Miracle-dz/UniFAR-FTUs huggingface dataset:
Grass Material as a Modern Process Standard for 14C Analysis of n-Alkanes

Abstract One of the difficulties in reporting accurate radiocarbon results from compound-specific radiocarbon analysis (CSRA) is the lack of suitable process standard materials to correct for the amount and 14C content of carbon added during extensive sample processing. We evaluated the use of n-alkanes extracted from modern grass material (1.224±0.006 fraction modern) as process standards for CSRA. The n-alkanes were isolated using preparative capillary gas chromatography (PCGC) from two independent chemical extraction methods applied to the grass. Since this was our first assessment of the 14C content of the grass n-alkanes, we corrected for extraneous carbon derived from PCGC isolation using commercially available single compounds of modern and 14C-free content. Results were consistent across the two extraction methods showing that the C29 n-alkane has a fraction modern value that is within 1σ of the bulk value of the grass while C31 n-alkane and less abundant n-alkanes have values within 2σ of the bulk value of the grass. C29 and C31 n-alkanes were the most abundant n-alkanes in the grass and, as such, the more feasible for collection of sufficient amounts of carbon for accelerator mass spectrometry (AMS) analysis. Our results suggest that choosing a grass n-alkane with an elution time closest to that of the unknowns may be advisable due to possibly greater effect from GC column bleed (14C-free) at later elution times. We conclude that C29 and C31 n-alkanes in modern grass of known 14C content can be used as in-house standards to correct for the addition of 14C-free carbon during sample preparation for 14C analysis of n-alkanes.
User Involvement in Large-Scale Egovernment Projects: Finding an Effective Combination of Strategies and Methods

Many methods have been developed for engaging users in systems design. Our research found that traditional methods for user involvement do not apply to the development of large-scale e-government systems. Nonetheless, involving the users in the design process of technological innovations is paramount for the success of these systems in terms of acceptance, trust and use. The aim of this paper is to shed some light on user involvement methods in e-government projects. To begin with, we argue that - especially for e-government - it is crucial for citizens to be involved in the design and implementation of new systems. Further, we will explain why involving users in large-scale innovations is different from traditional user engagement. Finally, we will briefly show how we tried to combine participatory design (PD) and technology assessment (TA) methods to effectively involve end-users in two e-government projects and discuss whether this was a fruitful exercise.
Group-by Query Verification by Untrusted Clients on Outsourced Data Streams

Outsourcing data streams and desired computations to a third party such as the cloud is practical for many companies due to overwhelming flow of information and excessively high resource requirements of their data stream applications. However, data outsourcing and remote computations intrinsically raise issues of trust, making it crucial to verify results returned by third parties. In this context, we propose a novel solution to verify outsourced Group-By, Sum (or histogram) queries that are common in many business applications. We consider a setting where a data owner employs an untrusted remote server to run continuous Group-By, Sum queries on a data stream it forwards to the server. Untrusted clients then query the server for the computed results. More importantly, a client can efficiently verify the correctness of the results by using a small and easy-to-compute signature provided by the data owner. Our work complements previous works on authenticating remote computation of selection and aggregation queries. Moreover, unlike prior work on remote Group-by queries, we support untrusted clients (who can collude with other clients or with the server) and provide stronger cryptographic guarantees. Experimental results on real and synthetic data show that our solution is practical and efficient.
A study of identity formation in the London investment banking sector

This thesis seeks to investigate identity formation within the London investment banking sector in the context of career development. The sector has undergone a host of changes in the past two decades. The ‘Old City’ was distinguished by trust, reputation and stability which was informally regulated through a kinship network comprised of a social elite. De-regulation in 1986 ushered in the ‘New City’ characterised by individualistic competition, inflated capital sums, truncated careers, volatility and diversification. Existing research concerning identity has largely focussed on how ‘Old City’ class and gender relations continue to predominate and shape career opportunities. Scholars have highlighted how patterns of privilege and exclusion are reproduced through a variety of ‘performances,’ disadvantaging those who are unable to access a limited range of acceptable class and gender positions. This study takes a different starting point to explore how ‘performance’ may play a role in identity work to further careers but in a way which is attentive to the distinctive conditions of the New City. Specifically, this research explores how identity may be constructed and constantly re-worked and revised, drawing upon a range of different resources within a highly diverse setting. The thesis seeks to engage with this research agenda by applying Giddens (1984; 1991) theoretical framework on self-identity, reflexivity and performance. A longitudinal research design was used to elicit qualitative data from six senior investment bank employees, gathering accounts on changes experienced over the period of a year as well as past events. The thesis investigates how a biographical narrative was reflexively maintained via the accommodation and perpetuation of a variety of different performances within a series of social terrains. These in turn served to reproduce the broader financial institutional context. A further contribution is developed which focuses on the theoretical interplays between selfidentity, reflexivity and performance through a detailed analysis of the empirical materials.
A STUDY OF FACTORS ASSOCIATED WITH STUDENT CHOICE IN THE UNIVERSITY SELECTION PROCESS

