User-defined Constraints Research Articles

Generating Global and Local Explanations for Tree-Ensemble Learning Methods by Answer Set Programming

Abstract We propose a method for generating rule sets as global and local explanations for tree-ensemble learning methods using answer set programming (ASP). To this end, we adopt a decompositional approach where the split structures of the base decision trees are exploited in the construction of rules, which in turn are assessed using pattern mining methods encoded in ASP to extract explanatory rules. For global explanations, candidate rules are chosen from the entire trained tree-ensemble models, whereas for local explanations, candidate rules are selected by only considering rules that are relevant to the particular predicted instance. We show how user-defined constraints and preferences can be represented declaratively in ASP to allow for transparent and flexible rule set generation, and how rules can be used as explanations to help the user better understand the models. Experimental evaluation with real-world datasets and popular tree-ensemble algorithms demonstrates that our approach is applicable to a wide range of classification tasks.

SparseAuto: An Auto-scheduler for Sparse Tensor Computations using Recursive Loop Nest Restructuring

Automated code generation and performance enhancements for sparse tensor algebra have become essential in many real-world applications, such as quantum computing, physical simulations, computational chemistry, and machine learning. General sparse tensor algebra compilers are not always versatile enough to generate asymptotically optimal code for sparse tensor contractions. This paper shows how to generate asymptotically better schedules for complex sparse tensor expressions using kernel fission and fusion. We present generalized loop restructuring transformations to reduce asymptotic time complexity and memory footprint. Furthermore, we present an auto-scheduler that uses a partially ordered set (poset)-based cost model that uses both time and auxiliary memory complexities to prune the search space of schedules. In addition, we highlight the use of Satisfiability Module Theory (SMT) solvers in sparse auto-schedulers to approximate the Pareto frontier of better schedules to the smallest number of possible schedules, with user-defined constraints available at compile-time. Finally, we show that our auto-scheduler can select better-performing schedules and generate code for them. Our results show that the auto-scheduler provided schedules achieve orders-of-magnitude speedup compared to the code generated by the Tensor Algebra Compiler (TACO) for several computations on different real-world tensors.

Conditioned Protein Structure Prediction

Deep-learning-based protein structure prediction has facilitated major breakthroughs in biological sciences. However, current methods struggle with alternative conformation prediction and offer limited integration of expert knowledge on protein dynamics. We introduce AFEXplorer, a generic approach that tailors AlphaFold predictions to user-defined constraints in coarse coordinate spaces by optimizing embedding features. Its effectiveness in generating functional protein conformations in accordance with predefined conditions is demonstrated through comprehensive examples. AFEXplorer serves as a versatile platform for conditioned protein structure prediction, bridging the gap between automated models and domain-specific insights. Published by the American Physical Society 2024

Optimizing scientific workflow scheduling in cloud computing: a multi-level approach using whale optimization algorithm

Cloud computing has evolved into an indispensable tool for facilitating scientific research due to its ability to efficiently distribute and process workloads in a virtual environment. Scientific tasks that involve complicated task dependencies and user-defined constraints related to quality of service (QoS) and time constraints require the efficient use of cloud resources. Planning these scientific workflow tasks represents an NP-complete problem, prompting researchers to explore various solutions, including conventional planners and evolutionary optimization algorithms. In this study, we present a novel, multistage algorithm specifically designed to schedule scientific workflows in cloud computing contexts. This approach addresses the challenges of efficiently mapping complex workflows onto distributed cloud resources while considering factors like resource heterogeneity, dynamic workloads, and stringent performance requirements. The algorithm uses the whale optimization algorithm (WOA) with a two-phase approach to shorten execution time, minimize financial costs, and effectively maintain load balancing.

Open-source synthetic photoplethysmographic signal generator with analog output.

The photoplethysmographic (PPG) signal of the finger is being used to create embedded devices that estimate physiological variables. This project outlines an innovative method for developing a synthetic PPG generator that produces both actual reference digital signals and their equivalent analog signals using open-source technology. A series of PPG profiles is synthesized using three variant Gaussian functions. A low-frequency trend induced by respiratory frequency and background noise are then added. To generate a diverse range of continuously variable PPG profiles within specified boundaries and customizable levels of interference, all parameters undergo random fluctuations on a cycle-by-cycle basis, as per user-defined constraints. The generated signal is then converted into its equivalent analog form through the use of an RC filter that low-frequency filters a Pulse-Width Modulation square wave that is modulated directly by the generated signal. The software returns different PPG profiles and allows the signal comparison before vs after the addition of different-intensity modulated respiratory trends and background noise. The digital signal is faithfully converted into an equivalent analog voltage signal capable of reproducing not only the waveform profile but also the respiratory trend and various levels of noise.

