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Neural compression has brought tremendous progress in designing lossy compressors with good rate-distortion (RD) performance at low complexity. Thus far, neural compression design involves transforming the source to a latent vector, which is then rounded to integers and entropy coded. While this approach has been shown to be optimal in a one-shot sense on certain sources, we show that it is highly sub-optimal on i.i.d. sequences, and in fact always recovers scalar quantization of the original source sequence. We demonstrate that the sub-optimality is due to the choice of quantization scheme in the latent space, and not the transform design. By employing lattice quantization instead of scalar quantization in the latent space, we demonstrate that Lattice Transform Coding (LTC) is able to recover optimal vector quantization at various dimensions and approach the asymptotically-achievable rate-distortion function at reasonable complexity. On general vector sources, LTC improves upon standard neural compressors in one-shot coding performance. LTC also enables neural compressors that perform block coding on i.i.d. vector sources, which yields coding gain over optimal one-shot coding.

We address the challenge of sampling and remote estimation for autoregressive Markovian processes in a multi-hop wireless network with statistically-identical agents. Agents cache the most recent samples from others and communicate over wireless collision channels governed by an underlying graph topology. Our goal is to minimize time-average estimation error and/or age of information with decentralized scalable sampling and transmission policies, considering both oblivious (where decision-making is independent of the physical processes) and non-oblivious policies (where decision-making depends on physical processes). We prove that in oblivious policies, minimizing estimation error is equivalent to minimizing the age of information. The complexity of the problem, especially the multi-dimensional action spaces and arbitrary network topologies, makes theoretical methods for finding optimal transmission policies intractable. We optimize the policies using a graphical multi-agent reinforcement learning framework, where each agent employs a permutation-equivariant graph neural network architecture. Theoretically, we prove that our proposed framework exhibits desirable transferability properties, allowing transmission policies trained on small- or moderate-size networks to be executed effectively on large-scale topologies. Numerical experiments demonstrate that (i) Our proposed framework outperforms state-of-the-art baselines; (ii) The trained policies are transferable to larger networks, and their performance gains increase with the number of agents; (iii) The training procedure withstands non-stationarity even if we utilize independent learning techniques; and, (iv) Recurrence is pivotal in both independent learning and centralized training and decentralized execution, and improves the resilience to non-stationarity in independent learning.

The spread of an undesirable contact process, such as an infectious disease (e.g. COVID-19), is contained through testing and isolation of infected nodes. The temporal and spatial evolution of the process (along with containment through isolation) render such detection as fundamentally different from active search detection strategies. In this work, through an active learning approach, we design testing and isolation strategies to contain the spread and minimize the cumulative infections under a given test budget. We prove that the objective can be optimized, with performance guarantees, by greedily selecting the nodes to test. We further design reward-based methodologies that effectively minimize an upper bound on the cumulative infections and are computationally more tractable in large networks. These policies, however, need knowledge about the nodes' infection probabilities which are dynamically changing and have to be learned by sequential testing. We develop a message-passing framework for this purpose and, building on that, show novel tradeoffs between exploitation of knowledge through reward-based heuristics and exploration of the unknown through a carefully designed probabilistic testing. The tradeoffs are fundamentally distinct from the classical counterparts under active search or multi-armed bandit problems (MABs). We provably show the necessity of exploration in a stylized network and show through simulations that exploration can outperform exploitation in various synthetic and real-data networks depending on the parameters of the network and the spread.

We discuss a federated learned compression problem, where the goal is to learn a compressor from real-world data which is scattered across clients and may be statistically heterogeneous, yet share a common underlying representation. We propose a distributed source model that encompasses both characteristics, and naturally suggests a compressor architecture that uses analysis and synthesis transforms shared by clients. Inspired by personalized federated learning methods, we employ an entropy model that is personalized to each client. This allows for a global latent space to be learned across clients, and personalized entropy models that adapt to the clients' latent distributions. We show empirically that this strategy outperforms solely local methods, which indicates that learned compression also benefits from a shared global representation in statistically heterogeneous federated settings., Comment: ISIT 2023

Recent advances in text-to-image generative models provide the ability to generate high-quality images from short text descriptions. These foundation models, when pre-trained on billion-scale datasets, are effective for various downstream tasks with little or no further training. A natural question to ask is how such models may be adapted for image compression. We investigate several techniques in which the pre-trained models can be directly used to implement compression schemes targeting novel low rate regimes. We show how text descriptions can be used in conjunction with side information to generate high-fidelity reconstructions that preserve both semantics and spatial structure of the original. We demonstrate that at very low bit-rates, our method can significantly improve upon learned compressors in terms of perceptual and semantic fidelity, despite no end-to-end training., Comment: ICML 2023 Neural Compression Workshop

We discuss a relationship between rate-distortion and optimal transport (OT) theory, even though they seem to be unrelated at first glance. In particular, we show that a function defined via an extremal entropic OT distance is equivalent to the rate-distortion function. We numerically verify this result as well as previous results that connect the Monge and Kantorovich problems to optimal scalar quantization. Thus, we unify solving scalar quantization and rate-distortion functions in an alternative fashion by using their respective optimal transport solvers., Comment: Published as a Tiny Paper at ICLR 2023; invited to present

A diamond network is considered in which the central processor is connected, via backhaul noiseless links, to multiple conferencing base stations, which communicate with a single user over a multiple access channel. We propose coding techniques along with lower and upper bounds on the capacity. Our achievability scheme uses a common cloud coding strategy based on the technique proposed by Wand, Wigger, and Zaidi (2018) and extends it beyond two relays. Our upper bounds generalize the method proposed by Bidokhti and Kramer for the two relay diamond network without cooperation (2016) and lead to new bounds for the multiple relay setting. Specializing our upper bounds for the two relay scenario (with cooperation), we provide new bounds and improve state-of-the-art., Comment: Complementary proofs for the ISIT2022 conference paper

