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Quantum networks of quantum objects promise to be exponentially more powerful than the objects considered independently. To live up to this promise will require the development of error mitigation and correction strategies to preserve quantum information as it is initialized, stored, transported, utilized, and measured. The quantum information could be encoded in discrete variables such as qubits, in continuous variables, or anything in-between. Quantum computational networks promise to enable simulation of physical phenomena of interest to the HEP community. Quantum sensor networks promise new measurement capability to test for new physics and improve upon existing measurements of fundamental constants. Such networks could exist at multiple scales from the nano-scale to a global-scale quantum network., Comment: contribution to Snowmass 2021

We study the behavior of an economic platform (e.g., Amazon, Uber Eats, Instacart) under shocks, such as COVID-19 lockdowns, and the effect of different regulation considerations imposed on a platform. To this end, we develop a multi-agent Gym environment of a platform economy in a dynamic, multi-period setting, with the possible occurrence of economic shocks. Buyers and sellers are modeled as economically-motivated agents, choosing whether or not to pay corresponding fees to use the platform. We formulate the platform's problem as a partially observable Markov decision process, and use deep reinforcement learning to model its fee setting and matching behavior. We consider two major types of regulation frameworks: (1) taxation policies and (2) platform fee restrictions, and offer extensive simulated experiments to characterize regulatory tradeoffs under optimal platform responses. Our results show that while many interventions are ineffective with a sophisticated platform actor, we identify a particular kind of regulation -- fixing fees to optimal, pre-shock fees while still allowing a platform to choose how to match buyer demands to sellers -- as promoting the efficiency, seller diversity, and resilience of the overall economic system.

In this study, 36 cores (30 starless and 6 protostellar) identified in Orion were surveyed to search for inward motions. We used the Nobeyama 45 m radio telescope, and mapped the cores in the $J = 1\rightarrow0$ transitions of HCO$^+$, H$^{13}$CO$^+$, N$_2$H$^+$, HNC, and HN$^{13}$C. The asymmetry parameter $\delta V$, which was the ratio of the difference between the HCO$^+$ and H$^{13}$CO$^+$ peak velocities to the H$^{13}$CO$^+$ line width, was biased toward negative values, suggesting that inward motions were more dominant than outward motions. Three starless cores (10% of all starless cores surveyed) were identified as cores with blue-skewed line profiles (asymmetric profiles with more intense blue-shifted emission), and another two starless cores (7%) were identified as candidate blue-skewed line profiles. The peak velocity difference between HCO$^+$ and H$^{13}$CO$^+$ of them was up to 0.9 km s$^{-1}$, suggesting that some inward motions exceeded the speed of sound for the quiescent gas ($\sim10-17$ K). The mean of $\delta V$ of the five aforementioned starless cores was derived to be $-$0.5$\pm$0.3. One core, G211.16$-$19.33North3, observed using the ALMA ACA in DCO$^+$ $J = 3\rightarrow2$ exhibited blue-skewed features. Velocity offset in the blue-skewed line profile with a dip in the DCO$^+$ $J = 3\rightarrow2$ line was larger ($\sim 0.5$ km s$^{-1}$) than that in HCO$^+$ $J = 1\rightarrow0$ ($\sim 0.2$ km s$^{-1}$), which may represent gravitational acceleration of inward motions. It seems that this core is at the last stage in the starless phase, judging from the chemical evolution factor version 2.0 (CEF2.0)., Comment: 45 pages, 28 figures, ApJ in press

We propose data-driven one-pass streaming algorithms for estimating the number of triangles and four cycles, two fundamental problems in graph analytics that are widely studied in the graph data stream literature. Recently, (Hsu 2018) and (Jiang 2020) applied machine learning techniques in other data stream problems, using a trained oracle that can predict certain properties of the stream elements to improve on prior "classical" algorithms that did not use oracles. In this paper, we explore the power of a "heavy edge" oracle in multiple graph edge streaming models. In the adjacency list model, we present a one-pass triangle counting algorithm improving upon the previous space upper bounds without such an oracle. In the arbitrary order model, we present algorithms for both triangle and four cycle estimation with fewer passes and the same space complexity as in previous algorithms, and we show several of these bounds are optimal. We analyze our algorithms under several noise models, showing that the algorithms perform well even when the oracle errs. Our methodology expands upon prior work on "classical" streaming algorithms, as previous multi-pass and random order streaming algorithms can be seen as special cases of our algorithms, where the first pass or random order was used to implement the heavy edge oracle. Lastly, our experiments demonstrate advantages of the proposed method compared to state-of-the-art streaming algorithms., Comment: To be presented at ICLR 2022

