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The cornerstone of neural algorithmic reasoning is the ability to solve algorithmic tasks, especially in a way that generalises out of distribution. While recent years have seen a surge in methodological improvements in this area, they mostly focused on building specialist models. Specialist models are capable of learning to neurally execute either only one algorithm or a collection of algorithms with identical control-flow backbone. Here, instead, we focus on constructing a generalist neural algorithmic learner -- a single graph neural network processor capable of learning to execute a wide range of algorithms, such as sorting, searching, dynamic programming, path-finding and geometry. We leverage the CLRS benchmark to empirically show that, much like recent successes in the domain of perception, generalist algorithmic learners can be built by "incorporating" knowledge. That is, it is possible to effectively learn algorithms in a multi-task manner, so long as we can learn to execute them well in a single-task regime. Motivated by this, we present a series of improvements to the input representation, training regime and processor architecture over CLRS, improving average single-task performance by over 20% from prior art. We then conduct a thorough ablation of multi-task learners leveraging these improvements. Our results demonstrate a generalist learner that effectively incorporates knowledge captured by specialist models., Comment: To appear at LoG 2022 (Spotlight talk). 23 pages, 11 figures

We construct a type-II holographic superconductor from an anisotropic rotating background metric. We study the effects of the magnetic field on the vortex structure, including continuous deformations both from triangular to square lattices or vice versa. Our holographic model reproduces known experimental vortex lattice deformations and the increment of the vortex population by increasing the external magnetic field., Comment: 5 pages, 3 figures. Published version

In this work, we develop a new quantum algorithm to solve a combinatorial problem with significant practical relevance occurring in clutch manufacturing. It is demonstrated how quantum optimization can play a role in real industrial applications in the manufacturing sector. Using the quantum annealer provided by D-Wave Systems, we analyze the performance of the quantum and quantum-classical hybrid solvers and compare them to deterministic- and random-algorithm classical benchmark solvers. The continued evolution of the quantum technology, indicating an expectation for even greater relevance in the future is discussed and the revolutionary potential it could have in the manufacturing sector is highlighted., Comment: 11 pages, 4 figures

Multi-functional wireless networks are rapidly evolving and aspire to become a promising attribute of the upcoming 6G networks. Enabling multiple simultaneous networking functions with a single radio fosters the development of more integrated and simpler equipment, overcoming design and technology barriers inherited from radio systems of the past. We are seeing numerous trends exploiting these features in newly designed radios, such as those operating on the mmWave band. In this article, however, we carefully analyze the challenges and opportunities for multi-functional wireless networks in UHF bands, advocating the reuse of existing infrastructures and technologies, and exploring the possibilities of expanding their functionality without requiring architectural changes. We believe that both modern and legacy technologies can be turned into multi-functional systems if the right scientific and technological challenges are properly addressed. This transformation can foster the development of new applications and extend the useful life of these systems, contributing to a more sustainable digitization by delaying equipment obsolescence.

We created the APOGEE-GALEX-\emph{Gaia} catalog to study white dwarfs binaries. This database aims to create a minimally biased sample of WD binary systems identified from a combination of GALEX, {\it Gaia}, and APOGEE data to increase the number of WD binaries with orbital parameters and chemical compositions. We identify 3,414 sources as WD binary candidates, with nondegenerate companions of spectral types between F and M, including main sequence, main sequence binaries, subgiants, sub-subgiants, red giants, and red clump stars. Among our findings are (a) a total of 1,806 systems having inferred WD radii $R < 25$ R$_{\Earth}$, which constitute a more reliable group of WD binary candidates within the main sample; (b) a difference in the metallicity distribution function between WD binary candidates and the control sample of most luminous giants ($M_H < -3.0$); (c) the existence of a population of sub-subgiants with WD companions; (d) evidence for shorter periods in binaries that contain WDs compared to those that do not, as shown by the cumulative distributions of APOGEE radial velocity shifts; (e) evidence for systemic orbital evolution in a sample of 252 WD binaries with orbital periods, based on differences in the period distribution between systems with red clump, main sequence binary, and sub-subgiant companions and systems with main sequence or red giant companions; and (f) evidence for chemical enrichment during common envelope (CE) evolution, shown by lower metallicities in wide WD binary candidates ($P > 100$ days) compared to post-CE ($P < 100$ days) WD binary candidates., Comment: 22 pages, 18 figures. Accepted for publication in AJ

