Miracle-dz/UniFAR-FTUs huggingface dataset:
K-PLUG: K NOWLEDGE - INJECTED P RE - TRAINED L ANGUAGE M ODEL FOR N ATURAL L ANGUAGE U N - DERSTANDING AND G ENERATION

Existing pre-trained language models (PLMs) have demonstrated the effectiveness of self-supervised learning for a broad range of natural language processing (NLP) tasks. However, most of them are not explicitly aware of domain-specific knowledge, which is essential for downstream tasks in many domains, such as tasks in e-commerce scenarios. In this paper, we propose K-PLUG, a knowledgeinjected pre-trained language model based on the encoder-decoder transformer that can be transferred to both natural language understanding and generation tasks. We verify our method in a diverse range of e-commerce scenarios that require domain-specific knowledge. Specifically, we propose five knowledgeaware self-supervised pre-training objectives to formulate the learning of domainspecific knowledge, including e-commerce domain-specific knowledge-bases, aspects of product entities, categories of product entities, and unique selling propositions of product entities. K-PLUG achieves new state-of-the-art results on a suite of domain-specific NLP tasks, including product knowledge base completion, abstractive product summarization, and multi-turn dialogue, significantly outperforms baselines across the board, which demonstrates that the proposed method effectively learns a diverse set of domain-specific knowledge for both language understanding and generation tasks. The code, data, and models will be publicly available1.
SHOULD THE COMMON LAW SYSTEM WELCOME ARTIFICIAL INTELLIGENCE : A CASE STUDY OF CHINA ’ S SAME-TYPE CASE REFERENCE SYSTEM

China has recently embraced a national use of “big data” through the implementation of judicial reforms that utilize new technology. These technological reforms have turned China’s judicial system into one closely resembling a common law system. Because of this, China’s use of artificial intelligence (AI) in the judicial setting provides a case study for general implementation of AI in common law systems.
Road killing of lizards and traf fi c density in central Italy

Road traffi c affects wildlife in several ways, among which direct killing of animals as a result of collision with cars is one of the main causes of mortality for many species of vertebrates in the European countries. The intensity of this impact on wildlife populations depends on many factors, concerning both ecoethological traits of different taxa and road features. For birds, it seems that the density of traffi c strongly affects collisions with vehicles, but detailed studies are scanty (Erritzoe et al., 2003). The occurrence of reptiles is generally documented in lists of road-killed species, but specifi c studies are scanty too. Concerning snakes, Bonnet et al. (1999) showed a risk of road-killing for adult males during the mating season and for juveniles during dispersion. Lizards use open habitats created by management of road sides: moreover, asphalt used as a substrate can result in increasing the basking performance. In this work we investigate the infl uence of traffi c density on (1) the use of roads by lizards and (2) the intensity of road-killing for lizards.
A Survey and Analysis on String Matching Techniques and its Applications

String matching is very popular as well as useful in many web related applications. String matching is related to information retrieval and helpful for performing search in search engines, social networking and other online applications as well as in software applications. This paper, deals with survey and analysis of state of art, exact string matching algorithms and approximate string matching algorithms with their applications including spell checking, plagiarism detection, intrusion detection and so on. An important objective of this survey is to describe that different string matching algorithms produce different results according to need and some of them are comparable as well as optimum in terms of efficiency. This paper also states that how different techniques, functions and approaches like preprocessing, character comparison, hash function, edit distance, n-gram and other, are used in string matching algorithms for making them efficient and optimum. Further, this study describes that why different string-matching algorithms that have been developed in last few years and how these are useful to achieve optimum results.
Photodegradation of Orange F3R Dyes: Effect of Light Sources and the Addition of CN-Codoped TiO2 Catalyst

The degradation of orange F3R textile dye (C.I.V at Orange 16; C29H12ClNO5) has been done by UV-light, visible-light and solar-light irradiation photolysis using C-N-codoped TiO2 catalyst. Orange F3R dyes are synthetic organic dyes which difficult degraded spontaneously in the environment. The heavy use of these dyes by industry will give negative impact to aquatic life. Degradation of orange F3R by UV-light photolysis is conducted using 10 watt UV lamp (λ=365 nm). Whereas, the intensity of visible LED light, Phillips 13 W is 2000 lux and solar-light during the degradation of orange F3R is 45.000 lux. All of photolysis method has been done without and with the addition of C-N-codoped catalyst on varied degradation times. Orange F3R dyes could be degraded by 11.82% after 180 min UV-light irradiation without catalyst, and the degradation percentage increase to 39.05% by the addition of 12 mg C-N-codoped TiO2 catalyst. The irradiation of visible light for 180 min can degrade the orange F3R dyes by 11.83% without catalyst and increase after addition 9 mg C-N-codoped TiO2 catalyst to 40.86%. While degradation percentage of solar irradiation photolysis is 22.81% without catalyst and increase to 64.12% by the addition of 6 mg C-N-codoped TiO2 catalyst.
The Role of Architectural Representation on the Formation of the Mental Image of Tourist Destination ( Case Study : Traditional Dwellings in the Data Theory Method )

