessions per week over 8 months in addition to regular math instruction. We find that the math growth scores were substantially higher for the treatment group with an effect size of 0.37, and that the results were statistically significant (p < 0.001). The fact that Rori works with basic mobile devices on low-bandwidth data networks gives the intervention strong potential to support personalized learning on other low-and-middle-income countries (LMICs), where laptop ownership and high-speed internet - prerequisite for many video-centered learning platforms - remain extremely limited. While the results should be interpreted judiciously, as they only report on year 1 of the intervention, and future research is necessary to better understand which conditions are necessary for successful implementation, they do suggest that chat-based tutoring solutions leveraging artificial intelligence could offer a costeffective approach to enhancing learning outcomes for millions of students globally.

Vision Transformer (ViT) models which were recently introduced by the transformer architecture have shown to be very competitive and often become a popular alternative to Convolutional Neural Networks (CNNs). However, the high computational requirements of these models limit their practical applicability especially on low-power devices. Current state-of-the-art employs approximate multipliers to address the highly increased compute demands of DNN accelerators but no prior research has explored their use on ViT models. In this work we propose TransAxx, a framework based on the popular PyTorch library that enables fast inherent support for approximate arithmetic to seamlessly evaluate the impact of approximate computing on DNNs such as ViT models. Using TransAxx we analyze the sensitivity of transformer models on the ImageNet dataset to approximate multiplications and perform approximate-aware finetuning to regain accuracy. Furthermore, we propose a methodology to generate approximate accelerators for ViT models. Our approach uses a Monte Carlo Tree Search (MCTS) algorithm to efficiently search the space of possible configurations using a hardware-driven hand-crafted policy. Our evaluation demonstrates the efficacy of our methodology in achieving significant trade-offs between accuracy and power, resulting in substantial gains without compromising on performance.

In response to the global shift towards renewable energy resources, the production of green hydrogen through electrolysis is emerging as a promising solution. Modular electrolysis plants, designed for flexibility and scalability, offer a dynamic response to the increasing demand for hydrogen while accommodating the fluctuations inherent in renewable energy sources. However, optimizing their operation is challenging, especially when a large number of electrolysis modules needs to be coordinated, each with potentially different characteristics. To address these challenges, this paper presents a decentralized scheduling model to optimize the operation of modular electrolysis plants using the Alternating Direction Method of Multipliers. The model aims to balance hydrogen production with fluctuating demand, to minimize the marginal Levelized Cost of Hydrogen (mLCOH), and to ensure adaptability to operational disturbances. A case study validates the accuracy of the model in calculating mLCOH values under nominal load conditions and demonstrates its responsiveness to dynamic changes, such as electrolyzer module malfunctions and scale-up scenarios.

Molecular lines are powerful diagnostics of the physical and chemical properties of the interstellar medium (ISM). These ISM properties, which affect future star formation, are expected to differ in starburst galaxies from those of more quiescent galaxies. We investigate the ISM properties in the central molecular zone of the nearby starburst galaxy NGC 253 using the ultra-wide millimeter spectral scan survey from the ALMA Large Program ALCHEMI. We present an atlas of velocity-integrated images at a 1".6 resolution of 148 unblended transitions from 44 species, including the first extragalactic detection of HCNH$^+$ and the first interferometric images of C$_3$H$^+$, NO, HCS$^+$. We conduct a principal component analysis (PCA) on these images to extract correlated chemical species and to identify key groups of diagnostic transitions. To the best of our knowledge, our dataset is currently the largest astronomical set of molecular lines to which PCA has been applied. The PCA can categorize transitions coming from different physical components in NGC 253 such as i) young starburst tracers characterized by high-excitation transitions of HC$_3$N and complex organic molecules (COMs) versus tracers of on-going star formation (radio recombination lines) and high-excitation transitions of CCH and CN tracing PDRs, ii) tracers of cloud-collision-induced shocks (low-excitation transitions of CH$_3$OH, HNCO, HOCO$^+$, and OCS) versus shocks from star-formation-induced outflows (high-excitation transitions of SiO), as well as iii) outflows showing emission from HOC$^+$, CCH, H$_3$O$^+$, CO isotopologues, HCN, HCO$^+$, CS, and CN. Our findings show these intensities vary with galactic dynamics, star formation activities, and stellar feedback., Comment: 65 pages, 39 figures. Accepted for publication in ApJS

