Miracle-dz/UniFAR-FTUs huggingface dataset:
Prospective study of TACE combined with sorafenib vs TACE combined with 125I seed implantation in the treatment of hepatocellular carcinoma with portal vein tumor thrombus and arterioportal fistulas

Purpose To compare the efficacy of TACE combined with sorafenib and TACE combined with 125I seed implantation in the treatment of hepatocellular carcinoma (HCC) with portal vein tumor thrombus (PVTT) combined with arterioportal fistulas (APFs), and discuss the efficacy and safety of TACE combined with 125I seed implantation. Patients and methods Between January 2017 and December 2018, the clinical data of patients with HCC complicated with PVTT and APFs who were admitted to the Affiliated Cancer Hospital of Zhengzhou University, First Affiliated Hospital of Zhengzhou University, and Henan Provincial People’s Hospital were prospectively collected. The patients were divided into the TACE+sorafenib (TACE-S) group based on their treatment willingness. There were 26 and 32 patients in the TACE-S and TACE-125I groups, respectively. Both groups of patients underwent APFs occlusion during TACE therapy. The embolization effect of APFs was observed and recorded in the two groups, the efficacy of intrahepatic lesions and PVTT was evaluated, and the effects of different treatment methods on the efficacy were analysed. Results All patients completed the 3 months follow-up. The improvement rates of APFs in TACE-S and TACE-125I groups were 30.77% (8/26) and 68.75% (22/32), respectively, and difference was statistically significant (χ2 = 8.287, P=0.004). The median survival time of TACE-S and TACE-125I groups was 8.00 months and 12.8 months, respectively (χ2 = 7.106, P=0.008). Multivariate analysis showed that the PVTT subtype (IIa/IIb) and treatment method (TACE-S or TACE-125I) were independent factors affecting the recanalization of APFs in patients (P<0.05). Conclusion For patients with HCC with PVTT and APFs, TACE combined with 125I seed implantation can effectively treat portal vein tumor thrombus, thereby reducing the recanalization of APFs and prolonging the survival time of patients.
Emotionally-Triggered Short Text Conversation ( STC-3 ) using Attention-Based Sequence Generation Model

Recent studies have significantly contributed to high-quality text generation. Different frameworks were leveraged to build sophisticated models for language generation. However, automatic generation of emotional content has barely been studied. We propose in this paper an Attention-Based Sequence Generation Model for Emotionally-Triggered Short-Text Conversation (STC). We use emotion category embeddings to represent different emotions and to trigger the generation of the stated emotion. The emotion vectors are learned during training allowing the model to have a degree of freedom to modify those vectors accordingly. Our attention mechanism is customized to include emotional information by using gated convolutional neural networks (GCNNs) in order to create an emotional context vector. Moreover, we utilize distinct StartOf-Sentence (SOS) token for each emotion category in order to further push our sequence-to-sequence model into a specific emotional generation mode. This approach avoids the implementation of different generative models for each emotion. Experimental results demonstrate the ability and difficulty of our architecture to generate affective answers.
Real-Time Massive Terrain Generation using Procedural Erosion on the GPU

Although terrain modeling is a widely explored field, problems such as scalability remain present when it comes to massive terrain sizes. While procedural methods have disadvantages regarding controllability and visual fidelity, it provides a great effectiveness handling with extremely large terrains due its computational performance. Our approach consists of generating the elevation data using a procedural function in real-time. We provide an intuitive set of parameters that allow control of several landscape characteristic. Real-world natural aspects were taken into account on the generation process. The terrain is represented by a dynamic quadtree structure and the elevation data is stored in a virtual texture. To address real-time performance, we adopt a GPU-based approach for the heightmap generation. Furthermore, a GPU instancing approach was used for rendering. Keywords-massive terrains, procedural generation, perlin noise, erosive noise, fractional brownian motion, real time rendering.
Analyzing Data Properties using Statistical Sampling - Illustrated on Scientific File Formats

