Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Query Execution: Dynamically Rewriting Compiled Queries

Nowadays, analytical database systems use highly sophisticated data processing techniques like query compilation or vectorization to evaluate queries on millions of tuples. In such systems, query runtimes depend on multiple factors like the query execution plan, the underlying hardware platform, and the operator implementation. Anticipating all these factors during query optimization is difficult, and finding the optimal implementation for a query is almost impossible and requires highly accurate cost models and estimations. Adaptive Query Processing overcomes these limitations by interleaving query optimization and query execution phases: An initial exploration phase explores different variations for a query and then chooses the best-performing one to process most of the data. Numerous optimizations are possible, like reordering joins and predicates or switching the operators’ implementations while executing them. However, the number of variations grows with the dynamic optimizations, and compiling all implementations is prohibitively expensive. We, therefore, propose a different approach that avoids recompilations entirely. Our Dynamic Blocks technique compiles the query once and embeds code fragments for all variants into the generated code. When a different variation is needed, we extract the relevant blocks from the original executable and assemble a new executable. Internally our Adaptive Query Processing technique consists of three components: a code generation framework to provide different variations for a query, an optimized compiler for lowering the variants to machine code, and a dynamic execution strategy that chooses the optimal implementation while executing the query. We integrate these three components into the research database system Umbra and implement three dynamic optimizations on top that adapt the query plan. Our results show that Adaptive Query Processing improves execution times in a compiling database system like Umbra by up to 2x.
A Robust Cybersecurity Topic Classification Tool

In this research, we use user defined labels from three internet text sources (Reddit, StackExchange, Arxiv) to train 21 different machine learning models for the topic classification task of detecting cybersecurity discussions in natural English text. We analyze the false positive and false negative rates of each of the 21 model’s in cross validation experiments. Then we present a Cybersecurity Topic Classification (CTC) tool, which takes the majority vote of the 21 trained machine learning models as the decision mechanism for detecting cybersecurity related text. We also show that the majority vote mechanism of the CTC tool provides lower false negative and false positive rates on average than any of the 21 individual models. We show that the CTC tool is scalable to the hundreds of thousands of documents with a wall clock time on the order of hours.
Research on structure selection and model test of cast-steel joint used in the Gymnasium of Shenzhen Universiade Sport Center

For designing the key joints of large-span steel structure in the Gymnasium of Shenzhen Universiade Sport Center,a study on the structure selection of cast-steel joint was carried out.According to the load characteristics of the prototype joint,a 1∶2 model and its loading device were made.By carrying out static loading test on the model,the stress condition and deformation of the joint was experimentally examined.Results of the experiment and analysis show that even if the test load reaches 1.3 times the design load,most parts of the joint remain elastic with little deformation,indicating the satisfaction of the joint design for requirement of safety and applicability.The connection plate and pin in the joint axis surface were deformed by the pressure,thus enhancement measures were taken press the plate on the joint directly,then the pin is used as a space limitling element not a shear resistant.It is further shown that testing and FEM analysis results are compared favorably.Proposal about the study,design and manufacture,materials inspection of the cast-steel joints was put forward combining with this research.
SPECKLE CORRELATION USING A DEEP LEARNING ALGORITHM ON NEWTONIAN FLUIDS

by Sans Basnet, DePaul University April 2021 Research Advisor: Eric Landahl In soft-matter physics, Dynamic Light Scattering (DLS) stands out as the non-invasive technique designed to measure the size and distribution of particles using speckle correlation. Traditional DLS analysis performs a one-to-one mapping of the size with diffusion which is computationally costly, in addition to lacks in generalisation to seek deeper relationships amongst speckle patterns. A recent advancement in double-pulse speckle imaging allows for the temporal-correlation problem presented by DLS to be transformed into an image classification problem. In this prototype study, I probe the feasibility and efficacy of DLS data analysis of speckle correlation using Deep Learning (DL). To reduce the loss of generality and retain the integrity of the speckles, the study implements an image-focused DL algorithm. A Convolutional Neural Network (CNN or ConvNet) is used for feature extraction of pixelated images that are time-series in nature. Each scan consists of 500 frames (images) taken with double-pulses of 100 nanoseconds duration at 17 time delays. The CNN model was trained with roughly 1000 observations per sample with labelled target to supervise the training. The model picked up patterns within image matrices producing best performance accuracy of 83, average accuracy hovered at 58 percent. Treating speckle images to artificial intelligence to classify to the impurity type opens a new avenue to perform analysis in DLS. This prototype study shows that CNN could be an alternative candidate to traditional speckle image analysis.
Blur Detection through Multinomial Logistic Regression based Adaptive Threshold