Building on the framework introduced by Xu and Raginksy [1] for supervised learning problems, we study the best achievable performance for model-based Bayesian reinforcement learning problems. With this purpose, we define minimum Bayesian regret (MBR) as the difference between the maximum expected cumulative reward obtainable either by learning from the collected data or by knowing the environment and its dynamics. We specialize this definition to reinforcement learning problems modeled as Markov decision processes (MDPs) whose kernel parameters are unknown to the agent and whose uncertainty is expressed by a prior distribution. One method for deriving upper bounds on the MBR is presented and specific bounds based on the relative entropy and the Wasserstein distance are given. We then focus on two particular cases of MDPs, the multi-armed bandit problem (MAB) and the online optimization with partial feedback problem. For the latter problem, we show that our bounds can recover from below the current information-theoretic bounds by Russo and Van Roy [2]., Comment: 10 pages: 6 of the main text, 1 of references, and 3 of appendices

Future and on-going infrared and radio observatories such as JWST, METIS or ALMA will increase the amount of rest-frame IR spectroscopic data for galaxies by several orders of magnitude. While studies of the chemical composition of the ISM based on optical observations have been widely spread over decades for SFG and, more recently, for AGN, similar studies need to be performed using IR data. This regime can be especially useful in the case of AGN given that it is less affected by temperature and dust extinction, traces higher ionic species and can also provide robust estimations of the chemical abundance ratio N/O. We present a new tool based on a bayesian-like methodology to estimate chemical abundances from IR emission lines in AGN. We use a sample of 58 AGN with IR spectroscopic data retrieved from the literature to probe the validity of our method. The estimations of the chemical abundances based on IR lines in our sample are later compared with the corresponding abundances derived from the optical emission lines in the same objects. HII-CHI-Mistry-IR takes advantage of photoionization models, characterized by the chemical abundance ratios O/H and N/O and the ionization parameter $U$, to compare their predicted emission-line fluxes with a set of observed values. Instead of matching single emission lines, the code uses some specific emission-line ratios sensitive to the above free parameters. We report mainly solar and also subsolar abundances for O/H in the nuclear region for our sample of AGN, whereas N/O clusters around solar values. We find a discrepancy between the chemical abundances derived from IR and optical emission lines, being the latter higher than the former. This discrepancy, also reported by previous studies of the composition of the ISM in AGN from IR observations, is independent from the gas density or the incident radiation field to the gas., Comment: 20 pages, 15 figures, 8 tables. Accepted for publication in A&A

Born-again planetary nebulae (PNe) allow investigating stellar evolution, dust production, and nebular shocks in human timescales. Here we present an analysis of multi-epoch optical spectroscopic observations of the born-again PN A 58 around V605 Aql, which experienced a very late thermal pulse (VLTP) about a century ago. The H-deficient ejecta has experienced a considerable brightening in the time period considered, from 1996 to 2021, with notable changes also in many emission line ratios. Neither the reduction of the extinction caused by the dilution of the ejecta nor the increase of the ionizing photon flux from the central star seem capable to produce these spectral changes, which are instead attributed to shocks in the bipolar H-poor outflow dissociating molecular material and propagating through the outer nebula., Comment: 9 pages, 5 figures

Inspection of cracks is an important process for properly monitoring and maintaining a building. However, manual crack inspection is time-consuming, inconsistent, and dangerous (e.g., in tall buildings). Due to the development of open-source AI technologies, the increase in available Unmanned Aerial Vehicles (UAVs) and the availability of smartphone cameras, it has become possible to automate the building crack inspection process. This study presents the development of an easy-to-use, free and open-source Automated Building Exterior Crack Inspection Software (ABECIS) for construction and facility managers, using state-of-the-art segmentation algorithms to identify concrete cracks and generate a quantitative and qualitative report. ABECIS was tested using images collected from a UAV and smartphone cameras in real-world conditions and a controlled laboratory environment. From the raw output of the algorithm, the median Intersection over Unions for the test experiments is (1) 0.686 for indoor crack detection experiment in a controlled lab environment using a commercial drone, (2) 0.186 for indoor crack detection at a construction site using a smartphone and (3) 0.958 for outdoor crack detection on university campus using a commercial drone. These IoU results can be improved significantly to over 0.8 when a human operator selectively removes the false positives. In general, ABECIS performs best for outdoor drone images, and combining the algorithm predictions with human verification/intervention offers very accurate crack detection results. The software is available publicly and can be downloaded for out-of-the-box use at: https://github.com/SMART-NYUAD/ABECIS

