Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual Systolic Graphs

We define a family of graphs we call dual systolic graphs. This definition comes from graphs that are duals of systolic simplicial complexes. Our main result is a sharp (up to constants) isoperimetric inequality for dual systolic graphs. The first step in the proof is an extension of the classical isoperimetric inequality of the boolean cube. The isoperimetric inequality for dual systolic graphs, however, is exponentially stronger than the one for the boolean cube. Interestingly, we know that dual systolic graphs exist, but we do not yet know how to efficiently construct them. We, therefore, define a weaker notion of dual systolicity. We prove the same isoperimetric inequality for weakly dual systolic graphs, and at the same time provide an efficient construction of a family of graphs that are weakly dual systolic. We call this family of graphs clique products. We show that there is a non-trivial connection between the small set expansion capabilities and the threshold rank of clique products, and believe they can find further applications.
Exploring the Magnetic Field Geometry in NGC 891 with SOFIA/HAWC+

Stratospheric Observatory For Infrared Astronomy/High-resolution Airborne Wideband Camera-plus 154 μm Far-Infrared polarimetry observations of the well-studied edge-on galaxy NGC 891 are analyzed and compared to simple disk models with ordered (planar) and turbulent magnetic fields. The overall low magnitude and the narrow dispersion of fractional polarization observed in the disk require significant turbulence and a large number of turbulent decorrelation cells along the line of sight through the plane. Higher surface brightness regions along the major axis to either side of the nucleus show a further reduction in polarization and are consistent with a view tangent to a spiral feature in our disk models. The nucleus also has a similar low polarization, and this is inconsistent with our model spiral galaxy where the ordered magnetic field component would be nearly perpendicular to the line of sight through the nucleus on an edge-on view. A model with a barred spiral morphology with a magnetic field geometry derived from radio synchrotron observations of face-on barred spirals fits the data much better. There is clear evidence for a vertical field extending into the halo from one location in the disk coincident with a polarization null point seen in near-infrared polarimetry, probably due to a blowout caused by star formation. Although our observations were capable of detecting a vertical magnetic field geometry elsewhere in the halo, no clear signature was found. A reduced polarization due to a mix of planar and vertical fields in the dusty regions of the halo best explains our observations, but unusually significant turbulence cannot be ruled out.
DistHD: A Learner-Aware Dynamic Encoding Method for Hyperdimensional Classification

The Internet of Things (IoT) has become an emerging trend that connects heterogeneous devices and enables them with new capabilities. Many applications exploit machine learning methodology to dissect collected data, and edge computing was introduced to enhance the efficiency and scalability in resource-constrained computing environments. Unfortunately, popular deep learning algorithms involve intensive computations that are overcomplicated for edge devices. Brain-inspired Hyperdimensional Computing (HDC) has been considered a promising approach to address this issue. However, existing HDC methods use static encoders, and thus require extremely high dimensionality and hundreds of training iterations to achieve reasonable accuracy. This results in a huge loss of efficiency and severely impedes the application of HDC algorithms in power-limited machines. In this paper, we propose DistHD, a novel HDC framework with a unique dynamic encoding technique consisting of two parts: top-2 classification and dimension regeneration. Our top-2 classification provides top-2 labels for each data sample based on cosine similarity, and dimension regeneration identifies and regenerates dimensions that mislead the classification and reduce the learning quality. The highly parallel algorithm of DistHD effectively accelerates the learning process and achieves the desired accuracy with considerably lower dimensionality. Our evaluation on a wide range of practical classification tasks shows that DistHD is capable of achieving on average 2.12% higher accuracy than state-of-the-art (SOTA) HDC approaches while reducing dimensionality by 8.0×. It delivers 5.97× faster training and 8.09× faster inference than SOTA learning algorithms. Additionally, the holographic distribution of patterns in high dimensional space provides DistHD with 12.90× higher robustness against hardware errors than SOTA DNNs. DistHD has been open-sourced to enable future research in this field.1
We Live in a Society: Motivators for Contributions in an OSS Ecosystem

Due to the increasing number of attacks targeting open source library ecosystems, assisting maintainers has become a top priority. This is especially important since maintainers are usually overworked. Although the motivation of Open Source developers has been widely studied, the extent to which maintainers assist libraries that they depend on is unknown. Surveying NPM developers, our early results indicate a difference in motivation between maintaining their own library (i.e., more person driven), as opposed to professional factors (i.e., focus on skills and expertise) when contributing to the software ecosystem. Finally, our thematic analysis shows different motivations and barriers developers face when contributing to the ecosystem. These results show that developers have different motivations and barriers depending on the role they play when making contributions to the ecosystem.
Dynamic Mixed Membership Stochastic Block Model for Weighted Labeled Networks

