Miracle-dz/UniFAR-FTUs huggingface dataset:
Construction of realistic gate sizing benchmarks with known optimal solutions

Gate sizing in VLSI design is a widely-used method for power or area recovery subject to timing constraints. Several previous works have proposed gate sizing heuristics for power and area optimization. However, finding the optimal gate sizing solution is NP-hard, and the suboptimality of sizing solutions has not been sufficiently quantified for each heuristic. Thus, the need for further research has been unclear. In this work, we describe a new benchmark generation approach for leakage power-driven gate sizing (the subject of the forthcoming ISPD-2012 contest) which constructs realistic circuit netlists with known optimal solutions. The generated netlists resemble real designs in terms of gate count, maximum path depth, interconnect complexity (Rent parameter), and net degree distributions. Using these benchmark circuits with known optimal gate size, we have studied the suboptimality of several leakage-driven gate sizing heuristics, including two commercial tools, with respect to key circuit topology parameters. Our study shows that common sizing methods are suboptimal for realistic benchmark circuits by up to 52.2% and 43.7% for Vt-assignment and gate sizing formulations, respectively. The results also suggest that (1) commercial tools may still suffer from significant suboptimality, and/or (2) existing methods have "similar" degrees of suboptimality.
Design, analysis and experiments of novel short-stroke linear loading system based on axial-magnetized voice-coil motor for linear oscillating actuator

Linear oscillating actuator is a kind of electromagnetic device, providing high frequency short-stroke reciprocating motion directly without extra motion transfer mechanisms. It shows satisfactory efficiency and power density especially with high frequency and large load. However, due to its special motion forms, researches into driving and control of linear oscillating actuator under loaded states are seldom conducted because traditional loading methods, such as magnetic powder brake and weight loading, are not suitable for high-frequency linear bi-direction loading applications. In order to deal with this problem, a novel linear loading system based on voice-coil motor is proposed in this paper to facilitate comprehensive testing and control of linear oscillating actuators with load. To achieve fast response, low inertia and stable loading force, symmetrical axial-magnetized array is adopted among the various magnetic configuration solutions. Moreover, non-ferromagnetic and non-conductive mover is designed for both low inertia and eddy current suppression. For validation, magnetic circuit configuration is firstly analyzed and leads to the analytical model of flux density and thrust. Based on the model, detailed structure optimization is conducted to instruct prototype design. Finally, complete experiments of proposed loading system is presented, including static output and thermal distribution. And it is expected to provide a practical solution to similar linear loading applications.
Four new species of Trigonopterus Fauvel from the island of New Britain (Coleoptera, Curculionidae)

Abstract The hyperdiverse genus Trigonopterus has its center of diversity in Melanesia, but only a single species is recorded from the Bismarck Archipelago to date. Here we describe four new species from the island of New Britain: Trigonopterus chewbacca sp. n. , Trigonopterus obsidianus sp. n. , Trigonopterus puncticollis sp. n. and Trigonopterus silaliensis sp. n. We provide cytochrome oxidase subunit I (cox1) sequences of the new species and a key to all five species known from the Bismarck Archipelago.
Le Cénomanien des Alpes-Maritimes : biozonation par les Globotruncanidés

Les formations marno-calcaires du Cenomanien des Alpes Maritimes ont livre une abondante microfaune de Foraminife-res planctoniques (Globotruncanides). Deux coupes, l'une au sud du Col de Braus, l'autre dans la Vallee de l'Esteron, ont ete etudiees plus particulierement, et malgre une variation de facies permettent de mettre en evidence des zones d'association de Globotruncanides, caracteristiques des diverses parties du Cenomanien. Cette biozonation peut etre correlee avec celle etablie par B. Porthault pour le "domaine vocontien".
A genetic algorithm based optimization method for low vertical link density 3-dimensional Networks-on-Chip many core systems