The co-integration of spin, superconducting, and topological systems is emerging as an exciting pathway for scalable and high-fidelity quantum information technology. High-mobility planar germanium is a front-runner semiconductor for building quantum processors with spin-qubits, but progress with hybrid superconductor-semiconductor devices is hindered because obtaining a superconducting gap free of subgap states (hard gap) has proven difficult. Here we solve this challenge by developing a low-disorder, oxide-free interface between high-mobility planar germanium and a germanosilicide parent superconductor. This superconducting contact is formed by the thermally-activated solid phase reaction between a metal (Pt) and the semiconductor heterostructure (Ge/SiGe). Electrical characterization reveals near-unity transparency in Josephson junctions and, importantly, a hard induced superconducting gap in quantum point contacts. Furthermore, we demonstrate phase control of a Josephson junction and study transport in a gated two-dimensional superconductor-semiconductor array towards scalable architectures. These results expand the quantum technology toolbox in germanium and provide new avenues for exploring monolithic superconductor-semiconductor quantum circuits towards scalable quantum information processing.

As in other cybersecurity areas, machine learning (ML) techniques have emerged as a promising solution to detect Android malware. In this sense, many proposals employing a variety of algorithms and feature sets have been presented to date, often reporting impresive detection performances. However, the lack of reproducibility and the absence of a standard evaluation framework make these proposals difficult to compare. In this paper, we perform an analysis of 10 influential research works on Android malware detection using a common evaluation framework. We have identified five factors that, if not taken into account when creating datasets and designing detectors, significantly affect the trained ML models and their performances. In particular, we analyze the effect of (1) the presence of duplicated samples, (2) label (goodware/greyware/malware) attribution, (3) class imbalance, (4) the presence of apps that use evasion techniques and, (5) the evolution of apps. Based on this extensive experimentation, we conclude that the studied ML-based detectors have been evaluated optimistically, which justifies the good published results. Our findings also highlight that it is imperative to generate realistic experimental scenarios, taking into account the aforementioned factors, to foster the rise of better ML-based Android malware detection solutions.

By starting from a Perelman entropy functional and considering the Ricci-DeTurck flow equations we analyze the behaviour of Einstein-Hilbert and Einstein-Proca theories with Lifshitz geometry as functions of a flow parameter. In the former case, we found one consistent fixed point that represents flat space-time as the flow parameter tends to infinity. Massive vector fields in the latter theory enrich the system under study and have the same fixed point achieved at the same rate as in the former case. The geometric flow is parametrized by the metric coefficients and represents a change in anisotropy of the geometry towards an isotropic flat space-time as the flow parameter evolves. Indeed, the flow of the Proca fields depends on certain coefficients that vanish when the flow parameter increases, rendering these fields constant. We have been able to write down the evolving Lifshitz metric solution with positive, but otherwise arbitrary, critical exponents relevant to geometries with spatially anisotropic holographic duals. We show that both the scalar curvature and matter contributions to the Ricci-DeTurck flow vanish under the flow at a fixed point consistent with flat space-time geometry. Thus, the behaviour of the scalar curvature always increases, homogenizing the geometry along the flow. Moreover, the theory under study keeps positive-definite but decreasing the entropy functional along the Ricci-DeTurck flow., Comment: 12 pages, 2 figures

Quantum-optical coherence tomography (QOCT) is an optical sectioning modality based on the quantum interference of photon pairs, obtained from a spontaneous parametric downconversion (SPDC) source. The promise of QOCT derives from two quantum-conferred advantages when compared to equivalent classical optical coherence tomography (OCT) systems: a factor of 2 axial resolution enhancement, as well as dispersion cancellation. However, the technique is far from being competitive with current OCT devices due to the long required acquisition times. Here, we on the one hand demonstrate a quantum optical coherence microscopy (QOCM) technique that is designed to overcome some of the limitations of previous QOCT implementations, and on the other hand test it on representative samples, including glass layers with manufactured transverse patterns and metal-coated biological specimens. We use a collinear SPDC source, so that the entire emitted photon pair flux may contribute to the measurements, together with a multi-mode detection design. We employ a Michelson interferometer with the sample placed as end-mirror in one of the interferometer arms, instead of the more typical Hong-Ou-Mandel used in QOCT implementations. In order to probe biological samples we transition from a Michelson to a Linnik interferometer by placing a microscope objective in the sample arm. In our setup, while the idler photon is collected with a multi-mode fiber, the signal photon is detected by an ICCD camera, leading to full-field transverse reconstruction through a single axial acquisition sequence. Interestingly, our setup permits concurrent OCT and QOCT trace acquisition, the former with greater counts and the latter with the benefit of quantum-conferred advantages. We hope that our current results will represent a significant step forward towards the actual applicability of QOCT, e.g. in clinical settings.

