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Register a new userHow Dense is Your Gas? On the recoverability of LVG model parameters
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We explore the recoverability of gas physical conditions with the Large Velocity Gradient (LVG) model, using the public code RADEX and the molecules HCN and CO. Examining a wide parameter range with a series of models of increasing complexity we use both grid and Monte Carlo Markov Chain (MCMC) methods to recover the input conditions, and quantify the inherent and noise induced uncertainties in the model results. We find that even with the benefit of generous assumptions the LVG models struggle to recover any parameter better than to within half a dex, although we find no evidence of systemic offsets. Examining isotopologue lines we demonstrate that it is always preferable to model the isotopologue abundance ratio as a free parameter, due to large biases introduced in all other parameters when an incorrect ratio is assumed. Finally, we explore the effects of the background radiation temperature on CO and HCN line ratios, with an emphasis on the effect of the CMB at $z>4$, and show that while the effect on the line ratios is minor, the effect on the SLED peak is significant and that the CO$(1-0)$ line luminosity to H$_2$ mass conversion factor ($alpha_{rm CO}$) needs to be altered to account for the loss of contrast against the hotter CMB as redshift increases.
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The proliferation of IoT devices in smart homes, hospitals, and enterprise networks is widespread and continuing to increase in a superlinear manner. With this unprecedented growth, how can one assess the security of an IoT network holistically? In this article, we explore two dimensions of security assessment, using vulnerability information of IoT devices and their underlying components ($textit{compositional security scores}$) and SIEM logs captured from the communications and operations of such devices in a network ($textit{dynamic activity metrics}$) to propose the notion of an $textit{attack circuit}$. These measures are used to evaluate the security of IoT devices and the overall IoT network, demonstrating the effectiveness of attack circuits as practical tools for computing security metrics (exploitability, impact, and risk to confidentiality, integrity, and availability) of heterogeneous networks. We propose methods for generating attack circuits with input/output pairs constructed from CVEs using natural language processing (NLP) and with weights computed using standard security scoring procedures, as well as efficient optimization methods for evaluating attack circuits. Our system provides insight into possible attack paths an adversary may utilize based on their exploitability, impact, or overall risk. We have performed experiments on IoT networks to demonstrate the efficacy of the proposed techniques.
In literature computer architectures are frequently claimed to be highly flexible, typically implying there exist trade-offs between flexibility and performance or energy efficiency. Processor flexibility, however, is not very sharply defined, and as such these claims can not be validated, nor can such hypothetical relations be fully understood and exploited in the design of computing systems. This paper is an attempt to introduce scientific rigour to the notion of flexibility in computing systems.
Pre-trained language models (LM) have become go-to text representation encoders. Prior research used deep LMs to encode text sequences such as sentences and passages into single dense vector representations. These dense representations have been used in efficient text comparison and embedding-based retrieval. However, dense encoders suffer in low resource situations. Many techniques have been developed to solve this problem. Despite their success, not much is known about why this happens. This paper shows that one cause lies in the readiness of the LM to expose its knowledge through dense representation in fine-tuning, which we term Optimization Readiness. To validate the theory, we present Condenser, a general pre-training architecture based on Transformer LMs, to improve dense optimization readiness. We show that fine-tuning from Condenser significantly improves performance for small and/or noisy training sets.
It remains unclear what sets the efficiency with which molecular gas transforms into stars. Here we present a new VLA map of the spiral galaxy M51 in 33GHz radio continuum, an extinction-free tracer of star formation, at 3 scales (~100pc). We combined this map with interferometric PdBI/NOEMA observations of CO(1-0) and HCN(1-0) at matched resolution for three regions in M51 (central molecular ring, northern and southern spiral arm segments). While our measurements roughly fall on the well-known correlation between total infrared and HCN luminosity, bridging the gap between Galactic and extragalactic observations, we find systematic offsets from that relation for different dynamical environments probed in M51, e.g. the southern arm segment is more quiescent due to low star formation efficiency (SFE) of the dense gas, despite having a high dense gas fraction. Combining our results with measurements from the literature at 100pc scales, we find that the SFE of the dense gas and the dense gas fraction anti-correlate and correlate, respectively, with the local stellar mass surface density. This is consistent with previous kpc-scale studies. In addition, we find a significant anti-correlation between the SFE and velocity dispersion of the dense gas. Finally, we confirm that a correlation also holds between star formation rate surface density and the dense gas fraction, but it is not stronger than the correlation with dense gas surface density. Our results are hard to reconcile with models relying on a universal gas density threshold for star formation and suggest that turbulence and galactic dynamics play a major role in setting how efficiently dense gas converts into stars.
Trading option strangles is a highly popular strategy often used by market participants to mitigate volatility risks in their portfolios. In this paper we propose a measure of the relative value of a delta-Symmetric Strangle and compute it under the standard Black-Scholes option pricing model. This new measure accounts for the price of the strangle, relative to the Present Value of the spread between the two strikes, all expressed, after a natural re-parameterization, in terms of delta and a volatility parameter. We show that under the standard BS option pricing model, this measure of relative value is bounded by a simple function of delta only and is independent of the time to expiry, the price of the underlying security or the prevailing volatility used in the pricing model. We demonstrate how this bound can be used as a quick {it benchmark} to assess, regardless the market volatility, the duration of the contract or the price of the underlying security, the market (relative) value of the $delta-$strangle in comparison to its BS (relative) price. In fact, the explicit and simple expression for this measure and bound allows us to also study in detail the strangles exit strategy and the corresponding {it optimal} choice for a value of delta.
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