This research develops a methodology to identify transactions through data-driven tracking and analysis of ransomware-Bitcoin payment networks [30]. We demonstrate the methodology by applying the GraphSAGE embedding algorithm to the WannaCry ransomware-Bitcoin cash-out network. The paper takes a data-driven approach to building a machine learning system that allows analysts to define features relevant to ransomware-Bitcoin payment networks.

This paper developed an ultrasound concave 2D ring array transducer for retinal visual stimulation of the brain to restore vision in blind people. Theory, simulation, and experiment were presented.Â

Impedance-based method has been increasingly adopted to assess the stability of inverter-based resources (IBRs). To get a better view of the state-of-art and challenges for implementing the impedance-based dynamic analysis, a survey with general/specific questions has been initiated by IEEE Task Force on Frequency-Domain Modeling and Dynamic Analysis of HVDC and FACTS. The feedbacks are collected from universities, national labs, transmission system operators (TSOs), power plant developers, as well as IBR vendors. It is interesting to note that while many common understandings have been established in practices, certain gaps still exist among different stakeholders. This article intends to bridge this gap by sharing a summary of the survey, including questionnaires, responses from different stakeholders, and the analysis of survey results. The challenges for different stakeholders using impedance-based method are identified, which hopefully shed a light on the future research work.

Sound event location is a momentous subtask of two-dimensional direction-of-arrival (2D-DOA) estimation to forecast the azimuth and elevation angle from each active sound event class of an audio fragment using multi-label regression in the 3D Cartesian coordinates. The main problem with the traditional multi-signal classification (MUSIC) algorithm and the existing baseline convolution recurrent neural network (BCRNN) are lower precision and huge computation in weaker signal-noise-ratio (SNR) environment. In particular, MUSIC algorithm will cause complete distortion when the SNR is lower than -5 dB. We thus design effective residual self-attention recurrent neural network (ESRNN) not only to overcome the distortion of traditional MUSIC algorithm under lower SNR but also further reduce predicted 2D-DOA error in different SNR reverberation environments. Two different filter structures, ESRNN-L and ESRNN-G, are designed to improve the SNR of our model when the SNR is above 0 dB or below -5 dB in a targeted manner respectively. To verify the efficiency and robustness, we train and test our model using TAU Spatial Sound Events 2019 datasets extracted the phase and magnitude spectrogram with different synthetic SNRs ranging from -10 dB to 30 dB. The experimental results show that the optimal 2D-DOA error of our ESRNN-L is 21% lower than BCRNN when the SNR is less than -5 dB, and ESRNN-G is 15% error lower with 10% parameters reduction when the SNR is higher than 0 dB.

Background: Relaxation methods determine residual stresses by measuring the deformations produced by incremental removal of a subdomain of the specimen. Measured strains at any given increment, determined by the cumulative effect of the relieved stresses, appear as an integral equation, which must be inverted to obtain residual stresses. In practice, stress distributions are discretized by a finite-dimensional basis, to transform the integral equations into a linear system of equations, which is often ill-conditioned. Objective: This article demonstrates that the problem is actually ill-posed and comes with an inherent bias-variance tradeoff. Methods: The hole drilling method is used as an example application, and the practical effects of ill-posedness are illustrated. Results: Traditional regularization of the solution by limiting the resolution of the discretization reduces solution variance (noise) at the expense of increased bias and often results in the ultimately harmful practice of taking fewer data points. A careful analysis including the alternate Tikhonov regularization approach shows that the highest number of measurements should always be taken to reduce the variance for a given regularization scheme. Unfortunately, the variability of a regularized solution cannot be used to build a valid confidence interval, since an unknown bias term is always present in the true overall error. Conclusions: The mathematical theory of ill-posed problems provides tools to manage the bias-variance tradeoff on a reasonable statistical basis, especially when the statistical properties of measurement errors are known. In the long run, physical arguments that provide constraints on the true solution would be of utmost importance, as they could regularize the problem without introducing an otherwise unknown bias. Constraining the minimum length scale to some physically meaningful value is one promising possibility.

Precise stock market prediction is crucial for investors, but the volatility of the stock market is influenced by multiple factors such as public sentiments, business news, and related product volatility. While several algorithms have been proposed to predict the stock exchange index based on historical data, they are not ideal as external factors play a critical role in market volatility. To address this issue, we proposed a machine learning model that incorporates historical data with external factors such as social media sentiments, oil and gold trends, and financial news data to enhance prediction accuracy. Our study used HPQ, IBM, ORCL, and MSFT stock market datasets to validate the effectiveness of the proposed model, including an analysis of the impact of Covid19 on companies. Our experimental results showed the highest accuracy of 87.2% using oil and sentiment datasets. Additionally, we identified that social media significantly affects IBM stocks, and the GBM (Gradient Boosting Classifier) classifier produced consistent results.

