AT&T

Traumatic injury imparts a physical and emotional response not only to the patients but also to the providers caring for this select population. The traumatic injuries to patients who require hospitalization are most often discussed, but there are a significant number of patients who experience bullet-related injuries that are seen at a trauma center but who do not have injuries that require intervention or hospitalization. Often, these patients are discharged and potentially followed up in the outpatient setting. There are a myriad of physical and emotional consequences to bullet-related injury that may go untreated or undertreated. Dr. LJ Punch discusses the experience transitioning from a surgeon who cuts to a surgeon who does not, and the move from Baltimore to St. Louis to create Power4STL to serve those at high risk for trauma-related physical and emotional challenges. Through focused efforts and multidisciplinary approaches, Dr. Punch and team effectively address patients’ bullet-related injury and their trauma.

In this study, manganese–nitrogen sites were incorporated into biochar (BC) to activate peroxymonosulfate (PMS) for the degradation of sulfamethoxazole (SMX). Characterization techniques, including scanning electron microscopy and others, confirmed the successful doping of Manganese–Nitrogen (Mn–N) sites into the BC (referred to as MnN@BC). The study revealed that the integration of Mn–N active sites in BC modified the electronic polarization and facilitated electron transfer. It is worth noting that a remarkable synergistic effect (SI = 6.92) was witnessed in the MnN@BC/PMS system. Under optimal conditions, SMX was nearly completely eliminated within 40 minutes. Radical scavenging experiments indicated that Hydroxyl Radical (•OH), Sulfate Radicals (SO4•−), superoxide radicals (O2•−), and singlet oxygen (¹O2) all played significant roles in the degradation of SMX. Density functional theory calculations were employed to further investigate the mechanism of enhanced electron transfer of PMS facilitated by the loading of BC on the Mn–N site. Cyclic experiments and characterizations conducted before and after recycling demonstrated that MnN@BC exhibited remarkable stability and reusability. This study probed into the mechanism of PMS activation by transition metal and non-metal dual active sites and offered strategies for more effective and sustainable degradation of pollutants.

In order to create a methodology for assessing the company’s main financial indicators, taking into account both business and financial risks, the CAPM and Fama-French models were included in the two main theories of capital structure - the Brusov-Filatova-Orekhova (BFO) theory and the Modigliani-Miller (MM) theory. CAPM takes into account systematic (business) risk, while capital structure theories take into account the financial risk of a specific company, associated with debt financing. As a result, generalized approaches (CAPM-BFO and CAPM–MM) were developed that take into account both types of risk: systematic (business) and financial. The Fama-French model with three and five factors is also considered and included. The latest versions of the two main theories of capital structure (BFO and MM), adapted to the established financial practice of the functioning of companies, are used, taking into account the real conditions of their work, such as variable income, frequent income tax payments, advance income tax payments, etc. Practical calculations have been made. They focus on (1) applying two versions of CAPM (market or industry) to real companies; (2) application to real companies of a new methodology developed by us for assessing the financial performance of a company, taking into account both business (market or industry) and financial risks. The calculations made for three real companies (Apple, Severstal, Polymetal) show that the financial performance of companies is highly dependent on the type of risks taken into account. Sometimes the difference between market and industry cases is small, sometimes it is significant. But the difference in financial indicators, while taking into account simultaneously financial and business risks, is always great. This means that taking into account simultaneously both financial and business risks is important for a correct assessment of the financial performance of companies. The developed approach makes it possible to use the powerful tools of these highly developed theories (BFO and MM) for the correct assessment of the main financial indicators of the company and their forecasting, taking into account both types of risks.

In recent decades, many phase II clinical trials have used survival outcomes as the primary endpoints. If radiotherapy is involved, the competing risk issue often arises because the time to disease progression can be censored by the time to normal tissue complications, and vice versa. Besides, many existing research has examined that patients receiving the same radiotherapy dose may yield distinct responses due to their heterogeneous radiation susceptibility statuses. Therefore, the “one-size-fits-all” strategy often fails, and it is more relevant to evaluate the subgroup-specific treatment effect with the subgroup defined by the radiation susceptibility status. In this paper, we propose a Bayesian adaptive biomarker stratified phase II trial design evaluating the subgroup-specific treatment effects of radiotherapy. We use the cause-specific hazard approach to model the competing risk survival outcomes. We propose restricting the candidate radiation doses based on each patient’s radiation susceptibility status. Only the clinically feasible personalized dose will be considered, which enhances the benefit for the patients in the trial. In addition, we propose a stratified Bayesian adaptive randomization scheme such that more patients will be randomized to the dose reporting more favorable survival outcomes. Numerical studies and an illustrative trial example have shown that the proposed design performed well and outperformed the conventional design ignoring the competing risk issue.

