Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deep CNN Architecture with Novel Pooling Layer Applied to Two Sudanese Arabic Sentiment Datasets

Arabic sentiment analysis has become an important research field in recent years. Initially, work focused on Modern Standard Arabic (MSA), which is the most widely used form. Since then, work has been carried out on several different dialects, including Egyptian, Levantine and Moroccan. Moreover, a number of data sets have been created to support such work. However, up until now, no work has been carried out on Sudanese Arabic, a dialect which has 32 million speakers. In this article, two new public data sets are introduced, the two-class Sudanese Sentiment Data set (SudSenti2) and the three-class Sudanese Sentiment Data set (SudSenti3). In the preparation phase, we establish a Sudanese stopword list. Furthermore, a convolutional neural network (CNN) architecture, Sentiment Convolutional MMA (SCM), is proposed, comprising five CNN layers together with a novel Mean Max Average (MMA) pooling layer, to extract the best features. This SCM model is applied to SudSenti2 and SudSenti3 and shown to be superior to the baseline models, with accuracies of 92.25% and 85.23% (Experiments 1 and 2). The performance of MMA is compared with Max, Avg and Min and shown to be better on SudSenti2, the Saudi Sentiment Data set and the MSA Hotel Arabic Review Data set by 1.00%, 0.83% and 0.74%, respectively (Experiment 3). Next, we conduct an ablation study to determine the contribution to performance of text normalisation and the Sudanese stopword list (Experiment 4). For normalisation, this makes a difference of 0.43% on two-class and 0.45% on three-class. For the custom stoplist, the differences are 0.82% and 0.72%, respectively. Finally, the model is compared with other deep learning classifiers, including transformer-based language models for Arabic, and shown to be comparable for SudSenti2 (Experiment 5).
Speed-ANN: Low-Latency and High-Accuracy Nearest Neighbor Search via Intra-Query Parallelism

Nearest Neighbor Search (NNS) has recently drawn a rapid increase of interest due to its core role in managing high-dimensional vector data in data science and AI applications. The interest is fueled by the success of neural embedding, where deep learning models transform unstructured data into semantically correlated feature vectors for data analysis, e.g., recommend popular items. Among several categories of methods for fast NNS, similarity graph is one of the most successful algorithmic trends. Several of the most popular and top-performing similarity graphs, such as NSG and HNSW, at their core employ best-first traversal along the underlying graph indices to search near neighbors. Maximizing the performance of the search is essential for many tasks, especially at the large-scale and high-recall regime. In this work, we provide an in-depth examination of the challenges of the state-of-the-art similarity search algorithms, revealing its challenges in leveraging multi-core processors to speed up the search efficiency. We also exploit whether similarity graph search is robust to deviation from maintaining strict order by allowing multiple walkers to simultaneously advance the search frontier. Based on our insights, we propose Speed-ANN, a parallel similarity search algorithm that exploits hidden intra-query parallelism and memory hierarchy that allows similarity search to take advantage of multiple CPU cores to significantly accelerate search speed while achieving high accuracy. We evaluate Speed-ANN on a wide range of datasets, ranging from million to billion data points, and show its shorter query latency than NSG and HNSW, respectively. Besides, with multicore support, we show that our approach offers faster search latency than highly-optimized GPU implementation and provides good scalability as the increase of the number of hardware resources (e.g., CPU cores) and graph sizes.
A Priori Denoising Strategies for Sparse Identification of Nonlinear Dynamical Systems: A Comparative Study

In recent years, identification of nonlinear dynamical systems from data has become increasingly popular. Sparse regression approaches, such as Sparse Identification of Nonlinear Dynamics (SINDy), fostered the development of novel governing equation identification algorithms assuming the state variables are known {\it a priori} and the governing equations lend themselves to sparse, linear expansions in a (nonlinear) basis of the state variables. In the context of the identification of governing equations of nonlinear dynamical systems, one faces the problem of identifiability of model parameters when state measurements are corrupted by noise. Measurement noise affects the stability of the recovery process yielding incorrect sparsity patterns and inaccurate estimation of coefficients of the governing equations. In this work, we investigate and compare the performance of several local and global smoothing techniques to {\it a priori} denoise the state measurements and numerically estimate the state time-derivatives to improve the accuracy and robustness of two sparse regression methods to recover governing equations: Sequentially Thresholded Least Squares (STLS) and Weighted Basis Pursuit Denoising (WBPDN) algorithms. We empirically show that, in general, global methods, which use the entire measurement data set, outperform local methods, which employ a neighboring data subset around a local point. We additionally compare Generalized Cross Validation (GCV) and Pareto curve criteria as model selection techniques to automatically estimate near optimal tuning parameters, and conclude that Pareto curves yield better results. The performance of the denoising strategies and sparse regression methods is empirically evaluated through well-known benchmark problems of nonlinear dynamical systems.
RIM-Net: Recursive Implicit Fields for Unsupervised Learning of Hierarchical Shape Structures

