Miracle-dz/UniFAR-FTUs huggingface dataset:
Rubik: A Hierarchical Architecture for Efficient Graph Learning

Graph convolutional network (GCN) emerges as a promising direction to learn the inductive representation in graph data commonly used in widespread applications, such as E-commerce, social networks, and knowledge graphs. However, learning from graphs is non-trivial because of its mixed computation model involving both graph analytics and neural network computing. To this end, we decompose the GCN learning into two hierarchical paradigms: graph-level and node-level computing. Such a hierarchical paradigm facilitates the software and hardware accelerations for GCN learning. We propose a lightweight graph reordering methodology, incorporated with a GCN accelerator architecture that equips a customized cache design to fully utilize the graph-level data reuse. We also propose a mapping methodology aware of data reuse and task-level parallelism to handle various graphs inputs effectively. Results show that Rubik accelerator design improves energy efficiency by 26.3x to 1375.2x than GPU platforms across different datasets and GCN models.
A non-local coupling model involving three fractional Laplacians

In this article the authors study a non-local diffusion problem that involves three different fractional laplacian operators acting on two domains. Each domain has an associated operator that governs the diffusion on it, and the third operator serves as a coupling mechanism between the two of them. The model proposed is the gradient flow of a non-local energy functional. In the first part of the article we provide results about existence of solutions and the conservation of mass. The second part is devoted to study the asymptotic behaviour of the solutions of the problem when the two domains are a ball and its complementary. Fractional Sobolev inequalities in exterior domains are also provided.
Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks

Multispectral images (e.g. visible and infrared) may be particularly useful when detecting objects with the same model in different environments (e.g. day/night outdoor scenes). To effectively use the different spectra, the main technical problem resides in the information fusion process. In this paper, we propose a new halfway feature fusion method for neural networks that leverages the complementary/consistency balance existing in multispectral features by adding to the network architecture, a particular module that cyclically fuses and refines each spectral feature. We evaluate the effectiveness of our fusion method on two challenging multispectral datasets for object detection. Our results show that implementing our Cyclic Fuse-and-Refine module in any network improves the performance on both datasets compared to other state-of-the-art multispectral object detection methods.
Why resampling outperforms reweighting for correcting sampling bias

A data set sampled from a certain population is biased if the subgroups of the population are sampled at proportions that are significantly different from their underlying proportions. Training machine learning models on biased data sets requires correction techniques to compensate for potential biases. We consider two commonly-used techniques, resampling and reweighting, that rebalance the proportions of the subgroups to maintain the desired objective function. Though statistically equivalent, it has been observed that reweighting outperforms resampling when combined with stochastic gradient algorithms. By analyzing illustrative examples, we explain the reason behind this phenomenon using tools from dynamical stability and stochastic asymptotics. We also present experiments from regression, classification, and off-policy prediction to demonstrate that this is a general phenomenon. We argue that it is imperative to consider the objective function design and the optimization algorithm together while addressing the sampling bias.
Joint User Scheduling and Resource Allocation for Millimeter Wave Systems Relying on Adaptive-Resolution ADCs

Millimeter wave (mmWave) communication systems using adaptive-resolution analog-to-digital converters (RADCs) have recently drawn considerable interests from the research community as benefit of their high energy efficiency and low implementation cost. In this paper, we focus on the mmWave uplink using RADCs and investigate the joint user scheduling and resource allocation problem. Specifically, we seek to maximize the system throughput of the scheduled users by jointly optimizing their transmit power level and hybrid combiners as well as the number of quantization bits, subject to practical constraints. By relying on fractional programming (FP) techniques, we first covert this problem into a form amenable to optimization and exploit the specific structures in its solutions with the aid of the so-called Ky Fan $n$-norm.Then, the resultant optimization problem is solved using a penalty block successive concave approximation (P-BSCA) algorithm. Our numerical results reveal that the proposed algorithm substantially enhances the throughput of the scheduled users compared to the state-of-the-art benchmark schemes and provides more flexible and efficient resource allocation control.
Topology Learning in Radial Dynamical Systems With Unreliable Data

