Diffusion Models with Deterministic Normalizing Flow Priors

For faster sampling and higher sample quality, we propose DiNof ($\textbf{Di}$ffusion with $\textbf{No}$rmalizing $\textbf{f}$low priors), a technique that makes use of normalizing flows and diffusion models. We use normalizing flows to parameterize the noisy data at any arbitrary step of the diffusion process and utilize it as the prior in the reverse diffusion process. More specifically, the forward noising process turns a data distribution into partially noisy data, which are subsequently transformed into a Gaussian distribution by a nonlinear process. The backward denoising procedure begins with a prior created by sampling from the Gaussian distribution and applying the invertible normalizing flow transformations deterministically. To generate the data distribution, the prior then undergoes the remaining diffusion stochastic denoising procedure. Through the reduction of the number of total diffusion steps, we are able to speed up both the forward and backward processes. More importantly, we improve the expressive power of diffusion models by employing both deterministic and stochastic mappings. Experiments on standard image generation datasets demonstrate the advantage of the proposed method over existing approaches. On the unconditional CIFAR10 dataset, for example, we achieve an FID of 2.01 and an Inception score of 9.96. Our method also demonstrates competitive performance on CelebA-HQ-256 dataset as it obtains an FID score of 7.11. Code is available at https://github.com/MohsenZand/DiNof.Comment: 12 pages, 7 figure

Flow-based Autoregressive Structured Prediction of Human Motion

A new method is proposed for human motion predition by learning temporal and spatial dependencies in an end-to-end deep neural network. The joint connectivity is explicitly modeled using a novel autoregressive structured prediction representation based on flow-based generative models. We learn a latent space of complex body poses in consecutive frames which is conditioned on the high-dimensional structure input sequence. To construct each latent variable, the general and local smoothness of the joint positions are considered in a generative process using conditional normalizing flows. As a result, all frame-level and joint-level continuities in the sequence are preserved in the model. This enables us to parameterize the inter-frame and intra-frame relationships and joint connectivity for robust long-term predictions as well as short-term prediction. Our experiments on two challenging benchmark datasets of Human3.6M and AMASS demonstrate that our proposed method is able to effectively model the sequence information for motion prediction and outperform other techniques in 42 of the 48 total experiment scenarios to set a new state-of-the-art

ObjectBox: From Centers to Boxes for Anchor-Free Object Detection

We present ObjectBox, a novel single-stage anchor-free and highly generalizable object detection approach. As opposed to both existing anchor-based and anchor-free detectors, which are more biased toward specific object scales in their label assignments, we use only object center locations as positive samples and treat all objects equally in different feature levels regardless of the objects' sizes or shapes. Specifically, our label assignment strategy considers the object center locations as shape- and size-agnostic anchors in an anchor-free fashion, and allows learning to occur at all scales for every object. To support this, we define new regression targets as the distances from two corners of the center cell location to the four sides of the bounding box. Moreover, to handle scale-variant objects, we propose a tailored IoU loss to deal with boxes with different sizes. As a result, our proposed object detector does not need any dataset-dependent hyperparameters to be tuned across datasets. We evaluate our method on MS-COCO 2017 and PASCAL VOC 2012 datasets, and compare our results to state-of-the-art methods. We observe that ObjectBox performs favorably in comparison to prior works. Furthermore, we perform rigorous ablation experiments to evaluate different components of our method. Our code is available at: https://github.com/MohsenZand/ObjectBox.Comment: ECCV 2022 Ora

Multiscale Residual Learning of Graph Convolutional Sequence Chunks for Human Motion Prediction