A fundamental question in designing lossy data compression schemes is how well one can do in comparison with the rate-distortion function, which describes the known theoretical limits of lossy compression. Motivated by the empirical success of deep neural network (DNN) compressors on large, real-world data, we investigate methods to estimate the rate-distortion function on such data, which would allow comparison of DNN compressors with optimality. While one could use the empirical distribution of the data and apply the Blahut-Arimoto algorithm, this approach presents several computational challenges and inaccuracies when the datasets are large and high-dimensional, such as the case of modern image datasets. Instead, we re-formulate the rate-distortion objective, and solve the resulting functional optimization problem using neural networks. We apply the resulting rate-distortion estimator, called NERD, on popular image datasets, and provide evidence that NERD can accurately estimate the rate-distortion function. Using our estimate, we show that the rate-distortion achievable by DNN compressors are within several bits of the rate-distortion function for real-world datasets. Additionally, NERD provides access to the rate-distortion achieving channel, as well as samples from its output marginal. Therefore, using recent results in reverse channel coding, we describe how NERD can be used to construct an operational one-shot lossy compression scheme with guarantees on the achievable rate and distortion. Experimental results demonstrate competitive performance with DNN compressors.

In applications of group testing in networks, e.g. identifying individuals who are infected by a disease spread over a network, exploiting correlation among network nodes provides fundamental opportunities in reducing the number of tests needed. We model and analyze group testing on $n$ correlated nodes whose interactions are specified by a graph $G$. We model correlation through an edge-faulty random graph formed from $G$ in which each edge is dropped with probability $1-r$, and all nodes in the same component have the same state. We consider three classes of graphs: cycles and trees, $d$-regular graphs and stochastic block models or SBM, and obtain lower and upper bounds on the number of tests needed to identify the defective nodes. Our results are expressed in terms of the number of tests needed when the nodes are independent and they are in terms of $n$, $r$, and the target error. In particular, we quantify the fundamental improvements that exploiting correlation offers by the ratio between the total number of nodes $n$ and the equivalent number of independent nodes in a classic group testing algorithm. The lower bounds are derived by illustrating a strong dependence of the number of tests needed on the expected number of components. In this regard, we establish a new approximation for the distribution of component sizes in "$d$-regular trees" which may be of independent interest and leads to a lower bound on the expected number of components in $d$-regular graphs. The upper bounds are found by forming dense subgraphs in which nodes are more likely to be in the same state. When $G$ is a cycle or tree, we show an improvement by a factor of $log(1/r)$. For grid, a graph with almost $2n$ edges, the improvement is by a factor of ${(1-r) \log(1/r)}$, indicating drastic improvement compared to trees. When $G$ has a larger number of edges, as in SBM, the improvement can scale in $n$.

Graph neural networks (GNNs) have recently been demonstrated to perform well on a variety of network-based tasks such as decentralized control and resource allocation, and provide computationally efficient methods for these tasks which have traditionally been challenging in that regard. However, like many neural-network based systems, GNNs are susceptible to shifts and perturbations on their inputs, which can include both node attributes and graph structure. In order to make them more useful for real-world applications, it is important to ensure their robustness post-deployment. Motivated by controlling the Lipschitz constant of GNN filters with respect to the node attributes, we propose to constrain the frequency response of the GNN's filter banks. We extend this formulation to the dynamic graph setting using a continuous frequency response constraint, and solve a relaxed variant of the problem via the scenario approach. This allows for the use of the same computationally efficient algorithm on sampled constraints, which provides PAC-style guarantees on the stability of the GNN using results in scenario optimization. We also highlight an important connection between this setup and GNN stability to graph perturbations, and provide experimental results which demonstrate the efficacy and broadness of our approach.

In recent years, deep neural network (DNN) compression systems have proved to be highly effective for designing source codes for many natural sources. However, like many other machine learning systems, these compressors suffer from vulnerabilities to distribution shifts as well as out-of-distribution (OOD) data, which reduces their real-world applications. In this paper, we initiate the study of OOD robust compression. Considering robustness to two types of ambiguity sets (Wasserstein balls and group shifts), we propose algorithmic and architectural frameworks built on two principled methods: one that trains DNN compressors using distributionally-robust optimization (DRO), and the other which uses a structured latent code. Our results demonstrate that both methods enforce robustness compared to a standard DNN compressor, and that using a structured code can be superior to the DRO compressor. We observe tradeoffs between robustness and distortion and corroborate these findings theoretically for a specific class of sources., Comment: Initially published at ICML-2021 ITR3 Workshop

The interplay between timeliness and rate efficiency is investigated in packet erasure broadcast channels with feedback. A scheduling framework is proposed in which coding actions, as opposed to users, are scheduled to attain desired tradeoffs between rate and age of information (AoI). This tradeoff is formalized by an upper bound on AoI as a function of the target rate constraints and two lower bounds: one as a function of the communication rate and one as a function of the arrival rate. Simulation results show that (i) coding can be beneficial in reducing AoI in the regime of moderate arrival rates even without rate constraints and the benefit increases with the number of users, and (ii) AoI increases with both the target rate constraint and the arrival rate when either is kept fixed, but decreases with them when they are set to be equal.

