Effectively leveraging diversity has been shown to improve performance for various machine learning models, including large language models (LLMs). However, determining the most effective way of using diversity remains a challenge. In this work, we compare two diversity approaches for answering binary questions using LLMs: model diversity, which relies on multiple models answering the same question, and question interpretation diversity, which relies on using the same model to answer the same question framed in different ways. For both cases, we apply majority voting as the ensemble consensus heuristic to determine the final answer. Our experiments on boolq, strategyqa, and pubmedqa show that question interpretation diversity consistently leads to better ensemble accuracy compared to model diversity. Furthermore, our analysis of GPT and LLaMa shows that model diversity typically produces results between the best and the worst ensemble members without clear improvement.

Managing Wireless networks, particularly in industrial and factory environments, to meet the escalating demands of time critical applications has become more complex and warrants proactive management strategies. This work introduces an innovative approach to wireless network management enabled by a Digital Twin (DT) designed and continuously enhanced by real-time device telemetry and user inputs through an Extended Reality interface. By collecting real telemetry data from network devices, our methodology defines and calibrates a DT representation of the network, enabling accurate prediction of wireless signal properties and network performance based on simulation models. The DT serves as an automation tool to analyze various scenarios, allowing for informed adjustments to user applications, devices and network configurations. The paper describes a real-life DT implementation of a wireless system in a real enterprise network scenario. Experimental results are provided demonstrating improved performance and user experience enabled by the proposed DT-based network management. The proposed methodology addresses the challenges of real-time network optimization and contributes to advance wireless network management based on device and network telemetry.

Following the Industry 4.0 vision of a full digitization of the industry, time-critical services and applications, allowing network infrastructures to deliver information with determinism and reliability, are becoming more and more relevant for a set of vertical sectors. As a consequence, deterministic network solutions are progressively emerging, albeit they are still bounded to specific technological domains. Even considering the existence of interconnected deterministic networks, the provision of an end-to-end (E2E) deterministic service over them must rely on a specific control plane architecture, capable of seamlessly integrate and control the underlying multi-technology data plane. In this work, we envision such a control plane solution, extending previous works and exploiting several innovations and novel architectural concepts. The proposed control architecture is service-centric, in order to provide the necessary flexibility, scalability, and modularity to deal with a heterogenous data plane. The architecture is hierarchical and encompasses a set of management platforms to interact with specific network technologies overarched by an E2E platform for the management, monitoring, and control of E2E deterministic services. Furthermore, Artificial Intelligence (AI) and Digital Twinning are used to enable network predictability and automation, as well as smart resource allocation, to ensure service reliability in dynamic scenarios where existing services may terminate and new ones may need to be deployed.

BACKGROUND: Cancer is a collection of diseases caused by the deregulation of cell processes, which is triggered by somatic mutations. The search for patterns in somatic mutations, known as mutational signatures, is a growing field of study that has already become a useful tool in oncology. Several algorithms have been proposed to perform one or both the following two tasks: (1) de novo estimation of signatures and their exposures, (2) estimation of the exposures of each one of a set of pre-defined signatures. RESULTS: Our group developed signeR, a Bayesian approach to both of these tasks. Here we present a new version of the software, signeR 2.0, which extends the possibilities of previous analyses to explore the relation of signature exposures to other data of clinical relevance. signeR 2.0 includes a user-friendly interface developed using the R-Shiny framework and improvements in performance. This version allows the analysis of submitted data or public TCGA data, which is embedded in the package for easy access. CONCLUSION: signeR 2.0 is a valuable tool to generate and explore exposure data, both from de novo or fitting analyses and is an open-source R package available through the Bioconductor project at ( https://doi.org/10.18129/B9.bioc.signeR ).

