Reviewerly/exHarmony huggingface dataset:
Encoding strongly-correlated many-boson wavefunctions on a photonic quantum computer: application to the attractive Bose-Hubbard model

Variational quantum algorithms (VQA) are considered as some of the most promising methods to determine the properties of complex strongly correlated quantum many-body systems, especially from the perspective of devices available in the near term. In this context, the development of efficient quantum circuit ansatze to encode a many-body wavefunction is one of the keys for the success of a VQA. Great efforts have been invested to study the potential of current quantum devices to encode the eigenstates of fermionic systems, but little is known about the encoding of bosonic systems. In this work, we investigate the encoding of the ground state of the (simple but rich) attractive Bose-Hubbard model using a Continuous-Variable (CV) photonic-based quantum circuit. We introduce two different ansatz architectures and demonstrate that the proposed continuous variable quantum circuits can efficiently encode (with a fidelity higher than 99%) the strongly correlated many-boson wavefunction with just a few layers, in all many-body regimes and for different number of bosons and initial states. Beyond the study of the suitability of the ansatz to approximate the ground states of many-boson systems, we also perform initial evaluations of the use of the ansatz in a variational quantum eigensolver algorithm to find it through energy minimization. To this end we also introduce a scheme to measure the Hamiltonian energy in an experimental system, and study the effect of sampling noise.
Verifiable Obstacle Detection

Perception of obstacles remains a critical safety concern for autonomous vehicles. Real-world collisions have shown that the autonomy faults leading to fatal collisions originate from obstacle existence detection. Open source autonomous driving implementations show a perception pipeline with complex interdependent Deep Neural Networks. These networks are not fully verifiable, making them unsuitable for safety-critical tasks. In this work, we present a safety verification of an existing LiDAR based classical obstacle detection algorithm. We establish strict bounds on the capabilities of this obstacle detection algorithm. Given safety standards, such bounds allow for determining LiDAR sensor properties that would reliably satisfy the standards. Such analysis has as yet been unattainable for neural network based perception systems. We provide a rigorous analysis of the obstacle detection system with empirical results based on real-world sensor data.
Image Enhancement using Fuzzy Intensity Measure and Adaptive Clipping Histogram Equalization.

Image enhancement aims at processing an inputimage so that the visual content of the output image is morepleasing or more useful for certain applications. Althoughhistogram equalization is widely used in image enhancement dueto its simplicity and effectiveness, it changes the mean brightnessof the enhanced image and introduces a high level of noise anddistortion. To address these problems, this paper proposesimage enhancement using fuzzy intensity measure and adaptiveclipping histogram equalization (FIMHE). FIMHE uses fuzzyintensity measure to first segment the histogram of the originalimage, and then clip the histogram adaptively in order toprevent excessive image enhancement. Experiments on theBerkeley database and CVF-UGR-Image database show thatFIMHE outperforms state-of-the-art histogram equalizationbased methods.
A Bayesian Approach for Model-Based Clustering of Several Binary Dissimilarity Matrices: The <b>dmbc</b> Package in <i>R</i>

We introduce the new package dmbc that implements a Bayesian algorithm for clustering a set of binary dissimilarity matrices within a model-based framework. Specifically, we consider the case when S matrices are available, each describing the dissimilarities among the same n objects, possibly expressed by S subjects (judges), or measured under different experimental conditions, or with reference to different characteristics of the objects themselves. In particular, we focus on binary dissimilarities, taking values 0 or 1 depending on whether or not two objects are deemed as dissimilar. We are interested in analyzing such data using multidimensional scaling (MDS). Differently from standard MDS algorithms, our goal is to cluster the dissimilarity matrices and, simultaneously, to extract an MDS configuration specific for each cluster. To this end, we develop a fully Bayesian three-way MDS approach, where the elements of each dissimilarity matrix are modeled as a mixture of Bernoulli random vectors. The parameter estimates and the MDS configurations are derived using a hybrid Metropolis-Gibbs Markov Chain Monte Carlo algorithm. We also propose a BIC-like criterion for jointly selecting the optimal number of clusters and latent space dimensions. We illustrate our approach referring both to synthetic data and to a publicly available data set taken from the literature. For the sake of efficiency, the core computations in the package are implemented in C/C++. The package also allows the simulation of multiple chains through the support of the parallel package.
Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays

The existing analysis of asynchronous stochastic gradient descent (SGD) degrades dramatically when any delay is large, giving the impression that performance depends primarily on the delay. On the contrary, we prove much better guarantees for the same asynchronous SGD algorithm regardless of the delays in the gradients, depending instead just on the number of parallel devices used to implement the algorithm. Our guarantees are strictly better than the existing analyses, and we also argue that asynchronous SGD outperforms synchronous minibatch SGD in the settings we consider. For our analysis, we introduce a novel recursion based on "virtual iterates" and delay-adaptive stepsizes, which allow us to derive state-of-the-art guarantees for both convex and non-convex objectives.
State estimation algorithms for linear systems with strong disturbance

This paper considers state estimation algorithms for linear systems contaminated by complex strong disturbance. The strong disturbance is characterized as a general random matrix, which can be non-diagonal, and its components (the parameter of each observation channel) can be simultaneously corrected. For this strong disturbance, we propose an optimal filtering algorithm to filter it in order to estimate the state by using projection theorem. We also present an optimal fixed-interval smoothing and a deconvolution algorithm to address it. Simulation results verify the presented algorithms.
Methods of long-term and short-term adaptation for biometric image of keystroke dynamics

This article discusses one of the main problems of user identification by keyboard handwriting - short-term changes in the keystroke dynamics of users in connection with its psychophysical state, as well as changes over a long time associated with the formation of keystroke dynamics by a new user or when switching to a new device. A method for determining the phase of working capacity by the time characteristics of the keystroke dynamics is proposed.
Semi Fragile Watermarking Technique using IWT and a Two Level Tamper Detection Scheme

A semi fragile watermarking technique using a two level thresholding scheme for tamper detection is proposed. The proposed embedding technique uses two level IWT (integer wavelet transform) to embed the authentication watermark. The authentication watermark generated from the approximate coefficients is stored in the detail coefficients using least significant substitution to form the watermarked image. The proposed tamper detection technique for identifying attacks in the watermarked image is a two level thresholding scheme using normalized hamming similarity (NHS) and a tamper detection map. The performance of the proposed technique was evaluated for a variety of content preserving manipulations and malicious attacks. The proposed technique produces a better performance in terms of an increased PSNR (Peak Signal to Noise Ratio) of the watermarked image and by localizing the malicious attacks when compared to the existing techniques. The significant performance of the proposed semi fragile watermarking technique is due to the combined results from both the NHS and the tamper detection map which helps in localizing the malicious attacks and identifying the incidental manipulations. Also, the authentication watermark which is a copy of the original image helps in identifying the tampered regions in the attacked watermarked image.
Backs and Fronts: Stitching Thread and Thought Through Manning, Methodology, and Art

This paper started with a messy embroidery back and a question: What happens when we play with/in mess, attending to how front/back, process/product, art/research, are counterparts in the event? To answer, we passed embroidery and writing techniques through each other, exploring what becomes possible when you think and make relationally, that is, between. Here, we stitch together Erin Manning’s theorization of the event with our experience. As we attuned ourselves to how thinking back-and-front together mattered, we found that our artful inquiry required us to rethink both what we thought and how we thought. Thus, we found that a methodology of front and back was made in this event, as a result of passing between. Finally, we invite readers to experiment with crafting their own methodologies of the between by flipping over their research as the embroidery hoop, and thereby opening the inquiry to its more-than.
Web Interface and Autodocking

This chapter explains how to implement web interfacing and autodocking using ROS.
Information Systems Analysis using the Technology Acceptance Model approach. Case Study: Faculty of Engineering, Manado State University

The information system is a technology application that provides information using the web. The Faculty of Engineering, Manado State University is an educational institution that has implemented an Information System as one of the conveniences in the student administration process starting from information on course grades received in each semester, grade point average, to filling out the Semester Plan. The use of this information system also requires both positive and negative responses. TAM is a model that is often used to measure user acceptance. With the TAM theory, it can be seen the response from users of the information system. The results of the analysis show that Perceived Usefulness, Perceived Ease of Use, and Intention are the main variables that influence User Acceptance of the information system. So, based on the analysis using TAM it can be concluded that the TAM variables are able to be a determining factor in the level of user acceptance of information systems at the Faculty of Engineering, Manado State University.
SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition

In this paper, we propose a new, simple, and effective Self-supervised Spatio-temporal Transformers (SPARTAN) approach to Group Activity Recognition (GAR) using unlabeled video data. Given a video, we create local and global Spatio-temporal views with varying spatial patch sizes and frame rates. The proposed self-supervised objective aims to match the features of these contrasting views representing the same video to be consistent with the variations in spatiotemporal domains. To the best of our knowledge, the proposed mechanism is one of the first works to alleviate the weakly supervised setting of GAR using the encoders in video transformers. Furthermore, using the advantage of transformer models, our proposed approach supports long-term relationship modeling along spatio-temporal dimensions. The proposed SPARTAN approach performs well on two group activity recognition benchmarks, including NBA and Volleyball datasets, by surpassing the state-of-the-art results by a significant margin in terms of MCA and MPCA metrics <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Impelemtasi Gerakan Literasi Sekolah (GLS) untuk Menumbuhkan Minat Baca Siswa di SMA Negeri 7 Banjarmasin