Understanding the characteristics of data stored in data centers helps computer scientists in identifying the most suitable storage infrastructure to deal with these workloads. For example, knowing the relevance of file formats allows optimizing the relevant formats but also helps in a procurement to define benchmarks that cover these formats. Existing studies that investigate performance improvements and techniques for data reduction such as deduplication and compression operate on a subset of data. Some of those studies claim the selected data is representative and scale their result to the scale of the data center. One hurdle of running novel schemes on the complete data is the vast amount of data stored and, thus, the resources required to analyze the complete data set. Even if this would be feasible, the costs for running many of those experiments must be justified. This paper investigates stochastic sampling methods to compute and analyze quantities of interest on file numbers but also on the occupied storage space. It will be demonstrated that on our production system, scanning 1% of files and data volume is sufficient to deduct conclusions. This speeds up the analysis process and reduces costs of such studies significantly.
Cell Classi(cid:12)cation from Multi-Focus Images by Deep Neural Network

This paper presents a new method for automatically classifying normal and abnormal cells from multi-focus images by deep neural networks (DNNs). There are the methods for classifying cells by using their images captured at a speci(cid:12)c focus point. However, the use of such single-focus images sometimes makes it diﬃcult to identify abnormal cells, especially, pre-cancerous cells with similar visual attributes with normal cells. To solve this problem, we propose a new method for automatically classifying normal and abnormal cells from multi-focus images by combining deep neural networks (DNNs) with diﬀerent architectures. From the experimental results, our proposed system can classify cells with high performance.
Factors that impact upon the use of e-learning

Many universities and higher educational institutio ns in different parts of the world are embracing the use of e-learning te chnologies to support and facilitate their teaching and learning activiti es, a development made possible by the rapid expansion of Internet technol ogies. These institutions are using various kinds of learning ma nagement system to enable e-learning. Divine Word University (DWU) in Papua New Guinea (PNG) uses the Moodle e-learning platform. W hen used effectively, e-learning can support and enhance the teaching and learning processes within the universities.
Measuring Fluency: Temporal Variables and Pausing Patterns in L2 English Speech

Park, Soohwan. Ph.D., Purdue University, May 2016. Measuring Fluency: Temporal Variables and Pausing Patterns in L2 English Speech. Major Professor: April Ginther. This paper examines temporal variables and pausing patterns in L2 English speech to investigate fluency as a measurable component of oral proficiency. Fluency can be defined as ‘speed and smoothness of oral delivery’. We can measure the speed of oral delivery through calculating temporal variables such as speech rate and mean syllables per run where ‘run’ is the vocal chunk between silent pauses. The smoothness of oral delivery can be measured through examination of pausing patterns by classifying the placement of pauses. Pauses may be placed in expected positions such as clause/phrase boundaries or in unexpected positions. Pause placement in unexpected positions may reduce the smoothness of oral delivery. The data sets are speech samples from the Oral English Proficiency Test (OEPT) but include the responses from two items (RAL: read aloud; NP: news passage). A total of 325 speakers across four different language groups (native speakers of Korean, Chinese, Hindi, and English) are represented across 6 proficiency levels (rated by holistic scoring based on the OEPT scale from 35 to 60). The speech samples were transcribed manually using a computer-assisted annotation tool that allowed capture of information about syllables, pausing boundaries, and types of pausing positions. Development of the annotation tool became a central concern of this study as
A Review on Impact of E-waste on Soil Microbial Community and Ecosystem Function