The advantages of moving from 2-Dimensional Networks-on-Chip (NoCs) to 3-Dimensional NoCs for any application must be justified by the improvements in performance, power, latency and the overall system costs, especially the cost of Through-Silicon-Via (TSV). The trade-off between the number of TSVs and the 3D NoCs system performance becomes one of the most critical design issues. In this paper, we demonstrate a genetic algorithm (GA) based system optimization method, which can deliver the advanced system design setup through topology, routing algorithm, task mapping and tile placement. In comparison to the simulated annealing (SA) based design optimization method, our GA based method can achieve significant advantages. All the experiments have been done in GSNOC framework (written in SystemC-RTL), which can achieve the cycle accuracy and good flexibility.
Big/little deep neural network for ultra low power inference

Deep neural networks (DNNs) have recently proved their effectiveness in complex data analyses such as object/speech recognition. As their applications are being expanded to mobile devices, their energy efficiencies are becoming critical. In this paper, we propose a novel concept called big/LITTLE DNN (BL-DNN) which significantly reduces energy consumption required for DNN execution at a negligible loss of inference accuracy. The BL-DNN consists of a little DNN (consuming low energy) and a full-fledged big DNN. In order to reduce energy consumption, the BL-DNN aims at avoiding the execution of the big DNN whenever possible. The key idea for this goal is to execute the little DNN first for inference (without big DNN execution) and simply use its result as the final inference result as long as the result is estimated to be accurate. On the other hand, if the result from the little DNN is not considered to be accurate, the big DNN is executed to give the final inference result. This approach reduces the total energy consumption by obtaining the inference result only with the little, energy-efficient DNN in most cases, while maintaining the similar level of inference accuracy through selectively utilizing the big DNN execution. We present design-time and runtime methods to control the execution of big DNN under a trade-off between energy consumption and inference accuracy. Experiments with state-of-the-art DNNs for ImageNet and MNIST show that our proposed BL-DNN can offer up to 53.7% (ImageNet) and 94.1% (MNIST) reductions in energy consumption at a loss of 0.90% (ImageNet) and 0.12% (MNIST) in inference accuracy, respectively.
Recognition of Characters by Adaptive Combination of Classifiers

In this paper, the visual feature space based on the long Horizontals, the long Verticals, and the radicals are given. An adaptive combination of classifiers, whose coefficients vary with the input pattern, is also proposed. Experiments show that the approach is promising for character recognition in video sequences. Recognition of Chinese characters is difficult. However, most of the Chinese characters can be decomposed into a couple of fundamental parts, such as left-hand radical, right-hand radical, upper radical, and so on. In other words, a large number of different characters can be composed by a simple combination of a small number of radicals. If each of these radicals is recognized as a single pattern, the number of categories, whose patterns have to be classified, can be greatly reduced. These radicals are taught to be the model as individual patterns during the training phase. And a lot of Chinese characters have Horizontals(called "HENG" in Chinese)and Verticals(called "SHU" in Chinese). Considering the robust of the Horizontal and the Vertical, only the long Horizontal and the long Vertical are extracted as visual feature in this paper. Recently, there is a popular belief that features and classifiers of different types could complement each other to some extent, therefore fine combination of multiple classifiers would result in improvement in performance (1) . The combination method is usually linear combination model and coefficients are usually constant, which restricts its adaptability and performance. In fact it is more appropriate and flexible to premise that the coefficients should vary with the input pattern. Based on this assumption, an adaptive combination model is proposed in this paper.
A Deep Learning-Based Remote Sensing Image Recognition Model for Ships

With the rapid development of remote sensing technology, remote sensing satellite image datasets have become an important resource for research, providing rich data for the acquisition of surface information. However, for vessel detection and identification, it is usually very difficult to distinguish the background of the picture from the vessel in high-resolution optical images, which leads to a large number of false alarm problems. In this study, we aim to solve the problem of vessel image classification in remote sensing images by using a dataset containing 20,000 images. In this paper, bilateral filter is chosen as the filtering method to maximize the clarity of vessel edges. In this paper, rotation and flipping are mainly chosen to enhance the images. In this paper, the Convolutional Neural Network (CNN) model and the Transformer classification model VIT are constructed respectively, and their performance is evaluated. And the performance of the two models is compared by training on different sized datasets. When the number of datasets is $2.0\times 10^{4}$, the recognition accuracy of Transformer classification model VIT is higher than 90.2%, which effectively solves the problem of vessel recognition in remote sensing images.
Umkomasia (megasporophyll): part 1 of a reassessment of Gondwana Triassic plant genera and a reclassification of some previously attributed