A new method is proposed for human motion prediction by learning temporal and spatial dependencies. Recently, multiscale graphs have been developed to model the human body at higher abstraction levels, resulting in more stable motion prediction. Current methods however predetermine scale levels and combine spatially proximal joints to generate coarser scales based on human priors, even though movement patterns in different motion sequences vary and do not fully comply with a fixed graph of spatially connected joints. Another problem with graph convolutional methods is mode collapse, in which predicted poses converge around a mean pose with no discernible movements, particularly in long-term predictions. To tackle these issues, we propose ResChunk, an end-to-end network which explores dynamically correlated body components based on the pairwise relationships between all joints in individual sequences. ResChunk is trained to learn the residuals between target sequence chunks in an autoregressive manner to enforce the temporal connectivities between consecutive chunks. It is hence a sequence-to-sequence prediction network which considers dynamic spatio-temporal features of sequences at multiple levels. Our experiments on two challenging benchmark datasets, CMU Mocap and Human3.6M, demonstrate that our proposed method is able to effectively model the sequence information for motion prediction and outperform other techniques to set a new state-of-the-art. Our code is available at https://github.com/MohsenZand/ResChunk.Comment: 13 page

Wear Resistant and Biocompatible Coatings for Medical Devices and Method of Fabrication

Abstract: A method of forming a biocompatible or biologically inert article for use in an application in which the article will make contact with at least one tissue, organ, or fluid within a human or animal body is provided. The method generally comprises providing an article having an external sur face; selecting chemical precursors; using a means to direct one or more chemical precursors towards or to apply such chemical precursors to the ex ternal surface; activating the chemical precursors by exposing said precursors to atmospheric pressure plasma; and grafting and/or cross-linking the chem ical precursors to form a solid coating adjacent to the external surface of the article

Molecular cloning and expression of Bacillus anthracis Lethal Factor domain 1 gene in Escherichia coli

زمینه و هدف: سیاه‌زخم (آنتراکس) یک بیماری مشترک بین انسان و دام است. عامل ایجاد کننده بیماری باکتری باسیلوس آنتراسیس می‌باشد که آنتی‌ژن حفاظت‌کننده (PA) و ناحیه یک فاکتور کشنده (LFD1) ایمونوژن‌های قوی این باکتری بوده و همواره به عنوان کاندیدای واکسن علیه باسیلوس آنتراسیس در نظر گرفته شده‌اند. هدف این مطالعه تولید آنتی‌ژن ناحیه یک فاکتور کشنده(LFD1) در باکتری Escherichia coli می‌باشد. روش بررسی: در این مطالعه تجربی آزمایشگاهی ژن LFD1 از پلاسمید pXO1 شناسایی و با واکنش PCR تکثیر شد. با جایگاه‌های آنزیمی BamH I و Xho Iدر وکتور (pGEM-T easy) همسانه‌سازی شد و بعد از جداسازی به وکتور بیانی pET28a(+) زیرهمسانه‌سازی گردید. این وکتور به باکتری E. coli-BL21 (DE3) تراریخت (ترانسفورم) شد. بیان ژن LFD1 تحت القای ایزوپروپیل-β -ِD -I-گالاکتوپیرانوزید (IPTG) انجام و پروتئین مورد نظر بیان شد. یافته‌ها: ژن ناحیه یک فاکتور کشنده (LFD1) کلون شده در وکتور بیانی pET28a(+) به وسیله‌ی توالی یابی، PCR و هضم به وسیله آنزیم‌های با اثر محدود تأیید گردید. همچنین پروتئین نوترکیب تولید شده به وسیله سدیم دودسیل سولفات پلی آکریل آمید ژل (SDS-PAGE) و لکه‌گذاری وسترن تایید گردید. نتیجه‌گیری: با توجه به ایمونوژن بودن پروتئین LFD1، پروتئین نوترکیب تولید شده در این تحقیق را می‌توان به‌صورت مجزا یا ترکیبی با یاورها و یا انتقال دهنده‌ها در طراحی واکسن برای بیماری سیاه‌زخم استفاده نمود

A General-Purpose Multiphase/Multispecies Model to Predict the Spread, Percutaneous Hazard, and Contact Dynamics for Nonporous and Porous Substrates and Membranes

A computational model to solve the coupled transport equations with chemical reaction and phase change for a liquid sessile droplet or the contact and spread of a sessile droplet between two approaching porous or non-porous surfaces, is developed. The model is general therefore it can be applied to toxic chemicals (contact hazard), drug delivery through porous organs and membranes, combustion processes within porous material, and liquid movements in the ground. The equation of motion and the spread of the incompressible liquid available on the primary surface for transfer into the contacting surface while reacting with other chemicals (or water) and/or the solid substrate are solved in a finite difference domain with adaptive meshing. The comparison with experimental data demonstrated the model is robust and accurate. The impact of the initial velocity on the spread topology and mass transfer into the pores is also addressed

