Miracle-dz/UniFAR-FTUs huggingface dataset:
Technology path-finding framework for directed-self assembly for via layers

Abstract. Directed self assembly (DSA) is a very promising patterning technology for the sub-7-nm technology nodes, especially for via/contact layers. In the graphoepitaxy type of DSA, a complementary lithography technique is used to print the guiding templates, where the block copolymer (BCP) phase-separates into regular structures. Accordingly, the design-friendliness of a DSA-based technology is affected by several factors: the complementary lithography technique, the legal guiding templates, the number of masks/exposures used to print the templates, the related design rules, the forbidden patterns (hotspots), and the characteristics of the BCP. Thus, foundries have a huge number of choices to make for a future DSA-based technology, affecting the design-friendliness, and the cost of the technology. We propose a framework for DSA technology path-finding, for via layers, to be used by the foundry as part of design and technology co-optimization. The framework optimally evaluates a DSA-based technology in which an arbitrary lithography technique is used to print the guiding templates, possibly using many masks/exposures, and provides a design-friendliness metric. In addition, if the evaluated technology is not design-friendly, the framework computes the minimum-cost technology change that makes the technology design-friendly. The framework is used to evaluate technologies like DSA+193-nm immersion (193i) lithography, DSA+extreme ultraviolet (EUV), and DSA+193i self-aligned double patterning. For example, one study showed that one mask of EUV in a DSA+EUV technology can replace three masks of 193i in a DSA+193i technology.
Twisting Linear to Orbital Angular Momentum in an Ultrasonic Motor

An ultrasonic motor built with a contactless meta engine block (MEB) is designed and experimentally demonstrated for twisting the linear momentum of sound emanating from a Helmholtz resonator‐based metasurface into orbital angular momentum (OAM). The MEB is capable of hosting highly efficient excitations of eigenmodes carrying desired OAM whose Bessel acoustic intensity patterns are enhanced by over ten times compared to the incident wave. Thanks to this efficiency, bidirectional ultrasonic OAM is capable of driving loads at speeds up to 1000 rpm at 4 W and remarkable sound radiation torque levels. Moreover, the possibility of using arbitrarily shaped MEBs is also demonstrated by engineering its physical boundary condition based on an analytically derived criterion to guarantee the high twisting efficiency of man‐made OAM. The results show how noninvasive driving of an ultrasonic motor can be made possible through appropriately designed momentum twisting, which opens the door to a new class of integrated mechanical devices solely powered by sound.
Methodology of a diabetes prevention translational research Methodology of a diabetes prevention translational research project utilizing a community-academic partnership for project utilizing a community-academic partnership for implementation in an underserved Latino community implementation in an

Background: Latinos comprise the largest racial/ethnic group in the United States and have 2–3 times the prevalence of type 2 diabetes mellitus as Caucasians. Methods and design: The Lawrence Latino Diabetes Prevention Project (LLDPP) is a community-based translational research study which aims to reduce the risk of diabetes among Latinos who have a ≥ 30% probability of developing diabetes in the next 7.5 years per a predictive equation. The project was conducted in Lawrence, Massachusetts, a predominantly Caribbean-origin urban Latino community. Individuals were identified primarily from a community health center's patient panel, screened for study eligibility, randomized to either a usual care or a lifestyle intervention condition, and followed for one year. Like the efficacious Diabetes Prevention Program (DPP), the LLDPP intervention targeted weight loss through dietary change
Altered photoreceptor metabolism in mouse causes late stage Altered photoreceptor metabolism in mouse causes late stage age-related macular degeneration-like pathologies age-related macular degeneration-like pathologies Let us know how access to this document benefits you.

