14 research outputs found

    Node Injection for Class-specific Network Poisoning

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    Graph Neural Networks (GNNs) are powerful in learning rich network representations that aid the performance of downstream tasks. However, recent studies showed that GNNs are vulnerable to adversarial attacks involving node injection and network perturbation. Among these, node injection attacks are more practical as they don't require manipulation in the existing network and can be performed more realistically. In this paper, we propose a novel problem statement - a class-specific poison attack on graphs in which the attacker aims to misclassify specific nodes in the target class into a different class using node injection. Additionally, nodes are injected in such a way that they camouflage as benign nodes. We propose NICKI, a novel attacking strategy that utilizes an optimization-based approach to sabotage the performance of GNN-based node classifiers. NICKI works in two phases - it first learns the node representation and then generates the features and edges of the injected nodes. Extensive experiments and ablation studies on four benchmark networks show that NICKI is consistently better than four baseline attacking strategies for misclassifying nodes in the target class. We also show that the injected nodes are properly camouflaged as benign, thus making the poisoned graph indistinguishable from its clean version w.r.t various topological properties.Comment: 28 pages, 5 figure

    Detecting pneumonia using convolutions and dynamic capsule routing for chest X-ray images

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    An entity\u27s existence in an image can be depicted by the activity instantiation vector from a group of neurons (called capsule). Recently, multi-layered capsules, called CapsNet, have proven to be state-of-the-art for image classification tasks. This research utilizes the prowess of this algorithm to detect pneumonia from chest X-ray (CXR) images. Here, an entity in the CXR image can help determine if the patient (whose CXR is used) is suffering from pneumonia or not. A simple model of capsules (also known as Simple CapsNet) has provided results comparable to best Deep Learning models that had been used earlier. Subsequently, a combination of convolutions and capsules is used to obtain two models that outperform all models previously proposed. These models-Integration of convolutions with capsules (ICC) and Ensemble of convolutions with capsules (ECC)-detect pneumonia with a test accuracy of 95.33% and 95.90%, respectively. The latter model is studied in detail to obtain a variant called EnCC, where n = 3, 4, 8, 16. Here, the E4CC model works optimally and gives test accuracy of 96.36%. All these models had been trained, validated, and tested on 5857 images from Mendeley

    Chalcogen Assisted Enhanced Atomic Orbital Interaction at TMDs - Metal Interface & Chalcogen Passivation of TMD Channel For Overall Performance Boost of 2D TMD FETs

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    Metal-semiconductor interface is a bottleneck for efficient transport of charge carriers through Transition Metal Dichalcogenide (TMD) based field-effect transistors (FETs). Injection of charge carriers across such interfaces is mostly limited by Schottky barrier at the contacts which must be reduced to achieve highly efficient contacts for carrier injection into the channel. Here we introduce a universal approach involving dry chemistry to enhance atomic orbital interaction between various TMDs (MoS2, WS2, MoSe2 and WSe2) & metal contacts has been experimentally demonstrated. Quantum chemistry between TMDs, Chalcogens and metals has been explored using detailed atomistic (DFT & NEGF) simulations, which is then verified using Raman, PL and XPS investigations. Atomistic investigations revealed lower contact resistance due to enhanced orbital interaction and unique physics of charge sharing between constituent atoms in TMDs with introduced Chalcogen atoms which is subsequently validated through experiments. Besides contact engineering, which lowered contact resistance by 72, 86, 1.8, 13 times in MoS2, WS2, MoSe2 and WSe2 respectively, a novel approach to cure / passivate dangling bonds present at the 2D TMD channel surface has been demonstrated. While the contact engineering improved the ON-state performance (ION, gm, mobility and RON) of 2D TMD FETs by orders of magnitude, Chalcogen based channel passivation was found to improve gate control (IOFF, SS, & VTH) significantly. This resulted in an overall performance boost. The engineered TMD FETs were shown to have performance on par with best reported till date

    Quantitative study of candlestick pattern & identifying candlestick patterns using deep learning for the Indian stock market