Enabling high data-rate uplink cellular connectivity for drones is a challenging problem, since a flying drone has a higher likelihood of having line-of-sight propagation to base stations that terrestrial UEs normally do not have line-of-sight to. This may result in uplink inter-cell interference and uplink performance degradation for the neighboring ground UEs when drones transmit at high data-rates (e.g., video streaming). We address this problem from a cellular operator’s standpoint to support drone-sourced video streaming of a point of interest. We propose a low-complexity, closed-loop control system for Open-RAN architectures that jointly optimizes the drone’s location in space and its transmission directionality to support video streaming and minimize its uplink interference impact on the network. We prototype and experimentally evaluate the proposed control system on a dedicated outdoor multi-cell RAN testbed, which is the first measurement campaign of its kind. Furthermore, we perform a large-scale simulation assessment of the proposed control system using the actual cell deployment topologies and cell load profiles of a major US cellular carrier. The proposed Open-RAN control scheme achieves an average $19\%$ network capacity gain over traditional BS-constrained control solutions and satisfies the application data-rate requirements of the drone (e.g., to stream an HD video).

Aromaticity is a well-known phenomenon in both physics and chemistry, and is responsible for many unique chemical and physical properties of aromatic molecules. The primary feature contributing to the stability of polycyclic aromatic hydrocarbons is the delocalised π-electron clouds in the 2p z orbitals of each of the N carbon atoms. While it is known that electrons delocalize among the hybridized sp ² orbitals, this paper proposes quantum walk as the mechanism by which the delocalization occurs, and also obtains how the functional chemical structures of these molecules arise naturally out of such a construction. We present results of computations performed for some benzoid polycyclic aromatic hydrocarbons in this regard, and show that the quantum walk-based approach does correctly predict the reactive sites and stability order of the molecules considered.

Herein we report the synthesis of ternary statistical methacrylate copolymers comprising cationic ammonium (amino-ethyl methacrylate: AEMA), carboxylic acid (propanoic acid methacrylate: PAMA) and hydrophobic (ethyl methacrylate: EMA) side chain monomers, to study the functional role of anionic groups on their antimicrobial and hemolytic activities as well as the conformation of polymer chains. The hydrophobic monomer EMA was maintained at 40 mol% in all the polymers, with different percentages of cationic ammonium (AEMA) and anionic carboxylate (PAMA) side chains, resulting in different total net charge for the polymers. The antimicrobial and hemolytic activities of the copolymer were determined by the net charge of +3 or larger, suggesting that there was no distinct effect of the anionic carboxylate groups on the antimicrobial and hemolytic activities of the copolymers. However, the pH titration and atomic molecular dynamics simulations suggest that anionic groups may play a strong role in controlling the polymer conformation. This was achievedviaformation of salt bridges between cationic and anionic groups, transiently crosslinking the polymer chain allowing dynamic switching between compact and extended conformations. These results suggest that inclusion of functional groups in general, other than the canonical hydrophobic and cationic groups in antimicrobial agents, may have broader implications in acquiring functional structures required for adequate antimicrobial activity. In order to explain the implications, we propose a molecular model in which formation of intra-chain, transient salt bridges, due to the presence of both anionic and cationic groups along the polymer, may function as “adhesives” which facilitate compact packing of the polymer chain to enable functional group interaction but without rigidly locking down the overall polymer structure, which may adversely affect their functional roles.

With the emergence of 5G, network densification, richer and more demanding applications, the Radio Access Network (RAN)---a key component of the cellular network infrastructure---will become increasingly complex. To tackle this complexity, it is critical for the RAN to be able to automate the process of deploying, optimizing, and operating while leveraging novel data-driven technologies to ultimately improve the end user Quality of Experience (QoE). In this article, we disaggregate the traditional monolithic control plane RAN architecture and introduce a RAN Intelligent Controller (RIC) platform decoupling the control and data planes of the RAN driving an intelligent and continuously evolving radio network by fostering network openness and empowering network intelligence with AI-enabled applications. We provide functional and software architectures of the RIC and discuss its design challenges. We elaborate how the RIC can enable near-real-time network optimization in 5G for Dual Connectivity use-case using machine learning control loops.

The quantum walk formalism is a widely used and highly successful framework for modeling quantum systems, such as simulations of the Dirac equation, different dynamics in both the low and high energy regime, and for developing a wide range of quantum algorithms. Here we present the circuit-based implementation of a discrete-time quantum walk in position space on a five-qubit trapped-ion quantum processor. We encode the space of walker positions in particular multi-qubit states and program the system to operate with different quantum walk parameters, experimentally realizing a Dirac cellular automaton with tunable mass parameter. The quantum walk circuits and position state mapping scale favorably to a larger model and physical systems, allowing the implementation of any algorithm based on discrete-time quantum walks algorithm and the dynamics associated with the discretized version of the Dirac equation. Implementations of quantum walks on ion trap quantum computers have been so far limited to the analogue simulation approach. Here, the authors implement a quantum-circuit-based discrete quantum walk in one-dimensional position space, realizing a Dirac cellular automaton with tunable mass parameter.