Many complex engineering systems admit bidirectional and linear couplings between their agents. Blind and passive methods to identify such influence pathways/couplings from data are central to many applications. However, dynamically related data streams originating at different sources are prone to corruption caused by asynchronous time stamps of different streams, packet drops, and noise. Such imperfect information may be present in the entire observation period and, hence, is not detected by change detection algorithms that require an initial clean observation period. In this article, we provide a novel approach to detect the location of corrupt agents as well as present an algorithm to learn the structure of radial dynamical systems despite corrupted data streams. In particular, we show that our approach provably learns the true radial structure if the unknown corrupted nodes are at least three hops away from each other. Our theoretical results are further validated in a test dynamical network.
Sensitivity and Resistance of Listeria monocytogenes ATCC 19115, Scott A, and UAL500 to Nisin.

Listeria monocytogenes ATCC 19115, Scott A, and UAL500 were evaluated for sensitivity to nisin (0 to 50 μg/ml) using a direct plating method. Nisin (10 μg/ml) decreased an initial population of L. monocytogenes (109 CFU per ml) by 6- to 7-log cycles. Sensitivity to nisin was enhanced by addition of 2% NaCl or by reduction of the medium pH from 6.5 to 5.5 with either hydrochloric or lactic acid. Mutants resistant to 50 μg/ml nisin were detected at frequencies of 10-6 to 10-8. Nisin-resistant L. monocytogenes mutants should be expected to arise when nisin is used as an antimicrobial in food systems.
Mixture of Spectral Generative Adversarial Networks for Imbalanced Hyperspectral Image Classification

We propose a three-player spectral generative adversarial network (GAN) architecture to afford GAN the ability to manage minority classes under imbalanced conditions. A class-dependent mixture generator spectral GAN (MGSGAN) was developed to force generated samples to remain within the actual distribution of the data. MGSGAN was able to generate minority classes, even when the imbalanced ratio of majority to minority classes was high. A classifier based on lower features was adopted along with a sequential discriminator to develop a three-player GAN game. The generative networks performed data augmentation to improve the classifier ’ s performance. The proposed method was validated using two hyperspectral image data sets and compared with state-of-the-art methods in two class-imbalanced settings corresponding with real data distributions.
A Comparative Study Between a Classical and Optimal Controller for a Quadrotor

This paper presents a comparison between the Proportional Integral Derivative (PID), a classical controller, and Linear Quadratic Regulator (LQR), an optimal controller, acting on a quadrotor model to control its translational and rotational movements. The complicated dynamic model of a quadrotor based on Newtonian and Euler's laws and fundamental physics principles is simplified to derive a linear mathematical model. The derivation gives the equations that govern a quadrotor's motion concerning the body frame and the inertial frame in the state-space form. This linearized model is utilized to develop the controllers. The performance of the PID control and the optimal regulator established on LQR is compared in various conditions subjected to the Quadrotor model.
Smart Irrigation IoT Solution using Transfer Learning for Neural Networks

In this paper we develop a reliable system for smart irrigation of greenhouses using artificial neural networks, and an IoT architecture. Our solution uses four sensors in different layers of soil to predict future moisture. Using a dataset we collected by running experiments on different soils, we show high performance of neural networks compared to existing alternative method of support vector regression. To reduce the processing power of neural network for the IoT edge devices, we propose using transfer learning. Transfer learning also speeds up training performance with small amount of training data, and allows integrating climate sensors to a pre-trained model, which are the other two challenges of smart irrigation of greenhouses. Our proposed IoT architecture shows a complete solution for smart irrigation.
Hypergraph $k$-cut for fixed $k$ in deterministic polynomial time

