nils-herrmann/clean_scirepeval huggingface dataset:
Quasi optical coherence vibration tomography technique for damage detection in beam like structures based on auxiliary mass induced frequency shift
Year: 2017

A novel quasi optical coherence vibration tomography (Quasi OCVT) measurement system suitable for structural damage detection is proposed by taking the concept of two dimensional optical coherence vibration tomography (2D OCVT) technique. An artificial quasi interferogram fringe pattern (QIFP) similar to the interferogram of 2D OCVT system, as a sensor, was pasted on the surface of a vibrating structure. Image sequences of QIFP were captured by a high speed camera that worked as a detector. The period density of the imaged QIFP changed due to the structural vibration, from which the vibration information of the structure could be obtained. Noise influence on the measurement accuracy, torsional sensitivity and optical distortion effect of the Quasi OCVT system were investigated. The efficiency and reliability of the proposed method were demonstrated by applying the system to damage detection of a cracked beam like structure with a roving auxiliary mass. The roving of the mass along the cracked beam brings about the change of natural frequencies that could be obtained by the Quasi OCVT technique. Therefore, frequency shift curves can be achieved and these curves provide additional spatial information for structural damage detection. Same cases were also analyzed by the finite element method (FEM) and conventional accelerometer based measurement method. Comparisons were carried out among these results. Results obtained by the proposed Quasi OCVT method had a good agreement with the ones obtained by FEM, from which the damage could be directly detected. However, the results obtained by conventional accelerometer showed misleading ambiguous peaks at damage position owing to the mass effect on the structure, where the damage location cannot be identified confidently without further confirmation. The good performance of the cost effective Quasi OCVT method makes it attractive for vibration measurement and damage detection of beam like structures.
Production planning with a two stage stochastic programming model in a kitting facility under demand and yield uncertainties
Year: 2020

The main objective of this study is to develop a two stage stochastic programming framework for lot sizing and scheduling the production activities at a kitting facility to support a manufacturing plant. The novelty of the study lies in modeling multiple uncertainties using two stage stochastic programming to solve a kitting specific production planning problem in a manufacturing setting. The demand for the kits and the yield of the kitting workers are the two sources of uncertainties considered in this study. The first stage decisions include the baseline production schedule and the workforce requirement, while the second stage makes recourse decisions on overtime production. The proposed decision making framework is validated on a multi period, multi product case study involving a kitting facility supporting a manufacturing plant producing braking equipment. The main conclusion of the study is that uncertainties have significant impacts on kitting planning decisions and that the proposed two stage stochastic programming model was robust in determining optimal production plans under uncertainty.
Artificial Intelligence Education and Tools for Medical and Health Informatics Students: Systematic Review
Venue: JMIR medical education
Year: 2020

The use of artificial intelligence (AI) in medicine will generate numerous application possibilities to improve patient care, provide real time data analytics, and enable continuous patient monitoring. Clinicians and health informaticians should become familiar with machine learning and deep learning. Additionally, they should have a strong background in data analytics and data visualization to use, evaluate, and develop AI applications in clinical practice.  The main objective of this study was to evaluate the current state of AI training and the use of AI tools to enhance the learning experience.  A comprehensive systematic review was conducted to analyze the use of AI in medical and health informatics education, and to evaluate existing AI training practices. PRISMA P (Preferred Reporting Items for Systematic Reviews and Meta Analysis Protocols) guidelines were followed. The studies that focused on the use of AI tools to enhance medical education and the studies that investigated teaching AI as a new competency were categorized separately to evaluate recent developments.  This systematic review revealed that recent publications recommend the integration of AI training into medical and health informatics curricula.  To the best of our knowledge, this is the first systematic review exploring the current state of AI education in both medicine and health informatics. Since AI curricula have not been standardized and competencies have not been determined, a framework for specialized AI training in medical and health informatics education is proposed.
Localized Aided Energy Efficient Optimal Relay Selection Protocol for Underwater Wireless Sensor Networks
Venue: 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)
Year: 2019

