Miracle-dz/UniFAR-FTUs huggingface dataset:
Method for generating gas leakage data based on Generative Adversarial Networks

To address the problem of small sample sizes in sound signal collection for $SF_{6}$ gas leak fault detection in high-voltage switchgear equipment, an improved StyleGAN model with a U-Net structure as the discriminator is proposed. The model uses a WGAN-GP loss term to reduce the problem of training mode collapse. A consistency loss term and a gradient penalty term are added to the loss evaluation to achieve regularization effects and generate spectrograms with corresponding features for data augmentation of small sample images of leak signal spectrograms. Feasibility experiments are conducted by simulating $SF_{6}$ gas leak audio signal collection. The t-SNE method is used to visually compare the spectrograms of real gas leak data and the “Pseudo-samples” generated by the improved StyleGAN model. The results prove the effectiveness of the proposed method.
Highway Traffic State Estimation Based on Data Fusion in a Connected Vehicle Environment

Traffic state estimation plays an important role in urban intelligent transportation management. Compared to traditional traffic data collection means using fixed and mobile detectors, connected vehicles can acquire richer and more diverse data of high precision, which is ideal for discerning traffic states. Nevertheless, the market penetration rate (MPR) of connected vehicles may remain low for a relatively long time. In order to predict traffic states more accurately using the connected vehicle data, in this paper, we propose a data fusion method based on Back-Propagation (BP) neural network model optimized by Particle Swarm optimization (PSO). The model considers the time correlation of traffic flow and combines the information of real-time and historical speeds of the connected vehicles to estimate the travel time on a highway section. The results show that the traffic data fusion method can reduce the estimation errors caused by the low penetration rate of connected vehicles, and improve the accuracy of road traffic state estimation in partially connected driving environments, compared to using only the current connected vehicle data.
Accelerating Sparse LU Factorization with Density-Aware Adaptive Matrix Multiplication for Circuit Simulation

Sparse LU factorization is considered to be one of the most time-consuming components in circuit simulation, particularly when dealing with circuits of considerable size in the advanced process era. Sparse LU factorization can be expedited by utilizing the supernode structure, which partitions the matrix into dense sub-matrices, thereby improving computational performance by utilizing level-3 Basic Linear Algebra Subprograms (BLAS) General Matrix Multiplication (GEMM) operations. The sparse and irregular structure of circuit matrices often impedes the formation of supernodes or results in the formation of supernodes with many zero elements, which in turn poses challenges for exploiting GEMM operations. In this paper, by fully utilizing the density in sub-matrices and combining GEMM with the Dense-Sparse Matrix Multiplication (SpMM), we propose a density-aware adaptive matrix multiplication equipped with machine learning techniques to optimize performance of the most-time consuming matrix multiplication operator so as to accelerate the sparse LU factorization. Numerical experiment results show that among the 6 circuit matrices tested, the average performance of matrix multiplication in our algorithm can be improved by 5.35x (up to 9.35x) compared to the performance of using GEMM directly in Schur-complement updates. Compared with state-of-the-art solver SuperLU_DIST, our method shows a substantial performance improvement.
Multichannel Subband-Fullband Gated Convolutional Recurrent Neural Network for Direction-Based Speech Enhancement with Head-Mounted Microphone Arrays

We present a method for direction-based multichannel speech enhancement with head-mounted microphone arrays. To target the task of direction-based enhancement of a single speaker, a direction-dependent feature extraction composed of matched filters and a maximum-directivity beamformer is employed. The extracted features are then first processed by a subband neural network with an inplace gated convolutional recurrent architecture. In a second stage, a fullband gated convolutional recurrent neural network processes the input features along with the output of the subband network. The proposed method outperforms subband-only and fullband-only approaches in PESQ and SI-SDR on the development datasets of the 2022/23 SPEAR challenge and was rated best among the submissions to the challenge in a paired-comparison listening experiment.
Invited: Autonomous Driving Digital Twin Empowered Design Automation: An Industry Perspective

Designing reliable computing systems for autonomous driving is extremely challenging, as the performance and reliability of the systems have to be thoroughly evaluated under an extremely large amount of driving scenarios. Physically constructing scenarios and conducting testing for autonomous driving systems is time-consuming and expensive. To minimize the need for physical testing and improve development efficiency, we developed a digital-twin-based simulation, which can generate an integral, precise, and comprehensive representation of physical scenarios. In this paper, we share our experiences with the digital-twin-based simulation for autonomous driving, particularly the design requirements and components demanded to facilitate virtual environment construction and design verification, which could greatly improve development efficiency.
Sound Source Distance Estimation in Diverse and Dynamic Acoustic Conditions