Blur detection and segmentation play vital role in many computer vision applications. Among various methods, local binary pattern based methods provide reasonable blur detection results. However, in conventional local binary pattern based methods, the blur map is computed by using a fixed threshold irrespective of the type and level of blur. It may not be suitable for images with variations in imaging conditions and blur. In this paper we propose an effective method based on local binary pattern with adaptive threshold for blur detection. The adaptive threshold is computed based on the model learned through the multinomial logistic regression. The performance of the proposed method is evaluated using different datasets. The comparative analysis not only demonstrates the effectiveness of the proposed method but also exhibits it superiority over the existing methods.
igh-Dose Carmustine, Etoposide, and Cisplatin for utologous Stem Cell Transplantation with or ithout Involved-Field Radiation for elapsed/Refractory Lymphoma: An Effective egimen with Low Morbidity and Mortality

Over a 10-year period (January 1993 to October 2002), 101 relapsed or refractory non-Hodgkin lymphoma patients were treated at our center with high-dose chemotherapy and autologous transplantation. The median patient age was 54 years (range, 25-70 years). Thirty-two patients had indolent (low-grade), 42 had aggressive (intermediate-grade), and 27 had very aggressive (high-grade) non-Hodgkin lymphoma. Thirty-six patients had primary refractory disease, 20 had a chemoresistant relapse, 35 patients had a chemosensitive relapse, and 10 patients were “initial high risk” patients. The median number of prior chemotherapy regimens was 2 (range, 1-5). The preparative regimen (BEP) was bischloroethylnitrosourea (BCNU) 600 mg/m, etoposide 2400 mg/m, and Platinol (cisplatin) 200 mg/m given intravenously over 5 days. Within 3 weeks before transplantation, 70 patients received involved-field radiotherapy (IFR) 20 Gy to sites of currently active (>2 cm) or prior bulky (>5 cm) disease. Most patients (n 93) received mobilized peripheral blood stem cells (median CD34 cell dose, 6.7 10/kg). Median neutrophil (>500/ L) and platelet (>20 000/ L, untransfused) recoveries were 11 days (range, 7-19 days) and 14 days (range, 7-36 days), respectively. At a median follow-up of 41 months (range, 4 to 118 months) for survivors, Kaplan-Meier 5-year probabilities of overall survival (OS) and disease-free survival (DFS) were 58.6% and 51.1%, respectively. Four patients (4%) died within 30 days of stem cell infusion (1 pulmonary embolism, 2 septicemias with multiorgan failure, and 1 progressive lymphoma). Two patients (2%) developed interstitial pneumonitis most likely secondary to high-dose BCNU. Three cases (3%) of secondary acute myelogenous leukemia occurred. On multivariate analysis, age (<60 or >60 years), histologic grade (low versus intermediate or high), the use of IFR, and chemotherapy response at baseline did not affect OS or DFS. Of 70 patients given IFR, 27 relapsed: 10 (37%) within and 17 (63%) outside the radiation field. The use of IFR did not affect either OS or DFS, probably because IFR was offered to patients with bulky or chemoresistant disease. BEP with or without IFR is a highly effective and well-tolerated regimen in the relapsed/refractory lymphoma setting. It has low morbidity and transplant-related mortality and a low incidence (3%) of posttransplantation malignancy. © 2005 American Society for Blood and Marrow Transplantation
Effect of Dietary Bidens pilosa L . leaves on Rats Suffering from Acute Liver toxicity

The study aimed to reveal the effect of three levels of Bidens pilosa L. (Asteraceae) leaves powder 2.5, 5 and 10 % on acute liver hepatotoxicity. Forty adult albino male rats Sprague Dawley strain were classified into five groups. One was fed on basal diet and kept as control (-ve) group. The other four hepatic rat groups were control group (+ve), 2.5, 5 and 10% BPP groups. At the end of the experimental period rats were fasted overnight and sacrificed, blood samples were collected from the aorta to determine lipids profiles and liver functions. Also, nutritional and biological parameters were recorded as well as histopahological studies for liver. The obtained results revealed that, injecting rats by CCL4 led to significant increase in acute liver toxicity, and increase in (triglycerides, LDL-c, ALT, AST and ALP). Feeding rats on basal diet supplemented with different levels from Bidens pilosa L. powder improved all parameters, reduced serum total cholesterol (TC), triglycerides (TG), low density lipoprotein cholesterol (LDL-c), and liver enzymes, while there was increase in serum high density lipoprotein cholesterol (HDL-c), and food intake especially when the high level 10% BPP is used. From the obtained results it can be concluded that, supplementation of bakery product with Bidens pilosa L. leaves powder exerts a positive impact on the liver functions, lipid profile and other biochemical parameters.
Designing for Cultural Values: Towards a Theory-motivated Method for Culture-sensitive Adaptation of Information Systems