In this paper, Underwater gadget Networks grouping of data type Sensors hub is intense by some submerged unforgiving drawback to beat these issues we tend to plot a fresh out of the plastic new convention to wipe out the information misfortune and improve the vitality power. In a submerged setting and guarantees solid conveyance of bundles from the underside to the outside of the water. amid this paper proposes a helpful vitality practical ideal hand off decision (Co ERRORS) convention for UWSNs. the anticipated subject joins area and profundity of the gadget hubs to select the goal hubs. Blend of those 2 parameters doesn't include realizing the position directions of the hubs and prompts the decision of the goal hubs closest to the water surface.
Influenced of threshold fluence, absorption coefficient and thermalloading in laser paint removal mechanisms
Year: 2015

This study aimed to investigate the influenced of FT, a and g in laser paint removal mechanisms over three types of car coated substrates irradiated by Cynosure Cynergy Pulse Nd:YAG laser. Depth of coating removed (d) versus laser Fluence (F) graphs were plotted for each Pulse Width (PW) Repetition Rate (RR) and Beam Size (BS) considered by measuring the average of coating removal efficiency The best threshold fluence for sample A, B and C were selected from the best interception of the fitted linear graph of with their X axis to estimate the general threshold fluence (FT) for each sample. By applying Beer Lambert law, absorption coefficient (a) and thermal loading (g) were also estimated. The study concludes the FT, a and g were obtained at 42 J/cm2 209.80 cm 1 and 8811.76 J/cm3 for sample A, 63 J/cm2 68.92 cm 1 and 4341.73 J/cm3 for sample B and 14 J/cm2 143.23 cm 1 and 2005.21 J/cm3 for sample C respectively.
Structured Sparse R CNN for Direct Scene Graph Generation
Venue: ArXiv
Year: 2021

Scene graph generation (SGG) is to detect entity pairs with their relations in an image. Existing SGG approaches often use multi stage pipelines to decompose this task into object detection, relation graph construction, and dense or dense to sparse relation prediction. Instead, from a perspective on SGG as a direct set prediction, this paper presents a simple, sparse, and unified framework for relation detection, termed as Structured Sparse R CNN. The key to our method is a set of learnable triplet queries and structured triplet detectors which could be jointly optimized from the training set in an end to end manner. Specifically, the triplet queries encode the general prior for entity pair locations, categories, and their relations, and provide an initial guess of relation detection for subsequent refinement. The triplet detector presents a cascaded dynamic head design to progressively refine the results of relation detection. In addition, to relieve the training difficulty of Structured Sparse R CNN, we propose a relaxed and enhanced training strategy based on knowledge distillation from a Siamese Sparse R CNN. We also propose adaptive focusing parameter and average logit approach for imbalance data distribution. We perform experiments on two benchmarks: Visual Genome and Open Images, and the results demonstrate that our method achieves the state of the art performance. Meanwhile, we perform in depth ablation studies to provide insights on our structured modeling in triplet detector design and training strategies.
Perspective on SCR NOx control for diesel vehicles
Year: 2019

