Design and implementation of a 10 Gigabit Ethernet XAUI test systems

10 Gigabit Ethernet has been standardized (IEEE 802.3ae), and products based on this standard are being deployed to interconnect MANs, WANs, Storage Area Networks, and very high speed LANs. The XAUI portion of the standard is primarily concerned with short range (up to 50 cm) chip-to-chip communication across printed circuit board traces. The UNH-IOL 10 Gigabit Ethernet Consortium, an industry-supported organization, performs PHY layer testing on products using a test system that has been partially implemented on a Xilinx ML321 evaluation board using the Virtex II-Pro FPGA. A new implementation of the 10 Gigabit Ethernet XAUI test system on the existing ML321 evaluation board is presented in this thesis. The new design removes a number of limitations present in the original Xilinx test system, and it adds new features to the existing transmit and receive sub-systems that enable test engineers to expand the range of test cases and analyze them while simultaneously increasing the speed of testing. The new test system also eliminates the need for expensive test instruments

Extracellular Vesicles from UVB Irradiated Keratinocytes Contain Cyclobutane Pyrimidine Dimers

Ultraviolet (UV) radiation induces the formation of cyclobutane pyrimidine dimers (CPDs) in genomic DNA, which are normally removed by nucleotide excision repair. However, the fate of these adducts remain largely unexplored. Detection of these photoproducts in body fluids could act as a predictor of UV exposure and enable a better understanding of the pathogenesis of photosensitive skin diseases, such as lupus. Using cultured human keratinocytes exposed to UVB radiation in vitro, ultracentrifugation of cell culture supernatants, and immunodot blot analysis of isolated DNA, we have found that a small fraction of CPDs is released from cells in a dose- and time-dependent manner in association with small extracellular vesicles (SEVs). Furthermore, pharmacological manipulation of cell signaling pathways revealed that caspase-dependent apoptotic signaling was critical to the release of SEVs containing CPDs. These results show for the first time that CPDs are released from UVB-irradiated cells and co-purify with SEVs

Disguised Face Identification (DFI) with Facial KeyPoints using Spatial Fusion Convolutional Network

Disguised face identification (DFI) is an extremely challenging problem due to the numerous variations that can be introduced using different disguises. This paper introduces a deep learning framework to first detect 14 facial key-points which are then utilized to perform disguised face identification. Since the training of deep learning architectures relies on large annotated datasets, two annotated facial key-points datasets are introduced. The effectiveness of the facial keypoint detection framework is presented for each keypoint. The superiority of the key-point detection framework is also demonstrated by a comparison with other deep networks. The effectiveness of classification performance is also demonstrated by comparison with the state-of-the-art face disguise classification methods.Comment: To Appear in the IEEE International Conference on Computer Vision Workshops (ICCVW) 201

The Number of Edges in Maximal 2-Planar Graphs

A graph is 2-planar if it has local crossing number two, that is, it can be drawn in the plane such that every edge has at most two crossings. A graph is maximal 2-planar if no edge can be added such that the resulting graph remains 2-planar. A 2-planar graph on n vertices has at most 5n-10 edges, and some (maximal) 2-planar graphs - referred to as optimal 2-planar - achieve this bound. However, in strong contrast to maximal planar graphs, a maximal 2-planar graph may have fewer than the maximum possible number of edges. In this paper, we determine the minimum edge density of maximal 2-planar graphs by proving that every maximal 2-planar graph on n ? 5 vertices has at least 2n edges. We also show that this bound is tight, up to an additive constant. The lower bound is based on an analysis of the degree distribution in specific classes of drawings of the graph. The upper bound construction is verified by carefully exploring the space of admissible drawings using computer support

Long Short-Term Memory with Spin-Based Binary and Non-Binary Neurons

Research in the field of neural networks has shown advancement in the device technology and machine learning application platforms of use. Some of the major applications of neural network prominent in recent scenarios include image recognition, machine translation, text classification and object categorization. With these advancements, there is a need for more energy-efficient and low area overhead circuits in the hardware implementations. Previous works have concentrated primarily on CMOS technology-based implementations which can face challenges of high energy consumption, memory wall, and volatility complications for standby modes. We herein developed a low-power and area-efficient hardware implementation for Long Short-Term Memory (LSTM) networks as a type of Recurrent Neural Network (RNN). To achieve energy efficiency while maintaining comparable accuracy commensurate with the ideal case, the LSTM network herein uses Resistive Random-Access Memory (ReRAM) based synapses along with spin-based non-binary neurons. The proposed neuron has a novel activation mechanism that mimics the ideal hyperbolic tangent (tanh) and sigmoid activation functions with five levels of output accuracy. Using ideal, binary, and the proposed non-binary neurons, we investigated the performance of an LSTM network for name prediction dataset. The comparison of the results shows that our proposed neuron can achieve up to 85% accuracy and perplexity of 1.56, which attains performance similar to algorithmic expectations of near-ideal neurons. The simulations show that our proposed neuron achieves up to 34-fold improvement in energy efficiency and 2-fold area reduction compared to the CMOS-based non-binary designs

