Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical wetting benchmarks -- advancing the plicRDF-isoAdvector unstructured Volume-of-Fluid (VOF) method

The numerical simulation of wetting and dewetting of geometrically complex surfaces benefits from unstructured numerical methods because they discretize the domain with second-order accuracy. A recently developed unstructured geometric Volume-of-Fluid (VOF) method, the plicRDF-isoAdvector method, is chosen to investigate wetting processes because of its volume conservation property and high computational efficiency. The present work verifies and validates the plicRDF-isoAdvector method for wetting problems. We present four verification studies. The first study investigates the accuracy of the interface advection near walls. The method is further investigated for the spreading of droplets on a flat and a spherical surface, respectively, for which excellent agreement with the reference solutions is obtained. Furthermore, a 2D capillary rise is considered, and a benchmark comparison based on results from previous work is performed. The benchmark suite, input data, and Jupyter Notebooks used in this study are publicly available to facilitate further research and comparison with other numerical codes.
BarrierBypass: Out-of-Sight Clean Voice Command Injection Attacks through Physical Barriers

The growing adoption of voice-enabled devices (e.g., smart speakers), particularly in smart home environments, has introduced many security vulnerabilities that pose significant threats to users' privacy and safety. When multiple devices are connected to a voice assistant, an attacker can cause serious damage if they can gain control of these devices. We ask where and how can an attacker issue clean voice commands stealthily across a physical barrier, and perform the first academic measurement study of this nature on the command injection attack. We present the BarrierBypass attack that can be launched against three different barrier-based scenarios termed across-door, across-window, and across-wall. We conduct a broad set of experiments to observe the command injection attack success rates for multiple speaker samples (TTS and live human recorded) at different command audio volumes (65, 75, 85 dB), and smart speaker locations (0.1-4.0m from barrier). Against Amazon Echo Dot 2, BarrierBypass is able to achieve 100% wake word and command injection success for the across-wall and across-window attacks, and for the across-door attack (up to 2 meters). At 4 meters for the across-door attack, BarrierBypass can achieve 90% and 80% injection accuracy for the wake word and command, respectively. Against Google Home mini BarrierBypass is able to achieve 100% wake word injection accuracy for all attack scenarios. For command injection BarrierBypass can achieve 100% accuracy for all the three barrier settings (up to 2 meters). For the across-door attack at 4 meters, BarrierBypass can achieve 80% command injection accuracy. Further, our demonstration using drones yielded high command injection success, up to 100%. Overall, our results demonstrate the potentially devastating nature of this vulnerability to control a user's device from outside of the device's physical space, and its limitations, without the need for complex and error-prone command injection.
Multipath agents for modular multitask ML systems

A standard ML model is commonly generated by a single method that specifies aspects such as architecture, initialization, training data and hyperparameters configuration. The presented work introduces a novel methodology allowing to define multiple methods as distinct agents. Agents can collaborate and compete to generate and improve ML models for a given tasks. The proposed methodology is demonstrated with the generation and extension of a dynamic modular multitask ML system solving more than one hundred image classification tasks. Diverse agents can compete to produce the best performing model for a task by reusing the modules introduced to the system by competing agents. The presented work focuses on the study of agents capable of: 1) reusing the modules generated by concurrent agents, 2) activating in parallel multiple modules in a frozen state by connecting them with trainable modules, 3) condition the activation mixture on each data sample by using a trainable router module. We demonstrate that this simple per-sample parallel routing method can boost the quality of the combined solutions by training a fraction of the activated parameters.
Matrix Estimation for Individual Fairness

In recent years, multiple notions of algorithmic fairness have arisen. One such notion is individual fairness (IF), which requires that individuals who are similar receive similar treatment. In parallel, matrix estimation (ME) has emerged as a natural paradigm for handling noisy data with missing values. In this work, we connect the two concepts. We show that pre-processing data using ME can improve an algorithm's IF without sacrificing performance. Specifically, we show that using a popular ME method known as singular value thresholding (SVT) to pre-process the data provides a strong IF guarantee under appropriate conditions. We then show that, under analogous conditions, SVT pre-processing also yields estimates that are consistent and approximately minimax optimal. As such, the ME pre-processing step does not, under the stated conditions, increase the prediction error of the base algorithm, i.e., does not impose a fairness-performance trade-off. We verify these results on synthetic and real data.
Semantic Diffusion Network for Semantic Segmentation

