Fundamental Tradeoff Research Articles

A 23 dBm Gain Shaping Stacked Power Block CMOS Power Amplifier Achieving 36% PAE

This paper introduces a design methodology that reduces the fundamental trade-off between linearity and power added efficiency (PAE) in CMOS power amplifier (PA). In our work, a stacked power block (SPB) has been proposed to mitigate the effect of gate–source capacitance (Cgs), thus linearizing the PA. Each stage is biased independently to shape the gain profile, either to be in the expanded mode or in the compressed mode, in which once combined it delivers a flat gain response and confirming the linearity performance. Efficient PI input and output matching networks are proposed to ensure no further distortion, once connected to the 50 Ω source and load. The PA achieves input and output return losses of less than −10 dB from 2.40 to 2.50 GHz. At the center frequency of 2.45 GHz, the SPB-PA achieves a gain of 10 dB and it is unconditionally stable. The proposed gain shaping linearization technique delivers a maximum linear output power (Poutlinmax) of 19.8 dBm with only 3.3 dB back-off from maximum output power (Poutmax) of 23.1 dBm. The SPB-PA meets the WLAN specification with linear PAE of 30% and peak PAE of 36.1%. The proposed SPB-PA reduces the fundamental trade-off between linearity and efficiency. Integration of this PA in wireless SoC shall reduce the chip’s overall power consumption.

An axiomatic perspective on the performance effects of end-host path selection

In various contexts of networking research, end-host path selection has recently regained momentum as a design principle. While such path selection has the potential to increase performance and security of networks, there is a prominent concern that it could also lead to network instability (i.e., flow-volume oscillation) if paths are selected in a greedy, load-adaptive fashion. However, the extent and the impact vectors of instability caused by path selection are rarely concretized or quantified, which is essential to discuss the merits and drawbacks of end-host path selection.In this work, we investigate the effect of end-host path selection on various metrics of networks both qualitatively and quantitatively. To achieve general and fundamental insights, we leverage the recently introduced axiomatic perspective on congestion control and adapt it to accommodate joint algorithms for path selection and congestion control, i.e., multi-path congestion-control protocols. Using this approach, we identify equilibria of the multi-path congestion-control dynamics and analytically characterize these equilibria with respect to important metrics of interest in networks (the “axioms”) such as efficiency, fairness, and loss avoidance. Moreover, we analyze how these axiomatic ratings for a general network change compared to a scenario without path selection, thereby obtaining an interpretable and quantitative formalization of the performance impact of end-host path-selection. Finally, we show that there is a fundamental trade-off in multi-path congestion-control protocol design between efficiency, stability, and loss avoidance on one side and fairness and responsiveness on the other side.

Foveated light-field display and real-time rendering for virtual reality.

Glasses-free light field displays have significantly progressed due to advances in high-resolution microdisplays and high-end graphics processing units (GPUs). However, for near-eye light-field displays requiring portability, the fundamental trade-off regarding achieved spatial resolution remains: retinal blur quality must be degraded; otherwise, computational consumption increases. This has prevented synthesizing the high-quality light field from being fast. By integrating off-the-shelf gaze tracking modules into near-eye light-field displays, we present wearable virtual reality prototypes supporting human visual system-oriented focus cues. An optimized, foveated light field is delivered to each eye subject to the gaze point, providing more natural visual experiences than state-of-the-art solutions. Importantly, the factorization runtime can be immensely reduced, since the image resolution is only high within the gaze cone. In addition, we demonstrate significant improvements in computation and retinal blur quality over counterpart near-eye displays.