Unraveling the tectonic evolution of the Andean hinterland (Argentina and Chile, 30°S) using multi-sample thermal history models

The Andean hinterland of Chile and Argentina defines a region of voluminous magmatism, polyphase deformation, and high topography above a long-lived subduction zone. Construction of this high-elevation domain has been variably attributed to isostatic uplift during crustal thickening induced by internal hinterland shortening, underthrusting during growth of the external thrust belt, or lower crustal flow. Alternatively, uplift may be related to dynamic processes associated with lithospheric foundering or flat-slab subduction. This study integrates geo/thermochronological, structural, magmatic, and sedimentological datasets to reconstruct hinterland evolution and evaluate potential drivers of exhumation. Our thermal history modeling approach implements a new time-depth extension to HeFTy 2 software, which enables simultaneous inversion of multiple samples along a structural or topographic profile. This extension addresses transient effects such as isotherm deflection and the transition from geothermal to atmospheric gradients and permits changes in the relative position among samples (folding, tilting) within user-defined constraints. Single- and multi-sample modeling results based on published apatite (U-Th)/He and fission track data (AHe, AFT) along the western Andean hinterland confirm late Eocene (∼30–40 Ma) cooling below at least ∼120°C, whereas thermal histories derived from new AHe, AFT, and zircon He analyses from the eastern hinterland resolve Mesozoic cooling below ∼80–160°C, followed by protracted residence at ∼60–80°C and rapid exhumational cooling in the early Miocene. Multi-sample models further require ∼10° eastward tilting of eastern sample locations, compatible with hinterland uplift via underthrusting and development of a crustal-scale fault-bend fold that was geometrically and kinematically linked to shortening in the external (eastern) thrust belt. Results underscore the enhanced resolution of multi-sample thermal history models in testing structural hypotheses and deciphering the timing, magnitude, and mechanisms of exhumational cooling during changing geodynamic conditions.

Adaptive laterally constrained inversion of time-domain electromagnetic data using hierarchical Bayes

Laterally constrained inversion (LCI) of time-domain electromagnetic (TEM) data is effective in recovering quasi-layered models, particularly in sedimentary environments. By incorporating lateral constraints, LCI enhances the stability of the inverse problem and improves the resolution of stratified interfaces. However, a limitation of the LCI is the recovery of laterally smooth transitions, even in regions unsupported by the available data sets. Therefore, we have developed an adaptive LCI scheme within a Bayesian framework. Our approach introduces user-defined constraints through a multivariate Gaussian prior, wherein the variances serve as hyperparameters in a hierarchical Bayes algorithm. By simultaneously sampling the model parameters and hyperparameters, our scheme allows for varying constraints throughout the model space, selectively preserving lateral constraints that align with the available data sets. We demonstrate the effectiveness of our adaptive LCI scheme through a synthetic example. The inversion results showcase the self-adaptive nature of the strength of the constraints, yielding models with smooth lateral transitions while accurately retaining sharp lateral interfaces. An application to field TEM data collected in Laizhou, China, supports the findings from the synthetic example. The adaptive LCI scheme successfully images quasi-layered environments and formations with well-defined lateral interfaces. Moreover, the Bayesian inversion provides a measure of certainty, allowing for a comprehensive illustration of the confidence in the inversion results.

Analysis of Performance Improvement of Planar Antenna Arrays With Optimized Square Rings for Massive MIMO Applications

The power pattern of the conventional fully optimized planar arrays can be properly reshaped according to the required user-defined constraints. However, the practical implementation of such fully optimized large planar arrays is complex and expensive. This paper introduces a new and efficient technique that is capable of providing better performance and almost the same power pattern shapes as that of the conventional fully optimized planar arrays by optimally adjusting the element amplitude and phase excitations of the outer-square rings instead of all elements' excitations. The proposed technique starts with a massive fully planar array then divides it into two contiguous sub-planar arrays which are both symmetric about the original array center. The elements excitation amplitudes or phases of the outer sub-planar array are only adjusted to form the desired power pattern shapes, while the amplitudes or phases of the central sub-planar array elements which have usually higher weights than the outer elements are made constants (i.e., they made ones for the case of amplitude-only control and zeros for the phase-only control). The results demonstrate the capability of the proposed planar array to form the required power patterns with far less number of the adjustable elements.

