Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-Objective Optimization Algorithm Based on Deep Learning for Circuit Partition

Circuit partition is a critical step during Three-dimensional integrated circuit(3D IC) physical design. The quality of circuit partition directly affects the subsequent processes(placement, clock-tree synchronization, route, etc.). Circuit partition is the process that divides a circuit composed of logic gates into subsets with the objective of minimizing the inter connection of subsets and balancing the sum of modules weights between subsets. It can be modeled as a graph partition problem, which is known to be NP-hard. Compared with the traditional graph partition problem, in order to make full use of the space resources of 3D chips, circuit partition pays more attention to the balance between subsets. In this paper, we propose a deep learning-based multi-objective optimization algorithm. Our algorithm uses multiple branch feature extractors(based on GraphSAGE) to learn the features and topological relationships of nodes, and then generates node embedding. Finally, our algorithm obtains the circuit partitioning strategy by using node allocator(based on FC). Compared to the state-of-the-art graph partition(metis), our algorithm improves the balance of partition(2×), and slightly reduces cut-edge quality.
Waveform Design for MIMO Radar Based on Riemannian Trust Region Method

This paper considers waveform design for collocated multiple-input multiple-output (MIMO) radar based on constant envelop (CE) constraint by taking advantage of the Riemannian manifold method. Using the characteristic of the CE constraint, we remodel the original optimization problem as an unconstrained optimization problem on the Riemannian manifold. Then, by exploiting Riemannian trust region manifold, the problem can be solved iteratively. Finally, the optimized waveform can be efficiently generated. Numerical results show the proposed method can suppress interference more heavily and achieve higher SINR compared with the counterparts.
ANYRES: Generating High-Resolution visible-face images from Low-Resolution thermal-face images

Cross-spectral Face Recognition (CFR) aims to compare facial images across different modalities, i.e., the visible and thermal spectra. CFR is more challenging than traditional face recognition (FR) due to the profound modality gap in-between spectra. As related applications range from night-vision FR to robust presentation attacks detection, acquisition involves capturing images at various distances, represented by different image resolutions. Prior approaches have addressed CFR by considering a fixed resolution, necessitating that a subject stands at a precise distance from a given sensor during acquisition, which constitutes an impractical scenario in real-life. Towards loosening this constraint, we propose ANYRES, a unified model endowed with the ability to handle a wide range of input resolutions. ANYRES generates high resolution visible images from low resolution thermal images, placing emphasis on maintaining the cross-spectral identity. We demonstrate the effectiveness of the method and present extensive FR experiments on multi-spectral paired face datasets.
EFT: Expert Fusion Transformer for Voice-Face Association Learning

Learning associations between voices and faces have recently attracted much research interest. Previous studies mainly focused on improving loss function design for learning better representations, neglecting that informative inputs are the prerequisite of association findings and effective representation alignments. Based on this motivation, we proposed an unsupervised transformer-based learning framework, which fuses the knowledge of readily available single-modal expert models to learn better representations. Benefiting from higher-quality inputs, a simple noise contrastive estimation (NCE) loss is used for training. In addition, we proposed a statistical batch construction (SBC) strategy, which obtains high-quality negative samples during the unsupervised learning process. Experiments on the VoxCeleb1 dataset demonstrate the effectiveness of our framework. It yields SOTA results in voice-face verification, matching, and retrieval tasks. In the challenging gender-constrained matching tasks, it achieves over 81% (ACC), which is 5% higher than the previous.
Surface Defect Detection for Die Castings Based on the Improved YOLOv5 Method

