Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling continuous-time financial markets

We formulate a model of continuous-time financial market consisting of a bank account with constant interest rate and one risky asset subject to transaction costs and capital gains taxes. We consider the problem of maximizing expected utility from future consumption in infinite horizon. This is the continuous-time version of the model introduced by Dammon, Spatt and Zhang [11]. The taxation rule is linear so that it allows for tax credits when capital gains losses are experienced. In this context, wash sales are optimal. Our main We are grateful to Stathis Tompaidis for numerous discussions on the modeling issue of this paper. We have also benefited from interesting comments and discussions with Victor DeMiguel, Chris Rogers, Jose Scheinkman, and Raman Uppal. TU Berlin, Germany, and CREST, Paris, imen@math.tu-berlin.de. Koç University, Istanbul, Turkey, msoner@ku.edu. tr. Member of the Turkish Academy of Sciences and this work was partly supported by the Turkish Academy of Sciences. CREST, Paris, France, touzi@ensae.fr, and Tanaka Business School, Imperial College London, England, n.touzi@imperial.ac. uk. 1 contribution is to derive lower and upper bounds on the value function in terms of the corresponding value in a tax-free and frictionless model. While the upper bound corresponds to the value function in a tax-free model, the lower bound is a consequence of wash sales. As an important implication of these bounds, we derive an explicit first order expansion of our value function for small interest rate and tax rate coefficients. In order to examine the accuracy of this approximation, we provide a characterization of the value function in terms of the associated dynamic programming equation, and we suggest a numerical approximation scheme based on finite differences and the Howard algorithm. The numerical results show that the first order Taylor expansion is very accurate for reasonable market data.
Toward Self-Learning Model-Based Evolutionary Algorithms

Model-based evolutionary algorithms (MBEAs) are praised for their broad applicability to black-box optimization problems. In practical applications however, they are mostly used to repeatedly optimize different instances of a single problem class, a setting in which specialized algorithms generally perform better. In this paper, we introduce the concept of a new type of MBEA that can automatically specialize its behavior to a given problem class using tabula rasa self-learning. For this, reinforcement learning (RL) is a naturally fitting paradigm. A proof-of-principle framework, called SL-ENDA, based on estimation of normal distribution algorithms in combination with reinforcement learning is defined. SL-ENDA uses an RL-agent to decide upon the next population mean while approaching the rest of the algorithm as the environment. A comparison of SL-ENDA to AMaLGaM and CMA-ES on unimodal noiseless functions shows mostly comparable performance and scalability to the broadly used and carefully manually crafted algorithms. This result, in combination with the inherent potential of self-learning model-based evolutionary algorithms with regard to specialization, opens the door to a new research direction with great potential impact on the field of model-based evolutionary algorithms.
USE OF DIFFERENT FRICTION MODELS ON THE AUTOMOTIVE CLUTCH ENERGY SIMULATION DURING VEHICLE LAUNCH

The clutch system works basically as an interface between the engine and the vehicle. The engine provides power and torque in a given revolution while the vehicle launches. The slipping time is a critical moment for the clutch during this phase. Then, many studies have been done to predict the total slip time, and the related amount of energy during clutch engagement. Considerations about this energy are highly important in clutch capacity analysis, and some available studies evaluate several aspects that take place during the slipping phase, where simple or complex models represent all the dynamics involved in related systems. Throughout a study case a proposed model based on Coulomb friction is compared to a static model found in the literature and to measurements done in a sample vehicle in order to check the accuracy of the model. Besides to the Coulomb, the dynamic Stribeck friction model is evaluated to check the one which better matches to this situation. Numerical simulation results are presented and discussed for both cases.
Health and mortality