The selective catalytic reduction of NOx with aqueous urea "urea SCR" is originally a steady state technology used in stationary source NOx control. Catalysts for this application are usually quite large, and typically consist of lower cost materials such as vanadia/titania or Fe/zeolite. The catalyst bed temperature may be controlled within an optimum NOx conversion window, and the gas flow is fairly constant; therefore, the aqueous urea injection is also fairly constant. Vehicle operation, on the other hand, is highly transient in flow and temperature based on customer demanded vehicle speed, load, road conditions, weather, etc. and may not be predicted. A catalyst must work in the vast majority of conditions found in normal vehicle use, and the catalyst size is limited by space and weight considerations. People drive vehicles, not catalysts, and emission control is not a feature that is highlighted in a marketing advertisement. Aqueous urea must be replenished for SCR to work, and the transient nature of the injection process makes it very different from stationary source NOx control. The SCR catalyst must function and survive in a multi component system that also controls hydrocarbons, carbon monoxide, and soot, and faces poisoning by sulfur, phosphorus, and a variety of other elements present in fuel, oil, and any upstream component including other catalysts. The SCR catalyst can experience temperatures below 200 degC and as high as 700 degC in typical vehicle operation, or even 900 degC when coated on a soot filter. The selection of functional materials, durability tests representing high mileage, and even more important, tests to failure, become critical decisions in the design of automotive urea SCR. This Prospective aims to summarize the key design questions and how to navigate the myriad of choices through careful research and practical considerations. The subsequent flurry of research on Cu/CHA after commercial launch on vehicles is considered, plus a look at the future applicability of the technology.
The Deconfounded Recommender: A Causal Inference Approach to Recommendation
Venue: ArXiv
Year: 2018

The goal of a recommender system is to show its users items that they will like. In forming its prediction, the recommender system tries to answer: "what would the rating be if we 'forced' the user to watch the movie?" This is a question about an intervention in the world, a causal question, and so traditional recommender systems are doing causal inference from observational data. This paper develops a causal inference approach to recommendation. Traditional recommenders are likely biased by unobserved confounders, variables that affect both the "treatment assignments" (which movies the users watch) and the "outcomes" (how they rate them) We develop the deconfounded recommender, a strategy to leverage classical recommendation models for causal predictions. The deconfounded recommender uses Poisson factorization on which movies users watched to infer latent confounders in the data; it then augments common recommendation models to correct for potential confounding bias. The deconfounded recommender improves recommendation and it enjoys stable performance against interventions on test sets.
Multi task learning for aspect term extraction and aspect sentiment classification
Venue: Neurocomputing
Year: 2020

Aspect sentiment classification has a dependency over the aspect term extraction. The majority of the existing studies tackle these two problems independently, i.e. while performing aspect sentiment classification, it is assumed that the aspect terms are pre identified. However, such assumptions are neither practical nor appropriate. In this paper, we address these impractical limitations and propose a multi task learning framework for the identification and classification of aspect terms in a unified model. At first, the proposed approach employs a BiLSTM followed by a self attention mechanism to identify the aspect terms in a given sentence. Subsequently, the architecture utilizes a CNN framework to predict the sentiments of the identified aspect terms. We evaluate our proposed approach for the three benchmark datasets across two languages, i.e. English and Hindi. Experimental results suggest that the proposed multi task model achieves competitive performance with reduced complexity (i.e. a single model for the two tasks compared to two separate models for each task) for both the languages.
Deletion of arcA, iclR, and tdcC in Escherichia coli to improve l threonine production
Venue: Biotechnology and applied biochemistry
Year: 2019

l Threonine is an important amino acid supplemented in food, medicine, or feed. Starting from glucose, l threonine production in Escherichia coli involves the glycolysis, TCA cycle, and the l threonine biosynthetic pathway. In this study, how the l threonine production in an l threonine producing E. coli TWF001 is controlled by the three regulators ArcA, Cra, and IclR, which control the expression of genes involved in the glycolysis and TCA cycle, has been investigated. Ten mutant strains were constructed from TWF001 by different combinations of deletion or overexpression of arcA, cra, iclR, and tdcC. l Threonine production was increased in the mutants TWF015 (DarcADcra) TWF016 (DarcAPcra::Ptrc) TWF017 (DarcADiclR) TWF018 (DarcADiclRDtdcC) and TWF019 (DarcADcraDiclRDtdcC) Among these mutant strains, the highest l threonine production (26.0 g/L) was obtained in TWF018, which was a 109.7% increase compared with the control TWF001. In addition, TWF018 could consume glucose more efficiently than TWF001 and produce less acetate. The results suggest that deletion of arcA, iclR, and tdcC could efficiently increase l threonine production in E. coli.
BiSeNet V2: Bilateral Network with Guided Aggregation for Real time Semantic Segmentation
Venue: ArXiv
Year: 2020

