Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Strategic Cultures Curriculum Project: Assessing Strategic Culture as a Methodological Approach to Understanding WMD Decision-Making by States and Non-State Actors

Abstract : This syllabus is intended to introduce the concept of strategic culture to upper division undergraduates seeking to deepen their understanding of international relations and security studies, and broaden their theoretical and methodological tool sets. Our objective is to provide the instructor with theoretical background on the concept of strategic culture as well as a framework for comparing case studies in this field. We do this through the use of comparative cases that examine a common international security phenomenon: a state's (and one non-state actor) decisions to acquire, proliferate, or use a weapon of mass destruction. Our purpose is to advance strategic culture analysis as a tool for understanding (and even forecasting) the foreign policy of other actors and of constructing successful foreign policy of our own. Practical applications of strategic culture include: * better understanding of the foreign lenses through which our policy moves will be viewed * more effective deterrence, because we understand what the other culture values and can better leverage their cost/benefit ratio * stronger cooperative relationships with allies * increased accuracy in the interpretation of intelligence collected abroad * insight that will allow for the creation of counter-narrative strategies aimed at weakening an adversary's propaganda campaign * more successful assimilation of immigrants * a stronger understanding of the repercussions of our foreign policy moves * illumination of our own cultural values * enhancement of predictive/forecasting capability
Using an Annotated Corpus as a Stochastic Grammar

In Data Oriented Parsing (DOP), an annotated corpus is used as a stochastic grammar. An input string is parsed by combining subtrees from the corpus. As a consequence, one parse tree can usually be generated by several derivations that involve different subtrees. This leads to a statistics where the probability of a parse is equal to the sum of the probabilities of all its derivations. In (Scha, 1990) an informal introduction to DOP is given, while (Bod, 1992a) provides a formalization of the theory. In this paper we compare DOP with other stochastic grammars in the context of Formal Language Theory. It it proved that it is not possible to create for every DOP-model a strongly equivalent stochastic CFG which also assigns the same probabilities to the parses. We show that the maximum probability parse can be estimated in polynomial time by applying Monte Carlo techniques. The model was tested on a set of hand-parsed strings from the Air Travel Information System (ATIS) spoken language corpus. Preliminary experiments yield 96% test set parsing accuracy.
Discriminative Language Modeling With Linguistic and Statistically Derived Features

This paper focuses on integrating linguistically motivated and statistically derived information into language modeling. We use discriminative language models (DLMs) as a complementary approach to the conventional n-gram language models to benefit from discriminatively trained parameter estimates for overlapping features. In our DLM approach, relevant information is encoded as features. Feature weights are discriminatively trained using training examples and used to re-rank the N -best hypotheses of the baseline automatic speech recognition (ASR) system. In addition to presenting a more complete picture of previously proposed feature sets that extract implicit information available at lexical and sub-lexical levels using both linguistic and statistical approaches, this paper attempts to incorporate semantic information in the form of topic sensitive features. We explore linguistic features to incorporate complex morphological and syntactic language characteristics of Turkish, an agglutinative language with rich morphology, into language modeling. We also apply DLMs to our sub-lexical-based ASR system where the vocabulary is composed of sub-lexical units. Obtaining implicit linguistic information from sub-lexical hypotheses is not as straightforward as word hypotheses, so we use statistical methods to derive useful information from sub-lexical units. DLMs with linguistic and statistical features yield significant, 0.8%-1.1% absolute, improvements over our baseline word-based and sub-word-based ASR systems. The explored features can be easily extended to DLM for other languages .
Interpreting Effective Field Theories

An effective field theory (EFT) is a theory of the dynamics of a physical system at energies small compared to a given cut-off. Low-energy states with respect to this cut-off are effectively independent of states at high energies; hence one may study the low-energy dynamics without the need for a detailed description of the high-energy dynamics. Many authors have suggested that, because of the essential role the cut-off plays in the standard (Wilsonian) method of constructing an EFT, an appropriate interpretation of an EFT requires a realistic interpretation of the cut-off. For some, this suggests an ontology of "quasi-autonomous domains" (Cao and Schweber 1993); for others, it suggests an ontology in which space is discrete and finite (Fraser 2009); and for yet others, it suggests that EFTs engage in idealizations and are inherently approximate (Fraser 2009, Castellani 2002). I argue that these interpretations are not forced upon us, in so far as there is an alternative to the Wilsonian method for constructing an EFT that does not explicitly employ a cut-off.
A magic lens for revealing device interactions in smart environments