We consider the Hypergraph-<tex>$k$</tex>-Cut problem. The input consists of a hypergraph <tex>$G= (V,\ E)$</tex> with nonnegative hyperedge-costs <tex>$c:E\rightarrow \mathbb{R}+$</tex> and a positive integer <tex>$k$</tex>. The objective is to find a least-cost subset <tex>$F\subseteq E$</tex> such that the number of connected components in <tex>$G-F$</tex> is at least <tex>$k$</tex>. An alternative formulation of the objective is to find a partition of <tex>$V$</tex> into <tex>$k$</tex> non-empty sets <tex>$V_{1}, V_{2}, \ldots, V_{k}$</tex> so as to minimize the cost of the hyperedges that cross the partition. Graph-<tex>$k$</tex>-Cut, the special case of Hypergraph-<tex>$k$</tex>-Cut obtained by restricting to graph inputs, has received considerable attention. Several different approaches lead to a polynomial-time algorithm for Graph-<tex>$k$</tex>-Cut when <tex>$k$</tex> is fixed, starting with the work of Goldschmidt and Hochbaum (1988) [1], [2]. In contrast, it is only recently that a randomized polynomial time algorithm for Hypergraph-<tex>$k$</tex>-Cut was developed [3] via a subtle generalization of Karger's random contraction approach for graphs. In this work, we develop the first deterministic polynomial time algorithm for Hypergraph-<tex>$k$</tex>-Cut for all fixed <tex>$k$</tex>. We describe two algorithms both of which are based on a divide and conquer approach. The first algorithm is simpler and runs in <tex>$n^{O(k^{2})}$</tex> time while the second one runs in <tex>$n^{O(k)}$</tex> time. Our proof relies on new structural results that allow for efficient recovery of the parts of an optimum <tex>$k$</tex>-partition by solving minimum (<tex>$S,\ T$</tex>) -terminal cuts. Our techniques give new insights even for Graph-<tex>$k$</tex>-Cut.
SceneGen: Generative Contextual Scene Augmentation using Scene Graph Priors

Spatial computing experiences are constrained by the real-world surroundings of the user. In such experiences, augmenting virtual objects to existing scenes require a contextual approach, where geometrical conflicts are avoided, and functional and plausible relationships to other objects are maintained in the target environment. Yet, due to the complexity and diversity of user environments, automatically calculating ideal positions of virtual content that is adaptive to the context of the scene is considered a challenging task. Motivated by this problem, in this paper we introduce SceneGen, a generative contextual augmentation framework that predicts virtual object positions and orientations within existing scenes. SceneGen takes a semantically segmented scene as input, and outputs positional and orientational probability maps for placing virtual content. We formulate a novel spatial Scene Graph representation, which encapsulates explicit topological properties between objects, object groups, and the room. We believe providing explicit and intuitive features plays an important role in informative content creation and user interaction of spatial computing settings, a quality that is not captured in implicit models. We use kernel density estimation (KDE) to build a multivariate conditional knowledge model trained using prior spatial Scene Graphs extracted from real-world 3D scanned data. To further capture orientational properties, we develop a fast pose annotation tool to extend current real-world datasets with orientational labels. Finally, to demonstrate our system in action, we develop an Augmented Reality application, in which objects can be contextually augmented in real-time.
Distribution Matching for Crowd Counting

In crowd counting, each training image contains multiple people, where each person is annotated by a dot. Existing crowd counting methods need to use a Gaussian to smooth each annotated dot or to estimate the likelihood of every pixel given the annotated point. In this paper, we show that imposing Gaussians to annotations hurts generalization performance. Instead, we propose to use Distribution Matching for crowd COUNTing (DM-Count). In DM-Count, we use Optimal Transport (OT) to measure the similarity between the normalized predicted density map and the normalized ground truth density map. To stabilize OT computation, we include a Total Variation loss in our model. We show that the generalization error bound of DM-Count is tighter than that of the Gaussian smoothed methods. In terms of Mean Absolute Error, DM-Count outperforms the previous state-of-the-art methods by a large margin on two large-scale counting datasets, UCF-QNRF and NWPU, and achieves the state-of-the-art results on the ShanghaiTech and UCF-CC50 datasets. Notably, DM-Count ranked first on the leaderboard for the NWPU benchmark, reducing the error of the state-of-the-art published result by approximately 16%. Code is available at this https URL.
Ferromagnetic-electrodes-induced Hall effect in topological Dirac semimetals

