Arbitrary Data Research Articles

Generation of Algebraic Data Type Values Using Evolutionary Algorithms

Automatic data generation is a key component of automated software testing. Random generation of test input data can uncover some bugs in software, but its effectiveness decreases when those inputs must satisfy complex properties in order to be meaningful. In this work, we study an evolutionary approach to generate values that can be encoded as algebraic data types plus additional properties. First, the approach is illustrated with the generation of sorted lists. Then, we generalize the technique to arbitrary algebraic data type definitions. Finally, we consider the problem of constrained data types where the data must satisfy some nontrivial property, using the well-known example of red-black trees for our experiments. This example will allow us to introduce the main principles of evolutionary algorithms and how these principles can be applied to obtain valid, nontrivial samples of a given data structure. Our experiments have revealed that this evolutionary approach is able to improve diversity, and increase the size of valid generated values with respect to simple random sampling techniques.

Investigation of liquid sloshing characteristics in tanks using an OpenFOAM solver with arbitrary excitation

A multiphase flow solver with mesh motion is typically used for simulations of liquid sloshing in containers, such as those in spacecraft, aircraft, and ships. The mode of the mesh motion should be predefined by functions, which is difficult for a sustained and arbitrary motion. In the present work, a general solver without mesh motion for liquid sloshing in tanks is proposed, based on the interFoam solver in OpenFOAM, by tracking the free surface using the volume of fluid method. An arbitrary excitation loading algorithm is implemented for automatically loading the three-dimensional arbitrary acceleration data. Moreover, the energy equation with phase change is also newly implemented in this solver so that the heat and mass transfer can be evaluated for cryogenic fuels during sloshing. In this way, arbitrary external excitations can be directly imported for any arbitrary sloshing case, and the characteristics of liquid sloshing and oscillation forces can be directly simulated without mesh motion. The solver is validated using different cases, and the influence of sloshing on the mass transfer rate and the effect of the baffle on the liquid sloshing characteristics are investigated. The results indicate that the solver can be easily applied to engineering problems involving liquid sloshing.

Integrating genomic and molecular data to predict antimicrobial minimum inhibitory concentration in Klebsiella pneumoniae

Minimum inhibitory concentration (MIC) denotes the in vitro benchmark indicating the quantity of antibiotic required to inhibit proliferation of specific bacterial strains. Determining MIC values corresponding to the infecting bacterial strain is paramount for tailoring appropriate antibiotic therapy. In the interim between specimen collection and laboratory-derived MIC outcomes, clinicians frequently resort to empirical therapy informed by retrospective analyses. Here introduces two deep learning approaches, a Convolutional Neural Network (CNN)-based model and an Enformer-based model, integrating genomic data of Klebsiella Pneumoniae and molecular structural data of 20 antibiotics to anticipate the MIC value of the bacterium for each antibiotic under consideration. These models demonstrate enhanced raw accuracy over the existing state-of-the-art model, which rely exclusively on genomic data. The CNN-based model achieves a notable 20% increase in raw accuracy while further mirroring the 1-tier accuracy of the state-of-the-art model. Although the Enformer-based model does not quite reach the performance levels of the CNN-based model, it offers an advantage by eliminating the need for arbitrary data processing steps. This streamlining of the data processing pipeline facilitates fast updates and improves the model interpretability. It is expected that these deep learning paradigms can significantly inform and bolster clinician decision-making during the empirical treatment phase.

Parallel molecular data storage by printing epigenetic bits on DNA

DNA storage has shown potential to transcend current silicon-based data storage technologies in storage density, longevity and energy consumption1–3. However, writing large-scale data directly into DNA sequences by de novo synthesis remains uneconomical in time and cost4. We present an alternative, parallel strategy that enables the writing of arbitrary data on DNA using premade nucleic acids. Through self-assembly guided enzymatic methylation, epigenetic modifications, as information bits, can be introduced precisely onto universal DNA templates to enact molecular movable-type printing. By programming with a finite set of 700 DNA movable types and five templates, we achieved the synthesis-free writing of approximately 275,000 bits on an automated platform with 350 bits written per reaction. The data encoded in complex epigenetic patterns were retrieved high-throughput by nanopore sequencing, and algorithms were developed to finely resolve 240 modification patterns per sequencing reaction. With the epigenetic information bits framework, distributed and bespoke DNA storage was implemented by 60 volunteers lacking professional biolab experience. Our framework presents a new modality of DNA data storage that is parallel, programmable, stable and scalable. Such an unconventional modality opens up avenues towards practical data storage and dual-mode data functions in biomolecular systems.