Every year university bound graduating high school students are faced with the problem of selecting a post secondary institution. The selection process typically spans a number of years and involves considering many factors. Identifying those factors that influence students during the selection process was the goal of this study. Two hundred twenty-seven first year university students attending one of three degree granting institutions in Alberta were surveyed by means of a questionnaire to determine those factors considered during the university selection process. Correlations, means, analyses of variance and qualitative data provided the statistical and descriptive information for interpretation. The principal finding of this study was that parents, particularily mothers, are the most influential persons reported to affect the process. The factors which tend to be important to students at the University of Alberta were not the same factors important to students attending Camrose Lutheran
Constructing scalable hierarchical switched openflow network using adaptive replacement of flow table management

The need for building large networks is on the rise due to rapid growth of number of internet users and also in data centers where large number of switched networks are formed. Hierarchical structure is the most preferable way of handling complex systems of any kind. Hence in this paper we devise a large hierarchical switched network using OpenFlow and propose the concept of adaptive replacement in flow table to evict uncertain flows at a particular time. Open Flow is a protocol which separates the control plane and data plane which helps in managing flow table and also to place controllers in large hierarchical networks.
Valuing Exotic Options by Approximating the SPD with Higher Moments

The financial economic No Arbitrage assumption implies that in a complete market the price of any derivative security is the discounted value of its payoff function integrated against the appropriate state-price density (SPD). For most exotic path dependent payoffs, it is quite difficult to obtain a closed form expression for the state-price density, thus eliminating the prized possibility of an analytic expression for the option in question. In this article we value exotic options by matching moments to an approximating SPD from a very general, robust, and convenient class of density functions, known in the statistical literature as the Johnson (1949) family. The end result is an analytic expression for a wide variety of European style exotic options. Our formula is tested on Asian and Basket options and is found to be extremely robust when compared with Monte Carlo simulations and other numerical techniques.
UGI: a multi-dimensional ultrasonic-based interaction approach

We are currently witnessing an era where interaction with computers is no longer limited to conventional methods (i.e. keyboard and mouse). Human Computer Interaction (HCI) as a progressive field of research, has opened up alternatives to the traditional interaction techniques. Embedded Infrared (IR) sensors, Accelerometers and RGBD cameras have become common inputs for devices to recognize gestures and body movements. These sensors are vision based and as a result the devices that incorporate them will be reliant on presence of light. Ultrasonic sensors on the other hand do not suffer this limitation as they utilize properties of sound waves. These sensors however, have been mainly used for distance detection and not with HCI devices. This paper presents our approach in developing a multi-dimensional interaction input method and tool Ultrasonic Gesture-based Interaction (UGI) that utilizes ultrasonic sensors. We demonstrate how these sensors can detect object movements and recognize gestures. We present our approach in building the device and demonstrate sample interactions with it. We have also conducted a user study to evaluate our tool and its distance and micro gesture detection accuracy. This paper reports these results and outlines our future work in the area.
3D Imaging and Modelling with a Space-qualified Laser Camera System: Development of Terrestrial Applications and Potential for Planetary Exploration

Applications and Potential for Planetary Exploration. R. Herd, J. Spray, C. Samson , S. Miller and I. Christie, Geological Survey of Canada, Natural Resources Canada, 601 Booth Street, Ottawa ON K1A 0E8 herd@nrcan.gc. ca , Planetary and Space Science Centre, University of New Brunswick, Fredericton NB E3B 5A3 jgs@unb.ca ,Neptec Design Group, 302 Legget Drive, Ottawa ON K2K 1Y5 csamson@neptec.com , smiller@neptec.com , ichristie@neptec.com .
The thin win : implicit preference for slim models in advertising