Efficient sampling and remote estimation are critical for a plethora of wireless-empowered applications in the Internet of Things and cyber-physical systems. Motivated by such applications, this work proposes decentralized policies for the real-time monitoring and estimation of autoregressive processes over random access channels. Two classes of policies are investigated: (i) oblivious schemes in which sampling and transmission policies are independent of the processes that are monitored, and (ii) non-oblivious schemes in which transmitters causally observe their corresponding processes for decision making. In the class of oblivious policies, we show that minimizing the expected time-average estimation error is equivalent to minimizing the expected age of information. Consequently, we prove lower and upper bounds on the minimum achievable estimation error in this class. Next, we consider non-oblivious policies and design a threshold policy, called error-based thinning, in which each source node becomes active if its instantaneous error has crossed a fixed threshold (which we optimize). Active nodes then transmit stochastically following a slotted ALOHA policy. A closed-form, approximately optimal, solution is found for the threshold as well as the resulting estimation error. It is shown that non-oblivious policies offer a multiplicative gain close to $3$ compared to oblivious policies. Moreover, it is shown that oblivious policies that use the age of information for decision making improve the state-of-the-art at least by the multiplicative factor $2$. The performance of all discussed policies is compared using simulations. The numerical comparison shows that the performance of the proposed decentralized policy is very close to that of centralized greedy scheduling.

A full understanding of the immune response to astrovirus (AstV) infection is required to treat and control AstV-induced gastroenteritis. Relative contributions of each arm of the immune system in restricting AstV infection remain unknown. In this study, two novel subunit AstV vaccines derived from capsid protein (CP) of mink AstV (MAstV) such as CPΔN (spanning amino acids 161-775) and CPΔC (spanning amino acids 1-621) were evaluated. Their immunogenicity and cytokine production in mice, as well as protective efficacy in mink litters via maternal immunization, were studied. Truncated CPs induced higher levels of serum anti-CP antibodies than CP, with the highest level for CPΔN. No seronegativity was detected after booster immunization with either AstV CP truncates in both mice and mink. All mink moms stayed seropositive during the entire 104-day study. Furthermore, lymphoproliferation responses and Th1/Th2 cytokine induction of mice splenocytes ex vivo re-stimulated by truncated CPs were significantly higher than those by CP, with the highest level for CPΔN. Immunization of mink moms with truncated CPs could suppress virus shedding and clinical signs in their litters during a 51-day study after challenge with a heterogeneous MAstV strain. Collectively, AstV truncated CPs exhibit better parameters for protection than full-length CP.

In applications of remote sensing, estimation, and control, timely communication is not always ensured by high-rate communication. This work proposes distributed age-efficient transmission policies for random access channels with $M$ transmitters. In the first part of this work, we analyze the age performance of stationary randomized policies by relating the problem of finding age to the absorption time of a related Markov chain. In the second part of this work, we propose the notion of \emph{age-gain} of a packet to quantify how much the packet will reduce the instantaneous age of information at the receiver side upon successful delivery. We then utilize this notion to propose a transmission policy in which transmitters act in a distributed manner based on the age-gain of their available packets. In particular, each transmitter sends its latest packet only if its corresponding age-gain is beyond a certain threshold which could be computed adaptively using the collision feedback or found as a fixed value analytically in advance. Both methods improve age of information significantly compared to the state of the art. In the limit of large $M$, we prove that when the arrival rate is small (below $\frac{1}{eM}$), slotted ALOHA-type algorithms are asymptotically optimal. As the arrival rate increases beyond $\frac{1}{eM}$, while age increases under slotted ALOHA, it decreases significantly under the proposed age-based policies. For arrival rates $\theta$, $\theta=\frac{1}{o(M)}$, the proposed algorithms provide a multiplicative factor of at least two compared to the minimum age under slotted ALOHA (minimum over all arrival rates). We conclude that, as opposed to the common practice, it is beneficial to increase the sampling rate (and hence the arrival rate) and transmit packets selectively based on their age-gain.

Many research areas in software engineering, such as mutation testing, automatic repair, fault localization, and fault injection, rely on empirical knowledge about recurring bug-fixing code changes. Previous studies in this field focus on what has been changed due to bug-fixes, such as in terms of code edit actions. However, such studies did not consider where the bug-fix change was made (i.e., the context of the change), but knowing about the context can potentially narrow the search space for many software engineering techniques (e.g., by focusing mutation only on specific parts of the software). Furthermore, most previous work on bug-fixing changes focused on C and Java projects, but there is little empirical evidence about Python software. Therefore, in this paper we perform a thorough empirical analysis of bug-fixing changes in three OpenStack projects, focusing on both the what and the where of the changes. We observed that all the recurring change patterns are not oblivious with respect to the surrounding code, but tend to occur in specific code contexts., Comment: 14 pages, The 30th International Symposium on Software Reliability Engineering (ISSRE 2019)

In order to plan for failure recovery, the designers of cloud systems need to understand how their system can potentially fail. Unfortunately, analyzing the failure behavior of such systems can be very difficult and time-consuming, due to the large volume of events, non-determinism, and reuse of third-party components. To address these issues, we propose a novel approach that joins fault injection with anomaly detection to identify the symptoms of failures. We evaluated the proposed approach in the context of the OpenStack cloud computing platform. We show that our model can significantly improve the accuracy of failure analysis in terms of false positives and negatives, with a low computational cost., Comment: 12 pages, The 30th International Symposium on Software Reliability Engineering (ISSRE 2019)

Cloud management systems provide abstractions and APIs for programmatically configuring cloud infrastructures. Unfortunately, residual software bugs in these systems can potentially lead to high-severity failures, such as prolonged outages and data losses. In this paper, we investigate the impact of failures in the context widespread OpenStack cloud management system, by performing fault injection and by analyzing the impact of the resulting failures in terms of fail-stop behavior, failure detection through logging, and failure propagation across components. The analysis points out that most of the failures are not timely detected and notified; moreover, many of these failures can silently propagate over time and through components of the cloud management system, which call for more thorough run-time checks and fault containment., Comment: 12 pages, ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE '19)

Age of Information (AoI) is studied in two-user broadcast networks with feedback, and lower and upper bounds are derived on the expected weighted sum AoI of the users. In particular, a class of simple coding actions is considered and within this class, randomized and deterministic policies are devised. Explicit conditions are found for symmetric dependent channels under which coded randomized policies strictly outperform the corresponding uncoded policies. Similar behavior is numerically shown for deterministic policies.