Anderson, H.M. , Barbacka, M.K. , Bamford, M. , Holmes, W.B. K. & Anderson, J.M. , 8 February 2019. Umkomasia (megasporophyll): Part 1 of a reassessment of Gondwana Triassic plant genera and a reclassification of some previously attributed. Alcheringa 43, 43–70. ISSN 0311-5518. The genus Umkomasia, a megasporophyll, belonging to the pteridosperms (seed ferns) in the family Umkomasiaceae (Corystospermaceae), is reassessed comprehensively worldwide. All previous records are analysed. Certain fertile structures previously attributed are reclassified. Umkomasia is shown to be restricted to the Triassic of Gondwana where it is associated with the genus Pteruchus, a microsporophyll, and the genus Dicroidium, a vegetative leaf. It is well represented from Argentina, Australia and southern Africa where the Molteno Formation is by far the most comprehensively sampled with eight species described. Two specimens from the upper Permian of India attributed to Umkomasia are reclassified as cf. Arberiopsis sp. A whorled fertile structure from Antarctica, previously assigned to Umkomasia, is reclassified in a new genus as Axsmithia uniramia. Another compression fossil and the permineralized Umkomasia resinosa remain as valid records from Antarctica. The material described as Umkomasia from the Triassic of China is reclassified as Stenorachis asiatica. The Lower Jurassic record from Germany is placed in a new genus as Kirchmuellia franconica. The records of Umkomasia sp. from the Rhaetic of Germany are reclassified as cf. Kirchmuellia sp. and the single specimen from the Jurassic of Libya as genus et sp. indet. The Lower Cretaceous record from Mongolia has been reclassified by other researchers as Doylea mongolica. A pictorial key to Umkomasia species is provided, geographic and stratigraphic distributions are tabulated. Heidi M. Anderson, [hmsholmes@googlemail.com], Evolutionary Studies Institute, University of the Witwatersrand, Johannesburg 20150, South Africa; Maria Barbacka [maria.barbacka@gmail.com] W. Szafer Institute of Botany, Polish Academy of Sciences, Lubicz 46, Kraków 31-512, Poland, Botanical Department, Hungarian Natural History Museumartmem H-1431, Budapest, Pf. 137, Hungary; Marion K. Bamford [marion.bamford@wits.ac. za] Evolutionary Studies Institute, University of the Witwatersrand, Johannesburg 20150, South Africa; W. B. Keith Holmes [wbkholmes@hotmail.com] 46 Kurrajong Street, Dorrigo, NSW 2453, Australia, University of New England, Armidale, NSW 2351, Australia; John M. Anderson [jmanderson.gondwana@googlemail.com] Evolutionary Studies Institute, University of the Witwatersrand, Johannesburg 20150, South Africa.
Challenges in assurance case application for industrial IoT

The purpose of this paper is to develop a framework to use Assurance Case methodology for Industrial IoT systems (IIoT). To achieve a general goal we discuss the three following issues. Firstly, a brief analysis of state-of-the-art researches and resources in area of Assurance Case is done. Secondly, we discuss specific components for IIoT Assurance Case with focus on Security Assurance. Thirdly, a learning course is developed to fulfill gap in security and safety demonstration of IIoT.
GSLAC: GPU Software Level Access Control for Information Isolation on Cloud Platforms