The ever increasing pile-up of electronic waste in dumping sites, especially in developing countries such as China, Pakistan, India and several African countries, might have caused a significant alteration in the microbial community of the contaminated sites. This change in the microbial population may have significant impact to the soil ecology function. The major pollutants of electronic waste are heavy metals like cadmium, lead, nickel, mercury, hexavalent chromium, arsenic and persistent organic pollutants like polychlorinated biphenyls and polybrominated diphenyl ethers. In general, the toxic pollutants reduce the normal soil microbial biota but give rise to increase in the heavy metal resistant and organic pollutants remediating microbes. With the development of culture- independent approach as a tool for studying microbial diversity, the microbial community structures in toxic waste contaminated sites have been revealed gradually. Studies on the microbial community structure of electronic waste contaminated sites show that there are significant differences between the contaminated and the non-contaminated sites. Soil pH in the e-waste contaminated sites of various regions has been reported in a wide range varying from pH 4 to pH 12. However, the predominant phyla so far identified in the electronic waste contaminated sites, based on studies through culture independent approach, are Firmicutes, Proteobacteria, Actinobacteria, Acidobacteria, Chloroflexi, Crenarchaeota and Bacteroidetes accounting for more than 80% of the total sequence reads on an average. The genera like Pseudomonas, Bacillus, Clostridium, Rhodococcus, Achromobacter and many unclassified bacteria are the common types in the contaminated sites.
Performance Analysis and Portability PLUM Load Balancing System of the

The ability to dynamically adapt an unstructured mesh is a powerful tool for solving computational problems with evolving physical features; however, an efficient parallel implementation is rather difficult. To address this problem, we have developed P[_0M, an automatic portable framework for performing adaptive numerical computations in a message-passing environment. PLUM requires that all data be globally redistributed after each mesh adaption to achieve load balance. We present an algorithm for minimizing this remapping overhead by guar-anteeing an optimal processor reassignment. We also show that the data redistribution cost can be significantly reduced by applying our heuristic processor reassignment algorithm to the default mapping of the parallel partitioner. Portability is examined by comparing performance on a SP2, an Origin2000, and a T3E. Results show that PLUM can be successfully ported to different platforms without any code modifications.
Folated-nanocarrier for curcumin drug delivery in breast cancer therapy

Among the potent anticancer agents, curcumin (CU) has been found to be very efficacious against many different types of cancer cells. Chitosan-coated magnetic nanoparticles (CS MNPs) were prepared and utilized as a nano-carrier for loading of curcumin (FA-CS-CU-MNPs) through a reverse micro emulsion method. This nano formulation was evaluated against breast cancer cell lines in the in vitro conditions. Both shape and size properties were studied by zeta sizer, AFM and FESEM and the cell internalization ability of prepared nanoparticles was determined by fluorescence microscopy. It was found that the synthesized FA-CS-CU-MNPs were spherical in shape with an average size of 90±15 nm, low aggregation and good magnetic responsive properties. Meanwhile, the high drug loading efficiency (~73%) was remarkable. These FA-CS-CU-MNPs also demonstrated sustained release of CU at 37 ◦C in different buffer solutions. Afterwards the suitable dose and therapeutic effects of (FA-CS-CU-MNPs) for both breast cancer and normal cell lines were evaluated by MTT assay. The result showed that FA-CS-CU-MNPs retained significant antitumor activities, no adverse effects was detected for normal cells. Additionally, it was observed that the FITC-labeled FA-CS-CU-MNPs could effectively enter into the cancer cells and induced cell apoptosis.
Finding Theme Communities from Database Networks

Given a database network where each vertex is associated with a transaction database, we are interested in finding theme communities. Here, a theme community is a cohesive subgraph such that a common pattern is frequent in all transaction databases associated with the vertices in the subgraph. Finding all theme communities from a database network enjoys many novel applications. However, it is challenging since even counting the number of all theme communities in a database network is #P-hard. Inspired by the observation that a theme community shrinks when the length of the pattern increases, we investigate several properties of theme communities and develop TCFI, a scalable algorithm that uses these properties to effectively prune the patterns that cannot form any theme community. We also design TC-Tree, a scalable algorithm that decomposes and indexes theme communities efficiently. Retrieving a ranked list of theme communities from a TC-Tree of hundreds of millions of theme communities takes less than 1 second. Extensive experiments and a case study demonstrate the effectiveness and scalability of TCFI and TC-Tree in discovering and querying meaningful theme communities from large database networks.
Effect of toilet tissue Paper on residential sewerage-line clogging