Global strong solutions to the radially symmetric compressible MHD equations in 2D solid balls with arbitrary large initial data

In this paper, we prove the global existence of strong solutions to the two-dimensional compressible MHD equations with density dependent viscosity coefficients (known as Kazhikhov-Vaigant model) on 2D solid balls with arbitrary large initial smooth data where shear viscosity μ being constant and the bulk viscosity λ be a polynomial of density up to power β. The global existence of the radially symmetric strong solutions was established under Dirichlet boundary conditions for β > 1. Moreover, as long as β>max{1,γ+24}, the density is shown to be uniformly bounded with respect to time. This generalizes the previous result [Fan et al., Arch. Ration. Mech. Anal. 245, 239–278 (2022)] of the compressible Navier-Stokes equations on 2D bounded domains where they require β > 4/3 and also improves the result [Chen et al., SIAM J. Math. Anal. 54(3), 3817–3847 (2022)] of of compressible MHD equations on 2D solid balls.

Device-Performance-Driven Heterogeneous Multiparty Learning for Arbitrary Images.

Multiparty learning (MPL) is an emerging framework for privacy-preserving collaborative learning. It enables individual devices to build a knowledge-shared model and remaining sensitive data locally. However, with the continuous increase of users, the heterogeneity gap between data and equipment becomes wider, which leads to the problem of model heterogeneous. In this article, we concentrate on two practical issues: data heterogeneous problem and model heterogeneous problem, and propose a novel personal MPL method named device-performance-driven heterogeneous MPL (HMPL). First, facing the data heterogeneous problem, we focus on the problem of various devices holding arbitrary data sizes. We introduce a heterogeneous feature-map integration method to adaptively unify the various feature maps. Meanwhile, to handle the model heterogeneous problem, as it is essential to customize models for adapting to the various computing performances, we propose a layer-wise model generation and aggregation strategy. The method can generate customized models based on the device's performance. In the aggregation process, the shared model parameters are updated through the rules that the network layers with the same semantics are aggregated with each other. Extensive experiments are conducted on four popular datasets, and the result demonstrates that our proposed framework outperforms the state of the art (SOTA).

ISubGen generates integrative disease subtypes by pairwise similarity assessment.

There are myriad types of biomedical data-molecular, clinical images, and others. When a group of patients with the same underlying disease exhibits similarities across multiple types of data, this is called a subtype. Existing subtyping approaches struggle to handle diverse data types with missing information. To improve subtype discovery, we exploited changes in the correlation-structure between different data types to create iSubGen, an algorithm for integrative subtype generation. iSubGen can accommodate any feature that can be compared with a similarity metric to create subtypes versatilely. It can combine arbitrary data types for subtype discovery, such as merging genetic, transcriptomic, proteomic, and pathway data. iSubGen recapitulates known subtypes across multiple cancers even with substantial missing data and identifies subtypes with distinct clinical behaviors. It performs equally with or superior to other subtyping methods, offering greater stability and robustness to missing data and flexibility to new data types. It is available at https://cran.r-project.org/web/packages/iSubGen.

Continuous solutions for a two-dimensional cross-diffusion problem involving doubly degenerate diffusion and logistic proliferation

Emerging from the modeling of intricate structures formed by bacterial motion in nutrient-deficient environments, the doubly degenerate nutrient taxis system [Formula: see text] is considered in smoothly bounded domains [Formula: see text]. In contrast to frameworks devoid of logistic growth where only small-data global solutions have so far been found in the literature, quadratic degradation in the logistic growth is shown to markedly amplify the overall dissipative effects to the extent that, for arbitrary smooth initial data, an associated no-flux type initial-boundary problem possesses global continuous weak solutions. Central to our findings is the identification of an advantageous dissipative structure subtly embedded in the system, the exploitation of which leads to the establishment of a positive lower bound for nutrients levels within any finite time interval, thereby ruling out the appearance of cross-degeneracies within such timeframes.

