nils-herrmann/clean_scirepeval huggingface dataset:
The Wisdom of Crowds in FinTech: Evidence from Initial Coin Offerings
Year: 2019

Certification by online analysts and early investors can generate excitement among potential token investors, leading to successful initial coin offerings (ICOs) We test the general notion of "wisdom of crowds" using novel data on nearly 3,400 ICOs, including sequential investor subscriptions during token sales. We find that favorable analyst opinions on the underlying projects are associated with aggressive initial token subscriptions, fundraising success, and exchange listing. Analyst ratings also predict long run token performance. Our results hold during the ICO market's boom and bust cycle. Overall, our results suggest analysts' intermediary role in ICOs, a type of direct placement in the FinTech era.
Empirical analysis on future cash arbitrage risk with portfolio VaR
Year: 2014

This paper constructs the positive arbitrage position by alternating the spot index with Chinese Exchange Traded Fund (ETF) portfolio and estimating the arbitrage free interval of futures with the latest trade data. Then, an improved Delta normal method was used, which replaces the simple linear correlation coefficient with tail dependence correlation coefficient, to measure VaR (Value at risk) of the arbitrage position. Analysis of VaR implies that the risk of future cash arbitrage is less than that of investing completely in either futures or spot market. Then according to the compositional VaR and the marginal VaR, we should increase the futures position and decrease the spot position appropriately to minimize the VaR, which can minimize risk subject to certain revenues.
Mathematics for Engineering Education: What Students Say
Year: 2008

In the context of curriculum renewal, 111 engineering students were surveyed about their perceptions of the relevance of topics in their core mathematics and science subjects to their later engineering studies. The mathematics topics rated most highly were trigonometric functions and linear algebra. Differences by student major and by stage through the degree are described. The implications of low ratings and ways to address these are discussed. This research was undertaken as part of a curriculum renewal project, funded as a Learning and Teaching Performance Fund initiative, in which academics from the Faculties of Science and of Engineering at the University of Technology, Sydney are collaborating to renew the mathematics and science curricula in the Engineering degree course. The curriculum renewal project aims to further enhance the relevance of mathematics and science to engineering students, so that students have the enabling mathematical and scientific knowledge and skills to engage confidently and effectively in their engineering studies and are prepared for life long professional learning. The project follows a design cycle of needs analysis, curriculum planning, design and development, implementation, and review and evaluation. This paper reports on part of the needs analysis, in particular the results of a survey of selected students in the degree.
GHT based automatic kidney image segmentation using modified AAM and GBDT
Venue: Health and Technology
Year: 2019

These days age development to be more prominent in biometric angle. Particularly CAD machine is essentially renowned for ordering and division. In this theory Kidney issue and the division systems are examined. The kidney inconvenience recognizable proof and the finding in logical region are well focused on kidney's external layer. Accordingly, the inward inconvenience isn't yet considered in each case. This is mulled over and another time is produced in this examination for division of Kidney using GBDT (Gradient Boosting Decision Tree) thought. The exploration managed with a novel proficient componentThis research was handled with novel efficient mechanism named as GBDT. A systematic technique termed GBDT was utilized to enhance the predictive model. In the process of renal cortex phase localization, a technique which integrates Generalized Hough Transform (GHT) with Active Appearance Models (AAM) was enforced for kidney localization to appraise the renal cortex thickness. The AAM method always matches a new data to the appearance model by minimizing the intensity of root mean square (RMS) between the new data as well as appearance model instance. And finally, from the result of the localization phase, the proposed method GBDT was employed to segregate the kidney into various components. Then an accumulator matrix indicating the possible position of an object was constructed pursuant to the R table, where the training set data is normally used in this form of table. The results were evaluated to reveal the higher achievement of the proposed system.
Extract rich in flavonoids from Hibiscus sabdariffa calyces: Optimizing microwave assisted extraction method and characterization through LC Q TOF MS analysis
Year: 2019