Localizing a moving sound source in the real world involves determining its direction-of-arrival (DOA) and distance relative to a microphone. Advancements in DOA estimation have been facilitated by data-driven methods optimized with large open-source datasets with microphone array recordings in diverse environments. In contrast, estimating a sound source’s distance remains understudied. Existing approaches assume recordings by non-coincident microphones to use methods that are susceptible to differences in room reverberation. We present a CRNN able to estimate the distance of moving sound sources across multiple datasets featuring diverse rooms, outperforming a recently-published approach. We also characterize our model’s performance as a function of sound source distance and different training losses. This analysis reveals optimal training using a loss that weighs model errors as an inverse function of the sound source true distance. Our study is the first to demonstrate that sound source distance estimation can be performed across diverse acoustic conditions using deep learning.
Analysis of passive intermodulation between rough rectangular waveguide flanges using fractal method

In nature, most of the surfaces are rough and waveguide flanges are not exception. And these surfaces topography are of high importance in the response of the waveguide flanges connection system. These surfaces topography are usually described by statistical parameters. And statistical parameters depend strongly on the resolution of the roughness‐measuring instrument, and hence the values of statistical parameters are not unique for a surface. As a result, the predictions of passive intermodulation (PIM) level of microwave devices based on these statistical parameters also may not be unique to a pair of contacting surfaces. Fortunately, fractal method is independent on the resolution of the roughness‐measuring instrument. Therefore, the randomness of the rough surface is not affected by the differences in the model. Consequently, if PIM level of rectangular waveguide is predicted with fractal parameters, the predicted value will be unique. Based on this motivation, a new model is developed. In this model, the fractal method is used to characterize the surface topography of waveguide flanges. Then, the third order PIM level of a rectangular waveguide flange is predicted. The analytic results are verified by PIM test set‐up. This work is help to understand PIM phenomenon and design the low‐PIM waveguide.
Deep learning-based detection of part deformation for laser powder bed fusion process

During the manufacturing process of parts made by laser powder bed fusion (LPBF), the dimensional accuracy of the part is one of the most important factors affecting the quality of the part and therefore in-situ real-time online monitoring of the part deformation is essential. To address these issues, a complete visualization method for LPBF part deformation detection has been developed in this study. Aiming at the problem that traditional digital image processing methods and machine vision used in the existing LPBF image segmentation are greatly affected by illumination and image quality, this paper uses a semantic segmentation model based on deep learning for the segmentation of powder bed background and parts. By comparing the segmentation performance of FCN, U-Net and DeepLabV3 models in different configurations, the U-Net(32) model with 32 basic convolutional kernels was chosen as the base framework, which were reduced by half compared to the U-Net model. The model has the advantages of effective segmentation, faster speed and smaller model, satisfying the basic requirements of real-time and easy to be deployed in industrial environments. In addition, this paper investigates the deformation intervals of parts without significant deformation and the variation pattern of deformation during the growth of warpage defects, which can provide reference information for feedback control in the future.
Rethinking Programming Frameworks for In-Storage Processing

In-storage processing (ISP) is the most commercialized implementation of the near-data processing (NDP) model that executes tasks near their storage locations. However, programming ISP devices is complicated as it requires programmers to work closely with the underlying hardware, and even highly-optimized code can easily lead to suboptimal performance.This paper introduces ActivePy. ActivePy makes the programmer completely agnostic to ISP hardware. ActivePy automatically, transpartently, and dynamically generates high-performance code to balance system-wide trade-offs and max-imize the benefits of ISP. Our real system implementation shows that ActivePy can use ISP as efficiently as conventional C-based frameworks.
Research on the trajectory planning method of inland waterway vessels based on Frenet coordinate system

With the continuous development of waterway traffic technology, the freight volume of inland waterway ships is increasing, and the waterway traffic environment becomes more and more complex. In the complex traffic environment of curved waterways with the participation of multiple ships, the traditional ship trajectory planning method is inefficient and often cannot meet the actual needs. To reduce the difficulty of trajectory planning and improve the planning efficiency, this paper proposes to complete the trajectory planning of inland waterway ships based on Frenet coordinate system, which converts the high-dimensional planning problem into multiple low-dimensional planning problems and greatly reduces the difficulty of ship planning. Frenet coordinates can intuitively reflect the motion relationship of different objects in the waterway, and the method is obvious for simplifying the trajectory planning problem and improving the efficiency of ship trajectory planning in the complex inland waterway traffic environment.
Single Channel Speech Presence Probability Estimation based on Hybrid Global-Local Information

