Miracle-dz/UniFAR-FTUs huggingface dataset:
Shizuoka City, Japan

Option is a one of the financial derivatives and its pricing is an important problem in practice. The process of stock prices are basically represented as Geometric Brownian motion or jump diffusion processes. In this package, algorithms and visualizations are implemented in order to calculate option price and plot its result. The implimented algorithms are Monte Carlo method for three equations. The first one is Geometric Brownian motion and the second is jump diffusion processes and furthermore a model that presents jumps among companies affect each other. Introduction In the early 1970’s, Back and Scholes[1] proposed a method in order to calculate option price. For option pricing, mainly three methods are used. The first one is the method to numerically solve Black–Scholes equation that represented as partial differential equation and the second is the method to solve equations directly. The last one is monte carlo method. The processes of stock prices are basically represented as Geometric Brownian motion. But the model can not explain the all aspect of a stock process. Because jump diffusion processes[2] that occur jump phenomena are researched. In this package, Monte Carlo method is implemented in order to calculate European option price for three equations as Geometric Brownian motion and jump diffusion processes and furthermore a model that presents jumps among companies affect each other. Finally we explain the models and how to use it in this paper. Background Geometric Brownian Motion The processes of stock prices are basically represented as Geometric Brownian motion. dS(t) = α(t)S(t)dt+ σ(t)S(t)dW (t), (1) where S(t) denotes stock price at time t. α is a drift parameter and σ is a volatility. W (t) is Brownian motion. Solution of the equation is given by S(t) = S(0)exp{σW (t) + (α− 1 2 σ )t}. (2) Price of European Option There are many types of options in the stock market. European call option can not execute until the duration of T is finished, and its strike price is K. Option prices are calculated under a risk-neutral probability. European call option price is given by Price = E[max(S(T )−K, 0)], (3)
Digital Commons @ University of South Florida Digital Commons @ University of South Florida

This study applies Channel Expansion Theory to assess the factors that influence analyst selection and use of a requirements elicitation techniques. The findings from a cross-sectional survey show that an analyst’s experience with a requirements elicitation technique is the strongest predictor of perceived richness from using that technique. Experience with the informant was only marginally significant. Results from a pilot study suggest further that analysts (in this organization) have a great deal of discretion in technique selection, and apply one particular technique more than others to gather and capture requirements. The results suggest that analysts need not only a broader repertoire of techniques, but also need to develop conceptual agility to enable them to articulate requirements more comprehensively and comprehensibly.
Empirical Foundations for Improved Engineering Education : Differences Between Engineering Students and Professional Expert Engineers while Designing

Engineering design education strives to equip students with the capability of becoming expert design engineers. To bridge the gap between the design competencies of undergraduate engineering students and expert engineers in the field, educators and researchers require a detailed knowledge of the cognitive behaviors of both cohorts. This research aims to identify novel pathways to approach the cognitive transformation from novice to expert in engineering education.
Everything You Wanted to Know About MPEG-7: Part 2

Audio-visual information must allow some degree of interpretation, which can be passed on to or accessed by a device or a computer code. MPEG-7 aims to create a standard for describing these operational requirements. This article describes MPEG-7's concepts, terminology and requirements. We also describe the influence of other approaches to multimedia content description on the development of the MPEG-7 standard and conclude with a short estimate of the time schedule for the standard.
University of Groningen Deep Colorization for Facial Gender Recognition Hogervorst ,

Recent research suggests that colorization models have the capability of generating plausible color versions from grayscale images. In this paper, we investigate whether colorization prior to gender classification improves classification performance on the FERET grayscale face dataset. For this, we colorize the images using an existing Lab colorization model, both with and without class rebalancing, and our novel HSV colorization model without class rebalancing. Then we construct gender classification models on the grayscale and colorized datasets using a reduced GoogLeNet convolutional neural network. Several models are trained using different loss functions (cross entropy loss, hinge loss) and gradient optimization solvers (Nesterov’s Accelerated Gradient Descent, Stochastic Gradient Descent), initialized using both random and pre-trained weights. Finally, we compare the gender classification accuracies of the models when applied to the face image color variants. The best performances are obtained by models initialized using pre-trained weights, and models using colorization without class rebalancing.
Visualizing an Execution Trace as a Compact Sequence Diagram Using Dominance Algorithms