Unemployment is related to poorer health and mortality. However, the extent to which this relationship is causal is a matter of continuing research. In particular, the issues of both direct selection (poor health causing unemployment) and indirect selection (differences in individual characteristics) are both suggested as possible explanations of the unemployment health association. In this paper we bring together routinely collected administrative data (from the Scottish Longitudinal Study) that is unique in the UK context and an innovative research design (propensity score matching) to ask; is there a mortality risk of unemployment in Scotland and to what extent is this risk attenuated by (health) selection? We discuss our findings in light of other recent studies conducted in Nordic countries that have also explored whether unemployment heightens mortality risk. Within this literature we focus particularly on the role of differences in welfare state provision between these Nordic countries and the UK.
Cyber Supply Chain Risk Management: Toward an Understanding Cyber Supply Chain Risk Management: Toward an Understanding of the Antecedents to Demand for Assurance of the Antecedents to Demand for Assurance

: Recognizing the need for effective cyber risk management processes across the supply chain, the AICPA issued a new SOC in March 2020 for assuring cyber supply chain risk management (C-SCRM) processes. This study examines supply chain relationship factors and cyber risk issues to better understand the demand for CSCRM assurance. Resource-Advantage Theory of Competition provides the conceptual foundation for assessing the dual drivers of relationship building and cyber risk management on demand for assurance. We use a field survey to collect data from 205 professionals enabling evaluation of the complex relationships in the theoretical model. Results support all hypotheses, provide satisfactory model fit, and support the underlying theory. Trust and cyber supply chain risk both positively influence demand for assurance over C-SCRM processes. This study expands the literature on cyber assurance by auditors and elaborates on overall supply chain processes that help drive value from auditors providing such assurance.
Journal of Applied Research in Memory and Cognition

Data visualizations and graphs are increasingly common in both scientiﬁc and mass media settings. While graphs are useful tools for communicating patterns in data, they also have the potential to mislead viewers. In ﬁve studies, we provide empirical evidence that y-axis truncation leads viewers to perceive illustrated differences as larger (i
R REpeating Pattern Extraction Technique (REPET): A Simple Method for Music/Voice Separation

— Repetition is a core principle in music. Many musical pieces are characterized by an underlying repeating structure over which varying elements are superimposed. This is especially true for pop songs where a singer often overlays varying vocals on a repeating accompaniment. On this basis, we present the REpeating Pattern Extraction Technique (REPET) , a novel and simple approach for separating the repeating “background” from the non-repeating “foreground” in a mixture. The basic idea is to identify the periodically repeating segments in the audio, compare them to a repeating segment model derived from them, and extract the repeating patterns via time-frequency masking. Experiments on data sets of 1,000 song clips and 14 full-track real-world songs showed that this method can be successfully applied for music/voice separation, competing with two recent state-of-the-art approaches. Further experiments showed that REPET can also be used as a preprocessor to pitch detection algorithms to improve melody extraction.
Department of Economics Working Paper Series How Does Foreign Direct Investment Promote Economic Growth? Exploring the Effects of Financial Markets on Linkages How Does Foreign Direct Investment Promote Economic Growth? Exploring the Effects of Financial Markets on Linkages *

The empirical literature finds mixed evidence on the existence of positive productivity externalities in the host country generated by foreign multinational companies. We propose a mechanism that emphasizes the role of local financial markets in enabling foreign direct investment (FDI) to promote growth through backward linkages, shedding light on this empirical ambiguity. In a small open economy, final goods production is carried out by foreign and domestic firms, which compete for skilled labor, unskilled labor, and intermediate products. To operate a firm in the intermediate goods sector, entrepreneurs must develop a new variety of intermediate good, a task that requires upfront capital investments. The more developed the local financial markets, the easier it is for credit constrained entrepreneurs to start their own firms. The increase in the number of varieties of intermediate goods leads to positive spillovers to the final goods sector. As a result financial markets allow the backward linkages between foreign and domestic firms to turn into FDI spillovers. Our calibration exercises indicate that a) holding the extent of foreign presence constant, financially well-developed economies experience growth rates that are almost twice those of economies with poor financial markets, b) increases in the share of FDI or the relative productivity of the foreign firm leads to higher additional growth in financially developed economies compared to those observed in financially underdeveloped ones, and c) other local conditions such as market structure and human capital are also important for the effect of FDI on economic growth.
Factors affecting ICT expansion in emerging economies: An analysis of ICT infrastructure expansion in five Latin American countries

