Miracle-dz/UniFAR-FTUs huggingface dataset:
Design Automation Flow using Library Adaptation for Variation Aware Logic Synthesis

As semiconductor technology reaches to nanometer scale, the impact of process uncertainties are increasing, leading to performance and power loss, and consequently reducing the yield. These process parameter variations necessitate the use of suitable variation-aware design techniques. There are some architecture level, circuit level, and post silicon techniques with certain overheads to reduce the effect of such variations. Along with good design techniques, variation-aware analysis plays a major role in determining the efficacy of variation tolerant design. Conventional way of min-max static timing analysis is no more a reliable option; we need to use Statistical Static Timing Analysis (SSTA). Although various techniques for variation tolerant design have been proposed, no major emphasis was given to the initial design phases of the ASIC design flow. In this work, we focused on logic synthesis stage to nullify the effects of process variations. For that, we proposed a novel technique called Library Adaptation for Variation Aware (LAVA) technique and automated the flow for the creation of process variation tolerant design. In LAVA technique a new approach is used to create variation aware libraries by re-characterization of existing libraries and new variation tolerant standard cells are created on demand. This work proposes a design methodology from RTL to GDSII that incorporates LAVA technique at logic synthesis stage for creating variation tolerant design with negligible overhead. The primary goal of our methodology is to capture the statistical aspects of variation from transistor-level of abstraction into gate-level i.e., standard cell library. This newly created variation-aware standard cell library is provided to the existing logic synthesis tool to select the better design at higher level of design cycle, thus making the design more robust to process variations. We have used accurate SSTA using PrimeTime VX by providing variation aware libraries and distribution of parameter values. To make the technique more efficient the design is taken to place and route stage using IC Compiler and verified again for the performance in presence of variations. Results using both combinational and sequential benchmarks clearly show the improvement of critical and near critical paths in presence of process variations with minimal power and area penalty. The use of multi-Vth libraries considerably reduced the power and area overhead introduced by this technique.
Actinodaphnine and Rutacridone as New T-Cell Protein Tyrosine Phosphatase Inhibitors for Drug Development of Obesity

T-Cell Protein Tyrosine Phosphatase (TCPTP) is an obesogenic enzyme that inactivates a Signal Transducer and Activator Transcription 3 (STAT3) protein, leading to inhibition of leptin and insulin signalling. This protein can be a target for development of anti-obesity drugs. This study aimed to identify Indonesian phytochemicals as in silico TCPTP inhibitor. This bioinformatics study used a molecular docking method with AutoDock Vina software version 1.1. 2. Three-dimensional structure of TCPTP protein and its Inhibitor (XIX, standard ligand) was obtained from Protein Data Bank (PDB) database with code 1L8K and PubChem database with code 9926586. Indonesian phytochemicals in this study were registered in the HerbalDB database and met criteria of Lipinski’s rule. The three-dimensional structure of phytochemicals was obtained from PubChem National Center for Biotechnology Information (NCBI). Binding affinity and molecular conformation of selected Indonesian phytochemicals were assessed and compared to the standard ligand. PyMol version 1.3 software was used to visualize molecular docking results. Inhibitor XIX interacted with TCPTP protein at Gln125 and Thr129 residues with -6.30 kcal/mol binding affinity. Actinodaphnine and Rutacridone had lower binding affinity (-6.40 kcal / mol) than the standard ligand. Actinodaphnine interacted with the TCPTP protein at Thr129 and Asp130 residues while Rutacridone had interaction at Thr129 only. However, both phytochemicals had different conformation from PTP inhibitor XIX. Actinodaphnine is more potential to become a TCPTP inhibitor in silico for treatment of obesity than Rutacridone. Further investigation is required to evaluate the inhibitory effect of both phytochemicals towards the TCPTP protein.
Ship recognition on the sea surface using aerial images taken by Uav : a deep learning approach

Oceans are very important for mankind, because they are a very important source of food, they have a very large impact on the global environmental equilibrium, and it is over the oceans that most of the world commerce is done. Thus, maritime surveillance and monitoring, in particular identifying the ships used, is of great importance to oversee activities like fishing, marine transportation, navigation in general, illegal border encroachment, and search and rescue operations. In this thesis, we used images obtained with Unmanned Aerial Vehicles (UAVs) over the Atlantic Ocean to identify what type of ship (if any) is present in a given location. Images generated from UAV cameras suffer from camera motion, scale variability, variability in the sea surface and sun glares. Extracting information from these images is challenging and is mostly done by human operators, but advances in computer vision technology and development of deep learning techniques in recent years have made it possible to do so automatically. We used four of the state-of-art pretrained deep learning network models, namely VGG16, Xception, ResNet and InceptionResNet trained on ImageNet dataset, modified their original structure using transfer learning based fine tuning techniques and then trained them on our dataset to create new models. We managed to achieve very high accuracy (99.6 to 99.9% correct classifications) when classifying the ships that appear on the images of our dataset. With such a high success rate (albeit at the cost of high computing power), we can proceed to implement these algorithms on maritime patrol UAVs, and thus improve Maritime Situational Awareness.
A narrative systematic review of medical registries.