Hibiscus sabdariffa comprises calyx which is rich in flavonoids and numerous phytochemical compounds. Nevertheless, the extraction parameters can influence the recoveries of these bioactive compounds. It is then important to investigate the contributing effects of the process parameters and optimized their conditions to achieve higher recovery yield. Hence, this study was carried out to examine the contributing effects of microwave assisted extraction (MAE) process factors (irradiation time, ethanol concentration, solvent/sample ratio, temperature, and microwave power) on the yield of total flavonoid (TF) contents from H. sabdariffa calyces using one variable at a time. The significant MAE process parameters were further optimized using Box Behnken design. At the MAE optimal conditions, the extract was analyzed to identify the flavonoid compounds using liquid chromatography mass spectrometry quadrupole time of flight. An optimum TF content of 94.32 mg QE/g extract from H. sabdariffa calyces was obtained at the following MAE conditions: Irradiation time of 4 min; ethanol concentration of 52% vol/vol; microwave power of 450 W and solvent/sample ratio 15:1 ml/g. Moreover, 95 flavonoid compounds were tentatively identified in the extract at optimized MAE conditions. Thus, these flavonoids can be further isolated in future studies to unveil embedded potentials of H. sabdariffa calyx extract.  The procedure described the microwave assisted extraction (MAE) which can serve as a guideline for extracting flavonoids from Hibiscus sabdariffa calyx. Moreover, the liquid chromatography mass spectrometry quadrupole time of flight analysis provided the developed conditions for identifying flavonoids from the calyces. The results obtained suggested the exact MAE conditions to achieve an optimum yield of flavonoids from H. sabdariffa calyces. Most importantly, the tentatively identified flavonoids can further be isolated and purified in future studies to unveil the embedded biological potentials.
In Search of the Ninja: The Historical Truth of Ninjutsu
Year: 2012

Lost in modern myth, false history and general misinterpretation, the Ninja have been misrepresented for many years. More recently, a desire for a more historical view of the ninja has become a popular theme in the history/martial arts community and Antony Cummins is the primary driving force behind that movement. In Search of the Ninja is based upon the Historical Ninjutsu Research Team's translations of the major ninja manuals and consists of genuinely new material. Little historical research has been done on the Ninja of Japan. Here for the first time the connection of the famous Hattori family warriors with the Ninja is explained. The Samurai versus Ninja myth is dispelled. The realities of Ninja skills are analysed. How did a Ninja work underwater when mining castle walls? How can a bird be used to set fire to the enemy's camp? The book explores newly discovered connections to ancient Chinese manuals, lost skills and the 'hidden' Zen philosophy that the Ninja followed. In Search of the Ninja is the first and only historical look at the shinobi of ancient Japan.
TagBreathe: Monitor Breathing with Commodity RFID Systems
Venue: IEEE Transactions on Mobile Computing
Year: 2020

Breath monitoring helps assess the general personal health and gives clues to chronic diseases. Yet, current breath monitoring technologies are inconvenient and intrusive. For instance, typical breath monitoring devices need to attach nasal probes or chest bands to users. Wireless sensing technologies have been applied to monitor breathing using radio waves without physical contact. Those wireless sensing technologies however require customized radios which are not readily available. More importantly, due to interference, such technologies do not work well with multiple users. When multiple users are present, the detection accuracy of existing systems decreases dramatically. In this paper, we propose to monitor users' breathing using commercial off the shelf (COTS) RFID systems. In our system, passive lightweight RFID tags are attached to users' clothes and backscatter radio waves, and commodity RFID readers report low level data (e.g. phase values) We reliably detect the effective human respiration corresponded signal and track periodic body movement due to inhaling and exhaling by analyzing the low level data reported by commodity readers. To enhance the measurement robustness, we synthesize data streams from an array of multiple tags to improve the monitoring accuracy. Our design follows the standard EPC protocol which arbitrates collisions in the presence of multiple tags. We implement a prototype for the breath monitoring system with commodity RFID systems. The experiment results show that the prototype system can simultaneously monitor breathing with high accuracy even with the presence of multiple users.
A Survey on Phrase Mining methods for Unstructured Data
Year: 2019

Phrase mining is a key research problem for semantic analysis and text based information retrieval. A phrase is a sequence of words that appear continuously in the text, and serves as a whole semantic unit in some particular context of documents. The aim of phrase mining involves extraction of quality phrases from large and structured sets of texts. The latest domain independent method of Seg Phrase outperforms the existing methods that rely on complex, linguistic analyzers. But still domain experts need to select quality phrases from abundant candidates. Bearing this in mind to reduce the human effort we propose the concept of automated phrase mining framework Autophrase, which supports any language without the need of POS tagger.
Consumer behaviour for wine 2.0: A review since 2003 and future directions
Year: 2012

This paper summarises the main findings concerning consumer behaviour for wine published in academic journals in the last ten years and provides some suggestions about strategic research directions to take in the next few years. One major finding was that few new or novel findings are occurring in some areas: the role of price, brand, region, grape variety, awards; comparisons of Old and New World; segmentation of wine consumers; the value of sustainable or 'green' wine practices to consumers. Another finding was the predominance of one off convenience sample studies that are difficult to interpret for generalisable results. Some areas with greatest research needs are: retail marketing and consumer response to the variety of techniques retailers use; on premise consumer behaviour; online and social media influences on consumers; premium and luxury wine behaviour and successful marketing practices; consumer behaviour in emerging markets; the value of wine tourism and marketing for value; the relationship between grape/wine quality and consumer behaviour; consumer response to wine and health issues.
Targeting KRAS dependent tumors with AZD4785, a high affinity therapeutic antisense oligonucleotide inhibitor of KRAS
Venue: Science Translational Medicine
Year: 2017

