Miracle-dz/UniFAR-FTUs huggingface dataset:
A Printable Paste Based on a Stable n-Type Poly[Ni-tto] Semiconducting Polymer

Polynickeltetrathiooxalate (poly[Ni-tto]) is an n-type semiconducting polymer having outstanding thermoelectric characteristics and exhibiting high stability under ambient conditions. However, its insolubility limits its use in organic electronics. This work is devoted to the production of a printable paste based on a poly[Ni-tto]/PVDF composite by thoroughly grinding the powder in a ball mill. The resulting paste has high homogeneity and is characterized by rheological properties that are well suited to the printing process. High-precision dispenser printing allows one to apply both narrow lines and films of poly[Ni-tto]-composite with a high degree of smoothness. The resulting films have slightly better thermoelectric properties compared to the original polymer powder. A flexible, fully organic double-leg thermoelectric generator with six thermocouples was printed by dispense printing using the poly[Ni-tto]-composite paste as n-type material and a commercial PEDOT-PSS paste as p-type material. A temperature gradient of 100 K produces a power output of about 20 nW.
Aprendizado ativo em modo batch ordenado

Com a enorme quantidade de informacao gerada todos os dias na Internet, fica cada vez mais dificil, se nao impossivel, processar e administrar manualmente esses dados. Como uma maneira de contornar este problema, algoritmos de Aprendizado de Maquina vemsendo cada vez mais utilizados nos mais distintos dominios. Um tipo de algoritmo de aprendizado, o Aprendizado Ativo, surgiu como uma maneira de otimizar a fase de treinamento de alguns desses metodos, com a premissa de que os algoritmos sao capazes de obter melhores resultados utilizando menos treinamento caso possam escolher quaisinstâncias devem ser rotuladas. Essa e um premissa especialmente interessante em cenarios em que o dado nao rotulado e abundante e existe um custo, nao desprezivel, associado ao processo de rotular uma determinada instância. Em sua forma original, uma instância e rotulada e utilizada pelo algoritmo a cada iteracao, o que impossibilita o uso de varios oraculos em paralelo. Para resolver este problema, surgiram osmetodos de Aprendizado Ativo em Modo Batch, que sao capazes de selecionar mais de uma instância a cada iteracao. Apesar de estes metodos resolverem o problema dos multiplos oraculos, ainda existe uma dependencia de se executar o algoritmo a cada batch analisado. Com o crescimento do uso desses metodos em ambientes corporativos, surgiu a necessidade de se evitar iteracoes constantes e, consequentemente, o tempo ocioso de analistas contratados, que esperam por um novo batch. Nesta dissertacao, o problema de Aprendizado Ativo em Modo Batch Ordenado e descrito propiciando umrelaxamento do metodo tradicional em Batch. Ao selecionar uma lista ordenada de instâncias e possivel gerar uma lista com um numero arbitrario de documentos a serem rotulados. Dessa maneira, as iteracoes do algoritmo podem ser espacadas conforme a necessidade do usuario. Tal fato possibilita que uma lista de instâncias suficientemente grande (para um dia completo de trabalho do analista) possa ser gerada fora do horario comercial. Alem da definicao formal deste problema, uma solucao e apresentada, utilizando um framework que constroi a lista iterativamente ponderando a utilidade dainstância para o classificador (incerteza) e a diversidade trazida ao modelo em relacao as instâncias ja selecionadas. A avaliacao experimental demonstra que o uso do Batch Ordenado prove uma reducao do numero de execucoes do algoritmo, mantendo a qual-idade das instâncias selecionadas. Em alguns casos, utilizando somente o conteudo nao rotulado disponivel, os resultados obtidos sao melhores que os obtidos utilizando metodos tradicionais. Em outras palavras, uma lista ordenada de instâncias, gerada a partir do conteudo nao analisado, foi responsavel por um processo de rotulacao com resultados estatisticamente melhores ou iguais ao de algoritmos tradicionais de aprendizado ativo mas sem suas limitacoes.
Eﬃcient Bayesian Inference for Nested Simulators