Research in consumer psychology suggests that much of human behaviour is influenced by non-conscious, uncontrolled, unobserved processes in memory (Bargh, 2002; Greenwald, Nosek & Banaji, 2003). However, increased attention to these implicit processes and associations within academic work did not necessarily stimulate the use of such methods in marketing research practice. Stüttgen, Vosgerau, Boatwright, & Messner, (2009) emphasise the attractiveness of implicit methods outside academia, but also mention the debate over these methods’ reliability and validity as a main hurdle for wide-spread use in commercial marketing research. Explicit measures remain the most important component of any marketing research program. This research therefore, intends to expand knowledge on consumer research using methods studying implicit processes. Non-conscious, automatic and unobserved processes in memory are important in advertising. Research in this field has relied almost exclusively on explicit measures, dependent on conscious evaluation and deliberation. Although these theories are essential to researchers in the field, they often neglect the potential role of implicit or automatic processes (Perkins, Forehand, Greenwald, Maison, 2008). This study demonstrates that implicit measures add incremental value beyond explicit measures.
Predicting Innovative Performance Through Proactive Personality: Examining its Criterion Validity and Incremental Validity Over the Five‐Factor Model

Innovation has emerged as a key work outcome for modern organizations. The present paper answers recent calls from several researchers for further empirical research on the validity of personality variables for predicting individual innovative performance. More specifically, it is focused on the validity of proactive personality. Using a sample of 170 software engineers and their direct supervisors from a multinational information technology firm, we found that this individual disposition represents a valid and meaningful predictor of individual innovative performance, when assessed through supervisor ratings. Further analyses have also shown that proactive personality remains a significant predictor of this criterion, when the big five factors of personality are taken into account. Theoretical and practical implications of these findings are presented and discussed.
Partially Linear Estimation With Application to Sparse Signal Recovery From Measurement Pairs

We address the problem of estimating a random vector from two sets of measurements and , such that the estimator is linear in . We show that the partially linear minimum mean-square error (PLMMSE) estimator does not require knowing the joint distribution of and in full, but rather only its second-order moments. This renders it of potential interest in various applications. We further show that the PLMMSE method is minimax-optimal among all estimators that solely depend on the second-order statistics of and . We demonstrate our approach in the context of recovering a signal, which is sparse in a unitary dictionary, from noisy observations of it and of a filtered version. We show that in this setting PLMMSE estimation has a clear computational advantage, while its performance is comparable to state-of-the-art algorithms. We apply our approach both in static and in dynamic estimation applications. In the former category, we treat the problem of image enhancement from blurred/noisy image pairs. We show that PLMMSE estimation performs only slightly worse than state-of-the art algorithms, while running an order of magnitude faster. In the dynamic setting, we provide a recursive implementation of the estimator and demonstrate its utility in tracking maneuvering targets from position and acceleration measurements.
Hidden Markov Acoustic Modeling With Bootstrap and Restructuring for Low-Resourced Languages

This paper proposes an acoustic modeling approach based on bootstrap and restructuring to dealing with data sparsity for low-resourced languages. The goal of the approach is to improve the statistical reliability of acoustic modeling for automatic speech recognition (ASR) in the context of speed, memory and response latency requirements for real-world applications. In this approach, randomized hidden Markov models (HMMs) estimated from the bootstrapped training data are aggregated for reliable sequence prediction. The aggregation leads to an HMM with superior prediction capability at cost of a substantially larger size. For practical usage the aggregated HMM is restructured by Gaussian clustering followed by model refinement. The restructuring aims at reducing the aggregated HMM to a desirable model size while maintaining its performance close to the original aggregated HMM. To that end, various Gaussian clustering criteria and model refinement algorithms have been investigated in the full covariance model space before the conversion to the diagonal covariance model space in the last stage of the restructuring. Large vocabulary continuous speech recognition (LVCSR) experiments on Pashto and Dari have shown that acoustic models obtained by the proposed approach can yield superior performance over the conventional training procedure with almost the same run-time memory consumption and decoding speed.
Poster abstract: Extreme learning machine for wireless indoor localization

Due to the widespread deployment and low cost, WLAN has drawn much attention for indoor localization. In this poster, an efficient indoor localization algorithm, which utilizes the WLAN received signal strength from each Access Point (AP), has been proposed. The algorithm is based on the Extreme Learning Machine (ELM), a Single layer Feed-forward neural Network (SLFN). It is competitive fast in offline learning and online localization. Also, compared with existing fingerprinting approach, it does not need the fingerprinting database in the online phase, which can substantially reduce the required storage space of the terminal devices.
Contact Recommendations from Aggegrated On-Line Activity

We describe a system for recommending people to contact based on similar interests and activities as part of a company-wide social networking site. Our contact recommendation service aggregates input from multiple on-line data sources and combines them using a Bayesian noisy-MAX function to generate a rating of the overall match between two users. The system is running as part of an experimental social networking site at MITRE. We present the results of a preliminary evaluation in which we compare the recommendations to existing friend rela-
Power Allocation for Amplify-and-Forward Cooperative Transmission Over Rayleigh-Fading Channels