Differential Privacy (DP) provides a formal privacy guarantee preventing adversaries with access to a machine learning model from extracting information about individual training points. Differentially Private Stochastic Gradient Descent (DP-SGD), the most popular DP training method for deep learning, realizes this protection by injecting noise during training. However previous works have found that DP-SGD often leads to a significant degradation in performance on standard image classification benchmarks. Furthermore, some authors have postulated that DP-SGD inherently performs poorly on large models, since the norm of the noise required to preserve privacy is proportional to the model dimension. In contrast, we demonstrate that DP-SGD on over-parameterized models can perform significantly better than previously thought. Combining careful hyper-parameter tuning with simple techniques to ensure signal propagation and improve the convergence rate, we obtain a new SOTA without extra data on CIFAR-10 of 81.4% under (8, 10^{-5})-DP using a 40-layer Wide-ResNet, improving over the previous SOTA of 71.7%. When fine-tuning a pre-trained NFNet-F3, we achieve a remarkable 83.8% top-1 accuracy on ImageNet under (0.5, 8*10^{-7})-DP. Additionally, we also achieve 86.7% top-1 accuracy under (8, 8 \cdot 10^{-7})-DP, which is just 4.3% below the current non-private SOTA for this task. We believe our results are a significant step towards closing the accuracy gap between private and non-private image classification.

We use time-resolved spectra from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) to examine the distribution of radial velocity (RV) variations in 249 stars identified as members of the Sagittarius (Sgr) dwarf spheroidal (dSph) galaxy by Hayes et al (2020). We select Milky Way (MW) stars that have stellar parameters ($log(g)$, $T_{eff}$, and $[Fe/H]$) similar to those of the Sagittarius members by means of a k-d tree of dimension 3. We find that the shape of the distribution of RV shifts in Sgr dSph stars is similar to that measured in their MW analogs, but the total fraction of RV variable stars in the Sgr dSph is larger by a factor of $\sim 2$. After ruling out other explanations for this difference, we conclude that the fraction of close binaries in the Sgr dSph is intrinsically higher than in the MW. We discuss the implications of this result for the physical processes leading to the formation of close binaries in dwarf spheroidal and spiral galaxies.

Galactic haloes in a $\Lambda$-CDM universe are predicted to host today a swarm of debris resulting from cannibalised dwarf galaxies. The chemo-dynamical information recorded in their stellar populations helps elucidate their nature, constraining the assembly history of the Galaxy. Using data from APOGEE and \textit{Gaia}, we examine the chemical properties of various halo substructures, considering elements that sample various nucleosynthetic pathways. The systems studied are Heracles, \textit{Gaia}-Enceladus/Sausage (GES), the Helmi stream, Sequoia, Thamnos, Aleph, LMS-1, Arjuna, I'itoi, Nyx, Icarus, and Pontus. Abundance patterns of all substructures are cross-compared in a statistically robust fashion. Our main findings include: {\it i)} the chemical properties of most substructures studied match qualitatively those of dwarf Milky Way satellites, such as the Sagittarius dSph. Exceptions are Nyx and Aleph, which are chemically similar to disc stars, implying that these substructures were likely formed \textit{in situ}; {\it ii)} Heracles differs chemically from {\it in situ} populations such as Aurora and its inner halo counterparts in a statistically significant way. The differences suggest that the star formation rate was lower in Heracles than in the early Milky Way; {\it iii)} the chemistry of Arjuna, LMS-1, and I'itoi is indistinguishable from that of GES, suggesting a possible common origin; {\it iv)} all three Sequoia samples studied are qualitatively similar. However, only two of those samples present chemistry that is consistent with GES in a statistically significant fashion; {\it v)} the abundance patterns of the Helmi stream and Thamnos are different from all other halo substructures., Comment: Accepted for publication in MNRAS. The revised version contains a detailed comparison of the chemical composition of Heracles against Aurora and its inner halo in situ counterparts as well as a revised discussion of the chemistry of various Sequoia samples in contrast with Gaia-Enceladus/Sausage