We introduce two approaches for conducting eﬃcient Bayesian inference in stochastic simulators containing nested stochastic sub-procedures, i.e., internal procedures for which the density cannot be calculated directly such as rejection sampling loops. The resulting class of simulators are used extensively throughout the sciences and can be interpreted as probabilistic generative models. However, drawing inferences from them poses a substantial challenge due to the inability to evaluate even their unnormalised density, preventing the use of many standard inference procedures like Markov Chain Monte Carlo (MCMC). To address this, we introduce inference algorithms based on a two-step approach that ﬁrst approximates the conditional densities of the individual sub-procedures, before using these approximations to run MCMC methods on the full program. Because the sub-procedures can be dealt with separately and are lower-dimensional than that of the overall problem, this two-step process allows them to be isolated and thus be tractably dealt with, without placing restrictions on the overall dimensionality of the problem. We demonstrate the utility of our approach on a simple, artiﬁcially constructed simulator.
Analysing the vegetation condition during the 2017 and 2018 growing seasons using indices derived from Sentinel-2 data : a case study over southern Sweden

The summer of 2017 was climatically close to normal in southern Sweden, whereas the following summer was unusually warm and dry. These two years therefore make an interesting case study for investigating the impact of severe drought on vegetation, particularly when considering that climate change is predicted to lead to an increased frequency of drought events in the study area. The comparison was done by calculating vegetation indices (VI) based on satellite imagery. The calculated indices are Normalised Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalised Difference Water Index (NDWI) based on four Sentinel-2 images from 2017 and four images from 2018. Rain Use Efficiency (RUE) was calculated based on NDVI and EVI and a precipitation data set from PERSIANN-CCS, in an attempt to measure drought resilience. The data was extracted for different land covers and crop types to determine where the largest differences were seen in the study area. It was found that the land cover with the smallest decreases (and some increases) in VI values between the two years was mixed forest. Both coniferous and deciduous forest had more negative changes between the two years; this suggests that the combination of tree types could increase the drought resilience. The land cover with the largest decrease was agricultural land which had decreasing values for all VI’s, suggesting that this might be the least drought resilient land cover. The break-down of the changes by crops showed that part of the decrease in VI values between 2017 and 2018 in agricultural land could be explained by a shift from winter crops to spring crops due to the late harvest in 2017. It could also be a result of the satellite imagery from the different years being taken at different dates, and capturing different stages of the growing cycle which would affect the results of cereal crops and rapeseed in particular. The largest decrease in VI values were seen in winter wheat and spring barley which indicates that these might be more drought sensitive. Both RUE and the standard deviation of RUE increased for the whole study area between the two years, which is likely a result of the large amount of precipitation amounts in 2017.
Automatic Semantic Categorization of News Headlines using Ensemble Machine Learning: A Comparative Study

Due to widespread availability of Internet there are a huge of sources that produce massive amounts of daily news. Moreover, the need for information by users has been increasing unprecedently, so it is critical that the news is automatically classified to permit users to access the required news instantly and effectively. One of the major problems with online news sets is the categorization of the vast number news and articles. In order to solve this problem, the machine learning model along with the Natural Language Processing (NLP) is widely used for automatic news classification to categorize topics of untracked news and individual opinion based on the user’s prior interests. However, the existing studies mostly rely on NLP but uses huge documents to train the prediction model, thus it is hard to classify a short text without using semantics. Few studies focus on exploring classifying the news headlines using the semantics. Therefore, this paper attempts to use semantics and ensemble learning to improve the short text classification. The proposed methodology starts with preprocessing stage then applying feature engineering using word2vec with TF-IDF vectorizer. Afterwards, the classification model was developed with different classifier KNN, SVM, Naive Bayes and Gradient boosting. The experimental results verify that Multinomial Naive Bayes shows the best performance with an accuracy of 90.12% and recall 90%.
Determinants of Successful Agri - Futures Contracts in India

