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This papers consider a two terminal problem, where Alice and Bob jointly want to perform a measurement on a bipartite quantum system \(\rho^{AB}\). Alice can transmit the results of her measurements to Bob on a classical channel, and Alice and Bob have common randomness. The question is what is the minimum amount of communications and common randomness needed. The paper derives an achievable rate region.

Charge transport in materials has an impact on a wide range of devices based on semiconductor, battery or superconductor technology. Charge transport in sliding Charge Density Waves (CDW) differs from all others in that the atomic lattice is directly involved in the transport process. To obtain an overall picture of the structural changes associated to the collective transport, the large coherent X-ray beam generated by an X-ray free-electron laser (XFEL) source was used. The CDW phase can be retrieved over the entire sample from diffracted intensities using a genetic algorithm. For currents below threshold, increasing shear deformation is observed in the central part of the sample while longitudinal deformation appears above threshold when shear relaxes. Shear thus precedes longitudinal deformation, with relaxation of one leading to the appearance of the other. Moreover, strain accumulates on surface steps in the sliding regime, demonstrating the strong pinning character of these surface discontinuities. The sliding process of nanometric CDW is based on an impressive spatial coherence involving the macroscopic sample dimensions., Comment: 20 pages, 5 figures

Understanding protein motion within the cell is crucial for predicting reaction rates and macromolecular transport in the cytoplasm. A key question is how crowded environments affect protein dynamics through hydrodynamic and direct interactions at molecular length scales. Using megahertz X-ray Photon Correlation Spectroscopy (MHz-XPCS) at the European X-ray Free Electron Laser (EuXFEL), we investigate ferritin diffusion at microsecond time scales. Our results reveal anomalous diffusion, indicated by the non-exponential decay of the intensity autocorrelation function $g_2(q,t)$ at high concentrations. This behavior is consistent with the presence of cage-trapping in between the short- and long-time protein diffusion regimes. Modeling with the $\delta\gamma$-theory of hydrodynamically interacting colloidal spheres successfully reproduces the experimental data by including a scaling factor linked to the protein direct interactions. These findings offer new insights into the complex molecular motion in crowded protein solutions, with potential applications for optimizing ferritin-based drug delivery, where protein diffusion is the rate-limiting step.

Multi-access edge computing (MEC) is seen as a vital component of forthcoming 6G wireless networks, aiming to support emerging applications that demand high service reliability and low latency. However, ensuring the ultra-reliable and low-latency performance of MEC networks poses a significant challenge due to uncertainties associated with wireless links, constraints imposed by communication and computing resources, and the dynamic nature of network traffic. Enabling ultra-reliable and low-latency MEC mandates efficient load balancing jointly with resource allocation. In this paper, we investigate the joint optimization problem of offloading decisions, computation and communication resource allocation to minimize the expected weighted sum of delivery latency and energy consumption in a non-orthogonal multiple access (NOMA)-assisted MEC network. Given the formulated problem is a mixed-integer non-linear programming (MINLP), a new multi-agent federated deep reinforcement learning (FDRL) solution based on double deep Q-network (DDQN) is developed to efficiently optimize the offloading strategies across the MEC network while accelerating the learning process of the Internet-of-Thing (IoT) devices. Simulation results show that the proposed FDRL scheme can effectively reduce the weighted sum of delivery latency and energy consumption of IoT devices in the MEC network and outperform the baseline approaches.

The ability to understand and ultimately control the transformations and properties of various nanoscale systems, from proteins to synthetic nanomaterial assemblies, hinges on the ability to uncover their dynamics on their characteristic length and time scales. Here, we use MHz X-ray photon correlation spectroscopy (XPCS) to directly elucidate the characteristic microsecond-dynamics of density fluctuations of semiconductor nanocrystals (NCs), not only in a colloidal dispersion but also in a liquid phase consisting of densely packed, yet mobile, NCs with no long-range order. We find the wavevector-dependent fluctuation rates in the liquid phase are suppressed relative to those in the colloidal phase and relative to observations of densely packed repulsive particles. We show that the suppressed rates are due to a substantial decrease in the self-diffusion of NCs in the liquid phase, which we attribute to explicit attractive interactions. Using coarse-grained simulations, we find that the extracted shape and strength of the interparticle potential explains the stability of the liquid phase, in contrast to the gelation observed via XPCS in many other charged colloidal systems. This work opens the door to elucidating fast, condensed phase dynamics in complex fluids and other nanoscale soft matter, such as densely packed proteins and non-equilibrium self-assembly processes, in addition to designing microscopic strategies to avert gelation., Comment: 7 pages, 4 figures