Speech presence probability (SPP) estimators work in the short-time Fourier transform domain to give a probability estimate of whether speech is present or absent at each time-frequency bin. Most existing SPP estimators have achieved a high SPP detection accuracy and are deployed successfully in speech enhancement and automatic speech recognition. In this work, we propose a single channel the a posteriori SPP estimator based on hybrid global-local information. In contrast to existing deep neural networks (DNNs) based SPP estimation approaches, our estimator DNN can effectively extract helpful speech representations to estimate SPP with a simpler architecture. Taking hybrid global-local information into account, an encoder is designed to extract high-dimensional global information into a low-dimensional latent space and then concatenate each frequency bin and the latent space to generate the hybrid information. Finally, an SPP decoder is used to decode the hybrid information into the SPP. Experimental results demonstrate that our proposed method provides a more effective way to estimate SPP, which can achieve high SPP estimation accuracy with low computational complexity, especially in low signal-to-noise ratio conditions.
Energy Efficient joint user association and power allocation using Parameterized Deep DQN

Using small cells to create an ultra-dense network for 5G and beyond is a promising strategy to improve network coverage, data demands and reduce latency. Despite using small cells, these dense wireless networks result in performance degradation and increased energy consumption. Energy consumption is a crucial parameter for sustainable future wireless networks. In order to improve quality of service (QoS) and Energy Efficiency (EE), efficient resource allocation strategies are required. This paper investigates a Parameterized Double Deep Q-Network (PDDQN) based framework for joint user association and power allocation to improve EE and throughput. Apart from other conventional machine learning approaches, considering single state space of the joint optimization problem, our proposed framework considers both discrete and continuous state spaces. Our proposed PDDQN technique also solves the generalization problem that occurs due to similar states. The simulation results indicate that the proposed work significantly improves energy EE and throughput in large-scale learning problems.
The output employment elasticity and the increased use of temporary contracts: Evidence from Poland

The paper investigates how the increased use of temporary contracts in Poland affected employment elasticity with respect to output. The analysis is based on Okun's law, and covers the period of 1996–2016, with particular focus on the years of 2001–2016 when temporary jobs became prevalent. We look at the relationships between output growth and the growths of aggregate, permanent and temporary employment separately. Our study finds that the responsiveness of aggregate employment to output is positive and changes through time. Interestingly, after 2007, when the use of temporary contracts stabilised at a high level, the employment intensity of growth started decreasing. We relate this to the opposite trends in output responsiveness of temporary and permanent jobs. Elasticity of temporary job was growing, while elasticity of permanent job was decreasing. Our study also shows that initially employers adapt to output changes replacing permanent job with temporary job, next temporary contracts become the main adjustment device.
Ground-water flow in the Central Valley, California

The agricultural productivity of the Central Valley is dependent on the availability of water from irrigation. About 7.3 million acres of cropland in the Central Valley receives about 22 million acre-feet of irrigation water annually. One half of this irrigation water is supplied by ground water, which amounts to about 20 percent of the Nation's ground-water pumpage. Ground water is important as a stable supply of irrigation water because of the high variability of surface-water supplies in the Central Valley. This large ground-water development during the past 100 years has had major impacts on the aquifer system, such as decline in water levels, land subsidence, depletion of the aquifer storage, and increase in recharge. The flow conditions before and during development were simulated on a regional scale using a three-dimensional finite-difference flow model. The concept presented in this report considers the entire thickness of the continental deposits as one aquifer system which has varying vertical leakance that depends on several factors, including amount of fine-grained sediments. The average horizontal hydraulic conductivity is about 6 feet per day, and the average thickness of the continental deposits is about 2,400 feet. The simulation results are shown on maps for comparison with observedmore » hydrologic data. A description of the computer-tape file, which contains estimates of recharge/discharge, and the aquifer properties used in the simulation are included in appendix A and B, respectively. The theoretical basis of calculating borehole hydraulic conductance of multilayer wells which cause increases in vertical leakance during the post-development period is discussed in appendix C. « less
A carbon fibre composite (CFC) Byelorussian peat corer

The design specification, development and manufacture of a Byelorussian (Russian) peat corer constructed from carbon fibre composite (CFC) are described. The availability of this new composite material introduces new possibilities for constructing field instruments that are as strong as, or stronger than, equipment made from steel and other metals. One advantage is a significant weight reduction. A 10.5 metre coring set in standard stainless and soft steel weighs around 16 kg, whereas the total weight of a similar CFC set is 5.2 kg, giving a weight reduction of almost 70%. The CFC sample chamber is 500 mm long with internal diameter 65 mm, and so contains almost twice the volume of peat that can be collected with a standard 45 mm diameter steel corer. The diameter of the rods is 30 mm, which improves ergonomics, and the CFC has better thermic properties for winter use. Another advantage is that the contamination of samples (notably by chromium and nickel) associated with the use of steel corers is eliminated. The CFC sampler works well in soft peats such as Sphagnum and Carex types. It is less suitable for little-decomposed fibrous and forest peats (e.g. Polytrichum type) and those containing hardwood remains, especially in the more compacted bottom layers. It should be totally satisfactory for organic lake sediments, but probably not for stiff and coarse mineral deposits.
Dopamine pathway characterization during the reproductive mode switch in