2020) Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly. While the histopathology of the different disease stages is well characterized, the cause underlying the pro- gression, from the early drusen stage to the advanced macular degeneration stage that leads to blindness, remains unknown. Here, we show that photoreceptors (PRs) of diseased individuals display increased expression of two key glycolytic genes, sugges- tive of a glucose shortage during disease. Mimicking aspects of this metabolic profile in PRs of wild-type mice by activation of the mammalian target of rapamycin complex 1 (mTORC1) caused early drusen-like pathologies, as well as advanced AMD-like pa- thologies. Mice with activated mTORC1 in PRs also displayed other early disease features, such as a delay in photoreceptor outer seg- ment (POS) clearance and accumulation of lipofuscin in the retinal-pigmented epithelium (RPE) and of lipoproteins at the Bruch ’ s membrane (BrM), as well as changes in complement accumulation. Interestingly, formation of drusen-like deposits was dependent on activation of mTORC1 in cones. Both major types of advanced AMD pathologies, including geographic atrophy (GA) and neovascular pathologies, were also seen. Finally, activated mTORC1 in PRs resulted in a threefold reduction in di-docosahexaenoic acid (DHA) – containing phospholipid species. Feeding mice a DHA-enriched diet alleviated most pathologies. The data recapitulate many aspects of the human disease, suggesting that metabolic adaptations in photoreceptors could contribute to disease progression in AMD. Identifying the changes downstream of mTORC1 that lead to ad- vanced pathologies in mouse might present new opportunities to study the role of PRs in AMD Here, we show that altering the metabolic profile of PRs is sufficient to cause 1) changes in ApoB, ApoE, C3, and CFH accumulation at the BrM, 2) formation of drusen-like deposits, and 3) GA and neovascular pathologies. The fact that these mice develop advanced AMD-like pathologies at the same frequency as humans, and that pathologies can be ame- liorated by dietary DHA supplementation, suggests that our mouse model may be a valuable tool to study the progression of human AMD. How does our data complement the current un-derstanding of AMD disease progression?
Design of optimal PI controller for Shunt Active Power Filter using Particle Swarm Optimization

In a recent electrical distribution network, there has been a increase currents harmonics injected by nonlinear loads, such as rectifier equipment used in telecommunication system, power suppliers, domestic appliances,ect. This work is performed to illustrate performance of optimized PI controller parameters of DC link voltage to a shunt active power filter using particle swarm optimization method. The control method is generally useful and applicable to any other active filters. Compared to the classical PI controller, the optimized PI controller shows the advantages of shorter response time and better control performance. The simulation results show that the particle swarm approach for finding the optimal controller parameters is not easy to be implemented, but very effective in reducing harmonic.
Editorial: Mathematical problems in physical fluid dynamics: part II

Fluid dynamics is a research area lying at the crossroads of physics and applied mathematics with an ever-expanding range of applications in natural sciences and engineering. However, despite decades of concerted research efforts, this area abounds with many fundamental questions that still remain unanswered. At the heart of these problems often lie mathematical models, usually in the form of partial differential equations, and many of the open questions concern the validity of these models and what can be learned from them about the physical problems. In recent years, significant progress has been made on a number of open problems in this area, often using approaches that transcend traditional discipline boundaries by combining modern methods of modelling, computation and mathematical analysis. The two-part theme issue aims to represent the breadth of these approaches, focusing on problems that are mathematical in nature but help to understand aspects of real physical importance such as fluid dynamical stability, transport, mixing, dissipation and vortex dynamics. This article is part of the theme issue ‘Mathematical problems in physical fluid dynamics (part 2)’.
The Pathogenic Yeast Candida parapsilosis Forms Pseudohyphae through Different Signaling Pathways Depending on the Available Carbon Source