Given a default distribution $P$ and a set of test data $x^M=\{x_1,x_2,\ldots,x_M\}$ this paper seeks to answer the question if it was likely that $x^M$ was generated by $P$. For discrete distributions, the definitive answer is in principle given by Kolmogorov-Martin-L\"{o}f randomness. In this paper we seek to generalize this to continuous distributions. We consider a set of statistics $T_1(x^M),T_2(x^M),\ldots$. To each statistic we associate its maximum entropy distribution and with this a universal source coder. The maximum entropy distributions are subsequently combined to give a total codelength, which is compared with $-\log P(x^M)$. We show that this approach satisfied a number of theoretical properties. For real world data $P$ usually is unknown. We transform data into a standard distribution in the latent space using a bidirectional generate network and use maximum entropy coding there. We compare the resulting method to other methods that also used generative neural networks to detect anomalies. In most cases, our results show better performance.

Immediate settlement, or single-slot finality (SSF), is a long-term goal for Ethereum. The growing active validator set size is placing an increasing computational burden on the network, making SSF more challenging. EIP-7251 aims to reduce the number of validators by giving stakers the option to merge existing validators. Key to the success of this proposal therefore is whether stakers choose to merge their validators once EIP-7251 is implemented. It is natural to assume stakers participate only if they anticipate greater expected utility (risk-adjusted returns) as a single large validator. In this paper, we focus on one of the duties that a validator performs, viz. being the proposer for the next block. This duty can be quite lucrative, but happens infrequently. Based on previous analysis, we may assume that EIP-7251 implies no change to the security of the protocol. We confirm that the probability of a validator being selected as block proposer is equivalent under each consolidation regime. This result ensures that the decision of one staker to merge has no impact on the opportunity of another to propose the next block, in turn ensuring there is no major systemic change to the economics of the protocol with respect to proposer selection., Comment: 15 pages

Dark field X-ray microscopy (DXFM) can visualize microstructural distortions in bulk crystals. Using the femtosecond X-ray pulses generated by X-ray free-electron lasers (XFEL), DFXM can achieve sub-{\mu}m spatial resolution and <100 fs time resolution simultaneously. In this paper, we demonstrate ultrafast DFXM measurements at the European XFEL to visualize an optically-driven longitudinal strain wave propagating through a diamond single crystal. We also present two DFXM scanning modalities that are new to the XFEL sources: spatially 3D and 2D axial-strain scans with sub-{\mu}m spatial resolution. With this progress in XFEL-based DFXM, we discuss new opportunities to study multi-timescale spatio-temporal dynamics of microstructures.

An unexpected dichotomic long time aging behaviour is observed in a glassy colloidal clay suspension investigated by X-Ray Photon Correlation Spectroscopy and Dynamic Light Scattering. In the long time aging regime the intensity autocorrelations are non-exponential, following the Kohlrausch-Williams-Watts functional form with exponent bQ. We show that for spontaneously aged samples a stretched behaviour (\beta_Q < 1) is always found. Surprisingly a compressed exponent (\beta_Q > 1) appears only when the system is rejuvenated by application of a shear field. In both cases the relaxation times scale as Q^{-1}. These observations shed light on the origin of compressed exponential behaviour and helps in classifying previous results in the literature on anomalous dynamics., Comment: 5 pages, 5 figures