AZD4785 is a therapeutic antisense oligonucleotide targeting KRAS mRNA with promising preclinical antitumor activity and a favorable safety profile. An antisensible approach to targeting KRAS Mutations that cause activation of the KRAS oncogene are common in human cancer, including treatment resistant tumor types such as lung and pancreatic cancer. KRAS has also proven to be notoriously difficult to target with small molecules. To overcome this issue, Ross et al. have turned to genetic technology, demonstrating an antisense oligonucleotide based therapy for inhibiting KRAS. The antisense oligonucleotide used in this study was chemically modified, allowing systemic delivery through subcutaneous injection and avoiding the need for a specialized delivery vehicle. The authors tested the efficacy of this therapy in multiple mouse models of non small cell lung cancer and evaluated its safety in primates, demonstrating its potential suitability for translation to humans. Activating mutations in KRAS underlie the pathogenesis of up to 20% of human tumors, and KRAS is one of the most frequently mutated genes in cancer. Developing therapeutics to block KRAS activity has proven difficult, and no direct inhibitor of KRAS function has entered clinical trials. We describe the preclinical evaluation of AZD4785, a high affinity constrained ethyl containing therapeutic antisense oligonucleotide (ASO) targeting KRAS mRNA. AZD4785 potently and selectively depleted cellular KRAS mRNA and protein, resulting in inhibition of downstream effector pathways and antiproliferative effects selectively in KRAS mutant cells. AZD4785 mediated depletion of KRAS was not associated with feedback activation of the mitogen activated protein kinase (MAPK) pathway, which is seen with RAS MAPK pathway inhibitors. Systemic delivery of AZD4785 to mice bearing KRAS mutant non small cell lung cancer cell line xenografts or patient derived xenografts resulted in inhibition of KRAS expression in tumors and antitumor activity. The safety of this approach was demonstrated in mice and monkeys with KRAS ASOs that produced robust target knockdown in a broad set of tissues without any adverse effects. Together, these data suggest that AZD4785 is an attractive therapeutic for the treatment of KRAS driven human cancers and warrants further development.
Hofstadter butterfly in a cavity induced dynamic synthetic magnetic field
Year: 2019

Energy bands of electrons in a square lattice potential threaded by a uniform magnetic field exhibit a fractal structure known as the Hofstadter butterfly. Here we study a Fermi gas in a 2D optical lattice within a linear cavity with a tilt along the cavity axis. The hopping along the cavity axis is only induced by resonant Raman scattering of transverse pump light into a standing wave cavity mode. Choosing a suitable pump geometry allows to realize the Hofstadter Harper model with a cavity induced dynamical synthetic magnetic field, which appears at the onset of the superradiant phase transition. The dynamical nature of this cavity induced synthetic magnetic field arises from the delicate interplay between collective superradiant scattering and the underlying fractal band structure. Using a sixth order expansion of the free energy as function of the order parameter and by numerical simulations we show that at low magnetic fluxes the superradiant ordering phase transition is first order, while it becomes second order for higher flux. The dynamic nature of the magnetic field induces a non trivial deformation of the Hofstadter butterfly in the superradiant phase. At strong pump far above the self ordering threshold we recover the Hofstadter butterfly one would obtain in a static magnetic field.
Impact of large scale climate oscillation on drought in West Africa
Year: 2019

Drought poses a significant threat to the delicate economies in subsaharan Africa. This study investigates the influence of large scale ocean oscillation on drought in West Africa. Standardized Precipitation Index for the region was computed using monthly precipitation data from the Climate Research Unit during the period 1961 1990. The impact of three ocean oscillation indices Southern Ocean Index (SOI) Pacific Decadal Oscillation (PDO) and North Atlantic Oscillation (NAO) on drought over West Africa was investigated using linear correlation, co integration test, mutual information and nonlinear synchronization methods. SOI showed predominantly positive correlation with drought over the region while PDO and NAO showed negative correlation. This was confirmed by the co integration tests. The nonlinear test revealed more complex relationship between the indices and drought. PDO has lesser influence or contribute less to the drought in the coastal region compared to the Sahel region of West Africa.
Secure Distributed Subgroup Discovery in Horizontally Partitioned Data
Venue: Trans. Data Priv.
Year: 2011