Keeping track of device interactions in smart environments is a challenging task for everyday users. Given the expected high number of communicating devices in future smart homes, it will become increasingly important to put users more in control of their smart environments by providing tools to monitor and control the interactions between smart objects and remote services. We present a system for collecting and visualizing interactions ofWeb-enabled smart things andWeb services in an intuitive and visually appealing way. Our tool displays device interactions both using a Web-based visualization application and in the form of a "magic lens" by augmenting the camera view of a tablet with relevant connections between recognized devices in the camera's field of view.
Non-Destructive Evaluation of the Leaf Nitrogen Concentration by In-Field Visible/Near-Infrared Spectroscopy in Pear Orchards

Non-destructive and timely determination of leaf nitrogen (N) concentration is urgently needed for N management in pear orchards. A two-year field experiment was conducted in a commercial pear orchard with five N application rates: 0 (N0), 165 (N1), 330 (N2), 660 (N3), and 990 (N4) kg·N·ha−1. The mid-portion leaves on the year’s shoot were selected for the spectral measurement first and then N concentration determination in the laboratory at 50 and 80 days after full bloom (DAB). Three methods of in-field spectral measurement (25° bare fibre under solar conditions, black background attached to plant probe, and white background attached to plant probe) were compared. We also investigated the modelling performances of four chemometric techniques (principal components regression, PCR; partial least squares regression, PLSR; stepwise multiple linear regression, SMLR; and back propagation neural network, BPNN) and three vegetation indices (difference spectral index, normalized difference spectral index, and ratio spectral index). Due to the low correlation of reflectance obtained by the 25° field of view method, all of the modelling was performed on two spectral datasets—both acquired by a plant probe. Results showed that the best modelling and prediction accuracy were found in the model established by PLSR and spectra measured with a black background. The randomly-separated subsets of calibration (n = 1000) and validation (n = 420) of this model resulted in high R2 values of 0.86 and 0.85, respectively, as well as a low mean relative error (<6%). Furthermore, a higher coefficient of determination between the leaf N concentration and fruit yield was found at 50 DAB samplings in both 2015 (R2 = 0.77) and 2014 (R2 = 0.59). Thus, the leaf N concentration was suggested to be determined at 50 DAB by visible/near-infrared spectroscopy and the threshold should be 24–27 g/kg.
Dendritic and Postsynaptic Protein Synthetic Machinery

There is a growing body of evidence that local protein synthesis beneath synapses may provide a novel mechanism underlying plastic phenomena. In vivo and in vitrobiochemical data show that dendrites can perform translation and glycosylation. Using antibodies directed against the eukaryotic protein synthetic machinery, we sought to identify the structures implicated in nonperinuclear translation, namely dendritic and postsynaptic protein synthesis. We performed a morphological and immunocytochemical analysis of ventromedial horn rat spinal cord neurons using both light and electron microscopy. We show at the cellular level that, in vivo, protein synthesis macrocomplexes (ribosomes and eIF-2) as well as the endomembranous system implicated in cotranslational and posttranslational modifications (endoplasmic reticulum and Golgi cisternae) penetrated some dendrites. Membrane-limited organelles of different shape and size are present close to the postsynaptic differentiations of most synapses, independently of their localization on the neuronal surface. We demonstrate (1) that some cisternae are immunoreactive for antibodies against ribosomal proteins and eIF-2, and (2) that markers of endoplasmic reticulum (BiP), intermediate compartment, and Golgi complex (rab1, CTR433, TGN38) label subsets of these subsynaptic organelles. Therefore, these findings indicate that synapses are equipped with the essential elements required for the synthesis and insertion of a well folded and glycosylated transmembrane protein.
UvA-DARE ( Digital Academic Repository ) Social status and group norms : Indirect reciprocity in a helping experiment

Experimental evidence is provided showing that indirect reciprocity is important in economic decision making and in the development of group norms. We study a so called repeated helping game with random pairing in large groups. Pairs consist of a donor and a recipient. Donors decide whether to help the individuals they are matched with against a certain cost or not to help, enduring no costs. We observe that many decision makers respond to the information we give them about previous decisions of the recipients, even though this information is based on transactions with third parties. (JEL C92)
Nutrient sensing and signaling in the yeast Saccharomyces cerevisiae

