Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Large Language Models to Generate JUnit Tests: An Empirical Study

A code generation model generates code by taking a prompt from a code comment, existing code, or a combination of both. Although code generation models (e.g., GitHub Copilot) are increasingly being adopted in practice, it is unclear whether they can successfully be used for unit test generation without fine-tuning for a strongly typed language like Java. To fill this gap, we investigated how well three models (Codex, GPT-3.5-Turbo, and StarCoder) can generate unit tests. We used two benchmarks (HumanEval and Evosuite SF110) to investigate the effect of context generation on the unit test generation process. We evaluated the models based on compilation rates, test correctness, test coverage, and test smells. We found that the Codex model achieved above 80% coverage for the HumanEval dataset, but no model had more than 2% coverage for the EvoSuite SF110 benchmark. The generated tests also suffered from test smells, such as Duplicated Asserts and Empty Tests.
An Efficient Plane Extraction Approach for Bundle Adjustment on LiDAR Point clouds

Bundle adjustment (BA) on LiDAR point clouds has been extensively investigated in recent years due to its ability to optimize multiple poses together, resulting in high accuracy and global consistency for point cloud. However, the accuracy and speed of LiDAR bundle adjustment depend on the quality of plane extraction, which provides point association for LiDAR BA. In this study, we propose a novel and efficient voxel-based approach for plane extraction that is specially designed to provide point association for LiDAR bundle adjustment. To begin, we partition the space into multiple voxels of a fixed size and then split these root voxels based on whether the points are on the same plane, using an octree structure. We also design a novel plane determination method based on principle component analysis (PCA), which segments the points into four even quarters and compare their minimum eigenvalues with that of the initial point cloud. Finally, we adopt a plane merging method to prevent too many small planes from being in a single voxel, which can increase the optimization time required for BA. Our experimental results on HILTI demonstrate that our approach achieves the best precision and least time cost compared to other plane extraction methods.
Monotonic Extensions of Lipschitz Maps

We study the problem of extending an order-preserving real-valued Lipschitz map defined on a subset of a partially ordered metric space without increasing its Lipschitz constant and preserving its monotonicity. We show that a certain type of relation between the metric and order of the space, which we call radiality, is necessary and sufficient for such an extension to exist. Radiality is automatically satisfied by the equality relation, so the classical McShane-Whitney extension theorem is a special case of our main characterization result. As applications, we obtain a similar generalization of McShane's uniformly continuous extension theorem, along with some functional representation results for radial partial orders.
Posterior Sampling for Deep Reinforcement Learning

Despite remarkable successes, deep reinforcement learning algorithms remain sample inefficient: they require an enormous amount of trial and error to find good policies. Model-based algorithms promise sample efficiency by building an environment model that can be used for planning. Posterior Sampling for Reinforcement Learning is such a model-based algorithm that has attracted significant interest due to its performance in the tabular setting. This paper introduces Posterior Sampling for Deep Reinforcement Learning (PSDRL), the first truly scalable approximation of Posterior Sampling for Reinforcement Learning that retains its model-based essence. PSDRL combines efficient uncertainty quantification over latent state space models with a specially tailored continual planning algorithm based on value-function approximation. Extensive experiments on the Atari benchmark show that PSDRL significantly outperforms previous state-of-the-art attempts at scaling up posterior sampling while being competitive with a state-of-the-art (model-based) reinforcement learning method, both in sample efficiency and computational efficiency.
Neurosymbolic AI - Why, What, and How