ALWAES

For an online delivery platform, accurate physical locations of merchants are essential for delivery scheduling. It is challenging to maintain tens of thousands of merchant locations accurately because of potential errors introduced by merchants for profits (e.g., potential fraud). In practice, a platform periodically sends a dedicated crew to survey limited locations due to high workforce costs, leaving many potential location errors. In this paper, we design and implement ALWAES, a system that automatically identifies and corrects location errors based on fundamental tradeoffs of five measurement strategies from manual, physical, and virtual data collection infrastructures for online delivery platforms. ALWAES explores delivery data already collected by platform infrastructures to measure the travel time of couriers between merchants and verify all merchants' locations by cross-validation automatically. We explore tradeoffs between performance and cost of different measurement approaches. By comparing with the manually-collected ground truth, the experimental results show that ALWAES outperforms three other baselines by 32.2%, 41.8%, and 47.2%, respectively. More importantly, ALWAES saves 3,846 hours of the delivery time of 35,005 orders in a month and finds new erroneous locations that initially were not in the ground truth but are verified by our field study later, accounting for 3% of all merchants with erroneous locations.

Fundamental tradeoff between packetized model predictive control and forward error correction in wireless feedback control systems

Fundamental tradeoff between packetized model predictive control and forward error correction in wireless feedback control systems

Performance Analysis of Coded OTFS Systems Over High-Mobility Channels

Orthogonal time frequency space (OTFS) modulation is a recently developed multi-carrier multi-slot transmission scheme for wireless communications in high-mobility environments. In this paper, the error performance of coded OTFS modulation over high-mobility channels is investigated. We start from the study of conditional pairwise-error probability (PEP) of the OTFS scheme, based on which its performance upper bound of the coded OTFS system is derived. Then, we show that the coding improvement for OTFS systems depends on the squared Euclidean distance among codeword pairs and the number of independent resolvable paths of the channel. More importantly, we show that there exists a fundamental trade-off between the coding gain and the diversity gain for OTFS systems, i.e., the diversity gain of OTFS systems improves with the number of resolvable paths, while the coding gain declines. Furthermore, based on our analysis, the impact of channel coding parameters on the performance of the coded OTFS systems is unveiled. The error performance of various coded OTFS systems over high-mobility channels is then evaluated. Simulation results demonstrate a significant performance improvement for OTFS modulation over the conventional orthogonal frequency division multiplexing (OFDM) modulation over high-mobility channels. Analytical results and the effectiveness of the proposed code design are also verified by simulations with the application of both classical and modern codes for OTFS systems.

Adaptive Social Learning

This work proposes a novel strategy for social learning by introducing the critical feature of adaptation. In social learning, several distributed agents update continually their belief about a phenomenon of interest through: i) direct observation of streaming data that they gather locally; and ii) diffusion of their beliefs through local cooperation with their neighbors. Traditional social learning implementations are known to learn well the underlying hypothesis (which means that the belief of every individual agent peaks at the true hypothesis), achieving steady improvement in the learning accuracy under stationary conditions. However, these algorithms do not perform well under nonstationary conditions commonly encountered in online learning, exhibiting a significant inertia to track drifts in the streaming data. In order to address this gap, we propose an Adaptive Social Learning (ASL) strategy, which relies on a small step-size parameter to tune the adaptation degree. First, we provide a detailed characterization of the learning performance by means of a steady-state analysis. Focusing on the small step-size regime, we establish that the ASL strategy achieves consistent learning under standard global identifiability assumptions. We derive reliable Gaussian approximations for the probability of error (i.e., of choosing a wrong hypothesis) at each individual agent. We carry out a large deviations analysis revealing the universal behavior of adaptive social learning: the error probabilities decrease exponentially fast with the inverse of the step-size, and we characterize the resulting exponential learning rate. Second, we characterize the adaptation performance by means of a detailed transient analysis, which allows us to obtain useful analytical formulas relating the adaptation time to the step-size. The revealed dependence of the adaptation time and the error probabilities on the step-size highlights the fundamental trade-off between adaptation and learning emerging in adaptive social learning.

Hysteretic Optimality of Container Warming Control in Serverless Computing Systems

Keeping containers warm for prompt service responses and reducing warm containers for light-weight system management exhibit a fundamental tradeoff in serverless computing systems. In this letter, we investigate the problem of container warming control for serverless computing, and we formulate it as a Markov decision process (MDP). By observing that the value functions corresponding to the MDP are partially submodular, we show that the derived optimal policy is hysteretic and partially non-decreasing. Our numerical results show that the derived optimal policy exhibits a hysteretic structure, which can be realized via switching-up/-down thresholds in practice.