Precise and accurate predictions over boundary areas are essential for semantic segmentation. However, the commonly-used convolutional operators tend to smooth and blur local detail cues, making it difficult for deep models to generate accurate boundary predictions. In this paper, we introduce an operator-level approach to enhance semantic boundary awareness, so as to improve the prediction of the deep semantic segmentation model. Specifically, we first formulate the boundary feature enhancement as an anisotropic diffusion process. We then propose a novel learnable approach called semantic diffusion network (SDN) to approximate the diffusion process, which contains a parameterized semantic difference convolution operator followed by a feature fusion module. Our SDN aims to construct a differentiable mapping from the original feature to the inter-class boundary-enhanced feature. The proposed SDN is an efficient and flexible module that can be easily plugged into existing encoder-decoder segmentation models. Extensive experiments show that our approach can achieve consistent improvements over several typical and state-of-the-art segmentation baseline models on challenging public benchmarks. The code will be released soon.
Detecting Security Patches via Behavioral Data in Code Repositories

The absolute majority of software today is developed collaboratively using collaborative version control tools such as Git. It is a common practice that once a vulnerability is detected and fixed, the developers behind the software issue a Common Vulnerabilities and Exposures or CVE record to alert the user community of the security hazard and urge them to integrate the security patch. However, some companies might not disclose their vulnerabilities and just update their repository. As a result, users are unaware of the vulnerability and may remain exposed. In this paper, we present a system to automatically identify security patches using only the developer behavior in the Git repository without analyzing the code itself or the remarks that accompanied the fix (commit message). We showed we can reveal concealed security patches with an accuracy of 88.3% and F1 Score of 89.8%. This is the first time that a language-oblivious solution for this problem is presented.
Interface Design for Crowdsourcing Hierarchical Multi-Label Text Annotations

Human data labeling is an important and expensive task at the heart of supervised learning systems. Hierarchies help humans understand and organize concepts. We ask whether and how concept hierarchies can inform the design of annotation interfaces to improve labeling quality and efficiency. We study this question through annotation of vaccine misinformation, where the labeling task is difficult and highly subjective. We investigate 6 user interface designs for crowdsourcing hierarchical labels by collecting over 18,000 individual annotations. Under a fixed budget, integrating hierarchies into the design improves crowdsource workers’ F1 scores. We attribute this to (1) Grouping similar concepts, improving F1 scores by +0.16 over random groupings, (2) Strong relative performance on high-difficulty examples (relative F1 score difference of +0.40), and (3) Filtering out obvious negatives, increasing precision by +0.07. Ultimately, labeling schemes integrating the hierarchy outperform those that do not — achieving mean F1 of 0.70.
Scene2BIR: Material-aware learning-based binaural impulse response generator for reconstructed real-world 3D scenes

Fig. 1. The binaural impulse responses (BIRs) generated for real-world 3D reconstructed scenes. We compare the accuracy of our learning method (Scene2BIR) with geometric sound propagation algorithms. These models were not used in the training data for Scene2BIR. Our Scene2BIR can generate binaural IRs corresponding to left and high channels by taking into account interaural level differences (ILD) and interaural time differences (ITD). In practice, Scene2BIR is two orders of magnitude faster than the interactive geometric sound propagation algorithm. We will release the code and the dataset at the time of publication.
Probabilistic-Bits Based on Ferroelectric Field-Effect Transistors for Probabilistic Computing

A probabilistic bit (p-bit) is the fundamental building block in the circuit of probabilistic computing (PC), and it produces a random binary bitstream with tunable probability. Utilizing the randomness induced by thermal noise-induced lattice vibration in the ferroelectric (FE) material, we propose the p-bits based on stochastic ferroelectric FET (FeFET). The domain dynamic is revealed to play crucial roles in FE p-bits’ stochasticity, as the domain coupling suppresses the dipole fluctuation. The proposed FE p-bits possess the advantages of both extremely low hardware cost and scalability for p-bit circuitry, rendering it a promising candidate for PC.
Goodness of Fit and Related Inference Processes for Quantile Regression

Abstract We introduce a goodness-of-fit process for quantile regression analogous to the conventional R2 statistic of least squares regression. Several related inference processes designed to test composite hypotheses about the combined effect of several covariates over an entire range of conditional quantile functions are also formulated. The asymptotic behavior of the inference processes is shown to be closely related to earlier p-sample goodness-of-fit theory involving Bessel processes. The approach is illustrated with some hypothetical examples, an application to recent empirical models of international economic growth, and some Monte Carlo evidence.
An Empirical Analysis of Fairness Notions under Differential Privacy