The study empirically examines the factors which impact the success of the Agri commodity futures contracts. Daily data from July 2012 to July 2017 is used for 22 commodities traded on the three national commodity derivatives exchanges and analyzed in a panel data analysis framework. The results of the study suggest that higher volatility in the spot and futures market, competition in terms of multiple exchange listing, the age of the futures contract, lot size and tick size increase the success potential of the agri futures contract. The international listing, the imposition of Commodity transaction tax, a higher number of levels in the value chain and greater geographical coverage in the spot market reduces the liquidity for the contract. Thus, the study identifies important market characteristics and elements of contract design that have implications for the success of agri commodities futures contract in India.
Methodology for validating Nest Memory Management Unit

The growing demand for performance makes the processor logic design more complex, thereby making post-silicon validation a critical and complex step in processor development life cycle. There are complex units with newer timing and control logic paths which are almost impossible to exercise in regular verification environments. One such unit to cater to newer workloads in recent superscalar processors is the Nest Memory Management Unit (NMMU), a memory management unit for all I/O devices. This paper presents some of the major challenges in validating Nest MMU. A postsilicon validation framework is proposed to mitigate these challenges. An asynchronous non-blocking accelerator job submission model is used in this approach to increase the translation traffic from the agent to NMMU. Core MMU translation is used as the reference model to validate nest MMU. The processor core storage exception handlers are leveraged to minimize the validation tool software development effort and to increase the efficiency of validation as well. This method makes use of an optimized threshold-based checker to detect potential NMMU hardware issues. The proposed methodology has been experimentally evaluated in Power9 NMMU to demonstrate the effectiveness of the method in providing considerable stress to the unit.
Survey of Techniques to Increase Accuracy of Touch Screen Devices

The objective of this project is to conduct exploratory research into the effort of increasing accurac on touch screen devices. Graphical password schemes have been proposed as a possible alternative to text-based schemes. Human can remember pictures better than text, thus may contribute to a more positive user experience. Graphical password techniques include recall-based click password (e.g. imposing background image so user can click on various locations on the image), and recognize-based selection password (e.g. selecting images or icons from an image pool). Keyword: Touch Screen Design, Human Computer Interactions, Touch Screen Device Accuracy
Exploration and Innovation in Creative Material Education

Dr. Heard holds a Teaching Professor in the Materials Science and Engineering Department at Carnegie Mellon University. Past work includes activities as an industrial consultant, entrepreneur/president of two companies, and vice president positions in several engineering companies. His experience lies largely in the development and application of specialized new technologies and business opportunities, having significant international business and project experience. He has served on the Board of Directors of the AIST, worked on several committees in professional societies, and is a member of MRS, ASM, TMS, Sigma Xi and ASEE. He has authored 30+ technical papers on a wide range of activities in materials science, including education, innovation management, environmental issues, nano-materials, steelmaking, casting, plasma and alternate iron technologies and authored a book on the Horizontal Continuous Casting of Steel.
“Essays on Policies to Reduce Agricultural Greenhouse Gas Emissions”

Agriculture is responsible for 25–30% of global anthropogenic greenhouse gas (GHG) emissions but has thus far been largely exempted from climate policies. Because of high monitoring costs and comparatively low technical potential for emission reductions in the agricultural sector, output taxes on emission-intensive agricultural goods may be an efficient policy instrument to deal with agricultural GHG emissions. In this study we assess the emission mitigation potential of GHG weighted consumption taxes on animal food products in the EU. We also estimate the decrease in agricultural land area through the related changes in food production and the additional mitigation potential in devoting this land to bioenergy production. Estimates are based on a model of food consumption and the related land use and GHG emissions in the EU. Results indicate that agricultural emissions in the EU27 can be reduced by approximately 32 million tons of CO2-eq with a GHG weighted tax on animal food products corresponding to C60 per ton CO2-eq. The effect of the tax is estimated to be six times higher if lignocellulosic crops are grown on the land made available and used to substitute for coal in power generation. Most of the effect of a GHG weighted tax on animal food can be captured by taxing the consumption of ruminant meat alone.
Running head: Context and timing in children and adults The dynamic effect of context on interval timing in children and adults