Introduction: Medical registries are valuable stores of data for research and public health monitoring purposes. This review aims to identify the benefits of medical registries to healthcare, determine the components of effective registries, assess concerns with patient privacy, and develop guidelines for designing quality medical registries. Method: Pubmed was screened for articles related to medical registries, data management, and patient privacy. Thirty-seven articles adhered to the selection criteria and were included in this review. Results: Benefits of medical registries: Registries facilitate health outcomes research and can improve patient prognosis by identifying effective care protocols. Data quality and error reduction: Data quality control rests upon clear and standardized processes for data collection and input. Error reduction can be achieved by reducing manual text input and automating data transfer between sources. Automatic feedback features in registries are the most feasible methods of identifying data error. Privacy: Patient privacy must be preserved by anonymizing or pseudonymizing registry data. The preservation of public trust is important to maintain participation in opt-out registries. Discussion and Conclusion: Registries can be designed to promote high quality data collection and storage by reducing systematic and random error. This review presents a framework for designing effective medical registries that fulfil research and health monitoring purposes while respecting patient privacy.
Multiframe interpolation for video using phase features

Abstract. Traditional frame interpolation algorithms typically find dense correspondences to synthesize an in-between frame. Finding correspondences is often sensitive to occlusion, disocclusion, and changes in color or luminance. We present a phase-feature-aided multiframe interpolation network that aims to estimate multiple in-between frames in one pass and handle challenging scenarios such as extreme light changes and occlusion. We first model the relation between multiple in-between frames together to enhance the temporal consistency. Two candidate optical flow fields are produced for a given in-between frame, one predicted from our network and the other estimated from those of neighboring frames using a flow fusion map. We also employ an image fusion map to combat occlusion problems in the warping processes, producing two candidate interpolated images that are fed to a shallow network with a residual structure to obtain the final interpolated image. To handle challenging scenarios, we apply a set of Gabor filters to extract phase variations in the feature domain with a multiscale phase subnetwork. Our entire neural network is end-to-end trainable. Our experiments show that this method outperforms the state-of-the-art approaches and achieves marked visual improvement in challenging scenarios.
Implementation and Application of the Shortest Path in GIS

The problem on the shortest path is a basic problem of traffic networks analysis system. It has been discussed widely and deeply. This paper discusses how to look up the shortest path with GIS from the application aspect, and enumerates an application example of the shortest path in GIS technique . It provides a reference for graphics management of information.
Short-term exposure to quinalphos induced biochemical and haematological changes in freshwater fish, Oreochromis mossambicus

The toxic impact of quinalphos, an organothiophosphate, on the biochemical as well as haematological parameters was studied in the adult freshwater fish, Oreochromis mossambicus. In the present study, 0. 5µl/ L quinalphos was chosen to represent sublethal concentration for 48 and 96 hours as short-term exposure and respective control animals were maintained. Quinalphos induced toxic stress to the exposed fishes, which is obvious by the reduction in the oxygen consumption of the fishes at the time of exposure and this could be due to shrinkage of the respiratory epithelium or possibly due to mucus accumulation on gills. Decrease in the haemoglobin content was observed and this may be due to either an increase in the rate at which the haemoglobin is destroyed or decreased rate of haemopoietic potential of the fish. In the present study, the significant increase in WBC count indicates hypersensitivity of leucocytes to quinalphos and these changes may be due to immunological reactions to produce antibodies to cope up with the stress. Decrease in the level of RBC count indicated decrease in erythropoietic activity or severe anemic state. Reduction in the plasma and tissue protein of quinalphos exposed fishes may be due to its utilization to mitigate the energy demand when the fishes are under stress. Increase of plasma glucose and the total glucose content in tissues like liver, muscle and gill might have resulted from gluconeogenesis to provide energy for the increased metabolic demands imposed by quinalphos stress. Thus the biochemical and hematological alterations due to acute short-term exposure to quinalphos were due to the toxic stress of the toxicant.
Development of an intuitive pedestrian interaction system for automated vehicles

