Miracle-dz/UniFAR-FTUs huggingface dataset:
Regional Balanced Index Measurement of Chinese High-tech Industry's Science and Technology Resource Allocation

High-tech industry's science and technology resource allocation has a characteristic of regional concentration, and optimizing the allocation is conductive to balanced development of regional high-tech industry. By constructing the measurement indicator system of the science and technology resource allocation level based on human resource, material resource and financial resource, doing sample empirical analysis on the data of science and technology resource allocation indicators of high-tech industry of 31 regions in China from 2005 to 2009, an important conclusion is drawn that the balanced index of science and technology resource allocation of high-tech industry of our country is relatively small, and regional high-tech industry's science and technology resource allocation is extremely unbalanced.
The smear layer in endodontic: To keep or remove – an updated overview

During mechanical preparation, the use of hand or rotary instruments results in the production of considerable amount of smear layer. The smear layer consists of two parts: a superficial layer that covers the dentinal wall and a smear plug which occludes that dentinal tubules. Researchers had reached to different conclusions on the importance of removing or maintaining this layer. Removing the smear layer allows for more cleaning and disinfecting root canal walls and better adaptation of root canal filling materials. However, the presence of smear layer can act as a seal to the dentinal tubules and minimize the ability of bacteria and its toxins from penetrating the dentinal tubules. The ability to remove smear layer depends primarily on chemomechanical preparation. There are three main methods to remove smear layer: chemically, mechanically (ultrasonically), laser, or combinations. No one single irrigant has the ability to kill microorganisms, dissolve organic tissues, and demineralize smear layer. Thus, alternating between organic and inorganic solvents and the use of different methods and techniques have been recommended. Indeed, there is little relevance attributed to the influence of smear layer on the clinical treatment outcomes. Moreover, there is critical lack of clinical studies to determine the role of smear layer since all previous studies were carried out on laboratory based. Further experimental model with a longitudinal observational characteristic should be applied.
From Self-assembled Brominated Diphenylacetylene Molecules to On-surface Organic Nanowires

The paper reports on the on-surface synthesis of a novel organic nanowire starting from the deposition of 1,2-bis(4bromophenyl) ethyne on the Ag (110) surface in ultra-high vacuum conditions. The molecular precursors self-assemble on the surface afret evaporation and form organo-metallic nanowires, wherein the silver atoms replace the bromine atoms at the two ends of the self-assembled molecules. The intermediate organometallic species eventually evolve to form all-organic ordered polymers by annealing the substrate at a higher temperature, which causes the release of silver adatoms and the formation of direct C-C bonds between adjacent molecular units.
DNA Sequencing using Brain-inspired Hyperdimensional Computing

—DNA sequencing has a vast number of applications in a multitude of applied ﬁelds including, but not limited to, medical diagnosis and biotechnology. In this paper, we propose HDNA to apply the concepts of hyperdimensional (HD) comput- ing (computing with hypervectors) to DNA sequencing. HDNA ﬁrst assigns holographic and (pseudo)random hypervectors to DNA bases. Using an encoder, it then exploits the orthogonality of these hypervectors to represent a DNA sequence by generating a class hypervector. The class hypervector keeps the information of combined individual hypervectors (i.e., the DNA bases) with high probability. HDNA uses the same encoding to map a DNA sequence with unknown labels to a query hypervectors and performs the classiﬁcation task by checking the similarity of the query hypervector against all class hypervectors. Our experimental evaluation shows that HDNA can achieve 99.7% classiﬁcation accuracy for Empirical dataset which is 5.2% higher than state-of-the-art techniques for the same dataset. Moreover, our HDNA can improve the execution time and energy consumption of classiﬁcation by 4.32 × and 2.05 × respectively, when compared against prior techniques.
Prognostic value of Wilms ’ tumor 1 in epithelial ovarian cancer : a systematic review and meta-analysis

Purpose: Although ovarian cancer has become a major cause of morbidity and mortality, the important question of specific prognostic factor has not been explicated thoroughly. Therefore, we conducted a systematic review and meta-analysis to investigate the prognostic value of Wilms’ tumor 1 (WT1) expression level in patients with ovarian cancer. Methods: A comprehensive search was made for studies that evaluated the prognostic value of WT1 in ovarian cancer patients. PubMed, Embase, the Cochrane Library, the clinical trial registration website (ClinicalTrials.gov) and Ovid databases from the earliest available date to July 10, 2018 were used to identify the studies. References of selected studies were checked manually for further potential trials. Random-effects model was used to estimate hazard ratio (HR) statistics. All statistical analyses were performed by STATA (version 13.0; College Station, Texas, USA). Results: Nine studies were involved in this meta-analysis. According to higher versus lower WT-1 expression, the pooled HR of overall survival is 1.05 (95% confidence interval, CI = 0.57-1.52, P = 0.125). The pooled HR is 1.33 (95% CI = 0.77-1.85, P = 0.191) in disease-specific survival (DSS), and 1.17 (95% CI = 0.66-1.69, P = 0.177) in disease-free survival (DFS)/progression-free survival (PFS). Moreover, subgroup analysis of OS revealed that higher WT1 expression in ovarian cancer patients with III-IV stage might be a poorer prognostic biomarker for OS (HR = 1.98, 95% CI = 1.15-3.41). Conclusion: WT1 expression level is not associated with the OS, DSS, DFS or PFS in ovarian cancer patients. However, higher WT-1 may cause poorer survival in cases with advanced cancer stage.
The Main Factors Influencing Consumer Behavior towards Bershka Store in Kazakhstan