Candida parapsilosis is one of the leading causes of hospital-acquired yeast infections and poses a significant risk to immunocompromised people. Two of its properties that contribute to infection are metabolic flexibility, to use a range of nutrients available in the host, and cellular dimorphism, to switch between round yeast cells and chains of elongated pseudohyphal cells. ABSTRACT Candida parapsilosis is an emerging fungal pathogen that primarily affects immunocompromised patients in hospitals. A significant risk factor is the use of implanted medical devices, which support the growth of biofilms composed of a mixture of individual yeast cells and chains of elongated pseudohyphal cells. The morphological switch between these two forms is triggered by cues from the environment, including nutrient availability and temperature. We examined how different nutrient sources affect the balance between yeast and pseudohyphae and found that cells grown in the presence of five- or six-carbon sugars form more pseudohyphae at 30°C than at 37°C. Conversely, cells grown on glycerol, a three-carbon polyalcohol, form more pseudohyphae at 37°C. Furthermore, we found that different regulators influence pseudohyphal growth on glucose at 30°C compared with those on glycerol at 37°C. In particular, cAMP signaling and the sirtuin deacetylase Hst1 were required for pseudohyphal growth on glycerol at 37°C but not on glucose at 30°C. Finally, we found that the carbon source on which C. parapsilosis is grown can influence its ability to establish an infection in a wax moth model. Overall, this study reveals that environmental conditions affect not only the extent of pseudohyphal growth but also which pathways and regulators govern pseudohyphal formation. IMPORTANCE Candida parapsilosis is one of the leading causes of hospital-acquired yeast infections and poses a significant risk to immunocompromised people. Two of its properties that contribute to infection are metabolic flexibility, to use a range of nutrients available in the host, and cellular dimorphism, to switch between round yeast cells and chains of elongated pseudohyphal cells. Uncovering the molecular mechanisms that regulate these processes could reveal new targets for antifungal drugs. We found that for C. parapsilosis, the balance between yeast and pseudohyphal cells depends on the nutrients available and the growth temperature. Moreover, these environmental changes can affect its ability to cause infections. Finally, we found that a potential sensor of the cell’s metabolic state, the sirtuin Hst1, contributes to pseudohyphal growth for cells grown on glycerol. These findings indicate that the shape and virulence of C. parapsilosis likely vary depending on its location in the host.
Modal analysis and demonstration of metastructure combining local resonators and an autonomous synchronized switch damping circuit

A locally resonant metastructure is a promising approach for low-frequency vibration attenuation, whereas the attachment of many resonators results in unnecessary and multiple resonance outside the bandgap. To address this issue, we propose a damping metastructure combining local resonators and an autonomous synchronized switch damping circuit. On the basis of modal analysis, we derive an electromechanically coupled equation of the proposed metastructure. The piezo ceramics, which are attached on a small portion of the metastructure and connected to the circuit, remarkably decrease the magnitude of the resonant vibration with no extra sensors, signal processors, or power sources. The displacement at unnecessary resonance was decreased by approximately 75%. The results of the coupled analysis were similar to the experimentally observed results in terms of the location and width of the bandgap on the frequency axis and the decreased displacement for the circuit. The proposed technique can overcome the disadvantage of the metastructure.
AdaTriplet: Adaptive Gradient Triplet Loss with Automatic Margin Learning for Forensic Medical Image Matching

This paper tackles the challenge of forensic medical image matching (FMIM) using deep neural networks (DNNs). FMIM is a particular case of content-based image retrieval (CBIR). The main challenge in FMIM compared to the general case of CBIR, is that the subject to whom a query image belongs may be affected by aging and progressive degenerative disorders, making it difficult to match data on a subject level. CBIR with DNNs is generally solved by minimizing a ranking loss, such as Triplet loss (TL), computed on image representations extracted by a DNN from the original data. TL, in particular, operates on triplets: anchor, positive (similar to anchor) and negative (dissimilar to anchor). Although TL has been shown to perform well in many CBIR tasks, it still has limitations, which we identify and analyze in this work. In this paper, we introduce (i) the AdaTriplet loss -- an extension of TL whose gradients adapt to different difficulty levels of negative samples, and (ii) the AutoMargin method -- a technique to adjust hyperparameters of margin-based losses such as TL and our proposed loss dynamically. Our results are evaluated on two large-scale benchmarks for FMIM based on the Osteoarthritis Initiative and Chest X-ray-14 datasets. The codes allowing replication of this study have been made publicly available at \url{https://github.com/Oulu-IMEDS/AdaTriplet}.
OROS: Online Operation and Orchestration of Collaborative Robots Using 5G