Recent works have shown that selecting an optimal model architecture suited to the differential privacy setting is necessary to achieve the best possible utility for a given privacy budget using differentially private stochastic gradient descent (DP-SGD)(Tramer and Boneh 2020; Cheng et al. 2022). In light of these findings, we empirically analyse how different fairness notions, belonging to distinct classes of statistical fairness criteria (independence, separation and sufficiency), are impacted when one selects a model architecture suitable for DP-SGD, optimized for utility. Using standard datasets from ML fairness literature, we show using a rigorous experimental protocol, that by selecting the optimal model architecture for DP-SGD, the differences across groups concerning the relevant fairness metrics (demographic parity, equalized odds and predictive parity) more often decrease or are negligibly impacted, compared to the non-private baseline, for which optimal model architecture has also been selected to maximize utility. These findings challenge the understanding that differential privacy will necessarily exacerbate unfairness in deep learning models trained on biased datasets.
Multiscale Graph Comparison via the Embedded Laplacian Discrepancy

Laplacian eigenvectors capture natural community structures on graphs and are widely used in spectral clustering and manifold learning. The use of Laplacian eigenvectors as embeddings for the purpose of multiscale graph comparison has however been limited. Here we propose the Embedded Laplacian Discrepancy (ELD) as a simple and fast approach to compare graphs (of potentially different sizes) based on the similarity of the graphs' community structures. The ELD operates by representing graphs as point clouds in a common, low-dimensional space, on which a natural Wasserstein-based distance can be efficiently computed. A main challenge in comparing graphs through any eigenvector-based approaches is the potential ambiguity that could arise due to sign-flips and basis symmetries. The ELD leverages a simple symmetrization trick to bypass any sign ambiguities. For comparing graphs that do not have any ambiguities due to basis symmetries (i.e. the spectrums are simple), we show that the ELD becomes a natural pseudo-metric that enjoys nice properties such as invariance under graph isomorphism. For comparing graphs with non-simple spectrums, we propose a procedure to approximate the ELD via a simple perturbation technique to resolve any ambiguity from basis symmetries. We show that such perturbations are stable using matrix perturbation theory under mild assumptions that are straightforward to verify in practice. We demonstrate the excellent applicability of the ELD approach on both simulated and real datasets.
FaaSLight: General Application-level Cold-start Latency Optimization for Function-as-a-Service in Serverless Computing

Serverless computing is a popular cloud computing paradigm that frees developers from server management. Function-as-a-Service (FaaS) is the most popular implementation of serverless computing, representing applications as event-driven and stateless functions. However, existing studies report that functions of FaaS applications severely suffer from cold-start latency. In this article, we propose an approach, namely, FaaSLight, to accelerating the cold start for FaaS applications through application-level optimization. We first conduct a measurement study to investigate the possible root cause of the cold-start problem of FaaS. The result shows that application code loading latency is a significant overhead. Therefore, loading only indispensable code from FaaS applications can be an adequate solution. Based on this insight, we identify code related to application functionalities by constructing the function-level call graph and separate other code (i.e., optional code) from FaaS applications. The separated optional code can be loaded on demand to avoid the inaccurate identification of indispensable code causing application failure. In particular, a key principle guiding the design of FaaSLight is inherently general, i.e., platform- and language-agnostic. In practice, FaaSLight can be effectively applied to FaaS applications developed in different programming languages (Python and JavaScript), and can be seamlessly deployed on popular serverless platforms such as AWS Lambda and Google Cloud Functions, without having to modify the underlying OSes or hypervisors, nor introducing any additional manual engineering efforts to developers. The evaluation results on real-world FaaS applications show that FaaSLight can significantly reduce the code loading latency (up to 78.95%, 28.78% on average), thereby reducing the cold-start latency. As a result, the total response latency of functions can be decreased by up to 42.05% (19.21% on average). Compared with the state-of-the-art, FaaSLight achieves a 21.25× improvement in reducing the average total response latency.
Boosting Exploration in Multi-Task Reinforcement Learning using Adversarial Networks

Advancements in reinforcement learning (RL) have been remarkable in recent years. However, the limitations of traditional training methods have become increasingly evident, particularly in meta-RL settings where agents face new, unseen tasks. Conventional training approaches are susceptible to failure in such situations as they need more robustness to adversity. Our proposed adversarial training regime for Multi-Task Reinforcement Learning (MT-RL) addresses the limitations of conventional training methods in RL, especially in meta-RL environments where the agent faces new tasks. The adversarial component challenges the agent, forcing it to improve its decision-making abilities in dynamic and unpredictable situations. This component operates without relying on manual intervention or domain-specific knowledge, making it a highly versatile solution. Experiments conducted in multiple MT-RL environments demonstrate that adversarial training leads to better exploration and a deeper understanding of the environment. The adversarial training regime for MT-RL presents a new perspective on training and development for RL agents and is a valuable contribution to the field.
Theoretical Performance Limit for Radar Parameter Estimation

