Reviewerly/exHarmony huggingface dataset:
Symbolic Replay: Scene Graph as Prompt for Continual Learning on VQA Task

VQA is an ambitious task aiming to answer any image-related question. However, in reality, it is hard to build such a system once for all since the needs of users are continuously updated, and the system has to implement new functions. Thus, Continual Learning (CL) ability is a must in developing advanced VQA systems. Recently, a pioneer work split a VQA dataset into disjoint answer sets to study this topic. However, CL on VQA involves not only the expansion of label sets (new Answer sets). It is crucial to study how to answer questions when deploying VQA systems to new environments (new Visual scenes) and how to answer questions requiring new functions (new Question types). Thus, we propose CLOVE, a benchmark for Continual Learning On Visual quEstion answering, which contains scene- and function-incremental settings for the two aforementioned CL scenarios. In terms of methodology, the main difference between CL on VQA and classification is that the former additionally involves expanding and preventing forgetting of reasoning mechanisms, while the latter focusing on class representation. Thus, we propose a real-data-free replay-based method tailored for CL on VQA, named Scene Graph as Prompt for Symbolic Replay. Using a piece of scene graph as a prompt, it replays pseudo scene graphs to represent the past images, along with correlated QA pairs. A unified VQA model is also proposed to utilize the current and replayed data to enhance its QA ability. Finally, experimental results reveal challenges in CLOVE and demonstrate the effectiveness of our method. Code and data are available at https://github.com/showlab/CLVQA.
Take Your MEDS: Digital Signatures from Matrix Code Equivalence

In this paper, we show how to use the Matrix Code Equivalence (MCE) problem as a new basis to construct signature schemes. This extends previous work on using isomorphism problems for signature schemes, a trend that has recently emerged in post-quantum cryptography. Our new formulation leverages a more general problem and allows for smaller data sizes, achieving competitive performance and great flexibility. Using MCE, we construct a zero-knowledge protocol which we turn into a signature scheme named Matrix Equivalence Digital Signature (MEDS). We provide an initial choice of parameters for MEDS, tailored to NIST’s Category 1 security level, yielding public keys as small as 2.8 kB and signatures ranging from 18 kB to just around 6.5 kB, along with a reference implementation in C.
First estimation of high-resolution solar photovoltaic resource maps over China with Fengyun-4A satellite and machine learning

Fengyun-4A (FY-4A), which is the latest-generation Chinese geostationary meteorological satellite, measures solar reflection and thermal emission with high temporal, spatial, and spectral resolutions. It is expected to be highly beneficial for solar resource assessment and forecasting in China. This study is the first to estimate, using FY-4A and a random forest model, the global horizontal irradiance (GHI) at a 4-km–15-min spatio-temporal resolution over China, as a means to arrive at a solar photovoltaic (PV) resource map. In terms of GHI estimates, the root mean square error and mean bias error between hourly measured and retrieved values are 147.02 (35.2%), −5.64 W/m2 (−1.4%), respectively, whereas the values of daily estimates are 29.20 (18.0%), −2.97 W/m2 (−1.3%). The retrieval accuracy is found much better for instances with solar zenith angles smaller than 60°. Relatively larger errors are found at locations in the Sichuan Basin and northeastern China, which can be attributed to bright surfaces and/or strong cloud transients. With the retrieved irradiance, PV resource is derived through a physical model chain. The annual mean PV resource map suggests that, over most of the west areas, the annual mean effective irradiance exceeds 1700 kWh/m2, with the highest value found in Tibet (around 2000 kWh/m2 per annum). Eastern China has an annual effective irradiance of only 1300–1500 kWh/m2. The region with poorest solar resource is the Sichuan Basin (less than 1100 kWh/m2 per annum).
Research on Traffic Sign Detection Based on Improved YOLOv8

Aiming at solving the problem of missed detection and low accuracy in detecting traffic signs in the wild, an improved method of YOLOv8 is proposed.Firstly, combined with the characteristics of small target objects in the actual scene, this paper further adds blur and noise operation.Then, the asymptotic feature pyramid network (AFPN) is introduced to highlight the influence of key layer features after feature fusion, and simultaneously solve the direct interaction of non-adjacent layers.Experimental results on the TT100K dataset show that compared with the YOLOv8, the detection accuracy and recall are higher.
Classical models may be a better explanation of the Jiuzhang 1.0 Gaussian Boson Sampler than its targeted squeezed light model