This article proposes a novel method for surface defect recognition of die-casting parts based on deep learning YOLOv5 network model. Previous methods, such as based on machine learning and based on template matching, can only classify defect type, and the accuracy and generalization of them are limited. The novel surface defects recognition method based on YOLOv5 algorithm can classify surface defects of die castings and accurately locate their positions which is import in powder metallurgy. To train the casting surface defect detection method based on the YOLOv5 algorithm, the transfer learning is initialized and trained on the Microsoft COCO dataset, we expanded the dataset based on the cyclegan algorithm, and used the kmeans++ algorithm to initialize the anchor-box size. We set up many groups of experiments, and experimental results show that our proposed method performed better than the previous method in joint identification of surface defects, and it can achieve very high mean of average precision (mAP@.5 and mAP@.5:.95) with more than 95%.
Decoupling Control for Heavy Quadruped Robot: A Hip-Based Optimal Virtual Force Distribution and Control Framework

The high self-weight and complex operating environment make implementing force sensor-based motion control of Heavy Quadruped Robots (HQR) challenging. In addition, for such robots, the mass of the heavy leg significantly impacts the ground reaction force (GRF) distribution and observation in the traditional control framework. This paper presents a GRF-Iess locomotion control framework for HQR based on five individual floating-base dynamics, fully considering the mass of legs and the coupled influence between the body and legs. We disassemble the full dynamics of the HQR into five spatially rigid parts with floating bases. The interaction effect between the body and legs is assumed to be a virtual spatial force (VSF), performing as the body's driving force and the loading force of the legs, which is obtained through the Nonlinear Disturbances Observer (NDOB). To realize the spatial trajectory control of the body, we utilize Quadratic Programming (QP) to solve the optimal VSF distribution on the body's hip joints. Furthermore, we employ Position-based Impedance Controllers (PIC) to build a VSF control loop to ensure that each grounded leg provides sufficient VSF to drive the body without slippage. Verification results show the promising locomotion control ability of the proposed framework for HQR.
Axial Ratio and Gain Enhancement of a Circular-Ring Slot Antenna Using a Pair of Asymmetrical Rectangular Slots and a Parasitic Patch for a Radio Beacon on a Nanosatellite

Radio beacons enable measurements of ionospheric radio scintillations and total electron content (TEC). These beacons transmit unmodulated, phase-coherent waves in S-band frequencies. Many satellite applications require circularly polarized (CP) wideband antennas. Their compact size, lightweight, and simple fabrication method make CP antennas suitable for small satellite systems. The slot antenna has wideband impedance, but the 3 dB axial ratio bandwidth (ARBW) is narrower compared to the impedance bandwidth (IBW). In this paper, a circularly polarized circular-slotted antenna (CSA) is proposed to enhance the ARBW and the antenna gain. A pair of asymmetrical rectangular slots, a simple 50 Ω feedline and a parasitic patch were introduced to a CSA to enhance the 3 dB ARBW and the antenna gain. Rectangular slots were inserted on the diagonal axis of the CSA, the feedline was shifted to the left side of the x-axis, and a parasitic patch was attached to the circular slot. The lengths of the rectangular slots correspond to the resonant frequency, and the parasitic patch width corresponds to the higher frequency of the 3 dB ARBW. The asymmetrical rectangular slots, the shifted feedline, and the parasitic patch successfully improved the measured 3 dB ARBW of the antenna by 787.5 MHz or 35.79%. The measured gain of a CSA with left-hand circular polarization (LHCP) was also improved by shifting the feedline and the rectangular slot, achieving a peak gain of 5 dBic.
Exploring the Impact of Student Learning and Programming Pedagogy while Adjusting to COVID-19 (An HBCU Case Study)

