Miracle-dz/UniFAR-FTUs huggingface dataset:
iPL-3D: A Novel Bilevel Programming Model for Die-to-Die Placement

Die-to-die (D2D) placement is a more challenging stage in achieving higher performance with complex constraints, critically impacting timing, power, yield, cost, etc. Existing placers often rely on indirect objectives (e.g., considering cut sizes in tier assignment), which can lead to a loss of the overall solution space utilization and may even deviate from the actual objective. To address this issue, this paper leverages the natural dominance relationship between decision variables to transform the original problem into a bilevel programming problem equivalently. Additionally, an alternating optimization framework is introduced to enhance the exploration of the overall solution space. On the one hand, we propose two tier optimization operators for simultaneous optimization of wirelength and #terminal in global and detailed perspectives; On the other hand, we present a near-optimal terminal legalization algorithm following an efficient multi-tier co-placement. Compared with the top three winners of the ICCAD'22 contest, our placer achieves 4.33%, 4.42%, and 5.88% smaller wire-length, 79.61 %, 16.74%, and 15.76% fewer #terminal and competitive runtime. Moreover, our placer always uses the fewest #terminal and achieves amazing wirelength reduction when the terminal size changes.
Adaptive critic control for spacecraft attitude tracking with appointed-time convergence

This paper investigates the appointed-time prescribed performance attitude tracking control problem of a rigid spacecraft using the adaptive critic control. Firstly, the error transformation technique is utilized to convert the system with the prescribed performance constraint into an equivalent unconstraint optimal control problem. Then in the framework of adaptive critics, the critic-only architecture is built to solve the Hamilton-Jacobi-Bellman equations associated with these solutions. A rigorous mathematical stability proof is given. Finally, numerical simulations are presented to demonstrate the effectiveness and robustness of the proposed controller.
Nighttime Lane Detection Based on Retinex Theory and Self-Attention Distillation

In the night scene, the lane marking image is overall dark and the illumination is uneven. These problems lead to low detection accuracy, high missed detection rate, and false detection rate. Aiming at these problems, this paper first uses the unsupervised learning network based on Retinex theory to enhance the night image and improve the contrast of the image, and then introduces self-attention distillation into the efficient residual factorized net (ERFNet) coding layer to make the high-level attention try to train the low-level attention map, to improve the accuracy of lane recognition. At the end of this paper, the method is verified on the constructed nightly Tusimple test dataset. The experimental results show that the lane detection model trained by this method is suitable for night environments and is robust in complex scenes.
Motor Imagery Recognition Based on an Optimized Probabilistic Neural Network with the Particle Swarm Optimization

At present, how to efficiently and effectively identify motor imagery tasks is still a huge challenge for the development of the Brain Computer Interface (BCI) systems in the field of human rehabilitation. Therefore, this paper proposes an optimized recognition method based on temporal features and spatial features re-representation. The Local Mean Decomposition (LMD) algorithm is used to extract the Product Functions (PFs) of the Motor Imagery Electroencephalogram (MI-EEG) signals and the Common Spatial Pattern (CSP) algorithm is applied to reconstruct the spatial distribution of each PF, then the MI-EEG signals are re-represented as features with temporal and spatial characteristics. The Probabilistic Neural Network (PNN) is constructed, in which the smoothing factor is optimized by the Particle Swarm Optimization (PSO). By introducing the PSO algorithm, the PNN can be adaptively determined according to the respective conditions of different subjects or datasets. The experimental results show, compared with the Support Vector Machine (SVM) based on feature re-representation and other state-of-the-art machine learning methods, the proposed method in this paper has both high recognition accuracy and adaptability. This optimized PNN with PSO method establishes a theoretical foundation and methodological guidance for the decoding of motor imagery recognition.
Advances in Postprocessing Technology for Laser Ultrasound Detection Signals: A Review

In the field of modern nondestructive testing (NDT), laser ultrasonic detection (LUD) technology has become a remarkable novel technology with its advantages of high-cost performance, noncontact detection, high accuracy, environmental adaptability, etc. Postprocessing of NDT ultrasonic waves to obtain more intuitive information reflecting the acoustic and mechanical properties of objects has also become an important step in modern quantitative NDT and one of the mainstream development directions. This article takes laser ultrasound detection as the entry point, introduces the typical techniques and principles of three major aspects including signal preprocessing, visualization processing, and intelligent defect reconstruction around the postprocessing of laser ultrasound detection signals after reception, compares and analyzes the results of different imaging algorithms, presents the intelligent processing methods commonly used in the field of ultrasound detection defect reconstruction, and briefly summarizes and outlooks on intelligent ultrasound signal processing technology.
READ: Reliability-Enhanced Accelerator Dataflow Optimization Using Critical Input Pattern Reduction