The low level details and high level semantics are both essential to the semantic segmentation task. However, to speed up the model inference, current approaches almost always sacrifice the low level details, which leads to a considerable accuracy decrease. We propose to treat these spatial details and categorical semantics separately to achieve high accuracy and high efficiency for realtime semantic segmentation. To this end, we propose an efficient and effective architecture with a good trade off between speed and accuracy, termed Bilateral Segmentation Network (BiSeNet V2) This architecture involves: (i) a Detail Branch, with wide channels and shallow layers to capture low level details and generate high resolution feature representation; (ii) a Semantic Branch, with narrow channels and deep layers to obtain high level semantic context. The Semantic Branch is lightweight due to reducing the channel capacity and a fast downsampling strategy. Furthermore, we design a Guided Aggregation Layer to enhance mutual connections and fuse both types of feature representation. Besides, a booster training strategy is designed to improve the segmentation performance without any extra inference cost. Extensive quantitative and qualitative evaluations demonstrate that the proposed architecture performs favourably against a few state of the art real time semantic segmentation approaches. Specifically, for a 2,048x1,024 input, we achieve 72.6% Mean IoU on the Cityscapes test set with a speed of 156 FPS on one NVIDIA GeForce GTX 1080 Ti card, which is significantly faster than existing methods, yet we achieve better segmentation accuracy.
Three professions come together for an interdisciplinary approach to 3D printing: occupational therapy, biomedical engineering, and medical librarianship
Venue: Journal of the Medical Library Association JMLA
Year: 2018

Although many libraries have offered 3D printing as a service or available technology, there is a lack of information on course integrated programs for 3D printing in which the library played a primary role. Therefore, librarians at the Touro College School of Health Sciences began exploring 3D printing for inclusion in the occupational and physical therapy curriculum.  The goal of this project was to educate occupational and physical therapy students and faculty about the potential applications of 3D printing in health care and provide hands on experience, while increasing collaboration between librarians and faculty. Students' tasks included designing and creating a 3D printed assistive device as part of their course.  Students were able to successfully print assistive devices, demonstrating the feasibility of 3D printing in a health sciences curriculum. Librarians involved with this project reached approximately 78 students and 200 other librarians and faculty members. 3D printing at Touro College continues to evolve and expand; the trial 3D printing course is being reviewed for formal adoption into the occupational therapy curriculum, and additional funding for 3D printing technologies is currently being allocated by Touro administration.
Demo: Using Qualitative Reasoning in Natural Language Question Answering
Year: 2017

One of the goals of qualitative reasoning is to capture aspects of human commonsense reasoning, a sizeable portion of which involves reasoning about continuous processes. Commonsense models of these processes provide the grounding for learning more sophisticated models in school, and this is particularly important in science. Learning and applying said models requires integrating qualitative reasoning with other cognitive capabilities, including natural language understanding. This demonstration will show how QP theory is being used in the Companion cognitive architecture in learning by reading and in answering science test questions posed in simplified English.
Federated Model Distillation with Noise Free Differential Privacy
Venue: IJCAI
Year: 2021