The yeast Saccharomyces cerevisiae has been a favorite organism for pioneering studies on nutrient-sensing and signaling mechanisms. Many specific nutrient responses have been elucidated in great detail. This has led to important new concepts and insight into nutrient-controlled cellular regulation. Major highlights include the central role of the Snf1 protein kinase in the glucose repression pathway, galactose induction, the discovery of a G-protein-coupled receptor system, and role of Ras in glucose-induced cAMP signaling, the role of the protein synthesis initiation machinery in general control of nitrogen metabolism, the cyclin-controlled protein kinase Pho85 in phosphate regulation, nitrogen catabolite repression and the nitrogen-sensing target of rapamycin pathway, and the discovery of transporter-like proteins acting as nutrient sensors. In addition, a number of cellular targets, like carbohydrate stores, stress tolerance, and ribosomal gene expression, are controlled by the presence of multiple nutrients. The protein kinase A signaling pathway plays a major role in this general nutrient response. It has led to the discovery of nutrient transceptors (transporter receptors) as nutrient sensors. Major shortcomings in our knowledge are the relationship between rapid and steady-state nutrient signaling, the role of metabolic intermediates in intracellular nutrient sensing, and the identity of the nutrient sensors controlling cellular growth.
Emergence of Spatial Structure in Cell Groups and the Evolution of Cooperation

On its own, a single cell cannot exert more than a microscopic influence on its immediate surroundings. However, via strength in numbers and the expression of cooperative phenotypes, such cells can enormously impact their environments. Simple cooperative phenotypes appear to abound in the microbial world, but explaining their evolution is challenging because they are often subject to exploitation by rapidly growing, non-cooperative cell lines. Population spatial structure may be critical for this problem because it influences the extent of interaction between cooperative and non-cooperative individuals. It is difficult for cooperative cells to succeed in competition if they become mixed with non-cooperative cells, which can exploit the public good without themselves paying a cost. However, if cooperative cells are segregated in space and preferentially interact with each other, they may prevail. Here we use a multi-agent computational model to study the origin of spatial structure within growing cell groups. Our simulations reveal that the spatial distribution of genetic lineages within these groups is linked to a small number of physical and biological parameters, including cell growth rate, nutrient availability, and nutrient diffusivity. Realistic changes in these parameters qualitatively alter the emergent structure of cell groups, and thereby determine whether cells with cooperative phenotypes can locally and globally outcompete exploitative cells. We argue that cooperative and exploitative cell lineages will spontaneously segregate in space under a wide range of conditions and, therefore, that cellular cooperation may evolve more readily than naively expected.
Persona-Scenario-Goal Methodology for User-Centered Requirements Engineering

This paper proposes Persona-Scenario-Goal (PSG) methodology as an extension of Persona-Scenario methodology, and its empirical study in the usercentered requirements engineering for a Web application system. The Persona-Scenario methodology demonstrated the effectiveness in capturing requirements of consumer products, such as mobile phones. However, the methodology has limitations to evaluate the multiple, possibly conflicting, requirements. The PSG methodology intended to identify a set of value requirements from multiple and even conflicting requirements by combining persona, scenario and goal-oriented techniques. This paper discusses the model and procedure of PSG methodology, and demonstrates its effectiveness by applying the methodology to reengineering a Web application system.
Person re-identification using matrix completion

Person re-identification is a challenging problem in multicamera surveillance systems. In this paper, we formulate person re-identification as a cross-camera feature construction problem to overcome the feature variation between different camera spaces. The linear transformation of color information between probe and gallery camera spaces makes the stacked matrix, which concatenates features from these two camera spaces, rank deficient. From the feature observed in probe camera space we can construct its corresponding feature in gallery camera space by completing unknown entries on the relevant positions of the stacked matrix, and then match the constructed probe feature with features in gallery camera space. We also introduce additive noise term into the model to deal with the adverse effects caused by illumination variation with time. Experimental results demonstrate the proposed approach outperforms the metric learning methods as well as simple nearest neighbor search, and obtains a competitive performance compared with the state-of-the-art methods.
Influence of Mouth Breathing on the Dentofacial Growth of Children: A Cephalometric Study

Background: The involvement of mouth breathing, facial, and structural growth alterations, especially during childhood has been discussed in medical and dental literature. The relevance of airway obstruction and its assumed effect on facial growth continues to be debated. Materials and Methods: This study was aimed at assessing the dental and soft tissue abnormalities in mouth breathing children with and without adenoid hypertrophy. Fifty children aged between 6 and 12 years following otolaryngological examination were divided into three groups: Group I (MBA): Twenty mouth breathing children with enlarged adenoids and 60% of nasopharynx obstruction; Group II (MB): Twenty mouth breathing children without any nasal obstruction; Group III (nasal breathers [NB]): Ten nose breathing healthy individuals (control group). Digital lateral cephalograms were obtained and the dental and soft tissue parameters were assessed using the cephalometric software, Dolphin Imaging 11.5 version. Comparison was done using one-way ANOVA and post-hoc analysis. Results: There was a significant increase in IMPA (P = 0.001 and 0.007 respectively), interlabial gap (P = 0.007 and 0.002 respectively) and facial convexity (P < 0.001 and 0.001 respectively) in both MBA and MB groups when compared to NB. The upper incisor proclination (P = 0.012) and facial convexity (P = 0.003) were significantly higher in mouthbreathers with adenoid hypertrophy. However, upper incisor proclination (P = 0.009) was statistically signifi cant only in group MB when compared to NB. Conclusion: All subjects with mouth-breathing habit exhibited a significant increase in lower incisor proclination, lip incompetency and convex facial profile. The presence of adenoids accentuated the facial convexity and mentolabial sulcus depth.
Evaluating Exchange Rate Management: An Application to Korea

