Miracle-dz/UniFAR-FTUs huggingface dataset:
Drawing Inductor Layout with a Reinforcement Learning Agent: Method and Application for VCO Inductors

Design of Voltage-Controlled Oscillator (VCO) inductors is a laborious and time-consuming task that is conventionally done manually by human experts. In this paper, we propose a framework for automating the design of VCO inductors, using Reinforcement Learning (RL). We formulate the problem as a sequential procedure, where wire segments are drawn one after another, until a complete inductor is created. We then employ an RL agent to learn to draw inductors that meet certain target specifications. In light of the need to tweak the target specifications throughout the circuit design cycle, we also develop a variant in which the agent can learn to quickly adapt to draw new inductors for moderately different target specifications. Our empirical results show that the proposed framework is successful at automatically generating VCO inductors that meet or exceed the target specification.
Testing Deep Learning Models: A First Comparative Study of Multiple Testing Techniques

Deep Learning (DL) has revolutionized the capabilities of vision-based systems (VBS) in critical applications such as autonomous driving, robotic surgery, critical infrastructure surveillance, air and maritime traffic control, etc. By analyzing images, voice, videos, or any type of complex signals, DL has considerably increased the situation awareness of these systems. At the same time, while relying more and more on trained DL models, the reliability and robustness of VBS have been challenged and it has become crucial to test thoroughly these models to assess their capabilities and potential errors. To discover faults in DL models, existing software testing methods have been adapted and refined accordingly. In this article, we provide an overview of these software testing methods, namely differential, metamorphic, mutation, and combinatorial testing, as well as adversarial perturbation testing and review some challenges in their deployment for boosting perception systems used in VBS. We also provide a first experimental comparative study on a classical benchmark used in VBS and discuss its results.
Cybersecurity Challenges in the Offshore Oil and Gas Industry: An Industrial Cyber-Physical Systems (ICPS) Perspective

There has been significant interest within the offshore oil and gas industry to utilise Industrial Internet of Things (IIoT) and Industrial Cyber-Physical Systems (ICPS). There has also been a corresponding increase in cyberattacks targeted at oil and gas companies. Offshore oil production requires remote access to and control of large and complex hardware resources. This is achieved by integrating ICPS, Supervisory, Control and Data Acquisition (SCADA) systems, and IIoT technologies. A successful cyberattack against an oil and gas (O&G) offshore asset could have a major impact on the environment, marine ecosystem and safety of personnel. Any disruption to the world’s supply of O&G can also have an effect on oil prices and the global economy. We describe the cyberattack surface within the oil and gas industry, discussing emerging trends in the offshore sub-sector and provide a historical perspective of known cyberattacks. We also present a case study of a subsea control system architecture typically used in offshore O&G operations and highlight potential vulnerabilities affecting the components of the system. This study is the first to provide a detailed analysis of attack vectors in a subsea control system. The analysis provided can be used to understand key vulnerabilities in such systems and may be used to implement efficient mitigation methods.
Coherence of Ion Cyclotron Resonance in Damped Ion Cyclotron Waves in Space Plasmas

Ion cyclotron resonance is one of the fundamental energy-conversion processes through field–particle interaction in collisionless plasmas. However, the key evidence for ion cyclotron resonance (i.e., the coherence between electromagnetic fields and the ion phase-space density) and the resulting damping of ion cyclotron waves (ICWs) has not yet been directly observed. Investigating the high-quality measurements of space plasmas by the Magnetospheric Multiscale (MMS) satellites, we find that both the wave electromagnetic field vectors and the bulk velocity of the disturbed ion velocity distribution rotate around the background magnetic field. Moreover, we find that the absolute gyrophase angle difference between the center of the fluctuations in the ion velocity distribution functions and the wave electric field vectors falls in the range of (0, 90)°, consistent with an ongoing energy conversion from wave fields to particles. By invoking plasma kinetic theory, we demonstrate that the field–particle correlation for the damped ICWs in our theoretical model matches well with our observations. Furthermore, the wave electric field vectors ( δEwave,⊥′ ), ion current density (δ J i,⊥), and energy transfer rate ( δJi,⊥·δEwave,⊥′ ) exhibit quasiperiodic oscillations, and the integrated work done by the electromagnetic field on the ions is positive, indicating that ions are mainly energized by the perpendicular component of the electric field via cyclotron resonance. Therefore, our combined analysis of MMS observations and kinetic theory provides direct, thorough, and comprehensive evidence for ICW damping in space plasmas.
Scenario Parameter Generation Method and Scenario Representativeness Metric for Scenario-Based Assessment of Automated Vehicles