To ensure that an intended target group accepts and uses an information system (IS) is a major challenge for service systems engineering and a key interest in IS research. On the one hand, individuals’ cultural values affect their willingness to use an IS; on the other hand, information technology (IT) is neither value-neutral and, thus, also affects IT acceptance and usage. Therefore, the adaptation of IS should consider both sources of value. Thus, in this paper, we present the theory-driven design of a method for culture-sensitive IS adaptation that draws on IT-culture conflict theory. Our two-fold evaluation approach results show that the method enables to create feasible results for developing culture-sensitive design solutions for IS. As a theoretical contribution, we contribute to the exploration of culture in IS development; as a practical contribution, we provide guidance in how to adapt IS for specific target groups.
Pet Breed Classification on Altered Images

Our project attempts to classify cats and dogs by breed in unusual settings: given corrupted images, can our model still infer the breed which the animal in the scene belongs to? We approach this task with four models: k-nearest neighbor, CNN, CNN with residual network, and transfer learning. Our evaluation metric shows that k-nearest neighbor achieves the best accuracy score of 98.34% on validation dataset while CNN with ResNet achieves the best accuracy score of 35.4%. The poor accuracy score of CNN’s can be attributed to a variety of factors within the data and model architecture. Nonetheless, transfer learning method seems to be the most promising method as it avoids overfitting problem found in other models.
Study of Mandatory Lane Change Execution Behavior Model for Heavy Vehicles and Passenger Cars

Lane changing is one of the basic driver behavior interactions in microscopic traffic simulations for traffic, safety, and transportation system analysis. Although many traffic simulations pay attention to the decision process for lane changing, the execution process is often simplified or even ignored. This paper presents a study of lane change execution of heavy vehicles and passenger cars in a traffic situation with a blocked lane. A video camera was used to collect data from an arterial road in Melbourne, Australia. This paper investigates the behavior of heavy vehicles and passenger cars in mandatory lane changes and proposes a model framework for the execution of lane changes with regard to the emergency status and the impact of surrounding traffic for individual drivers who are changing lanes. The distance to the blockage was used to denote the emergency status. For the impact of the surrounding traffic, it was assumed that the driver would adjust the execution if a traffic conflict were detected and would continue the lane change if there were no conflict. A probability model was developed to interpret the driver’s choice of whether to continue the lane change. The data analysis found that the lane change performance of heavy vehicles differed from that of passenger cars. Therefore, the model was calibrated for heavy vehicles and passenger cars separately. In conclusion, the paper provides a framework of the future work of lane change execution models on traffic simulation to assess the traffic safety and road efficiency.
Cognitive Hierarchies in Extensive Form Games

The cognitive hierarchy (CH) approach posits that players in a game are heterogeneous with respect to levels of strategic sophistication. A level- k player believes all other players in the game have lower levels of sophistication distributed from 0 to k - 1, and these beliefs correspond to the truncated distribution of a “true” distribution of levels. We extend the CH framework to extensive form games, where these initial beliefs over lower levels are updated as the history of play in the game unfolds, providing information to players about other players’ levels of sophistication. For a class of centipede games with a linearly increasing pie, we fully characterize the dynamic CH solution and show that it leads to the game terminating earlier than in the static CH solution for the centipede game in reduced normal form
Shock Parameterization for the Compressible Euler Equations using Deep Learning

The design of high-speed aerospace vehicles is enhanced through the use of computational fluid dynamics (CFD). CFD allows for the numerical simulation of the physical flow around the geometric representation of these systems. In our examples listed above, each has the common inclusion of shocks, or regions of compressed air that result in sharp gradients of the physical states on either side of the shock. Shocks can lead to large heat fluxes on vehicle bodies and have led to catastrophic failures in several situations.
A Review of Advanced Bridge Inspection Technologies Based on Robotic Systems and Image Processing

To ensure safety of bridges, it is critical to inspect and assess physical and functional conditions regularly. Presently, most highway bridges in the U.S. are inspected visually. However, this method of inspection is often influenced by the bridge inspector's knowledge and experience. So, reliability and accuracy of inspection results may be problematic. To solve such problems, an extensive number of robotics systems and image processing techniques for bridge inspection methods have been proposed. These robotics systems and image processing techniques are used to measure various bridge conditions, such as apparent damage, displacement and dynamic characteristics. This paper provides a comprehensive review of robotics systems and image processing technologies used in bridge inspection.
Design and characteristics of 3D magnetically steerable guidewire system for minimally invasive surgery