Learning to program can be a challenge, but it is an essential skill that all computer science (CS) majors are expected to develop. Much attention has been rendered towards ways to enhance student learning and program pedagogy in the CS classroom. One reason is to improve the retention of CS majors, while also aiding them with the ability to showcase their learned skills in practical and professional settings. Due to COVID-19 pandemic, the way students learn have shifted significantly. One notable shift being transitioning from in-class learning to virtual/online learning. Recent literature indicate that student learning has declined because of this transition. Moreover, emphasis on how COVID-19 has impacted student learning in the CS classroom is needed. This article discusses a case-study conducted on CS majors enrolled at an Historically Black University (HBCU) in the mid-Atlantic Region of the United States. This study is comprised of pedagogical coding review (PCR) assessments and analyses conducted over a span of six semesters (Fall 2018 to Spring 2021) involving two CS courses, CS2 and an Object-Oriented Programming course. The objective was to determine if a decline in the students' learning and performance existed from these PCRs during the semesters where online learning was prevalent due to COVID-19. Results revealed that a slight decline occurred during the semesters where COVID-19 was prevalent. These findings contribute to current literature as it pertains to underlying challenges of COVID-19 in higher education. Moreover, this case study directly emphasizes the impacts of COVID-19 on student learning in the CS classroom.
A Learning-Based Method for Gate Delay Prediction under NBTI and Process Variation Effects

As the transistor process technology shrinks to nanoscale, aging and process variation effects become dominant issues affecting the chip reliability. Analytical evaluation at the early stages of chip design is critical to ensure that the chip will function correctly throughout its entire life cycle. In this paper, a machine learning model named Gaussian Process Regression (GPR) is proposed for predicting gate delay, which considers aging and process variation effects jointly. The experiment uses commercial 28 nm technology to simulate typical types of logic gates and verify the performance of GPR. Our approach can obtain accurate prediction with rRMSE score of 0.19% and MAE score of 0.14%, and significant runtime speed-up with 1000X speedup, with minimal loss of accuracy compared to the golden data.
Netzbetriebsverfahren zur Koordinierung vonPhasenschiebertransformatoren und HGÜ-Verbindungen imVerbundnetz

Die Ubertragungsnetzbetreiber reagieren auf entstehende Uberlastungen einzelner AC-Leitungen sowohl mit dem Ausbau des AC-Netzes als auch mit der vermehrten Integration von Phasenschiebertransformatoren (PST) und Hochspannungsgleichstromubertragungen (HGU), die eine definierte Beeinflussung der AC-Wirkleistungsflusse ermoglichen. Zur Vermeidung von negativen Wechselwirkungen zwischen diesen Betriebsmitteln widmet sich die Arbeit der Fragestellung, wie sich geeignete Arbeitspunkte von PSTs und HGUs zur Wahrung der Netzsicherheit bestimmen lassen. Dazu wird ein einheitliches Netzbetriebsverfahren entworfen, dass unterschiedliche Methoden zur Koordinierung von PST und HGU verwendet, um so auf die verschiedenen zeitlichen Gegebenheiten wahrend der Netzbetriebsplanung und der Netzbetriebsfuhrung einzugehen. Diese Methoden werden mithilfe numerischer Fallstudien an einem entworfenen Testnetz untersucht und deren Eignung analysiert.
Approximate Bayesian State Estimation for Active Fault Diagnosis of Large-Scale Systems

Active fault diagnosis (AFD) of stochastic large-scale systems in multiple model framework involves two stages: offline and online. In the offline stage, an excitation input generator is designed based on a Bellman function. In the online stage, the generator is utilized together with an estimator of the model indices. A similar estimator is used in the offline stage for the Bellman function calculation using the value iteration technique. However, due to the high dimensions of information states of the associated perfect state information problem, the estimator in the offline stage must involve approximations. The paper provides the relations for the estimate calculation using the Bayesian recursive relations, proposes four algorithms, and studies effects of such approximations on the AFD decisions. In particular, the quality of the model index estimates is analyzed using a power network model.
Proposal of a Multi-Purpose Marketing Data Analysis Program Design Methodology

Marketing is a strategy for the sale of goods, and shopping malls and vendors consider it important. Companies set up strategies through marketing departments or professional companies and collect related data directly, but data analysis eventually requires collecting data directly, even if some tools are used, and most of the automatically collected data is only normalized data. In this paper, we present the necessity and method of performing a program that can be used fundamentally by automatically performing the functions of existing data analysis tools. As a method, crawling and deep learning techniques are utilized, and the results of simple analysis for automatic data collection and evaluation are provided. Start-ups and small businesses can reduce the need for primary preparation and basic costs and efforts to use the data analysis tools used in marketing.
Acquisition and Remote Transfer of Operative Field View During Open Surgery