The 5G mobile networks extend the capability for supporting collaborative robot operations in outdoor scenarios. However, the restricted battery life of robots still poses a major obstacle to their effective implementation and utilization in real scenarios. One of the most challenging situations is the execution of mission-critical tasks that require the use of various on-board sensors to perform simultaneous localization and mapping (SLAM) of unexplored environments. Given the time-sensitive nature of these tasks, completing them in the shortest possible time is of the highest importance. In this paper, we analyze the benefits of 5G-enabled collaborative robots by enhancing the intelligence of the robot operation through joint orchestration of Robot Operating System (ROS) and 5G resources for energy-saving goals, addressing the problem from both offline and online manners. We propose OROS, a novel orchestration approach that minimizes mission-critical task completion times as well as overall energy consumption of 5G-connected robots by jointly optimizing robotic navigation and sensing together with infrastructure resources. We validate our 5G-enabled collaborative framework by means of MATLAB/Simulink, ROS software and Gazebo simulator. Our results show an improvement between 3.65% and 11.98% in exploration task by exploiting 5G orchestration features for battery savings when using 3 robots.
Efficient Minimax Optimal Estimators For Multivariate Convex Regression

We study the computational aspects of the task of multivariate convex regression in dimension $d \geq 5$. We present the first computationally efficient minimax optimal (up to logarithmic factors) estimators for the tasks of (i) $L$-Lipschitz convex regression (ii) $\Gamma$-bounded convex regression under polytopal support. The proof of the correctness of these estimators uses a variety of tools from different disciplines, among them empirical process theory, stochastic geometry, and potential theory. This work is the first to show the existence of efficient minimax optimal estimators for non-Donsker classes that their corresponding Least Squares Estimators are provably minimax sub-optimal; a result of independent interest.
All Grains, One Scheme (AGOS): Learning Multigrain Instance Representation for Aerial Scene Classification

Aerial scene classification remains challenging as: 1) the size of key objects in determining the scene scheme varies greatly and 2) many objects irrelevant to the scene scheme are often flooded in the image. Hence, how to effectively perceive the region of interests (RoIs) from a variety of sizes and build more discriminative representation from such complicated object distribution is vital to understand an aerial scene. In this article, we propose a novel all grains, one scheme (AGOS) framework to tackle these challenges. To the best of our knowledge, it is the first work to extend the classic multiple instance learning (MIL) into multigrain formulation. Specifically, it consists of a multigrain perception (MGP) module, a multibranch multi-instance representation (MBMIR) module, and a self-aligned semantic fusion (SSF) module. First, our MGP module preserves the differential dilated convolutional features from the backbone, which magnifies the discriminative information from multigrains. Then, our MBMIR module highlights the key instances in the multigrain representation under the MIL formulation. Finally, our SSF module allows our framework to learn the same scene scheme from multigrain instance representations and fuses them, so that the entire framework is optimized as a whole. Notably, our AGOS is flexible and can be easily adapted to existing convolutional neural networks (CNNs) in a plug-and-play manner. Extensive experiments on UCM, aerial image dataset (AID), and Northwestern Polytechnical University (NWPU) benchmarks demonstrate that our AGOS achieves a comparable performance against the state-of-the-art methods.
Learning Scalable Policies over Graphs for Multi-Robot Task Allocation using Capsule Attention Networks