The massive parallel architecture makes Graphics Processing Unit (GPU) a powerful accelerator for various computational intensive tasks, such as computer games, scientific computation, cryptocurrency, and AI model training and inferences. In many cloud platforms, GPUs are scarce computing resources and shared by multiple users. To achieve information isolation among different user programs, GPU access control is an essential technology to prevent the information leaking for program execution and data access when using GPUs. However, the lack of a zeroing mechanism in GPUs, combined with vulnerabilities in user-land drivers, poses risks to both data confidentiality and system integrity. In this paper, we propose a novel system architecture, called GSLAC, to provide GPU System Level Access Control for information isolation on cloud platforms. GSLAC combines resource isolation and mandatory access control measures with the aim of establishing a secure computing environment. It encompasses an authentication mechanism for authorized GPU access, as well as the integration of mandatory access control mechanisms to safeguard sensitive resources. Furthermore, with a careful design, user programs can be compiled and executed as they do in a normal environment without sacrifying the desired performance.
Model-based decision support for integrated management and control of coastal lagoons

This paper addresses some results obtained within the EU funded project DITTY as concerns the development of a decision support system for the management of Southern European lagoons. The first contribution is a general model-based decision support structure, whose development was motivated by the need for a common and flexible framework to ease the integration of the outputs of different project work-packages, as well as to deal with the diversity of socio-economic and environmental characteristics of the project case studies. The proposed structure helps integrate and manage in a clear and structured fashion the information provided by different kinds of mathematical and analytical models (e.g., biogeochemical, hydrodynamic, ecological, socio-economic models of a lagoon ecosystem). Data and information obtained from the models can be used to accomplish the decision task by application of multicriteria analysis approaches. Robustness of the decision with respect to external factors beyond the control of the decision maker, is considered. As a second contribution, a real application of the proposed decision support structure to the management of clam farming in the Sacca di Goro lagoon (Italy), is described.
Efficient Constrained k-Center Clustering with Background Knowledge

Center-based clustering has attracted significant research interest from both theory and practice. In many practical applications, input data often contain background knowledge that can be used to improve clustering results. In this work, we build on widely adopted k-center clustering and model its input background knowledge as must-link (ML) and cannot-link (CL) constraint sets. However, most clustering problems including k-center are inherently NP-hard, while the more complex constrained variants are known to suffer severer approximation and computation barriers that significantly limit their applicability. By employing a suite of techniques including reverse dominating sets, linear programming (LP) integral polyhedron, and LP duality, we arrive at the first efficient approximation algorithm for constrained k-center with the best possible ratio of 2. We also construct competitive baseline algorithms and empirically evaluate our approximation algorithm against them on a variety of real datasets. The results validate our theoretical findings and demonstrate the great advantages of our algorithm in terms of clustering cost, clustering quality, and running time.
Reflectarray Antennas

Printed reflectarrays combine certain advantages of reflector antennas and phased arrays. One major hurdle in the designing of reflectarrays is the compensation of phase difference between various elements. This paper presents reviews of various techniques for phase compensation, like using variable sizes of the patch, adding stubs of variable lengths and use of variable size slots on the ground plane. Further, techniques for circular polarization in space fed arrays are reviewed. They are achieved by rotating the patches at certain angles. Techniques for orthogonal polarization have also been reviewed. Towards the end of the paper recent and future applications for reflectarrays have been reviewed.
Agents Vs. Users: Visual Recommendation of Research Talks with Multiple Dimension of Relevance

Several approaches have been researched to help people deal with abundance of information. An important feature pioneered by social tagging systems and later used in other kinds of social systems is the ability to explore different community relevance prospects by examining items bookmarked by a specific user or items associated by various users with a specific tag. A ranked list of recommended items offered by a specific recommender engine can be considered as another relevance prospect. The problem that we address is that existing personalized social systems do not allow their users to explore and combine multiple relevance prospects. Only one prospect can be explored at any given time—a list of recommended items, a list of items bookmarked by a specific user, or a list of items marked with a specific tag. In this article, we explore the notion of combining multiple relevance prospects as a way to increase effectiveness and trust. We used a visual approach to recommend articles at a conference by explicitly presenting multiple dimensions of relevance. Suggestions offered by different recommendation techniques were embodied as recommender agents to put them on the same ground as users and tags. The results of two user studies performed at academic conferences allowed us to obtain interesting insights to enhance user interfaces of personalized social systems. More specifically, effectiveness and probability of item selection increase when users are able to explore and interrelate prospects of items relevance—that is, items bookmarked by users, recommendations and tags. Nevertheless, a less-technical audience may require guidance to understand the rationale of such intersections.
Inhibition of Phosphorylation of BAD and Raf-1 by Akt Sensitizes Human Ovarian Cancer Cells to Paclitaxel*