We propose an unconventional type of Hall effect in a topological Dirac semimetal with ferromagnetic electrodes. The topological Dirac semimetal itself has time-reversal symmetry, whereas attached ferromagnetic electrodes break it, causing the large Hall response. This induced Hall effect is a characteristic of the helical surface states of topological Dirac semimetals and the helical edge states of quantum spin Hall insulators. We compute the Hall conductance/resistance and the Hall angle by using a lattice model with four-terminal geometry. For topological Dirac semimetals with four electrodes, the induced Hall effect occurs whether the current electrodes or the voltage electrodes are ferromagnetic. When the spins in electrodes are almost fully polarized, the Hall angle becomes as large as that of quantum Hall states or ideal magnetic Weyl semimetals. We show the robustness of the induced Hall effect against impurities and also discuss the spin injection and spin decay problems. This Hall response can be used to detect whether the magnetizations of the two ferromagnetic electrodes are parallel or antiparallel.
Improved generalization by noise enhancement

Recent studies have demonstrated that noise in stochastic gradient descent (SGD) is closely related to generalization: A larger SGD noise, if not too large, results in better generalization. Since the covariance of the SGD noise is proportional to $\eta^2/B$, where $\eta$ is the learning rate and $B$ is the minibatch size of SGD, the SGD noise has so far been controlled by changing $\eta$ and/or $B$. However, too large $\eta$ results in instability in the training dynamics and a small $B$ prevents scalable parallel computation. It is thus desirable to develop a method of controlling the SGD noise without changing $\eta$ and $B$. In this paper, we propose a method that achieves this goal using ``noise enhancement'', which is easily implemented in practice. We expound the underlying theoretical idea and demonstrate that the noise enhancement actually improves generalization for real datasets. It turns out that large-batch training with the noise enhancement even shows better generalization compared with small-batch training.
Conditions for Regional Frequency Stability in Power Systems - Part I: Theory

This paper considers the phenomenon of distinct regional frequencies recently observed in some power systems. We first study the causes through dynamic simulations and then develop the mathematical model describing this behaviour. Then, techniques to solve the model are discussed, demonstrating that the post-fault frequency evolution in any given region is equal to the frequency evolution of the Centre Of Inertia plus certain inter-area oscillations. This finding leads to the deduction of conditions for guaranteeing frequency stability in all regions of a power system, a deduction performed using a mixed analytical-numerical approach that combines mathematical analysis with regression methods on simulation samples. The proposed stability conditions are linear inequalities that can be implemented in any optimisation routine allowing the co-optimisation of all existing ancillary services for frequency support: inertia, multi-speed frequency response, load damping and an optimised largest power infeed. This is the first reported mathematical framework with explicit conditions to maintain frequency stability in a power system exhibiting inter-area oscillations in frequency.
A simple and efficient ensemble classifier combining multiple neural network models on social media datasets in Vietnamese

Text classification is a popular topic of natural language processing, which has currently attracted numerous research efforts worldwide. The significant increase of data in social media requires the vast attention of researchers to analyze such data. There are various studies in this field in many languages but limited to the Vietnamese language. Therefore, this study aims to classify Vietnamese texts on social media from three different Vietnamese benchmark datasets. Advanced deep learning models are used and optimized in this study, including CNN, LSTM, and their variants. We also implement the BERT, which has never been applied to the datasets. Our experiments find a suitable model for classification tasks on each specific dataset. To take advantage of single models, we propose an ensemble model, combining the highest-performance models. Our single models reach positive results on each dataset. Moreover, our ensemble model achieves the best performance on all three datasets. We reach 86.96% of F1- score for the HSD-VLSP dataset, 65.79% of F1-score for the UIT-VSMEC dataset, 92.79% and 89.70% for sentiments and topics on the UIT-VSFC dataset, respectively. Therefore, our models achieve better performances as compared to previous studies on these datasets.
Graph-based Multi-hop Reasoning for Long Text Generation