9 Aphids are major pests of most of the crops worldwide. Such a success is largely explained by the 10 remarkable plasticity of their reproductive mode. They reproduce efficiently by viviparous 11 parthenogenesis during spring and summer generating important damage on crops. At the end of the 12 summer, viviparous parthenogenetic females perceive the photoperiod shortening and transduce this 13 signal to their embryos that change their reproductive fate to produce sexual individuals: oviparous 14 females and males. After mating, those females lay cold-resistant eggs. Earlier studies showed that some 15 transcripts coding for key components of dopamine pathway were regulated between long days and 16 short days conditions suggesting that dopamine might be involved in the transduction of seasonal cues 17 prior to reproductive mode switch. In this study, we aimed at going deeper into the characterization of 18 the expression dynamics of this pathway but also in the analysis of its functional role in this context in 19 the pea aphid Acyrthosiphon pisum . We first analysed the level of expression of ten genes of this 20 pathway in embryos and larval heads of aphids reared under long days (asexual producers) or short days 21 (sexual producers) conditions. We then performed in situ hybridization experiments to localize in 22 embryos the ddc and pale transcripts that are coding for two key enzymes in dopamine synthesis. Finally, 23 Using CRISPR-Cas9 mutagenesis in eggs produced after the mating of sexual individuals, we targeted 24 the ddc gene. We could observe strong melanization defaults in ddc mutated eggs, which confidently 25 mimicked the Drosophila ddc phenotype. Nevertheless, such a lethal phenotype did not allow us to 26 validate the involvement of dopamine as a signaling pathway necessary to trigger the reproductive mode 27 switch in embryos.
Simultaneous Inversion of Receiver Functions, Multi-Mode Dispersion, and Travel-Time Tomography for Lithospheric Structure Beneath the Middle East and North Africa

Abstract : Understanding the evolution of the continental lithosphere requires improving our knowledge on lower continental composition. Towards this goal, we perform receiver function analysis using teleseismic waveforms recorded at permanent and temporary broadband seismic stations located in Middle East, Europe, Asia, and North Africa. Two hundred and twenty six stations recording a total of about 6,000 teleseismic events producing more than 100,000 seismograms have been investigated. We have examined receiver functions for 213 of stations (best data) in the period of 1990 - 2004 and applied the receiver function stacking procedure of Zhuand Kanamori [2000] to estimate Poisson's ratio and crustal thickness. We have divided the research area according to five tectonics environments, explicitly Shields, Platform, Paleozoic orogenic belts, MesozoicCenozoic orogenic belts, and rift zones based on Condie's [1989] simplified classifications. The results from this study shows lower value of Poisson's ratio sigma = 0.25 for Shield and Platform compare to the Orogenic belts with sigma = 0.27. Crustal thickness for Shield and Platform show the value of 38 km and 43 km respectively, while for the Orogenic belts we found a value of 37 km for Paleozoic belts and 39 km for Mesozoic-Cenozoic belts, although the range of thicknesses for the younger active regions is large. Since our ultimate goal is to provide an improved imaged of global continental structure and composition, we combine our observations with receiver functions results from other published analysis. In total we have integrated observations from 374 stations located in different geologic setting and the results indicate the value of sigma = 0.26 for Poisson's ratio and H = 38 km for crustal thickness in Shield, sigma = 0.27 with H = 43 km for the Platform and sigma + 0.28 with H = 36-39 km for the Orogenic belts.
A Study of the Impact of DNS Resolvers on Performance Using a Causal Approach

For a user to access any resource on the Internet, it is necessary to first locate a server hosting the requested resource. The Domain Name System service (DNS) represents the first step in this process, translating a human readable name, the resource host name, into an IP address. With the expansion of Content Distribution Networks (CDNs), the DNS service has seen its importance increase. In a CDN, objects are replicated on different servers to decrease the distance from the client to a server hosting the object that needs to be accessed. The DNS service should improve user experience by directing its demand to the optimal CDN server. While most of the Internet Service Providers (ISPs) offer a DNS service to their customers, it is now common to see clients using a public DNS service instead. This choice may have an impact on Web browsing performance. In this paper we study the impact of choosing one DNS service instead of another and we compare the performance of a large European ISP DNS service with the one of a public DNS service, Google DNS. We propose a causal approach to expose the structural dependencies of the different parameters impacted by the DNS service used and we show how to model these dependencies with a Bayesian network. This model allows us to explain and quantify the benefits obtained by clients using their ISP DNS service and to propose a solution to further improve their performance.