Simulating approaches to emergency department pandemic physician staffing during COVID-19.

During pandemics, emergency departments (EDs) are challenged by the need to replace quarantined ED staff and avoid staffing EDs with nonemergency medicine (EM) trained physicians. We sought to design and examine three feasible ED staffing models intended to safely schedule EM physicians to staff three EDs within a health system during a prolonged infectious disease outbreak. We conducted simulation analyses examining the strengths and limitations of three ED clinician staffing models: two-team and three-team fixed cohort, and three-team unfixed cohort. Each model was assessed with and without immunity, and by varying infection rates. We assumed a 12-week pandemic disaster requiring a 2-week quarantine. The outcome, time to staffing shortage, was defined as depletion of available physicians in both 8- and 12-hour shift duration scenarios. All staffing models initially showed linear physician attrition with higher infection rates resulting in faster staffing shortages. The three-team fixed cohort model without immunity was not viable beyond 11 weeks. The three-team unfixed cohort model without immunity avoided staffing shortage for the duration of the pandemic up to an infection rate of 50 percent. The two-team model without immunity also avoided staffing shortage up to 30 percent infection rate. When accounting for immunity, all models behaved similarly initially but returned to adequate staffing during week 5 of the pandemic. Simulation analyses reveal fundamental tradeoffs that are critical to designing feasible pandemic disaster staffing models. Emergency physicians should test similar models based on local assumptions and capacity to ensure adequate staffing preparedness for prolonged pandemics.

Mapping and Monitoring Zero-Deforestation Commitments.

A growing number of companies have announced zero-deforestation commitments (ZDCs) to eliminate commodities produced at the expense of forests from their supply chains. Translating these aspirational goals into forest conservation requires forest mapping and monitoring (M&M) systems that are technically adequate and therefore credible, salient so that they address the needs of decision makers, legitimate in that they are fair and unbiased, and scalable over space and time. We identify 12 attributes of M&M that contribute to these goals and assess how two prominent ZDC programs, the Amazon Soy Moratorium and the High Carbon Stock Approach, integrate these attributes into their M&M systems. These programs prioritize different attributes, highlighting fundamental trade-offs in M&M design. Rather than prescribe a one-size-fits-all solution, we provide policymakers and practitioners with guidance on the design of ZDC M&M systems that fit their specific use case and that may contribute to more effective implementation of ZDCs.

Biometric Identification Systems with Noisy Enrollment for Gaussian Sources and Channels.

In the present paper, we investigate the fundamental trade-off of identification, secret-key, storage, and privacy-leakage rates in biometric identification systems for remote or hidden Gaussian sources. We use a technique of converting the system to one where the data flow is in one-way direction to derive the capacity region of these rates. Also, we provide numerical calculations of three different examples for the system. The numerical results imply that it seems hard to achieve both high secret-key and small privacy-leakage rates simultaneously.

Towards a data‐driven framework for optimizing security‐efficiency tradeoff in QUIC

AbstractAdvances in computing and compression technology, coupled with high‐speed networks, has beaconed an era of video streaming on the Internet. This has led to a need to enhance the security of communications transporting data without degrading its performance. The transport layer security (TLS) protocol negotiates configurations for securing communication channels. Such conversations adversely impact latency, thereby presenting a fundamental tradeoff between security and efficiency. In this work, we present a conceptual framework, called SEC‐QUIC (Secure and Efficient Configurations for QUIC), that focuses on optimizing this tradeoff specifically for video transmissions by investigating various factors in Quick UDP Internet Connections (QUIC). Transport‐layer‐related elements, such as maximum transmission unit (MTU) sizes, cipher suites, and ACK timer, are examined to evaluate the impact on the security‐efficiency tradeoff in QUIC‐based video transmissions using platform‐based experiments. Subsequently, we develop a conceptual framework to leverage QUIC's dynamics based on the context of a connection to optimize the security‐efficiency tradeoff. Our findings demonstrate the need to alter default configurations based on the contextual factors of a connection (eg, resource constraints and network conditions) in QUIC‐based video transmissions to balance the tradeoff. Experiments reveal an MTU of 1400 bytes is found to have 60% better throughput compared to an MTU of 1200 bytes while also 4% less CPU usage on average for the transmission of 100 MB video files. Overall, our experiments suggest that fine‐tuning performance and security related configurations is an effective approach to optimizing the security‐efficiency tradeoff in video transmissions.