Human reproductions of time intervals are often biased towards previously 2 perceived durations, resulting in a central tendency effect. The aim of the current study was to 3 compare this effect of temporal context on time reproductions within children and adults. 4 Children aged from 5 to 7 years, as well as adults, performed a ready-set-go reproduction task 5 with a short and a long duration distribution. A central tendency effect was observed both in 6 children and adults, with no age-difference in the effect of global context on temporal 7 performance. However, the analysis of the effect of local context (trial-by-trial) indicated that 8 younger children relied more on the duration (objective duration) presented in the most recent 9 trial than adults. In addition, statistical analyses of the influence on temporal performance of 10 recently reproduced durations by subjects (subjective duration) revealed that temporal 11 reproductions in adults were influenced by performance drifts, i.e., their evaluation of their 12 temporal error, while children simply relied on the value of reproduced durations on the 13 recent trials. We argue that the central tendency effect was larger in young children due to 14 their noisier internal representation of durations: A noisy system led participants to base their 15 estimation on experienced duration rather than on the evaluation of their judgment. 16 17
DELIVERABLE 7.3 FINAL INTEGRATED SYSTEM: PROTOTYPE AND EVALUATION

We address the question of whether service robots that interact with humans in public spaces must express socially appropriate behaviour. To do so, we implemented a robot bartender which is able to take drink orders from humans and serve drinks to them. By using a high-level automated planner, we explore two different robot interaction styles: in the task only setting, the robot simply fulfils its goal of asking customers for drink orders and serving them drinks; in the socially intelligent setting, the robot additionally acts in a manner socially appropriate to the bartender scenario, based on the behaviour of humans observed in natural bar interactions. The results of a user study show that the interactions with the socially intelligent robot were somewhat more efficient, but the two implemented behaviour settings had only a small influence on the subjective ratings. However, there were objective factors that influenced participant ratings: the overall duration of the interaction had a positive influence on the ratings, while the number of system order requests had a negative influence. We also found a cultural difference: German participants gave the system higher pre-test ratings than participants who interacted in English, although the post-test scores were similar.
A Study of Various Load Balancing Techniques in Cloud Computing and their Challenges

Cloud Computing is an emerging distributed computing paradigm. A framework which enables convenient, on-demand access to network, aiming to share data, calculations and service transparently over a shared pool of scalable network of nodes. Load balancing is one of the main challenges in cloud computing which is required to distribute the dynamic workload across multiple nodes to ensure that no single node is overloaded. It helps in optimal utilization of resources and hence in enhancing the performance of the system. There are few existing scheduling algorithms that can maintain load balancing and provide better approaches through efficient job scheduling and resource allocation techniques as well. It becomes necessary to utilize the available resources efficiently in order to maximum profits with optimized load balancing algorithms. This paper discusses some of the load balancing algorithms in cloud computing and the various challenges faced by the organizations using them.
Efficiency of Low Cost Adsorbents for the Removal of Arsenic from Water