Snapshottable Stores

We say that an imperative data structure is snapshottable or supports snapshots if we can efficiently capture its current state, and restore a previously captured state to become the current state again. This is useful, for example, to implement backtracking search processes that update the data structure during search. Inspired by a data structure proposed in 1978 by Baker, we present a snapshottable store , a bag of mutable references that supports snapshots. Instead of capturing and restoring an array, we can capture an arbitrary set of references (of any type) and restore all of them at once. This snapshottable store can be used as a building block to support snapshots for arbitrary data structures, by simply replacing all mutable references in the data structure by our store references. We present use-cases of a snapshottable store when implementing type-checkers and automated theorem provers. Our implementation is designed to provide a very low overhead over normal references, in the common case where the capture/restore operations are infrequent. Read and write in store references are essentially as fast as in plain references in most situations, thanks to a key optimisation we call record elision . In comparison, the common approach of replacing references by integer indices into a persistent map incurs a logarithmic overhead on reads and writes, and sophisticated algorithms typically impose much larger constant factors. The implementation, which is inspired by Baker’s and the OCaml implementation of persistent arrays by Conchon and Filliâtre, is both fairly short and very hard to understand: it relies on shared mutable state in subtle ways. We provide a mechanized proof of correctness of its core using the Iris framework for the Coq proof assistant.

Geo-Hgan: Unsupervised anomaly detection in geochemical data via latent space learning

Reconstructing geochemical data for anomaly detection using Generative Adversarial Networks (GANs) has become a prevalent method in identifying geochemical anomalies. However, injecting random noise into GANs can induce model instability. To mitigate this issue, we propose a novel anomaly detection model, Geo-Hgan, which integrates a dual adversarial network architecture with a Latent Space Adversarial Module (LSAM) to learn the distribution of latent variables from arbitrary data and optimize the sample reconstruction process, thereby alleviating instability during GAN training. Additionally, an encoder guided by the LSAM-pretrained GAN is employed to extract variational features, facilitating rapid and effective sample mapping into the latent space defined by LSAM. Experimental results demonstrate that under unsupervised conditions, Geo-Hgan achieves an Area Under the Curve (AUC) score of 85% across three geochemical datasets, outperforming similar models in accuracy and reconstruction capabilities. To assess its versatility and generalization ability, we extend Geo-Hgan to anomaly detection tasks in computer vision, where it achieves an average AUC score of 98.7% on the MvtecAD dataset, setting a new state-of-the-art performance in the domain. Furthermore, we propose AnomFilter, a method for setting anomaly thresholds based on the clustering hypothesis. AnomFilter identifies high-confidence anomaly samples identified by Geo-Hgan in the source domain and iteratively transfers them to the target domain. These high-confidence anomaly samples, combined with a small number of known positive samples in the target domain, enhance the accuracy of supervised geochemical anomaly detection in the target domain, which achieved an AUC score of 94%. The utilization of anomaly detection models for sample transfer learning offers a novel perspective for future work.

Detecting malicious encrypted traffic with privacy set intersection in cloud-assisted industrial internet

Encryption technology provides the ability of confidential transmission to ensure the security of Industrial Internet communication, but it makes detecting malicious encrypted traffic very difficult. To resolve the conflict between the difficulty of malicious encrypted traffic detection and the requirements of traffic privacy protection, we propose a cloud-assisted Industrial Internet malicious encrypted traffic detection scheme with privacy protection. To accurately match the encrypted traffic and the detection rules, a privacy set intersection protocol based on the oblivious pseudorandom function and random garbled Bloom filter is constructed, which can detect malicious traffic without revealing data content. Meanwhile, our scheme can allow semi-trusted cloud servers to assist resource-constrained end devices to participate in private calculations. The key-homomorphic encryption is introduced to obfuscate the detection rules, making the detection rules always transparent to end users and semi-trusted cloud servers. We also design the random input verification to make the malicious end users do not have any opportunity to participate in the privacy set intersection calculation using arbitrary data. The scheme analysis and performance evaluation results show that our scheme can effectively guarantee the security of encrypted traffic detection with better detection performance and limited resource consumption.

Accelerated relaxation enhancing flows cause total dissipation

We show that by ‘accelerating’ relaxation enhancing flows, one can construct a flow that is smooth on [0,1)×Td but highly singular at t = 1 so that for any positive diffusivity, the advection–diffusion equation associated to the accelerated flow totally dissipates solutions, taking arbitrary initial data to the constant function at t = 1.

Cross-Blockchain Communication Using Oracles With an Off-Chain Aggregation Mechanism Based on zk-SNARKs

The closed architecture of prevailing blockchain systems renders the usage of this technology mostly infeasible for a wide range of real-world problems. Most blockchains trap users and applications in their isolated space without the possibility of cooperating or switching to other blockchains. Therefore, blockchains need additional mechanisms for seamless communication and arbitrary data exchange between each other and external systems. Unfortunately, current approaches for cross-blockchain communication are resource-intensive or require additional blockchains or tailored solutions depending on the applied consensus mechanisms of the connected blockchains. Therefore, we propose an oracle with an off-chain aggregation mechanism based on Zero-Knowledge Succinct Non-interactive Arguments of Knowledge (zk-SNARKs) to facilitate cross-blockchain communication. The oracle queries data from another blockchain and applies a rollup-like mechanism to move state and computation off-chain. The zkOracle contract only expects the transferred data, an updated state root, and proof of the correct execution of the aggregation mechanism. The proposed solution only requires constant 378 kgas to submit data on the Ethereum blockchain and is primarily independent of the underlying technology of the queried blockchains.

