Miracle-dz/UniFAR-FTUs huggingface dataset:
Computed NMR Shielding Effects over Fused Aromatic / Antiaromatic Hydrocarbons

Ned H. Martin *, Mathew R. Teague and Katherine H. Mills Department of Chemistry and Biochemistry, University of North Carolina Wilmington, 601 South College Road, Wilmington, NC 28403-5932, USA; E-Mails: mattrteague@gmail.com (M.R.T.); khm0327@alum.uncw.edu (K.H.M.) * Author to whom correspondence should be addressed; E-Mail: martinn@uncw.edu. Received: 14 January 2010; in revised form: 17 March 2010 / Accepted: 19 March 2010 / Published: 22 March 2010 Abstract: Through-space isotropic NMR shielding values of a perpendicular diatomic hydrogen probe moved in a 0.5 A grid 2.5 A above several polycyclic aromatic/antiaromatic ring and aromatic/aromatic hydrocarbons were computed with Gaussian 03 at the GIAO HF/6-31G(d,p) level. Combinations of benzene fused with cyclobutadiene, with the tropylium ion, and with the cyclopentadienyl anion were investigated. Subtraction of the isolated H
The Speed of Information Revelation and Eventual Price Quality in Markets with Insiders: Comparing Two Theories

Two theoretical literatures, one using Bayesian Nash equilibrium (BNE), and the other using noisy rational expectations equilibrium (NREE), both provide a foundation for understanding how private information is impounded into asset prices, yet some of their predictions are conflicting. Here, we compare for the first time, the two theories using data from carefully controlled laboratory asset markets. In the dynamics, we find strong evidence for BNE theory, although final prices support predictions of the NREE theory. Finally, we document that price volatility increases when information is being impounded in prices.
CDSEG: Community detection for extracting dominant segments in color images

Segmentation plays an important role in the machine vision field. Extraction of dominant segments with large number of pixels is essential for some applications such as object detection. In this paper, a new approach is proposed for color image segmentation which uses ideas behind the social science and complex networks to find dominant segments. At first, we extract the color and texture information for each pixel of input image. A network that consists of some nodes and edges is constructed based on the extracted information. The idea of community detection in social networks is used to partition a color image into disjoint segments. Community detection means partitioning vertices of a network into different non-overlapped groups (communities) such that the density of intra-group edges is much higher than the density of intergroup edges. There is a very close relation between communities in the social network and segments in an image. Our results show that community detection approaches will improve the segmentation output compare to other methods available in the literature.
Differences of Photographs Inducing Craving Between Alcoholics and Non-alcoholics

Many researchers have used cue reactivity paradigm to study alcohol craving. But the difference of craving response to drinks between alcoholic patients and social drinkers was little evaluated. To investigate characteristics of alcohol-related visual cues which induce alcohol craving in alcoholism, we examined the response of subjects to alcohol-related cues considering qualitative aspects. The authors developed 27 photographs related to alcohol as candidate visual cues. Thirty five patients with alcohol dependence, 35 heavy drinkers and 35 social drinkers were shown these pictures and asked to rate these 6 pictures in order of inducing alcohol craving the most. 'A glass of Soju' and 'A Party scene' were chosen as the alcohol-related visual cues which induced craving the most in the patients and heavy drinkers, respectively. The results suggest that the patients with alcohol dependence are more absorbed by alcohol without drinking context such as an atmosphere or situation involving drinking. Heavy drinkers may experience craving in anticipation of being in a drinking situation.
Mature students, transformation and transition