We re-consider operator mixing in the so-called $SU(2)$ sector of ${\cal N} \, = \, 4$ super Yang-Mills theory with gauge group $SU(N)$. Where possible, single-trace operators of moderate length are completed by higher-trace admixtures so as to yield large $N$ tree level eigenstates. We are particularly interested in parity pairs with three excitations. Since parity is respected in the mixing, the odd single-trace operators at low length cannot receive too many admixtures. We reproduce the tree-level norms of a set of large $N$ eigenstates up to order $1/N^4$ by integrability methods. This involves evaluating two-point functions on the sphere, the torus, and the double-torus. A perfect match is found as long as descendents are absent from the mixing. Using twist to make the descendents appear in the integrability picture immediately leads to the question how to modify the entangled states occurring in the hexagon tessellations. We take a closer look at the double-trace admixtures to the parity even three-excitation operator at length seven, which are both products of a primary state and a descendent. Their two-point functions are sensitive to the twist introduced into the Bethe equations. For transverse scalar excitations we succeed in recovering the corresponding field theory results. For longitudinal magnons our methods fail, pointing at a potential weakness of the formalism., Comment: LaTeX, 27 pages, 7 figures

We present one of the very first extensive classifications of a large sample of molecular clouds based on their morphology. This is achieved using a recently published catalogue of 10663 clouds obtained from the first data release of the SEDIGISM survey. The clouds are classified into four different morphologies by visual inspection and using an automated algorithm -- J plots. The visual inspection also serves as a test for the J plots algorithm, as this is the first time it has been used on molecular gas. Generally, it has been found that the structure of molecular clouds is highly filamentary and our observations indeed verify that most of our molecular clouds are elongated structures. Based on our visual classification of the 10663 SEDIGISM clouds, 15% are ring-like, 57% are elongated, 15% are concentrated and 10% are clumpy clouds. The remaining clouds do not belong to any of these morphology classes and are termed unclassified. We compare the SEDIGISM molecular clouds with structures identified through other surveys, i.e. ATLASGAL elongated structures and the bubbles from Milky Way Project (MWP). We find that many of the ATLASGAL and MWP structures are velocity coherent. ATLASGAL elongated structures overlap with ~ 21% of the SEDIGISM elongated structures (elongated and clumpy clouds) and MWP bubbles overlap with ~ 25% of the SEDIGISM ring-like clouds. We also analyse the star-formation associated with different cloud morphologies using two different techniques. The first technique examines star formation efficiency (SFE) and the dense gas fraction (DGF), based on SEDIGISM clouds and ATLASGAL clumps data. The second technique uses the high-mass star formation (HMSF) threshold for molecular clouds. The results indicate that clouds with ring-like and clumpy morphologies show a higher degree of star formation., Comment: Accepted for publication in A&A. 40 pages (26 of Appendices), 55 figures, 13 tables. The updated SEDIGISM cloud catalogue, containing cloud morphology, will be available as part of the SEDIGISM database

Despite artificial intelligence (AI) having transformed major aspects of our society, less than a fraction of its potential has been explored, let alone deployed, for education. AI-powered learning can provide millions of learners with a highly personalized, active and practical learning experience, which is key to successful learning. This is especially relevant in the context of online learning platforms. In this paper, we present the results of a comparative head-to-head study on learning outcomes for two popular online learning platforms (n=199 participants): A MOOC platform following a traditional model delivering content using lecture videos and multiple-choice quizzes, and the Korbit learning platform providing a highly personalized, active and practical learning experience. We observe a huge and statistically significant increase in the learning outcomes, with students on the Korbit platform providing full feedback resulting in higher course completion rates and achieving learning gains 2 to 2.5 times higher than both students on the MOOC platform and students in a control group who don't receive personalized feedback on the Korbit platform. The results demonstrate the tremendous impact that can be achieved with a personalized, active learning AI-powered system. Making this technology and learning experience available to millions of learners around the world will represent a significant leap forward towards the democratization of education., Comment: 9 pages, 6 figures