The development of assessment methods for the performance of Automated Vehicles (AVs) is essential to enable the deployment of automated driving technologies, due to the complex operational domain of AVs. One candidate is scenario-based assessment, in which test cases are derived from real-world road traffic scenarios obtained from driving data. Because of the high variety of the possible scenarios, using only observed scenarios for the assessment is not sufficient. Therefore, methods for generating additional scenarios are necessary. Our contribution is twofold. First, we propose a method to determine the parameters that describe the scenarios to a sufficient degree while relying less on strong assumptions on the parameters that characterize the scenarios. By estimating the probability density function (pdf) of these parameters, realistic parameter values can be generated. Second, we present the Scenario Representativeness (SR) metric based on the Wasserstein distance, which quantifies to what extent the scenarios with the generated parameter values are representative of real-world scenarios while covering the actual variety found in the real-world scenarios. A comparison of our proposed method with methods relying on assumptions of the scenario parameterization and pdf estimation shows that the proposed method can automatically determine the optimal scenario parameterization and pdf estimation. Furthermore, it is demonstrated that our SR metric can be used to choose the (number of) parameters that best describe a scenario. The presented method is promising, because the parameterization and pdf estimation can directly be applied to already available importance sampling strategies for accelerating the evaluation of AVs.
First implementation of full-workflow automation in radiotherapy: the All-in-One solution on rectal cancer

The aim of this work is to describe the technical characteristics of an AI-powered radiotherapy workflow that enables full-process automation (All-in-One), evaluate its performance implemented for on-couch initial treatment of rectal cancer, and provide insight into the behavior of full-workflow automation in the specialty of radiotherapy. The All-in-One workflow was developed based on a CT-integrated linear accelerator. It incorporates routine radiotherapy procedures from simulation, autosegmentation, autoplanning, image guidance, beam delivery, and in vivo quality assurance (QA) into one scheme, with critical decision points involved, while the patient is on the treatment
Constraints on dark matter self-interaction from galactic core size

Self-interaction of particulate dark matter may help thermalising the central region of the galactic halo and driving core formation. The core radius is expectedly sensitive to the self-interaction strength of dark matter (DM). In this paper we study the feasibility of constraining dark matter self-interaction from the distribution of the core radius in isolated haloes. We perform systematic DM only N-body simulations of spherically symmetric isolated galactic haloes in the mass range of 1010-1015 M ⊙, incorporating the impact of isotropic DM self-interaction. Comparing the simulated profiles with the observational data, we provide a conservative upper limit on the self-interaction cross-section, σ/m < 9.8 cm2 / gm at 95 % confidence level. We report significant dependence of the derived bounds on the galactic density distribution models assumed for the analysis.
Slow-Fast Visual Tempo Learning for Video-based Action Recognition