Telementoring in open surgery has emerged as an alternative to on-site teaching. To ensure effective communication between the mentee and the mentor, the view of surgical operations in the operating room needs to be shared remotely in real-time. This study is carried out to propose a multi-threaded system that takes input from multi-color/depth cameras, aligns and creates a single point cloud representing the operative field in three-dimension, transfers it over the network, and reconstructs it for visualization for mentors. The system's performance is measured in terms of execution time of each threaded unit, and latency for transfer of multi-color/depth data over the network. The achieved registration accuracy is benchmarked for multiple cameras. The results show the potential usage of the proposed system for near real-time telementoring during open surgeries.
A New Practical Robust Control Design for Model-based Uncertain Collaborative Robot

A new practical robust control method is proposed for uncertain collaborative robots with six degrees of freedom, improving their trajectory tracking performance. The MPDP controller combines a model-based PD control and a robust control algorithm. Lyapunov theory analysis proves that this method ensures uniform boundedness and final boundedness of the system. Simulation results show that the MPDP controller outperforms the traditional PID controller, providing better robustness and smaller steady-state tracking error.
Ground Surveillance Radar Target Classification Based on 2D Selective CA CNN

In this paper, a new 2D convolutional neural networks (CNN) model is proposed for classifying targets detected by low-resolution ground surveillance radar. The targets echoes can be disposed in different domains to obtain their unique features which can be used in classification. Using the proposed network for radar target classification consists of two steps, using Toeplitz matrix to reconstruct original 1D signal to 2D and constructing the Selective CA (coordinate attention) model for target classification. In first step, Toeplitz matrix is made use of reconstructing Radar signal to build 2D data without changing the characteristic distribution of the signal, which makes it possible to train radar signal with 2D CNN. In second step, we construct the 2D Selective CA model. Each block of the model we propose applies the CA to different receptive fields for feature fusion. The residual structure is also included, which can make the information spread more smoothly in the network and effectively solve the problem of vanishing/exploding gradients int the back propagation process. The model was trained and tested on actual collected database containing human and car, which finally achieve 99.2% accuracy on the original frequency domain test set and 97.89% accuracy on the original time domain test set.
A Survey on the Control Techniques used In Process Control

Major works in the world is carried out in automation. Manual labor is being reduced due to certain factors like staff maintenance charges which includes wages and incentives, safety and precautionary measures, enormous requirement of products, fast action, human errors etc. In this automation era, various control techniques are developed which required manpower or supervision. This review paper consists of Part I Introduction representing the present scenario developed in modern era. Part II describes the Role of controllers in industries. Part III explains the different control techniques like conventional P/PI/PID controllers, Model Predictive Control, Fuzzy Logic, Neural Network, Reinforcement Learning, Deep learning control strategies. Part IV identifies the constraints faced in each technique and the development to other methods. Furthermore, future work section concludes by the upcoming advancements like RNN to forecast the action required in the area of process control. This review holds an insight to the researchers to enhance the level of development to meet the industry requirements.
Sensitivity to keV-MeV dark matter from cosmic-ray scattering with current and the upcoming ground-based arrays CTA and SWGO