We introduce RIM-Net, a neural network which learns recursive implicit fields for unsupervised inference of hierarchical shape structures. Our network recursively decomposes an input 3D shape into two parts, resulting in a binary tree hierarchy. Each level of the tree corresponds to an assembly of shape parts, represented as implicit functions, to reconstruct the input shape. At each node of the tree, simultaneous feature decoding and shape decomposition are carried out by their respective feature and part decoders, with weight sharing across the same hierarchy level. As an implicit field decoder, the part decoder is designed to decompose a sub-shape, via a two-way branched reconstruction, where each branch predicts a set of parameters defining a Gaussian to serve as a local point distribution for shape reconstruction. With reconstruction losses accounted for at each hierarchy level and a decomposition loss at each node, our network training does not require any ground-truth segmentations, let alone hierarchies. Through extensive experiments and comparisons to state-of-the-art alternatives, we demonstrate the quality, consistency, and interpretability of hierarchical structural inference by RIM-Net.
Online Assessment Misconduct Detection using Internet Protocol and Behavioural Classification

With the recent prevalence of remote education, academic assessments are often conducted online, leading to further concerns surrounding assessment misconducts. This paper investigates the potentials of online assessment misconduct (e-cheating) and proposes practical countermeasures against them. The mechanism for detecting the practices of online cheating is presented in the form of an e-cheating intelligent agent, comprising of an internet protocol (IP) detector and a behavioural monitor. The IP detector is an auxiliary detector which assigns randomised and unique assessment sets as an early procedure to reduce potential misconducts. The behavioural monitor scans for irregularities in assessment responses from the candidates, further reducing any misconduct attempts. This is highlighted through the proposal of the DenseLSTM using a deep learning approach. Additionally, a new PT Behavioural Database is presented and made publicly available. Experiments conducted on this dataset confirm the effectiveness of the DenseLSTM, resulting in classification accuracies of up to 90.7%.
A structurally based investigation of abdominal aortic aneurysms in mouse models

A Structurally Based Investigation of Abdominal Aortic Aneurysms in Mouse Models. (December 2011) Melissa Jill Collins, B.S., University of Nebraska Lincoln Chair of Advisory Committee: Dr. Mary McDougall Understanding the mechanical properties of Abdominal Aortic Aneurysms is paramount to improving treatment of this deadly condition. Here, we present work that makes strides in understanding not only the mechanical behavior and constitutive parameters of the two vessels that experience AAAs in different models, but also the effects of three major components of AAA formation. Biaxial mechanical tests were performed using a modified computer-controlled device. We examined the solid mechanics of the infrarenal and suprarenal aorta to examine why non-targeted models of AAAs (like Ang-II infusion) form exclusively in the suprarenal aorta, whereas in humans the lesions preferentially form in the infrarenal aorta. The major differences between the two vessels are the elastin content and lamellar thickness in the suprarenal aorta. We analyzed the mechanical and constitutive effects of an acute loss of functional elastin via intraluminal exposure to elastase. We found that, after elastase exposure, vessels were less distensible and experienced non-uniform, but modest
Observation of Phase Controllable Majorana-like Bound States in Metamaterial-based Kitaev Chain Analogues

We experimentally demonstrate that Majorana-like bound states (MLBSs) can occur in quasione-dimensional metamaterials, analogous to Majorana zero modes (MZM) in the Kitaev chain. In a mechanical spinner ladder system, we observe a topological phase transition and spectral-gapprotected edge MLBSs. We characterize the decaying and oscillatory nature of these MLBS pairs and their phase-dependent hybridization. It is shown that the hybridization can be tuned to yield the analogue of parity switching in MZMs, a key element of topological qubits. We find strong agreements with theory.
Interconnect Parasitics and Partitioning in Fully-Analog In-Memory Computing Architectures

Fully-analog in-memory computing (IMC) architectures that implement both matrix-vector multiplication and nonlinear vector operations within the same memory array have shown promising performance benefits over conventional IMC systems due to the removal of energy-hungry signal conversion units. However, maintaining the computation in the analog domain for the entire deep neural network (DNN) comes with potential sensitivity to interconnect parasitics. Thus, in this paper, we investigate the effect of wire parasitic resistance and capacitance on the accuracy of DNN models deployed on fully-analog IMC architectures. Moreover, we propose a partitioning mechanism to alleviate the impact of the parasitic while keeping the computation in the analog domain through dividing large results for a $400 \times 120 \times 84 \times 10$ DNN model deployed on a results for a $400 \times 120 \times 84 \times 10$ DNN model deployed on a fully-analog IMC circuit show that a 94.84 % accuracy could be achieved for MNIST classification application with 16,8, and 8 horizontal partitions, as well as 8,8, and 1 vertical partitions for first, second, and third layers of the DNN, respectively, which is comparable to the $\sim 97$ % accuracy realized by digital implementation on CPU. It is shown that accuracy benefits are extra circuitry required for handling partitioning.
Equivariant neural networks for recovery of Hadamard matrices