Function is dynamic and originates at atomic interfaces. Combining the degrees of freedom of molecules with the peculiar properties of 2D quantum materials can create novel functionality. Here, we report the manipulation and ultrafast imaging of a unidirectional gearing motion in molecules on a 2D quantum material. To visualize and disentangle the intertwined structural and electronic dynamics of such a hybrid interface, we record a 'full molecular movie' by imaging the atomic positions, the evolution of the molecular orbital wavefunctions and the modification of electronic states of the substrate. In a multimodal investigation in a single setup, we disentangle dynamics in valence and core electrons of both the molecule and the surface with femtosecond and sub-{\aa}ngstr\"om precision. The ultrafast rotational motion is fueled by the transfer of hot holes into the molecules that results in 'supercharging' of the film. As hot carriers move through the interface, we track a transient modification of the frontier molecular orbitals and observe a chiral symmetry breaking associated with local structural rearrangements. Our calculations show that the 'supercharging' changes the interfacial potential energy landscape and triggers the gearing motion. The experiment offers all-in-one imaging of the electronic, molecular orbital, chemical and structural dynamics during the flow of charge and energy across the hybrid interface. Our approach provides detailed dynamical information on the mechanism underlying surface-adsorbed molecular gears and enables tailoring novel functionalities in hybrid active matter., Comment: 19 pages, 6 figures

Compress-forward (CF) relays can improve communication rates even when the relay cannot decode the source signal. Efficient implementation of CF is a topic of contemporary interest, in part because of its potential impact on wireless technologies such as cloud-RAN. There exists a gap between the performance of CF implementations in the high spectral efficiency regime and the corresponding information theoretic achievable rates. We begin by re-framing a dilemma causing this gap, and propose an approach for its mitigation. We utilize trellis coded quantization (TCQ) at the relay together with multi-level coding at the source and relay, in a manner that facilitates the calculation of bit LLRs at the destination for joint decoding. The contributions of this work include designing TCQ for end-to-end relay performance, since a distortion-minimizing TCQ is suboptimum. The reported improvements include a 1dB gain over prior results for PSK modulation.

We consider the following problem: we have a large dataset of normal data available. We are now given a new, possibly quite small, set of data, and we are to decide if these are normal data, or if they are indicating a new phenomenon. This is a novelty detection or out-of-distribution detection problem. An example is in medicine, where the normal data is for people with no known disease, and the new dataset people with symptoms. Other examples could be in security. We solve this problem by training a bidirectional generative adversarial network (BiGAN) on the normal data and using a Gaussian graphical model to model the output. We then use universal source coding, or minimum description length (MDL) on the output to decide if it is a new distribution, in an implementation of Kolmogorov and Martin-L\"{o}f randomness. We apply the methodology to both MNIST data and a real-world electrocardiogram (ECG) dataset of healthy and patients with Kawasaki disease, and show better performance in terms of the ROC curve than similar methods.

The concept of 'Stateless Ethereum' was conceived with the primary aim of mitigating Ethereum's unbounded state growth. The key facilitator of Stateless Ethereum is through the introduction of 'witnesses' into the ecosystem. The changes and potential consequences that these additional data packets pose on the network need to be identified and analysed to ensure that the Ethereum ecosystem can continue operating securely and efficiently. In this paper we propose a Bayesian Network model, a probabilistic graphical modelling approach, to capture the key factors and their interactions in Ethereum mainnet, the public Ethereum blockchain, focussing on the changes being introduced by Stateless Ethereum to estimate the health of the resulting Ethereum ecosystem. We use a mixture of empirical data and expert knowledge, where data are unavailable, to quantify the model. Based on the data and expert knowledge available to use at the time of modelling, the Ethereum ecosystem is expected to remain healthy following the introduction of Stateless Ethereum., Comment: In Proceedings MARS 2022, arXiv:2203.09299

Multilayer Laue lenses were used for the first time to focus x-rays from an X-ray Free Electron Laser (XFEL). In an experiment, which was performed at the European XFEL, we demonstrated focusing to a spot size of a few tens of nanometers. A series of runs in which the number of pulses per train was increased from 1 to 2, 3, 4, 5, 6, 7, 10, 20 and 30 pulses per train, all with a pulse separation of 3.55 us, was done using the same set of lenses. The increase in the number of pulses per train was accompanied with an increase of x-ray intensity (transmission) from 9% to 92% at 5 pulses per train, and then the transmission was reduced to 23.5 % when the pulses were increased further. The final working condition was 30 pulses per train and 23.5% transmission. Only at this condition we saw that the diffraction efficiency of the MLLs changed over the course of a pulse train, and this variation was reproducible from train to train. We present the procedure to align and characterize these lenses and discuss challenges working with the pulse trains from this unique x-ray source.