Despite of the fast development of highly effective vaccines to control the current COVID$-$19 pandemic, the unequal distribution and availability of these vaccines worldwide and the number of people infected in the world lead to the continuous emergence of SARS-CoV-2 (Severe Acute Respiratory Syndrome coronavirus 2) variants of concern. It is likely that real-time genomic surveillance will be continuously needed as an unceasing monitoring tool, necessary to follow the spillover of the disease spread and the evolution of the virus. In this context, new genomic variants of SARS-CoV-2 that may emerge as a response to selective pressure, including variants refractory to current vaccines, makes genomic surveillance programs tools of utmost importance. Here propose a statistical model for the estimation of the relative frequencies of SARS-CoV-2 variants in pooled samples. This model is built by considering a previously defined selection of genomic polymorphisms that characterize SARS-CoV-2 variants. The methods described here support both raw sequencing reads for polymorphisms-based markers calling and predefined markers in the VCF format. Results obtained by using simulated data show that our method is quite effective in recovering the correct variant proportions. Further, results obtained by considering longitudinal data from wastewater samples of two locations in Switzerland agree well with those describing the epidemiological evolution of COVID-19 variants in clinical samples of these locations. Our results show that the described method can be a valuable tool for tracking the proportions of SARS-CoV-2 variants.

Time plays a major role in the specification of Cyber-physical Systems (CPS) behavior with concurrency, timeliness, asynchrony, and resource limits as their main characteristics. In addition to timeliness , the specification of CPS needs to assess and unambiguously define its behavior with respect to the other Quality-of-Information (QoI) properties: (1) Correctness, (2) Completeness, (3) Consistency, and (4) Accuracy. Very often, CPS need to handle these QoI properties, and any combination thereof, multiple times when performing computation and communication processes. However, a model-driven and systematic approach to specify CPS behavior that jointly considers combined QoI aspects is possible but missing in existing methodologies. As the first contribution of this work, we provide an extension to an established model of computation (MoC) based on “Functions driven by Finite State Machine” (FunState) to enable a model-driven composition mechanism to create CPS behavior specifications from reusable components. Second, we present a novel set of design patterns to illustrate the modeling of QoI-aware CPS specifications that can be applied in several state-of-the-art Electronic System Level (ESL) methodologies. The time semantics of the MoC are formalized using the tagged-signal-model, and the presented model-driven approach enables the composition of multiple design patterns. The main benefits of the presented model-driven approach and design patterns to create CPS specifications are as follows: (a) reduce modeling effort, errors, and time through the reuse of known recipes to re-incurring tasks and allow to automatically generate repetitive control flows based on extended Finite State Machines; (b) increase system robustness and facilitate the creation of holistic QoI management allowing to unambiguously define system behavior for scenarios with single/multiple QoI requirement violations in different models of computation; (c) dynamically validate timing behavior of system implementations to enable a multi-objective optimization of nonfunctional properties that influence CPS timing. We demonstrate the aforementioned benefits through the modeling and evaluation of an infrastructure-assisted automated driving case study using Infrastructure-to-Vehicle (I2V) communications to distribute QoI critical road environment information.

MOTIVATION: Mutational signatures can be used to understand cancer origins and provide a unique opportunity to group tumor types that share the same origins and result from similar processes. These signatures have been identified from high throughput sequencing data generated from cancer genomes by using non-negative matrix factorisation (NMF) techniques. Current methods based on optimization techniques are strongly sensitive to initial conditions due to high dimensionality and nonconvexity of the NMF paradigm. In this context, an important question consists in the determination of the actual number of signatures that best represent the data. The extraction of mutational signatures from high-throughput data still remains a daunting task. RESULTS: Here we present a new method for the statistical estimation of mutational signatures based on an empirical Bayesian treatment of the NMF model. While requiring minimal intervention from the user, our method addresses the determination of the number of signatures directly as a model selection problem. In addition, we introduce two new concepts of significant clinical relevance for evaluating the mutational profile. The advantages brought by our approach are shown by the analysis of real and synthetic data. The later is used to compare our approach against two alternative methods mostly used in the literature and with the same NMF parametrization as the one considered here. Our approach is robust to initial conditions and more accurate than competing alternatives. It also estimates the correct number of signatures even when other methods fail. Results on real data agree well with current knowledge. AVAILABILITY AND IMPLEMENTATION: signeR is implemented in R and C ++, and is available as a R package at http://bioconductor.org/packages/signeR CONTACT: [email protected] information: Supplementary data are available at Bioinformatics online.