With the rapid advancements of deep learning in recent years, hardware accelerators are continuously deployed in more and more safety-critical applications such as autonomous driving and robotics. While the accelerators are usually fabricated with advanced technology nodes for high performance and energy efficiency, they are also more prone to timing errors under process, voltage, temperature, and aging (PVTA) variations. By revisiting the physical sources of timing errors, we show that most of the timing errors in the accelerator are caused by a specific subset of input patterns, defined as critical input patterns. To improve the timing error resilience of the accelerator, in this paper, we propose READ, a reliability-enhanced acceler-tor dataflow optimization technique that can effectively reduce timing errors. READ reduces the occurrence of critical input patterns by exploring the optimal computing sequence when mapping a trained deep neural network to accelerators. READ only changes the order of multiply-accumulate operations in a convolution, which introduces negligible hardware overhead and no impact on accuracy. The experimental results on VGG and ResNet demonstrate on average $\mathbf{7.8}\times$ timing error rate (TER) reduction and up to $\mathbf{37.9}\times$ TER reduction for certain layers. The results also show that READ enables the accelerator to maintain accuracy over a wide range of PVTA variations, making it a promising approach for robust deep-learning design.
Prescribed Performance Position Control for PMSM System via a Novel Non-Recursive Design Framework

In order to optimize the transient-time control performance and facilitate the implementation of the industrial application for the permanent magnet synchronous motor (PMSM) position control system, a non-recursive prescribed performance control strategy has been proposed in this paper. Firstly, a higher-order sliding mode disturbance observer is constructed to exactly estimate the disturbance terms. Secondly, we introduce a performance function to realize the asymmetric prescribed performance constraint of the PMSM position. Based on the accurate estimations of the disturbance, the reference signals of each state can be calculated, and eventually, a concise control law is obtained directly in the subsequence. As a new feature, the proposed non-recursive control framework ensures the simplicity of the PMSM position controller design process by maintaining a high position control performance for the PMSM system. Several simulations verify the effectiveness and disturbance rejection ability of the proposed strategy.
Bearing fault diagnosis algorithm based on multi-source data and auxiliary classifier

In recent years, with the development of sensor technology and deep learning, intelligent bearing diagnosis algorithms using multi-source data have achieved great success. However, when faced with the input of single sensor data, the deep learning model trained with multi-source data cannot work, which affects the flexibility of model application. Meanwhile, the method of only adding a loss function in the final classification layer is insufficient to restrict each branch model, and will also affect the performance of the model. Based on this, this paper proposes a parallel convolutional neural network with auxiliary classifier. The auxiliary classifiers added in each branch can not only give fault diagnosis results when only a single data is used, but also improve the performance of the final multi-data fusion by optimizing the output features of each branch, and broaden the application range of the diagnosis model. The proposed method is tested on the bearing fault dataset, the experimental results show that, after adding multiple losses, the structural form of the auxiliary classifier can effectively improve the fault diagnosis accuracy and robustness of the parallel convolutional neural network. Meanwhile, the auxiliary classifiers of each branch can also obtain good classification performance when facing single sensor data, which better improves the flexibility of the model.
Real-time Detection of Two Subjects' Respiratory Rates Using Wavelet-Transform-Based 5.8 GHz CW Doppler Radar

Using the continuous-wave (CW) Doppler radar to identify and extract the respiratory rates of two subjects is challenging, especially when the subjects have time-varying respiratory rates. In this work, a novel wavelet-transform-based algorithm is proposed to obtain the respiratory rates for the scenario when two subjects are presenting in the CW Doppler radar detection field. A two-point time-frequency spectrum was obtained from the continuous wavelet transform spectrogram, then a peak power density was calculated based on the two-point time-frequency spectrum, with which the respiratory rates can be eventually extracted. The algorithm was evaluated with numerical simulation and tested with human subjects using a 5.8-GHz continuous-wave Doppler radar platform.
Foresight Social-aware Reinforcement Learning for Robot Navigation

When robots handle navigation tasks while avoiding collisions, they perform in crowded and complex environments not as good as in stable and homogeneous environments. This often results in a low success rate and poor efficiency. Therefore, we propose a novel Foresight Social-aware Reinforcement Learning (FSRL) framework for mobile robots to achieve collision-free navigation. Compared to previous learning-based methods, our approach is foresighted. It not only considers the current human-robot interaction to avoid an immediate collision, but also estimates upcoming social interactions to still keep distance in the future. Furthermore, an efficiency constraint is introduced in our approach that significantly reduces navigation time. Comparative experiments are performed to verify the effectiveness and efficiency of our proposed method under more realistic and challenging simulated environments.
Deep Reinforcement Learning-based Edge Caching for Industrial Control Applications