Conventional federated learning directly averaging model weights is only possible if all local models have the same model structure. Naturally, it poses a restrictive constraint for collaboration between models with heterogeneous architectures. Sharing prediction instead of weight removes this obstacle and eliminates the risk of white box inference attacks in conventional federated learning. However, the predictions from local models are sensitive and would leak private information to the public. Currently, there is no theoretic guarantee that sharing prediction is private and secure. To address this issue, one naive approach is adding the differentially private random noise to the predictions like previous privacy works related to federated learning. Although the privacy concern is mitigated with random noise perturbation, it brings a new problem with a substantial trade off between privacy budget and model performance. In this paper, we fill in this gap by proposing a novel framework called FedMD NFDP, which applies the new proposed Noise Free Differential Privacy (NFDP) mechanism into a federated model distillation framework. NFDP can effectively protect the privacy of local data with the least sacrifice of the model utility. Our extensive experimental results on various datasets validate that FedMD NFDP can deliver not only comparable utility, communication efficiency but also provide a noise free differential privacy guarantee. We also demonstrate the feasibility of our FedMD NFDP by considering both IID and non IID setting, heterogeneous model architectures, and unlabelled public datasets from a different distribution.
Neural Machine Translation with Gumbel Greedy Decoding
Venue: AAAI
Year: 2018

Previous neural machine translation models used some heuristic search algorithms (e.g. beam search) in order to avoid solving the maximum a posteriori problem over translation sentences at test time. In this paper, we propose the Gumbel Greedy Decoding which trains a generative network to predict translation under a trained model. We solve such a problem using the Gumbel Softmax reparameterization, which makes our generative network differentiable and trainable through standard stochastic gradient methods. We empirically demonstrate that our proposed model is effective for generating sequences of discrete words.
Holistic and Deep Feature Pyramids for Saliency Detection
Venue: BMVC
Year: 2018

Saliency detection has been increasingly gaining research interest in recent years since many computer vision applications need to derive object attentions from images in the first steps. Multi scale awareness of the saliency detector becomes essential to find thin and small attention regions as well as keeping high level semantics. In this paper, we propose a novel holistic and deep feature pyramid neural network architecture that can leverage multi scale semantics in feature encoding stage and saliency region prediction (decoding) stage. In the encoding stage, we exploit multi scale and pyramidal hierarchy of feature maps via the densely connected network with variable size dilated convolutions as well as a pyramid pooling. In the decoding stage, we fuse multi level feature maps via up sampling and convolution. In addition, we utilize the multi level deep supervision via plugging in loss functions at every feature fusion level. Multi loss supervision regularizes weights searching space among different tasks minimizing overfitting and enhances gradient signal during backpropagation, and thus enables us training the network from scratch. This architecture builds an inherent multi level semantic pyramidal feature maps at different scales and enhances model's capability in the saliency detection task. We validated our approach on six benchmark datasets and compared with Corresponding authors: Zhifan Gao (gaozhifan@gmail.com) and Heye Zhang (hy.zhang@siat.ac.cn) The National Natural Science Foundation of China (No: 61525106, 61427807,61771464) shenzhen innovation funding (JCYJ20170307165309009, JCYJ20170413114916687,SGLH20161212104605195) c (c) 2018. The copyright of this document resides with its authors. 2 29TH BRITISH MACHINE VISION CONFERENCE: BMVC2018 eleven state of the art methods. The results demonstrated that the design effectiveness and our approach outperformed the compared methods.
Progress in the Pathogenesis of Liver Injury in Sepsis
Year: 2014

Liver injury plays an important role in the development of sepsis,and improving liver function helps to improve the prognosis of patients. Therefore,the pathogenesis of liver injury in sepsis research has important clinical value. The current study suggests that the pathogenesis of septic liver is as follows: sepsis caused hepatic microcirculation and hepatic blood flow changes lead to hypoxic ischemic liver cells,and if combined energy metabolism of liver cells exists,liver injury is then caused. Endotoxin directly damages Kupffer cells,while the endotoxin can cause inflammatory cytokines changes which aggravates the liver injury. The massive release of oxygen free radicals and lipid peroxidation product malondialdehyde in sepsis cause damage to the integrity of mitochondrial membrane and cell membranes,causing liver injury. The role of platelet activating factor,hepatocyte apoptosis,and some other processes together cause the liver injury in sepsis,and here is to make a review of the above mentioned mechanisms of septic liver injury.