High-quality information and communication technology (ICT) infrastructure is essential for developing countries to achieve rapid economic growth. International trade and the structure of the global economy require a level of integration that is achievable only with sophisticated infrastructure. Since the early 1990s, international institutions have been pushing developing nations to deregulate and heavily invest in ICT infrastructure as a strategy for accelerating socioeconomic development. After more than a decade of continued investments, some countries have still not achieved expected outcomes. Recently, the International Telecommunications Union (ITU) has called for empirical research to assess the performance and impact of ICT expansion in developing countries. In this article, we respond to this call by investigating factors affecting the efficiency of ICT expansion in five emerging economies in Latin America. Our findings demonstrate that deregulation is not enough to effect efficient ICT expansion, and we argue that existing conditions (economic factors, human capital, geography, and civil infrastructure factors) must also be considered. We conclude by asserting that policy makers can more easily realize socioeconomic development via ICTs if they consider these conditions while cultivating their technology strategies. © 2009 Wiley Periodicals, Inc.
Individual reactions to using information technology Intentions to use information technology Actual use of information technology

Information technology (IT) acceptance research has yielded many competing models, each with different sets of acceptance determinants. In this paper, we (1) review user acceptance literature and discuss eight prominent models, (2) empirically compare the eight models and their extensions , (3) formulate a unified model that integrates elements across the eight models, and (4) empirically validate the unified model. The eight models reviewed are the theory of reasoned action, the technology acceptance model, the motivational model, the theory of planned behavior, a model combining the technology acceptance model and the theory of planned behavior, the model of PC utilization, the innovation diffusion theory, and the social cognitive theory. Using data from four organizations over a six-month period with three points of measurement, the eight models explained between 17 percent and 53 percent of the variance in user intentions to use information technology. Next, a unified model, called the Unified Theory of Acceptance and Use of Technology (UTAUT), was formulated, with four core determinants of intention and usage, and up to four moderators of key relationships. UTAUT was then tested using the original data and found to outperform the eight individual models (adjusted R 2 of 69 percent). UTAUT was then confirmed with data from two new organizations with similar results (adjusted R 2 of 70 percent). UTAUT thus provides a useful tool for managers needing to Venkatesh et al./User Acceptance of IT 426 MIS Quarterly Vol. 27 No. 3/September 2003 assess the likelihood of success for new technology introductions and helps them understand the drivers of acceptance in order to proactively design interventions (including training, marketing, etc.) targeted at populations of users that may be less inclined to adopt and use new systems. The paper also makes several recommendations for future research including developing a deeper understanding of the dynamic influences studied here, refining measurement of the core constructs used in UTAUT, and understanding the organizational outcomes associated with new technology use.
A Graph Convolutional Topic Model for Short and Noisy Text Streams

Learning hidden topics from data streams has become absolutely necessary but posed challenging problems such as concept drift as well as short and noisy data. Using prior knowledge to enrich a topic model is one of potential solutions to cope with these challenges. Prior knowledge that is derived from human knowledge (e.g. Wordnet) or a pre-trained model (e.g. Word2vec) is very valuable and useful to help topic models work better. However, in a streaming environment where data arrives continually and infinitely, existing studies are limited to exploiting these resources effectively. Especially, a knowledge graph, that contains meaningful word relations, is ignored. In this paper, to aim at exploiting a knowledge graph effectively, we propose a novel graph convolutional topic model (GCTM) which integrates graph convolutional networks (GCN) into a topic model and a learning method which learns the networks and the topic model simultaneously for data streams. In each minibatch, our method not only can exploit an external knowledge graph but also can balance the external and old knowledge to perform well on new data. We conduct extensive experiments to evaluate our method with both a human knowledge graph (Wordnet) and a graph built from pre-trained word embeddings (Word2vec). The experimental results show that our method achieves significantly better performances than state-of-the-art baselines in terms of probabilistic predictive measure and topic coherence. In particular, our method can work well when dealing with short texts as well as concept drift. The implementation of GCTM is available at \url{https://github.com/bachtranxuan/GCTM.git}.
MS-TR: A Morphologically enriched sentiment Treebank and recursive deep models for compositional sem