Background: Research on the biogenesis of tick-borne encephalitis virus (TBEV) would benefit gene therapy. Due to specific arrangements of genes along the TBEV genome, its viral-like particles (VLPs) could be exploited as shuttles to deliver their replicon, which carries therapeutic genes, to immune system cells. Objective: To develop a flaviviral vector for gene delivery as a part of gene therapy research that can be expressed in secretable VLP suicidal shuttles and provide abundant unique molecular and structural data supporting this gene therapy concept. Method: TBEV structural gene constructs of a Swedish Torö strain were cloned into plasmids driven by the promoters CAG and CMV and then transfected into various cell lines, including COS-1 and BHK-21. Time-course sampling of the cells, culture fluid, cell lysate supernatant, and pellet specimens were performed. Western blotting and electron microscopy analyses of collected specimens were used to investigate molecular and structural processing of TBEV structural proteins. Results: Western blotting analysis showed differences between promoters in directing the gene expression of the VLPs constructs. The premature flaviviral polypeptides as well as mature VLPs could be traced. Using electron microscopy, the premature and mature VLP accumulation in cellular compartments—and also endoplasmic reticulum proliferation as a virus factory platform—were observed in addition to secreted VLPs. Conclusions: The abundant virologic and cellular findings in this study show the natural processing and safety of inserting flaviviral structural genes into suicidal VLP shuttles. Thus, we propose that these VLPs are a suitable gene delivering system model in gene therapy.

Background: Research on the biogenesis of tick-borne encephalitis virus (TBEV) would benefit gene therapy. Due to specific arrangements of genes along the TBEV genome, its viral-like particles (VLPs) could be exploited as shuttles to deliver their replicon, which carries therapeutic genes, to immune system cells. Objective: To develop a flaviviral vector for gene delivery as a part of gene therapy research that can be expressed in secretable VLP suicidal shuttles and provide abundant unique molecular and structural data supporting this gene therapy concept. Method: TBEV structural gene constructs of a Swedish Torö strain were cloned into plasmids driven by the promoters CAG and CMV and then transfected into various cell lines, including COS-1 and BHK-21. Time-course sampling of the cells, culture fluid, cell lysate supernatant, and pellet specimens were performed. Western blotting and electron microscopy analyses of collected specimens were used to investigate molecular and structural processing of TBEV structural proteins. Results: Western blotting analysis showed differences between promoters in directing the gene expression of the VLPs constructs. The premature flaviviral polypeptides as well as mature VLPs could be traced. Using electron microscopy, the premature and mature VLP accumulation in cellular compartments—and also endoplasmic reticulum proliferation as a virus factory platform—were observed in addition to secreted VLPs. Conclusions: The abundant virologic and cellular findings in this study show the natural processing and safety of inserting flaviviral structural genes into suicidal VLP shuttles. Thus, we propose that these VLPs are a suitable gene delivering system model in gene therapy.

Background: Research on the biogenesis of tick-borne encephalitis virus (TBEV) would benefit gene therapy. Due to specific arrangements of genes along the TBEV genome, its viral-like particles (VLPs) could be exploited as shuttles to deliver their replicon, which carries therapeutic genes, to immune system cells. Objective: To develop a flaviviral vector for gene delivery as a part of gene therapy research that can be expressed in secretable VLP suicidal shuttles and provide abundant unique molecular and structural data supporting this gene therapy concept. Method: TBEV structural gene constructs of a Swedish Torö strain were cloned into plasmids driven by the promoters CAG and CMV and then transfected into various cell lines, including COS-1 and BHK-21. Time-course sampling of the cells, culture fluid, cell lysate supernatant, and pellet specimens were performed. Western blotting and electron microscopy analyses of collected specimens were used to investigate molecular and structural processing of TBEV structural proteins. Results: Western blotting analysis showed differences between promoters in directing the gene expression of the VLPs constructs. The premature flaviviral polypeptides as well as mature VLPs could be traced. Using electron microscopy, the premature and mature VLP accumulation in cellular compartments—and also endoplasmic reticulum proliferation as a virus factory platform—were observed in addition to secreted VLPs. Conclusions: The abundant virologic and cellular findings in this study show the natural processing and safety of inserting flaviviral structural genes into suicidal VLP shuttles. Thus, we propose that these VLPs are a suitable gene delivering system model in gene therapy.

Background: Research on the biogenesis of tick-borne encephalitis virus (TBEV) would benefit gene therapy. Due to specific arrangements of genes along the TBEV genome, its viral-like particles (VLPs) could be exploited as shuttles to deliver their replicon, which carries therapeutic genes, to immune system cells. Objective: To develop a flaviviral vector for gene delivery as a part of gene therapy research that can be expressed in secretable VLP suicidal shuttles and provide abundant unique molecular and structural data supporting this gene therapy concept. Method: TBEV structural gene constructs of a Swedish Torö strain were cloned into plasmids driven by the promoters CAG and CMV and then transfected into various cell lines, including COS-1 and BHK-21. Time-course sampling of the cells, culture fluid, cell lysate supernatant, and pellet specimens were performed. Western blotting and electron microscopy analyses of collected specimens were used to investigate molecular and structural processing of TBEV structural proteins. Results: Western blotting analysis showed differences between promoters in directing the gene expression of the VLPs constructs. The premature flaviviral polypeptides as well as mature VLPs could be traced. Using electron microscopy, the premature and mature VLP accumulation in cellular compartments—and also endoplasmic reticulum proliferation as a virus factory platform—were observed in addition to secreted VLPs. Conclusions: The abundant virologic and cellular findings in this study show the natural processing and safety of inserting flaviviral structural genes into suicidal VLP shuttles. Thus, we propose that these VLPs are a suitable gene delivering system model in gene therapy.