Cooperative Enclosing Control With Modified Guaranteed Performance and Aperiodic Communication for Unmanned Vehicles: A Path-Following Solution

Existing cooperative enclosing outcomes are primarily conducted relying on continuous transmission flow among agents, and transient specifications are not explicitly incorporated into the design, which may consume a large amount of communication resources and violate the preset transient constraints. To address abovementioned issues, by resorting to a path-following framework, this paper proposes a novel cooperative enclosing control with aperiodic communication and modified guaranteed performance for unmanned vehicles, consisting of communication and guidance levels. At communication level, by constructing an event-triggered rule and a continuous-time state predictor, an aperiodic communication tactic is established to mitigate the real-time transmission cost of network medium and obviate unnecessary resource wastage. At guidance level, a modified guaranteed performance control (MGPC) with the deployment of novel unsymmetrical envelopes is proposed to prescribe path-following errors within user-defined constraints while excluding any overshoots. The striking innovation includes two aspects, one is that by utilizing the aperiodic communication tactic, transmission data amount among multiple vehicles in experimental results can be reduced by approximately 54.7% compared with the time-triggered alternative, another is that the performance guarantees (e.g., overshoot-free property and preassigned convergence rate) can be retained by resorting to suggested MGPC. All deviations of the entire system are analyzed to be constrained without triggering Zeno behaviors. The effectiveness is further validated by simulation and experimental outcomes.

Evolution-Based Shape and Behavior Co-Design of Virtual Agents.

We introduce a novel co-design method for autonomous moving agents' shape attributes and locomotion by combining deep reinforcement learning and evolution with user control. Our main inspiration comes from evolution, which has led to wide variability and adaptation in Nature and has significantly improved design and behavior simultaneously. Our method takes an input agent with optional user-defined constraints, such as leg parts that should not evolve or are only within the allowed ranges of changes. It uses physics-based simulation to determine its locomotion and finds a behavior policy for the input design that is used as a baseline for comparison. The agent is randomly modified within the allowed ranges, creating a new generation of several hundred agents. The generation is trained by transferring the previous policy, which significantly speeds up the training. The best-performing agents are selected, and a new generation is formed using their crossover and mutations. The next generations are then trained until satisfactory results are reached. We show a wide variety of evolved agents, and our results show that even with only 10% of allowed changes, the overall performance of the evolved agents improves by 50%. If more significant changes to the initial design are allowed, our experiments' performance will improve even more to 150%. Our method significantly improved motion tasks without changing body structures, and it does not require considerable computation resources as it works on a single GPU and provides results by training thousands of agents within 30 minutes.

AutoML in heavily constrained applications

Optimizing a machine learning pipeline for a task at hand requires careful configuration of various hyperparameters, typically supported by an AutoML system that optimizes the hyperparameters for the given training dataset. Yet, depending on the AutoML system’s own second-order meta-configuration, the performance of the AutoML process can vary significantly. Current AutoML systems cannot automatically adapt their own configuration to a specific use case. Further, they cannot compile user-defined application constraints on the effectiveness and efficiency of the pipeline and its generation. In this paper, we propose Caml, which uses meta-learning to automatically adapt its own AutoML parameters, such as the search strategy, the validation strategy, and the search space, for a task at hand. The dynamic AutoML strategy of Caml takes user-defined constraints into account and obtains constraint-satisfying pipelines with high predictive performance.

Design and Analysis of High Performance Heterogeneous Block-based Approximate Adders

Approximate computing is an emerging paradigm to improve the power and performance efficiency of error-resilient applications. As adders are one of the key components in almost all processing systems, a significant amount of research has been carried out toward designing approximate adders that can offer better efficiency than conventional designs; however, at the cost of some accuracy loss. In this article, we highlight a new class of energy-efficient approximate adders, namely, Heterogeneous Block-based Approximate Adders (HBAAs), and propose a generic configurable adder model that can be configured to represent a particular HBAA configuration. An HBAA, in general, is composed of heterogeneous sub-adder blocks of equal length, where each sub-adder can be an approximate sub-adder and have a different configuration. The sub-adders are mainly approximated through inexact logic and carry truncation. Compared to the existing design space, HBAAs provide additional design points that fall on the Pareto-front and offer a better quality-efficiency tradeoff in certain scenarios. Furthermore, to enable efficient design space exploration based on user-defined constraints, we propose an analytical model to efficiently evaluate the Probability Mass Function (PMF) of approximation error and other error metrics, such as Mean Error Distance (MED), Normalized Mean Error Distance (NMED), and Error Rate (ER) of HBAAs. The results show that HBAA configurations can provide around 15% reduction in area and up to 17% reduction in energy compared to state-of-the-art approximate adders.