The classical interpretation of Young’s double slit experiment is that, before and after passing through the diaphragm of the double slit, the light behaves as waves. Feynman called the double slit experiment “the only mystery [of quantum mechanics]. We have shown for the first time that in the classical wave experiments: (1) Particle patterns, which are non-interference patterns indicating the particle nature of light, are near the diaphragm, and Interference patterns are near the screen; (2) Particle patterns evolve to Transition patterns that are also non-interference patterns, and, finally, evolve to Interference patterns; (3) Postulates of lens for utilizing lens in wave experiments. blob:https://www.techrxiv.org/615e9312-3e89-48d5-99fe-bbad6cef8a75 blob:https://www.techrxiv.org/4fad8e66-75e1-47db-af51-d9e445461b9b blob:https://www.techrxiv.org/e4274387-92e5-41bb-9185-038555bb2f13 In this article, we experimentally show that Particle patterns, Transition patterns and Interference patterns of the double slit experiments are simultaneously emerged on the screen. We suggest that these phenomena and the physical mechanism of photons distributing as waves are new mystery.

Sparse Regression Code (SPARC) connects the sparse signal recovery framework with the error control coding techniques. In this paper, we focus on improving the block error performance of SPARC in short block length regime over the AWGN channel. Towards that, we introduce suitable candidates for dictionary matrices in real and complex fields using Gold sequences and mutually unbiased bases (MUB). We propose two generalizations of SPARC (GSPARC), develop a greedy decoder called Match and Decode (MAD) algorithm, and provide its analytical noiseless recovery guarantees. We propose a parallel greedy search technique called parallel MAD (PMAD) to improve performance. We describe the applicability of GSPARC with PMAD decoder for multi-user channels, providing a non-orthogonal multiple access scheme. We present numerical results comparing the block error rate (BLER) performance of the proposed algorithms for GSPARC in AWGN channels in the short block length regime. The PMAD decoder gives better BLER than the approximate message-passing decoder for SPARC. GSPARC with PMAD gives comparable and competitive BLER performance compared to other existing codes. In multi-user channels, GSPARC with PMAD decoder outperforms the sphere packing lower bounds of an orthogonal multiple access scheme with the same spectral efficiency.

The challenge of query execution in MySQL involves the efficient and accurate processing of SQL queries to retrieve, manipulate, and store data in a database. It requires optimizing the query execution plan, identifying and resolving performance issues, and ensuring data integrity and security. The challenge lies in balancing the trade-offs between query complexity, database size, system resources, and user requirements. Choosing the right approach depends on the specific characteristics of the data and workload. Therefore, in this paper, we have applied various techniques to get optimized results.

The journey of research for Document Analysis and Recognition (DAR) started with the problem of automatic character recognition. Today, it has covered a vast span of recognition tasks such as text recognition, script identification, writer identification, word spotting, signature verification etc., in scripts like Roman, Arabic, Chinese, Japanese, Brahmi etc. Extensive advancements in deep learning techniques have achieved state-of-the-art results for various DAR tasks. This work explores the challenges from different perspectives and reviews the techniques for online/offline and printed/handwritten DAR tasks. We examine the research works with the view of script-related challenges. Various datasets for DAR are categorized into historic, printed and handwritten datasets. We present a comprehensive survey of challenges, techniques, datasets, evaluation metrics, script-related perspectives and potential future directions for DAR.

We present a deep neural network-based framework for designing multi-band microstrip antennas given a desired impedance matching spectrum. The approach enables a design methodology that generates the desired antenna structures rapidly (under a second) through an effective deep learning- enabled search of a large design space and eliminates the need for extensive domain knowledge of antenna design. The framework is built on our innovations in tandem neural networks consisting of two cascaded neural networks. Our structures are parameterized in an exponentially large design space of discrete variables (pixels), leading to the realization of nonintuitive structures. This end-to-end synthesis in terms of discrete variables is enabled by introducing a new type of “smooth thresholding” activation function, which, along with crucial regularization terms in the network loss function, aids in designing our structures. We perform extensive neural network optimizations and study various trade-offs in the design process. We demonstrate the efficacy of our methods by generating single and dual-band resonant structures, which can be up to 50% more compact in terms of area, and up to 18 % thinner in terms of substrate height than conventional structures, while retaining competitive performance parameters in terms of gain, polarization properties, radiation efficiency, and fractional bandwidth.

This work introduces an open-source hardware and software expansion for digital microfluidics (DMF) devices that integrates programmatic temperature and magnetic control. Our aim was to create a versatile, modular, extension that may aid in biomedical applications and has proven potential for use in bacteriophage research. To achieve this, we developed an accessible DMF device extension called PhageBox. PhageBox incorporates temperature and magnetic control modules at the hardware level. At the software level, it is controlled by embedded software that includes a unique model for bio-protocol programming and a graphical user interface for visual feedback and operation of the device. We evaluated PhageBox’s effectiveness in biomedical applications using functional testing and validated the temperature control using thermography, achieving a range of +/-0.2 Celsius. The electromagnets produced a magnetic force of 15 milliTesla, which precisely immobilized magnetic beads. Furthermore, we demonstrate the potential of PhageBox in bacteriophage research through three initial proof of principle protocols: (1) a universal framework for PCR, (2) T7 bacteriophage restriction enzyme digestion, and (3) the concentration of PhiX174 RF genomic DNA. Overall, our study presents an open-source solution for DMF devices to integrate temperature regulation and magnetism, facilitating various applications in biomedical research and bacteriophage studies. (GitHub: https://github.com/Dreycey/PhageBox)

This letter derives and discusses the superiority of a simple dc-link capacitor voltage control configuration for multilevel neutral-point-clamped converters with any number of levels. The control involves n−2 control loops regulating the difference between the voltage of neighbor capacitors. These control loops are inherently decoupled; i.e., they are independent and the control action of one loop does not affect the others. The good performance of such control is confirmed through simulations and experiments.