Background: Research on the biogenesis of tick-borne encephalitis virus (TBEV) would benefit gene therapy. Due to specific arrangements of genes along the TBEV genome, its viral-like particles (VLPs) could be exploited as shuttles to deliver their replicon, which carries therapeutic genes, to immune system cells. Objective: To develop a flaviviral vector for gene delivery as a part of gene therapy research that can be expressed in secretable VLP suicidal shuttles and provide abundant unique molecular and structural data supporting this gene therapy concept. Method: TBEV structural gene constructs of a Swedish Torö strain were cloned into plasmids driven by the promoters CAG and CMV and then transfected into various cell lines, including COS-1 and BHK-21. Time-course sampling of the cells, culture fluid, cell lysate supernatant, and pellet specimens were performed. Western blotting and electron microscopy analyses of collected specimens were used to investigate molecular and structural processing of TBEV structural proteins. Results: Western blotting analysis showed differences between promoters in directing the gene expression of the VLPs constructs. The premature flaviviral polypeptides as well as mature VLPs could be traced. Using electron microscopy, the premature and mature VLP accumulation in cellular compartments—and also endoplasmic reticulum proliferation as a virus factory platform—were observed in addition to secreted VLPs. Conclusions: The abundant virologic and cellular findings in this study show the natural processing and safety of inserting flaviviral structural genes into suicidal VLP shuttles. Thus, we propose that these VLPs are a suitable gene delivering system model in gene therapy.

Coded-caching delivery is considered over a symmetric noisy broadcast channel whose state is unknown at the transmitter during the cache placement phase. In particular, the delivery phase is modeled by a state-dependent broadcast channel where the state remains constant over each transmission block and is learned by the transmitter (and the receivers) only at the beginning of each block. A state-adaptive coded caching scheme is proposed that improves either on rate or decoding latency over two baseline schemes that are based on standard coded caching., Comment: The paper has appeared at the 52nd Annual Asilomar Conference on Signals, Systems, and Computers

Degraded K-user broadcast channels (BC) are studied when receivers are facilitated with cache memories. Lower and upper bounds are derived on the capacity-memory tradeoff, i.e., on the largest rate of reliable communication over the BC as a function of the receivers' cache sizes, and the bounds are shown to match for some special cases. The lower bounds are achieved by two new coding schemes that benefit from non-uniform cache assignment. Lower and upper bounds are also established on the global capacity-memory tradeoff, i.e., on the largest capacity-memory tradeoff that can be attained by optimizing the receivers' cache sizes subject to a total cache memory budget. The bounds coincide when the total cache memory budget is sufficiently small or sufficiently large, characterized in terms of the BC statistics. For small cache memories, it is optimal to assign all the cache memory to the weakest receiver. In this regime, the global capacity-memory tradeoff grows as the total cache memory budget divided by the number of files in the system. In other words, a perfect global caching gain is achievable in this regime and the performance corresponds to a system where all cache contents in the network are available to all receivers. For large cache memories, it is optimal to assign a positive cache memory to every receiver such that the weaker receivers are assigned larger cache memories compared to the stronger receivers. In this regime, the growth rate of the global capacity-memory tradeoff is further divided by the number of users, which corresponds to a local caching gain. Numerical indicate suggest that a uniform cache-assignment of the total cache memory is suboptimal in all regimes unless the BC is completely symmetric. For erasure BCs, this claim is proved analytically in the regime of small cache-sizes., Comment: Submitted to IEEE Transactions on Information Theory

Improved lower bounds on the average and the worst-case rate-memory tradeoffs for the Maddah-Ali&Niesen coded caching scenario are presented. For any number of users and files and for arbitrary cache sizes, the multiplicative gap between the exact rate-memory tradeoff and the new lower bound is less than 2.315 in the worst-case scenario and less than 2.507 in the average-case scenario.

The downlink of symmetric Cloud Radio Access Networks (C-RANs) with multiple relays and a single receiver is studied. Lower and upper bounds are derived on the capacity. The lower bound is achieved by Marton's coding which facilitates dependence among the multiple-access channel inputs. The upper bound uses Ozarow's technique to augment the system with an auxiliary random variable. The bounds are studied over scalar Gaussian C-RANs and are shown to meet and characterize the capacity for interesting regimes of operation.

We study noisy broadcast networks with local cache memories at the receivers, where the transmitter can pre-store information even before learning the receivers' requests. We mostly focus on packet-erasure broadcast networks with two disjoint sets of receivers: a set of weak receivers with all-equal erasure probabilities and equal cache sizes and a set of strong receivers with all-equal erasure probabilities and no cache memories. We present lower and upper bounds on the capacity-memory tradeoff of this network. The lower bound is achieved by a new joint cache-channel coding idea and significantly improves on schemes that are based on separate cache-channel coding. We discuss how this coding idea could be extended to more general discrete memoryless broadcast channels and to unequal cache sizes. Our upper bound holds for all stochastically degraded broadcast channels. For the described packet-erasure broadcast network, our lower and upper bounds are tight when there is a single weak receiver (and any number of strong receivers) and the cache memory size does not exceed a given threshold. When there are a single weak receiver, a single strong receiver, and two files, then we can strengthen our upper and lower bounds so as they coincide over a wide regime of cache sizes. Finally, we completely characterise the rate-memory tradeoff for general discrete-memoryless broadcast channels with arbitrary cache memory sizes and arbitrary (asymmetric) rates when all receivers always demand exactly the same file.