Endovascular techniques have been increasingly adapted to medical applications as a minimally-invasive treatment approach to diagnose and treat various vascular diseases. Guidewires are the basic devices in endovascular surgery. The conventional practice of endovascular procedures has a number of drawbacks such as prolonged exposure to high doses of X-ray radiation, long operation time and undesired damage by surgeon's unskilled operation. In this paper, we proposed a magnetically steerable guidewire and designed a three-dimensional magnetically actuating system. The proposed magnetically steerable guidewire consists of a commercial guidewire connecting to a soft actuator made of Ecoflex and magnetic powder. The actuating system contains two symmetrical parts, each consisting of three linear motors. It has great workspace of 700mm*500mm*500mm, which can help the proposed guidewire to achieve a wide range of deflection angle. The relationship between the proposed guidewire's deflection angle and the magnitude of the external magnetic field are explored, as well as the direction of the magnetic field. The proposed guidewire is flexible enough to make sharp turns up to 180 in three dimensional workspace. The guidwire has achieved navigating through a set of rings and in a complex and constrained vascular phantom by programmed actuating magnetic fields.
Hybrid electric excursion ships power supply system based on a multiple energy storage system

This study presents the multiple energy storage elements usability for ships using a passive hybrid topology. The considered hybridisation is based on a passive parallel topology connecting NiMH batteries and SuperCapacitors to a DC power distribution by a bidirectional DC/DC converter. The overall propulsion architecture is a hybrid series system where an engine-generator group is the main energy source and the multiple energy storage system (ESS) answer the intermittent power demanded by the on-board loads. First, the multiple ESS is sized using voltages, storage elements' characteristics, and typical power demand profile. Thereafter, a control scheme of this hybridisation is deduced through a cascade of current and voltage linear controllers. The energy management strategy based on the stability of the DC power distribution voltage and the inherent storage elements' characteristics is fully addressed in order to reduce weight and space on-board, fuel consumption, pollution, and optimise the global efficiency. Experimental results show that the engine-generator fulfil a constant power, meanwhile the multiple ESS stabilises the DC-link voltage with unknown power demand profile. The effectiveness of the proposed passive hybrid topology with reduced control layer complexity based on the DC-link voltage stabilisation as an energy management strategy is validated.
Wavelength Division Multiplexing: A Practical Engineering Guide

In this book, Optical Wavelength Division Multiplexing (WDM) is approached from a strictly practical and application-oriented point of view. Based on the characteristics and constraints of modern fiber-optic components, transport systems and fibers, the textprovides relevant rules of thumb and practical hints for technology selection, WDM system and link dimensioning, and also for network-related aspects such as wavelength assignment and resilience mechanisms. Actual 10/40 Gb/s WDM systems are considered, and apreview ofthe upcoming 100 Gb/s systems and technologies for even higher bit rates is given as well.Key features:Considers WDM from ULH backbone (big picture view)down to PON access (micro view). Includes allmajor telecom and datacom applications. Provides the relevant background for state-of-the-art and next-gen systems. Offerspractical guidelines for system / link engineering.
A Review on Deep Neural Networks for ICD Coding

The International Classification of Diseases (ICD) is a standard for categorizing physical conditions, which has been widely used for analyzing clinical data and monitoring health issues. Manual ICD coding takes a long time and is vulnerable to errors, so researchers pay more and more attention to the application of deep neural networks in ICD automatic coding. However, there is still no comprehensive review of these studies and prospects for further research. This paper is not limited to the study of deep neural networks, but gives a formal definition of ICD coding problems, and then systematically reviews the existing literature on how to design deep neural networks to address the four major challenges of ICD coding tasks. This paper also summarizes the public data sets and future research directions, to provide guidance for the research of ICD coding in medical field.
On the SIR Meta Distribution for Cache-Enabled Wireless Networks with Random Discontinuous Transmission: Analysis and Optimization

A fine-grained analysis of the cache-enabled networks is crucial for system design. In this paper, we focus on the meta distribution of the signal-to-interference ratio for the cache-enabled networks where the locations of the base stations are modeled as a Poisson point process. With the application of the random caching and the random discontinuous transmission schemes, we derive the moments of the conditional successful transmission probability, the exact meta distribution and its beta approximation by utilizing stochastic geometry. The closed-form expressions of the mean and variance of the local delay (i.e., the jitter) are also derived. We then consider the maximization of the mean successful transmission probability and the minimization of the average system transmission delay by jointly optimizing the caching probability and the BS active probability. Finally, the numerical results demonstrate the superiority of the proposed optimization schemes over the existing caching strategies and reveal the impacts of the key network parameters on the cache-enabled networks in terms of successful transmission probability, successful transmission probability variance, meta distribution, mean local delay and jitter.
Passive or Active? Behavioral Changes in Different Designs of Search Experiments

While search experiments are available in several designs, accumulating experimental evidence suggests that individual search behavior depends on design details. This paper reports the ﬁrst classiﬁcation and comparison of several search experiment designs widely accepted in search studies. These designs can be categorized as passive, quasi-active, and active. We found individual-and aggregate-level signiﬁcant diﬀerences in the results across designs, despite identical models. In the passive design, subjects tended to be more reluctant to search than in the active design, and risk-averse subjects quickly terminated their search. Our results highlight the importance and potentials of designing search environments in practice