Humans interact with the environment using a combination of perception - transforming sensory inputs from their environment into symbols, and cognition - mapping symbols to knowledge about the environment for supporting abstraction, reasoning by analogy, and long-term planning. Human perception-inspired machine perception, in the context of AI, refers to large-scale pattern recognition from raw data using neural networks trained using self-supervised learning objectives such as next-word prediction or object recognition. On the other hand, machine cognition encompasses more complex computations, such as using knowledge of the environment to guide reasoning, analogy, and long-term planning. Humans can also control and explain their cognitive functions. This seems to require the retention of symbolic mappings from perception outputs to knowledge about their environment. For example, humans can follow and explain the guidelines and safety constraints driving their decision-making in safety-critical applications such as healthcare, criminal justice, and autonomous driving. This article introduces the rapidly emerging paradigm of Neurosymbolic AI combines neural networks and knowledge-guided symbolic approaches to create more capable and flexible AI systems. These systems have immense potential to advance both algorithm-level (e.g., abstraction, analogy, reasoning) and application-level (e.g., explainable and safety-constrained decision-making) capabilities of AI systems.
Learned Focused Plenoptic Image Compression With Microimage Preprocessing and Global Attention

Focused plenoptic cameras can record spatial and angular information of the light field (LF) simultaneously with higher spatial resolution relative to traditional plenoptic cameras, which facilitate various applications in computer vision. However, the existing plenoptic image compression methods present ineffectiveness to the captured images due to the complex micro-textures generated by the microlens relay imaging and long-distance correlations among the microimages. In this article, a lossy end-to-end learning architecture is proposed to compress the focused plenoptic images efficiently. First, a data preprocessing scheme is designed according to the imaging principle to remove the sub-aperture image ineffective pixels in the recorded light field and align the microimages to the rectangular grid. Then, the global attention module with large receptive field is proposed to capture the global correlation among the feature maps using pixel-wise vector attention computed in the resampling process. Also, a new image dataset consisting of 1910 focused plenoptic images with content and depth diversity is built to benefit training and testing. Extensive experimental evaluations demonstrate the effectiveness of the proposed approach. It outperforms intra coding of HEVC and VVC by an average of 62.57% and 51.67% bitrate reduction on the 20 preprocessed focused plenoptic images, respectively. Also, it achieves 18.73% bitrate saving and generates perceptually pleasant reconstructions compared to the state-of-the-art end-to-end image compression methods, which benefits the applications of focused plenoptic cameras greatly. The dataset and code are publicly available at https://github.com/VincentChandelier/GACN.
Interacting tachyonic scalar field II

The existence of dark energy is essential to explain the cosmic accelerated expansion. We consider a homogenous interacting tachyonic scalar field as a possible candidate for the dynamical dark energy. The interaction between the tachyonic field and matter can be gauged to be linear in the energy density of matter (or the tachyonic field) and Hubble's parameter. We estimate the rate of expansion, the age of the universe, the evolution of energy density of matter and tachyonic field, and the coupling strength of the interaction for a spatially flat ($k=0$) universe. We observed that the upper limit of coupling strength is 1, and it is the same whether the interaction term depends on the energy density of matter or the energy density of tachyonic scalar field.
IoTFlowGenerator: Crafting Synthetic IoT Device Traffic Flows for Cyber Deception

Over the years, honeypots emerged as an important security tool to understand attacker intent and deceive attackers to spend time and resources. Recently, honeypots are being deployed for Internet of things (IoT) devices to lure attackers, and learn their behavior. However, most of the existing IoT honeypots, even the high interaction ones, are easily detected by an attacker who can observe honeypot traffic due to lack of real network traffic originating from the honeypot. This implies that, to build better honeypots and enhance cyber deception capabilities, IoT honeypots need to generate realistic network traffic flows. To achieve this goal, we propose a novel deep learning based approach for generating traffic flows that mimic real network traffic due to user and IoT device interactions.A key technical challenge that our approach overcomes is scarcity of device-specific IoT traffic data to effectively train a generator.We address this challenge by leveraging a core generative adversarial learning algorithm for sequences along with domain specific knowledge common to IoT devices.Through an extensive experimental evaluation with 18 IoT devices, we demonstrate that the proposed synthetic IoT traffic generation tool significantly outperforms state of the art sequence and packet generators in remaining indistinguishable from real traffic even to an adaptive attacker.
Environmental Sound Synthesis from Vocal Imitations and Sound Event Labels