Deep learning approaches are successful in a wide range of AI problems and in particular for visual recognition tasks. However, there are still open problems among which is the capacity to handle streams of visual information and the management of class imbalance in datasets. Existing research approaches these two problems separately while they co-occur in real world applications. Here, we study the problem of learning incrementally from imbalanced datasets. We focus on algorithms which have a constant deep model complexity and use a bounded memory to store exemplars of old classes across incremental states. Since memory is bounded, old classes are learned with fewer images than new classes and an imbalance due to incremental learning is added to the initial dataset imbalance. A score prediction bias in favor of new classes appears and we evaluate a comprehensive set of score calibration methods to reduce it. Evaluation is carried with three datasets, using two dataset imbalance configurations and three bounded memory sizes. Results show that most calibration methods have beneficial effect and that they are most useful for lower bounded memory sizes, which are most interesting in practice. As a secondary contribution, we remove the usual distillation component from the loss function of incremental learning algorithms. We show that simpler vanilla fine tuning is a stronger backbone for imbalanced incremental learning algorithms.

We present 850 $\mu$m polarimetric observations toward the Serpens Main molecular cloud obtained using the POL-2 polarimeter on the James Clerk Maxwell Telescope (JCMT) as part of the B-fields In STar-forming Region Observations (BISTRO) survey. These observations probe the magnetic field morphology of the Serpens Main molecular cloud on about 6000 au scales, which consists of cores and six filaments with different physical properties such as density and star formation activity. Using the histogram of relative orientation (HRO) technique, we find that magnetic fields are parallel to filaments in less dense filamentary structures where $N_{H_2} < 0.93\times 10^{22}$ cm$^{-2}$ (magnetic fields perpendicular to density gradients), while being perpendicular to filaments (magnetic fields parallel to density gradients) in dense filamentary structures with star formation activity. Moreover, applying the HRO technique to denser core regions, we find that magnetic field orientations change to become perpendicular to density gradients again at $N_{H_2} \approx 4.6 \times 10^{22}$ cm$^{-2}$. This can be interpreted as a signature of core formation. At $N_{H_2} \approx 16 \times 10^{22}$ cm$^{-2}$ magnetic fields change back to being parallel to density gradients once again, which can be understood to be due to magnetic fields being dragged in by infalling material. In addition, we estimate the magnetic field strengths of the filaments ($B_{POS} = 60-300~\mu$G)) using the Davis-Chandrasekhar-Fermi method and discuss whether the filaments are gravitationally unstable based on magnetic field and turbulence energy densities., Comment: 18 pages, accepted for publication in ApJ

The presence of complex organic molecules (COMs) in the interstellar medium (ISM) is of great interest since it may link to the origin and prevalence of life in the universe. Aiming to investigate the occurrence of COMs and their possible origins, we conducted a chemical census toward a sample of protostellar cores as part of the ALMA Survey of Orion Planck Galactic Cold Clumps (ALMASOP) project. We report the detection of 11 hot corino sources, which exhibit compact emissions from warm and abundant COMs, among 56 Class 0/I protostellar cores. All the hot corino sources discovered are likely Class 0 and their sizes of the warm region ($>$ 100 K) are comparable to 100 au. The luminosity of the hot corino sources exhibits positive correlations with the total number of methanol and the extent of its emissions. Such correlations are consistent with the thermal desorption picture for the presence of hot corino and suggest that the lower luminosity (Class 0) sources likely have a smaller region with COMs emissions. With the same sample selection method and detection criteria being applied, the detection rates of the warm methanol in the Orion cloud (15/37) and the Perseus cloud (28/50) are statistically similar when the cloud distances and the limited sample size are considered. Observing the same set of COM transitions will bring a more informative comparison between the cloud properties., Comment: 34 pages. 14 figures. 1 figure set. 1 machine-readable table. Accepted for publication in ApJ