Long text generation is an important but challenging task.The main problem lies in learning sentence-level semantic dependencies which traditional generative models often suffer from. To address this problem, we propose a Multi-hop Reasoning Generation (MRG) approach that incorporates multi-hop reasoning over a knowledge graph to learn semantic dependencies among sentences. MRG consists of twoparts, a graph-based multi-hop reasoning module and a path-aware sentence realization module. The reasoning module is responsible for searching skeleton paths from a knowledge graph to imitate the imagination process in the human writing for semantic transfer. Based on the inferred paths, the sentence realization module then generates a complete sentence. Unlike previous black-box models, MRG explicitly infers the skeleton path, which provides explanatory views tounderstand how the proposed model works. We conduct experiments on three representative tasks, including story generation, review generation, and product description generation. Automatic and manual evaluation show that our proposed method can generate more informative and coherentlong text than strong baselines, such as pre-trained models(e.g. GPT-2) and knowledge-enhanced models.
Effective-one-body waveforms from dynamical captures in black hole binaries

Dynamical capture is a possible formation channel for BBH mergers leading to highly eccentric merger dynamics and to gravitational wave (GW) signals that are morphologically different from those of quasi-circular mergers. The future detection of these mergers by ground-based or space-based GW interferometers can provide invaluable insights on astrophysical black holes, but it requires precise predictions and dedicated waveform models for the analysis. We present a state-of-the-art effective-one-body (EOB) model for the multipolar merger-ringdown waveform from dynamical capture black-hole mergers with arbitrary mass-ratio and nonprecessing spins. The model relies on analytical descriptions of the radiation reaction and waveform along generic orbits that are obtained by incorporating generic Newtonian prefactors in the expressions used in the quasi-circular case. It provides a tool for generating waveforms for generic binary black hole coalescences and for GW data analysis. We demonstrate that the model reliably accounts for the rich phenomenology of dynamical captures, from direct plunge to successive close encounters up to merger. The parameter space is fully characterized in terms of the initial energy and angular momentum. Our model reproduces to few percent the scattering angle from ten equal-mass, nonspinning, hyperbolic encounter numerical-relativity (NR) simulations. The agreement can be further improved to the by incorporating 6PN-results in one of the EOB potentials and tuning currently unknown analytical parameters. Our results suggest that NR simulations of hyperbolic encounters (and dynamical captures) can be used to inform EOB waveform models for generic BBH mergers/encounters for present and future GW detectors.
Zero-shot Multi-Domain Dialog State Tracking Using Prescriptive Rules

In this work, we present a framework for incorporating descriptive logical rules in state-of-the-art neural networks, enabling them to learn how to handle unseen labels without the introduction of any new training data. The rules are integrated into existing networks without modifying their architecture, through an additional term in the network's loss function that penalizes states of the network that do not obey the designed rules. As a case of study, the framework is applied to an existing neural-based Dialog State Tracker. Our experiments demonstrate that the inclusion of logical rules allows the prediction of unseen labels, without deteriorating the predictive capacity of the original system.
A Machine Learning Approach to DoA Estimation and Model Order Selection for Antenna Arrays With Subarray Sampling

In this paper, we study the problem of direction of arrival estimation and model order selection for systems employing subarray sampling. Thereby, we focus on scenarios, where the number of resolvable active sources is not smaller than the number of simultaneously sampled antenna elements, i.e., we operate above the conventional limit for most estimators. For this purpose, we propose new schemes based on neural networks and estimators that combine neural networks with gradient steps on the likelihood function. These methods are able to outperform existing estimators in terms of mean squared error and model selection accuracy, especially in the low snapshot domain, at a drastically lower computational complexity.