One way of expressing an environmental sound is using vocal imitations, which involve the process of replicating or mimicking the rhythm and pitch of sounds by voice. We can effectively express the features of environmental sounds, such as rhythm and pitch, using vocal imitations, which cannot be expressed by conventional input information, such as sound event labels, images, or texts, in an environmental sound synthesis model. In this paper, we propose a framework for environmental sound synthesis from vocal imitations and sound event labels based on a framework of a vector quantized encoder and the Tacotron2 decoder. Using vocal imitations is expected to control the pitch and rhythm of the synthesized sound, which only sound event labels cannot control. Our objective and subjective experimental results show that vocal imitations effectively control the pitch and rhythm of synthesized sounds.
Addressing Age-Related Accessibility Needs of Senior Users Through Model-Driven Engineering

One of the main reasons that cause seniors to face accessibility barriers when trying to use software applications is that the age-related user interface (UI) needs of seniors (e.g., physical and cognitive limitations) are not properly addressed in software user interfaces. The existing literature proposes model-driven engineering based UI adaptations as a prominent solution for this phenomenon. But in our exploration into the domain, we identified that the existing work lacks comprehensiveness when it comes to integrating accessibility into software modelling tools and methods when compared to a well-recognised accessibility standard such as the Web Content Accessibility Guidelines (WCAG). Thus in this paper, we outline a research roadmap that aims to use WCAG as a reference framework to design domain-specific languages that model the diverse accessibility scenarios of senior users via user context information and UI adaptation rules modelling so that they meet the accessibility standards specified in WCAG.
Multipath Time Delay Estimation in Multi Channel Disturbance Case

Taking into account the multi channels disturbance and noises with the non stationary characteristic,time delay estimation based on cyclic correlation parametric approach is proposed in this paper.Multi path time delays are estimated precisely with this method,and disturbances and noises can be refrained effectively.This method has better performance than the time delay estimation based on correlative and has less calculating amount and more wide applicability than the estimation based on the higher order cumulant approach.The relevant conclusions are got and verified by the simulation experiments.
Methods and prospects for gravitational-wave searches targeting ultralight vector-boson clouds around known black holes

Ultralight bosons are predicted in many extensions to the Standard Model and are popular dark matter candidates. The black hole superradiance mechanism allows for these particles to be probed using only their gravitational interaction. In this scenario, an ultralight boson cloud may form spontaneously around a spinning black hole and extract a non-negligible fraction of the black hole's mass. These oscillating clouds produce quasi-monochromatic, long-duration gravitational waves that may be detectable by ground-based or space-based gravitational wave detectors. We discuss the capability of a new long-duration signal tracking method, based on a hidden Markov model, to detect gravitational wave signals generated by ultralight vector boson clouds, including cases where the signal frequency evolution timescale is much shorter than that of a typical continuous wave signal. We quantify the detection horizon distances for vector boson clouds with current- and next-generation ground-based detectors. We demonstrate that vector clouds hosted by black holes with mass $\gtrsim 60 M_{\odot}$ and spin $\gtrsim 0.6$ are within the reach of current-generation detectors up to a luminosity distance of $\sim 1$ Gpc. This search method enables one to target vector boson clouds around remnant black holes from compact binary mergers detected by gravitational-wave detectors. We discuss the impact of the sky localization of the merger events and demonstrate that a typical remnant black hole reasonably well-localized by the current generation detector network is accessible in a follow-up search.
Diffusion models for time-series applications: a survey