NC92Soil: A computer code for deterministic and stochastic 1D equivalent linear seismic site response analyses

The extensive evaluation of the impact of local seismo-stratigraphic configurations on seismic ground motion presents significant challenges due to the necessity of considering the combined effects of uncertainty and small-scale lateral variability of the relevant parameters. To effectively explore these sources of uncertainty, a new Python-based computer program is proposed for one-dimensional seismic site response simulations, adopting the equivalent linear viscoelastic approach in the frequency domain. With respect to existing software, the code introduces new pre- and post-processing features, which also meet the specific requirements of seismic microzonation studies. Within the code, the complete spectrum of uncertainties related to local seismo-stratigraphic configurations, including lithotype successions, layer thicknesses, and seismic and geotechnical properties for the considered lithotypes, is managed by considering user-defined constraints and statistical properties of the relevant parameters. Additionally, a batch approach is offered, enabling the application of the procedure to an unlimited number of different scenarios. To demonstrate the potentiality of the proposed code, a comprehensive set of 90,000 local seismic site response analyses was conducted, showing a clear correlation between the amplification factors, the mean shear wave velocity in the upper 30 m, the fundamental frequency of the deposit and the depth to the seismic bedrock.

PAGE: Parallel Scalable Regionalization Framework

Regionalization techniques group spatial areas into a set of homogeneous regions to analyze and draw conclusions about spatial phenomena. A recent regionalization problem, called MP-regions, groups spatial areas to produce a maximum number of regions by enforcing a user-defined constraint at the regional level. The MP-regions problem is NP-hard. Existing approximate algorithms for MP-regions do not scale for large datasets due to their high computational cost and inherently centralized approaches to process data. This article introduces a parallel scalable regionalization framework ( PAGE ) to support MP-regions on large datasets. The proposed framework works in two stages. The first stage finds an initial solution through randomized search, and the second stage improves this solution through efficient heuristic search. To build an initial solution efficiently, we extend traditional spatial partitioning techniques to enable parallelized region building without violating the spatial constraints. Furthermore, we optimize the region building efficiency and quality by tuning the randomized area selection to trade off runtime with region homogeneity. The experimental evaluation shows the superiority of our framework to support an order of magnitude larger datasets efficiently compared to the state-of-the-art techniques while producing high-quality solutions.

Matching algorithms to assist in designing with reclaimed building elements

Reuse of building components is one of the recommended circular strategies to reduce the environmental impact of new buildings. However, reclaimed building components are more difficult to design with than new products. While new products can be made to match exact needs, the salvaged components have predefined dimensions and quality limitations. Following the Design Science Research methodology, we attempt to answer how the reuse design can be aided by a digital design tool. The developed matching algorithms suggest the optimal assignment of available elements for the desired configuration, considering user-defined constraints and optimisation criteria. In the test cases, we seek to optimise the global warming potential of timber framing elements, defined by life cycle assessment, though the tool is not limited to this objective. The implementation includes greedy algorithms, bipartite graphs, and mixed integer linear programming. The usefulness of the proposed solution is evaluated on simulated sets of building elements in terms of embodied emission reduction and speed of the calculation. The paper contributes with methodologies, algorithms, and test cases to assess their performance. Practitioners can apply the proposed solution to reduce the time of designing with salvaged materials, which can lead to the popularisation of the circular design.

DeepAltTrip: Top-K Alternative Itineraries for Trip Recommendation

Trip itinerary recommendation finds an ordered sequence of Points-of-Interest (POIs) from a large number of candidate POIs in a city. In this paper, we propose a deep learning-based framework, called DeepAltTrip, that learns to recommend top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> alternative itineraries for given source and destination POIs. These alternative itineraries would be not only popular given the historical routes adopted by past users but also dissimilar (or diverse) to each other. The DeepAltTrip consists of two major components: (i) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Itinerary Net</i> (ITRNet) which estimates the likelihood of POIs on an itinerary by using graph autoencoders and two (forward and backward) LSTMs; and (ii) a route generation procedure to generate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> diverse itineraries passing through relevant POIs obtained using ITRNet. For the route generation step, we propose a novel sampling algorithm that can seamlessly handle a wide variety of user-defined constraints. To the best of our knowledge, this is the first work that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learns</i> from historical trips to provide a set of alternative itineraries to the users. Extensive experiments conducted on eight popular real-world datasets show the effectiveness and efficacy of our approach over state-of-the-art methods.

