Reviewerly/exHarmony huggingface dataset:
Testing for market efficiency in cryptocurrencies: evidence from a non-linear conditional quantile framework

This study tests for the weak-form market efficiency of 15 cryptocurrency prices. The conventional unit root tests and stationary test results reveal that most cryptocurrency markets are efficient markets. However, the non-linear quantile unit root test proposed by Li and Park (2018) rejects the unit root null hypothesis over the whole quantile level. To derive more informative ideas, we split the whole quantile interval to several sub-intervals and find asymmetric behaviour of the market efficiency across the lower and upper sub-intervals in several cryptocurrency markets. Moreover, non-linear quantile unit root tests for Chainlink, Bitcoin Cash, Binance Coin, EOS, Tron, and Stellar indicate that markets for these cryptocurrencies are efficient at the upper sub-intervals.
Quantum Error Correction at the Threshold: If technologists don't get beyond it, quantum computers will never be big

Dates Chiseled into an ancient tombstone have more in common with the data in your phone or laptop than you may realize. They both involve conventional, classical information, carried by hard-ware that is relatively immune to errors. The situation inside a quantum computer is far different: The information itself has its own idiosyncratic properties, and compared with standard digital microelectronics, state-of-the-art quantum-computer hardware is more than a billion trillion times as likely to suffer a fault. This tremendous susceptibility to errors is the single biggest problem holding back quantum computing from realizing its great promise.
LMISA: A lightweight multi-modality image segmentation network via domain adaptation using gradient magnitude and shape constraint

In medical image segmentation, supervised machine learning models trained using one image modality (e.g. computed tomography (CT)) are often prone to failure when applied to another image modality (e.g. magnetic resonance imaging (MRI)) even for the same organ. This is due to the significant intensity variations of different image modalities. In this paper, we propose a novel end-to-end deep neural network to achieve multi-modality image segmentation, where image labels of only one modality (source domain) are available for model training and the image labels for the other modality (target domain) are not available. In our method, a multi-resolution locally normalized gradient magnitude approach is firstly applied to images of both domains for minimizing the intensity discrepancy. Subsequently, a dual task encoder-decoder network including image segmentation and reconstruction is utilized to effectively adapt a segmentation network to the unlabeled target domain. Additionally, a shape constraint is imposed by leveraging adversarial learning. Finally, images from the target domain are segmented, as the network learns a consistent latent feature representation with shape awareness from both domains. We implement both 2D and 3D versions of our method, in which we evaluate CT and MRI images for kidney and cardiac tissue segmentation. For kidney, a public CT dataset (KiTS19, MICCAI 2019) and a local MRI dataset were utilized. The cardiac dataset was from the Multi-Modality Whole Heart Segmentation (MMWHS) challenge 2017. Experimental results reveal that our proposed method achieves significantly higher performance with a much lower model complexity in comparison with other state-of-the-art methods. More importantly, our method is also capable of producing superior segmentation results than other methods for images of an unseen target domain without model retraining. The code is available at GitHub (https://github.com/MinaJf/LMISA) to encourage method comparison and further research.
Weakly Supervised Part-Based Method for Combined Object Detection in Remote Sensing Imagery

Deep learning methods have reached considerable achievement on remote sensing object detection in recent years. However, most methods are designed for single object detection, such as vehicles and ships, and have limited detection capabilities for the combined object with large scale and complex part structure. In this paper, we propose a Part-based Topology Distillation Network (PTDNet) for accurate and efficient combined object detection in remote sensing imagery. Specifically, a Part-based Feature Module (PFM) is designed to extract the key parts information of combined object in a weakly supervised manner. Besides, to balance the accuracy and efficiency of the model, with considering the topology structure of multiple parts in combined objects, a lightweight network training method based on partial topological feature distillation is proposed to improve the model performance without additional parameters. Experiments show that the PTDNet outperforms the state-of-the-art methods and achieves 65.4% mAP and 84.1% accuracy for combined object detection.
Effort inference and prediction by acoustic and movement descriptors in interactions with imaginary objects during Dhrupad vocal improvisation