Purpose The purpose of this paper is to argue for a transformative education that acknowledges and values the capacities of mature students, where higher education institutions reflect on their own assumptions and practices in order to become more inclusive and open to difference. Design/methodology/approach The stories told by Eliza, a mature student, are analysed through narrative inquiry; this approach uses a narrative as a means of capturing and analysing experience. In this case, Eliza’s stories about transition and transformation were collected over three years. Eliza made the transition from her “Access to HE course” to a degree programme in textiles. She was crossing a boundary between further and higher education, a time which could impact positively or negatively on her future achievements. The conclusions drawn from this study are not easily turned into generalisations or “truths” as they are contingent on the contexts in which the narratives were produced. Narrative is a representation of experience which is mediated by the social and cultural positions of the narrators and their audiences. Findings This study found that Eliza was confronted by many difficulties and misunderstandings around time management, pedagogy and assessment. Ineffective communication between Eliza and her tutors led to a growing frustration resulting in her considering leaving the course. Eliza’s institution sometimes seemed inflexible and was unable to respond effectively to her needs as a part-time student. Practical implications The implications for educators are that they should think about strategies for adapting to a diverse student body. Originality/value The previous experiences and backgrounds of “newcomers” should be celebrated rather than being perceived as “issues” that need to be fixed. In other words, when “non-traditional” students move through the stages of their education, their learning contexts may also need to be transformed.
Rank Aggregation using Multi Objective Genetic Algorithm

Rank Aggregation is needed to combine many different rank orderings on the same set of alternatives, or candidates, in order to obtain a better ordering. The aim of this field is to somehow merge a number of ranked lists in order to build a single superior ranked list. Various methods exist for dealing with the problem of Rank Aggregation problem. In this paper, Rank Aggregation is implemented using Genetic Approach. Multiple objectives have been achieved using genetic approach. So, this approach is called Multi-Objective Genetic Algorithm. The results of the genetic Approach are compared with that of Stuart method and Mean Method. From the experiments, It is concluded that Performance of GA lies between the Sturat and Mean method. In most cases, Stuart method gives better results than GA and GA gives better results than Mean method.
Network Reliability Evaluation and Optimization : Methods , Algorithms and Software Tools

Networks reliability analysis consists of determining the probability of correct operations of a system. Generally, the network is modeled as a connected graph and formally approached as a stochastic coherent binary system (SCBS). It represents the functioning of a system such as telecommunication and transportation networks, or simply hardware/software devices. Real networks model could be very larges and complexes which provoke the reliability evaluation to become intractable with traditional algorithms, so it is needed to invent more efficient techniques. Approaches used for determining the reliability of a system involve exact and approximate techniques. This paper introduces the problem of evaluating and optimizing the reliability of networks. It presents the most important methods, algorithms and software tools, and an interesting review of the literature. This work is based on our experiment to conducting projects in reliability engineering, essentially in telecommunication and transport networks.
A quantification of predation rates, indirect positive effects on plants, and foraging variation of the giant tropical ant, Paraponera clavata

Abstract While a clear consensus is emerging that predators can play a major role in shaping terrestrial communities, basic natural history observations and simple quantifications of predation rates in complex terrestrial systems are lacking. The potential indirect effect of a large predatory ant, Paraponera clavata Fabricius (Formicidae: Ponerinae), on herbivores was determined on rainforest trees at La Selva Biological Station in Costa Rica and Barro Colorado Island in Panama. Prey and other food brought back to nests by 75 colonies of P. clavata were quantified, taking into account temporal, seasonal, and microhabitat variation for both foraging activity and composition of foraging booty. The dispersion and density of ant colonies and combined density with the mean amounts of prey retrieval were used to calculate rates of predation per hectare in the two forests. In addition, herbivory was measured on trees containing P. clavata and on trees where the ants were not foraging. Colonies at La Selva brought back significantly more nectar plus prey than those at Barro Colorado Island, but foraging patterns were similar in the two forests. At both forests, the ants were more active at night, and there was no significant seasonal or colonial variation in consumption of nectar, composition of foraging booty, and overall activity of the colonies. At La Selva, trees containing P. clavata colonies had the same levels of folivory as nearest neighbor trees without P. clavata but had significantly lower folivory than randomly selected trees. Predation by this ant was high in both forests, despite its omnivorous diet. This insect predator is part of potentially important top-down controls in these wet and moist forests.
Biospecific Binding of Carbonic Anhydrase to Mixed SAMs Presenting Benzenesulfonamide Ligands: A Model System for Studying Lateral Steric Effects