This paper presents a novel graph reinforcement learning (RL) architecture to solve multi-robot task allocation (MRTA) problems that involve tasks with deadlines and workload, and robot constraints such as work capacity. While drawing motivation from recent graph learning methods that learn to solve combinatorial optimization (CO) problems such as multi-Traveling Salesman and Vehicle Routing Problems using RL, this paper seeks to provide better performance (compared to non-learning methods) and important scalability (compared to existing learning architectures) for the stated class of MRTA problems. The proposed neural architecture, called Capsule Attention-based Mechanism or CapAM acts as the policy network, and includes three main components: 1) an encoder: a Capsule Network based node embedding model to represent each task as a learnable feature vector; 2) a decoder: an attention-based model to facilitate a sequential output; and 3) context: that encodes the states of the mission and the robots. To train the CapAM model, the policy-gradient method based on REINFORCE is used. When evaluated over unseen scenar-ios, CapAM demonstrates better task completion performance and > 10 times faster decision-making compared to standard non-learning based online MRTA methods. CapAM's advantage in generalizability, and scalability to test problems of size larger than those used in training, are also successfully demonstrated in comparison to a popular approach for learning to solve CO problems, namely the purely attention mechanism.
Domain-Level Detection and Disruption of Disinformation

How, in 20 short years, did we go from the promise of the internet to democratize access to knowledge and make the world more understanding and enlightened, to the litany of daily horrors that is today's internet? We are awash in disinformation consisting of lies, conspiracies, and general nonsense, all with real-world implications ranging from horrific humans rights violations to threats to our democracy and global public health. Although the internet is vast, the peddlers of disinformation appear to be more localized. To this end, we describe a domain-level analysis for predicting if a domain is complicit in distributing or amplifying disinformation. This process analyzes the underlying domain content and the hyperlinking connectivity between domains to predict if a domain is peddling in disinformation. These basic insights extend to an analysis of disinformation on Telegram and Twitter. From these insights, we propose that search engines and social-media recommendation algorithms can systematically discover and demote the worst disinformation offenders, returning some trust and sanity to our online communities.
Invariant Content Synergistic Learning for Domain Generalization of Medical Image Segmentation

While achieving remarkable success for medical image segmentation, deep convolution neural networks (DCNNs) often fail to maintain their robustness when confronting test data with the novel distribution. To address such a drawback, the inductive bias of DCNNs is recently well-recognized. Specifically, DCNNs exhibit an inductive bias towards image style (e.g., superficial texture) rather than invariant content (e.g., object shapes). In this paper, we propose a method, named Invariant Content Synergistic Learning (ICSL), to improve the generalization ability of DCNNs on unseen datasets by controlling the inductive bias. First, ICSL mixes the style of training instances to perturb the training distribution. That is to say, more diverse domains or styles would be made available for training DCNNs. Based on the perturbed distribution, we carefully design a dual-branches invariant content synergistic learning strategy to prevent style-biased predictions and focus more on the invariant content. Extensive experimental results on two typical medical image segmentation tasks show that our approach performs better than state-of-the-art domain generalization methods.
The role of atomization in the coupling between doped droplets dynamics and their flames

The droplet and flame chemiluminescence dynamics as well as their coupling during atomization events of graphene oxide doped diesel are investigated experimentally. The tested doping concentrations are 0, 0.001, 0.005, 0.01, and 0.02% by weight. Small diameter fibers are used for the droplet suspension. Separate shadowgraphy and OH* chemiluminescence measurements are performed at 4000 Hz to study the droplet and flame dynamics, respectively. The results show that both the droplet diameter squared and the flame chemiluminescence feature intermittent oscillations. The root mean square of the droplet diameter squared oscillations is positively related to the number and intensity of the atomization events and the graphene oxide doping concentration. The probability density function of the inverse of the time separation between two consecutive atomization events and the power spectrum density of the droplet diameter squared oscillations feature dominant large probabilities and powers at about 25 Hz prior to the occurrence of the first intense atomization event. After the occurrence of the first intense atomization event, this frequency decreases to about 5 Hz for both. Although the intense atomization triggers the large amplitude oscillations at 5 Hz, it was argued that the retracting motion of the igniter induces the oscillations at 25 Hz. Our findings suggest that the atomization events are the root cause of the smaller frequency coupling between liquid fuel droplets doped with graphene oxide and their flames. This has implications for spray combustion research.