Visualizing an execution trace of an object-oriented system as sequence diagrams is effective to understand the behavior of the system. However, sequence diagrams extracted from an execution trace are too large for developers to inspect since a trace involves a large number of objects and method calls. To support developers to understand extracted sequence diagrams, it is necessary to remove the less important details of the diagrams. In this paper, we apply a dominance algorithm to a dynamic call graph among objects in order to detect and remove local objects contributing to internal behavior of dominator objects. The case study shows our approach automatically removed about 40 percent of the objects from execution traces on average.
Bryant Digital Repository Bryant Digital Repository

Increasingly, research across many disciplines has recognized the shortcomings of the traditional “in-tegration prescription” for inter-organizational knowledge management. This research conducts several simulation experiments to study the effects of different rates of product change, different demand environments, and different economies of scale on the level of integration between firms at different levels in the supply chain. The underlying paradigm shifts from a static, steady state view to a dynamic, complex adaptive systems and knowledge-based view of supply chain networks. Several research propositions are presented that use the role of knowledge in the supply chain to provide predictive power for how supply chain collaborations or integration should evolve. Suggestions and implications are suggested for managerial and research purposes.
Beyond the Worst-Case Analysis of Algorithms

Learning in the presence of outliers is a fundamental problem in statistics. Until recently, all known efficient unsupervised learning algorithms were very sensitive to outliers in high dimensions. In particular, even for the task of robust mean estimation under natural distributional assumptions, no efficient algorithm was known. A recent line of work gave the first efficient robust estimators for a number of fundamental statistical tasks, including mean and covariance estimation. This chapter introduces the core ideas and techniques in the emerging area of algorithmic highdimensional robust statistics with a focus on robust mean estimation.
Journal of Applied Research in Memory and Cognition

How does being a national of a country, steeped in its cultural context, affect our perception of that country’s past? Despite demonstrations of “national narcissism” in prior work on collective memory, the present studies suggest that pride alone does not shape national identity. Considering collective memories for one’s own and the other nation, American and German participants agreed more on which events nations should be ashamed of. When asked to generate events that shaped national identity, for both sets of participants, only a few of the most frequently nominated events were categorized as proud events. The rest were mixed or predominantly categorized as ashamed events or as evoking neither pride nor shame. Across studies, similar events were generated from the “self” vs. “other” perspective, indicating that national collective memories can, to an extent, be understood by non-nationals. Variations in speciﬁcity, location, and timing of events were also examined.
Simple but effective techniques to reduce biases

There have been several studies recently showing that strong natural language understanding (NLU) models are prone to relying on unwanted dataset biases without learning the underlying task, resulting in models which fail to generalize to out-of-domain datasets, and are likely to perform poorly in real-world scenarios. We propose several learning strategies to train neural models which are more robust to such biases and transfer better to out-of-domain datasets. We introduce an additional lightweight bias-only model which learns dataset biases and uses its prediction to adjust the loss of the base model to reduce the biases. In other words, our methods down-weight the importance of the biased examples, and focus training on hard examples, i.e. examples that cannot be correctly classified by only relying on biases. Our approaches are model agnostic and simple to implement. We experiment on large-scale natural language inference and fact verification datasets and their out-of-domain datasets and show that our debiased models significantly improve the robustness in all settings, including gaining 9.76 points on the FEVER symmetric evaluation dataset, 5.45 on the HANS dataset and 4.78 points on the SNLI hard set. These datasets are specifically designed to assess the robustness of models in the out-of-domain setting where typical biases in the training data do not exist in the evaluation set.
Channel Codes: Classical and Modern

Preface 1. Coding and capacity 2. Finite fields, vector spaces, finite geometries and graphs 3. Linear block codes 4. Convolutional codes 5. Low-density parity-check codes 6. Computer-based design of LDPC codes 7. Turbo codes 8. Ensemble enumerators for turbo and LDPC codes 9. Ensemble decoding thresholds for LDPC and turbo codes 10. Finite geometry LDPC codes 11. Constructions of LDPC codes 12. LDPC codes based on combinatorial designs, graphs, and superposition 13. LDPC codes for binary erasure channels 14. Non-binary LDPC codes 15. LDPC code applications and advanced topics Index.
USE OF STRUCTURE COEFFICIENTS IN PUBLISHED MULTIPLE REGRESSION ARTICLES: β IS NOT ENOUGH

can be found at: Educational and Psychological Measurement Additional services and information for http://epm.sagepub. com/cgi/alerts Email Alerts: http://epm.sagepub. com/subscriptions Subscriptions: http://www.sagepub. com/journalsReprints.nav Reprints: http://www.sagepub. com/journalsPermissions.nav Permissions: http://epm.sagepub. com/cgi/content/refs/61/2/229 SAGE Journals Online and HighWire Press platforms): (this article cites 22 articles hosted on the Citations
NeuroImage: Clinical