This thesis has examined the lack of communication between pedestrians and automated vehicles (AVs). As a result, a light-based interface with animated signals was developed as a proposed solution to this problem. The goal was to make the interface as intuitive as possible. The communication approach that was chosen for this thesis was communicating the AV intentions and actions to the pedestrians rather than giving them explicit directions. If there is to be an AV-pedestrian communication interface implemented, it would be very beneficial for users if it was standardized across all vehicle brands and models. If every brand develops an individual way of communication it might create confusing, disorienting and ultimately ineffective experience for the pedestrians. Previous research has presented contradictory findings in regard to the need of an AV-pedestrian communication interface. Therefore, it is important to further investigate the role of such an interface. Previous solutions to the problem have been analysed and negative and positive aspects of each have been identified. There are several approaches to AV-pedestrian communication with various levels of testing and effectiveness. The user system consisting of the categories user, objective, product and context was subsequently analysed before going into the ideation phase. The ideation generated five concepts that were evaluated with a focus group consisting of industry and research experts. Through evaluation analysis, the concepts were refined and narrowed down to three concepts. This time it was evaluated through interviews and surveys of 18 test participants similar to end users. Analysis from the result of this evaluation combined with a last iteration of the concepts generated one final concept. Future work needs to examine the use of sound signals and test the impact of varying parameters like placement, frequency, colour or timing.
Determination of Zn, Cu, Fe and Mn in Muscle Cells as Potential Markers of Oxidative Stress by Laser Ablation and Solution Based ICP-MS

Oxidative stress is imbalance between oxidant and antioxidant levels in living systems. Human cells are protected from reactive oxygen species (ROS) by endogenous enzymatic antioxidants, such as superoxide dismutase (SOD) and catalase. Most of these compounds require particular redox metals in their structures as cofactors to allow them to scavenge the free radicals, in particular Cu, Zn or Mn-with SOD and Fe with catalase. The aim of this study was to quantify these metals in human cells to evaluate their effectiveness as novel biomarkers for measuring oxidative stress. The metals (Zn, Cu, Fe, Mn) were measured in vitro in skeletal muscle cells (C2C12) which were incubated under hypoxia or hyperoxia conditions generated by varying oxygen levels from 1% - 60% for 24 and 48 hours. Two methods were used to perform the analyses. Solution-based inductively coupled plasma mass spectrometry (ICP-MS) was applied to quantify Zn, Cu, Fe and Mn in cell populations, and laser ablation (LA)-ICP-MS was employed to compare their relative levels in individual cells. The data acquired from both techniques were positively correlated confirming the validity of the two approaches. The results showed that the concentration of the measured elements increased dramatically in cells grown at 25% - 60% O2, the most significant increase being in Cu at 60% O2. None showed any increase at 5% - 15% O2, indicating normoxia states. At 1% O2, all elements, except Fe, showed a significant increase and the most remarkable growth was in Mn. Increasing the incubation time to 48 hours had differing effects on the elements. Zn and Cu concentrations were unaffected by increasing incubation time except at 60% O2 where they showed further growth. In contrast, Mn concentration grew sharply for oxygen levels of 30% - 50% with no further effect at 1%, while Fe concentration decreased at 1% O2 and grew steadily for oxygen levels of 5% - 60%. It can be concluded that all four elements were significantly affected by stress conditions applied to cells, but at different rates. Importantly, this paper describes a novel method for estimating oxidative stress in cells based on the determination of redox elements in single cells and cell populations using ICP-MS.
A Review on Tungsten Oxide (WO3) and their Derivatives for Sensor Applications

International Journal of Advanced Science and Engineering www.mahendrapublications. com A Review on Tungsten Oxide (WO3) and their Derivatives for Sensor Applications V. Hariharan1*, B. Gnanavel2, R. Sathiyapriya3,4, V. Aroulmoji3 1PG & Research Department of Physics, Mahendra Arts and Science College, Kalipatti, Namakkal District – 637 501, Tamilnadu, India 2PG & Research Department of Physics, Chikkaiah Naicker College, Erode – 638 004, Tamilnadu, India 3Center for Research and Development, Mahendra Engineering College, Mallasamudram, Namakkal District 637 503, Tamilnadu, India 4Department of Physics, Mahendra Engineering College, Mallasamudram, Namakkal District 637 503, Tamilnadu, India
Static Watson-Crick Context-Free Grammars