Most real-world networks evolve over time. Existing literature proposes models for dynamic networks that are either unlabeled or assumed to have a single membership structure. On the other hand, a new family of Mixed Membership Stochastic Block Models (MMSBM) allows to model static labeled networks under the assumption of mixed-membership clustering. In this work, we propose to extend this later class of models to infer dynamic labeled networks under a mixed membership assumption. Our approach takes the form of a temporal prior on the model's parameters. It relies on the single assumption that dynamics are not abrupt. We show that our method significantly differs from existing approaches, and allows to model more complex systems --dynamic labeled networks. We demonstrate the robustness of our method with several experiments on both synthetic and real-world datasets. A key interest of our approach is that it needs very few training data to yield good results. The performance gain under challenging conditions broadens the variety of possible applications of automated learning tools --as in social sciences, which comprise many fields where small datasets are a major obstacle to the introduction of machine learning methods.
Real-Time Trajectory-Based Social Group Detection

Social group detection is a crucial aspect of various robotic applications, including robot navigation and human-robot interactions. To date, a range of model-based techniques have been employed to address this challenge, such as the F-formation and trajectory similarity frameworks. However, these approaches often fail to provide reliable results in crowded and dynamic scenarios. Recent advancements in this area have mainly focused on learning-based methods, such as deep neural networks that use visual content or human pose. Although visual content based methods have demonstrated promising performance on large-scale datasets, their computational complexity poses a significant barrier to their practical use in real-time applications. To address these issues, we propose a simple and efficient framework for social group detection. Our approach explores the impact of motion trajectory on social grouping and utilizes a novel, reliable, and fast data-driven method. We formulate the individuals in a scene as a graph, where the nodes are represented by LSTM-encoded trajectories and the edges are defined by the distances between each pair of tracks. Our framework employs a modified graph transformer module and graph clustering losses to detect social groups. Our experiments on the popular JRDB-Act dataset reveal noticeable improvements in performance, with relative improvements ranging from 2% to 11%. Furthermore, our framework is significantly faster, with up to 12x faster inference times compared to state-of-the-art methods under the same computation resources. These results demonstrate that our proposed method is suitable for real-time robotic applications.
Frame Error Rate Prediction for Non-Stationary Wireless Vehicular Communication Links

Wireless vehicular communication will increase the safety of road users. The reliability of vehicular communication links is of high importance as links with low reliability may diminish the advantage of having situational traffic information. The goal of our investigation is to obtain a reliable coverage area for non-stationary vehicular scenarios. Therefore we propose a deep neural network (DNN) for predicting the expected frame error rate (FER). The DNN is trained in a supervised fashion, where a time-limited sequence of channel frequency responses has been labeled with its corresponding FER values assuming an underlying wireless communication system, i.e. IEEE 802.11p. For generating the training dataset we use a geometry-based stochastic channel model (GSCM). We obtain the ground truth FER by emulating the time-varying frequency responses using a hardware-in-the-loop setup. Our GSCM provides the propagation path parameters which we use to fix the statistics of the fading process at one point in space for an arbitrary amount of time, enabling accurate FER estimation. Using this dataset we achieve an accuracy of 85% of the DNN. We use the trained model to predict the FER for measured time-varying channel transfer functions obtained during a measurement campaign. We compare the predicted output of the DNN to the measured FER on the road and obtain a prediction accuracy of 78%.
Real-time Nonlinear Model Predictive Control using One-step Optimizations and Reachable Sets

Model predictive control allows solving complex control tasks with control and state constraints. However, an optimal control problem must be solved in real-time to predict the future system behavior, which is hardly possible on embedded hardware. To solve this problem, this paper proposes to compute a sequence of one-step optimizations aided by pre-computed inner approximations of reachable sets rather than solving the full-horizon optimal control problem at once. This feature can be used to virtually predict the future system behavior with a low computational footprint. Proofs for recursive feasibility and for the sufficient conditions for asymptotic stability under mild assumptions are given. The presented approach is demonstrated in simulation for functional verification.
Measuring a Soft Resistive Strain Sensor Array by Solving the Resistor Network Inverse Problem

Soft robotics is applicable to a variety of domains due to the adaptability offered by the soft and compliant materials. To develop future intelligent soft robots, soft sensors that can capture deformations with nearly infinite degrees of freedom are necessary. Soft sensor networks can address this problem, however, measuring all sensor values throughout the body requires excessive wiring and complex fabrication that may hinder robot performance. We circumvent these challenges by developing a non-invasive measurement technique, which is based on an algorithm that solves the inverse problem of resistor network, and implement this algorithm on a soft resistive, strain sensor network. Our algorithm works by iteratively computing the resistor values based on the applied boundary voltage and current responses, and we analyze the reconstruction error of the algorithm as a function of network size and measurement error. We further develop electronics setup to implement our algorithm on a stretchable resistive strain sensor network made of soft conductive silicone, and show the response of the measured network to different deformation modes. Our work opens a new path to addressing the challenge of measuring many sensor values in soft sensors, and could be applied to soft robotic sensor systems.
HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting

Medical decision-making processes can be enhanced by comprehensive biomedical knowledge bases, which require fusing knowledge graphs constructed from different sources via a uniform index system. The index system often organizes biomedical terms in a hierarchy to provide the aligned entities with fine-grained granularity. To address the challenge of scarce supervision in the biomedical knowledge fusion (BKF) task, researchers have proposed various unsupervised methods. However, these methods heavily rely on ad-hoc lexical and structural matching algorithms, which fail to capture the rich semantics conveyed by biomedical entities and terms. Recently, neural embedding models have proved effective in semantic-rich tasks, but they rely on sufficient labeled data to be adequately trained. To bridge the gap between the scarce-labeled BKF and neural embedding models, we propose HiPrompt, a supervision-efficient knowledge fusion framework that elicits the few-shot reasoning ability of large language models through hierarchy-oriented prompts. Empirical results on the collected KG-Hi-BKF benchmark datasets demonstrate the effectiveness of HiPrompt.
Speech Reconstruction from Silent Tongue and Lip Articulation by Pseudo Target Generation and Domain Adversarial Training

This paper studies the task of speech reconstruction from ultrasound tongue images and optical lip videos recorded in a silent speaking mode, where people only activate their intra-oral and extra-oral articulators without producing sound. This task falls under the umbrella of articulatory-to-acoustic conversion, and may also be refered to as a silent speech interface. We propose to employ a method built on pseudo target generation and domain adversarial training with an iterative training strategy to improve the intelligibility and naturalness of the speech recovered from silent tongue and lip articulation. Experiments show that our proposed method significantly improves the intelligibility and naturalness of the reconstructed speech in silent speaking mode compared to the baseline TaLNet model. When using an automatic speech recognition (ASR) model to measure intelligibility, the word error rate (WER) of our proposed method decreases by over 15% compared to the baseline. In addition, our proposed method also outperforms the baseline on the intelligibility of the speech reconstructed in vocalized articulating mode, reducing the WER by approximately 10%.
A Game-theoretic Framework for Privacy-preserving Federated Learning

In federated learning, benign participants aim to optimize a global model collaboratively. However, the risk of \textit{privacy leakage} cannot be ignored in the presence of \textit{semi-honest} adversaries. Existing research has focused either on designing protection mechanisms or on inventing attacking mechanisms. While the battle between defenders and attackers seems never-ending, we are concerned with one critical question: is it possible to prevent potential attacks in advance? To address this, we propose the first game-theoretic framework that considers both FL defenders and attackers in terms of their respective payoffs, which include computational costs, FL model utilities, and privacy leakage risks. We name this game the federated learning privacy game (FLPG), in which neither defenders nor attackers are aware of all participants' payoffs. To handle the \textit{incomplete information} inherent in this situation, we propose associating the FLPG with an \textit{oracle} that has two primary responsibilities. First, the oracle provides lower and upper bounds of the payoffs for the players. Second, the oracle acts as a correlation device, privately providing suggested actions to each player. With this novel framework, we analyze the optimal strategies of defenders and attackers. Furthermore, we derive and demonstrate conditions under which the attacker, as a rational decision-maker, should always follow the oracle's suggestion \textit{not to attack}.
Finding AGN remnant candidates based on radio morphology with machine learning

Remnant radio galaxies represent the dying phase of radio-loud active galactic nuclei (AGN). Large samples of remnant radio galaxies are important for quantifying the radio galaxy life cycle. The remnants of radio-loud AGN can be identified in radio sky surveys based on their spectral index, or, complementary, through visual inspection based on their radio morphology. However, this is extremely time-consuming when applied to the new large and sensitive radio surveys. Here we aim to reduce the amount of visual inspection required to find AGN remnants based on their morphology, through supervised machine learning trained on an existing sample of remnant candidates. For a dataset of 4107 radio sources, with angular sizes larger than 60 arcsec, from the LOw Frequency ARray (LOFAR) Two-Metre Sky Survey second data release (LoTSS-DR2), we started with 151 radio sources that were visually classified as 'AGN remnant candidate'. We derived a wide range of morphological features for all radio sources from their corresponding Stokes-I images: from simple source catalogue-derived properties, to clustered Haralick-features, and self-organising map (SOM) derived morphological features. We trained a random forest classifier to separate the 'AGN remnant candidates' from the not yet inspected sources. The SOM-derived features and the total to peak flux ratio of a source are shown to be most salient to the classifier. We estimate that $31\pm5\%$ of sources with positive predictions from our classifier will be labelled 'AGN remnant candidates' upon visual inspection, while we estimate the upper bound of the $95\%$ confidence interval for 'AGN remnant candidates' in the negative predictions at $8\%$. Visual inspection of just the positive predictions reduces the number of radio sources requiring visual inspection by $73\%$.
Two-Dimensional Simulation of Argon Dielectric Barrier Discharge (DBD) in Plasma Actuator Structure with COMSOL Multiphysics