ATLASGAL is a 870-mircon dust survey of 420 square degrees of the inner Galactic plane and has been used to identify ~10 000 dense molecular clumps. Dedicated follow-up observations and complementary surveys are used to characterise the physical properties of these clumps, map their Galactic distribution and investigate the evolutionary sequence for high-mass star formation. The analysis of the ATLASGAL data is ongoing: we present an up-to-date version of the catalogue. We have classified 5007 clumps into four evolutionary stages (quiescent, protostellar, young stellar objects and HII regions) and find similar numbers of clumps in each stage, suggesting a similar lifetime. The luminosity-to-mass (L/M) ratio curve shows a smooth distribution with no significant kinks or discontinuities when compared to the mean values for evolutionary stages indicating that the star-formation process is continuous and that the observational stages do not represent fundamentally different stages or changes in the physical mechanisms involved. We compare the evolutionary sample with other star-formation tracers (methanol and water masers, extended green objects and molecular outflows) and find that the association rates with these increases as a function of evolutionary stage, confirming that our classification is reliable. This also reveals a high association rate between quiescent sources and molecular outflows, revealing that outflows are the earliest indication that star formation has begun and that star formation is already ongoing in many of the clumps that are dark even at 70 micron., Comment: Accepted for publication in MNRAS. Consists of 20 pages, 15 figures, 8 table. The complete tables will be available from CDS and upon request

Context. The formation processes of massive stars are still unclear but a picture is emerging involving accretion disks and molecular outflows in what appears to be a scaled-up version of low-mass star formation. A census of outflow activity towards massive star-forming clumps in various evolutionary stages has the potential to shed light on massive star formation (MSF). Aims. We conducted an outflow survey towards ATLASGAL clumps using SEDIGISM data and aimed to obtain a large sample of clumps exhibiting outflows in different evolutionary stages. Methods. We identify the high-velocity wings of the 13CO lines toward ATLASGAL clumps by (1) extracting the simultaneously observed 13CO and C18O spectra from SEDIGISM, and (2) subtracting Gaussian fits to the scaled C18O from the 13CO, line after considering opacity broadening. Results. We have detected high-velocity gas towards 1192 clumps out of a total sample of 2052, giving an overall detection rate of 58%. Outflow activity has been detected in the earliest quiescent clumps (i.e., 70$\mu$m weak), to the most evolved HII region stages i.e., 8$\mu$m bright with MSF tracers. The detection rate increases as a function of evolution (quiescent=51%, protostellar=47%, YSO=57%, UCHII regions=76%). Conclusion. Our sample is the largest outflow sample identified so far. The high-detection rate from this large sample is consistent with previous results and supports that outflows are a ubiquitous feature of MSF. The lower detection rate in early evolutionary stages may be due to that outflows in the early stages are weak and difficult to detect. We obtain a statistically significant sample of outflow clumps for every evolutionary stage, especially for outflow clumps in the 70$\mu$m dark stage. This suggests that the absence of 70$\mu$m emission is not a robust indicator of starless/pre-stellar cores., Comment: 13 pages, 10 figures, accepted by Astronomy & Astrophysics (A&A)

A* is a classic and popular method for graphs search and path finding. It assumes the existence of a heuristic function $h(u,t)$ that estimates the shortest distance from any input node $u$ to the destination $t$. Traditionally, heuristics have been handcrafted by domain experts. However, over the last few years, there has been a growing interest in learning heuristic functions. Such learned heuristics estimate the distance between given nodes based on "features" of those nodes. In this paper we formalize and initiate the study of such feature-based heuristics. In particular, we consider heuristics induced by norm embeddings and distance labeling schemes, and provide lower bounds for the tradeoffs between the number of dimensions or bits used to represent each graph node, and the running time of the A* algorithm. We also show that, under natural assumptions, our lower bounds are almost optimal., Comment: ITCS 2022

We introduce the $(p,q)$-Fair Clustering problem. In this problem, we are given a set of points $P$ and a collection of different weight functions $W$. We would like to find a clustering which minimizes the $\ell_q$-norm of the vector over $W$ of the $\ell_p$-norms of the weighted distances of points in $P$ from the centers. This generalizes various clustering problems, including Socially Fair $k$-Median and $k$-Means, and is closely connected to other problems such as Densest $k$-Subgraph and Min $k$-Union. We utilize convex programming techniques to approximate the $(p,q)$-Fair Clustering problem for different values of $p$ and $q$. When $p\geq q$, we get an $O(k^{(p-q)/(2pq)})$, which nearly matches a $k^{\Omega((p-q)/(pq))}$ lower bound based on conjectured hardness of Min $k$-Union and other problems. When $q\geq p$, we get an approximation which is independent of the size of the input for bounded $p,q$, and also matches the recent $O((\log n/(\log\log n))^{1/p})$-approximation for $(p, \infty)$-Fair Clustering by Makarychev and Vakilian (COLT 2021)., Comment: SODA 2022