Cooperative transmission has been used to achieve diversity gain through partner to create multiple independent fading channels. It has attracted lots of research attention in recent years. An important question in cooperative communication is power allocation. Given the same total power, how much should be allocated to self-information transmission and how much to relayinginformation transmission. In this paper, we investigate power allocation problem using amplify-and-forward (AF) protocol considering bit error rate (BER) performance of both users and the work region of two cooperative users. We solve cooperative ratio, which is defined as the ratio of power used for cooperative transmission over the total power. We first consider both users applying the same ratio, and then extend to two-dimensional cooperative ratio problem. Our results show that it is sufficient to allocate both users the same ratio. This value is specified by the user with a weaker channel gain to the destination, i.e., bottleneck link.With appropriate selection of the cooperative ratio, the BER performance of both users can be improved significantly. User fairness is also considered in the analysis.
On Jacket transforms over finite fields

In 2001 Lee, Rajan and Park introduced (among other things) the concept of extended Complex Reverse Jacket (CRJ) transform over finite field. In this paper, we prove that this transform is equivalent to Galois Field Fourier (GFF) transform and possesses an additional feature of having a matrix with specific border. We also, present examples of Jacket transforms over finite fields applicable to odd dimensions extending in this way previous works on the subject.
Statistical Properties of Accountability Measures Based on ACT's Educational Planning and Assessment System. ACT Research Report Series, 2009-1.

Educational accountability has grown substantially over the last decade, due in large part to the No Child Left Behind Act of 2001. Accordingly, educational researchers and policymakers are interested in the statistical properties of accountability models used for NCLB, such as status, improvement, and growth models; as well as others that are not currently used for NCLB, such as value-added models. This study examines the statistical properties of accountability measures that are based on ACT’s Educational Planning and Assessment System (EPAS). Utilizing data on 1,019 high school cohorts and over 70,000 students with test scores from three time points (8, 10, and 11 /12 grades), different types of accountability measures are contrasted and key statistical properties are discussed including reliability, associations with prior mean academic achievement and school contextual factors, and associations with college enrollment and retention rates. Our findings highlight how status, improvement, growth, and value-added models can lead to different conclusions about a school’s effectiveness. Unlike status, improvement, and growth models, value-added models attempt to isolate and measure the school’s effect on student’s learning. Thus, value-added measures have smaller associations with prior mean academic achievement and, by extension, school contextual factors such as poverty level and proportion of racial/ethnic minority students. This study also highlights the need for reporting the statistical uncertainty about estimates of schools’ effects so that results can be properly interpreted.
Tumor Necrosis Factor Inhibits Neurite Outgrowth and Branching of Hippocampal Neurons by a Rho-Dependent Mechanism

In response to injury and inflammation of the CNS, brain cells including microglia and astrocytes secrete tumor necrosis factor-α (TNF). This pro-inflammatory cytokine has been implicated in both neuronal cell death and survival. We now provide evidence that TNF affects the formation of neurites. Neurons cultured on astrocytic glial cells exhibited reduced outgrowth and branching of neurites after addition of recombinant TNF or prestimulation of glial cells to secrete TNF. This effect was absent in neurons of TNF receptor-deficient mice cultured on prestimulated glia of wild-type mice and was reverted by blocking TNF with soluble TNF receptor IgG fusion protein. TNF activated in neurons the small GTPase RhoA. By inactivating Rho with C3 transferase, the inhibitory effect of TNF on neurite outgrowth and branching was abolished. These results suggest that glia-derived TNF, as part of an injury or inflammatory process, can inhibit neurite elongation and branching during development and regeneration.
BOOK: PRCs in Neuroscience; Theory, Experiment and Analysis Section CB: Prediction of Network States; Experiment

In this chapter, we describe in detail how phase-locking in electrically coupled networks of spiking neurons can be understood using the framework of PRCs and weak coupling theory. We provide the necessary mathematical background and biological context to allow the reader to acquire an understanding of the network dynamics. We present the work using neuronal representations that include general integrate-and-fire and conductance-based models as well as spatially distributed, compartmental models.
Texture Segmentation Using Multichannel Gabor Filtering

Texture is defined as the regular repetition of an element or pattern on a surface. Texture is an important property of surfaces which characterizes the nature of the surface. An important task in image processing and machine vision is the task of segmenting the texture in an image. Texture segmentation is the process of partitioning an image into regions based on their texture. In this project we would like to propose a new approach using multichannel Gabor filters for the segmentation o f colour and grayscale multi textured images. The proposed method includes the design of the constituent Gabor filters. Based on these new Gabor filters, we develop new algorithms for the texture segmentation. Keywords - clustering, Gabor filter, Multichannel, segmentation, Texture