To enhance the quality of service for field users in industrial control applications, a suitable caching strategy at edge servers is essential. This paper proposes a cache replacement strategy based on deep reinforcement learning. Status space, action space and reward function are defined considering the varying real-time requirements of the application files. The performance of the proposed algorithm is compared with baseline algorithms using user requests with dynamic popularities. The experimental results demonstrate that the proposed algorithm can effectively enhance the hit rate of control files while maintaining the overall cache hit rate, without sacrificing performance.
Automatic Velocity Picking Based on Improved Mask R-CNN

Velocity spectrum analysis is the main method used to determine the normal moveout (NMO) velocity. However, conventional velocity spectrum analysis methods are inefficient and are typically affected by subjective factors, resulting in a deviation of the selected velocity from the actual velocity. Consequently, we developed a velocity-picking method that uses a mask region-based convolutional neural network (Mask R-CNN). The proposed method first generates a large number of training datasets, including velocity spectrum and labels, and then feeds them to the network for training. After the network is trained with the training data, it automatically extracts time–velocity functions from the velocity spectrum and outputs them for subsequent data processing. The proposed method converts the picking of velocity-energy clusters in seismic data processing into object detection in image processing. Therefore, to enable the method to accurately detect velocity-energy clusters in the velocity spectrum, according to the characteristics of velocity-energy clusters, the anchor scale and anchor ratio of the traditional Mask R-CNN are improved and a Mask R-CNN structure suitable for velocity-energy cluster detection is constructed. We solved the problem of object duplicate detection in the traditional Mask R-CNN by improving the nonmaximum suppression algorithm. The results of experiments conducted on synthetic and field data indicate that the proposed method quickly and accurately extracts time–velocity functions from the velocity spectrum. Furthermore, it obtains better results for velocity picking, NMO-corrected common midpoint gathers, and stacked sections than the traditional Mask R-CNN and the man–computer interaction velocity-picking (MIVP) method.
Preliminary assessment of species richness and avian community dynamics in the Madrean Sky Islands, Arizona

The Sky Island mountain ranges of southeastern Arizona contain a unique and rich avifaunal community, including many Neotropical migratory species whose northern breeding range extends to these mountains along with many species typical of similar habitats throughout western North America. Understanding ecological factors that influence species richness and biological diversity of both resident and migratory species is important for conservation of this unique bird assemblage. We used a 5-year data set to evaluate avian species distribution across montane habitat types within the Santa Rita, Santa Catalina, Huachuca, Chiricahua, and Pinaleno Mountains. Using point-count data from spring-summer breeding seasons, we describe avian diversity and community dynamics. We use a Bayesian hierarchical model to describe occupancy as a function of vegetative cover type and mountain range latitude, and detection probability as a function of species heterogeneity and sampling effort. By identifying important habitat correlates for avian species, these results can help guide management decisions to minimize loss of key habitats and guide restoration efforts in response to disturbance events in the Madrean Archipelago.
GRAFT: Graph-Assisted Reinforcement Learning for Automated SSD Firmware Testing

Well-designed test cases (TCs) are crucial for en-suring the quality of Solid-State Drive (SSD) products. Indeed, validating SSD firmware code by the TCs is indispensable to check if there are no defects during the SSD development process. Accordingly, it is necessary to create short TCs covering firmware code as much as possible for efficient and precise validation. While various methods are available for generating TCs, existing automated approaches overlook backward compatibility, a key property in the SSD development process. To utilize the property, we introduce a novel deep-learning approach called GRAFT, which combines graph representation learning and reinforcement learning (RL) for automated TC generation in SSDs by leveraging pre-collected data. G RAFT trains a graph neural network to extract the underlying structure of the SSD firmware code from a given SSD simulator. The resulting graph embeddings serve as observations in the RL process. To address the challenge of over- estimation in an external domain in the RL process, conservative Q-Iearning, an offline RL technique, is employed using the pre- collected data. Despite the limitation of not being able to interact with the SSD simulator for training, we demonstrate that GRAFT successfully trains RL agents that generate TCs. The TCs are not only significantly more efficient with 3.5x shorter than randomly generated TCs by a black-box fuzzer but also exhibit comparable coverage and efficiency to those created by human experts with domain knowledge, which fully took three days. Moreover, the TCs achieves maximum coverage more reliably than any other methods in the experiments.
A Many-to-Many UAV Pursuit and Interception Strategy Based on PERMADDPG