Parkinson is a very prevalent neurodegenerative disease impacting the life of millions of people worldwide. Although its cause remains unknown, its functional and structural analysis is fundamental to advance in the search of a cure or symptomatic treatment. The automatic segmentation of deep brain structures related to Parkinson`s disease could be bene ﬁ cial for the follow up and treatment planning. Unfortunately, there is not broadly available segmentation software to automatically measure Parkinson related structures. In this paper, we present a novel pipeline to segment three deep brain structures related to Parkinson's disease (substantia nigra, subthalamic nucleus and red nucleus). The proposed method is based on the multi-atlas label fusion technology that works on standard and high-resolution T2-weighted images. The proposed method also includes as post-processing a new neural network-based error correction step to minimize systematic segmentation errors. The proposed method has been compared to other state-of-the-art methods showing competitive results in terms of accuracy and execution time.
A 1998–2013 climatology of Kyushu, Japan: seasonal variations of stability and rainfall

The seasonal variation of the atmospheric structure, vertical shear, stability, and rainfall distribution over the island of Kyushu, southern Japan, is studied using 16 years of observational data, from 1998 to 2013. Over 20 000 twice‐daily rawinsonde observations from the cities of Kagoshima (southern Kyushu) and Fukuoka (northern Kyushu) are utilized along with daily precipitation data from 120 Japan Meteorological Agency stations located across the island. Understanding the local atmospheric circulation and climatological behaviour of the island is important both locally due to the island's large population and regionally, due to its position in relation to the tracks of typhoons generated annually over the Pacific ocean and make landfall here, the rainy season associated with the Asian monsoon, and the large number of active volcanoes located on or near the island, emitting volcanic gases and ash on a daily basis.
Relationships and Data Sanitization: A Study in Scarlet

eScholarship provides open access, scholarly publishing services to the University of California and delivers a dynamic research platform to scholars worldwide. Abstract: Research in data sanitization (including anonymization) emphasizes ways to prevent an adversary from desanitizing data. Most work focuses on using mathematical mappings to sanitize data. A few papers examine incorporation of privacy requirements, either in the guise of templates or prioritization. Essentially these approaches reduce the information that can be gleaned from a data set. In contrast, this paper considers both the need to "desanitize" and the need to support privacy. We consider conflicts between privacy requirements and the needs of analysts examining the redacted data. Our goal is to enable an informed decision about the effects of redacting, and failing to redact data. We begin with relationships among the data being examined, including relationships with a known data set and other, additional, external data. By capturing these relationships, desanitization techniques that exploit them can be identified, and the information that must be concealed in order to thwart them can be determined. Knowing that, a realistic assessment of whether the information and relationships are already widely known or available will enable the sanitizers to assess whether irreversible sanitization is possible, and if so, what to conceal to prevent desanitization. ABSTRACT Research in data sanitization (including anonymization) emphasizes ways to prevent an adversary from desanitizing data. Most work focuses on using mathematical mappings to sanitize data. A few papers examine incorporation of privacy requirements, either in the guise of templates or pri-oritization. Essentially these approaches reduce the information that can be gleaned from a data set. In contrast, this paper considers both the need to “desanitize” and the need to support privacy. We consider conﬂicts between privacy requirements and the needs of analysts examining the redacted data. Our goal is to enable an informed decision about the eﬀects of redacting, and failing to redact data. We begin with relationships among the data being examined, including relationships with a known data set and other, additional, external data. By capturing these relationships, desanitization techniques that exploit them can be identi-ﬁed, and the information that must be concealed in order to thwart them can be determined. Knowing that, a realistic assessment of whether the information and relationships are already widely known or available will enable the sanitizers to assess whether irreversible sanitization is possible, and if so, what to conceal to prevent desanitization.
The barrier of objects : From dynamical systems to boundedorganizations