Line waves are modes that propagate along a line connecting two-dimensional impedance surfaces with dual electromagnetic responses. In general, line waves can be implemented using complementary impedances or impedances with a gain-loss effect. On the other hand, if the two impedance surfaces are non-complementary, they can support a different type of mode called a “quasi-line wave”. We propose a different configuration to implement quasi-line waves. Indeed, non-dual structures with purely inductive impedances are used in order to drive the mode. We investigate the implementation of quasi-line modes using multilayer graphenes. This mode has a wide bandwidth in the terahertz range and a significant propagation length. The proposed structure guides one-way modes in which the fields are highly concentrated around the edges of two inductive impedance structures. Furthermore, incorporating graphene into the proposed waveguide allows for control of bandwidth and transmission characteristic of the waveguide.

A generative adversarial network (GAN) enables mapping a data sample from one domain to another one. It has extensively been employed in image-to-image and text-toimage translation. We propose an EEG-to-EEG translation model based on a GAN structure in which a conditional GAN (cGAN) is combined with a variational autoencoder (VAE), named as VAE-cGAN. Scalp EEG (sEEG) is plagued by noise and suffers from low resolution. On the other hand, intracranial EEG (iEEG) recordings enjoy high resolution. Here, we consider the task of mapping the sEEG to iEEG to enhance the sEEG resolution.  

In this work, we propose a pseudospin-polarized closed waveguide whose walls are formed by dual electromagnetic boundary conditions.  The structure has a very wide bandwidth so that it covers an extremely wide electromagnetic spectrum.  The proposed waveguide supports one-way pseudospin states resulting from the establishment of dual relationships at the waveguide boundaries, and hence the establishment of mirror reflection symmetry. This provides the proposed waveguide with the ability to suppress backscattering.  The boundary conditions of the proposed structure consist of complementary metasurfaces which have dual surface impedances.  The presence of these boundary conditions leads to forming a pseudospin-polarized waveguide having the ability to suppress and filter the backward modes that may be excited along bent paths or discontinuities.  To the best of our knowledge, this is the widest bandwidth waveguide that has been proposed so far, so that the isolation of the waveguide in the frequency range of 7- 350 GHz is above -0.5 dB.  In addition, we use a very precise method based on the variational method to calculate the surface impedance of the metasurfaces forming the waveguide boundaries so as to increase the accuracy of the obtained results.

Measurements of the average properties of vehicular traffic are inherently noisy. The distributions of flow and speed measurements at any particular density are non-Gaussian with density-dependent variance, skewness, and kurtosis. Previous studies have failed to properly account for these complicated noise properties. In remediation, we present FitFun, a general framework for modelling any observed flow-density-speed relationship. Models specified within FitFun incorporate components for both the functional form and the noise. We define three flexible noise model components and we fit 200 different models to a high-quality sample of 10,150 observed urban flow-occupancy relationships. We compare the fits using information criteria and assess fit quality through analysis of the residuals. We find that the non-parametric Sun model for the functional form component combined with a Skew Exponential Power Type III noise component significantly outperforms all of the other models. Interestingly, we find that the city, country, road topology, and detector location have virtually no impact on model performance and fit quality, which is very convenient for model selection. The only factor of relevance from those that we studied is the effective occupancy coverage of the data. We conclude that certain models specified judiciously within FitFun can successfully capture the functional form and noise of observed flow-density-speed relationships without the need to discard data taken during non-stationary conditions. This is particularly advantageous for urban data where stationary traffic conditions are rarely observed. Accepted by Transportation Research Part C on 16th Feb 2023.

In this work, we propose and implement a bijunction depletion-type silicon electro-optic phase shifter. The phase shifter has a lateral profile of implants, that closely resembles that of a common bipolar junction transistor, and thus, has two polarities. These are acceptor-donor-acceptor (PNP) as well as donor-acceptor-donor (NPN). We realize both variants in IMEC ISIPP50G open-access silicon photonic technology and compare them to lateral and interleaved phase shifters. Both PNP and NPN phase shifters exhibit a VπLπ figure that is at least 14.47% and up to 45.1% lower than that of the lateral and interleaved phase shifters realized in the same technology. Bi-junction phase shifters can be implemented in any planar silicon photonic technology that offers bipolar implantations within silicon photonic waveguides.