Mobile devices aggregate various types of data from sensitive corporate documents to personal content. While users desire to access this content on a single device via a unified user experience and through any mobile app, protecting this data is challenging. Even though different data types have different security and privacy needs, mobile operating systems include only few, if any, functionalities for fine-grained data protection. We present SWIRLS, an Android-based mobile OS that provides a policy-based information-flow data protection abstraction for mobile apps to support BYOD (bring-your-own-device) use cases. SWIRLS attaches security policies to individual pieces of data and enforces these policies as the data flows through the device. Unlike current BYOD solutions like VMs that create duplication overload, SWIRLS provides a single environment to access content from different security contexts using the same applications while monitoring for malicious data leakage. SWIRLS leverages a two-level hybrid information flow tracking (IFT) mechanism to track both intra-application flows and a higher level IFT based on processes for applications isolation. Our evaluation presents BYOD data protection use-cases such as limiting document sharing, preventing leakage based on document classification and security policies based on geo-fencing. SWIRLS only imposes a low battery consumption and performance overhead.

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that such deployments can also be used to enable advanced data-driven and Machine Learning (ML) applications in mobile networks. We propose an edge-controller-based architecture for cellular networks and evaluate its performance with real data from hundreds of base stations of a major U.S. operator. In this regard, we will provide insights on how to dynamically cluster and associate base stations and controllers, according to the global mobility patterns of the users. Then, we will describe how the controllers can be used to run ML algorithms to predict the number of users in each base station, and a use case in which these predictions are exploited by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that the prediction accuracy improves when based on machine learning algorithms that rely on the controllers' view and, consequently, on the spatial correlation introduced by the user mobility, with respect to when the prediction is based only on the local data of each single base station.

Real graphs often contain edge and node weights, representing, for instance, penalty, distance or uncertainty. We study the problem of keyword search over weighted node-labeled graphs, in which a query consists of a set of keywords and an answer is a subgraph whose nodes contain the keywords. We evaluate answers using three ranking strategies: optimizing edge weights, optimizing node weights, and a bi-objective combination of both node and edge weights. We prove that optimizing node weights and the bi-objective function are NP-hard. We propose an algorithm that optimizes edge weights and has an approximation ratio of two for the unique node enumeration paradigm. To optimize node weights and the bi-objective function, we propose transformations that distribute node weights onto the edges. We then prove that our transformations allow our algorithm to also optimize node weights and the bi-objective function with the same approximation ratio of two. Notably, the proposed transformations are compatible with existing algorithms that only optimize edge weights. We empirically show that in many natural examples, incorporating node weights (both keyword holders and middle nodes) produces more relevant answers than ranking methods based only on edge weights. Extensive experiments over real-life datasets verify the effectiveness and efficiency of our solution.

Adaptive bitrate streaming (ABR) has become the de-facto technique for video streaming over the Internet. Despite a flurry of techniques, achieving high quality ABR streaming over cellular networks remains a tremendous challenge. ABR streaming can be naturally modeled as a control problem. There has been some initial work on using PID, a widely used feedback control technique, for ABR streaming. Existing studies, however, either use PID control directly without fully considering the special requirements of ABR streaming, leading to suboptimal results, or conclude that PID is not a suitable approach. In this paper, we take a fresh look at PID-based control for ABR streaming. We design a framework called PIA (PID-control based ABR streaming) that strategically leverages PID control concepts and incorporates several novel strategies to account for the various requirements of ABR streaming. We evaluate PIA using simulation based on LTE network traces, as well as using real DASH implementation. The results demonstrate that PIA outperforms state-of-the-art schemes in providing high average bitrate with significantly lower bitrate changes (reduction up to 40%) and stalls (reduction up to 85%), while incurring very small runtime overhead. We further design PIA-E to improve the performance of PIA in the initial playback phase.

Traffic for internet video streaming has been rapidly increasing and is further expected to increase with the higher definition videos and IoT applications, such as 360 degree videos and augmented virtual reality applications. While efficient management of heterogeneous cloud resources to optimize the quality of experience is important, existing work in this problem space often left out important factors. In this paper, we present a model for describing a today's representative system architecture for video streaming applications, typically composed of a centralized origin server and several CDN sites. Our model comprehensively considers the following factors: limited caching spaces at the CDN sites, allocation of CDN for a video request, choice of different ports from the CDN, and the central storage and bandwidth allocation. With the model, we focus on minimizing a performance metric, stall duration tail probability (SDTP), and present a novel, yet efficient, algorithm to solve the formulated optimization problem. The theoretical bounds with respect to the SDTP metric are also analyzed and presented. Our extensive simulation results demonstrate that the proposed algorithms can significantly improve the SDTP metric, compared to the baseline strategies. Small-scale video streaming system implementation in a real cloud environment further validates our results.