Diffusion models, a family of generative models based on deep learning, have become increasingly prominent in cutting-edge machine learning research. With distinguished performance in generating samples that resemble the observed data, diffusion models are widely used in image, video, and text synthesis nowadays. In recent years, the concept of diffusion has been extended to time-series applications, and many powerful models have been developed. Considering the deficiency of a methodical summary and discourse on these models, we provide this survey as an elementary resource for new researchers in this area and to provide inspiration to motivate future research. For better understanding, we include an introduction about the basics of diffusion models. Except for this, we primarily focus on diffusion-based methods for time-series forecasting, imputation, and generation, and present them, separately, in three individual sections. We also compare different methods for the same application and highlight their connections if applicable. Finally, we conclude with the common limitation of diffusion-based methods and highlight potential future research directions. 扩散模型,一类基于深度学习的生成模型家族,在前沿机器学习研究中变得日益重要。 扩散模型以在生成与观察数据相似样本方面的卓越性能而著称,如今广泛用于图像、视频和文本合成。 近年来,扩散的概念已扩展到时间序列应用领域,涌现出许多强大的模型。 鉴于这些模型缺乏系统性总结和讨论,我们提供此综述作为此领域新研究人员的基础资源,并为激发未来研究提供灵感。 为更好理解,引入了有关扩散模型基础知识的介绍。 除此之外,主要关注基于扩散的时间序列预测、插补和生成方法,并将它们分别在三个独立章节中呈现。 还比较了同一应用的不同方法,并强调它们之间的关联(若适用)。 最后,总结了扩散方法的共同局限性,并突出强调潜在的未来研究方向。
Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning

Conventionally, federated learning aims to optimize a single objective, typically the utility. However, for a federated learning system to be trustworthy, it needs to simultaneously satisfy multiple/many objectives, such as maximizing model performance, minimizing privacy leakage and training cost, and being robust to malicious attacks. Multi-Objective Optimization (MOO) aiming to optimize multiple conflicting objectives at the same time is quite suitable for solving the optimization problem of Trustworthy Federated Learning (TFL). In this paper, we unify MOO and TFL by formulating the problem of constrained multi-objective federated learning (CMOFL). Under this formulation, existing MOO algorithms can be adapted to TFL straightforwardly. Different from existing CMOFL works focusing on utility, efficiency, fairness, and robustness, we consider optimizing privacy leakage along with utility loss and training cost, the three primary objectives of a TFL system. We develop two improved CMOFL algorithms based on NSGA-II and PSL, respectively, for effectively and efficiently finding Pareto optimal solutions, and we provide theoretical analysis on their convergence. We design specific measurements of privacy leakage, utility loss, and training cost for three privacy protection mechanisms: Randomization, BatchCrypt (An efficient version of homomorphic encryption), and Sparsification. Empirical experiments conducted under each of the three protection mechanisms demonstrate the effectiveness of our proposed algorithms.
A Theory of the NEPv Approach for Optimization On the Stiefel Manifold

The NEPv approach has been increasingly used lately for optimization on the Stiefel manifold arising from machine learning. General speaking, the approach first turns the first order optimality condition, also known as the KKT condition, into a nonlinear eigenvalue problem with eigenvector dependency (NEPv) or a nonlinear polar decomposition with orthogonal factor dependency (NPDo) and then solve the nonlinear problem via some variations of the self-consistent-field (SCF) iteration. The difficulty, however, lies in designing a proper SCF iteration so that a maximizer is found at the end. Currently, each use of the approach is very much individualized, especially in its convergence analysis to show that the approach does work or otherwise. In this paper, a unifying framework is established. The framework is built upon some basic assumptions. If the basic assumptions are satisfied, globally convergence is guaranteed to a stationary point and during the SCF iterative process that leads to the stationary point, the objective function increases monotonically. Also a notion of atomic functions is proposed, which include commonly used matrix traces of linear and quadratic forms as special ones. It is shown that the basic assumptions are satisfied by atomic functions and by convex compositions of atomic functions. Together they provide a large collection of objectives for which the NEPv/NPDo approach is guaranteed to work.
Unraveling Latch Locking Using Machine Learning, Boolean Analysis, and ILP