We propose a message passing neural network architecture designed to be equivariant to column and row permutations of a matrix. We illustrate its advantages over traditional architectures like multi-layer perceptrons (MLPs), convolutional neural networks (CNNs) and even Transformers, on the combinatorial optimization task of recovering a set of deleted entries of a Hadamard matrix. We argue that this is a powerful application of the principles of Geometric Deep Learning to fundamental mathematics, and a potential stepping stone toward more insights on the Hadamard conjecture using Machine Learning techniques.
Part of Speech Tagging (POST) of a Low-resource Language using another Language (Developing a POS-Tagged Lexicon for Kurdish (Sorani) using a Tagged Persian (Farsi) Corpus)

Tagged corpora play a crucial role in a wide range of Natural Language Processing. The Part of Speech Tagging (POST) is essential in developing tagged corpora. It is time-and-effort-consuming and costly, and therefore, it could be more affordable if it is automated. The Kurdish language currently lacks publicly available tagged corpora of proper sizes. Tagging the publicly available Kurdish corpora can leverage the capability of those resources to a higher level than what raw or segmented corpora can provide. Developing POS-tagged lexicons can assist the mentioned task. We use a tagged corpus (Bijankhan corpus) in Persian (Farsi) as a close language to Kurdish to develop a POS-tagged lexicon. This paper presents the approach of leveraging the resource of a close language to Kurdish to enrich its resources. A partial dataset of the results is publicly available for non-commercial use under CC BY-NC-SA 4.0 license at https://kurdishblark.github. io/. We plan to make the whole tagged corpus available after further investigation on the outcome. The dataset can help in developing POS-tagged lexicons for other Kurdish dialects and automated Kurdish corpora tagging.
Improvement of the Performance of DC/DC Converter Used for Transportations Applications. Test of Different Control Strategies

Abstract This paper presents a study on DC/DC converter with supercapacitor stack for a trolleybus. After the description of transportation application, the performance of the system is studied by testing different control strategies. The comparison of SMC (Sliding mode Control) and PI controller on a developed test bench has shown the benefit of SMC in this type of applications.
The curvature effect in Gaussian random fields

Random field models are mathematical structures used in the study of stochastic complex systems. In this paper, we compute the shape operator of Gaussian random field manifolds using the first and second fundamental forms (Fisher information matrices). Using Markov chain Monte Carlo techniques, we simulate the dynamics of these random fields and compute the Gaussian, mean and principal curvatures of the parametric space, analyzing how these quantities change along dynamics exhibiting phase transitions. During the simulations, we have observed an unexpected phenomenon that we called the curvature effect, which indicates that a highly asymmetric geometric deformation happens in the underlying parametric space when there are significant increase/decrease in the system’s entropy. When the system undergoes a phase transition from randomness to clustered behavior the curvature is smaller than that observed in the reverse phase transition. This asymmetric pattern relates to the emergence of hysteresis phenomenon, leading to an intrinsic arrow of time along the random field dynamics.
Neural Network Training With Asymmetric Crosspoint Elements

Analog crossbar arrays comprising programmable non-volatile resistors are under intense investigation for acceleration of deep neural network training. However, the ubiquitous asymmetric conductance modulation of practical resistive devices critically degrades the classification performance of networks trained with conventional algorithms. Here we first describe the fundamental reasons behind this incompatibility. Then, we explain the theoretical underpinnings of a novel fully-parallel training algorithm that is compatible with asymmetric crosspoint elements. By establishing a powerful analogy with classical mechanics, we explain how device asymmetry can be exploited as a useful feature for analog deep learning processors. Instead of conventionally tuning weights in the direction of the error function gradient, network parameters can be programmed to successfully minimize the total energy (Hamiltonian) of the system that incorporates the effects of device asymmetry. Our technique enables immediate realization of analog deep learning accelerators based on readily available device technologies.
CP Violation for the Heavens and the Earth

Electroweak baryogenesis can be driven by the top quark in a general two Higgs doublet model with extra Yukawa couplings. Higgs quartics provide the first order phase transition, while extra top Yukawa coupling ρtt can fuel the cosmic baryon asymmetry through the λtImρtt product, with flavor-changing ρtc coupling as backup. The impressive ACME 2018 bound on the electron electric dipole moment calls for an extra electron coupling ρee for exquisite cancellation among dangerous diagrams, broadening the baryogenesis solution space. The mechanism suggests that extra Yukawa couplings echo the hierarchical structure of standard Yukawa couplings. Phenomenological consequences in the Higgs search and flavor physics are discussed, with μ and τ EDM touched upon.
A quantum model of lasing without inversion

