Individual-level Evaluation of the Exposure Notification Cascade in the SwissCovid Digital Proximity Tracing App: An Observational Study (Preprint)

BACKGROUND Digital proximity tracing (DPT) aims to complement manual contact tracing (MCT) in identifying exposed contacts and preventing further transmission of SARS-CoV-2 in the population. While several DPT apps, including SwissCovid, have shown to have promising effects on mitigating the pandemic, several challenges have impeded them from fully achieving the desired results. A key question now relates to how the effectiveness of DPT can be improved which requires better understanding of factors influencing its processes. OBJECTIVE In this study, we aimed to provide a detailed examination of the exposure notification (EN) cascade and to evaluate potential contextual influences for successful receipt of EN and subsequent actions taken by cases and contacts in different exposure settings. METHODS We used data from 285 pairs of SARS-CoV-2-infected cases and their contacts within an observational cohort study of cases and contacts identified by MCT and enrolled between 06 August and 17 January 2021 in the Canton of Zurich, Switzerland. We surveyed participants with electronic questionnaires. Data were summarized descriptively and stratified by exposure setting. RESULTS We found that only 60% of contacts using the app whose corresponding case reported to have triggered the EN also received one. Among those, 23% received the EN before being contacted by MCT. Compared to those receiving an EN after MCT, we observed that a higher proportion of contacts receiving an EN before MCT were exposed in non-household settings (67% versus 56%) and their corresponding cases had more frequently reported mild to moderate symptoms (78% versus 69%). Among the 18 contacts receiving an EN before MCT, 14 (78%) took preventive measures: 12 (67%) were tested for SARS-CoV-2 and 7 (39%) called the SwissCovid Infoline. In non-household settings, the proportion of contacts taking preventive actions after receiving an EN was higher compared to same-household settings (82% versus 67%). One in eleven ENs received before MCT led to the identification of a SARS-CoV-2-infected case by prompting the contact to get tested. This corresponds to one in 85 exposures of a contact to a case in a non-household setting, in which both were app users and the case triggered the EN. CONCLUSIONS Our descriptive evaluation of the DPT notification cascade provides further evidence that DPT is an important complementary tool in pandemic mitigation, especially in non-household exposure settings. However, the effect of DPT apps can only be exerted if code generation processes are efficient and exposed contacts are willing to undertake preventive actions. This highlights the need to focus efforts on keeping barriers to efficient code generation as low as possible and promoting not only app adoption but also compliance with the recommended measures upon EN. CLINICALTRIAL ISRCTN14990068

Sensory coding and contrast invariance emerge from the control of plastic inhibition over emergent selectivity

Visual stimuli are represented by a highly efficient code in the primary visual cortex, but the development of this code is still unclear. Two distinct factors control coding efficiency: Representational efficiency, which is determined by neuronal tuning diversity, and metabolic efficiency, which is influenced by neuronal gain. How these determinants of coding efficiency are shaped during development, supported by excitatory and inhibitory plasticity, is only partially understood. We investigate a fully plastic spiking network of the primary visual cortex, building on phenomenological plasticity rules. Our results suggest that inhibitory plasticity is key to the emergence of tuning diversity and accurate input encoding. We show that inhibitory feedback (random and specific) increases the metabolic efficiency by implementing a gain control mechanism. Interestingly, this led to the spontaneous emergence of contrast-invariant tuning curves. Our findings highlight that (1) interneuron plasticity is key to the development of tuning diversity and (2) that efficient sensory representations are an emergent property of the resulting network.

Compilation of sparse array programming models

This paper shows how to compile sparse array programming languages. A sparse array programming language is an array programming language that supports element-wise application, reduction, and broadcasting of arbitrary functions over dense and sparse arrays with any fill value. Such a language has great expressive power and can express sparse and dense linear and tensor algebra, functions over images, exclusion and inclusion filters, and even graph algorithms. Our compiler strategy generalizes prior work in the literature on sparse tensor algebra compilation to support any function applied to sparse arrays, instead of only addition and multiplication. To achieve this, we generalize the notion of sparse iteration spaces beyond intersections and unions. These iteration spaces are automatically derived by considering how algebraic properties annotated onto functions interact with the fill values of the arrays. We then show how to compile these iteration spaces to efficient code. When compared with two widely-used Python sparse array packages, our evaluation shows that we generate built-in sparse array library features with a performance of 1.4× to 53.7× when measured against PyData/Sparse for user-defined functions and between 0.98× and 5.53× when measured against SciPy/Sparse for sparse array slicing. Our technique outperforms PyData/Sparse by 6.58× to 70.3×, and (where applicable) performs between 0.96× and 28.9× that of a dense NumPy implementation, on end-to-end sparse array applications. We also implement graph linear algebra kernels in our system with a performance of between 0.56× and 3.50× compared to that of the hand-optimized SuiteSparse:GraphBLAS library.