X-ray free-electron lasers (XFELs) with megahertz repetition rate can provide novel insights into structural dynamics of biological macromolecule solutions. However, very high dose rates can lead to beam-induced dynamics and structural changes due to radiation damage. Here, we probe the dynamics of dense antibody protein (Ig-PEG) solutions using megahertz X-ray photon correlation spectroscopy (MHz-XPCS) at the European XFEL. By varying the total dose and dose rate, we identify a regime for measuring the motion of proteins in their first coordination shell, quantify XFEL-induced effects such as driven motion, and map out the extent of agglomeration dynamics. The results indicate that for average dose rates below $1.06\,\mathrm{kGy}\mathrm{\mu s}^{-1}$ in a time window up to $10\,\mathrm{\mu s}$, it is possible to capture the protein dynamics before the onset of beam induced aggregation. We refer to this approach as correlation before aggregation and demonstrate that MHz-XPCS bridges an important spatio-temporal gap in measurement techniques for biological samples., Comment: 22 pages, 6 figures

Many multivariate data such as social and biological data exhibit complex dependencies that are best characterized by graphs. Unlike sequential data, graphs are, in general, unordered structures. This means we can no longer use classic, sequential-based compression methods on these graph-based data. Therefore, it is necessary to develop new methods for graph compression. In this paper, we present universal source coding methods for the lossless compression of unweighted, undirected, unlabelled graphs. We encode in two steps: 1) transforming graph into a rooted binary tree, 2) the encoding rooted binary tree using graph statistics. Our coders showed better compression performance than other source coding methods on both synthetic and real-world graphs. We then applied our graph coding methods for model selection of Gaussian graphical models using minimum description length (MDL) principle finding the description length of the conditional independence graph. Experiments on synthetic data show that our approach gives better performance compared to common model selection methods. We also applied our approach to electrocardiogram (ECG) data in order to explore the differences between graph models of two groups of subjects., Comment: Submitted to IEEE Transactions on Signal Processing

We demonstrate the successful application of X-ray Speckle Visibility Spectroscopy (XSVS) experiments to study the dynamics of radiation sensitive, biological samples with unprecedentedly small X-ray doses of 45 Gy and below. Using XSVS, we track the dynamics of casein micelles in native, concentrated, and acidified solution conditions, while substantially reducing the deposited dose as compared to alternative techniques like sequential X-ray photon correlation spectroscopy (XPCS). The Brownian motion in a skim milk sample yields the hydrodynamic radius of the casein micelles while deviations from Brownian motion with a characteristic $q$-dependent diffusion coefficient $D(q)$ can be observed in more concentrated solution conditions. The low dose applied in our experiments allows the observation of static, frozen speckle patterns from gelled acidic milk. We show that the XSVS technique is especially suitable for tracking dynamics of radiation sensitive samples in combination with the improved coherent properties of new generation X-ray sources, emphasizing the great potential for further investigations of protein dynamics using fourth generation synchrotrons and free electron lasers.