We studied the roles of the phosphatidylinositol 3-kinase (PI-3K)-Akt-BAD cascade, ERK-BAD cascade, and Akt-Raf-1 cascade in the paclitaxel-resistant SW626 human ovarian cancer cell line, which lacks functional p53. Treatment of SW626 cells with paclitaxel activates Akt and ERK with different time frames. Interference with the Akt cascade either by treatment with PI-3K inhibitor (wortmannin or LY294002) or by exogenous expression of a dominant negative Akt in SW626 cells caused decreased cell viability following treatment with paclitaxel. Interference with the ERK cascade by treatment with an MEK inhibitor, PD98059, in SW626 cells also caused decreased cell viability following treatment with paclitaxel. Treatment of cells with paclitaxel also stimulated the phosphorylation of BAD at both the Ser-112 and Ser-136 sites. The phosphorylation of BAD at Ser-136 was blocked by treatment with wortmannin or cotransfection with the dominant negative Akt. On the other hand, the phosphorylation of BAD at Ser-112 was blocked by PD98059. We further examined the role of BAD in the viability following paclitaxel treatment using BAD mutants. Exogenous expression of doubly substituted BAD2SA in SW626 cells caused decreased viability following treatment with paclitaxel. Moreover, because paclitaxel-induced apoptosis is mediated by activated Raf-1 and the region surrounding Ser-259 in Raf-1 conforms to a consensus sequence for phosphorylation by Akt, the regulation of Raf-1 by Akt was examined. We demonstrated an association between Akt and Raf-1 and showed that the phosphorylation of Raf-1 on Ser-259 induced by paclitaxel was blocked by treatment with wortmannin or LY294002. Furthermore, interference with the Akt cascade induced by paclitaxel up-regulated Raf-1 activity, and expression of constitutively active Akt inhibited Raf-1 activity, suggesting that Akt negatively regulates Raf-1. Our findings suggest that paclitaxel induces the phosphorylation of BAD Ser-112 via the ERK cascade, and the phosphorylation of both BAD Ser-136 and Raf-1 Ser-259 via the PI-3K-Akt cascade, and that inhibition of either of these cascades sensitizes ovarian cancer cells to paclitaxel.
Indoor Positioning: Novel Approach for Bluetooth Networks using RSSI Smoothing

In recent years, localization and navigation have been important topics in research. The most popular navigation system is an outdoor navigation with “GPS”; however, the positioning within indoor environments is not possible with “GPS”, so it is resulting in limited operation for indoor environments. In order to overcome this limitation, this paper discussed Bluetooth Low Energy technology based localization model. The “BLE” provides several major forms of parameters linked to location estimation such as “RSSI” and “LQI”. In real time applications such as object tracking and distance estimates require continuous reception of RSSI measurements to estimate the position of the object accurately. Nevertheless, on that point, there are some constraints such as signal attenuation, signal loss, multipath effects, temperature, reflection, a human body and other communication signals. Hence, this research work considered the “RSSI” smoothing approaches. Although there are so many solutions, no RSSI smoothing method has been recognized as a standard method. This paper presents a Feedback filter together with shifting technique at distance domain to reduce fluctuations of the real-time signals. Experiments show that the probability of locating errorless and it is better than the other existing interference avoidance algorithms. General Terms Indoor navigation, Localization, i-Beacon