Sticker systems and Watson-Crick automata are two modellings of DNA molecules in DNA computing. A sticker system is a computational model which is coded with single and double-stranded DNA molecules; while Watson-Crick automata is the automata counterpart of sticker system which represents the biological properties of DNA. Both of these models use the feature of Watson-Crick complementarity in DNA computing. Previously, the grammar counterpart of the Watson-Crick automata have been introduced, known as Watson-Crick grammars which are classified into three classes: Watson-Crick regular grammars, Watson-Crick linear grammars and Watson-Crick context-free grammars. In this research, a new variant of Watson-Crick grammar called a static Watson-Crick context-free grammar, which is a grammar counterpart of sticker systems that generates the double-stranded strings and uses rule as in context-free grammar, is introduced. The static Watson-Crick context-free grammar differs from a dynamic Watson-Crick context-free grammar in generating double-stranded strings, as well as for regular and linear grammars. The main result of the paper is to determine the generative powers of static Watson-Crick context-free grammars. Besides, the relationship of the families of languages generated by Chomsky grammars, sticker systems and Watson-Crick grammars are presented in terms of their hierarchy.
Tensor-structured Dictionaries for Hyperspectral Imaging

—Dictionary learning, paired with sparse coding, aims at providing sparse data representations. When dealing with large datasets, the dictionary obtained by applying unstructured dictionary learning methods may be of considerable size, posing both memory and computa-tional complexity issues. We show how a previously proposed structured dictionary learning model, HO-SuKro, can be used to obtain more compact and readily-applicable dictionaries when the targeted data is a collection of multiway arrays. We introduce an alternating optimization learning algorithm and apply it to a hyperspectral image denoising task.
Numerical Analysis of Layered Materials Models

Most real-world materials are composed of multiple layers , whose physical properties impact the appearance of objects. The accurate reproduction of layered material properties is thus an important part of physically-based rendering applications. Since no exact analytical model exists for arbitrary configurations of layer stacks, available models make a number of approximations. In this technical report, we propose to evaluate these approximations with a numerical approach: we simulate BRDFs and BTDFs for layered materials in order to compare existing models against a common reference. More specifically, we consider 60 layered material configurations organized in three categories: plastics, metals and transparent slabs. Our results show that: (1) no single model systematically outperforms the others on all categories; and (2) significant discrepancies remain between simulated and modeled materials. We analyse the reasons for these discrepancies and introduce immediate corrections that improve models accuracy with little effort. Finally, we provide a few challenging cases for future layered material models.
A Review of Open Information Extraction Techniques

Nowadays, massive amount of data flows all the time. Approximately between 20 or 30 percent of these data is text. This data is always organized in semi-structured text, which cannot be used directly. To make use of such huge amounts of textual data, there is a need to detect, extract, and structure the information conveyed through this data in a fast and scalable manner. This can be performed using Information Extraction Techniques. However, the task of information extraction is one of the main challenges in Natural Language Processing and there are limitations for its implementation on a large scale of data. Open Information Extraction (OIE) is an open-domain and relation-independent paradigm to perform information extraction in an unsupervised manner. This technique can lead to high-speed and scalable performance. The review of previous research proposals reveals that there are OIE experiments among different languages, such as English, Portuguese, Spanish, Vietnamese, Chinese, and Germany. This paper reviews the OIE techniques, compare their performance in some languages, and then integrates these results with the languages complexity levels to reveal the relationship between the suitable model and the language complexity level. Nowadays, massive amount of data flows all the time. Approximately between 20 or 30 percent of these data is text. This data is always organized in semi-structured text, which cannot be used directly. To make use of such huge amounts of textual data, there is a need to detect, extract, and structure the information conveyed through this data in a fast and scalable manner. This can be performed using Information Extraction Techniques. However, the task of information extraction is one of the main challenges in Natural Language Processing and there are limitations for its implementation on a large scale of data. Open Information Extraction (OIE) is an open-domain and relation-independent paradigm to perform information extraction in an unsupervised manner. This technique can lead to high-speed and scalable performance. The review of previous research proposals reveals that there are OIE experiments among different languages, such as English, Portuguese, Spanish, Vietnamese, Chinese, and Germany. This paper reviews the OIE techniques, compare their performance in some languages, and then integrates these results with the languages complexity levels to reveal the relationship between the suitable model and the language complexity level.Keywords—Open Information Extraction; Natural Language Processing
Structural summaries for visual provenance analysis

Many systems today exist to collect provenance that describes how some data was derived. Such provenance represents useful information in many use cases, e.g., reproducibility or derivation process quality. Depending on the use case, collected provenance traces need to be explored and analyzed. Therefore, various approaches, including visual analysis approaches have been proposed. However, these typically focus on analyzing individual provenance traces. We propose to create structure-based summaries of provenance by aggregating many provenance traces provided in W3C-PROV representation. We further describe the analysis tasks that apply on these summaries. We showcase the usefulness of structural summaries based on several use cases, when using appropriate visualization and interaction techniques.