Recently, Zhong et al. performed landmark Gaussian boson sampling experiments with up to 144 modes using threshold detectors. The authors claim to have achieved quantum computational advantage with the implementation of these experiments, named Jiuzhang 1.0 and Jiuzhang 2.0. Their experimental results are validated against several classical hypotheses and adversaries using tests such as the comparison of statistical correlations between modes, Bayesian hypothesis testing and the Heavy Output Generation (HOG) test. We propose an alternative classical hypothesis for the validation of these experiments using the probability distribution of mixtures of coherent states sent into a lossy interferometer; these input mixed states, which we term squashed states, have vacuum fluctuations in one quadrature and excess fluctuations in the other. We find that for configurations in the high photon number density regime, the comparison of statistical correlations does not tell apart the ground truth of the experiment (two-mode squeezed states sent into an interferometer) from our alternative hypothesis. The Bayesian test indicates that, for all configurations excepting Jiuzhang 1.0, the ground truth is a more likely explanation of the experimental data than our alternative hypothesis. A similar result is obtained for the HOG test: for all configurations of Jiuzhang 2.0, the test indicates that the experimental samples have higher ground truth probability than the samples obtained form our alternative distribution; for Jiuzhang 1.0 the test is inconclusive. Our results provide a new hypothesis that should be considered in the validation of future GBS experiments, and shed light into the need to identify proper metrics to verify quantum advantage in the context of GBS. They also indicate that a classical explanation of the Jiuzhang 1.0 experiment, lacking any quantum features, has not been ruled out.
Cryptocurrency energy consumption: Analysis, global trends and interaction

The rapid spread of cryptocurrencies is one of the most relevant trends today. One of the significant risks of their spread is the increase in energy consumption, which has a negative impact on the environment due to carbon emissions. This requires the development of a scientific toolkit for assessing relationships and predicting the impact of cryptocurrencies on energy consumption, which is the aim of this paper.With the correlational regression analysis, the model of the dependence of spending on IT sector, energy consumption of Bitcoin, Ethereum and global capitalization of the cryptocurrency market was conducted, based on statistical data from Statista.com, Сoinmarketcap.com and International Data Corporation. To check the possible relationship, tests for the adequacy of the results obtained (Fisher’s test, Student’s t-test) confirmed the correctness of coefficients for independent variables.The results showed a significant direct correlation (Multiple R is 95%) of spending on IT sector, energy consumption and global capitalization of the cryptocurrency market. The established relationships allowed predicting that Bitcoin energy consumption may reach 142 Terawatt hours per year in 2026. And its impact on environment by mining in 2022 was at least 27.4 Mt of CO2 emission.As a proposal, a conclusion was made on the expediency of linking mining to the use of certain sources of electricity production, such as “residual” natural gas, nuclear power, renewable energy sources. The obtained results and conclusions may be used as a basis for political decisions in the field of energy efficiency and climate change mitigation.
Machine Learning-Based Mapping for Mineral Exploration

We briefly review the state-of-the-art machine learning (ML) algorithms for mineral exploration, which mainly include random forest (RF), convolutional neural network (CNN), and graph convolutional network (GCN). In recent years, RF, a representative shallow machine learning algorithm, and CNN, a representative deep learning approach, have been proved to be powerful tools for ML-based mapping for mineral exploration. In the future, GCN deserves more attention for ML-based mapping for mineral exploration because of its ability to capture the spatial anisotropy of mineralization and its applicability within irregular study areas. Finally, we summarize the original contributions of the six papers comprising this special issue.
Experimental Demonstration of High‐Performance Physical Reservoir Computing with Nonlinear Interfered Spin Wave Multidetection

Physical reservoir computing, which is a promising method for the implementation of highly efficient artificial intelligence devices, requires a physical system with nonlinearity, fading memory, and the ability to map in high dimensions. Although it is expected that spin wave interference can perform as highly efficient reservoir computing in some micromagnetic simulations, there has been no experimental verification to date. Herein, reservoir computing is demonstrated that utilizes multidetected nonlinear spin wave interference in an yttrium‐iron‐garnet single crystal. The subject computing system achieves excellent performance when used for hand‐written digit recognition, second‐order nonlinear dynamical tasks, and nonlinear autoregressive moving average (NARMA). It is of particular note that normalized mean square errors for NARMA2 and second‐order nonlinear dynamical tasks are 1.81 × 10 −2 and 8.37 × 10 −5 , respectively, which are the lowest figures for any experimental physical reservoir so far reported. Said high performance is achieved with higher nonlinearity and the large memory capacity of interfered spin wave multidetection.
Image encryption using binary polarization states of light beam