Vote from the Center: 6 DoF Pose Estimation in RGB-D Images by Radial Keypoint Voting

We propose a novel keypoint voting scheme based on intersecting spheres, that is more accurate than existing schemes and allows for a smaller set of more disperse keypoints. The scheme is based upon the distance between points, which as a 1D quantity can be regressed more accurately than the 2D and 3D vector and offset quantities regressed in previous work, yielding more accurate keypoint localization. The scheme forms the basis of the proposed RCVPose method for 6 DoF pose estimation of 3D objects in RGB-D data, which is particularly effective at handling occlusions. A CNN is trained to estimate the distance between the 3D point corresponding to the depth mode of each RGB pixel, and a set of 3 disperse keypoints defined in the object frame. At inference, a sphere centered at each 3D point is generated, of radius equal to this estimated distance. The surfaces of these spheres vote to increment a 3D accumulator space, the peaks of which indicate keypoint locations. The proposed radial voting scheme is more accurate than previous vector or offset schemes, and is robust to disperse keypoints. Experiments demonstrate RCVPose to be highly accurate and competitive, achieving state-of-the-art results on the LINEMOD 99.7% and YCB-Video 97.2% datasets, notably scoring +7.9% higher (71.1%) than previous methods on the challenging Occlusion LINEMOD dataset

POISED: Spotting Twitter Spam Off the Beaten Paths

Cybercriminals have found in online social networks a propitious medium to spread spam and malicious content. Existing techniques for detecting spam include predicting the trustworthiness of accounts and analyzing the content of these messages. However, advanced attackers can still successfully evade these defenses. Online social networks bring people who have personal connections or share common interests to form communities. In this paper, we first show that users within a networked community share some topics of interest. Moreover, content shared on these social network tend to propagate according to the interests of people. Dissemination paths may emerge where some communities post similar messages, based on the interests of those communities. Spam and other malicious content, on the other hand, follow different spreading patterns. In this paper, we follow this insight and present POISED, a system that leverages the differences in propagation between benign and malicious messages on social networks to identify spam and other unwanted content. We test our system on a dataset of 1.3M tweets collected from 64K users, and we show that our approach is effective in detecting malicious messages, reaching 91% precision and 93% recall. We also show that POISED's detection is more comprehensive than previous systems, by comparing it to three state-of-the-art spam detection systems that have been proposed by the research community in the past. POISED significantly outperforms each of these systems. Moreover, through simulations, we show how POISED is effective in the early detection of spam messages and how it is resilient against two well-known adversarial machine learning attacks

Bio-compatible polymer coatings using low temperature, atmospheric pressure plasma

Current research has been dedicated to investigating the viability of atmospheric pressure plasmas for use in coating technology. In addition to being more cost effective and efficient, atmospheric pressure plasma offers a more streamlined process, as it can be employed directly into the production line. Atmospheric pressure plasma has been used in applications including biocompatibility, hydrophilicity/hydrophobicity, and coating with antibacterial films. Polyethylene is used as a biocompatible surface for ball and socket joint replacements, which are under constant wear. Atmospheric pressure plasma treatment was used to change the surface chemistry by grafting various biocompatible polymers to the polyethylene surface, as methods of providing wear resistance as well as providing a self-lubricating surface. The organic coatings included biocompatible polymers, such as poly(2- hydroxyethylmethacrylate), polyethylenimine, and polyethylene glycol. Low temperature, atmospheric pressure plasma was used, along with an in-house constructed spray delivery system, to coat high density polyethylene substrates. Coatings were characterized with Fourier transform infrared spectroscopy (FTIR), contact angle analysis, and adhesion testing. A significant decrease in contact angle was noted for various coatings produced with this method, indicating an increased wettability. Plasma processing conditions, specifically the pretreatment of the substrate and the input power, greatly affected the adhesion and uniformity of the polymerized layer. Keywords: atmospheric pressure plasma, coatings, hydrophilicity