The aim of this study was to explain the role of place representation in shaping the mental image of tourists from traditional residences. Representing the architecture of traditional residences in cyberspace as one of the most important sources of effective information in shaping the image of tourist destinations is of great importance. Today, traditional resorts as one of the most important tourist attractions of historic cities, are of interest to tourists. These tourists make extensive use of the visual and verbal information contained in the online media to identify and select their destinations. Using a qualitative approach, in addition to field observations, research data were collected through semi-structured interviews with 32 domestic and foreign tourists, experienced tour guides, content production experts, and managers of traditional accommodations. To analyze the data and present the final theory, the data theory method of Corbin and Strauss Innovative Foundation was used. Based on this method, through open, central and selective coding, research findings were drawn and presented in the form of a paradigm model consisting of three dimensions of conditions, strategies and consequences. The findings include 9 sub-categories, consisting of cyberspace, location-based representation, cultural tourism, geographical context of the destination, managerial factors, tourism, location-based evaluation, online interaction and exchange, and mental image enhancement. The main category of this research is location-based experience, which includes the sub-categories. Providing a conceptual model to explain the relationships between categories shows that the category of locationbased representation, as the most important reason for creating experience through space, allows the development of perceptions of the environment, which is classified into three categories: architectural representation, representation of semantic aspects and representation of activities and events. The role of architectural representation was done by explaining how the mental image of tourists is formed from traditional accommodations in cyberspace. The results of this study can help architects to understand the perceptions and knowledge of tourists of traditional accommodations in cyberspace, as well as how historical monuments are represented in the media.
The Labor Market Integration of Refugees and other Migrants in Germany GLO Discussion

Using the panel data from 1995 to 2019, this paper investigates the labor market integration of non-EU immigrants in Germany. The existing evidence shows that the economic outcomes of migrants are far behind natives. However, immigrants are a heterogeneous group in terms of their motives for migration and skills composition. In this paper, I disentangle immigrants into refugees and other migrants and compare the employment probability gap between refugees, other migrants, and natives. I also examine whether refugees have a lower employment outcome than other migrants. The result confirms that refugees and other migrants are less likely to be employed than natives and the employment gap is much higher for refugees. I also find evidence of heterogeneity across gender. Other migrant men do not significantly differ from native men in the probability of being employed. In contrast, refugee women have an economic disadvantage than other migrant women and native women. I find no evidence that health status differences attribute to the employment gap between refugees, other migrants, and natives. Finally, this paper highlights the importance of the migration category when assessing the integration of immigrants into the labor market. JEL Classifications: J15; J21; J61; F22
A DVERSARIALLY R OBUST F EDERATED L EARNING FOR N EURAL N ETWORKS

In federated learning, data is distributed among local clients which collaboratively train a prediction model using secure aggregation. To preserve the privacy of the clients, the federated learning paradigm requires each client to maintain a private local training data set, and only uploads its summarized model updates to the server. In this work, we show that this paradigm could lead to a vulnerable model, which collapses in performance when the corrupted data samples (under adversarial manipulations) are used for prediction after model deployment. To improve model robustness, we first decompose the aggregation error of the central server into bias and variance, and then, propose a robust federated learning framework, named Fed BVA, that performs on-device adversarial training using the bias-variance oriented adversarial examples supplied by the server via asymmetrical communications. The experiments are conducted on multiple benchmark data sets using several prevalent neural network models, and the empirical results show that our framework is robust against white-box and black-box adversarial corruptions under both IID and non-IID settings.
The Clinical Data Intelligence Project

This article is about a new project that combines clinical data intelligence and smart data. It provides an introduction to the “Klini-sche Datenintelligenz” (KDI) project which is founded by the Federal Ministry for Economic Affairs and Energy (BMWi); we transfer research and development results (R&D) of the analysis of data which are generated in the clinical routine in speciﬁc medical domain. We present the project structure and goals, how patient care should be improved, and the joint efforts of data and knowledge engineering, information extraction (from textual and other unstructured data), statistical machine learning, decision support, and their integration into special use cases moving towards individualised medicine. In particular, we describe some details of our medical use cases and co-operation with two major German university hospitals.
Convolutional Neural Networks for Modeling and Forecasting Nonlinear Nonstationary Processes