Abstract In electronic musical instruments (EMIs), the concept of “sound sculpting” was proposed by Mulder, in which imaginary objects are manually sculpted to produce sounds, although promising has had some limitations: driven by pure intuition, only the objects’ geometrical properties were mapped to sound, while effort—which is often regarded as a key factor of expressivity in music performance—was neglected. The aim of this paper is to enhance such digital interactions by accounting for the perceptual measure of effort that is conveyed through well-established gesture-sound links in the ecologically valid conditions of non-digital music performances. Thus, it reports on the systematic exploration of effort in Dhrupad vocal improvisation, in which singers are often observed to engage with melodic ideas by manipulating intangible, imaginary objects with their hands. The focus is devising formalized descriptions to infer the amount of effort that such interactions are perceived to require and classify gestures as interactions with elastic versus rigid objects, based on original multimodal data collected in India for the specific study. Results suggest that a good part of variance for both effort levels and gesture classes can be explained through a small set of statistically significant acoustic and movement features extracted from the raw data and lead to rejecting the null hypothesis that effort is unrelated to the musical context. This may have implications on how EMIs could benefit from effort as an intermediate mapping layer and naturally opens discussions on whether physiological data may offer a more intuitive measure of effort in wearable technologies.
An Assessment on Bone Cancer Detection Using Various Techniques in Image Processing

Cancer cells are abnormal cells in the body that uncontrollably divide themselves and spreads over the body. Bone cancer, one kind of cancer, is a dreadful and threatening disease, usually caused due to the uncontrolled split up of bone cells. The cause for all other cancer types can be identified but this is not the case for bone cancer. It affects people in almost all age groups. The most challenging task is to detect bone cancer in the earlier stages. Hence for survival, the chances of detecting bone cancer in the earlier stages must be increased. Medical imaging like X-ray, MRI, or CT imaging in association with the techniques of image processing can provide remarkable results while detecting bone tumors. In this paper, existing works related to the detection of bone cancer are examined and further studied about bone cancer and their features for predicting its type. Several Image processing techniques are discussed for estimating X-Ray and MRI image interpretation.
Real-Time Visual Analytics for Air Quality

Raise collective awareness about the daily levels of humans exposure to toxic chemicals in the air is of great significance in motivating citizen to act and embrace a more sustainable life style. For this reason, Public Administrations are involved in effectively monitoring urban air quality with high-resolution and provide understandable visualization of the air quality conditions in their cities. Moreover, collecting data for a long period can help to estimate the impact of the policies adopted to reduce air pollutant concentration in the air. The easiest and most cost-effective way to monitor air quality is by employing low-cost sensors distributed in urban areas. These sensors generate a real-time data stream that needs elaboration to generate adequate visualizations. The TRAFAIR Air Quality dashboard proposed in this paper is a web application to inform citizens and decision-makers on the current, past, and future air quality conditions of three European cities: Modena, Santiago de Compostela, and Zaragoza. Air quality data are multidimensional observations update in real-time. Moreover, each observation has both space and a time reference. Interpolation techniques are employed to generate space-continuous visualizations that estimate the concentration of the pollutants where sensors are not available. The TRAFAIR project consists of a chain of simulation models that estimates the levels of NO and NO2 for up to 2 days. Furthermore, new future air quality scenarios evaluating the impact on air quality according to changes in urban traffic can be explored. All these processes generate heterogeneous data: coming from different sources, some continuous and others discrete in the space-time domain, some historical and others in real-time. The dashboard provides a unique environment where all these data and the derived statistics can be observed and understood.
An Improved Tiered Head Pose Estimation Network with Self-Adjust Loss Function

As an important task in computer vision, head pose estimation has been widely applied in both academia and industry. However, there remains two challenges in the field of head pose estimation: (1) even given the same task (e.g., tiredness detection), the existing algorithms usually consider the estimation of the three angles (i.e., roll, yaw, and pitch) as separate facets, which disregard their interplay as well as differences and thus share the same parameters for all layers; and (2) the discontinuity in angle estimation definitely reduces the accuracy. To solve these two problems, a THESL-Net (tiered head pose estimation with self-adjust loss network) model is proposed in this study. Specifically, first, an idea of stepped estimation using distinct network layers is proposed, gaining a greater freedom during angle estimation. Furthermore, the reasons for the discontinuity in angle estimation are revealed, including not only labeling the dataset with quaternions or Euler angles, but also the loss function that simply adds the classification and regression losses. Subsequently, a self-adjustment constraint on the loss function is applied, making the angle estimation more consistent. Finally, to examine the influence of different angle ranges on the proposed model, experiments are conducted on three popular public benchmark datasets, BIWI, AFLW2000, and UPNA, demonstrating that the proposed model outperforms the state-of-the-art approaches.
A Conflict-Driven Interface Between Symbolic Planning and Nonlinear Constraint Solving