Toilet paper is widely used in residences. The low biodegradability of toilet paper’s pulp fibers can cause residence sewer lines to clog, which may be due to some additives or the presence of nonwoven fiber products. In this study, the disintegration rate of toilet paper was compared against various physical factors of the toilet paper sheet. The samples were disintegrated in water at varying pH levels. In the manufacturing of toilet paper, there must be a balance between the desired softness and the necessary wet strength. Twelve commonly used brands of toilet paper were purchased locally. Physical factors of toilet paper samples such as degree of polymerization, thickness, grammage, and softness were determined. The samples were evaluated based on 9 variables using correlation and multivariate linear regression analyses. A strong positive relationship was found between the degree of disintegration of toilet tissue paper and its physical factors. These were the degree of polymerization, the grammage, thickness, and the softness. Additionally, the amount of polymers applied to toilet paper decreased the degree of softness and adversely affected the redispersion of fibers. Thus, this work supports the idea that toilet paper can contribute to clogging of residential sewerage lines.
Stance Detection Attending External Knowledge on Topics ∗

This paper presents a novel approach to stance detection for unseen topics that takes advantage of external knowledge about the topics. We build a new stance detection dataset consisting of 6,701 tweets on seven topics with associated Wikipedia articles. An analysis of this dataset confirms the necessity of external knowledge for this task. This paper also presents a method of extracting related concepts and events from Wikipedia articles. To incorporate this extracted knowledge into stance detection, we propose a novel neural network model that can attend to such related concepts and events when encoding the given text using bi-directional long short-term memories. Our experimental results demonstrate that the proposed method, using knowledge extracted from Wikipedia, can improve stance detection performance.
Research on Traffic Load Characteristics and Simulation Methods

By adopting sine formula with parameters in simulating the traffic loads,the simulation formula was derived and then fitted by the application to the relative parameters of a typical highway in Yangzhou area. With the assistance of the finite element simulation software ABAQUS,simulation verifications were carried out by using its subroutine- DLOAD in both 2-D and 3-D. Under 2-D situation,the load transfer laws of sine-wave was verified and the influence depth of the traffic loads was calculated. Furtherly,under 3-D situation,the impact that the moving loads poses on the deformation of road hierarchies was verified. Result reveals that the sine formula with parameters is a more suitable formula to simulate traffic loads.
The evolving biology of Mycobacterium tuberculosis drug resistance

Tuberculosis, caused by Mycobacterium tuberculosis (Mtb) is an ancient disease that has remained a leading cause of infectious death. Mtb has evolved drug resistance to every antibiotic regimen ever introduced, greatly complicating treatment, lowering rates of cure and menacing TB control in parts of the world. As technology has advanced, our understanding of antimicrobial resistance has improved, and our models of the phenomenon have evolved. In this review, we focus on recent research progress that supports an updated model for the evolution of drug resistance in Mtb. We highlight the contribution of drug tolerance on the path to resistance, and the influence of heterogeneity on tolerance. Resistance is likely to remain an issue for as long as drugs are needed to treat TB. However, with technology driving new insights and careful management of newly developed resources, antimicrobial resistance need not continue to threaten global progress against TB, as it has done for decades.
Optimization Algorithms for Deep Learning

Gradient descent algorithms are the most important and popular techniques for optimizing deep learning related models. Considering the large scale dataset and the limited computation memory especially on GPUs, the traditional typical batch gradient decent method and stochastic gradient decent method cannot conduct the training effectively and efficiently. Moreover, tricks of tuning of step size (learning rate) also play an critical role during training procedure. To address this problem, several variations of gradient methods have been proposed. In this paper, we investigate the basic methodology and property of the methods. Moreover, we also conduct several experiments on various of tasks to compare the performance.
Smart Products: Liability, Timing of Market Introduction, and Investments in Product Safety