The fashion industry has made a considerable growth in the global market all over the world comparing to the rest of market representatives. Many experienced entrepreneurs strive to invest their money in this area of business because they find it profitable to set up and develop in Asian and Central Asian countries. However, many young companies suffer from different problems while penetrating this type of market and getting to the same level of success that fashion giant companies achieved, the fashion industry is still expected to be one of the fastest developing and competitive industries in the world. The fashion market in Kazakhstan has made several steps to improve the current situation in the local market taking into account that giant companies such as Zara, Bershka, H & M penetrated the Kazakh clothing market not a long time ago and gained its success by paying attention to some factors that have a significant impact on the consumer attitude in Kazakhstan. This makes sense to study the main aspects influencing on the behavior of customers in Kazakhstan based on previous works which were made in this particular field. Studying these factors might help to understand the consumer behavior from it’s the most important side of management. Customer behavior is an activity that is directly involved in the acquisition and consumption of goods and services, including the decision processes that precede and follow that activity. Understanding the factors that affect consumers to make their purchases can help to improve the current situation and make important steps to increase the sales in the future. Keywords— Consumer Behavior, Fashion Industry, Global Market, Kazakh Clothing Market
Agronomic and quality characteristics of old , modern and mixture wheat ( Triticum aestivum spp . ) varieties and landrace for

Wheat landraces and old varieties could have an important role for food security not only as source of gene readily available for breeders, but also because they perform well in marginal environments and are more resilient as compared to the modern cultivars. The Italian cereal sector suffers from lack of seed companies that breed specifically adapted varieties for organic and biodynamic farms. Participatory and evolutionary plant breeding (PPB and EPB) have been used in this research to (i) evaluate the agronomic characteristics of old, modern and mixture of varieties and landrace of bread wheat (Triticum aestivum spp.) and their adaptability to organic farming in hilly and mountainous areas; (ii) assess the technological, nutritional and functional properties of grains (rheological characteristic, macro and micro elements contents and antioxidants); (iii) explore the consumers’ preferences for breads obtained by old and modern varieties. Between five and seven old (Sieve, Verna, Gentil Rosso, Andriolo, Gambo di ferro, Frassineto and Abbondanza), two mixtures and four modern (Bolero, Blasco, Arabia and Bologna) varieties were tested for two years in between two and three organic farms (FARM1, FARM2 and FARM3) in hilly areas of Piedmont. Agronomic characteristic were strongly affected by locations and years. On average, Bologna, Abbondanza and Arabia, and the two mixtures were the highest yielding varieties. Flour strength (W) varied greatly ranging from 230 in 2011 for Andriolo to 38 in 2012 for Gambo di ferro. Gluten quality, expressed by GI, was found to be almost within the optimal range but was affected by the year. All six bread samples were acceptable to the 233 consumers who scored them, but the bread produced with old wheat varieties, particularly with Andriolo and Gambo di Ferro, was the preferred one. The old varieties and their mixtures yielded less than the modern varieties but with higher stability as shown by the inability of the modern varieties in FARM1 to survive the winter (they were not harvested) while the old varieties reached maturity showing higher robustness, Therefore, the use of old bread wheat varieties and their mixture, assessed with participatory and evolutionary plant breeding, could represent a strategy for local communities to cope with climate change while improving food security and food quality. © 2016 Elsevier B.V. All rights reserved.
The Multimodal Correction Detection Problem Extended Abstract

In order for socially aware agents to be truly useful, they should have abilities associated with human intelligence, such as the ability to detect their own mistakes from user reactions. This is an instance of implicit feedback. In this work we address the problem of detecting an agent’s mistakes by identifying when the user tries to correct the agent. We refer to this problem as the Correction Detection task. We use a multimodal approach, using both the voice (acoustics and non-verbal sounds) as well as the transcript of the user’s spoken commands.
TUA 1 at the NTCIR-14 STC-3 Task