To address the problem of multiple unmanned aerial vehicles (UAVs) cooperatively pursuing and intercepting non-cooperative UAVs in complex environments, we propose a prioritized experience replay multi-agent deep deterministic policy gradient (PERMADDPG) algorithm based on deep reinforcement learning. By designing algorithm models, state spaces, action spaces, reward functions, and incorporating prioritized experience replay techniques, the UAVs are trained to learn pursuit and interception strategies within a centralized training and distributed execution framework. The algorithm expands research on scenario where the “escaping UAVs have superior speed and maneuverability compared to the pursuing UAVs’’ and alleviates the large amplitude and slow convergence of the MADDPG algorithm when facing complex environments. Finally, through simulation experiments, the effectiveness of the proposed algorithm in solving the many-to-many UAV pursuit and interception problem is verified. By comparing it with the traditional MADDPG algorithm, the proposed algorithm demonstrates the ability of improving pursuit success rate and reducing response time effectively, while converging faster and minimizing oscillation amplitude as well.
PBA: Percentile-Based Level Allocation for Multiple-Bits-Per-Cell RRAM

Recently, researchers have demonstrated multiple-bits-per-cell (MBPC) data storage using resistive random access memory (RRAM) device technologies. In MBPC storage, a level allocation algorithm identifies a level allocation that maps resistance ranges to bit combinations. State-of-the-art level allocation algorithms, such as sigma-based allocation (SBA), fit cell characterization data to parameterized distributions and then use distribution parameters (i.e., programmed resistance standard deviation σ) to find level allocations. However, from the datasets we collected, the data points do not actually conform to the chosen distribution, and therefore the real-world analog behaviors are poorly approximated by the parameterized distribution-based approach. We present PBA, a percentile-based level allocation algorithm that computes level allocations directly from characterization data. We show that PBA level allocations have 30%-71% lower bit-error rates and 22%-41% lower ECC storage overheads than SBA on three fabricated RRAM storage arrays.
Monitoring coughs using a chest-wearable Respeck

This paper describes an unobtrusive cough monitor based on the wireless Respeck sensor worn as a patch on the chest, in tandem with a deep learning-based cough classification method for the automatic detection of instances of coughs in the Respeck sensor data. The cough monitor was evaluated on an unseen, 2.5h-long, Respeck dataset which mimicked real-life settings and achieved an accuracy of greater than 82% using a one-dimensional convolutional neural network. Results are presented on testing the Respeck cough monitor in the wild on asthma and COPD patients which provided insights validated by independent publications.
Task-Related and Resting-State EEG Classification of Adult Patients with ADHD Using Machine Learning

Attention-deficit hyperactivity disorder (ADHD) is a prevalent psychological disorder characterized by attention deficits and high impulsivity, impacting both adults and children. This study aims to assess the effectiveness of task-related electroencephalography (EEG) and resting-state EEG in distinguishing adult patients with ADHD from healthy controls. Machine learning techniques are employed to classify the patients’ status based on EEG features. The primary objective of this investigation is to determine whether the classification performance of task-based EEG data recorded during a stop-signal task recruiting inhibitory processes outperforms that of resting-state EEG data. We hypothesize that task-based EEG data contains valuable biomarkers related to inhibitory control that can be utilized to detect ADHD, whereas resting-state EEG data does not possess such useful biomarkers.
Track Irregularity Detection Based on Lateral Offset of High-Speed Railway

Track irregularity detection is crucial to assure passenger comfort and operation security of the train. In this paper, a novel track irregularity estimation model based on Temporal Convolution Network (TCN) and Long Short-Term Memory (LSTM) is proposed to monitor the track state promptly and accurately. Firstly, a denoising method combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and grey theory is established to realize the noise reduction of the train attitude data with a poor signal-to-noise ratio (SNR). Then, the vehicle attitude characteristic information extracted from TCN is input into LSTM, and the complex model between train body lateral response and track irregularity is constructed. Finally, utilizing a dataset made up of real track irregularities and the vehicle-rail model, experimental validation is performed. The experiment results show that the track irregularity is detected effectively. Compared to the TCN, LSTM and CNN-LSTM, the detection error of TCN-LSTM is the lowest. The feasibility and effectiveness of the proposed method can be demonstrated.
Exploring Error Bits for Memory Failure Prediction: An In-Depth Correlative Study

In large-scale datacenters, memory failure is a common cause of server crashes, with uncorrectable errors (UEs) being a major indicator of Dual Inline Memory Module (DIMM) defects. Existing approaches primarily focus on predicting UEs using correctable errors (CEs), without fully considering the information provided by error bits. However, error bit patterns have a strong correlation with the occurrence of uncorrectable errors (UEs). In this paper, we present a comprehensive study on the correlation between CEs and UEs, specifically emphasizing the importance of spatio-temporal error bit information. Our analysis reveals a strong correlation between spatio-temporal error bits and UE occurrence. Through evaluations using real-world datasets, we demonstrate that our approach significantly improves prediction performance by 15% in F1-score compared to the state-of-the-art algorithms. Overall, our approach effectively reduces the number of virtual machine interruptions caused by UEs by approximately 59%.