A wealth of astrophysical and cosmological observational evidence shows that the matter content of the universe is made of about 85$\%$ of non-baryonic dark matter. Huge experimental efforts have been deployed to look for the direct detection of dark matter via their scattering on target nucleons, their production in colliders, and their indirect detection via their annihilation products. Inelastic scattering of high-energy cosmic rays off dark matter particles populating the Milky Way halo would produce secondary gamma rays in the final state from the decay of the neutral pions produced in such interactions, providing a new avenue to probe dark matter properties. We compute here the sensitivity for H.E. S.S. -like observatory, a current-generation ground-based Cherenkov telescopes, to the expected gamma-ray flux from collisions of Galactic cosmic rays and dark matter in the center of the Milky Way. We also derive sensitivity prospects for the upcoming Cherenkov Telescope Array (CTA) and Southern Wide-field Gamma-ray Observatory (SWGO). The expected sensitivity allows us to probe a poorly-constrained range of dark matter masses so far, ranging from keV to sub-GeV, and provide complementary constraints on the dark matter-proton scattering cross section traditionally probed by deep underground direct dark matter experiments.
Effects of Individual Cognitive Stimulation Therapy on People With Dementia: A PRISMA- Guided Systematic Review

Background: Individual Cognitive stimulation therapy (iCST) has been used to improve cognitive function and quality of life. Aim: To analyze the efficacy of the CST among people with dementia. Design: Systematic review and meta-analysis. Results: A total of 17 studies were included in the review. Individual Cognitive stimulation therapy can be used as an intervention to improve cognition and quality of life. Conclusions: Individual Cognitive Stimulation Therapy improves the quality of life of the participants including people with dementia and their carer. It can potentially be used to improve cognition, but further research may consider the implementation of a standard protocol with a rigorous study design to provide sufficient evidence to address this area.
Biosorption of Heavy Metals

Only within the past decade has the potential of metal biosorption by biomass materials been well established. For economic reasons, of particular interest are abundant biomass types generated as a waste byproduct of large‐scale industrial fermentations or certain metal‐binding algae found in large quantities in the sea. These biomass types serve as a basis for newly developed metal biosorption processes foreseen particularly as a very competitive means for the detoxification of metal‐bearing industrial effluents. The assessment of the metal‐binding capacity of some new biosorbents is discussed. Lead and cadmium, for instance, have been effectively removed from very dilute solutions by the dried biomass of some ubiquitous species of brown marine algae such as Ascophyllum and Sargassum, which accumulate more than 30% of biomass dry weight in the metal. Mycelia of the industrial steroid‐transforming fungi Rhizopus and Absidia are excellent biosorbents for lead, cadmium, copper, zinc, and uranium and also bind other heavy metals up to 25% of the biomass dry weight. Biosorption isotherm curves, derived from equilibrium batch sorption experiments, are used in the evaluation of metal uptake by different biosorbents. Further studies are focusing on the assessment of biosorbent performance in dynamic continuous‐flow sorption systems. In the course of this work, new methodologies are being developed that are aimed at mathematical modeling of biosorption systems and their effective optimization. Elucidation of mechanisms active in metal biosorption is essential for successful exploitation of the phenomenon and for regeneration of biosorbent materials in multiple reuse cycles. The complex nature of biosorbent materials makes this task particularly challenging. Discussion focuses on the composition of marine algae polysaccharide structures, which seem instrumental in metal uptake and binding. The state of the art in the field of biosorption is reviewed in this article, with many references to recent reviews and key individual contributions.
A Pyrrhic Victory? – Bank Bailouts and Sovereign Credit Risk

We develop a model in which financial sector bailout and sovereign credit risk are intimately linked. The bailout ameliorates the under-investment problem of the financial sector. However, as the bailout is ultimately funded through taxation of the future profits of the non-financial sectors, it weakens their incentives to invest. This can adversely affect the sovereign’s own credit risk which severely limits the size of the efficient bailout. In the shortrun, the bailout is funded through issuance of government bonds, which erodes the value of existing bonds held by the financial sector and further reduces the size of the efficient bailout. The model provides testable implications concerning the relation between the credit risk of the sovereign and its financial sector. We provide supporting empirical evidence using data from the credit default swaps market and bank stress tests conducted during the financial and sovereign crises of 2007-10. J.E. L. Classification: G21, G28, G38, E58, D62.