The object of research. The object of research is modeling and forecasting nonlinear nonstationary processes presented in the form of time-series data. Investigated problem. There are several popular approaches to solving the problems of adequate model constructing and forecasting nonlinear nonstationary processes, such as autoregressive models and recurrent neural networks. However, each of them has its advantages and drawbacks. Autoregressive models cannot deal with the nonlinear or combined influence of previous states or external factors. Recurrent neural networks are computationally expensive and cannot work with sequences of high length or frequency. The main scientific result. The model for forecasting nonlinear nonstationary processes presented in the form of the time series data was built using convolutional neural networks. The current study shows results in which convolutional networks are superior to recurrent ones in terms of both accuracy and complexity. It was possible to build a more accurate model with a much fewer number of parameters. It indicates that one-dimensional convolutional neural networks can be a quite reasonable choice for solving time series forecasting problems. The area of practical use of the research results. Forecasting dynamics of processes in economy, finances, ecology, healthcare, technical systems and other areas exhibiting the types of nonlinear nonstationary processes. Innovative technological product. Methodology of using convolutional neural networks for modeling and forecasting nonlinear nonstationary processes presented in the form of time-series data. Scope of the innovative technological product. Nonlinear nonstationary processes presented in the form of time-series data.
What Explains the Heterogeneous Decline in Services Trade Observed during the Pandemic?

Global services exports and imports declined by 20% each in 2020 from their value in 2019 with significant heterogeneity across countries and sectors. The decline is found to be correlated with the incidence of COVID-19 cases and mortalities, the decline in merchandise trade and with the different ways in which services trade is transacted across sectors. The latter depends on the sectoral composition of services trade across countries, which in turn emanates from more fundamental determinants of comparative advantage in services. Results from empirical analysis suggest that larger, more capital- and PTA-intensive economies with more restrictive policies on digital trade and lower ability to leverage ICT infrastructure were associated with relatively larger declines in services trade. Regulatory quality is found to have played both alleviating and intensifying roles in the decline, while geographical remoteness is inversely related. However, the expected role of GVC-integration in accentuating the services trade decline finds little support in the GMM results.
The impact of non-professional analyst coverage on stock market liquidity: An instrumental variables approach

Against the backdrop of the recent decline of professional analyst (PA) stock coverage, we study whether research support by non-professional analysts (NPAs) can increase stock market liquidity. In particular, we employ an instrumental variables approach to estimate the causal impact of NPA coverage on the market liquidity of covered stocks. We find that firms that are covered by NPAs in a given quarter display substantially higher market liquidity (as measured by trading volume). To identify the mechanism underlying this relationship, we investigate which types of firms exhibit the highest liquidity increase from NPA coverage. We find that the impact of NPA coverage is stronger when there is no simultaneous PA coverage, as well as for smaller and growth stocks. These findings suggest that NPA coverage stimulates market liquidity by increasing investor recognition and reducing information asymmetry, where investor recognition seems to be the dominant channel.
Assessing the antimicrobial and antioxidant properties of Moringa oleifera – A comparative study between deionised water and Zamzam water as solvent

The aim of this study was to assess the antimicrobial and antioxidant properties of different M. oleifera leaf extracts while exploiting the potential of using Zamzam water as a solvent for the phytochemical extraction. Six solvents were used in the extraction protocol: deionised water (DW), Zamzam water (ZW), deionised water/ethanol (DWE), Zamzam water/ethanol (ZWE), deionised water/acetone (DWA) and Zamzam water/acetone (ZWA). The antimicrobial activities of the extracts were tested against the following microorganisms: S. aureus, B. cereus, B. subtilis, P. aeruginosa, E. coli, P. mirabilis and C. albicans. The least MIC value reported was 6.25 mg/ml with B. subtilis with ZWA while having an MBC value of 50 mg/ml. The highest phenolic content (122.18± 0.45 mg GAE/g extract) as well as the flavonoid content (316.0 ± 6.5 mg QE/g extract) was observed with ZWA. With the DPPH assay, the IC50 values varied from 5.362 ± 0.146 mg/ml to 0.617 ± 0.005 mg/ml with ZWA having the lowest value. Overall, the acetone extracts showed higher biological activity in almost all the assays performed. Interesting findings were also obtained when the DPPH assay was done; revealing the possibility of the two types of water used affecting the results obtained.
KETOCONAZOLE HPLC METHOD DEVELOPMENT AND VALIDATION: A NOVEL APPROACH