The two-receiver broadcast packet erasure channel with feedback and memory is studied. Memory is modeled using a finite-state Markov chain representing a channel state. Two scenarios are considered: (i) when the transmitter has causal knowledge of the channel state (i.e., the state is visible), and (ii) when the channel state is unknown at the transmitter, but observations of it are available at the transmitter through feedback (i.e., the state is hidden). In both scenarios, matching outer and inner bounds on the rates of communication are derived and the capacity region is determined. It is shown that similar results carry over to channels with memory and delayed feedback and memoryless compound channels with feedback. When the state is visible, the capacity region has a single-letter characterization and is in terms of a linear program. Two optimal coding schemes are devised that use feedback to keep track of the sent/received packets via a network of queues: a probabilistic scheme and a deterministic backpressure-like algorithm. The former bases its decisions solely on the past channel state information and the latter follows a max-weight queue-based policy. The performance of the algorithms are analyzed using the frameworks of rate stability in networks of queues, max-flow min-cut duality in networks, and finite-horizon Lyapunov drift analysis. When the state is hidden, the capacity region does not have a single-letter characterization and is, in this sense, uncomputable. Approximations of the capacity region are provided and two optimal coding algorithms are outlined. The first algorithm is a probabilistic coding scheme that bases its decisions on the past L acknowledgments and its achievable rate region approaches the capacity region exponentially fast in L. The second algorithm is a backpressure-like algorithm that performs optimally in the long run., Comment: revised version

This paper takes a rate-distortion approach to understanding the information-theoretic laws governing cache-aided communications systems. Specifically, we characterise the optimal tradeoffs between the delivery rate, cache capacity and reconstruction distortions for a single-user problem and some special cases of a two-user problem. Our analysis considers discrete memoryless sources, expected- and excess-distortion constraints, and separable and f-separable distortion functions. We also establish a strong converse for separable-distortion functions, and we show that lossy versions of common information (G\'{a}cs-K\"{o}rner and Wyner) play an important role in caching. Finally, we illustrate and explicitly evaluate these laws for multivariate Gaussian sources and binary symmetric sources.

A class of diamond networks is studied where the broadcast component is orthogonal and modeled by two independent bit-pipes. New upper and lower bounds on the capacity are derived. The proof technique for the upper bound generalizes bounding techniques of Ozarow for the Gaussian multiple description problem (1981) and Kang and Liu for the Gaussian diamond network (2011). The lower bound is based on Marton's coding technique and superposition coding. The bounds are evaluated for Gaussian and binary adder multiple access channels (MACs). For Gaussian MACs, both the lower and upper bounds strengthen the Kang-Liu bounds and establish capacity for interesting ranges of bit-pipe capacities. For binary adder MACs, the capacity is established for all ranges of bit-pipe capacities.

The two-receiver broadcast packet erasure channel with feedback and memory is studied. Memory is modeled using a finite-state Markov chain representing a channel state. The channel state is unknown at the transmitter, but observations of this hidden Markov chain are available at the transmitter through feedback. Matching outer and inner bounds are derived and the capacity region is determined. The capacity region does not have a single-letter characterization and is, in this sense, uncomputable. Approximations of the capacity region are provided and two optimal coding algorithms are outlined. The first algorithm is a probabilistic coding scheme that bases its decisions on the past L feedback sequences. Its achievable rate-region approaches the capacity region exponentially fast in L. The second algorithm is a backpressure-like algorithm that performs optimally in the long run., Comment: extended version of ISIT paper

The problem of multicasting two nested messages is studied over a class of networks known as combination networks. A source multicasts two messages, a common and a private message, to several receivers. A subset of the receivers (called the public receivers) only demand the common message and the rest of the receivers (called the private receivers) demand both the common and the private message. Three encoding schemes are discussed that employ linear superposition coding and their optimality is proved in special cases. The standard linear superposition scheme is shown to be optimal for networks with two public receivers and any number of private receivers. When the number of public receivers increases, this scheme stops being optimal. Two improvements are discussed: one using pre-encoding at the source, and one using a block Markov encoding scheme. The rate-regions that are achieved by the two schemes are characterized in terms of feasibility problems. Both inner-bounds are shown to be the capacity region for networks with three (or fewer) public and any number of private receivers. Although the inner bounds are not comparable in general, it is shown through an example that the region achieved by the block Markov encoding scheme may strictly include the region achieved by the pre-encoding/linear superposition scheme. Optimality results are founded on the general framework of Balister and Bollob\'as (2012) for sub-modularity of the entropy function. An equivalent graphical representation is introduced and a lemma is proved that might be of independent interest. Motivated by the connections between combination networks and broadcast channels, a new block Markov encoding scheme is proposed for broadcast channels with two nested messages. The rate-region that is obtained includes the previously known rate-regions. It remains open whether this inclusion is strict.

The two-receiver broadcast packet erasure channel with feedback and memory is studied. Memory is modeled using a finite-state Markov chain representing a channel state. Outer and inner bounds on the capacity region are derived when the channel state is strictly causally known at the transmitter. The bounds are both formulated in terms of feasibility problems and they are matching in all but one of the constraints. The results are extended to feedback with larger delay. Numerical results show that the bounds are close in many examples and the gains offered through feedback can be quite large. The presented outer bound meets the inner bound recently derived in \cite{Kuo_Wang2014} and hence describes the capacity region., Comment: Corrected version, extended version of work presented at Allerton '14

A class of diamond networks are studied where the broadcast component is modelled by two independent bit-pipes. New upper and low bounds are derived on the capacity which improve previous bounds. The upper bound is in the form of a max-min problem, where the maximization is over a coding distribution and the minimization is over an auxiliary channel. The proof technique generalizes bounding techniques of Ozarow for the Gaussian multiple description problem (1981), and Kang and Liu for the Gaussian diamond network (2011). The bounds are evaluated for a Gaussian multiple access channel (MAC) and the binary adder MAC, and the capacity is found for interesting ranges of the bit-pipe capacities.