In this paper, we employ the thoughts and methodologies of Shannon's information theory to solve the problem of the optimal radar parameter estimation. Based on a general radar system model, the \textit{a posteriori} probability density function of targets' parameters is derived. Range information (RI) and entropy error (EE) are defined to evaluate the performance. It is proved that acquiring 1 bit of the range information is equivalent to reducing estimation deviation by half. The closed-form approximation for the EE is deduced in all signal-to-noise ratio (SNR) regions, which demonstrates that the EE degenerates to the mean square error (MSE) when the SNR is tending to infinity. Parameter estimation theorem is then proved, which claims that the theoretical RI is achievable. The converse claims that there exists no unbiased estimator whose empirical RI is larger than the theoretical RI. Simulation result demonstrates that the theoretical EE is tighter than the commonly used Cram\'er-Rao bound and the ZivZakai bound.
Iterative decoding of generalized low-density parity-check codes

A generalization of Gallager's low-density parity-check codes is introduced, where as component codes single-error correcting Hamming codes are used instead of single-error detecting parity-check codes. Low-density (LD) parity-check codes were first introduced by Gallager in 1963. These codes are, in combination with iterative decoding, very promising for achieving low error probabilities at a reasonable cost. Results of computer simulations for long LD codes show, that a performance close to the Shannon capacity limit can be achieved. In this work, we consider a generalized version of low-density parity-check codes, where the decoding procedure can be based on the decoding of Hamming component codes.
Simultaneous and Group-Sparse Multi-Task Learning of Gaussian Graphical Models

In this paper, we present $\ell_{1,p}$ multi-task structure learning for Gaussian graphical models. We discuss the uniqueness and boundedness of the optimal solution of the maximization problem. A block coordinate descent method leads to a provably convergent algorithm that generates a sequence of positive definite solutions. Thus, we reduce the original problem into a sequence of strictly convex $\ell_p$ regularized quadratic minimization subproblems. We further show that this subproblem leads to the continuous quadratic knapsack problem for $p=\infty$ and to a separable version of the well-known quadratic trust-region problem for $p=2$, for which very efficient methods exist. Finally, we show promising results in synthetic experiments as well as in two real-world datasets.
Computations of Light-Scattering and Extinction by Spheres According to Diffraction and Geometrical Optics, and Some Comparisons with the Mie Theory

The scattering and extinction of light by systems of transparent, spherical particles have been calculated on the basis of classical diffraction and of geometrical scattering by external reflection and transmission, for the refractive index range 1.1 to 2.0 and scattering angles up to about 40°, and presented as a set of graphs for general use. Provided that the particles are larger than 3 or 4 wavelengths of light, and that there is a sufficient range (say 2:1) of particle sizes present to average out effects of phase, the scattering and extinction calculated in this way agree well with such direct calculations by the Mie theory as are at present available. It is thought that the treatment should also be successful with irregular particles, and to some extent with nontransparent ones, such as often occur in airborne dusts in industry. The conditions under which scattering and extinction measurements can be used to estimate the surface-area of particles in suspension in air or liquid are discussed.
Some Examples in the Theory of Factorization in Integral Domains

Let R be a UFD, S a subring of R and P be a nonzero commom ideal of R and S such that (i) R/P is a torsion-free S/P-module, (ii) if x,y ∈ S\P and c ∈ R with y = cx then c ∈ S. In this note we study the factorization properties of such subrings and thereby obtain some examples.
Mining Teacher Informal Online Learning Networks: Insights From Massive Educational Chat Tweets

Social-media-based teacher learning networks have the affordance to grant flexibility of time and space for teachers’ professional learning, support the development and sustainability of social networking, and meet their just-in-time needs for exchanging knowledge, negotiating meaning and accessing resources. However, most existing research on teacher online learning networks relies on qualitative methods and self-report data. There is a lack of study using quantitative methods to study large networks, especially using authentic data from social media. This work adds to the literature through mining teacher informal online learning networks using authentic data retrieved from Twitter. Specifically, we collected around half a million tweets and developed a network with the data. Then, various social network analysis techniques were utilized to explore the network structure and characteristics, participants’ behavioral patterns and how individuals connected with each other. We found that members of massive teacher informal online learning networks tended to communicate more with others of similar characteristics forming homogeneous communities, while hub participants connected many small communities which are significantly from one another, and hence, are the key to degree heterogeneity in a large network.