Low reading skills will affect the quality of student learning which will have an impact on student achievement in school. To overcome these problems, SMA Negeri 7 Banjarmasin implement School Literacy Movement has been running from 2016 to the present. This is field research using a qualitative descriptive approach. Data collection techniques in this study used the methods of observation, interview, and documentation. The subjects in this study were the headmaster, Indonesian Subject teacher, homeroom teachers, and the librarian of SMA Negeri 7 Banjarmasin. The object of this research is the implementation of the School Literacy Movement to foster reading interest at SMA Negeri 7 Banjarmasin as well as the supporting and inhibiting factors for the implementation of the School Literacy Movement to foster reading interest in SMA Negeri 7 Banjarmasin. The data analysis process includes data reduction, data presentation, and drawing conclusions.The results of this study are as follows: 1. Implementation of School Literacy Movement at SMA Negeri 7 Banjarmasin is realized by implementing a socialization program for implementation of School Literacy Movement, reading literacy 15 minutes before starting the classes, library utilization, reading corner, literacy competition, writing Scientific Papers, as well as socialization and assistance to other schools. The implementation of the School Literacy Movement at SMA Negeri 7 Banjarmasin has been carried out all stages, but at the stage of development and learning does not hold follow-up literacy activities consistently and sustainably, so that the implementation is still in the habituation/interest growth stage. 2. Supporting factors: Adequate Human Resources, complete facilities and infrastructure, and funds. Inhibiting factors include low parental attention, low awareness, and willingness of students, and lack of coordination between teachers and the school management team.Keywords: School Literacy Movement, Reading Interest, Literacy, Library
How to Estimate Model Transferability of Pre-Trained Speech Models?

In this work, we introduce a "score-based assessment" framework for estimating the transferability of pre-trained speech models (PSMs) for fine-tuning target tasks.We leverage upon two representation theories, Bayesian likelihood estimation and optimal transport, to generate rank scores for the PSM candidates using the extracted representations.Our framework efficiently computes transferability scores without actual finetuning of candidate models or layers by making a temporal independent hypothesis.We evaluate some popular supervised speech models (e.g., Conformer RNN-Transducer) and selfsupervised speech models (e.g., HuBERT) in cross-layer and cross-model settings using public data.Experimental results show a high Spearman's rank correlation and low p-value between our estimation framework and fine-tuning ground truth.Our proposed transferability framework requires less computational time and resources, making it a resource-saving and timeefficient approach for tuning speech foundation models.
A Comprehensive Introduction to the Intel Quantum SDK

The Intel(R) Quantum Software Development Kit (SDK) offers a comprehensive platform for developers to create quantum applications using an LLVM-based compiler with user-friendly C++ language extensions. This full-stack solution facilitates the expression and optimization of quantum circuits and includes a quantum runtime library for seamless hybrid execution between classical and quantum kernels. The runtime enables dynamic parameter determination for quantum circuits, allowing both quantum and classical components of variational algorithms to be defined within a single program, compiled once, and significantly reducing execution latency. The SDK features a suite of quantum simulators, including simulations of Intel's quantum hardware components such as qubit control processors, control electronics, and quantum dot qubits. With a unified interface, the Intel Quantum SDK allows users to target ideal qubit simulators, realistic Intel quantum hardware simulators, and future Intel quantum hardware, enabling efficient optimization, compilation, and execution of scalable hybrid quantum-classical variational algorithms.
Incremental Audio-Visual Fusion for Person Recognition in Earthquake Scene