Contents Overview 3 1 The barrier of objects 4 2 Towards a speciication language for chemistry 8 2.1 Minimal Chemistry 2 Overview Self-maintaining natural systems include the global climate system, all living organisms , many cognitive processes, and a diversity of human social institutions. The capacity to construct artiicial systems that are self-maintaining would be highly desirable. Yet, curiously, there exists no readily identiiable scientiic tradition that seeks to understand what classes of such systems are possible or to discover conditions necessary to achieve them. Given the ubiquity of such systems naturally and the desirability of self-maintenance as a feature of design, any credible approach to establishing such a tradition merits serious attention. We have recently developed and implemented a framework for approaching the problem 26, 27]. It is based on the premise that the constituent entities of a self-maintaining system characteristically engage in interactions whose direct outcome is the construction of other entities in the same class. Self-maintenance, then, is the consequence of a constructive feedback loop: it occurs when the construction processes induced by the entities of a system permit the continuous regeneration of these same entities 88]. The speciic functional relationships between entities which collectively insure their continuous regeneration, we deene as an organization. A theory of organization, so deened, is a theory of self-maintaining systems. A prototypical instance of entities are molecules. And organisms are a particularly interesting class of self-maintaining systems generated by their constructive interactions. The atmosphere is another example. And so, perhaps, is the sun at the nuclear level. The overarching long-term goal of our program is to develop a formal understanding of self-maintaining organizations. Our eeorts in doing so, which we summarize here, have led us to appreciate a fundamental problem in methodology: the traditional theory of \dynamical systems" is not equipped for dealing with constructive processes. Indeed, the very notion of \construction" requires a description that involves the structure of objects. Yet, it was precisely the elimination of objects from the formalism that make dynamical systems approaches so tremendously successful. We seek to solve this impasse by connecting dynamical systems with fundamental research in computer science, whose theoretical foundations are about \objects" and their constructive interrelations. Our long-term goal, then, becomes equivalent to the task of expanding dynamical systems theory to include object construction, to become what we have come to call constructive dynamical systems 26]. 3 1 The barrier of …
A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem

Bandit learning is characterized by the tension between long-term exploration and short-term exploitation. However, as has recently been noted, in settings in which the choices of the learning algorithm correspond to important decisions about individual people (such as criminal recidivism prediction, lending, and sequential drug trials), exploration corresponds to explicitly sacrificing the well-being of one individual for the potential future benefit of others. This raises a fairness concern. In such settings, one might like to run a "greedy" algorithm, which always makes the (myopically) optimal decision for the individuals at hand - but doing this can result in a catastrophic failure to learn. In this paper, we consider the linear contextual bandit problem and revisit the performance of the greedy algorithm. We give a smoothed analysis, showing that even when contexts may be chosen by an adversary, small perturbations of the adversary's choices suffice for the algorithm to achieve "no regret", perhaps (depending on the specifics of the setting) with a constant amount of initial training data. This suggests that "generically" (i.e. in slightly perturbed environments), exploration and exploitation need not be in conflict in the linear setting.
Guidance manual on valuation and accounting of ecosystem services for small island developing states

Contiene: 1. Introduction to the guidance manual -- 2. Valuation and accounting of island ecosystem services -- 3. Critical analysis of techniques for the valuation and accounting of ecosystem services in small island developing states -- 4. Step-by-step guide on valuation techniques for small island developing states, with case studies -- 5. Step-by-step guide to ecosystem service accounting for small island developing states -- 6. Current initiatives and working groups with a focus on the valuation and accounting of island ecosystem services -- 7. Policy application case studies
Impact of Transformational Leadership on Employee Resilience during a Crisis Situation

The business world is currently presented with the pinnacle of adversity and turbulence due to Covid-19. While some organizations collapsed, certain leaders have succeeded to navigate their business towards recovery by understanding suitable strategies, innovation and identifying new opportunities. Given the rate of uncertainty in the contemporary business landscape, a need of further study on employee resilience has arisen to assist organizations understand how to respond to crises. Hence, this research aims to investigate crisis leadership and its impact, under the research topic “Transformational leadership to boost employee resilience amid crisis induced pressure”. The research was conducted basing Earl’s Regency Resort, Kandy, Sri Lanka. Data were collected through a five point likert scale questionnaire, and the sample was decided through simple random sampling method resulting in 63 valid responses. Minitab-19 software was employed for data processing. The findings of this study indicated that there was a positive and significant impact of transformational leadership in boosting employee resilience. Furthermore the findings recommended future studies based on other organizational factors affecting employee resilience, aside from transformational leadership.
INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY

: This paper provides an introduction to Automatic image annotation and also will converse the literature survey on various approaches for automatic annotation on digital images. Nowadays digital photography is a common technology for capturing and archiving images due to the falling price of storage devices and digital cameras. In this paper we focus on different techniques of automating the process of annotating images as an intermediate step in retrieval process. There are some researches on image annotation which provides very good knowledge theoretically or technically that lead to produce such promising surveys. Automatic image annotation is the process of assigning keywords to digital images depending on the content information. Many techniques have been proposed for image annotation in the last decade that gives reasonable performance on standard datasets. Summary and analysis of some of the approaches have been used as references to create a framework in designing an automatic image annotation