Reviewerly/exHarmony huggingface dataset:
Rome to Milan, AMD Continues Its Tour of Italy

AMD’s recently released EPYC 7003 series CPUs (codename Milan) deliver performance and power efficiency improvements over its previous generation of data center CPUs (codename Rome). Various architectural improvements were made such as an uplift from the Zen 2 core to the Zen 3 core, unifying the L3 cache, and adding security features. Relative to the competition, AMD’s latest data center CPUs are very competitive. However, the looming threat of vertical integration plagues the entire market.
Resurrecting Address Clustering in Bitcoin

Blockchain analysis is essential for understanding how cryptocurrencies like Bitcoin are used in practice, and address clustering is a cornerstone of blockchain analysis. However, current techniques rely on heuristics that have not been rigorously evaluated or optimized. In this paper, we tackle several challenges of change address identification and clustering. First, we build a ground truth set of transactions with known change from the Bitcoin blockchain that can be used to validate the efficacy of individual change address detection heuristics. Equipped with this data set, we develop new techniques to predict change outputs with low false positive rates. After applying our prediction model to the Bitcoin blockchain, we analyze the resulting clustering and develop ways to detect and prevent cluster collapse. Finally, we assess the impact our enhanced clustering has on two exemplary applications.
Learning to Read L’Infinito: Handwritten Text Recognition with Synthetic Training Data

Deep learning-based approaches to Handwritten Text Recognition (HTR) have shown remarkable results on publicly available large datasets, both modern and historical. However, it is often the case that historical manuscripts are preserved in small collections, most of the time with unique characteristics in terms of paper support, author handwriting style, and language. State-of-the-art HTR approaches struggle to obtain good performance on such small manuscript collections, for which few training samples are available. In this paper, we focus on HTR on small historical datasets and propose a new historical dataset, which we call Leopardi, with the typical characteristics of small manuscript collections, consisting of letters by the poet Giacomo Leopardi, and devise strategies to deal with the training data scarcity scenario. In particular, we explore the use of carefully designed but cost-effective synthetic data for pre-training HTR models to be applied to small single-author manuscripts. Extensive experiments validate the suitability of the proposed approach, and both the Leopardi dataset and synthetic data will be available to favor further research in this direction.
Interconnect-Free Multibit Arithmetic and Logic Unit in a Single Reconfigurable 3 μm<sup>2</sup> Plasmonic Cavity

Processing information with conventional integrated circuits remains beset by the interconnect bottleneck: circuits made of smaller active devices need longer and narrower interconnects, which have become the prime source of power dissipation and clock rate saturation. Optical interchip communication provides a fast and energy-saving option that still misses a generic on-chip optical information processing by interconnect-free and reconfigurable Boolean arithmetic logic units (ALU). Considering metal plasmons as a platform with dual optical and electronic compatibilities, we forge interconnect-free, ultracompact plasmonic Boolean logic gates and reconfigure them, at will, into computing ALU without any redesign nor cascaded circuitry. We tailor the plasmon mode landscape of a single 2.6 μm2 planar gold cavity and demonstrate the operation and facile reconfiguration of all 2-input logic gates. The potential for higher complexity of the same logic unit is shown by a multi-input excitation and a phase control to realize an arithmetic 2-bit adder.
Minimizing the Computational Effort to Optimize Solar Concentrators with the Open-Source Tools SunPATH and Tonatiuh++

Integrals that are of interest in the analysis, design, and optimization of concentrating solar thermal systems (CST), such as the annual optical efficiency of the light collection and concentration (LCC) subsystem, can be accurately computed or estimated in two distinct ways: on the time domain and on the spatial domain. This article explores these two ways, using a case study that is highly representative of the commercial CST systems being deployed worldwide. In the time domain, the computation of these integrals are explored using 1-min, 10-min, and 1-h solar DNI input data and using The Cyprus Institute (CyI)’s High-Performance Computing (HPC) system and an open-source ray tracer, Tonatiuh++, being actively developed at CyI. In the spatial domain, the computation of these integrals is explored using SunPATH, another open-source software tool being actively developed at CyI, in tandem with Tonatiuh++. The comparison between the time and spatial domain approach clearly indicate that the spatial domain approach using SunPATH is dramatically more computationally efficient than the time domain approach. According to the results obtained, at least for the case study analyzed in this article, to compute the annual energy delivered by the LCC subsystem with a relative error less than 0.1%, it is enough to provide SunPATH with 1-h DNI data as input, request from SunPATH the sun position and weights of just 30 points in the celestial sphere, and run Tonatiuh++ to simulate these 30 points using 15 million rays per run. As the test case is highly representative, it is expected that this approach will yield similar results for most CST systems of interest.
Ultrastrong Tunable Coupler Between Superconducting <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mml:mi>L</mml:mi><mml:mi>C</mml:mi></mml:math> Resonators