Robotic planning in real-world scenarios typically requires joint optimization of logic and continuous variables. A core challenge to combine the strengths of logic planners and continuous solvers is the design of an efficient interface that informs the logical search about continuous infeasibilities. In this paper we present a novel iterative algorithm that connects logic planning with nonlinear optimization through a bidirectional interface, achieved by the detection of minimal subsets of nonlinear constraints that are infeasible. The algorithm continuously builds a database of graphs that represent (in)feasible subsets of continuous variables and constraints, and encodes this knowledge in the logical description. As a foundation for this algorithm, we introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Planning with Nonlinear Transition Constraints (PNTC)</i> , a novel planning formulation that clarifies the exact assumptions our algorithm requires and can be applied to model Task and Motion Planning (TAMP) efficiently. Our experimental results show that our framework significantly outperforms alternative optimization-based approaches for TAMP.
Self-verifying Web Resource Representations Using Solid, RDF-Star and Signed URIs

Our demo showcases a Solid-based Web where the integrity of a Web resource’s representation is directly verifiable using its content and its identifier: A Web resource is available at some URI described in RDF. Each such representation includes a Linked Data Signature, which we model using RDF-star. In addition, each Web resource’s URI includes the signature’s value as a suffix, which we call Signed URI. In a Web of such resource representations, modifications to a resource are detectable unless all resources that transitively reference the original are updated as well. We present a Solid-based Web application where such a Web of resources with self-verifying representations can be created and verified.
Using Linked Data Sources to Enhance Catalog Discovery

Our research explores how linked data sources and non-library metadata can support open-ended discovery of library resources. We also consider which experimental methods are best suited to the improvement of library catalog systems. We provide an overview of the questions driving our discovery experiments with linked data, a summary of some of our usability findings, as well as our design and implementation approach. In addition, we situate the discussion of our work within the larger framework of library cataloging and curation practices.
Survey of Methods for Automated Code-Reuse Exploit Generation

This paper provides a survey of methods and tools for automated code-reuse exploit generation. Such exploits use code that is already contained in a vulnerable program. The code-reuse approach allows one to exploit vulnerabilities in the presence of operating system protection that prohibits data memory execution. This paper contains a description of various code-reuse methods: return-to-libc attack, return-oriented programming, jump-oriented programming, and others. We define fundamental terms: gadget, gadget frame, gadget catalog. Moreover, we show that, in fact, a gadget is an instruction, and a set of gadgets defines a virtual machine. We can reduce an exploit creation problem to code generation for this virtual machine. Each particular executable file defines a virtual machine instruction set. We provide a survey of methods for gadgets searching and determining their semantics (creating a gadget catalog). These methods allow one to get the virtual machine instruction set. If a set of gadgets is Turing-complete, then a compiler can use a gadget catalog as a target architecture. However, some instructions can be absent. Hence we discuss several approaches to replace missing instructions with multiple gadgets. An exploit generation tool can chain gadgets by pattern searching (regular expressions) or considering gadgets semantics. Furthermore, some chaining methods use genetic algorithms, while others use SMT-solvers. We compare existing open-source tools and propose a testing system rop-benchmark that can be used to verify whether a generated chain successfully opens a shell.
Transfer of zero-order coherence matrix along spin-1/2 chain

In this work, we study transfer of coherence matrices along spin-1/2 chains of various length. Unlike higher order coherence matrices, zero-order coherence matrix can be perfectly transferred if its elements are properly fixed. In certain cases, to provide the perfect transfer, an extended receiver together with optimized its unitary transformation has to be included into the protocol.In this work, the asymptotic perfectly transferable zero-order coherence matrix for an infinitely long chain is considered and deviation of a perfectly transferred state from this asymptotic state is studied as a function of the chain length for various sizes of the extended receiver. The problem of arbitrary parameter transfer via the nondiagonal elements of the zero-order coherence matrix is also considered and optimized using the unitary transformation of the extended receiver.
An Example of Use of Variational Methods in Quantum Machine Learning

This paper introduces a deep learning system based on a quantum neural network for the binary classification of points of a specific geometric pattern (Two-Moons Classification problem) on a plane. We believe that the use of hybrid deep learning systems (classical + quantum) can reasonably bring benefits, not only in terms of computational acceleration but in understanding the underlying phenomena and mechanisms; that will lead to the creation of new forms of machine learning, as well as to a strong development in the world of quantum computation. The chosen dataset is based on a 2D binary classification generator, which helps test the effectiveness of specific algorithms; it is a set of 2D points forming two interspersed semicircles. It displays two disjointed data sets in a two-dimensional representation space: the features are, therefore, the individual points' two coordinates, $$x_1$$ and $$x_2$$ . The intention was to produce a quantum deep neural network with the minimum number of trainable parameters capable of correctly recognising and classifying points.
Nelder–Mead Simplex Search Method - A Study