Adsorption is one of the primary processes for removing arsenic from drinking water. This study focuses on developing inexpensive and effective adsorbents to remove arsenic from ground water. Eight different types of adsorbents were prepared. Some of these materials were chemically modified. The efficiency of percentage adsorption of arsenite, As(+III) on different materials were investigated at different pH, contact time and initial concentrations. Out of eight different types of adsorbents, the iron-loaded xanthated orange waste (Fe-XOW) showed high efficiency for the removal of arsenic. It was found that approximately 83 % of arsenite , As(+III) and 87% of arsenate, As(+V) removal could be achieved at optimum pH of 10 and 4 respectively. The significant effect of pH was in the range of 9 to12 for As (+III) and 3 to 5 for As (+V). Time dependency experiments for the arsenite uptake showed that the adsorption rate on Fe-XOW was fast initially for 1 hour, followed by slow attainment of equilibrium at 2.15 hour. Adsorption isotherm test showed that equilibrium adsorption data were better represented by Langmuir model than the Freundlich model and the maximum adsorption (qmax) for As (+III) onto Fe-XOW was found to be 53.47 mg/gm. The concentration of arsenic in water sample was determined by standard silver diethyldithiocarbamate spectrophotometric method (SDDC method).
Investing in Superheroes? Comic Art as a New Alternative Investment

We build quarterly and semi-annual indexes for American and European comic art based on a dataset of more than 106,000 comic art items sold at auction. We find that this new alternative investment can outperform US and European equities and bonds. Between 2002 and 2017, annualized returns of US comic artworks outperformed most asset classes with a solid 11% annualized return, while European comic art achieved 25% yearly returns on average after 2009. We show that comic art delivers significant diversification benefits to an investment portfolio due to low correlations with other assets and geographical diversification between European and American markets. These outcomes contrast with fine art in general, which delivered few diversification benefits when compared to equities and bonds between 2002 and 2017, and whose geographical markets are closely tied.
University of Birmingham A qualitative exploration of 'thrivership' among women who have experienced domestic violence and abuse

Background: Domestic violence and abuse (DVA) is a serious public health issue, threatening the health of individuals the world over. Whilst DVA can be experienced by both men and women, the majority is still experienced by women; around 30% of women worldwide who have been in a relationship report that they have experienced violence at the hands of their partner, and every week in England and Wales two women are killed by their current or ex-partner. The purpose of this study was to explore the concept of thrivership with women who have experienced DVA, to contribute to our understandings of what constitutes ‘ thriving ’ post-abuse, and how women affected can move from surviving to thriving. Methods: Thirty-seven women took part in this qualitative study which consisted of six focus groups and four in-depth interviews undertaken in one region of the UK in 2018. Data were analysed using a thematic analysis approach. Initial findings were reported back to a group of participants to invite respondent validation and ensure co-production of data. Results: The process of ‘ thrivership ’ – moving from surviving to thriving after DVA - is a fluid, non-linear journey of self-discovery featuring three ‘ stages ’ of victim, survivor, and thriver. Thriving after DVA is characterised by a positive outlook and looking to the future, improved health and well-being, a reclamation of the self, and a new social network. Crucial to ensuring ‘ thrivership ’ are three key components that we propose as the ‘ Thrivership Model ’ , all of which are underpinned by education and awareness building at different levels: (1) Provision of Safety, (2) Sharing the Story, (3) Social Response. Conclusions: The study findings provide a new view of thriving post-abuse by women who have lived through it. The proposed Thrivership Model has been developed to illustrate what is required from DVA-services and public health practitioners for the thrivership process to take place, so that more women may be supported towards ‘ thriving ’ after abuse.
for “On the Convergence of a Bayesian Algorithm for Joint Dictionary Learning and Sparse Recovery”

In this section, we provide the details of the EM-algorithm development, explaining how to obtain (3)-(8), and the γk update equations in Algorithm 1 and Algorithm 2. The EM algorithm computes the unknown parameter set Λ by minimizing the negative log likelihood − log p(y ; Λ). To compute the likelihood, we first note that the SBL framework imposes a Gaussian prior on the unknown vector xk ∼ N (0,Γk), where Γk is an unknown diagonal matrix. Thus, yk also follows a Gaussian distribution: yk ∼ N (0, σI + AΓkA ) because the noise term wk ∼ N (0, σI). Therefore, we have