—Action visual tempo characterizes the dynamics and the temporal scale of an action, which is helpful to distinguish human actions that share high similarities in visual dynamics and appearance. Previous methods capture the visual tempo either by sampling raw videos with multiple rates, which requires a costly multi-layer network to handle each rate, or by hierarchically sampling backbone features, which relies heavily on high-level features that miss ﬁne-grained temporal dynamics. In this work, we propose a Temporal Correlation Module (TCM), which can be easily embedded into the current action recognition backbones in a plug-in-and-play manner, to extract action visual tempo from low-level backbone features at single-layer remarkably. Speciﬁcally, our TCM contains two main components: a Multi-scale Temporal Dynamics Module (MTDM) and a Temporal Attention Module (TAM). MTDM applies a correlation operation to learn pixel-wise ﬁne-grained temporal dynamics for both fast-tempo and slow-tempo. TAM adaptively emphasizes expressive features and suppresses inessential ones via analyzing the global information across various tempos. Extensive experiments conducted on several action recognition benchmarks, e.g. Something-Something V1 & V2, Kinetics-400, UCF-101, and HMDB-51, have demonstrated that the proposed TCM is effective to promote the performance of the existing video-based action recognition models for a large margin. The source code is publicly released at https://github.com/zphyix/TCM.
Ausschöpfen von Stellgrößenbeschränkungen mittels weicher strukturvariabler Regelung / Exhausting bounds on control by means of soft variable structure control

Es wird ein dynamischer, strukturvariabler Regler vorgestellt, der Stellgrößenbeschränkungen in linearen, zeitinvarianten Strecken voll auszuschöpfen vermag. Dies geschieht jedoch nicht nach Art einer hart schaltenden Regelung, sondern mit frei einstellbarer Verstellgeschwindigkeit. Die global asymptotische Stabilität des nichtlinearen Gesamtsystems kann gesichert werden, sofern die Strecke selbst asymptotisch stabil ist. Der Entwurf basiert auf den in [1] angegebenen Stabilitätskriterien und erstreckt sich sowohl auf Eingrößensysteme als auch auf Mehrgrößensysteme mit konzentrierten oder verteilten Parametern. An einem Beispiel wird gezeigt, wie man unter bestimmten Voraussetzungen mit der ohnehin verfügbaren Ausgangsinformation der Strecke und damit ohne Beobachter auskommt.
Life History Stage and Sex-specific Survival Rates for the Japanese Pond Turtle, Mauremys japonica, in the Foothill Region of Chiba Prefecture, Japan

Abstract: Life history parameters are essential when we consider appropriate strategy for conservation of wildlife based on population dynamics modelling. In this study, life history stage and sex-specific survival rates of a Japanese pond turtle (Mauremys japonica) population in foothill region were estimated. Life history was divided into three stages based on the age, separately for males and females, estimated by counting the number of annual rings on scutes: juvenile (1–2 years old), young adult (3–4), and adult (>5) in males, and juvenile (1–4), subadult (5–7), and adult (>8) in females. The median annual survival of subadult and adult females was 0.84 (95% credible interval: 0.54–0.99) and 0.94 (0.77–1.00), respectively, and higher than the survival of juvenile females. In contrast, adult survival of males was estimated as 0.79 (0.44–0.99), and it was higher than the survival of young adult and juvenile males. This study showed that the patterns of survivorship of M. japonica coincide with those of other chelonians.
Building Equitable Access and Inclusion for Children Growing up in the Digital age

Beginning during infancy, digital media are a pervasive part of family life, affecting opportunities to learn and time in family relationships. Research showing the potentially negative impacts of media on very young children led to recommendations of restricted media usage. Other research has examined how educational media can promote child outcomes and well-being. However, stark issues of digital inequity remain. Many families experience underconnectivity, with both income and geography limiting access to adequate bandwidth. Finally, cracks in the democratic structure of the Internet are emerging. Software engineers and social scientists revealed that algorithms determine children's media content and exploitative features manipulate the duration of media exposure. The article evaluates media usage for very young children. Based on this risk–benefit analysis, the article proposes a policy to increase the inclusiveness and safety of the digital space for all young children.
How Infants Learn From a World of Faces: Implications for Racial Biases and Mask-Wearing