Supervised descriptive rule discovery techniques like subgroup discovery are quite popular in applications like fraud detection or clinical studies. Compared with other descriptive techniques, like classical support/confidence association rules, subgroup discovery has the advantage that it comes up with only the top k patterns, and that it makes use of a quality function that avoids patterns uncorrelated with the target. If these techniques are to be applied in privacy sensitive scenarios involving distributed data, precise guarantees are needed regarding the amount of information leaked during the execution of the data mining. Unfortunately, the adaptation of secure multi party protocols for classical support/confidence association rule mining to the task of subgroup discovery is impossible for fundamental reasons. The source is the different quality function and the restriction to a fixed number of patterns i.e. exactly the desired features of subgroup discovery. In this paper, we present new protocols which allow distributed subgroup discovery while avoiding the disclosure of the individual databases. We analyze the properties of the protocols, describe a prototypical implementation and present experiments that demonstrate the feasibility of the approach.
Influence of Delayed Graft Function and Acute Rejection on Outcomes After Kidney Transplantation From Donors After Cardiac Death
Venue: Transplantation
Year: 2012

Delayed graft function (DGF) and acute rejection (AR) exert an adverse impact on graft outcomes after kidney transplantation using organs from donation after brain stem death (DBD) donors. Here, we examine the impact of DGF and AR on graft survival in kidney transplants using organs from donation after cardiac death (DCD) donors.  We conducted a single center retrospective study of DCD and DBD donor kidney transplants. We compared 1 and 4 year graft and patient survival rates, as well as death censored graft survival (DCGS) rates, between the two groups using univariate analysis, and the impact of DGF and AR on graft function was compared using multivariate analysis.  Eighty DCD and 206 DBD donor transplants were analyzed. Median follow up was 4.5 years. The incidence of DGF was higher among DCD recipients (73% vs. 27% P<0.001) and AR was higher among DBD recipients (23% vs. 9% P<0.001) One year and 4 year graft survival rates were similar (DCD 94% and 79% vs. DBD 90% and 82% Among recipients with DGF, the 4 year DCGS rate was better for DCD recipients compared with DBD recipients (100% vs. 92% P=0.04) Neither DGF nor AR affected the 1 year graft survival rate in DCD recipients, whereas in DBD recipients, the 1 year graft survival rate was worse in the presence of DGF (88% vs. 96% P=0.04) and the 4 year DCGS rate was worse in the presence of AR (88% vs. 96% P=0.04)  Despite the high incidence of DGF, medium term outcomes of DCD kidney transplants are comparable to those from DBD transplants. Short term graft survival from DCD transplants is not adversely influenced by DGF and AR, unlike in DBD transplants.
Joint Reconstruction and Anomaly Detection From Compressive Hyperspectral Images Using Mahalanobis Distance Regularized Tensor RPCA
Venue: IEEE Transactions on Geoscience and Remote Sensing
Year: 2018

Anomaly detection plays an important role in remotely sensed hyperspectral image (HSI) processing. Recently, compressive sensing technology has been widely used in hyperspectral imaging. However, the reconstruction from compressive HSI and detection are commonly completed independently, which will reduce the processing's efficiency and accuracy. In this paper, we propose a framework for hyperspectral compressive sensing with anomaly detection which reconstruct the HSI and detect the anomalies simultaneously. In the proposed method, the HSI is composed of the background and anomaly parts in the tensor robust principal component analysis model. To characterize the low dimensional structure of the background, a novel tensor nuclear norm is used to constrain the background tensor. As the anomaly part is formed by a few anomalous spectra, the anomaly part is assumed to be a tuber wise sparse tensor. In addition, to enhance the separation of the background and anomaly, we minimize the sum of Mahalanobis distance of the background pixels. Experiments on four HSIs demonstrate that the proposed method outperforms several state of the art methods on both reconstruction and anomaly detection accuracies.
Business model innovation for sustainability: a food start up case study
Year: 2018

The purpose of this paper is to investigate and present the way in which a food start up can develop business model innovations, taking into account the importance of social and environmental issues.,The authors studied an Italian pizzeria, implementing the illustrative single case study methodology, to answer the research question: "how can food start ups develop a sustainable business model innovation?",The study highlights sustainable elements for each component of the business model configuration provided by Osterwalder to provide a deeper view of the sustainable business model concept.,The study contributes to the extant research introducing the concept of sustainable innovation within the business model literature. It concludes that, within the food industry, especially for start ups, the development of sustainable business model innovation is particularly important because the industry is itself linked with nature and human respect. Moreover, it suggests a methodological analysis of the business model configuration, which will help to develop clearer and more accurate and influential research.
Competitiveness and Industrial Policy: From Rationalities of Failure Towards the Ability to Evolve
Year: 2016

This paper elaborates a dynamic rationale of industrial policy, focusing on how to strengthen the socio economic system's ability to evolve, i.e. how to achieve high real income together with qualitative change. It highlights that the ubiquitous rationalities of failure, be it of markets, governments, or systems, are rooted in a peculiar habit of accepting hypothetical perfect states as normative benchmarks. In contrast, a dynamic logic of intervention should start from the question, what the system aims to accomplish. Combining the structuralist ontology of micro, meso and macro with the functional principles of evolutionary change, the paper proposes a general typology of economic policies based on their respective contributions to the system's ability to evolve.