In this paper, we describe the overview of our work in STC-3 Chinese Emotional Conversation Generation (CECG) subtask at NTCIR14. We propose a Post & Emotion to Response (P&E2R) model to train emotions together with posts to obtain the responses. We then propose another model called Post to Response & Emotion to Response (P2R&E2R) model to separate the training of emotions from that of grammar and semantics on the basis of the prior model. We try to use these models to explore how to combine emotions with the generation model better. In the evaluation section, the average scores of our models are both over 0.8, which suggests that our proposed models have emotional output capabilities in Chinese.
Trusted D2D-Based IoT Resource Access Using Smart Contracts

We present and evaluate models that allow clients to access IoT resources using secure and trusted device-to-device (D2D) communication, while utilizing smart contracts to obtain the benefits of blockcain technology. These benefits include decentralized trust, immutability, transparency, and high availability. The models consider different network connection capabilities of the clients and the IoT resources, namely continuous network connectivity and D2D-only connectivity. We describe two approaches for utilizing blockchains and smart contracts in the authorization process: in the first approach, only hashes of the authorization information are recorded on the blockchain. In the second approach, a smart contract handles authorization requests. We implement the approaches using the OAuth 2.0 delegated authorization framework and evaluate the implementations on the public Ethereum testnet Rinkeby, in terms of execution cost, contract creation cost, and delay. Our evaluation quantifies the tradeoffs of blockchain cost and smart contract functionality, such as blocking and non-blocking operation, and the reduction of the transaction cost that can be achieved when multiple authorization requests are concatenated in a single transaction.
Transport of Nanomaterials in Complex Media

Potential benefits of nanotechnology are immense, also its dangers. Nanomaterials are developed for their new radical properties involving to completely new and amazing results. Although exists a wide consensus about the advances of nanotechnology in fields like materials, medicine and energy, there is also huge doubts about biological, environmental and security implications. Some studies had identified knowledge gaps that must be addressed immediately, some of the recommendations are: to determine the kinetics and transport mechanism of nanoparticles in human body, and their characterization. The main objective of this paper is to describe the progress made in the design of equipment and protocols for future study of nanomaterials transport in biological media.
A branch-and-price-and-cut method for train unit scheduling with complex minimum turnround time requirements

We propose a branch-and-price-and-cut method with warm-start for solving the integer fixed-charge multicommodity flow (IFMCF) model for the network flow level of the train unit scheduling problem, in particular with complex minimum turnround time requirements. This problem is regarded to be difficult due to its nature in integer flows and fixed-charge variables. The key components of this method are a warm-start strategy and a dynamic cut generation scheme that will only add certain constraints when needed. We also study the theoretical perspective in finding strong valid inequalities associated with the dynamic cuts for preventing time allowance violations due to coupling/decoupling.
Moses - statistical machine translation system

Abstract This document serves as user manual and code guide for the Moses machine translation decoder. The decoder was mainly developed by Hieu Hoang and Philipp Koehn at the University of Edinburgh and extended during a Johns Hopkins University Summer Workshop and further developed under EuroMatrix and GALE project funding. The decoder (which is part of a complete statistical machine translation toolkit) is the de facto benchmark for research in the field. This document serves two purposes: a user manual for the functions of the Moses decoder and a code guide for developers. In large parts, this manual is identical to documentation available at the official Moses decoder web site http://www.statmt. org/. This document does not describe in depth the underlying methods, which are described in the text book Statistical Machine Translation (Philipp Koehn, Cambridge University Press, 2009).
Empirical Study of Robust State Estimation for Power Systems

This paper provides insights into the existence of spurious local optima for the nonlinear `1-norm state estimator in power system applications. The linear least-absolute-value (LAV) estimator has attracted considerable attentions over the past few years, especially in the machine learning community, mainly due to its convexity and robustness with respect to sparse noise. Due to these properties, recent studies have attempted to apply the nonlinear version of the LAV to problems such as topological error detection in power systems. However, there has been no study so far that provides theoretical guarantees for finding the global solution of the nonlinear least-absolute-value (NLAV) estimator. In this study, we analyze the performance of NLAV on different real-world scenarios and compare it with the nonlinear least-squares (NLS), which is the common practice in power industry. In our study, we consider various factors, such as the number of measurements, available prior information, and the presence of bad data, on the performance of these estimators by performing more than 260000 simulations on several IEEE benchmark systems. Additionally, we use a recent result on robust principal component analysis and low-rank matrix completion to justify our observations and provide recommendations for realworld applications of these estimators.
Phillips Curves in the Euro Area

We perform a robust estimation of the Phillips curve in the euro area using a battery of 630 theory-driven models. We extend the existing literature by adding model specifications, taking into account the uncertainty in the measurement of variables and testing for potential non-linearities and structural changes. Using Dynamic Model Averaging, we identify the most important determinants of inflation over the sample. We then forecast core inflation 12 quarters ahead and present its probability distribution. We compare the distribution of forecasts performed in recent years, and we assess, in a probabilistic manner, the convergence towards a sustainable path of inflation. JEL Classification: C30, E52, F41, E32
Cybersecurity Vital Signs : The Role of Anomaly Detection on Insider Threat Triage