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    The stock market is an integral aspect of any country’s economic infrastructure. Analyzing and attempting to play the markets to maximize profits is an endeavor a large fraction of the population aspires to. Candlestick patterns are the backbone of Technical Analysis, used for trading in the stock market. There are a number of candlestick patterns in the market, each with its own benefits and downsides. Due to this, the task that befalls the hands of analysts is deciding which patterns provide the most effective gauge of the current market situation. Due to the large level of noise and widely recognized semi-strong form of market efficiency, analyzing and forecasting the stock market is infamously difficult. For traders that use Technical Analysis to trade, it's critical to be able to recognize candlestick patterns quickly. We will be attempting to determine their respective effectiveness with respect to the Indian Stock Market via exploratory analysis conducted on real-world market data. Also, we'll use candlestick charts to train neural networks and subsequently find patterns. Deep Learning will be used to recognize candlestick patterns in large-cap Indian equities

    Selective Electron or Hole Conduction in Tungsten Diselenide (WSe2) Field-Effect Transistors by Sulfur-Assisted Metal-Induced Gap State Engineering

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    For semiconductor industry to replace silicon CMOS integrated circuits by 2-D semiconductors or transition metal dichalcogenides (TMDs), TMD-based n-FETs as well as p-FETs having performance better than Si FETs are a must. While a lot of literature demonstrates n-channel characteristics, the major roadblocks in the realization of TMD-based CMOS integrated circuit are the lack of approach to realize p-channel transistors having performance comparable to n-channel transistors, all realized over the same TMD substrate. To address this, we propose a new technique by engineering WSe2/metal interface to realize WSe2-based high-performance p-and n-channel transistors and therefore unveil its potential toward CMOS-integrated technology. The technique involves a dry process, based on the chemistry between the sulfur atom and WSe2 surface, that induces unique metal-induced gap states in the source/drain (S/D) contact area, which causes improved hole (electron) injection when Cr (Ni) as S/D metal was used. This has enabled the controlled realization of high-performance WSe2 FETs with desired polarity (N, P, or ambipolar), which solely depends on the contact metal used and contact engineering (CE)/surface engineering. Fundamental investigations on the effect of the proposed CE on metal-WSe2 interface revealed interesting and counter-intuitive facts, which very well corroborate with experimental observations

    A Physical Synthesis Flow for Early Technology Evaluation of Silicon Nanowire based Reconfigurable FETs

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    Silicon Nanowire (SiNW) based reconfigurable fieldeffect transistors (RFETs) provide an additional gate terminal called the program gate which gives the freedom of programming p-type or n-type functionality for the same device at runtime. This enables the circuit designers to pack more functionality per computational unit. This saves processing costs as only one device type is required, and no doping and associated lithography steps are needed for this technology. In this paper, we present a complete design flow including both logic and physical synthesis for circuits based on SiNW RFETs. We propose layouts of logic gates, Liberty and LEF (Library Exchange Format) files to enable further research in the domain of these novel, functionally enhanced transistors. We show that in the first of its kind comparison, for these fully symmetrical reconfigurable transistors, the area after placement and routing for SiNW based circuits is 17% more than that of CMOS for MCNC benchmarks. Further, we discuss areas of improvement for obtaining better area results from the SiNW based RFETs from a fabrication and technology point of view. The future use of self-aligned techniques to structure two independent gates within a smaller pitch holds the promise of substantial area reduction

    Role of innate immunological/inflammatory pathways in myelodysplastic syndromes and AML: a narrative review

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    Abstract Dysregulation of the innate immune system and inflammatory-related pathways has been implicated in hematopoietic defects in the bone marrow microenvironment and associated with aging, clonal hematopoiesis, myelodysplastic syndromes (MDS), and acute myeloid leukemia (AML). As the innate immune system and its pathway regulators have been implicated in the pathogenesis of MDS/AML, novel approaches targeting these pathways have shown promising results. Variability in expression of Toll like receptors (TLRs), abnormal levels of MyD88 and subsequent activation of NF-κβ, dysregulated IL1-receptor associated kinases (IRAK), alterations in TGF-β and SMAD signaling, high levels of S100A8/A9 have all been implicated in pathogenesis of MDS/AML. In this review we not only discuss the interplay of various innate immune pathways in MDS pathogenesis but also focus on potential therapeutic targets from recent clinical trials including the use of monoclonal antibodies and small molecule inhibitors against these pathways
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