Concern about how to aggregate sensitive user data without compromising individual privacy is a major barrier to greater availability of data. Differential privacy has emerged as an accepted model to release sensitive information while giving a statistical guarantee for privacy. Many different algorithms are possible to address different target functions. We focus on the core problem of count queries, and seek to design mechanisms to release data associated with a group of n individuals. Prior work has focused on designing mechanisms by raw optimization of a loss function, without regard to the consequences on the results. This can leads to mechanisms with undesirable properties, such as never reporting some outputs (gaps), and overreporting others (spikes). We tame these pathological behaviors by introducing a set of desirable properties that mechanisms can obey. Any combination of these can be satisfied by solving a linear program (LP) which minimizes a cost function, with constraints enforcing the properties. We focus on a particular cost function, and provide explicit constructions that are optimal for certain combinations of properties, and show a closed form for their cost. In the end, there are only a handful of distinct optimal mechanisms to choose between: one is the well-known (truncated) geometric mechanism; the second a novel mechanism that we introduce here, and the remainder are found as the solution to particular LPs. These all avoid the bad behaviors we identify. We demonstrate in a set of experiments on real and synthetic data which is preferable in practice, for different combinations of data distributions, constraints, and privacy parameters.

LTE evolved Multimedia Broadcast/Multicast Service (eMBMS) is an attractive solution for video delivery to very large groups in crowded venues. However, the deployment and management of eMBMS systems are challenging, due to the lack of real-time feedback from the user equipments (UEs). Therefore, we present the Dynamic Monitoring (DyMo) system for low-overhead feedback collection. DyMo leverages eMBMS for broadcasting stochastic group instructions to all UEs. These instructions indicate the reporting rates as a function of the observed quality of service (QoS). This simple feedback mechanism collects very limited QoS reports from the UEs. The reports are used for network optimization, thereby ensuring high QoS to the UEs. We present the design aspects of DyMo and evaluate its performance analytically and via extensive simulations. Specifically, we show that DyMo infers the optimal eMBMS settings with extremely low overhead while meeting strict QoS requirements under different UE mobility patterns and presence of network component failures. For instance, DyMo can detect the eMBMS signal-to-noise ratio experienced by the 0.1th percentile of the UEs with a root mean square error of 0.05% with only 5 to 10 reports per second regardless of the number of UEs.

The increasing popularity of IoT devices in both residences and enterprises has widened the attack surface for network connected devices. Many popular IoT devices have unpatched vulnerabilities or default passwords and lack basic security mechanisms, making them easy prey for malware and botnets. In this paper, we share our experience of designing and using an experimental deployment of network gateways to provide IoT security, to both the IoT devices and the gateways themselves. We propose three approaches for framework design and collecting the network data, each providing different levels of visibility into IoT device behavior. Finally we present our methodology and experimental evaluation of a small-scale deployment of gateways and IoT devices for volumetric anomaly detection and IoT device identification using the data collected by the gateways behind the NAT, or in the cloud, outside the NAT. We believe that securing IoT devices can be more efficient and effective when there is more visibility into device activity and security capabilities are deployed close to the devices, in the gateway. However, a hybrid approach in which data is collected on the gateways and analyzed in the cloud can be more practical; special considerations regarding sensitive data storage and privacy guarantees have to be taken into account.

Objective: The current state of the art for compartment modeling of dynamic PET data can be described as a two-stage approach. In Stage 1, individual estimates of kinetic parameters are obtained by fitting models using standard techniques, such as nonlinear least squares (NLS), to each individual's data one subject at a time. Population-level effects, such as the difference between diagnostic groups, are analyzed in Stage 2 using standard statistical methods by treating the individual estimates as if they were observed data. While this approach is generally valid, it is possible to increase efficiency and precision of the analysis, allow more complex models to be fit, and also to permit parameter-specific investigation by fitting data across subjects simultaneously. We explore the application of nonlinear mixed-effects (NLME) models for estimation and inference in this setting. Methods: In the NLME framework, subjects are modeled simultaneously through the inclusion of random effects of subjects for each kinetic parameter; meanwhile, population parameters are estimated directly in a joint model. Results: Simulation results indicate that NLME outperforms the two-stage approach in estimating group-level effects and also has improved power to detect differences across groups. We applied our NLME approach to clinical PET data and found effects not detected by the two-stage approach. Conclusion: The proposed NLME approach is more accurate and correspondingly more powerful than the two-stage approach in compartment modeling of PET data. Significance: The NLME method can broaden the methodological scope of PET modeling because of its efficiency and stability.