In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning., Comment: 288 pages, 92 figures. We have a publishing contract with Cambridge University Press. Figures and tex files are available at https://github.com/Shmoo137/Lecture-Notes

We examine effects of protein and energy intakes on height and weight growth for children between 6 and 24 months old in Guatemala and the Philippines. Using instrumental variables to control for endogeneity and estimating multiple specifications, we find that protein intake plays an important and positive role in height and weight growth in the 6-24 month period. Energy from other macronutrients, however, does not have a robust relation with these two anthropometric measures. Our estimates indicate that in contexts with substantial child undernutrition, increases in protein-rich food intake in the first 24 months can have important growth effects, which previous studies indicate are related significantly to a range of outcomes over the life cycle.

We present a framework that gives a deep insight into the link between physical and technical-tactical aspects of soccer and it allows associating physical performance with value generation thanks to a top-down approach. First, we estimate physical indicators from tracking data. Then, we contextualize each player's run to understand better the purpose and circumstances in which it is done, adding a new dimension to the creation of team and player profiles. Finally, we assess the value-added by off-ball high-intensity runs by linking with a possession-value model. This novel approach allows answering practical questions from very different profiles of practitioners within a soccer club, from analysts, coaches, and scouts to physical coaches and readaptation physiotherapists., Comment: 17 pages, 16 figures

An important aspect of many particle accelerators is the constant evolution and frequent configuration changes that are needed to perform the experiments they are designed for. This often leads to the design of configurable software that can absorb these changes and perform the required control and protection actions. This design strategy minimizes the engineering and maintenance costs, but it makes the software verification activities more challenging since safety properties must be guaranteed for any of the possible configurations. Software model checking is a popular automated verification technique in many industries. This verification method explores all possible combinations of the system model to guarantee its compliance with certain properties or specification. This is a very appropriate technique for highly configurable software, since there is usually an enormous amount of combinations to be checked. This paper presents how PLCverif, a CERN model checking platform, has been applied to a highly configurable Programmable Logic Controller (PLC) program, the SPS Personnel Protection System (PPS). The benefits and challenges of this verification approach are also discussed., Comment: 18th International Conference on Accelerator and Large Experimental Physics Control Systems (ICALEPCS2021)

Programmable Logic Controllers (PLC) are widely used for industrial automation including safety systems at CERN. The incorrect behaviour of the PLC control system logic can cause significant financial losses by damage of property or the environment or even injuries in some cases, therefore ensuring their correct behaviour is essential. While testing has been for many years the traditional way of validating the PLC control system logic, CERN developed a model checking platform to go one step further and formally verify PLC logic. This platform, called PLCverif, first released internally for CERN usage in 2019, is now available to anyone since September 2020 via an open source licence. In this paper, we will first give an overview of the PLCverif platform capabilities before focusing on the improvements done since 2019 such as the larger support coverage of the Siemens PLC programming languages, the better support of the C Bounded Model Checker backend (CBMC) and the process of releasing PLCverif as an open-source software., Comment: 18th International Conference on Accelerator and Large Experimental Physics Control Systems (ICALEPCS2021)

This paper investigates a distributed formation tracking control law for large-scale networks of mechanical systems. In particular, the formation network is represented by a directed communication graph with leaders and followers, where each agent is described as a port-Hamiltonian system with a constant mass matrix. Moreover, we adopt a distributed parameter approach to prove the scalable asymptotic stability of the network formation, i.e., the scalability with respect to the network size and the specific formation preservation. A simulation case illustrates the effectiveness of the proposed control approach., Comment: 6 pages, 3 figures

After evaporation of the organic solvents, benzene, toluene, and cyclohexane on gold substrates, Scanning Tunneling Microscope (STM) shows the presence of a remaining adsorbed layer. The different solvent molecules were individually observed at ambient conditions, and their electronic transport properties characterized through the STM in the Break Junction approach. The combination of both techniques reveals, on one hand, that solvents are not fully evaporated over the gold electrode and, secondly, determines the role of the electronic transport of the solvents in molecular electronics., Comment: 7 pages, 5 figures