This paper addresses the role of product liability for the emergence and development of smart products such as autonomous vehicles (AVs). We analyze how the liability regime affects innovative activities, as well as the timing of market introduction and market penetration of such smart products. We develop a dynamic model in which at each point in time, a potential (monopolistic) innovator decides on how much to invest in the safety stock of the smart product and on the product price, once it has been launched. Calibrating the model to the U.S. car market, our analysis reveals policy-relevant trade-offs when shifting more liability on the producers of AVs. First, while this improves the safety of AVs in the long run, the safety stock is accumulated more slowly. Second, it delays the market introduction of AVs, and also slows down market penetration, which hampers the innovator’s incentives for safety investments in the short- and intermediate term. As a result, the safety level of AVs at a given point in time decreases as the liability regime becomes more stringent. Furthermore, there is a threshold for the innovator’s burden of liability beyond which she forgoes to develop the AV altogether. Finally, we find that direct AV safety regulation is welfare-superior compared to a stringent liability regime, as it induces higher levels of AV safety in the short and intermediate term.
Actual SystemCurrent Linear Dynamics Controller Cost Function Bayesian Optimization Cost Evaluator Output Cost New Linear Dynamics Optimal Control Scheme

Real-world robots are becoming increasingly complex and commonly act in poorly understood environments where it is extremely challenging to model or learn their true dynamics. Therefore, it might be desirable to take a taskspecific approach, wherein the focus is on explicitly learning the dynamics model which achieves the best control performance for the task at hand, rather than learning the true dynamics. In this work, we use Bayesian optimization in an active learning framework where a locally linear dynamics model is learned with the intent of maximizing the control performance, and used in conjunction with optimal control schemes to efficiently design a controller for a given task. This model is updated directly based on the performance observed in experiments on the physical system in an iterative manner until a desired performance is achieved. We demonstrate the efficacy of the proposed approach through simulations and real experiments on a quadrotor testbed.
Lipid Emulsion Treatment ( LET ) of Post-Operative Cognitive Dysfunction ( POCD )

Postoperative delirium (POD) or Post-operative cognitive dysfunction (POCD) is a common and serious adverse event in the elderly patient and is associated with significant morbidity and mortality. A new treatment for POD/POCD by intravenous Intralipid (lipid emulsion) injection in the recovery room was first suggested by Eldor on 2017 (http:// medcraveonline.com/JACCOA/JACCOA-07-00273.pdf). The 4 case reports in this article describing the successful use of lipid emulsions (Smoflipid 20% and Lipidem 20%) are the first case reports of Lipid Emulsion Treatment (LET) of Post-operative cognitive dysfunction (POCD) in the medical literature.
A Dynamic web service scheduling and deployment framework for grid workflow.

Grid computing boosts productivity by maximizing resource utilization and simplifying access to resources which are shared among virtual organizations. Recently, the Grid and Web Service communities have established a set of common interests and requirements. The latest version of the Globus Toolkit implements the Web Service Resource Framework (WSRF) specifications which have been formulated to cover these interests. However, it has some limitations in supporting the dynamic nature of large-scale Grid and data-intensive workflow executions. Dynamic Web Service deployment fits well into the dynamic nature of the Grid and opens new ways of managing workflow executions on the Grid. So far, there is little work on deploying Web Services dynamically on Grid computing resources. In this thesis, we design a dynamic Web Service scheduling and deployment framework that supports the workflow management of dynamic services. Dynamic Web Service deployment on the Grid enables the system to distribute workflow jobs on the Grid computing resources and allows jobs to be executed on the same site as where the input data is located. The empirical studies show that the designed framework increases resource utilization and decreases workflow execution time. We argue that the framework ensures more flexible, fault-tolerant workflows and also reduces the likelihood and frequency of bottlenecks. The system is based on Open Grid Services Architecture specifications and is WSRF-compliant. ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library