Single-pointing observations of NH$_3$ (1,1) and (2,2) were conducted towards 672 Planck Early Release Cold Cores (ECCs) using the Nanshan 26-m radio telescope. Out of these sources, a detection rate of 37% (249 cores) was achieved, with NH$_3$(1,1) hyperfine structure detected in 187 and NH$_3$(2,2) emission lines detected in 76 cores. The detection rate of NH3 is positively correlated with the continuum emission fluxes at a frequency of 857 GHz. Among the observed 672 cores, ~22% have associated stellar and IR objects within the beam size (~2$\arcmin$). This suggests that most of the cores in our sample may be starless. The kinetic temperatures of the cores range from 8.9 to 20.7 K, with an average of 12.3 K, indicating a coupling between gas and dust temperatures. The ammonia column densities range from 0.36 to 6.07$\times10^{15}$ cm$^{-2}$, with a median value of 2.04$\times10^{15}$ cm$^{-2}$. The fractional abundances of ammonia range from 0.3 to 9.7$\times10^{-7}$, with an average of 2.7 $\times10^{-7}$, which is one order of magnitude larger than that of Massive Star-Forming (MSF) regions and Infrared Dark Clouds (IRDCs). The correlation between thermal and non-thermal velocity dispersion of the NH$_3$(1,1) inversion transition indicates the dominance of supersonic non-thermal motions in the dense gas traced by NH$_3$, and the relationship between these two parameters in Planck cold cores is weaker, with lower values observed for both parameters relative to other samples under our examination. The cumulative distribution shapes of line widths in the Planck cold cores closely resemble those of the dense cores found in regions of Cepheus, and Orion L1630 and L1641, with higher values compared to Ophiuchus. A comparison of NH3 line-center velocities with those of $^{13}$CO and C$^{18}$O shows small differences (0.13 and 0.12 km s$^{-1}$ ), suggesting quiescence on small scales., Comment: 29 pages, 13 figures

High precision mapping of H2O megamaser emission from active galaxies has revealed more than a dozen Keplerian H2O maser disks, which enable a ~4% uncertainty estimate of the Hubble constant as well as providing accurate masses for the central black holes. These disks often have well-defined inner and outer boundaries of maser emission on sub-parsec scales. In order to better understand the physical conditions that determine the inner and outer radii of a maser disk, we examine the distributions of gas density and X-ray heating rate in a warped molecular disk described by a power-law surface density profile. For a suitable choice of the disk mass, we find that the outer radius R_out of the maser disk predicted from our model can match the observed value, with R_out mainly determined by the maximum heating rate or the minimum density for efficient maser action, depending on the combination of the Eddington ratio, black hole mass, and disk mass. Our analysis also indicates that the inner radius for maser action is comparable to the dust sublimation radius, suggesting that dust may play a role in determining the inner radius of a maser disk. Finally, our model predicts that H2O gigamaser disks could exist at the centers of high-z quasars, with disk sizes of >~ 10-30 pc., Comment: Accepted by MNRAS, 17 pages, 8 figures, 2 tables

One of the major barriers that CMOS devices face at nanometer scale is increasing parameter variation due to manufacturing imperfections. Process variations severely inhibit the reliable operation of circuits, as the operational frequency at the nominal process corner is insufficient to suppress timing violations across the entire variability spectrum. To avoid variability-induced timing errors, previous efforts impose pessimistic and performance-degrading timing guardbands atop the operating frequency. In this work, we employ approximate computing principles and propose a circuit-agnostic automated framework for generating variability-aware approximate circuits that eliminate process-induced timing guardbands. Variability effects are accurately portrayed with the creation of variation-aware standard cell libraries, fully compatible with standard EDA tools. The underlying transistors are fully calibrated against industrial measurements from Intel 14nm FinFET in which both electrical characteristics of transistors and variability effects are accurately captured. In this work, we explore the design space of approximate variability-aware designs to automatically generate circuits of reduced variability and increased performance without the need for timing guardbands. Experimental results show that by introducing negligible functional error of merely $5.3\times 10^{-3}$, our variability-aware approximate circuits can be reliably operated under process variations without sacrificing the application performance., Comment: 13 pages, 14 figures

Deep Neural Networks (DNNs) have shown significant advantages in a wide variety of domains. However, DNNs are becoming computationally intensive and energy hungry at an exponential pace, while at the same time, there is a vast demand for running sophisticated DNN-based services on resource constrained embedded devices. In this paper, we target energy-efficient inference on embedded DNN accelerators. To that end, we propose an automated framework to compress DNNs in a hardware-aware manner by jointly employing pruning and quantization. We explore, for the first time, per-layer fine- and coarse-grained pruning, in the same DNN architecture, in addition to low bit-width mixed-precision quantization for weights and activations. Reinforcement Learning (RL) is used to explore the associated design space and identify the pruning-quantization configuration so that the energy consumption is minimized whilst the prediction accuracy loss is retained at acceptable levels. Using our novel composite RL agent we are able to extract energy-efficient solutions without requiring retraining and/or fine tuning. Our extensive experimental evaluation over widely used DNNs and the CIFAR-10/100 and ImageNet datasets demonstrates that our framework achieves $39\%$ average energy reduction for $1.7\%$ average accuracy loss and outperforms significantly the state-of-the-art approaches., Comment: 14 pages, 9 figures

In a conducting medium held at finite temperature, free carriers are performing Brownian motion and generate fluctuating electromagnetic fields. We compute the averaged Lorentz force density that turns out nonzero in a thin sub-surface layer, pointing towards the surface, while vanishing in the bulk. This is an elementary example of rectified fluctuations, similar to the Casimir force or radiative heat transport. Our results also provide an experimental way to distinguish between the Drude and so-called plasma models., Comment: 7 pages, 5 figures, added estimates for zero temperature (quantum) contribution, accepted for publication in Physics (MDPI)