We investigate the ultrastrong tunable coupler for coupling of superconducting resonators. The obtained coupling constant exceeds 1 GHz, and the wide-range tunability is achieved for both antiferromagnetics and ferromagnetics from $\ensuremath{-}1086$ to 604 MHz. The ultrastrong coupler is composed of a rf superconducting quantum interference device (SQUID) and dc SQUID as tunable junctions, which connect to resonators via shared aluminum thin-film meander lines enabling such a huge coupling constant. The spectrum of the coupler obviously shows the breaking of the rotating-wave approximation, and our circuit model treating the Josephson junction as a tunable inductance reproduces the experimental results well. The ultrastrong coupler is expected to be utilized in quantum annealing circuits and/or noisy intermediate-scale quantum devices with dense connections between qubits.
MapGo: Model-Assisted Policy Optimization for Goal-Oriented Tasks

In Goal-oriented Reinforcement learning, relabeling the raw goals in past experience to provide agents with hindsight ability is a major solution to the reward sparsity problem. In this paper, to enhance the diversity of relabeled goals, we develop FGI (Foresight Goal Inference), a new relabeling strategy that relabels the goals by looking into the future with a learned dynamics model. Besides, to improve sample efficiency, we propose to use the dynamics model to generate simulated trajectories for policy training. By integrating these two improvements, we introduce the MapGo framework (Model-Assisted Policy optimization for Goal-oriented tasks). In our experiments, we first show the effectiveness of the FGI strategy compared with the hindsight one, and then show that the MapGo framework achieves higher sample efficiency when compared to model-free baselines on a set of complicated tasks.
Implementation of Verification and Matching E-KTP with Faster R-CNN and ORB

needs a solid validation that has verification and matching uploaded images. To solve this problem, this paper implementing a detection model using Faster R-CNN and a matching method using ORB (Oriented FAST and Rotated BRIEF) and KNN-BFM (K-Nearest Neighbor Brute Force Matcher). The goal of the implementations is to reach both an 80% mark of accuracy and prove matching using ORB only can be a replaced OCR technique. The implementation accuracy results in the detection model reach mAP (Mean Average Precision) of 94%. But, the matching process only achieves an accuracy of 43,46%. The matching process using only image feature matching underperforms the previous OCR technique but improves processing time from 4510ms to 60m). Image matching accuracy has proven to increase by using a high-quality dan high quantity dataset, extracting features on the important area of EKTP card images.
Single-shot circular fringe projection for the profiling of objects having surface discontinuities

Fringe projection profilometry (FPP) is a widely used non-contact optical method for 3D profiling of objects. The commonly used linear fringe pattern in FPP has periodic intensity variations along the lateral direction. As a result, the linear fringe pattern used in FPP cannot uniquely represent the lateral shift induced by the objects having surface discontinuities. Thus, unambiguous surface profiling of objects, especially with surface discontinuities, using a single linear fringe image having a single fringe frequency, is unfeasible. This paper proposes using a radially symmetric circular fringe pattern as the structured light pattern for accurate unambiguous surface profiling of sudden height-discontinuous objects. To the best of our knowledge, this is the only method that can reconstruct discontinuous height profiles with the help of a single fringe image having a single frequency. The performance of the proposed algorithm is evaluated on several synthetic and real objects having smooth variations and discontinuities. Compared to the well-known fringe projection methods, the results depict that for a tolerable range of error, the proposed method can be applied for the reconstruction of objects with 4 times higher dynamic range and even at much lower fringe frequencies.
Discrete-variable quantum key distribution with homodyne detection

Most quantum key distribution (QKD) protocols can be classified as either a discrete-variable (DV) protocol or continuous-variable (CV) protocol, based on how classical information is being encoded. We propose a protocol that combines the best of both worlds -- the simplicity of quantum state preparation in DV-QKD together with the cost-effective and high-bandwidth of homodyne detectors used in CV-QKD. Our proposed protocol has two highly practical features: (1) it does not require the honest parties to share the same reference phase (as required in CV-QKD) and (2) the selection of decoding basis can be performed after measurement. We also prove the security of the proposed protocol in the asymptotic limit under the assumption of collective attacks. Our simulation suggests that the protocol is suitable for secure and high-speed practical key distribution over metropolitan distances.
Data Augmentation Methods for End-to-End Speech Recognition on Distant-Talk Scenarios