Local Complexity of Polygons

Many problems in Discrete and Computational Geometry deal with simple polygons or polygonal regions. Many algorithms and data-structures perform considerably faster, if the underlying polygonal region has low local complexity. One obstacle to make this intuition rigorous, is the lack of a formal definition of local complexity. Here, we give two possible definitions and show how they are related in a combinatorial sense. We say that a polygon $P$ has point visibility width $w=pvw$, if there is no point $q\in P$ that sees more than $w$ reflex vertices. We say that a polygon $P$ has chord visibility width $w=cvw$, if there is no chord $c=\textrm{seg}(a,b)\subset P$ that sees more than w reflex vertices. We show that $$cvw \leq pvw ^{O( pvw )},$$ for any simple polygon. Furthermore, we show that there exists a simple polygon with $$cvw \geq 2^{\Omega( pvw )}.$$Comment: 7 pages, 5 figure

A comparison of various machine learning algorithms and execution of flask deployment on essay grading

Students’ performance can be assessed based on grading the answers written by the students during their examination. Currently, students are assessed manually by the teachers. This is a cumbersome task due to an increase in the student-teacher ratio. Moreover, due to coronavirus disease (COVID-19) pandemic, most of the educational institutions have adopted online teaching and assessment. To measure the learning ability of a student, we need to assess them. The current grading system works well for multiple choice questions, but there is no grading system for evaluating the essays. In this paper, we studied different machine learning and natural language processing techniques for automated essay scoring/grading (AES/G). Data imbalance is an issue which creates the problem in predicting the essay score due to uneven distribution of essay scores in the training data. We handled this issue using random over sampling technique which generates even distribution of essay scores. Also, we built a web application using flask and deployed the machine learning models. Subsequently, all the models have been evaluated using accuracy, precision, recall, and F1-score. It is found that random forest algorithm outperformed the other algorithms with an accuracy of 97.67%, precision of 97.62%, recall of 97.67%, and F1-score of 97.58%

Simple Topological Drawings of kk-Planar Graphs

Every finite graph admits a \emph{simple (topological) drawing}, that is, a drawing where every pair of edges intersects in at most one point. However, in combination with other restrictions simple drawings do not universally exist. For instance, \emph{$k$-planar graphs} are those graphs that can be drawn so that every edge has at most $k$ crossings (i.e., they admit a \emph{$k$-plane drawing}). It is known that for $k\le 3$, every $k$-planar graph admits a $k$-plane simple drawing. But for $k\ge 4$, there exist $k$-planar graphs that do not admit a $k$-plane simple drawing. Answering a question by Schaefer, we show that there exists a function $f : \mathbb{N}\rightarrow\mathbb{N}$ such that every $k$-planar graph admits an $f(k)$-plane simple drawing, for all $k\in\mathbb{N}$. Note that the function $f$ depends on $k$ only and is independent of the size of the graph. Furthermore, we develop an algorithm to show that every $4$-planar graph admits an $8$-plane simple drawing.Comment: Appears in the Proceedings of the 28th International Symposium on Graph Drawing and Network Visualization (GD 2020

Scenario Diffusion: Controllable Driving Scenario Generation With Diffusion

Automated creation of synthetic traffic scenarios is a key part of validating the safety of autonomous vehicles (AVs). In this paper, we propose Scenario Diffusion, a novel diffusion-based architecture for generating traffic scenarios that enables controllable scenario generation. We combine latent diffusion, object detection and trajectory regression to generate distributions of synthetic agent poses, orientations and trajectories simultaneously. To provide additional control over the generated scenario, this distribution is conditioned on a map and sets of tokens describing the desired scenario. We show that our approach has sufficient expressive capacity to model diverse traffic patterns and generalizes to different geographical regions.Comment: NeurIPS 202