We consider the single-item interdependent value setting, where there is a monopolist, $n$ buyers, and each buyer has a private signal $s_i$ describing a piece of information about the item. Each bidder $i$ also has a valuation function $v_i(s_1,\ldots,s_n)$ mapping the (private) signals of all buyers to a positive real number representing their value for the item. This setting captures scenarios where the item's information is asymmetric or dispersed among agents, such as in competitions for oil drilling rights, or in auctions for art pieces. Due to the increased complexity of this model compared to standard private values, it is generally assumed that each bidder's valuation function $v_i$ is public knowledge. But in many situations, the seller may not know how a bidder aggregates signals into a valuation. In this paper, we design mechanisms that guarantee approximately-optimal social welfare while satisfying ex-post incentive compatibility and individual rationality for the case where the valuation functions are private to the bidders. When the valuations are public, it is possible for optimal social welfare to be attained by a deterministic mechanism under a single-crossing condition. In contrast, when the valuations are the bidders' private information, we show that no finite bound can be achieved by any deterministic mechanism even under single-crossing. Moreover, no randomized mechanism can guarantee better than an $n$-approximation. We thus consider valuation functions that are submodular over signals (SOS), introduced in the context of combinatorial auctions in a recent breakthrough paper by Eden et al. [EC'19]. Our main result is an $O(\log^2 n)$-approximation for buyers with private signals and valuations under the SOS condition. We also give a tight $\Theta(k)$-approximation for the case each agent's valuation depends on at most $k$ other signals even for unknown $k$., Comment: To appear in SODA 2022

We consider the problem of approximating the arboricity of a graph $G= (V,E)$, which we denote by $\mathsf{arb}(G)$, in sublinear time, where the arboricity of a graph is the minimal number of forests required to cover its edges. An algorithm for this problem may perform degree and neighbor queries, and is allowed a small error probability. We design an algorithm that outputs an estimate $\hat{\alpha}$, such that with probability $1-1/\textrm{poly}(n)$, $\mathsf{arb}(G)/c\log^2 n \leq \hat{\alpha} \leq \mathsf{arb}(G)$, where $n=|V|$ and $c$ is a constant. The expected query complexity and running time of the algorithm are $O(n/\mathsf{arb}(G))\cdot \textrm{poly}(\log n)$, and this upper bound also holds with high probability. %($\widetilde{O}(\cdot)$ is used to suppress $\textrm{poly}(\log n)$ dependencies). This bound is optimal for such an approximation up to a $\textrm{poly}(\log n)$ factor.

Jets and outflows trace the accretion history of protostars. High-velocity molecular jets have been observed from several protostars in the early Class\,0 phase of star formation, detected with the high-density tracer SiO. Until now, no clear jet has been detected with SiO emission from isolated evolved Class\,I protostellar systems. We report a prominent dense SiO jet from a Class\,I source G205S3 (HOPS\,315: T$_{bol}$ $\sim$ 180 K, spectral index $\sim$ 0.417), with a moderately high mass-loss rate ($\sim$ 0.59 $\times$ 10$^{-6}$ M$_\odot$ yr$^{-1}$) estimated from CO emission. Together, these features suggest that G205S3 is still in a high accretion phase, similar to that expected of Class\,0 objects. We compare G205S3 to a representative Class\,0 system G206W2 (HOPS\,399) and literature Class\,0/I sources to explore the possible explanations behind the SiO emission seen at the later phase. We estimate a high inclination angle ($\sim$ 40$^\circ$) for G205S3 from CO emission, which may expose the infrared emission from the central core and mislead the spectral classification. However, the compact 1.3\,mm continuum, C$^{18}$O emission, location in the bolometric luminosity to sub-millimeter fluxes diagram, outflow force ($\sim$ 3.26 $\times$ 10$^{-5}$ M$_\odot$km s$^{-1}$/yr) are also analogous to that of Class\,I systems. We thus consider G205S3 to be at the very early phase of Class\,I, and in the late phase of ``high-accretion". The episodic ejection could be due to the presence of an unknown binary, a planetary companion, or dense clumps, where the required mass for such high accretion could be supplied by a massive circumbinary disk., Comment: 13 pages, 5 figures, Accepted for publication in the Astrophysical Journal