A classic application of description length is for model selection with the minimum description length (MDL) principle. The focus of this paper is to extend description length for data analysis beyond simple model selection and sequences of scalars. More specifically, we extend the description length for data analysis in Gaussian graphical models. These are powerful tools to model interactions among variables in a sequence of i.i.d Gaussian data in the form of a graph. Our method uses universal graph coding methods to accurately account for model complexity, and therefore provide a more rigorous approach for graph model selection. The developed method is tested with synthetic and electrocardiogram (ECG) data to find the graph model and anomaly in Gaussian graphical models. The experiments show that our method gives better performance compared to commonly used methods., Comment: Submitted to ISIT 2021

This paper asks a basic question: how much training is required to beat a universal source coder? Traditionally, there have been two types of source coders: fixed, optimum coders such as Huffman coders; and universal source coders, such as Lempel-Ziv The paper considers a third type of source coders: learned coders. These are coders that are trained on data of a particular type, and then used to encode new data of that type. This is a type of coder that has recently become very popular for (lossy) image and video coding. The paper consider two criteria for performance of learned coders: the average performance over training data, and a guaranteed performance over all training except for some error probability $P_e$. In both cases the coders are evaluated with respect to redundancy. The paper considers the IID binary case and binary Markov chains. In both cases it is shown that the amount of training data required is very moderate: to code sequences of length $l$ the amount of training data required to beat a universal source coder is $m=K\frac{l}{\log l}$, where the constant in front depends the case considered., Comment: Submitted to IEEE Transactions on Information Theory

This study explores the possibility to measure dynamics of proteins in solution using X-ray photon correlation spectroscopy (XPCS) at nearly diffraction limited storage rings (DLSR). We calculate the signal to noise ratio (SNR) of XPCS experiments from a concentrated lysozyme solution at the length scale of the hydrodynamic radius of the protein molecule. We take limitations given by the critical X-ray dose into account and find expressions for the SNR as a function of beam size, sample-detector distance and photon energy. Specifically, we show that the combined increase in coherent flux and coherence lengths at the DLSR PETRA IV will yield an increase in SNR of more than one order of magnitude. The resulting SNR values indicate that XPCS experiments of biological macromolecules on nm length scales will become feasible with the advent of a new generation of synchrotron sources. Our findings provide valuable input for the design and construction of future XPCS beamlines at DLSRs.

An X-ray free-electron laser oscillator (XFELO) is a new type of hard X-ray source that would produce fully coherent pulses with meV bandwidth and stable intensity. The XFELO complements existing sources based on self-amplified spontaneous emission (SASE) from high-gain X-ray free-electron lasers (XFEL) that produce ultra-short pulses with broad-band chaotic spectra. This report is based on discussions of scientific opportunities enabled by an XFELO during a workshop held at SLAC on June 29 - July 1, 2016, Comment: 21 pages, 12 figures

This paper introduces a new method for model selection and more generally hyperparameter selection in machine learning. Minimum description length (MDL) is an established method for model selection, which is however not directly aimed at minimizing generalization error, which is often the primary goal in machine learning. The paper demonstrates a relationship between generalization error and a difference of description lengths of the training data; we call this difference differential description length (DDL). This allows prediction of generalization error from the training data alone by performing encoding of the training data. DDL can then be used for model selection by choosing the model with the smallest predicted generalization error. We show how this method can be used for linear regression and neural networks and deep learning. Experimental results show that DDL leads to smaller generalization error than cross-validation and traditional MDL and Bayes methods., Comment: Submitted to NeurIPS 2019

This paper has dual aims. First is to develop practical universal coding methods for unlabeled graphs. Second is to use these for graph anomaly detection. The paper develops two coding methods for unlabeled graphs: one based on the degree distribution, the second based on the triangle distribution. It is shown that these are efficient for different types of random graphs, and on real-world graphs. These coding methods is then used for detecting anomalous graphs, based on structure alone. It is shown that anomalous graphs can be detected with high probability., Comment: To be presented at ISIT'18

Heart rate variability (HRV) is a vital measure of the autonomic nervous system functionality and a key indicator of cardiovascular condition. This paper proposes a novel method, called pattern tree which is an extension of Willem's context tree to real-valued data, to investigate HRV via an atypicality framework. In a previous paper atypicality was developed as method for mining and discovery in "Big Data," which requires a universal approach. Using the proposed pattern tree as a universal source coder in this framework led to discovery of arrhythmias and unknown patterns in HRV Holter Monitoring., Comment: 5 pages