MS-TR: A Morphologically enriched sentiment Treebank and recursive deep models for compositional semantics in Turkish Sultan Zeybek, Ebubekir Koç & Aydın Seçer | To cite this article: Sultan Zeybek, Ebubekir Koç & Aydın Seçer | (2021) MS-TR: A Morphologically enriched sentiment Treebank and recursive deep models for compositional semantics in Turkish, Cogent Engineering, 8:1, 1893621, DOI: 10.1080/23311916.2021. 1893621 To link to this article: https://doi.org/10.1080/23311916.2021. 1893621
Accumulation of Oncogenic Mutations During Progression from Healthy Tissue to Cancer

Cancers typically result from sequential accumulation of driver mutations in a previously healthy cell. Some of these mutations, such as inactivation of the first copy of a tumor suppressor gene, can be neutral, and some, like those resulting in activation of oncogenes, may provide cells with a selective growth advantage. We study a multi-type branching process that starts with healthy tissue in home-ostasis and models accumulation of neutral and advantageous mutations on the way to cancer. We provide results regarding the sizes of premalignant populations and the waiting times to the first cell with a particular combination of mutations, including the waiting time to malignancy. Finally, we apply our results to two specific biological settings: initiation of colorectal cancer and age incidence of chronic myeloid leukemia. Our model allows for any order of neutral and advantageous mutations and can be applied to other evolutionary settings.
Tertiary lymphoid structure-related immune infiltrates in NSCLC tumor lesions correlate with low tumor-reactivity of TIL products

Adoptive transfer of tumor infiltrating lymphocytes (TIL therapy) has shown great potential for the treatment of solid cancers, including non-small cell lung cancer (NSCLC). However, not all patients benefit from this therapy, and the parameters that define the likelihood of TIL products to be tumor reactive are to date unknown. Defining prognostic markers that correlate with high level of tumor-reactivity is key for achieving better tailored immunotherapies. To determine whether the composition of immune cell infiltrates correlates with the tumor-reactivity of expanded TIL products, we employed multi-parameter flow cytometry to characterize the immune cell infiltrates from 26 early-stage, and 20 late-stage NSCLC tumor lesions. Unbiased flow cytometry analysis with Cytotree and Spearman’s Rank Correlation was used to correlate immune infiltrates with the expansion rate, immune cell activation and T cell differentiation state, and the anti-tumor response of TIL products generated from the same lesions. The composition of tumor immune infiltrates was highly variable between patients, irrespective of the disease stage. High percentages of B cell infiltrates positively correlated with the presence of conventional CD4+ T cells, and an overall increase of naïve T cell infiltrates. In contrast, high B cell infiltrates negatively correlated with the tumor-reactivity of expanded TIL products, as defined by cytokine production upon exposure to autologous tumor digest. Tumors with high B cell infiltrates contained IgD+BCL6+ B cells and CXCR5+BLC6+ CD4+ T cell infiltrates and an increased percentage of naïve CD8+ T cells, indicative of the presence of tertiary lymphoid structures (TLS) in tumors with high B cell infiltrates. This study reveals that the composition of immune cell infiltrates in NSCLC tumors associates with the functionality of expanded TIL products from NSCLC tumor lesions. Importantly, the tumor-responsiveness of TIL products negatively correlated with the presence of TLS-associated immune infiltrates in tumors. Our finding may thus help improve patient selection for TIL therapy.
Gut feeling: host and habitat as drivers of the microbiome in blackbuck (Antilope cervicapra)