This paper uses data-rich estimation techniques to study monetary policy in an open economy. We apply the techniques to a small, forward-looking model and explore the importance of the exchange rate in the monetary policy rule. This approach allows us to discern whether a monetary authority targets the exchange rate per se, or instead simply responds to the exchange rate in order to achieve its other objectives. The approach also removes a downward bias on the estimate of the extent of inflation targeting. We find that this bias is important in the case of Korea, a de jure inflation targeter. In contrast to previous studies, our findings suggest that the Bank of Korea actively targets inflation, not the exchange rate. Apparently, the exchange rate has been only indirectly important in Korea's monetary policy.
Design and Evaluation of Interactive Instruction System: Impression for a Distance Class

In order to perform effective real-time online distance classes with multimedia educational materials, we have developed interactive instruction system named IMPRESSION which facilitates interactive presentation of several multimedia educational materials provided by Web server on the Internet. In this paper, we clarify the concept of our system, design and implement it. And we evaluate effectiveness of our system through the practical use in a two-day online distance class for high school students with JGN-II. As a result of this class, we confirmed we were able to perform a flexible distance class
Power demand prediction in smart microgrids using interacting multiple model Kalman filtering

Optimized management of energy resources within smart microgrids may require an approximation of near future power demands to institute efficient scheduling of tasks. Since demands are volatile in shorter time spans, localized short-term prediction of demand is non-trivial. Local prediction requires efficiency of calculations to minimize computational resources. To address this concern we present a predictor based on the Interacting Multiple Model Kalman filters. This approach supports effective year round prediction while only storing two model demand profiles. We demonstrate that the approach provides consistency in intra-day demand prediction across an entire year.
Energy Aware Intra Cluster Routing for Wireless Sensor Networks

Wireless Sensor Network (WSN) is an emerging technology that is predicted to change the human life in future. This technology is composed of tiny sensing objects called sensors that are wirelessly scattered in the environment. Due to wireless nature and having limited lifetime (battery operated) there are many challenges for researchers to make this technology more useful. In this research work an energy efficient routing technique Energy Aware Intra Cluster Routing (EAICR) is presented that has increased energy efficiency up to 17% and increased the network lifetime up to 12% when compared with a well known routing algorithm MultiHop Router [1] .
Design for brawling-Exploring emotional issues for concept design

This paper describes a case study of a student course about design for user experience. The theme of the course was emotionally loaded behaviour, brawling. Our objective was to teach, how to tackle a combination of emotional behaviour, user centered design approach and designer’s personal interpretation during product concept creation. The students were first introduced to the theme, then they did a user study and analysed the data to find opportunities for concept design. The paper presents the process of the course with some of the results.
MMToC: A Multimodal Method for Table of Content Creation in Educational Videos

In this paper we propose a multimodal method called MMToC for automatically creating a table of content for educational videos. MMToC defines and quantifies word saliency for visual words extracted from the slides and spoken words obtained from the speech transcript. The saliency scores from these two modalities are combined to obtain a ranked list of salient words. These ranked words along with their saliency scores are used to formulate a topic segmentation cost function. The cost function is optimized using a dynamic program framework to obtain the topic segments of the video. These segments are labelled with their corresponding topic names for creating the table of content. We perform experiments on 24 hours of lectures spread across 23 videos ranging over 20-75 minutes duration each. We compare the proposed method with LDA-based video segmentation approaches and show that the proposed MMToC method is significantly better (F-score improvement of 0.19 and 0.24 on two datasets). We also perform a user study to demonstrate the effectiveness of MMToC for navigating educational videos.
Model and Algorithm of Dynamic Wagon-flow Allocation on Uncertainty Conditions

Under the target of maximum number of vehicles in a stage plan, the model of dynamic wagon-flow allocating under the uncertain condition can be built. The improved approximate nondeterministic tree search (ANTS) algorithm whose parameter α fixed by chaos sequence generator will be introduced by means of defining the possibility break up set, waiting set and choice set to mapping dynamic wagon-flow allocating onto scheme tree. Improved route construction rule and pheromone updating, and the judgment of constraint condition in the process of transfer can enhance the efficiency of solution and convergence speed of the algorithm. The example demonstrates that the algorithm mentioned can find out a global scheme in a relatively efficient way.