Nelder-Mead in n dimensions facilitates the set of n + 1 test points. It finds a new test point, Makes one of the old test points new, and so the technique progresses into objective behavior process is measured at each test point. The Nelder-Mead Simplex system uses Simplex to find the minimum space. The algorithm operates using a design framework with n + 1 points (called simplex), Where n is for simplex based operation Number of input dimensions. The Nelder-Mead method is one of the most popular non-derivative methods, using only the values of f to search. Only in the simplex formation of n + 1 will the points / moving / contraction of this simplex be in a positive direction. Strictly speaking, Nelder-Mead is not a truly universal optimization algorithm; however, in it works reasonably well for many non-local problems. Direct search is the gradient of the objective process Optimization is a method for solving problems that require no information. All of the points approaching an optimal point Pattern search algorithms that calculate the sequence. The existence of local trust is a key factor in defining the difficulty of the global optimization problem because it is relatively easy to improve locally and relatively difficult to improve locally. Slope Descent is an optimal method Machine learning models and to train neurological networks commonly used. Training data these models allow learning over time, and pricing function is particularly active in gradient descent. The barometer is an optimization algorithm that measures its accuracy at each parameter update and can be repeated by comparing optimal or different solutions. A satisfactory the solution will be found. With the advent of computers, optimization has become of computer aided design activities has become a part of Gradient Decent (GT) is a functional first-order upgrade algorithm Local minimum of the given function and Used to determine the maximum. This method is commonly used to reduce cost / loss performance in machine learning (ML) and deep learning (DL). The problem with finding optimal points in such situations is referred to as derivative-free optimization, and algorithms that do not use derivatives or defined variants are called derivative-free algorithms.
Statistical Properties of Bit Strings Sampled from Sycamore Random Quantum Circuits

Quantum supremacy has been recently reported for random circuit sampling on the Sycamore processor with 53 qubits. Here, we analyze the statistical properties of bit strings sampled from random quantum circuits. In contrast to classical random bit strings, bit strings sampled from Sycamore random circuits give rise to heat maps with stripe patterns at specific qubits, have more bit 1 than 0, and do not pass the NIST random number tests. The difference between the Sycamore bit strings and classical random bit strings is also demonstrated by the Marchenko-Pastur distribution and the Girko circular law of random matrices. The calculation of Wasserstein distances shows that the Sycamore bit strings are farther from bit strings sampled from Haar-measure random quantum circuits than classical random bit strings. Our results show that random matrices and Wasserstein distances could be used to analyze the performance of quantum computers.
Literature review: Anomaly detection approaches on digital business financial systems

Anomaly detection approaches have become critically important to enhance decision-making systems, especially regarding the process of risk reduction in the economic performance of an organisation and the consumer costs. Previous studies on anomaly detection have examined mainly abnormalities that translate into fraud, such as fraudulent credit card transactions or fraud in insurance systems. However, anomalies represent irregularities in system patterns data, which may arise from deviations, adulterations or inconsistencies. Further, its study encompasses not only fraud, but also any behavioural abnormalities that signal risks. This paper proposes a literature review of methods and techniques to detect anomalies on diverse financial systems using a five-step technique. In our proposed method, we created a classification framework using codes to systematize the main techniques and knowledge on the subject, in addition to identifying research opportunities. Furthermore, the statistical results show several research gaps, among which three main ones should be explored for developing this area: a common database, tests with different dimensional sizes of data and indicators of the detection models' effectiveness. Therefore, the proposed framework is pertinent to comprehending an existing scientific knowledge base and signals important gaps for a research agenda considering the topic of anomalies in financial systems.
Wave front phase imaging for silicon wafer metrology

Wave Front Phase Imaging (WFPI) is a new wafer shape measurement technique that acquires millions of data points in just seconds or less, on a full 300mm silicon wafer. This provides lateral resolution well below 100μm with the possibility of reaching the lens’ optical resolution limitation between 3-4μm. The system has high repeatability with root-mean-square (RMS) standard deviation (σRMS) in the single digit nm for the global wafer shape geometry and for nanotopography it reaches in the sub ångström (Å = 10-10 m) range. WFPI can collect data on the entire wafer to within a single pixel away from the wafer edge roll off1. The flatness of the silicon wafers used to manufacture integrated circuits (IC) is controlled to tight tolerances to help ensure that the full wafer is sufficiently flat for lithographic processing. Advanced lithographic patterning processes require a detailed map of the wafer shape to avoid overlay errors caused by depth-of-focus issues2. In this paper we go deep into the theoretical explanation as to how the wave front phase sensor works.