Bacterial Multicellularity: The Biology of Escherichia coli Building Large-Scale Biofilm Communities.

Biofilms are a widespread multicellular form of bacterial life. The spatial structure and emergent properties of these communities depend on a polymeric extracellular matrix architecture that is orders of magnitude larger than the cells that build it. Using as a model the wrinkly macrocolony biofilms of Escherichia coli, which contain amyloid curli fibers and phosphoethanolamine (pEtN)-modified cellulose as matrix components, we summarize here the structure, building, and function of this large-scale matrix architecture. Based on different sigma and other transcription factors as well as second messengers, the underlying regulatory network reflects the fundamental trade-off between growth and survival. It controls matrix production spatially in response to long-range chemical gradients, but it also generates distinct patterns of short-range matrix heterogeneity that are crucial for tissue-like elasticity and macroscopic morphogenesis. Overall, these biofilms confer protection and a potential for homeostasis, thereby reducing maintenance energy, which makes multicellularity an emergent property of life itself.

Optimal Distinctiveness across Revenue Models: Differentiation of Paid and Free Mobile Apps

A critical strategic choice facing firms is how to position products within market categories. The optimal distinctiveness literature highlights a fundamental trade-off in this choice: products should be distinct from other products in order to minimize competition, but similar to other products in order to build legitimacy. In an attempt to resolve mixed evidence about the optimal level of distinctiveness, research has recently begun to investigate how the distinctiveness-performance relationship varies across different contexts. We add to this debate as we link this literature with the business model literature to argue that the shape of the distinctiveness-performance relationship depends on products’ revenue models. More specifically, we argue that differences in revenue models affect customer expectations, and this leads us to predict that the distinctiveness-performance relationship varies fundamentally for paid versus free products. Within the class of free products, we further contend that the shape of the distinctiveness-performance relationship depends on whether the free product adopts a freemium revenue model. Hypotheses are tested in a sample of over 250,000 mobile apps from the Apple iOS App Store. We find an inverted U-shaped relationship for paid products and a U-shaped relationship for free products, which becomes flatter for free products using a freemium revenue model.

Trace Logic Locking: Improving the Parametric Space of Logic Locking

To protect against an untrusted foundry, logic locking must 1) inject sufficient error to ensure critical application failures for any wrong key (error severity) and 2) resist any attack against it (attack resilient). We begin our work by deriving a fundamental tradeoff between these two goals which exists underlying all logic locking, regardless of construction. This relationship forces integrated circuit (IC) designers to sacrifice the error severity of logic locking to increase its attack resilience and vice versa. We proceed by exploring the consequences of this tradeoff through architectural simulations of ICs incorporating locking sweeping over the derived parametric space. We find that the efficacy of logic locking is severely limited by this tradeoff. In response, we propose trace logic locking (TLL), a novel enhancement of module level logic locking which enables existing art to secure arbitrary length sequences of input minterms, referred to as traces. Doing so injects an additional degree of freedom into the parametric space of locking, enabling locking techniques to overcome the limitations of our derived tradeoff. We both theoretically and empirically prove this by using TLL to enhance cutting edge locking. In ten large benchmarks, we show that TLL-enhanced logic locking provides exponentially stronger attack resilience than conventional locking with only modest additional overhead. Finally, we demonstrate the efficacy of TLL in a processor IC using architectural simulations. Despite prior art being unable to secure this IC, we find that TLL concurrently achieves strong error severity and attack resilience.

Pushing the Limits of the Donor-Acceptor Copolymer Strategy for Intramolecular Singlet Fission.