Application of triple-branch artificial neural network system for catalytic pellets combustion

On the international level, it is common to act on reducing emissions from energy systems. However, in addition to industrial emissions, low-stack emissions also make a significant contribution. A good step in reducing its environmental impact, is to move to biofuels, including biomass. This paper examines the impact of placing a catalytic system in a retort boiler to minimize emissions of greenhouse gases, dust and other pollutants when burning pellets. The effect of platinum, and oxides of selected metals placed on the deflector as a solid catalyst was studied. Based on the experimental data, a branched artificial neural network was constructed and trained. The routing of three parallel topologies made it possible to achieve high accuracy while keeping the input data relatively simple. The system showed an average error of 3.54% against arbitrary test data. On the basis of experimental data as well as predictions returned by the artificial neural network, recommendations were shown for the catalysts used and their amounts. Depending on the biomass from which the pellet was produced, the experiment suggested the use of titanium or copper oxides. In the case of the neural network, it was able to select a better system, based on platinum, improving emission reductions by up to more than 19%, depending on the type of pellet used.

General inferential limits under differential and Pufferfish privacy

Differential privacy (DP) is a class of mathematical standards for assessing the privacy provided by a data-release mechanism. This work concerns two important flavors of DP that are related yet conceptually distinct: pure ε-differential privacy (ε-DP) and Pufferfish privacy. We restate ε-DP and Pufferfish privacy as Lipschitz continuity conditions and provide their formulations in terms of an object from the imprecise probability literature: the interval of measures. We use these formulations to derive limits on key quantities in frequentist hypothesis testing and in Bayesian inference using data that are sanitised according to either of these two privacy standards. Under very mild conditions, the results in this work are valid for arbitrary parameters, priors and data generating models. These bounds are weaker than those attainable when analysing specific data generating models or data-release mechanisms. However, they provide generally applicable limits on the ability to learn from differentially private data – even when the analyst's knowledge of the model or mechanism is limited. They also shed light on the semantic interpretations of the two DP flavors under examination, a subject of contention in the current literature.1

CT reconstruction using diffusion posterior sampling conditioned on a nonlinear measurement model.

Recently, diffusion posterior sampling (DPS), where score-based diffusion priors are combined with likelihood models, has been used to produce high-quality computed tomography (CT) images given low-quality measurements. This technique permits one-time, unsupervised training of a CT prior, which can then be incorporated with an arbitrary data model. However, current methods rely on a linear model of X-ray CT physics to reconstruct. Although it is common to linearize the transmission tomography reconstruction problem, this is an approximation to the true and inherently nonlinear forward model. We propose a DPS method that integrates a general nonlinear measurement model. We implement a traditional unconditional diffusion model by training a prior score function estimator and apply Bayes' rule to combine this prior with a measurement likelihood score function derived from the nonlinear physical model to arrive at a posterior score function that can be used to sample the reverse-time diffusion process. We develop computational enhancements for the approach and evaluate the reconstruction approach in several simulation studies. The proposed nonlinear DPS provides improved performance over traditional reconstruction methods and DPS with a linear model. Moreover, as compared with a conditionally trained deep learning approach, the nonlinear DPS approach shows a better ability to provide high-quality images for different acquisition protocols. This plug-and-play method allows the incorporation of a diffusion-based prior with a general nonlinear CT measurement model. This permits the application of the approach to different systems, protocols, etc., without the need for any additional training.

Semi-supervised prototype network based on compact-uniform-sparse representation for rotating machinery few-shot class incremental fault diagnosis