Faces are special to infants from birth, and experiences with faces in infancy are critical to developing brain circuits that support face processing skills through adulthood. Infants learn to extract rich information from faces, including recognizing people, tracking their gaze and expressions, and lip-reading. As infants learn to interact with the people around them, their responses to and understanding of these communicative facial cues become more connected to their social understanding and reflect their developmental context. Infants’ face perception is particularly responsive to experience, with some degree of plasticity present through middle childhood. Opportunities to interact with people from diverse racial backgrounds in infancy may help prevent perceptual and social biases toward different groups. Variations in experience with faces beyond face race and gender, such as the use of face coverings, may impact how and what infants learn from faces.
Apoptotic cell-derived micro/nanosized extracellular vesicles in tissue regeneration

Abstract Extracellular vesicles (EVs), products released by cells in multiple biological activities, are currently widely accepted as functional particles and intercellular communicators. From the orthodox perspective, EVs derived from apoptotic cells (apoEVs) are responsible for cell debris clearance, while recent studies have demonstrated that apoEVs participate in tissue regeneration. However, the underlying mechanisms and particular functions in tissue regeneration promotion of apoEVs remain ambiguous. Some molecules active during apoptosis also function in tissue regeneration triggered by apoptosis, such as caspases. ApoEVs are generated in the process of apoptosis, carrying cell contents to manifest biological effects and possess biomarkers to target phagocytes. The regenerative effect of apoEVs might be due to their abilities to facilitate cell proliferation and regulate inflammation. Such regenerative effect has been observed in various tissues, including skin, bone, cardiovascular system, and kidneys. Engineered apoEVs are produced to amplify the biological benefits of apoEVs, rendering them optional for drug delivery. Meanwhile, challenges exist in thorough mechanistic exploration and standardization of production. In this review, we discussed the link between apoptosis and regeneration, current comprehension of the origination and investigation strategies of apoEVs, and mechanisms in tissue regeneration of apoEVs and their applications. Challenges and prospects are also addressed here.
Dominant follicle and gonadotropin dynamics before ovulation in postpartum lactating mares

The aim of this study was to compare the dominant follicle (DF) and gonadotropin dynamics for 9 days before ovulation in postpartum lactating (PP Lactating) versus non‐postpartum cycling (N‐PP Cycling) mares. Every PP Lactating mare on the day of parturition was paired with a N‐PP Cycling mare, and the data analyses considered the partum‐ovulation interval (POI) and the postpartum interovulatory interval (PPIOI) in PP Lactating mares and two interovulatory intervals in N‐PP Cycling mares. The results of the present study revealed several novel and unique aspects of DF development and FSH and LH dynamics before ovulation in PP Lactating mares when compared with N‐PP Cycling mares. The most remarkable differences between both groups of mares were the following: (1) a shorter interval to ovulation in PP Lactating mares during the foal heat (POI ≤22 days) compared with all other intervals; (2) a larger DF in PP Lactating mares, and an earlier day of DF at maximum diameter during the foal heat; and (3) lower gonadotropin levels in PP Lactating mares. Regarding the particularities, PP Lactating mares had lower LH levels during the POI than the PPIOI, demonstrating a strong partum effect; spring‐foaled mares had longer POIs, larger DF diameters, and lower LH levels; and lower body condition scores and higher body‐weight loss led to longer POIs and smaller DF diameters in PP Lactating mares. This study contributes to a broad understanding of ovarian function in the postpartum mare.
Dual Partial Recurrent Networks for Hyperspectral Image Change Detection

This paper presents a Dual Partial Recurrent Networks (DUAL-PRNs) which can project more accurate and effective image features by learning invariant pixel pairs with high confidence. The Change Vector Analysis provides a reference for the model to select invariant pixel pairs with high confidence as training samples. Then, the Unsupervised Slow Feature Analysis (USFA) is utilized to suppress the invariant pixel features projected by DUAL-PRNs, and highlight the variant pixel features, respectively. Thus, more obvious discrimination between the invariant and variant pixels can be achieved. Two groups of features are then obtained by passing bi-temporal remote sensing images through DUAL-PRNs and USFA. Chi-square distance is employed to calculate the divergence between two groups of features and thus generate the Change Intensity Map. Finally, the thresholding algorithm transforms the change intensity map into binary change map. Experimental results show that the proposed change detection model DUAL-PRNs performs better than the advanced model DSFA-128-2.
GOVID: Repurposing Serious Game for Enterprise COVID-19 Awareness