Finding the best possible encoding decisions for compressing a video sequence is a highly complex problem. In this work, we propose a multi-objective Design Space Exploration (DSE) method to automatically find HEVC encoder implementations that are optimized for several different criteria. The DSE shall optimize the coding mode evaluation order of the mode decision process and jointly explore early skip conditions to minimize the four objectives a) bitrate, b) distortion, c) encoding time, and d) decoding energy. In this context, we use a SystemC-based actor model of the HM test model encoder for the evaluation of each explored solution. The evaluation that is based on real measurements shows that our framework can automatically generate encoder solutions that save more than 60% of encoding time or 3% of decoding energy when accepting bitrate increases of around 3%.

MOTIVATION: The detection of genomic regions unusually rich in a given pattern is an important undertaking in the analysis of next-generation sequencing data. Recent studies of chromosomal translocations in activated B lymphocytes have identified regions that are frequently translocated to c-myc oncogene. A quantitative method for the identification of translocation hotspots was crucial to this study. Here we improve this analysis by using a simple probabilistic model and the framework provided by scan statistics to define the number and location of translocation breakpoint hotspots. A key feature of our method is that it provides a global chromosome-wide nominal control level to clustering, as opposed to previous methods based on local criteria. While being motivated by a specific application, the detection of unusual clusters is a widespread problem in bioinformatics. We expect our method to be useful in the analysis of data from other experimental approaches such as of ChIP-seq and 4C-seq. RESULTS: The analysis of translocations from B lymphocytes with the method described here reveals the presence of longer hotspots when compared with those defined previously. Further, we show that the hotspot size changes substantially in the absence of DNA repair protein 53BP1. When 53BP1 deficiency is combined with overexpression of activation-induced cytidine deaminase, the hotspot length increases even further. These changes are not detected by previous methods that use local significance criteria for clustering. Our method is also able to identify several exclusive translocation hotspots located in genes of known tumor supressors. AVAILABILITY AND IMPLEMENTATION: The detection of translocation hotspots is done with hot_scan, a program implemented in R and Perl. Source code and documentation are freely available for download at https://github.com/itojal/hot_scan.

Modeling and evaluating nonfunctional properties such as performance, power, and reliability of embedded systems are tasks of utmost importance. In this article, we introduce M AESTRO , a methodology for the modeling and evaluation of nonfunctional properties and embedded firmware of MPSoC architecture components at the Electronic System Level (ESL). In contrast to existing design flows that provide predefined performance models, M AESTRO defines a flexible approach that allows to define virtual prototypes that can be easily customized and extended to evaluate multiple nonfunctional properties of interest at different levels of abstraction. In M AESTRO , a design is composed purely from actor-oriented models. This enables typical ESL features such as automatic design space exploration and synthesizability of HW and SW components, typically missing in very general design flows. Unique to M AESTRO is the separation and coordination of the interaction between application functionality, firmware, and performance models for the evaluation of nonfunctional properties, and their complex interactions within a single Model-of-Computation (MoC). The main advantages of M AESTRO are: (I) Extensible modeling of interdependent nonfunctional properties of heterogeneous MPSoC components; (II) high flexibility to investigate the appropriate trade-off between modeling effort and accuracy of nonfunctional property evaluators; (III) a holistic approach for modeling application functionality as well as firmware affecting the evaluation of nonfunctional properties. Regarding (II), we present a mobile baseband processor platform use-case, executing a GSM paging application. To demonstrate (I) and (III), we present the modeling of a complex ESL processor virtual prototype, running a soft real-time application and equipped with both a power and reliability manager.

Task-accurate performance estimation methods are widely applied in early design phases to explore different architecture options. These methods rely on accurate annotations generated by software profiling or real measurements to guarantee accurate results. However, in practice, such accurate annotations are not available in early design phases due to lack of source code and hardware platform. Instead, estimated mean or worst-case annotations are usually used, which makes the final result inaccurate because of the errors induced by the estimations, especially for designs with tight time constraints. In this paper, we propose a novel methodology that combines Distributionally Robust Monte Carlo Simulation with task-accurate performance estimation method to guarantee robust system performance estimation in early design phases, i.e., determining the lower bound of the confidence level of fulfilling a specific time constraint. Instead of using accurate annotations, our method only uses estimated annotations in the form of intervals and it does not make any assumptions of the distribution types of these intervals.