Intelligent reflected surfaces assisted code domain non-orthogonal multiple access scheme

The propagation medium plays a crucial role in any wireless communication networks, the channel between the transmitter and the receiver, deteriorate the quality of the received signal due to the uncontrollable interactions such as scattering, reflection, and refraction in the channel with the surrounding objects. To overcome this challenge, the recent advent of recongurable intelligent surfaces can be helpful, in which the network operators can control the radio waves, eg, the phase, amplitude, frequency, and even polarization, of the impinging signals without the need of complex decoding, encoding, and radio frequency processing operations. On the other hand, few research papers reported an efficient code domain non orthogonal multiple access (NOMA) such as Interleave division multiple access (IDMA) system for wireless information transfer. Persuaded by the capability of this arising RIS technology, the present article is aimed to provide the modified framework of IDMA (code-domain NOMA) communication system based on RIS technology. Simulation results demonstrate that the proposed system achieves better SNR performance than the conventional IDMA framework.

Grafs: declarative graph analytics

Graph analytics elicits insights from large graphs to inform critical decisions for business, safety and security. Several large-scale graph processing frameworks feature efficient runtime systems; however, they often provide programming models that are low-level and subtly different from each other. Therefore, end users can find implementation and specially optimization of graph analytics error-prone and time-consuming. This paper regards the abstract interface of the graph processing frameworks as the instruction set for graph analytics, and presents Grafs, a high-level declarative specification language for graph analytics and a synthesizer that automatically generates efficient code for five high-performance graph processing frameworks. It features novel semantics-preserving fusion transformations that optimize the specifications and reduce them to three primitives: reduction over paths, mapping over vertices and reduction over vertices. Reductions over paths are commonly calculated based on push or pull models that iteratively apply kernel functions at the vertices. This paper presents conditions, parametric in terms of the kernel functions, for the correctness and termination of the iterative models, and uses these conditions as specifications to automatically synthesize the kernel functions. Experimental results show that the generated code matches or outperforms handwritten code, and that fusion accelerates execution.

Does the Zebra Finch Mating Song Circuit Use Spike Times Efficiently?

Precise and reliable spike times are thought to subserve multiple possible functions, including improving the accuracy of encoding stimuli or behaviours relative to other coding schemes. Indeed, repeating sequences of spikes with sub-millisecond precision exist in nature, such as the synfire chain of spikes in area HVC of the zebra-finch mating-song circuit. Here, we analyzed what impact precise and reliable spikes have on the encoding accuracy for both the zebra-finch and more generic neural circuits using computational modelling. Our results show that neural circuits can use precisely timed spikes to encode signals with a higher-order accuracy than a conventional rate code. Circuits with precisely timed and reliably emitted spikes increase their encoding accuracy linearly with network size, which is the hallmark signature of an efficient code. This qualitatively differs from circuits that employ a rate code which increase their encoding accuracy with the square-root of network size. However, this improved scaling is dependent on the spikes becoming more accurate and more reliable with larger networks. Finally, we discuss how to test this scaling relationship in the zebra mating song circuit using both neural data and song-spectrogram-based recordings while taking advantage of the natural fluctuation in HVC network size due to neurogenesis. The zebra-finch mating-song circuit may represent the most likely candidate system for the use of spike-timing-based, efficient coding strategies in nature.

Robustly Safe Compilation, an Efficient Form of Secure Compilation

Security-preserving compilers generate compiled code that withstands target-level attacks such as alteration of control flow, data leaks, or memory corruption. Many existing security-preserving compilers are proven to be fully abstract, meaning that they reflect and preserve observational equivalence. Fully abstract compilation is strong and useful but, in certain cases, comes at the cost of requiring expensive runtime constructs in compiled code. These constructs may have no relevance for security, but are needed to accommodate differences between the source and target languages that fully abstract compilation necessarily needs. As an alternative to fully abstract compilation, this article explores a different criterion for secure compilation called robustly safe compilation or RSC . Briefly, this criterion means that the compiled code preserves relevant safety properties of the source program against all adversarial contexts interacting with the compiled program. We show that RSC can be proved more easily than fully abstract compilation and also often results in more efficient code. We also present two different proof techniques for establishing that a compiler attains RSC and, to illustrate them, develop three illustrative robustly safe compilers that rely on different target-level protection mechanisms. We then proceed to turn one of our compilers into a fully abstract one and through this example argue that proving RSC can be simpler than proving full abstraction. To better explain and clarify notions, this article uses syntax highlighting in a way that colourblind and black-8-white readers can benefit from Reference [58]. For a better experience, please print or view this article in colour . 1