Earthquakes have a profound impact on social harmony and property, resulting in damage to buildings and infrastructure. Effective earthquake rescue efforts require rapid and accurate determination of whether any survivors are trapped in the rubble of collapsed buildings. While deep learning algorithms can enhance the speed of rescue operations using single-modal data (either visual or audio), they are confronted with two primary challenges: insufficient information provided by single-modal data and catastrophic forgetting. In particular, the complexity of earthquake scenes means that single-modal features may not provide adequate information. Additionally, catastrophic forgetting occurs when the model loses the information learned in a previous task after training on subsequent tasks, due to non-stationary data distributions in changing earthquake scenes. To address these challenges, we propose an innovative approach that utilizes an incremental audio-visual fusion model for person recognition in earthquake rescue scenarios. Firstly, we leverage a cross-modal hybrid attention network to capture discriminative temporal context embedding, which uses self-attention and cross-modal attention mechanisms to combine multi-modality information, enhancing the accuracy and reliability of person recognition. Secondly, an incremental learning model is proposed to overcome catastrophic forgetting, which includes elastic weight consolidation and feature replay modules. Specifically, the elastic weight consolidation module slows down learning on certain weights based on their importance to previously learned tasks. The feature replay module reviews the learned knowledge by reusing the features conserved from the previous task, thus preventing catastrophic forgetting in dynamic environments. To validate the proposed algorithm, we collected the Audio-Visual Earthquake Person Recognition (AVEPR) dataset from earthquake films and real scenes. Furthermore, the proposed method gets 85.41% accuracy while learning the 10th new task, which demonstrates the effectiveness of the proposed method and highlights its potential to significantly improve earthquake rescue efforts.
CASN:Class-Aware Score Network for Textual Adversarial Detection

Adversarial detection aims to detect adversarial samples that threaten the security of deep neural networks, which is an essential step toward building robust AI systems.Density-based estimation is widely considered as an effective technique by explicitly modeling the distribution of normal data and identifying adversarial ones as outliers.However, these methods suffer from significant performance degradation when the adversarial samples lie close to the non-adversarial data manifold.To address this limitation, we propose a score-based generative method to implicitly model the data distribution.Our approach utilizes the gradient of the log-density data distribution and calculates the distribution gap between adversarial and normal samples through multi-step iterations using Langevin dynamics.In addition, we use supervised contrastive learning to guide the gradient estimation using label information, which avoids collapsing to a single data manifold and better preserves the anisotropy of the different labeled data distributions.Experimental results on three text classification tasks upon four advanced attack algorithms show that our approach is a significant improvement (+15.2F1 score on average against previous SOTA) over previous detection methods.
Local Path Planning for Nonholonomic Mobile Robots Based on Planar G2 Pythagorean Hodograph Degree 7 Bezier Transition Curves

The paper proposes a method that joins two line segments using planar Pythagorean hodograph (PH) degree 7 Bezier transition curves. The objective is to make use of such curvature-continuous curves for the path planning of nonholonomic constrained vehicles moving in 2D space, which requires designing a smooth path while satisfying the constraint of minimum turning radius. We initially present a novel idea that employs PH degree 7 Bezier transition curves for dealing with curvature constraints, the result of which proves to be better than the traditional method based on the “forward simulation”. The properties of PH degree 7 Bezier transition curves are then fully analyzed showing that the curves can be determined by a minimum of three parameters, each of which has an intuitive geometric meaning. The paper also proposes a path planner with direct curvature constraints during the process of path generation in 2D space. The objective function of the optimal path generation can be adjusted to minimize the length of the whole path or to closely follow the original global nonsmooth sequences of line segments. Here, the optimization problem is tactfully converted to a linear programming problem that can be solved efficiently. Finally, we use a kinematic bicycle model for simulation and compare the results with those from Dubins path showing that a [Formula: see text] curve is easier to follow than a [Formula: see text] one.
Passive frustrated nanomagnet reservoir computing

Abstract Reservoir computing (RC) has received recent interest because reservoir weights do not need to be trained, enabling extremely low-resource consumption implementations, which could have a transformative impact on edge computing and in-situ learning where resources are severely constrained. Ideally, a natural hardware reservoir should be passive, minimal, expressive, and feasible; to date, proposed hardware reservoirs have had difficulty meeting all of these criteria. We, therefore, propose a reservoir that meets all of these criteria by leveraging the passive interactions of dipole-coupled, frustrated nanomagnets. The frustration significantly increases the number of stable reservoir states, enriching reservoir dynamics, and as such these frustrated nanomagnets fulfill all of the criteria for a natural hardware reservoir. We likewise propose a complete frustrated nanomagnet reservoir computing (NMRC) system with low-power complementary metal-oxide semiconductor (CMOS) circuitry to interface with the reservoir, and initial experimental results demonstrate the reservoir’s feasibility. The reservoir is verified with micromagnetic simulations on three separate tasks demonstrating expressivity. The proposed system is compared with a CMOS echo state network (ESN), demonstrating an overall resource decrease by a factor of over 10,000,000, demonstrating that because NMRC is naturally passive and minimal it has the potential to be extremely resource efficient.