Detecting cybersecurity insider threat has become progressively challenging, as an over saturation of data has made it increasingly difficult to parse and consume information. In the past intrusion detection systems (IDS) were used to identify anomalies and potential misuse. However, IDSs do not specialize in the identification of anomalous activities. Thus, the development of anomaly detection systems (ADS) tailored toward the identification of deviations in behaviors is more sufficient. Though the use of anomaly detection systems have grown within cybersecurity, cybersecurity analysts face the problematic task of focusing on the right information in order to identify potentially malicious insider threats. In this paper, we will provide empirical evidence toward the identification and validation of cybersecurity vital signs that will aid cybersecurity analysts with triage for potentially malicious insider threats. A comparison of IDS and anomaly detection systems will be presented to depict the importance of separating anomaly detection from intrusion detection systems. We will also present the development of a prototype focused on effectively visualizing cybersecurity vital signs.
Management of Opioid Tolerability and Related Adverse Effects

Opioid analgesics are effective but are not always well tolerated by patients. Adverse effects of opioids range from the common ones (constipation, nausea) to the less common (hiccups, lower leg edema), from the relatively mild (dry skin, runny nose) to the severe (respiratory depression). Adverse effects associated with opioid use can be treatment limiting, but a variety of management strategies and tactics exists. The adverse effects of opioids may be classed into inhibitory and excitatory, although they sometimes occur simultaneously. Ultra-low doses of opioid antagonists can reduce excitatory effects, which, in turn, can heighten the inhibitory response (and possibly potentiate analgesia). Patients on opioid therapy may benefit from a change in drugs (ideally to a non-opioid altogether, but possibly to a lower dose or different opioid) or combination therapy. Specific medications or therapies have been shown to be effective in managing some adverse effects of opioid therapy and are discussed here, but there is considerable intra-patient variability. Opioid therapy confers analgesic benefits to well selected patients, but adverse effects of this therapy are common and may require physician management.
Performance Evaluation Of Spam Detection Techniques In Relation To Stream Computing

This dissertation explores an alternative to load shedding in a stream computing environment. The hypothesis which leads this research states: “Is there viability to implement adapting/switching out the computational algorithm in a stream computing system under load, where the computations being done are sufficiently complex and alternatives with a trade of in effectiveness and regained cost are available”. To explore this, the application of content based spam detection was chosen as it fits a stream computing environment and requires sufficient computations. A number of machine learning models were implemented and evaluated in terms of effectiveness and cost and conclusions were drawn on the effectiveness of algorithmic adaption as an alternative to load shedding.
Using Low-threshold Applications and Software Templates to Improve Efficiency in an Introductory Statistics Course

This study was an exploratory study in improving efficiency in university courses by using low-cost methods of design and development that can be easily managed by university faculty. To explore this issue, we developed a lesson for the Statistics department at Brigham Young University using low-threshold applications (uses of technology that are low-cost and easy to learn) and software templates. We evaluated the lesson as a possible method to decrease the number of hours instructors were required to spend teaching in class. We discovered that students responded positively to the lesson, and that the methods of lesson design and development did provide advantages to the faculty of the Statistics department. Students reported that they learned from the lesson, liked the lesson, and felt comfortable using the lesson. Students who were most likely to react positively to the lesson were those who had experience with similar instructional models, those who used advanced features of the lesson, and those who used practice problems provided with the lesson. The advantages of using low-threshold applications and software templates included a decrease in development and maintenance costs, as well as a method to quickly analyze possible design alternatives.
Nonlinear Model Predictive Control of Coupled Rotational-Translational Spacecraft Relative Motion

In this paper, a nonlinear model predictive control (NMPC) policy is developed for kinematically and dynamically coupled rotational-translational motion of a chaser spacecraft relative to an uncooperative, tumbling target asteroid. The goal of the NMPC policy is to rendezvous the chaser spacecraft, equipped with a robotic grasper, to the asteroid surface to collect a sample rock. The relative spacecraft motion model is kinematically coupled due to the non-center-of-mass points on both the target and chaser. Additionally, the chaser spacecraft is actuated by eight gimbaled thrusters, introducing dynamic coupling via control that simultaneously produces forces and torques. The combined 6-degree-of-freedom kinematically and dynamically coupled relative motion model is constrained by the NMPC policy to approach the asteroid surface via a line-of-sight cone, while enforcing thruster gimbal limit constraints. Simulations demonstrate the effectiveness of the NMPC policy in bringing the chaser spacecraft to rest relative the tumbling asteroid while satisfying state and input constraints.