Robots operating in human-centered environments should have the ability to understand how objects function: what can be done with each object, where this interaction may occur, and how the object is used to achieve a goal. To this end, we propose a novel approach that extracts a self-supervised visual affordance model from human teleoperated play data and leverages it to enable efficient policy learning and motion planning. We combine model-based planning with model-free deep reinforcement learning (RL) to learn policies that favor the same object regions favored by people, while requiring minimal robot interactions with the environment. We evaluate our algorithm, Visual Affordance-guided Policy Optimization (VAPO), with both diverse simulation manipulation tasks and real world robot tidy-up experiments to demonstrate the effectiveness of our affordance-guided policies. We find that our policies train 4x faster than the baselines and generalize better to novel objects because our visual affordance model can anticipate their affordance regions., Comment: Accepted at the 2022 IEEE International Conference on Robotics and Automation (ICRA). Videos at http://vapo.cs.uni-freiburg.de/

We report the first 3-D kinematical measurements of 88 stars in the direction of several recently discovered substructures in the southern periphery of the Large Magellanic Cloud (LMC) using a combination of Gaia proper motions and radial velocities from the APOGEE-2 survey. More specifically, we explore stars lie in assorted APOGEE-2 pointings in a region of the LMC periphery where various overdensities of stars have previously been identified in maps of stars from Gaia and DECam. By using a model of the LMC disk rotation, we find that a sizeable fraction of the APOGEE-2 stars have extreme space velocities that are distinct from, and not a simple extension of, the LMC disk. Using N-body hydrodynamical simulations of the past dynamical evolution and interaction of the LMC and Small Magellanic Cloud (SMC), we explore whether the extreme velocity stars may be accounted for as tidal debris created in the course of that interaction. We conclude that the combination of LMC and SMC debris produced from their interaction is a promising explanation, although we cannot rule out other possible origins, and that these new data should be used to constrain future simulations of the LMC-SMC interaction. We also conclude that many of the stars in the southern periphery of the LMC lie out of the LMC plane by several kpc. Given that the metallicity of these stars suggest they are likely of Magellanic origin, our results suggest that a wider exploration of the past interaction history of the Magellanic Clouds is needed.

This manuscript describes several approaches to tune the parameters of a class of passivity-based controllers for standard nonlinear mechanical systems. In particular, we are interested in controllers that preserve the mechanical system structure in closed-loop. To this end, first, we provide tuning rules for stabilization, i.e., the rate of convergence (exponential stability) and stability margin (input-to-state stability). Then, we provide guidelines to remove the overshoot while prescribing the rise time. Additionally, we propose a methodology to tune the gyroscopic-related parameters. We also provide remarks on the damping phenomena to facilitate the practical implementation of our approaches. We conclude this paper with experimental results obtained from applying our tuning rules to an underactuated and a fully-actuated mechanical configuration.

A primary source of increased read time on NAND flash comes from the fact that in the presence of noise, the flash medium must be read several times using different read threshold voltages for the decoder to succeed. This paper proposes an algorithm that uses a limited number of re-reads to characterize the noise distribution and recover the stored information. Both hard and soft decoding are considered. For hard decoding, the paper attempts to find a read threshold minimizing bit-error-rate (BER) and derives an expression for the resulting codeword-error-rate. For soft decoding, it shows that minimizing BER and minimizing codeword-error-rate are competing objectives in the presence of a limited number of allowed re-reads, and proposes a trade-off between the two. The proposed method does not require any prior knowledge about the noise distribution, but can take advantage of such information when it is available. Each read threshold is chosen based on the results of previous reads, following an optimal policy derived through a dynamic programming backward recursion. The method and results are studied from the perspective of an SLC Flash memory with Gaussian noise for each level but the paper explains how the method could be extended to other scenarios.

This paper introduces an efficient method for communication resource use in dense wireless areas where all nodes must communicate with a common destination node. The proposed method groups nodes based on their \newt{distance from the destination} and creates a structured multi-hop configuration in which each group can relay its neighbor's data. \newt{The large number of active radio nodes and the common direction of communication toward a single destination are exploited to reuse the limited spectrum resources in spatially separated groups}. Spectrum allocation constraints among groups are then embedded in a joint routing and resource allocation framework to optimize the route and amount of resources allocated to each node. \newt{The solution to this problem uses coordination among the lower-layers of the wireless-network protocol stack to outperform conventional approaches where these layers are decoupled. Furthermore, the structure of this problem is exploited to obtain} a semi-distributed optimization algorithm based on the alternating direction method of multipliers (ADMM) where each node can optimize its resources independently based on local channel information.