Optical image/data encryption techniques are mostly based on the manipulation of spatial distributions of light's amplitude, phase, and polarization. Information encoding with phase involves complex interferometric set-up and polarization encoding requires Stoke's parameter measurement. Hence, they create difficulties in optical implementation. Considering the practical limitations, in this study, we demonstrate a method of single-shot intensity recording-based color image encryption by encoding the information in binary polarization states. The proposed method does not require Stoke parameter calculation. As a proof-of-concept, we demonstrated the technique with coherent and partially coherent light sources. Use of partially coherent light overcomes the speckle problem and makes the system cost-effective, useful for practical applications.
A Field Note Describing the Development and Dissemination of Clear Language Research Summaries for University-Based Knowledge Mobilization

This field note places research summaries in the context of the knowledge mobilization literature and then describes the development, evaluation and dissemination of clear language research summaries as tools for research outreach, research communication and knowledge mobilization. The ResearchSnapshot clear language research summary format was developed with input from a Steering Committee comprised of academic and non-academic members. The research summary format was tested in roundtables of diverse research stakeholders and received positive feedback. The research summaries were then made accessible online and disseminated using a social media strategy. The use of twitter to enhance dissemination of the research summaries increased web traffic to the research summary database by 270%. This field note concludes with clear recommendations for individuals and organizations interested in developing their own clear language research summaries.
Voice Pathologies Classification Using GMM And SVM Classifiers

In this paper we investigate the proprieties of automatic speaker recognition (ASR) to develop a system for voice pathologies detection, where the model does not correspond to a speaker but it corresponds to group of patients who shares the same diagnostic. One of essential part in this topic is the database (described later), the samples voices (healthy and pathological) are chosen from a German database which contains many diseases, spasmodic dysphonia is proposed for this study. This problematic can be solved by statistical pattern recognition techniques where we have proposed the mel frequency cepstral coefficients (MFCC) to be modeled first, with gaussian mixture model (GMM) massively used in ASR then, they are modeled with support vector machine (SVM). The obtained results are compared in order to evaluate the more preferment classifier. The performance of each method is evaluated in a term of the accuracy, sensitivity, specificity. The best performance is obtained with 12 coefficientsMFCC, energy and second derivate along SVM with a polynomial kernel function, the classification rate is 90% for normal class and 93% for pathological class.This work is developed under MATLAB
Technical Factors Affecting Ultrasound Breast Tumor Size as Correlated with Pathological Type

Background and Objectives: Accurate breast tumor sizing is very important in treatment planning; as a result, ultrasound (US) plays an important role in diagnosing breast masses, due to its non-magnified image and its availability. The continuous change in the disease pathogenesis of breast cancer and tremendous advances in US imaging technology require the continuous evaluation of this imaging modality. In this study, our aim was to determine the accuracy of US in measuring the size of breast mass, and if there is an influence of the different pathological types on this accuracy. Materials and Methods: This study contained 66 specimens of breast masses that underwent surgical excision and pathological examination of the resected masses; the mean difference between the size taken by US and the size taken by pathology was calculated to the patients as a whole and for each tumor type in this study. Results: The result was that US underestimates the size of the tumor by 0.5 cm for all pathological types, and the US size is in agreement with the pathology size. Conclusions: US is an accurate method in measuring breast lesions with a degree of underestimation that may be related to many factors such as the tumor type, size, and margins. Complementary MRI is recommended in case of ILC and architectural distortion.
Generating Narrative Action Schemas for Suspense

A bottleneck in interactive storytelling is the authorial burden of writing narrative units, and connecting them to the interactive narrative structure. To address this problem, we present a hybrid approach that combines AI planning and evolutionary optimization in order to generated new plan operators representing possible story actions, within the framework of a planning-based interactive narrative system. We focus our work on inventing plan operators that are useful for contributing to suspenseful interactive stories, using suspense metrics that have been proposed in the literature. We devise an encoding scheme for converting a plan operator into a genetic-algorithm chromosome and vice versa, respecting constraints that are needed for an operator to be well-formed. We discuss the performance of the system, and several examples from preliminary experiments carried out to evaluate the evolved operators.
ACCELERATING FPGA-SURF FEATURE DETECTION MODULE BY MEMORY ACCESS REDUCTION

Feature detection is an important concept in the area of image processing to compute image abstractions of image information, which is used for image recognition and many other applications. One of the popular algorithm used is called the Speeded-Up Robust Features (SURF), which realized the scale space pyramid to detect the features. For this reason, prior researchers concentrate on applying parallelism onto the SURF multiple layers using technology such as Field Programmable Gate Array (FPGA). However, prior FPGA-SURF implementation does not emphasis on memory access limitation that can affect the overall performance of a system. This paper proposes a study on FPGA-SURF and memory access implementation in feature detection area. We conduct a profiling test and founds that the external memory access to fetch the integral image data in SURF highly affects the overall performance. We also found that the SURF algorithm memory access has redundant repeating pattern that can be reduced. Therefore, a controller design that stores repeating data (for the subsequent process) in an on-chip memory is proposed. This method reduces the external memory access and can increase the overall performance. The result shows that our proposed method improves the existing method (i.e. without the memory access reduction) by 1.23 times when the external memory latency is 20ns.
Work in Progress: Analysis of Mobile Technology Impact on STEM-Based Courses, Specifically Introductions to Engineering in the Era of the iPad