The present research article describes the method development and validation of ketoconazole by an innovative HPLC method in dosage form that are solid in nature i.e tablets. Inertsil ODS-C18 column (150mm,4.6mm,5μm) was used for the study and mobile phase consists of buffer (pH-4) and Methanol in the mixture of 30:70. Wavelength for the chromatographic separation used was 274nm. Injection volume was maintained at 20μl. A wide variety of mobile phase combinations used for the study and system contains integrated degasser. Flow rates was maintained a t 1ml/min throughout the process for mobile phase combinations. In this method ICH guidelines were used for validation studies. After analysis of different research and review articles it came to light that HPLC method development of ketoconazole has been done in different dosage forms but less work is done on solid dosage forms and hence still there is enormous potential for new methods to be developed in solid dosage form with different mobile phase combinations.
Towards Demonstrating Fault Tolerance in Small Circuits Using Bacon-Shor Codes

Quantum error correction is necessary to perform large-scale quantum computations in the presence of noise and decoherence. As a result, several aspects of quantum error correction have already been explored. These have been primarily studies of quantum memory[1, 2], an important first step towards quantum computation, where the objective is to increase the lifetime of the encoded quantum information. Additionally, several works have explored the implementation of logical gates[3-5]. In this work we study a next step - fault-tolerantly implementing quantum circuits. We choose the $[[4, 1, 2]]$ Bacon-Shor subsystem code, which has a particularly simple error-detection circuit. Through both numerics and site-counting arguments, we compute pseudo-thresholds for the Pauli error rate $p$ in a depolarizing noise model, below which the encoded circuits outperform the unencoded circuits. These pseudo-threshold values are shown to be as high as $p=3\%$ for short circuits, and $p=0.6\%$ for circuits of moderate depth. Additionally, we see that multiple rounds of stabilizer measurements give an improvement over performing a single round at the end. This provides a concrete suggestion for a small-scale fault-tolerant demonstration of a quantum algorithm that could be accessible with existing hardware.
Curriculum learning for language modeling

Language Models like ELMo and BERT have provided robust representations of natural language, which serve as the language understanding component for a diverse range of downstream tasks.Curriculum learning is a method that employs a structured training regime instead, which has been leveraged in computer vision and machine translation to improve model training speed and model performance. While language models have proven transformational for the natural language processing community, these models have proven expensive, energy-intensive, and challenging to train. In this work, we explore the effect of curriculum learning on language model pretraining using various linguistically motivated curricula and evaluate transfer performance on the GLUE Benchmark. Despite a broad variety of training methodologies and experiments we do not find compelling evidence that curriculum learning methods improve language model training.
Holographic nuclear matter in presence of chemical potential at finite temperature

We study the dense nuclear matter within the holographic Sakai-Sugimoto model. The nuclear matter is described via instantons in bulk, whose size has the new temperature dependency. Then, properties of nuclear matter have been studied for different temperatures. For example, free energy and baryon density are examined for different temperature values. Also, nuclear matter properties, like the speed of sound and connection of nuclear matter and quark matter, are discussed. As we have increased the temperature, the critical chemical potential has been changed from baryon to quark. In our results, this point is non-physical also, see continuity between the baryonic and quark phases. Speed of sound at the low chemical potential in the presence of temperature is different, but when the potential increases, the effect of temperature on the speed of sound will remain unchanged. In the phase diagram, for significant chemical potential, chiral symmetry is restored and provides all things that we have the realistic model.
TextCNN with Attention for Text Classification

The vast majority of textual content is unstructured, making automated classification an important task for many applications. The goal of text classification is to automatically classify text documents into one or more predefined categories. Recently proposed simple architectures for text classification such as Convolutional Neural Networks for Sentence Classification by Kim, Yoon showed promising results. In this paper, we propose incorporating an attention mechanism into the network to boost its performance, we also propose WordRank for vocabulary selection to reduce the network embedding parameters and speed up training with minimum accuracy loss. By adopting the proposed ideas TextCNN accuracy on 20News increased from 94.79 to 96.88, moreover, the number of parameters for the embedding layer can be reduced substantially with little accuracy loss by using WordRank. By using WordRank for vocabulary selection we can reduce the number of parameters by more than 5x from 7.9M to 1.5M, and the accuracy will only decrease by 1.2%.
Exact and Heuristic Approaches to Drone Delivery Problems

The Flying Sidekick Traveling Salesman Problem (FSTSP) considers a delivery system composed by a truck and a drone. The drone launches from the truck with a single package to deliver to a customer. Each drone must return to the truck to recharge batteries, pick up another package, and launch again to a new customer location. This work proposes a novel Mixed Integer Programming (MIP) formulation and a heuristic approach to address the problem. The proposedMIP formulation yields better linear relaxation bounds than previously proposed formulations for all instances, and was capable of optimally solving several unsolved instances from the literature. A hybrid heuristic based on the General Variable Neighborhood Search metaheuristic combining Tabu Search concepts is employed to obtain high-quality solutions for large-size instances. The efficiency of the algorithm was evaluated on 1415 benchmark instances from the literature, and over 80% of the best known solutions were improved.