Donor-acceptor (D-A) copolymers have shown great potential for intramolecular singlet fission (iSF). Nonetheless, very few design principles exist for optimizing these systems for iSF, with very little knowledge about how to engineer them for this purpose. In recent work, a fundamental trade-off between the main electronic ingredients required for iSF capable D-A coplanar copolymers was revealed. Still, further investigations are needed to understand these limitations and learn how to bypass them. In this work, we propose to induce torsion as an effective way to circumvent the limits of the coplanar approach. We disclose the potential of noncoplanar copolymers with inherently low triplet energies that encompass all the characteristics required for iSF beyond the limiting values associated with fully coplanar systems. Our findings shed some light on the electronic structure aspects of D-A copolymers for iSF and offer a new avenue for the rational design of novel promising candidates.

Human Autonomy in Future Drone Traffic: Joint Human-AI Control in Temporal Cognitive Work.

The roles of human operators are changing due to increased intelligence and autonomy of computer systems. Humans will interact with systems at a more overarching level or only in specific situations. This involves learning new practices and changing habitual ways of thinking and acting, including reconsidering human autonomy in relation to autonomous systems. This paper describes a design case of a future autonomous management system for drone traffic in cities in a key scenario we call The Computer in Brussels. Our approach to designing for human collaboration with autonomous systems builds on scenario-based design and cognitive work analysis facilitated by computer simulations. We use a temporal method, called the Joint Control Framework to describe human and automated work in an abstraction hierarchy labeled Levels of Autonomy in Cognitive Control. We use the Score notation to analyze patterns of temporal developments that span levels of the abstraction hierarchy and discuss implications for human-automation communication in traffic management. We discuss how autonomy at a lower level can prevent autonomy on higher levels, and vice versa. We also discuss the temporal nature of autonomy in minute-to-minute operative work. Our conclusion is that human autonomy in relation to autonomous systems is based on fundamental trade-offs between technological opportunities to automate and values of what human actors find meaningful.

Online Service with Delay

In this article, we introduce the online service with delay problem. In this problem, there are n points in a metric space that issue service requests over time, and there is a server that serves these requests. The goal is to minimize the sum of distance traveled by the server and the total delay (or a penalty function thereof) in serving the requests. This problem models the fundamental tradeoff between batching requests to improve locality and reducing delay to improve response time, which has many applications in operations management, operating systems, logistics, supply chain management, and scheduling. Our main result is to show a poly-logarithmic competitive ratio for the online service with delay problem. This result is obtained by an algorithm that we call the preemptive service algorithm . The salient feature of this algorithm is a process called preemptive service, which uses a novel combination of (recursive) time forwarding and spatial exploration on a metric space. We also generalize our results to k > 1 servers and obtain stronger results for special metrics such as uniform and star metrics that correspond to (weighted) paging problems.

A lean view of lean

A lean view of lean

Optical mesoscopy, machine learning, and computational microscopy enable high information content diagnostic imaging of blood films.

Automated image-based assessment of blood films has tremendous potential to support clinical haematology within overstretched healthcare systems. To achieve this, efficient and reliable digital capture of the rich diagnostic information contained within a blood film is a critical first step. However, this is often challenging, and in many cases entirely unfeasible, with the microscopes typically used in haematology due to the fundamental trade-off between magnification and spatial resolution. To address this, we investigated three state-of-the-art approaches to microscopic imaging of blood films which leverage recent advances in optical and computational imaging and analysis to increase the information capture capacity of the optical microscope: optical mesoscopy, which uses a giant microscope objective (Mesolens) to enable high-resolution imaging at low magnification; Fourier ptychographic microscopy, a computational imaging method which relies on oblique illumination with a series of LEDs to capture high-resolution information; and deep neural networks which can be trained to increase the quality of low magnification, low resolution images. We compare and contrast the performance of these techniques for blood film imaging for the exemplar case of Giemsa-stained peripheral blood smears. Using computational image analysis and shape-based object classification, we demonstrate their use for automated analysis of red blood cell morphology and visualization and detection of small blood-borne parasites such as the malarial parasite Plasmodium falciparum. Our results demonstrate that these new methods greatly increase the information capturing capacity of the light microscope, with transformative potential for haematology and more generally across digital pathology. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.