This work describes the binding of carbonic anhydrase (CA) to mixed self-assembled monolayers (SAMs) presenting benzenesulfonamide ligands at a surface consisting primarily of tri(ethylene glycol) [(EG)3OH] groups. Surface plasmon resonance (SPR) quantified the binding of CA to the benzenesulfonamide groups. Two factors influenced the binding of CA: (a) the density of benzenesulfonamide groups at the surface, and (b) the coverage of the surface with molecules of CA adsorbed to these benzenesulfonamide groups. At low mole-fractions of benzenesulfonamide groups in the mixed SAM where the binding of CA is highly (>90%) reversible, we observe: (a) an approximately 10-fold decrease in the observed bimolecular rate constant for association, kon,obs, during the binding of CA (as the fraction of the surface covered by adsorbed CA increases from ∼0.15 to ∼0.35, the value of kon,obs decreases from ∼40 × 103 M-1 s-1 to ∼4 × 103 M-1 s-1); (b) almost no corresponding changes in the observed unimolecular rate constan.
The influence of city centre environments on the affective and restorative walking experience: PhD thesis extended abstract

This PhD thesis explored the influence of the built environment on the affective walking experience. In fact, while urbanisation trends are increasing, levels of walking in urban settings are decreasing, despite to the important health, social, and environmental benefits of walking (Robertson et al., 2012). However, while there is a rich literature on the restorative benefits of walking in nature, not many studies addressed the potential positive benefits of walking in attractive urban areas. However, according to the United Nations (2014), by 2050 66 per cent of the world’s population will live in cities. In addition, recent research has shown how experiencing nature is a rarity for most urban dwellers (Cox et al., 2017). Therefore, it is important to explore what elements of the urban realm can have positive effects on people’s health, especially during walking.
Delaunay simplexes in physics

Delaunay simplexes are very helpful instrument for analysis of computer models of atomic, molecular and granular systems. This paper illustrates their application to the current physical problems. The first one is the structure of non-crystalline packing of spheres. Recently it got the advance thanks to application of the Delaunay simplexes with quantitative measures of simplex shape. The second important problem is recognition and analysis of intermolecular voids. Delaunay simplex represents “the simplicial cavities”, and any complex void is a cluster of such cavities.
A History-Based Backoff Algorithm for Mobile Ad Hoc Networks S .

the standard IEEE 802.11 Medium Access Control protocol uses the Binary Exponential Backoff algorithm. The Binary Exponential Backoff makes exponential increments to contention window sizes. This work has studied the effect of choosing a combination between linear, exponential and logarithmic increments to contention windows. Results have shown that choosing the right increment based on network status enhances the data delivery ratio up to 37% compared to the Binary Exponential Backoff, and up to 38 % compared to the Pessimistic Linear Exponential Backoff algorithms. KeywordsMANETs, MAC, CW, Backoff algorithms
The Association between Serum GGT Concentration and Diabetic Peripheral Polyneuropathy in Type 2 Diabetic Patients