Starting from a quantum description of multiple Λ-type three-level atoms driven with a coherent microwave field and incoherent optical pumping, we derive a microscopic model of lasing from which we move towards a consistent macroscopic picture. Our analysis applies across the range of system sizes from nanolasers to the thermodynamic limit of conventional lasing. We explore the necessary conditions to achieve lasing without inversion in certain regimes by calculating the non-equilibrium steady state solutions of the model at, and between, its microscopic and macroscopic limits. For the macroscopic picture, we use mean-field theory to present a thorough analysis of the lasing phase transition. In the microscopic case, we exploit the underlying permutation symmetry of the density matrix to calculate exact solutions for N three-level systems. This allows us to show that the steady state solutions approach the thermodynamic limit as N increases, restoring the sharp non-equilibrium phase transition in this limit. We demonstrate how the lasing phase transition and degree of population inversion can be adjusted by simply varying the phase of the coherent driving field. The high level of quantum control presented by this microscopic model and the framework outlined here have applications to further understanding and developing nanophotonic technology.
Sim-to-Real for Projective Dynamics on Soft Robots via Differentiability: Meshing, Damping, and Actuation

—An accurate, physically-based model of soft robots can unlock downstream applications in optimal control. The Finite Element Method (FEM) is an expressive approach for modeling highly deformable structures such as dynamic, elastomeric soft robots. Recently, Projective Dynamics (PD) has been proposed as a fast FEM; however, PD lacks rigorous benchmarking against reality. In this paper, we compare virtual robot models simulated using PD with measurements from their physical counterparts. In particular, we examine several soft structures with different morphologies: a clamped beam under external force, a pneumatically actuated soft robotic arm, and a soft robotic ﬁsh tail. We benchmark and analyze different meshing resolutions and elements (tetrahedra and hexahedra), numerical damping, and the differentiability of PD through a differentiable solution (DiffPD). We also advance PD in application to soft robotics by proposing a predictive model for pneumatic soft robot actuation. Through our case-studies, we provide strategies and algorithms for matching real-world physics in simulation, making PD useful for soft robots 1
Transport Capacity Optimization for Resource Allocation in Tera-IoT Networks

We present a new adaptive resource optimization strategy that jointly allocates the subwindow and transmit power in multidevice terahertz (THz) band Internet of Things (Tera-IoT) networks. Unlike the prior studies focusing mostly on maximizing the sum distance, we incorporate both rate and transmission distance into the objective function of our problem formulation with key features of THz bands, including the spreading and molecular absorption losses. More specifically, as a performance metric of Tera-IoT networks, we adopt the transport capacity (TC), which is defined as the sum of the rate–distance products over all users. This metric has been widely adopted in large-scale ad hoc networks and would also be appropriate for evaluating the performance of various Tera-IoT applications. We then formulate an optimization problem that aims at maximizing the TC. Moreover, motivated by the importance of the transmission distance that is very limited due to the high path loss in THz bands, our optimization problem is extended to the case of allocating the subwindow, transmit power, and transmission distance. We show how to solve our problems via an effective two-stage resource allocation strategy. We demonstrate the superiority of our adaptive solution over benchmark methods via intensive numerical evaluations for various environmental setups of large-scale Tera-IoT networks.
Low-rank features based double transformation matrices learning for image classification

Linear regression is a supervised method that has been widely used in classification tasks. In order to apply linear regression to classification tasks, a technique for relaxing regression targets was proposed. However, methods based on this technique ignore the pressure on a single transformation matrix due to the complex information contained in the data. A single transformation matrix in this case is too strict to provide a flexible projection, thus it is necessary to adopt relaxation on transformation matrix. This paper proposes a double transformation matrices learning method based on latent low-rank feature extraction. The core idea is to use double transformation matrices for relaxation, and jointly projecting the learned principal and salient features from two directions into the label space, which can share the pressure of a single transformation matrix. Firstly, the low-rank features are learned by the latent low rank representation (LatLRR) method which processes the original data from two directions. In this process, sparse noise is also separated, which alleviates its interference on projection learning to some extent. Then, two transformation matrices are introduced to process the two features separately, and the information useful for the classification is extracted. Finally, the two transformation matrices can be easily obtained by alternate optimization methods. Through such processing, even when a large amount of redundant information is contained in samples, our method can also obtain projection results that are easy to classify. Experiments on multiple data sets demonstrate the effectiveness of our approach for classification, especially for complex scenarios.