Along with the COVID-19 pandemic came another major threat of pandemic-related misinformation spreading across social media. It has affected all walks of life, not sparing employees of organizations as well. As an employee awareness measure to reduce the misinformation and panic, we developed a serious game to train various do's and don'ts of the pandemic. We chose an enterprise awareness game Phishy, having a ‘survival’ theme, and suitably repurposed it to create COVID-19 awareness game Govid. Appropriate modifications were made to the original game so that there is synergy between the new game story and the learning content. Repurposing greatly reduced the development time, which helped us to develop and deploy the game within just seven days. Govid was played by 2,372 employees of our organization. Using a pre-test and post-test method, we observed that Govid participants had a correctness percentage of 95.6% in answering COVID-19-related questions after playing the game. Our results show that a repurposed game with an engaging gameplay coupled with appropriate learning content shows positive results in the learning outcome. With this, we present an early investigation of the effectiveness of repurposed serious games for an enterprise context.
Audio-visual Speech Enhancement by Using the Correlation Deeply

Audio-visual speech separation aims to separate distinct voice from an audio mixture of multiple simultaneous speakers. The problem is that visual information can’t be fully utilized. We propose a network which can improve the functioning of speech enhancement by using the correlation between audio and vision deeply. We propose that we could extract visual feature information by utilizing speech before using visual information to improve speech enhancement performance. We make a continuous dataset, CRF, to fit the network at the same time. The results demonstrate that the combination of them can improve the performance of speech enhancement effectively.
Attention Distribution Graph: Visualizing Student’s Attention Transition in Error-finding Tasks

Various visualization methods have been proposed for eye movement analysis in programming education. However, most of them were utilized for the general scenario of program comprehension. In this paper, we focus on the eye movement visualization in a specified scenario of error-finding test, in which the students are asked to find multiple errors in a C-language program and submit the result by clicking the corresponding checkbox. A novel visualization scheme namely Attention Distribution Graph (ADG), is proposed to describe the line-level transition of student’s visual attention. We adopt time distance to click event and line number to describe the spatio-temporal space of eye movement, and color the space grids according to the proportion of fixation duration. Experiment results show that, from the distribution of colored grids in ADG, we can infer the student’s cognitive process in a successful error-finding task. It provides a normalized view to analyze the students’ eye movement toward a same bug, especially on the influence of preceding reading to the later decision.
Region-Level Attention Network for Food and Ingredient Joint Recognition

Food image recognition is a challenging task that predicts the image’s food category or ingredient composition. It is an essential and fundamental step to realize automatic dietary recognition and assessment. Many food images, especially Chinese food, lack distinctive structured information and fixed semantic patterns. The current approaches have proposed to capture more discriminative features from local/attention-based perspectives to overcome this problem. However, ingredient composition is often overlooked, and it is worth noticing that the relation of food category and ingredient composition may also be conducive to image recognition. Therefore, in this paper, we propose a Region-Level Attention Network (RLA-Net) for food and ingredient joint classification, which is composed of two stage modules. More specifically, in the Feature Extraction Stage, a two-branch structure is designed to extract global food features and local-region ingredient features under the supervision of the ground-truth label. In the Relation Fusion Stage, by utilizing the mutual relationship between the food category and ingredients, we propose a Region-Weighted Module (RWM) to obtain relation fusion features for better performance. The experimental results demonstrate that our model achieves the state-of-the-art performance in ingredient recognition on the Chinese Food dataset VIREO Food-172, and the results of food classification are also competitive.