Background Diabetic peripheral polyneuropathy (DPP) is one of the common complications of diabetes mellitus (DM) and can lead to foot ulcers or amputation. The pathophysiology of DPP includes several factors such as metabolic, vascular, autoimmune, oxidative stress and neurohormonal growth-factor deficiency and recent studies have suggested the use of serum gamma-glutamyl transferase (GGT) as an early marker of oxidative stress. Therefore, we investigated whether serum GGT may be useful in predicting DPP. Methods We assessed 90 patients with type 2 DM who were evaluated for the presence of DPP using clnical neurologic examinations including nerve conduction velocity studies. We evaluated the association between serum GGT and the presence of DPP. Results The prevalence of DPP was 40% (36 cases) according to clinical neurological examinations. The serum GGT concentration was significantly elevated in type 2 diabetic patients with DPP compared to patients without DPP (P < 0.01). There were other factors significantly associated with DPP including smoking (P = 0.019), retinopathy (P = 0.014), blood pressure (P < 0.05), aspartate aminotransferase (P = 0.022), C-reactive protein (P = 0.036) and urine microalbumin/creatinine ratio (P = 0.004). Serum GGT was independently related with DPP according to multiple logistic analysis (P < 0.01). Conclusion This study shows that increased levels of serum GGT may have important clinical implications in the presence of DPP in patients with type 2 diabetes.
Semantic proximity search on graphs with metagraph-based learning

Given ubiquitous graph data such as the Web and social networks, proximity search on graphs has been an active research topic. The task boils down to measuring the proximity between two nodes on a graph. Although most earlier studies deal with homogeneous or bipartite graphs only, many real-world graphs are heterogeneous with objects of various types, giving rise to different semantic classes of proximity. For instance, on a social network two users can be close for different reasons, such as being classmates or family members, which represent two distinct classes of proximity. Thus, it becomes inadequate to only measure a “generic” form of proximity as previous works have focused on. In this paper, we identify metagraphs as a novel and effective means to characterize the common structures for a desired class of proximity. Subsequently, we propose a family of metagraph-based proximity, and employ a supervised technique to automatically learn the right form of proximity within its family to suit the desired class. As it is expensive to match (i.e., find the instances of) a metagraph, we propose the novel approaches of dual-stage training and symmetry-based matching to speed up. Finally, our experiments reveal that our approach is significantly more accurate and efficient. For accuracy, we outperform the baselines by 11% and 16% in NDCG and MAP, respectively. For efficiency, dual-stage training reduces the overall matching cost by 83%, and symmetry-based matching further decreases the cost of individual metagraphs by 52%.
Automatic Gaze-Based Detection of Mind Wandering during Reading

We present a fully-automated person-independent approach to track mind wandering by monitoring eye gaze during reading. We tracked eye gaze of 84 students who engaged in an approximately 30-minute self-paced reading task on research methods. Mind wandering reports were collected by auditorily probing students in-between and after reading certain pages. Supervised classifiers trained on global and local features extracted from students’ gaze fixations 3, 5, 10, and 15 seconds before each probe were used to predict mind wandering with a leave-several-subjects-out cross validation procedure. The most accurate model tracked both global and local eye gaze in a 5-second window before a probe and yielded a kappa (accuracy after correcting for chance) of 0.23 on a downsampled corpus containing 50% yes and 50% no responses to probes. Implications of our findings for adaptive interventions that restore attention when mind wandering is detected are discussed.
Design of robust linear dispersion codes based on imperfect CSI for ML receivers

This paper concerns the design of codes for multiple-input multiple-output communication systems. The transmission scheme utilizes imperfect channel state information (CSI) in the design, assuming that maximum-likelihood detection is employed at the receiver. It is argued that channel diagonalizing codes are not robust to imperfections in the CSI. A robust non-diagonalizing code with good minimum distance separation between received codewords is proposed. The design is very suitable for systems operating at high data rates since the complexity scales nicely with the number of antennas. Numerical results show that the proposed code outperforms a state-of-the-art diagonalizing precoder.
The Accuracy of 6D SLAM using the AIS 3D Laser Scanner

Automatic sensing of the environment is a fundamental scientific issue in robotics, since it is essential for autonomous mobile robot systems. In previous works, we presented a 6D SLAM algorithm which is based on the spatial data from the AIS 3D laser scanner and a variant of the iterative closest points algorithm (ICP). In this paper we focused on the reachable accuracy of the whole approach and therefore performed several ground truth experiments. We will show that the 6D SLAM algorithm can compensate for erroneous data of the 3D laser scanner, at least in a limited range. Furthermore we will discuss different aspects which influences the accuracy of our approach
Spatiotemporal Neurodynamics Underlying Internally and Externally Driven Temporal Prediction: A High Spatial Resolution ERP Study