Logic locking has become a promising approach to provide hardware security in the face of a possibly insecure fabrication supply chain. While many techniques have focused on locking combinational logic (CL), an alternative latch-locking approach in which the sequential elements are locked has also gained significant attention. Latch (LAT) locking duplicates a subset of the flip-flops (FF) of a design, retimes these FFs and replaces them with latches, and adds two types of decoy latches to obfuscate the netlist. It then adds control circuitry (CC) such that all latches must be correctly keyed for the circuit to function correctly. This paper presents a two-phase attack on latch-locked circuits that uses a novel combination of deep learning, Boolean analysis, and integer linear programming (ILP). The attack requires access to the reverse-engineered netlist but, unlike SAT attacks, is oracle-less, not needing access to the unlocked circuit or correct input/output pairs. We trained and evaluated the attack using the ISCAS’89 and ITC’99 benchmark circuits. The attack successfully identifies a key that is, on average, 96.9% accurate and fully discloses the correct functionality in 8 of the tested 19 circuits and leads to low function corruptibility (less than 4%) in 3 additional circuits. The attack run-times are manageable.
A New Stock Market Valuation Measure with Applications to Retirement Planning

We generalize the classic Shiller cyclically adjusted price-earnings ratio (CAPE) used for prediction of future total returns of the stock market. We treat earnings growth as exogenous. The difference between log wealth and log earnings is modeled as an autoregression of order 1 with linear trend 4.5\% and Gaussian innovations. Detrending gives us a new valuation measure. This autoregression is significantly different from the random walk. Therefore, our results disprove the Efficient Market Hypothesis. Therefore, long-run total returns equal long-run earnings growth plus 4.5\%. We apply results to retirement planning. A withdrawal process governs how a retired capital owner withdraws a certain fraction of wealth annually. The fraction can vary from year to year. We study the long-term behavior of such processes.
Graph Representation Learning via Diversity-preserving Graph Reﬁnement

—A real-world graph has a complex topological structure, which is often formed by the interaction of different latent factors. Disentanglement of these latent factors can effectively improve the robustness and expressiveness of the node representation of a graph. However, most existing methods lack consideration of the intrinsic differences in relations between nodes caused by factor entanglement. In this paper, we pro-pose an Adversarial Disentangled Graph Convolutional Network (ADGCN) for disentangled graph representation learning. To begin with, we point out two aspects of graph disentanglement that need to be considered, i.e., micro-disentanglement and macro-disentanglement. For these two aspects, a component-speciﬁc aggregation approach is proposed to achieve micro-disentanglement by inferring latent components that caused the links between nodes. On the basis of micro-disentanglement, we further propose a macro-disentanglement adversarial regularizer to improve the separability among component distributions, thus restricting the interdependence among components. Additionally, to reveal the topological graph structure, a diversity-preserving node sampling approach is proposed, by which the graph structure can be progressively reﬁned in a way of local structure awareness.
Evolution of clinical and radiological presentations of spondyloepimetaphyseal dysplasia, RPL13‐related: Description of 11 further cases

Spondyloepimetaphyseal dysplasia (SEMD), RPL13‐related is caused by heterozygous variants in RPL13, which encodes the ribosomal protein eL13, a component of the 60S human ribosomal subunit. Here, we describe the clinical and radiological evolution of 11 individuals, 7 children and 4 adults, from 6 families. Some of the skeletal features improved during the course of this condition, whilst others worsened. We describe for the first time “corner fractures” as a feature of this dysplasia which as with other dysplasias disappear with age. In addition, we review the heights and skeletal anomalies of these reported here and previously in a total of 25 individuals from 15 families. In this study, six different RPL13 variants were identified, five of which were novel. All were located in the apparently hotspot region, located in intron 5 and exon 6. Splicing assays were performed for two of the three previously undescribed splicing variants. Until now, all splice variants have occurred in the intron 5 splice donor site, incorporating an additional 18 amino acids to the mutant protein. Here, we report the first variant in intron 5 splice acceptor site which generates two aberrant transcripts, deleting the first three and four amino acids encoded by exon 6. Thus, this study doubles the number of SEMD‐RPL13‐related cases and variants reported to date and describes unreported age‐related clinical and radiological features.