Optimizing efficiency in the creation of patient-specific plates through field-driven generative design in maxillofacial surgery

Field driven design is a novel approach that allows to define through equations geometrical entities known as implicit bodies. This technology does not rely upon conventional geometry subunits, such as polygons or edges, rather it represents spatial shapes through mathematical functions within a geometrical field. The advantages in terms of computational speed and automation are conspicuous, and well acknowledged in engineering, especially for lattice structures. Moreover, field-driven design amplifies the possibilities for generative design, facilitating the creation of shapes generated by the software on the basis of user-defined constraints. Given such potential, this paper suggests the possibility to use the software nTopology, which is currently the only software for field-driven generative design, in the context of patient-specific implant creation for maxillofacial surgery. Clinical scenarios of applicability, including trauma and orthognathic surgery, are discussed, as well as the integration of this new technology with current workflows of virtual surgical planning. This paper represents the first application of field-driven design in maxillofacial surgery and, although its results are very preliminary as it is limited in considering only the distance field elaborated from specific points of reconstructed anatomy, it introduces the importance of this new technology for the future of personalized implant design in surgery.

Enabling Real-Time Remote Monitoring of Ships by Lossless Protocol Transformations

This paper uses data processing techniques to reduce the required transmission bandwidth in ship-to-shore communications. The proposed framework (ONline Efficient Sources Transmission Optimizer -) leverages state-of-the-art technologies and novel algorithms to automatically optimize transmissions under structural (e.g., available bandwidth, fixed packet overhead) and user-defined (e.g., maximum latency) constraints. In addition, authenticates and encrypts the communication between the ship and the shore via mainstream free and open-source software components. Initially, we present the abstract mathematical formulation of the problem, with its assumptions, goal function, constraints, and significant quantities. Then, we introduce the architecture of a system capable of continuously estimating the compressibility, processing and transmission time of streaming data. Such estimations allow to calculate and apply optimal parameters for achieving the best compression ratio. Lastly, using a prototypical implementation, we evaluate the system performance with a Class B ship simulator on two realistic use cases. Our experiments show an excellent compression ratio with maritime protocols (more than 40:1) and a limited latency impact, demonstrating the approach’s viability.

Optimal Demand Response Using Battery Storage Systems and Electric Vehicles in Community Home Energy Management System-Based Microgrids

Demand response (DR) strategies are recieving much attention recently for their applications in the residential sector. Electric vehicles (EVs), which are considered to be a fairly new consumer load in the power sector, have opened up new opportunities by providing the active utilization of EVs as a storage unit. Considering their storage capacities, they can be used in vehicle-to-grid (V2G) or vehicle-to-community (V2C) options instead of taking power in peak times from the grid itself. This paper suggests a community-based home energy management system for microgrids to achieve flatter power demand and peak demand shaving using particle swarm optimization (PSO) and user-defined constraints. A dynamic clustered load scheduling scheme is proposed, including a method for managing peak shaving using rules specifically designed for PV systems that are grid-connected alongside battery energy storage systems and electric vehicles. The technique being proposed involves determining the limits of feed-in and demand dynamically, using estimated load demands and profiles of PV power for the following day. Additionally, an optimal rule-based management technique is presented for the peak shaving of utility grid power that sets the charge/discharge schedules of the battery and EV one day ahead. Utilizing the PSO algorithm, the optimal inputs for implementing the rule-based peak shaving management strategy are calculated, resulting in an average improvement of about 7% in percentage peak shaving (PPS) when tested using MATLAB for numerous case studies.

Auto-tuning of reference models in direct data-driven control

Designing controllers directly from data often requires choosing a reference closed-loop model, whose behavior should be reproduced as tightly as possible by the actual closed-loop system via the selected controller structure (e.g., PID). Within a linear setting, we present a derivative-based approach to jointly select the reference model and controller parameters directly from data. The proposed strategy allows one to maximize closed-loop performance while enforcing user-defined constraints, and it is designed to handle non-minimum phase dynamics. The effectiveness of the proposed approach is shown through three numerical case studies.