Developed over 5 new courses, including UTEP technology & society core curriculum classes specifically for incoming freshman with a STEM background.Mr. Perez has six years of
Probability Based Independence Sampler for Bayesian Quantitative Learning in Graphical Log-Linear Marginal Models

We introduce a novel Bayesian approach for quantitative learning for graphical log-linear marginal models.These models belong to curved exponential families that are difficult to handle from a Bayesian perspective.The likelihood cannot be analytically expressed as a function of the marginal log-linear interactions, but only in terms of cell counts or probabilities.Posterior distributions cannot be directly obtained, and Markov Chain Monte Carlo (MCMC) methods are needed.Finally, a well-defined model requires parameter values that lead to compatible marginal probabilities.Hence, any MCMC should account for this important restriction.We construct a fully automatic and efficient MCMC strategy for quantitative learning for such models that handles these problems.While the prior is expressed in terms of the marginal log-linear interactions, we build an MCMC algorithm that employs a proposal on the probability parameter space.The corresponding proposal on the marginal log-linear interactions is obtained via parameter transformation.We exploit a conditional conjugate setup to build an efficient proposal on probability parameters.The proposed methodology is illustrated by a simulation study and a real dataset.
Domains and stochastic processes

Domain theory has a long history of applications in theoretical computer science and mathematics. In this article, we explore the relation of domain theory to probability theory and stochastic processes. The goal is to establish a theory in which Polish spaces are replaced by domains, and measurable maps are replaced by Scott-continuous functions. We illustrate the approach by recasting one of the fundamental results of stochastic process theory -- Skorohod's Representation Theorem -- in domain-theoretic terms. We anticipate the domain-theoretic version of results like Skorohod's Theorem will improve our understanding of probabilistic choice in computational models, and help devise models of probabilistic programming, with its focus on programming languages that support sampling from distributions where the results are applied to Bayesian reasoning.
Improving the lower bound to the secret-key capacity of the thermal amplifier channel

We consider the noisy thermal amplifier channel, where signal modes are amplified together with environmental thermal modes. We focus on the secret-key capacity of this channel, which is the maximum amount of secret bits that two remote parties can generate by means of the most general adaptive protocol, assisted by unlimited and two-way classical communication. For this channel only upper and lower bounds are known, and in this work we improve the lower bound. We consider a protocol based on squeezed states and homodyne detections, in both direct and reverse reconciliation. In particular, we assume that trusted thermal noise is mixed on beam splitters controlled by the parties in a way to assist their homodyne detections. The new improved lower bounds to the secret-key capacity are obtained by optimizing the key rates over the variance of the trusted noise injected, and the transmissivity of the parties' beam splitters. Our results confirm that there is a separation between the coherent information of the thermal amplifier channel and its secret key capacity.
Parameter Measurement of Biaxial Braided Composite Preform Based on Phase Congruency

Abstract In this paper, a new method based on phase congruency is proposed to measure pitch lengths and surface braiding angles of two-dimensional biaxial braided composite preforms. Lab space transform and BM3D (block-matching and 3D filter) are used first to preprocess the original acquired images. A corner detection algorithm based on phase congruency is then proposed to detect the corners of the preprocessed images. Pitch lengths and surface braiding angles are finally measured based on the detected corner maps. Experimental results show that our method achieves the automatic measurement of pitch lengths and the surface braiding angles of biaxial braided composite preforms with high accuracy.
Decoding the three-dimensional toric codes and welded codes on cubic lattices

The recent years have seen a growing interest in quantum codes in three dimensions (3D). One of the earliest proposed 3D quantum codes is the 3D toric code. It has been shown that 3D color codes can be mapped to 3D toric codes. The 3D toric code on cubic lattice is also a building block for the welded code which has highest energy barrier to date. Although well known, the performance of the 3D toric code has not been studied extensively. In this paper, we propose efficient decoding algorithms for the 3D toric code on a cubic lattice with and without boundaries and report their performance for various quantum channels. We observe a threshold of $\gtrsim 12\%$ for the bit flip errors, $\approx 3\%$ for phase flip errors and $ 24.8\%$ for erasure channel. We also study the performance of the welded 3D toric code on the quantum erasure channel. We did not observe a threshold for the welded code over the erasure channel.