Temporal prediction (TP) is a flexible and dynamic cognitive ability. Depending on the internal or external nature of information exploited to generate TP, distinct cognitive and brain mechanisms are engaged with the same final goal of reducing uncertainty about the future. In this study, we investigated the specific brain mechanisms involved in internally and externally driven TP. To this end, we employed an experimental paradigm purposely designed to elicit and compare externally and internally driven TP and a combined approach based on the application of a distributed source reconstruction modeling on a high spatial resolution electrophysiological data array. Specific spatiotemporal ERP signatures were identified, with significant modulation of contingent negative variation and frontal late sustained positivity in external and internal TP contexts, respectively. These different electrophysiological patterns were supported by the engagement of distinct neural networks, including a left sensorimotor and a prefrontal circuit for externally and internally driven TP, respectively.
A New Calculator and Why it is Necessary

Conventional calculators are badly designed: they suffer from bad computer science — they are unnecessarily difficult to use and bug ridden. I describe a solution, avoiding the problems caused by conventional calculators that is more powerful and arguably much easier to use. The solution has been implemented, and design issues are discussed. This paper shows an interactive system that is declarative, with the advantages of clarity and power that entails. It frees people from working out how a calculation should be expressed to concentrating on what they want solved. An important contribution is to demonstrate the very serious problems users face when using conventional calculators, and hence what a freedom a declarative design brings.
Security-By-Contract ( S × C ) for Mobile Systems

We present the notion of Security-by-Contract (S×C), a mobile contract that an application carries with itself. The key idea of the framework is that a digital signature should not just certify the origin of the code but rather bind together the code with a contract. In a nutshell, a contract describes the security relevant interactions that the mobile application could have with the mobile device. The contract should be accepted by the platform (if compatible with the policy) at deployment time, and its enforcement guaranteed, for instance by in-line monitoring. A short live demo with real phones will be done at the end of the talk.
Time Consistency Versus Law Invariance in Multistage Stochastic Optimization with Coherent Risk Measures : Multilevel Optimization Modeling and Computational Complexity

Coherent risk measures have become a popular tool for incorporating risk aversion into stochastic optimization models. For dynamic models in which uncertainly is resolved at more than one stage, however, use of coherent risk measures within a standard single-level optimization framework presents the modeler with an uncomfortable choice between two desirable model properties, time consistency and law invariance. Prior published work has favored maintaining time consistency, but the absence of law invariance makes the resulting models unattractive to practical decision makers. This paper summarizes these issues and then presents an alternative multilevel optimization modeling approach that preserves law invariance, yet leads to models that are time-consistent even while using time-inconsistent risk measures. It argues that this approach should be the starting point for all multistage optimization modeling; however, when performing classical risk-neutral modeling, it simplifies to a more familiar single-objective form. Unfortunately, this paper also shows that its proposed approach leads to NPhard models, even in the simplest imaginable setting in which it would be needed: three-stage linear problems on a finite probability space, using the standard meansemideviation and average-value-at-risk measures. While not necessarily indicating that solution of such models is impractical, these results suggest that it will likely require approximation or implicit enumeration methods. We close with some preliminary computational results showing that high-quality local optimum solutions of models of the kind we propose are in fact practically computable, hence that the complexity results should not be taken as completely discouraging. Acknowledgements: This work was supported by a grant from the Air Force Office of Scientific Research, award FA9550-11-1-0164. The author would like to thank Andrzej Ruszczyński for his very helpful assistance, and his student Jingnan Fan for performing the computational experiments described in Section 8. The results in Sections 5-7 also appeared in Eckstein (2012), an earlier, unpublished technical report.