X-ray radiation damage provides a serious bottle neck for investigating {\mu}s to s dynamics on nanometer length scales employing X-ray photon correlation spectroscopy. This limitation hinders the investigation of real time dynamics in most soft matter and biological materials which can tolerate only X-ray doses of kGy and below. Here, we show that this bottleneck can be overcome by low dose X-ray speckle visibility spectroscopy. Employing X-ray doses of 22 kGy to 438 kGy and analyzing the sparse speckle pattern of count rates as low as 6.7x10-3 per pixel we follow the slow nanoscale dynamics of an ionic liquid (IL) at the glass transition. At the pre-peak of nanoscale order in the IL we observe complex dynamics upon approaching the glass transition temperature TG with a freezing in of the alpha relaxation and a multitude of milli-second local relaxations existing well below TG. We identify this fast relaxation as being responsible for the increasing development of nanoscale order observed in ILs at temperatures below TG., Comment: 7 pages, 5 figures

A central question in the era of 'big data' is what to do with the enormous amount of information. One possibility is to characterize it through statistics, e.g., averages, or classify it using machine learning, in order to understand the general structure of the overall data. The perspective in this paper is the opposite, namely that most of the value in the information in some applications is in the parts that deviate from the average, that are unusual, atypical. We define what we mean by 'atypical' in an axiomatic way as data that can be encoded with fewer bits in itself rather than using the code for the typical data. We show that this definition has good theoretical properties. We then develop an implementation based on universal source coding, and apply this to a number of real world data sets., Comment: 40 pages

The aim of atypicality is to extract small, rare, unusual and interesting pieces out of big data. This complements statistics about typical data to give insight into data. In order to find such "interesting" parts of data, universal approaches are required, since it is not known in advance what we are looking for. We therefore base the atypicality criterion on codelength. In a prior paper we developed the methodology for discrete-valued data, and the the current paper extends this to real-valued data. This is done by using minimum description length (MDL). We show that this shares a number of theoretical properties with the discrete-valued case. We develop the methodology for a number of "universal" signal processing models, and finally apply them to recorded hydrophone data., Comment: 13 pages, two columns

Making inferences from data streams is a pervasive problem in many modern data analysis applications. But it requires to address the problem of continuous model updating and adapt to changes or drifts in the underlying data generating distribution. In this paper, we approach these problems from a Bayesian perspective covering general conjugate exponential models. Our proposal makes use of non-conjugate hierarchical priors to explicitly model temporal changes of the model parameters. We also derive a novel variational inference scheme which overcomes the use of non-conjugate priors while maintaining the computational efficiency of variational methods over conjugate models. The approach is validated on three real data sets over three latent variable models., Comment: ICML 2017

In multiple descriptions on distributed storage, a source is stored in a shared fashion on multiple servers. When a subset of servers are contacted, the source should be estimated with a certain maximum distortion depending on the number of servers. The problem considered in this paper is how to restore the system operation when one of the servers fail and a new server replaces it, that is, repair. The requirement is that the distortions in the restored system should be no more than in the original system. The question is how many extra bits are needed for repair. We find the optimum solution for a two server problem in the Gaussian case, and an achievable rate for general $n$ nodes. One conclusion is that it is necessary to design the multiple description codes with repair in mind; just using an existing multiple description code results in unnecessary high repair rates., Comment: Preliminary journal version of ISIT'18 submission. Includes formal proofs

The AMIDST Toolbox is a software for scalable probabilistic machine learning with a spe- cial focus on (massive) streaming data. The toolbox supports a flexible modeling language based on probabilistic graphical models with latent variables and temporal dependencies. The specified models can be learnt from large data sets using parallel or distributed implementa- tions of Bayesian learning algorithms for either streaming or batch data. These algorithms are based on a flexible variational message passing scheme, which supports discrete and continu- ous variables from a wide range of probability distributions. AMIDST also leverages existing functionality and algorithms by interfacing to software tools such as Flink, Spark, MOA, Weka, R and HUGIN. AMIDST is an open source toolbox written in Java and available at http://www.amidsttoolbox.com under the Apache Software License version 2.0.