Dielectric barrier discharge (DBD) plasma is used for various applications. DBD is also one of the most efficient and low-cost methods for active fluid flow control. In this study, a detailed physical model of DBD in atmospheric pressure at 1kV DC voltage is developed with COMSOL Multiphysics software. Argon gas is also used as a background gas and electrodes are assumed to be copper. Plasma parameters such as electron and ion density, electric field, potential, and temperature for different distances of electrodes (1.0mm, 0.9mm, 0.8mm) have been investigated. Moreover, the effect of dielectric type (Quartz, Silica Glass, Mica) on these key parameters is investigated. The results of the simulation show that the longitudinal distance of the buried electrodes to the exposed electrodes has a direct influence on parameters such as electron temperature, and electron and ion density which are the main factors of fluid flow control. These parameters have the maximum value when mica is used as a dielectric and the lowest value when silica glass is utilized.
DOSM: Demand-Prediction based Online Service Management for Vehicular Edge Computing Networks

In this work, we investigate an online service management problem in vehicular edge computing networks. To satisfy the varying service demands of mobile vehicles, a service management framework is required to make decisions on the service lifecycle to maintain good network performance. We describe the service lifecycle consists of creating an instance of a given service (scale-out), moving an instance to a different edge node (migration), and/or termination of an underutilized instance (scale-in). In this paper, we propose an efficient online algorithm to perform service management in each time slot, where performance quality in the current time slot, the service demand in future time slots, and the minimal observed delay by vehicles and the minimal migration delay are considered while making the decisions on service lifecycle. Here, the future service demand is computed from a gated recurrent unit (GRU)based prediction model, and the network performance quality is estimated using a deep reinforcement learning (DRL) model which has the ability to interact with the vehicular environment in real-time. The choice of optimal edge location to deploy a service instance at different times is based on our proposed optimization formulations. Simulation experiments using real-world vehicle trajectories are carried out to evaluate the performance of our proposed demand-prediction based online service management (DOSM) framework against different state-of-the-art solutions using several performance metrics.
Testing Sufficiency for Transfer Learning

Modern statistical analysis often encounters high dimensional models but with limited sample sizes. This makes the target data based statistical estimation very difficult. Then how to borrow information from another large sized source data for more accurate target model estimation becomes an interesting problem. This leads to the useful idea of transfer learning. Various estimation methods in this regard have been developed recently. In this work, we study transfer learning from a different perspective. Specifically, we consider here the problem of testing for transfer learning sufficiency. By transfer learning sufficiency (denoted as the null hypothesis), we mean that, with the help of the source data, the useful information contained in the feature vectors of the target data can be sufficiently extracted for predicting the interested target response. Therefore, the rejection of the null hypothesis implies that information useful for prediction remains in the feature vectors of the target data and thus calls for further exploration. To this end, we develop a novel testing procedure and a centralized and standardized test statistic, whose asymptotic null distribution is analytically derived. Simulation studies are presented to demonstrate the finite sample performance of the proposed method. A deep learning related real data example is presented for illustration purpose.
State and Parameter Estimation of a CSTR

In the continuation of authors’ studies on estimation and control for Continuous Stirred-Tank Reactors (CSTR), a new structure to estimate the concentration of reactive state, the global heat transfer coefficient, and the heat of reaction parameters is proposed here. This scheme consist of an Observer Based Estimator (OBE) connected in cascade with a High Order Sliding Mode Observer (HOSMO). The OBE estimates the global heat transfer coefficient, and the HOSMO estimates the heat of reaction, and the concentration of reactive. Numerical simulations show that the whole structure presents a good performance in presence of parametric variations, which often are presented in chemical processes.
Power control in wireless systems: a stochastic control formulation

This paper considers power control for log-normal fading channels. A rate based power set point control model and an associated performance measure are introduced. Within this framework, a unique stochastic optimal power control law exists, and the value function of the optimal control is a viscosity solution to the corresponding HJB equation. An interpretation of the nature of the resulting control laws is presented.