Coronaviruses demonstrate great potential for interspecies transmission, including zoonotic outbreaks. Although bovine coronavirus (BCoV) strains are frequently circulating in cattle farms worldwide, causing both enteric and respiratory disease, little is known about their genomic evolution. We sequenced and analysed the full-length spike (S) protein gene of 33 BCoV strains from dairy and feedlot farms collected during outbreaks that occurred from 2002 to 2010 in Sweden and Denmark. Amino acid identities were.97% for the BCoV strains analysed in this work. These strains formed a clade together with Italian BCoV strains and were highly similar to human enteric coronavirus HECV-4408/US/94. A high similarity was observed between BCoV, canine respiratory coronavirus (CRCoV) and human coronavirus OC43 (HCoV-OC43). Molecular clock analysis of the S gene sequences estimated BCoV and CRCoV diverged from a common ancestor in 1951, while the time of divergence from a common ancestor of BCoV and HCoVOC43 was estimated to be 1899. BCoV strains showed the lowest similarity to equine coronavirus, placing the date of divergence at the end of the eighteenth century. Two strongly positive selection sites were detected along the receptor-binding subunit of the S protein gene: spanning amino acid residues 109-131 and 495-527. By contrast, the fusion subunit was observed to be under negative selection. The selection pattern along the S glycoprotein implies adaptive evolution of BCoVs, suggesting a successful mechanism for BCoV to continuously circulate among cattle and other ruminants without disappearance.

Astroviruses are becoming a growing concern in veterinary and public health. To date there are no registered vaccines against astrovirus-induced disease, mostly due to the difficulty to cultivate astroviruses to high titer for vaccine development using conventional techniques. As means to circumvent this drawback, we have developed stably transfected mink fetal cells and BHK21 cells constitutively expressing the full-length and truncated capsid proteins of two distinct genotypes of mink astrovirus. Protein expression in these stably transfected cells was demonstrated by strong signals as evaluated by in-situ PLA and IFA, and confirmed by Western blotting. The recombinant full-length and truncated proteins induced a high level of antibodies in mink, evaluated by ELISA, demonstrating their immunogenicity. In a challenge experiment in mink, a reduction in presentation clinical signs and virus shedding was observed in mink kits born from immunized females. The gene integration and protein expression were sustained through cell passage, showing that the used approach is robust and reliable for expression of functional capsid proteins for vaccine and diagnostic applications.

Astroviruses are becoming a growing concern in veterinary and public health. To date there are no registered vaccines against astrovirus-induced disease, mostly due to the difficulty to cultivate astroviruses to high titer for vaccine development using conventional techniques. As means to circumvent this drawback, we have developed stably transfected mink fetal cells and BHK21 cells constitutively expressing the full-length and truncated capsid proteins of two distinct genotypes of mink astrovirus. Protein expression in these stably transfected cells was demonstrated by strong signals as evaluated by in-situ PLA and IFA, and confirmed by Western blotting. The recombinant full-length and truncated proteins induced a high level of antibodies in mink, evaluated by ELISA, demonstrating their immunogenicity. In a challenge experiment in mink, a reduction in presentation clinical signs and virus shedding was observed in mink kits born from immunized females. The gene integration and protein expression were sustained through cell passage, showing that the used approach is robust and reliable for expression of functional capsid proteins for vaccine and diagnostic applications.

Coronaviruses demonstrate great potential for interspecies transmission, including zoonotic outbreaks. Although bovine coronavirus (BCoV) strains are frequently circulating in cattle farms worldwide, causing both enteric and respiratory disease, little is known about their genomic evolution. We sequenced and analysed the full-length spike (S) protein gene of 33 BCoV strains from dairy and feedlot farms collected during outbreaks that occurred from 2002 to 2010 in Sweden and Denmark. Amino acid identities were.97% for the BCoV strains analysed in this work. These strains formed a clade together with Italian BCoV strains and were highly similar to human enteric coronavirus HECV-4408/US/94. A high similarity was observed between BCoV, canine respiratory coronavirus (CRCoV) and human coronavirus OC43 (HCoV-OC43). Molecular clock analysis of the S gene sequences estimated BCoV and CRCoV diverged from a common ancestor in 1951, while the time of divergence from a common ancestor of BCoV and HCoVOC43 was estimated to be 1899. BCoV strains showed the lowest similarity to equine coronavirus, placing the date of divergence at the end of the eighteenth century. Two strongly positive selection sites were detected along the receptor-binding subunit of the S protein gene: spanning amino acid residues 109-131 and 495-527. By contrast, the fusion subunit was observed to be under negative selection. The selection pattern along the S glycoprotein implies adaptive evolution of BCoVs, suggesting a successful mechanism for BCoV to continuously circulate among cattle and other ruminants without disappearance.

In multi-terminal communication systems, signals carrying messages meant for different destinations are often observed together at any given destination receiver. Han and Kobayashi (1981) proposed a receiving strategy which performs a joint unique decoding of messages of interest along with a subset of messages which are not of interest. It is now well-known that this provides an achievable region which is, in general, larger than if the receiver treats all messages not of interest as noise. Nair and El Gamal (2009) and Chong, Motani, Garg, and El Gamal (2008) independently proposed a generalization called indirect or non-unique decoding where the receiver uses the codebook structure of the messages to uniquely decode only its messages of interest. Non-unique decoding has since been used in various scenarios. The main result in this paper is to provide an interpretation and a systematic proof technique for why non-unique decoding, in all known cases where it has been employed, can be replaced by a particularly designed joint unique decoding strategy, without any penalty from a rate region viewpoint.