Sampling random nodes is a fundamental algorithmic primitive in the analysis of massive networks, with many modern graph mining algorithms critically relying on it. We consider the task of generating a large collection of random nodes in the network assuming limited query access (where querying a node reveals its set of neighbors). In current approaches, based on long random walks, the number of queries per sample scales linearly with the mixing time of the network, which can be prohibitive for large real-world networks. We propose a new method for sampling multiple nodes that bypasses the dependence in the mixing time by explicitly searching for less accessible components in the network. We test our approach on a variety of real-world and synthetic networks with up to tens of millions of nodes, demonstrating a query complexity improvement of up to $\times 20$ compared to the state of the art., Comment: To appear in 15th ACM International Conference on Web Search and Data Mining (WSDM 2022). Code available soon at: https://github.com/omribene/sampling-nodes

Incremental learning enables artificial agents to learn from sequential data. While important progress was made by exploiting deep neural networks, incremental learning remains very challenging. This is particularly the case when no memory of past data is allowed and catastrophic forgetting has a strong negative effect. We tackle class-incremental learning without memory by adapting prediction bias correction, a method which makes predictions of past and new classes more comparable. It was proposed when a memory is allowed and cannot be directly used without memory, since samples of past classes are required. We introduce a two-step learning process which allows the transfer of bias correction parameters between reference and target datasets. Bias correction is first optimized offline on reference datasets which have an associated validation memory. The obtained correction parameters are then transferred to target datasets, for which no memory is available. The second contribution is to introduce a finer modeling of bias correction by learning its parameters per incremental state instead of the usual past vs. new class modeling. The proposed dataset knowledge transfer is applicable to any incremental method which works without memory. We test its effectiveness by applying it to four existing methods. Evaluation with four target datasets and different configurations shows consistent improvement, with practically no computational and memory overhead., Comment: Accepted to WACV 2022

The morphology of the Milky Way is still a matter of debate. In order to shed light on uncertainties surrounding the structure of the Galaxy, in this paper, we study the imprint of spiral arms on the distribution and properties of its molecular gas. To do so, we take full advantage of the SEDIGISM survey that observed a large area of the inner Galaxy in the $^{13}$CO(2-1) line at an angular resolution of 28". We analyse the influences of the spiral arms by considering the features of the molecular gas emission as a whole across the longitude-velocity map built from the full survey. Additionally, we examine the properties of the molecular clouds in the spiral arms compared to the properties of their counterparts in the inter-arm regions. Through flux and luminosity probability distribution functions, we find that the molecular gas emission associated with the spiral arms does not differ significantly from the emission between the arms. On average, spiral arms show masses per unit length of $\sim10^5-10^6$ M$_{\odot} $kpc$^{-1}$. This is similar to values inferred from data sets in which emission distributions were segmented into molecular clouds. By examining the cloud distribution across the Galactic plane, we infer that the molecular mass in the spiral arms is a factor of 1.5 higher than that of the inter-arm medium, similar to what is found for other spiral galaxies in the local Universe. We observe that only the distributions of cloud mass surface densities and aspect ratio in the spiral arms show significant differences compared to those of the inter-arm medium; other observed differences appear instead to be driven by a distance bias. By comparing our results with simulations and observations of nearby galaxies, we conclude that the measured quantities would classify the Milky Way as a flocculent spiral galaxy, rather than as a grand-design one., Comment: Accepted for publication in A&A. 38 pages (17 of Appendices), 26 figures, 7 tables. The updated SEDIGISM cloud catalogue, containing spiral arm association information, will be available as part of the SEDIGISM database ( https://sedigism.mpifr-bonn.mpg.de/index.html )

We simulate entanglement sharing between two end-nodes of a quantum network using SeQUeNCe, an open-source simulation package for quantum networks. Our focus is on the rate of entanglement generation between the end-nodes with many repeaters with a finite quantum memory lifetime. Our findings demonstrate that the performance of quantum connection depends highly on the entanglement management protocol scheduling entanglement generation and swapping, resulting in the final end-to-end entanglement. Numerical and analytical simulations show limits of connection performance for a given number of repeaters involved, memory lifetimes for a given distance between the end nodes, and an entanglement management protocol., Comment: 9 pages, 5 figures