The gut microbiome can be shaped by both intrinsic host factors and extrinsic environmental factors. However, the relative importance of intrinsic and extrinsic factors in gut microbial composition has rarely been investigated, particularly for a single host across its natural range. Here, we characterise the gut microbiome of an endemic, endangered antelope, the blackbuck or Antilope cervicapra. We evaluated the influence of seven predictor variables, which were classified into intrinsic and extrinsic factors, on the gut microbiome. We determined which of these seven variables explains greater variation in the microbiome within (α-diversity) and between (β-diversity) the blackbuck host. We analysed the microbiome of 60 blackbuck hosts from ten different populations across India. We recorded 11800 unique OTUs across 30 known phyla and 2.9 million reads. We find an average of 2056 OTUs per individual, with firmicutes and bacteroidetes being the most dominant phyla. We find that nucleotide diversity (intrinsic), blackbuck population density (intrinsic), distance to human settlement (extrinsic), and anthropogenic land-use (extrinsic) explain within-host variation. In contrast, precipitation (extrinsic), nucleotide diversity, distance to human settlement, and anthropogenic land-use explain between-host variation. Overall, we also show that the genetic diversity of the host is more important than their environment for both within- and between-host variation in the microbiome, in blackbuck. Therefore, conservation efforts should be directed to not only preserve natural habitats but also increase the genetic pool of the blackbuck populations, which will positively impact their survival through diverse gut microbiomes.
Design and Analysis of a Developed Multiport High Step-Up DC–DC Converter With Reduced Device Count and Normalized Peak Inverse Voltage on the Switches/Diodes

In this paper, a developed structure is proposed for high step-up nonisolated noncoupled inductor based multiport dc–dc converters. The voltage gain per number of devices of the proposed topology is higher than that of similar topologies. This means that, by using the same number of devices, the proposed topology can generate higher gains than other structures. In other words, the proposed topology utilizes less number of devices for producing the same voltage gain, which leads to less total cost, size, weight, and complexity. Despite high voltage gain, the normalized peak inverse voltage (NPIV) on switches/diodes is low. Higher number of input ports leads to higher gains and lower NPIVs on devices. The load energy can be absorbed and stored in first input unit. Modularity, simple structure, continuous input currents, and low input current ripple are the other profits of proposed topology. Low/medium power applications are suggested for the proposed structure. Proposed topology has been explained and operational modes along with steady-state analysis have been discussed. For better verification, the proposed topology has been compared with several multiport high gain converters. Comparison results confirm the advantages of the proposed topology. Finally, the proper performance of the proposed topology has been validated by experimental results.
Acoustic modeling with bootstrap and restructuring for low-resourced languages

This paper investigates an acoustic modeling approach for lowresourced languages based on bootstrap and model restructuring. The approach first creates an acoustic model with redundancy by averaging over bootstrapped models from resampled subsets of sparse training data, which is followed by model restructuring to scale down the model to a desired cardinality. A variety of techniques for Gaussian clustering and model refinement are discussed for the model restructuring. LVCSR experiments are carried out on Pashto language with up to 105 hours of training data. The proposed approach is shown to yield more robust acoustic models given sparse training data and obtain superior performance over the traditional training procedure.
GENRE CLASSIFICATION BASED ON TONE OBJECTS ( MIREX 2012 SUBMISSION — DRAFT )

This extended abstract details the submission to the 2012 Music Information Retrieval Evaluation eXchange in the Audio Classification Train/Test task. The proposed system is designed to perform improved genre classification by combining temporal segmentation and source separation of music signals into so-called tone objects. The audio features extracted from these tone objects are low-level and well-known. Support Vector Machines are used for classification. This submission is motivated by promising results obtained on other genre data sets.