Astroviruses are becoming a growing concern in veterinary and public health. To date there are no registered vaccines against astrovirus-induced disease, mostly due to the difficulty to cultivate astroviruses to high titer for vaccine development using conventional techniques. As means to circumvent this drawback, we have developed stably transfected mink fetal cells and BHK21 cells constitutively expressing the full-length and truncated capsid proteins of two distinct genotypes of mink astrovirus. Protein expression in these stably transfected cells was demonstrated by strong signals as evaluated by in-situ PLA and IFA, and confirmed by Western blotting. The recombinant full-length and truncated proteins induced a high level of antibodies in mink, evaluated by ELISA, demonstrating their immunogenicity. In a challenge experiment in mink, a reduction in presentation clinical signs and virus shedding was observed in mink kits born from immunized females. The gene integration and protein expression were sustained through cell passage, showing that the used approach is robust and reliable for expression of functional capsid proteins for vaccine and diagnostic applications.

Bovine coronavirus (BCV) and bovine respiratory syncytial virus (BRSV) infections are spread in cattle herds worldwide. When introduced in to a susceptible herd, both infections cause respiratory disease. BCV is also associated with diarrhea in both calves and adult cattle. The purpose of this study was to investigate the seroprevalence of BCV and BRSV in dairy cattle herds in an area of Sweden. Specific goals were to determine the immunity against these infections in both conventional and organic dairy herds during two sampling occasions, and if the prevalence of these infections in conventional herds differ from organic herds. The influence of different risk factors for seropositivity was also investigated. Around 700 serum samples, taken from 20 conventional and 20 organic dairy herds in south eastern Sweden on two sampling occasions with one year interval, were tested by ELISA for presence of antibodies to BCV and BRSV. On individual level, the seroprevalence at both occasions varied between 82-86% to BCV and 79-82% BRSV. Analyzing the data on herdlevel revealed that the conventional herds had a significantly higher mean seroprevalence to BCV and BRSV than the organic (P< 0.001). A significant association was found between age and seroprevalence (P< 0.001). Cows younger than 5 years old in conventional herds had significantly higher mean seroprevalence than the organic (P< 0.001). Only 30% of the youngest cows in organic herds compared to 70% in conventional herds were positive to both BCV and BRSV which increased up to 100% in the oldest cows. The mean absorbance value in positive cows older than 5 years was significantly higher compared with those younger than 5 years (P<0.001). Higher mean titres of antibody in the oldest cows most likely make their enriched colostrum a valuable tool in herds with neonatal infection problems. This study suggests that organic management may be associated with a lower incidence of BCV and BRSV infections compared with conventio

The prevalence of antibodies to bovine coronavirus (BCV) and bovine respiratory syncytial virus (BRSV) infections was studied in 20 conventional and 20 organic dairy herds The organic farms had produced 'certified' milk for at least 2 years On two occasions, with a 1-year interval, 699 serum samples from 624 peri-parturient cows were tested by ELISA for antibodies to BCV and BRSV. Accompanying data relating to the sampled animals were collected in order to identify potential factors associated with increased antibody prevalence. The antibody prevalence was high at both sampling times with approximately 85% and 80% of animals positive for antibodies to BCV and to BRSV, respectively Conventional herds had a significantly higher mean antibody prevalence to BCV and BRSV than the organically managed herds (P < 0.01) Animal age was significantly associated with increased antibody prevalence (P < 0 001). The findings of this study suggest that organic farm management may be effective in reducing the seroprevalence of these viruses relative to conventional Farming methods

The prevalence of antibodies to bovine coronavirus (BCV) and bovine respiratory syncytial virus (BRSV) infections was studied in 20 conventional and 20 organic dairy herds The organic farms had produced 'certified' milk for at least 2 years On two occasions, with a 1-year interval, 699 serum samples from 624 peri-parturient cows were tested by ELISA for antibodies to BCV and BRSV. Accompanying data relating to the sampled animals were collected in order to identify potential factors associated with increased antibody prevalence. The antibody prevalence was high at both sampling times with approximately 85% and 80% of animals positive for antibodies to BCV and to BRSV, respectively Conventional herds had a significantly higher mean antibody prevalence to BCV and BRSV than the organically managed herds (P < 0.01) Animal age was significantly associated with increased antibody prevalence (P < 0 001). The findings of this study suggest that organic farm management may be effective in reducing the seroprevalence of these viruses relative to conventional Farming methods

The prevalence of antibodies to bovine coronavirus (BCV) and bovine respiratory syncytial virus (BRSV) infections was studied in 20 conventional and 20 organic dairy herds The organic farms had produced 'certified' milk for at least 2 years On two occasions, with a 1-year interval, 699 serum samples from 624 peri-parturient cows were tested by ELISA for antibodies to BCV and BRSV. Accompanying data relating to the sampled animals were collected in order to identify potential factors associated with increased antibody prevalence. The antibody prevalence was high at both sampling times with approximately 85% and 80% of animals positive for antibodies to BCV and to BRSV, respectively Conventional herds had a significantly higher mean antibody prevalence to BCV and BRSV than the organically managed herds (P < 0.01) Animal age was significantly associated with increased antibody prevalence (P < 0 001). The findings of this study suggest that organic farm management may be effective in reducing the seroprevalence of these viruses relative to conventional Farming methods