Distinct from the ideal ‘closed-set’ assumption of comprehensive fault types and arbitrary data access, the entangled challenges of labeled instance paucity and fault increasing with operation are difficult to circumvent in industrial diagnostics. However, conventional deep learning-based diagnostic models lack the capacity for dynamic and incremental acceptance of new faults under limited labeled instances. When confronted with this more industrially realistic diagnostic task, their performance is constrained by overfitting, imbalance, and catastrophic forgetting, thus diminish the feasibility of engineering deployment. In this research, we define this task as a few-shot class incremental fault diagnosis (FS-CIFD) problem and develop a semi-supervised prototype network based on compact-uniform-sparse representation (CUS-SSPN) for this purpose. Structurally, CUS-SSPN incorporates the designed compact-uniform-sparse (CUS) composite loss, optimal transport (OT)-based semi-supervised strategy, and multi-level knowledge distillation (KD) strategy into the prototype network. With the CUS composite loss and OT-based semi-supervised strategy, CUS-SSPN can assign representative prototypes for each incremental fault and form an intra-class compact and inter-class uniform and sparse representation space. This allows the model to incrementally learn new faults while effectively suppressing the overfitting triggered by instance paucity and the biased diagnosis triggered by unbalanced labeling. Combining the designed multi-level KD strategy to convey category centroid information and classification decision knowledge from both prototype and logits levels, catastrophic forgetting of known faults can be effectively mitigated. Extensive FS-CIFD tasks are constructed on both publicly available dataset and practical wind turbine dataset to validate the effectiveness of CUS-SSPN and the feasibility of engineering diagnostics.

High-speed two-color scanning volumetric laser-induced fluorescence

Many problems in fluid mechanics require single-shot 3D measurements of fluid flows, but are limited by available techniques. Here, we design and build a novel flexible high-speed two-color scanning volumetric laser-induced fluorescence (H2C-SVLIF) technique. The technique is readily adaptable to a range of temporal and spatial resolutions, rendering it easily applicable to a wide spectrum of experiments. The core equipment consists of a single monochrome high-speed camera and a pair of ND: YAG lasers pulsing at different wavelengths. The use of a single camera for direct 3D imaging eliminates the need for complex volume reconstruction algorithms and easily allows for the correction of distortion defects. Motivated by the large data loads that result from high-speed imaging techniques, we develop a custom, open-source, software package, which allows for real time playback with correction of perspective defects while simultaneously overlaying arbitrary 3D data. The technique is capable of simultaneous measurement of 3D velocity fields and a secondary tracer in the flow. To showcase the flexibility and adaptability of our technique, we present a set of experiments: (1) the flow past a sphere, and (2) vortices embedded in laminar pipe flow. In the first experiment, two channel measurements are taken at a resolution of 512 × 512 × 512 with volume rates of 65.1 Hz. In the second experiment, a single-color SVLIF system is integrated on a moving stage, providing imaging at 1280 × 304 × 256 with volume rates of 34.8 Hz. Although this second experiment is only single channel, it uses identical software and much of the same hardware to demonstrate the extraction of multiple information channels from single channel volumetric images.

Trading T gates for dirty qubits in state preparation and unitary synthesis

Efficient synthesis of arbitrary quantum states and unitaries from a universal fault-tolerant gate-set e.g. Clifford+T is a key subroutine in quantum computation. As large quantum algorithms feature many qubits that encode coherent quantum information but remain idle for parts of the computation, these should be used if it minimizes overall gate counts, especially that of the expensive T-gates. We present a quantum algorithm for preparing any dimension-N pure quantum state specified by a list of N classical numbers, that realizes a trade-off between space and T-gates. Our scheme uses O(log⁡(N/ϵ)) clean qubits and a tunable number of ∼(λlog⁡(log⁡Nϵ)) dirty qubits, to reduce the T-gate cost to O(Nλ+λlog⁡Nϵlog⁡log⁡Nϵ). This trade-off is optimal up to logarithmic factors, proven through an unconditional gate counting lower bound, and is, in the best case, a quadratic improvement in T-count over prior ancillary-free approaches. We prove similar statements for unitary synthesis by reduction to state preparation. Underlying our constructions is a T-efficient circuit implementation of a quantum oracle for arbitrary classical data.

The Impact of Missing Continuous Blood Glucose Samples on Machine Learning Models for Predicting Postprandial Hypoglycemia: An Experimental Analysis

This study investigates how missing data samples in continuous blood glucose data affect the prediction of postprandial hypoglycemia, which is crucial for diabetes management. We analyzed the impact of missing samples at different times before meals using two datasets: virtual patient data and real patient data. The study uses six commonly used machine learning models under varying conditions of missing samples, including custom and random patterns reflective of device failures and arbitrary data loss, with different levels of data removal before mealtimes. Additionally, the study explored different interpolation techniques to counter the effects of missing data samples. The research shows that missing samples generally reduce the model performance, but random forest is more robust to missing samples. The study concludes that the adverse effects of missing samples can be mitigated by leveraging complementary and informative non-point features. Consequently, our research highlights the importance of strategically handling missing data, selecting appropriate machine learning models, and considering feature types to enhance the performance of postprandial hypoglycemia predictions, thereby improving diabetes management.