Although end-to-end automatic speech recognition (E2E ASR) has achieved great performance in tasks that have numerous paired data, it is still challenging to make E2E ASR robust against noisy and low-resource conditions. In this study, we investigated data augmentation methods for E2E ASR in distant-talk scenarios. E2E ASR models are trained on the series of CHiME challenge datasets, which are suitable tasks for studying robustness against noisy and spontaneous speech. We propose to use three augmentation methods and thier combinations: 1) data augmentation using text-to-speech (TTS) data, 2) cycle-consistent generative adversarial network (Cycle-GAN) augmentation trained to map two different audio characteristics, the one of clean speech and of noisy recordings, to match the testing condition, and 3) pseudo-label augmentation provided by the pretrained ASR module for smoothing label distributions. Experimental results using the CHiME-6/CHiME-4 datasets show that each augmentation method individually improves the accuracy on top of the conventional SpecAugment; further improvements are obtained by combining these approaches. We achieved 4.3\% word error rate (WER) reduction, which was more significant than that of the SpecAugment, when we combine all three augmentations for the CHiME-6 task.
Leveraging Pre-Trained Language Model for Speech Sentiment Analysis

In this paper, we explore the use of pre-trained language models to learn sentiment information of written texts for speech sentiment analysis. First, we investigate how useful a pre-trained language model would be in a 2-step pipeline approach employing Automatic Speech Recognition (ASR) and transcripts-based sentiment analysis separately. Second, we propose a pseudo label-based semi-supervised training strategy using a language model on an end-to-end speech sentiment approach to take advantage of a large, but unlabeled speech dataset for training. Although spoken and written texts have different linguistic characteristics, they can complement each other in understanding sentiment. Therefore, the proposed system can not only model acoustic characteristics to bear sentiment-specific information in speech signals, but learn latent information to carry sentiments in the text representation. In these experiments, we demonstrate the proposed approaches improve F1 scores consistently compared to systems without a language model. Moreover, we also show that the proposed framework can reduce 65% of human supervision by leveraging a large amount of data without human sentiment annotation and boost performance in a low-resource condition where the human sentiment annotation is not available enough.
Visibility estimation via deep label distribution learning in cloud environment

Abstract The visibility estimation of the environment has great research and application value in the fields of production. To estimate the visibility, we can utilize the camera to obtain some images as evidence. However, the camera only solves the image acquisition problem, and the analysis of image visibility requires strong computational power. To realize effective and efficient visibility estimation, we employ the cloud computing technique to realize high-through image analysis. Our method combines cloud computing and image-based visibility estimation into a powerful and efficient monitoring framework. To train an accurate model for visibility estimation, it is important to obtain the precise ground truth for every image. However, the ground-truth visibility is difficult to be labeled due to its high ambiguity. To solve this problem, we associate a label distribution to each image. The label distribution contains all the possible visibilities with their probabilities. To learn from such annotation, we employ a CNN-RNN model for visibility-aware feature extraction and a conditional probability neural network for distribution prediction. The estimation result can be improved by fusing the predicting results of multiple images from different views. Our experiment shows that labeling the image with visibility distribution can boost the learning performance, and our method can obtain the visibility from the image efficiently.
Many-to-Many Voice Conversion Based Feature Disentanglement Using Variational Autoencoder

Voice conversion is a challenging task which transforms the voice characteristics of a source speaker to a target speaker without changing linguistic content. Recently, there have been many works on many-to-many Voice Conversion (VC) based on Variational Autoencoder (VAEs) achieving good results, however, these methods lack the ability to disentangle speaker identity and linguistic content to achieve good performance on unseen speaker scenarios. In this paper, we propose a new method based on feature disentanglement to tackle many to many voice conversion. The method has the capability to disentangle speaker identity and linguistic content from utterances, it can convert from many source speakers to many target speakers with a single autoencoder network. Moreover, it naturally deals with the unseen target speaker scenarios. We perform both objective and subjective evaluations to show the competitive performance of our proposed method compared with other state-of-the-art models in terms of naturalness and target speaker similarity.
Quantum steering as a witness of quantum scrambling

Quantum information scrambling describes the delocalization of local information to global information in the form of entanglement throughout all possible degrees of freedom. A natural measure of scrambling is the tripartite mutual information, which quantifies the amount of delocalized information for a given quantum channel with its state representation, i.e., the Choi state. In this work, we show that quantum information scrambling can also be witnessed by temporal quantum steering for qubit systems. We can do so because there is a fundamental equivalence between the Choi state and the pseudodensity matrix formalism used in temporal quantum correlations. In particular, we propose a quantity as a scrambling witness, based on a measure of temporal steering called temporal steerable weight. We justify the scrambling witness for unitary qubit channels by proving that the quantity vanishes whenever the channel is nonscrambling.
Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge

The detection of mitotic figures from different scanners/sites remains an important topic of research, owing to its potential in assisting clinicians with tumour grading. The MItosis DOmain Generalization (MIDOG) challenge aims to test the robustness of detection models on unseen data from multiple scanners for this task. We present a short summary of the approach employed by the TIA Centre team to address this challenge. Our approach is based on a hybrid detection model, where mitotic candidates are segmented on stain normalised images, before being refined by a deep learning classifier. Cross-validation on the training images achieved the F1-score of 0.786 and 0.765 on the preliminary test set, demonstrating the generalizability of our model to unseen data from new scanners.
Multiscale analysis of multi-directional composite laminates to predict stiffness and strength in the presence of micro-defects

A methodology is proposed to incorporate spatial distributions of micro-defects within a three dimensional multiscale modeling framework for Carbon Fiber Reinforced Polymer (CFRP) composites to predict stiffness and strength properties of laminates for use in structural design and in establishing nondestructive inspection protocols. Defects in both the fiber and matrix due to variations in the manufacturing process for fiber reinforced composite laminates are considered. Stochastic sampling of micro-defects based on experimental data using Ripley’s K-function is proposed. Three different laminate void volume fractions are used. Spatial statistical analysis is performed for the micro-defects, and then a methodology is presented for predicting stiffness and ultimate strength of three multi-directional composite laminates with distributions of microstructural defects. The results indicate that, in addition to the volume of micro-defects that are present, the spatial distribution of the micro-defects plays a significant role in mechanical properties of laminates, especially the ultimate strength for which a 3.62% reduction is seen in the simulations of laminates containing distributions of micro-defects. Based on these results, it is recommended that the spatial statistics of micro-defects in manufactured composites be incorporated into both design criteria and nondestructive inspection criteria for composite laminates.
Anovel HEOMGA Approach for Class Imbalance Problem in the Application of Customer Churn Prediction

Making class balance is essential when learning from highly skewed datasets; otherwise, a learner may classify all instances to a negative class, resulting in a high false-negative rate. As a result, a precise balancing strategy is required. Many researchers have investigated class imbalance using Machine Learning (ML) methods due to their powerful generalization performance and interpreting capabilities, comparing with random sampling techniques, to handle the problem of class imbalance in the preprocessing phase to facilitate learning process and improve performance results of learners. In this research, an effective method called HEOMGA is presented by combining Heterogeneous Euclidean-Overlap Metric (HEOM) and Genetic Algorithm (GA) for oversampling minority class. The HEOM is employed to define a fitness function for the GA. To assess the performance of the proposed HEOMGA method, three benchmark datasets from UCI repository in the domain of customer churn prediction are examined using three different ML learners and evaluated with three performance metrics. The experiment results show the effectiveness of the proposed method compared to some popular oversample methods, such as SMOTE, ADASYN, G SMOTE, and Gaussian oversampling methods. The HEOMGA method significantly outperformed the other oversampling methods in terms of recall, G mean, and AUC when the Wilcoxon signed-rank test is used.
Modeling the Dynamics of Living Standards Based on Factors of the Remuneration System

The concepts "standard of living" and "quality of life" play an essential role in society. They are indicators of assessing the socio-economic situation in a country. In the paper the model the dynamics of living standards which makes it possible to substantiate management decisions on the formation of a remuneration system at enterprises, taking into account the financial security of employees is developed. The use of the model showed that the financial indicators of the salary system effectiveness at enterprises do not reflect its adequate impact on the living standard of employees. Namely, the positive tendencies of an increase in average salary and an increase in the salary bill do not fully ensure an increase in the level indicator reflecting the volume of consumption of basic food products. This is due to the fact that the level of the real salary index is insufficient to fully meet the needs of employees.
An internal-external optimized convolutional neural network for arbitrary orientated object detection from optical remote sensing images

(2021). An internal-external optimized convolutional neural network for arbitrary orientated object detection from optical remote sensing images. Geo-spatial Information Science: Vol. 24, No. 4, pp. 654-665.
A Reconstruction of Total Solar Irradiance Based on Wavelet Analysis

Abstract As an important indicator of solar activity, the long‐term total solar irradiance (TSI) observations are needed to uncover the impact of solar activity on Earth's climate. In this paper, the periodic variation of TSI and its relationships with sunspot number and Ca II K index are analyzed by using the satellite observation data from 1979 to 2015 and the wavelet method. The results of continuous wavelet analysis show that TSI, sunspot number and Ca II K index all have significant and stable oscillation periods of 9∼13 years and intermittent oscillation periods of 2∼6 months only during the time of intense solar activity. Moreover, the results of cross wavelet analysis indicate that the effect of sunspot number and Ca II K index on TSI is mainly reflected in the period of 9∼13 years with one month phase lag, and sunspot number and Ca II K index cannot explain the TSI variation in the period of 3∼6 months. Under the condition of considering the phase relationship and the model order, the TSI reconstruction model is established and the monthly TSI time series from 1907 to 1978 is restored.