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We study information-theoretic security for discrete memoryless interference and broadcast channels with independent confidential messages sent to two receivers. Confidential messages are transmitted to their respective receivers with informationtheoretic secrecy. That is, each receiver is kept in total ignorance with respect to the message intended for the other receiver. The secrecy level is measured by the equivocation rate at the eavesdropping receiver. In this paper, we present inner and outer bounds on secrecy capacity regions for these two communication systems. The derived outer bounds have an identical mutual information expression that applies to both channel models. The difference is in the input distributions over which the expression is optimized. The inner bound rate regions are achieved by random binning techniques. For the broadcast channel, a doublebinning coding scheme allows for both joint encoding and preserving of confidentiality. Furthermore, we show that, for a special case of the interference channel, referred to as the switch channel, the two bound bounds meet. Finally, we describe several transmission schemes for Gaussian interference channels and derive their achievable rate regions while ensuring mutual information-theoretic secrecy. An encoding scheme in which transmitters dedicate some of their power to create artificial noise is proposed and shown to outperform both time-sharing and simple multiplexed transmission of the confidential messages.

Abstract-Capacity regions are established for several twosender, two-receiver channels with partial transmitter cooperation. First, the capacity regions are determined for compound multipleaccess channels (MACs) with common information and compound MACs with conferencing. Next, two interference channel models are considered: an interference channel with common information (ICCI) and an interference channel with unidirectional cooperation (ICUC) in which the message sent by one of the encoders is known to the other encoder. The capacity regions of both of these channels are determined when there is strong interference, i.e., the interference is such that both receivers can decode all messages with no rate penalty. The resulting capacity regions coincide with the capacity region of the compound MAC with common information.

This article reviews progress in cooperative communication networks. Our survey is by no means exhaustive. Instead, we assemble a representative sample of recent results to serve as a roadmap for the area. Our emphasis is on wireless networks, but many of the results apply to cooperation in wireline networks and mixed wireless/wireline networks. We intend our presentation to be a tutorial for the reader who is familiar with information theory concepts but has not actively followed the field. For the active researcher, this contribution should serve as a useful digest of significant results. This article is meant to encourage readers to find new ways to apply the ideas of network cooperation and should make the area sufficiently accessible to network designers to contribute to the advancement of networking practice. Overview IntroductionThe classic representation of a communication network is a graph, as in Figure 1.1, with a set of nodes and edges. The nodes usually represent devices such as a router, a wireless access point, or a mobile telephone. The edges usually represent communication links or channels, for example a fiber-optic cable or a wireless link. Both the devices and the channels may have constraints on their operation. For example, a router might have limited processing power, or perhaps it can accept data from only a few of its ports simultaneously. A fiber-optic link has a limited bandwidth (which can be quite large!). A wireless phone, on the other hand, has limited battery resources and likely wishes to conserve energy. A wireless link can have rapid time variations arising from mobility and multipath propagation of signals. Some of these properties are collected in Table 1.1 and are described in more detail in this text.The purpose of a communication network is to enable the exchange of messages between its nodes. These messages, as generated by an application, are organized into data packets. In the traditional model of a network, the nodes operate as store-and-forward packet routers that transmit packets over point-to-point links. However, this model is unnecessarily restrictive as it ignores two important possibilities:• Node Coding: Nodes can combine, or encode, any of their received information and symbol streams.• Broadcasting: Nodes overhear the transmissions of other nodes from which they are not required to receive messages.Node coding is possible in any network, while the ability to overhear transmissions is a property of the physical communication channel. In particular, wireless devices inherently broadcast information in that a signal to a particular receiving node can be overheard by other nodes. Typically, the wireless nodes treat these overheard signals as interference and the system provides mechanisms to mitigate this interference. For example, many second generation cellular phones employ code division multiple access (CDMA) to permit signal decoding in 274 Overview the presence of interference [185]. As a second example, 802.11× wireless LANs employ a media access...

Abstract-Inner and outer bounds are established on the capacity region of two-sender, two-receiver interference channels where one transmitter knows both messages. The transmitter with extra knowledge is referred to as being cognitive. The inner bound is based on strategies that generalize prior work, and include rate-splitting, Gel'fand-Pinsker coding and cooperative transmission. A general outer bound is based on the Nair-El Gamal outer bound for broadcast channels. A simpler bound is presented for the case in which one of the decoders can decode both messages. The bounds are evaluated and compared for Gaussian channels.

Motivation: Multiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia. Results: We performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified. Availability and implementation: Datasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.

IMPORTANCE Antidepressant use may be associated with reduced levels of several proinflammatory cytokines suggested to be involved with the development of severe An association between the use of selective serotonin reuptake inhibitors (SSRIs)-specifically fluoxetine hydrochloride and fluvoxamine maleate-with decreased mortality among patients with COVID-19 has been reported in recent studies; however, these studies had limited power due to their small size. OBJECTIVETo investigate the association of SSRIs with outcomes in patients with COVID-19 by analyzing electronic health records (EHRs). DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study used propensity score matching by demographic characteristics, comorbidities, and medication indication to compare SSRItreated patients with matched control patients not treated with SSRIs within a large EHR database representing a diverse population of 83 584 patients diagnosed with COVID-19 from January to September 2020 and with a duration of follow-up of as long as 8 months in 87 health care centers across the US. EXPOSURES Selective serotonin reuptake inhibitors and specifically (1) fluoxetine, (2) fluoxetine or fluvoxamine, and (3) other SSRIs (ie, not fluoxetine or fluvoxamine). MAIN OUTCOMES AND MEASURES Death. RESULTS A total of 3401 adult patients with COVID-19 prescribed SSRIs (2033 women [59.8%]; mean [SD] age, 63.8 [18.1] years) were identified, with 470 receiving fluoxetine only (280 women [59.6%]; mean [SD] age, 58.5 [18.1] years), 481 receiving fluoxetine or fluvoxamine (285 women [59.3%]; mean [SD] age, 58.7 [18.0] years), and 2898 receiving other SSRIs (1733 women [59.8%]; mean [SD] age, 64.7 [18.0] years) within a defined time frame. When compared with matched untreated control patients, relative risk (RR) of mortality was reduced among patients prescribed any SSRI (497 of 3401 [14.6%] vs 1130 of 6802 [16.6%]; RR, 0.92 [95% CI, 0.85-0.99]; adjusted P = .03);

Abstract-We address the minimum-energy broadcast problem under the assumption that nodes beyond the nominal range of a transmitter can collect the energy of unreliably received overheard signals. As a message is forwarded through the network, a node will have multiple opportunities to reliably receive the message by collecting energy during each retransmission. We refer to this cooperative strategy as accumulative broadcast. We seek to employ accumulative broadcast in a large scale loosely synchronized, low-power network. Therefore, we focus on distributed network layer approaches for accumulative broadcast in which loosely synchronized nodes use only local information. To further simplify the system architecture, we assume that nodes forward only reliably decoded messages.Under these assumptions, we formulate the minimum-energy accumulative broadcast problem. We present a solution employing two subproblems. First, we identify the ordering in which nodes should transmit. Second, we determine the optimum power levels for that ordering. While the second subproblem can be solved by means of linear programming, the ordering subproblem is found to be NP-complete. We devise a heuristic algorithm to find a good ordering. Simulation results show the performance of the algorithm to be close to optimum and a significant improvement over the well known BIP algorithm for constructing energy-efficient broadcast trees. We then formulate a distributed version of the acumulative broadcast algorithm that uses only local information at the nodes and has performance close to its centralized counterpart.Index Terms-Minimum-energy broadcast, reliable forwarding, wideband regime, distributed algorithm.