Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling category-level purchase timing with brand-level marketing variables

Purchase timing of households is usually modeled at the category level. Marketing eorts are however only available at the brand level. Hence, to describe category-level interpurchase times using marketing eorts one has to construct a category-level measure of marketing eorts from the marketing mix of individual brands. In this paper we discuss two standard approaches suggested in the literature to solve this problem, that is, using individual choice shares as weights to average the marketing mix, and the inclusive value approach. Additionally, we propose three alternative novel solutions, which have less limitations than the two standard approaches. The new approaches use brand preferences following from a brand choice model to capture the relevance of the marketing mix of individual brands. One of these approaches integrates the purchase timing model with a brand preference model. To empirically compare the two standard and the three new approaches, we consider household scanner data in three product categories. One of the main conclusions is that the inclusive value approach performs worse than the other approaches. This holds in-sample as well as out-of-sample. The performance of the individual choice share approach is best unless one allows for unobserved heterogeneity in the brand choice models, in which case the three new approaches based on modeled brand preferences are superior.
Asset–liability modelling and pension schemes: the application of robust optimization to USS

This paper uses a novel numerical optimization technique – robust optimization – that is well suited to solving the asset–liability management (ALM) problem for pension schemes. It requires the estimation of fewer stochastic parameters, reduces estimation risk and adopts a prudent approach to asset allocation. This study is the first to apply it to a real-world pension scheme, and the first ALM model of a pension scheme to maximize the Sharpe ratio. We disaggregate pension liabilities into three components – active members, deferred members and pensioners, and transform the optimal asset allocation into the scheme's projected contribution rate. The robust optimization model is extended to include liabilities and used to derive optimal investment policies for the Universities Superannuation Scheme (USS), benchmarked against the Sharpe and Tint, Bayes–Stein and Black–Litterman models as well as the actual USS investment decisions. Over a 144-month out-of-sample period, robust optimization is superior to the four benchmarks across 20 performance criteria and has a remarkably stable asset allocation – essentially fix-mix. These conclusions are supported by six robustness checks.
Sorting 4DCT Images Based on Manifold Learning

Respiratory motion degrades anatomic position reproducibility, and result in significant errors in radiotherapy. 4D computed tomography (4DCT) can characterize anatomy motion during breathing. Usually, the acquired 4DCT images sequences is out of order. How to rearrange the sequence, i.e. sort 4DCT images has been the focus of 4DCT. In this paper we propose a method based on manifold learning, Isomap technique to reconstruct time-resolved CT volumes. By mapping high dimensional image data with Isomap into low dimensional space, each image is assigned a value, then 4DCT images is sorted according to the value to reconstruct a respiratory cycle. Experiments result shows that the method is feasible to sort 4 DCT images without using any external motion monitoring systems.
Investigating the tolerance response of early vegetative stage soybeans to Aphis glycines Matsumura

Investigating the tolerance response of early vegetative stage soybeans to Aphis glycines Matsumura" (2012). The objectives of this research were to evaluate the impact of soybean aphid (Aphis glycines Matsumura) feeding on the yield response of V1, V3, and R1 KS4202 soybean plants and examine the effect of soybean aphid feeding on peroxidase activity in V1 and V3 KS4202 plants. KS4202 plants infested during the early vegetative stages (VC, VE and V1) were identified as highly susceptible based upon plant damage and stunting of the plants. In contrast, V3, V4, and V5 stage KS4202 soybeans were classified as moderately resistant. In the yield response study, V1, V3 and R1 plants had aphid numbers that exceeded the average economic injury level threshold of 674 aphids per plant. Despite exceeding this level, V3 and R1 aphid-infested plants were not statistically different from their respective control plants for any of the yield parameters evaluated except average pod weight, which was statistically higher for plants exposed to the high aphid treatment than to the control treatment. Changes in protein content, peroxidase activity and isozyme profiles in response to aphid feeding were documented in V1 and V3 stages of tolerant (KS4202) and susceptible (SD76R) soybeans at 6, 16, and 22 days after aphid introduction. Protein content was similar between infested and control V1 and V3 stage plants for both KS4202 and SD76R at 6, 16, and 22 days after aphid introduction. Enzyme kinetics studies documented that KS4202 V1 and SD76R V1 and V3 control and aphid-infested soybean had similar levels of peroxidase activity at the three time points evaluated. By contrast, KS4202 aphid-infested plants at the V3 stage had significantly higher peroxidase activity levels than control plants at 6 and 22 days after aphid introduction. The differences in peroxidase activity observed between infested and control V3 KS4202 plants throughout the course of the experiment suggest that peroxidases may be playing multiple roles in the tolerant plant. Gels stained for peroxidases identified differences in the isozyme profiles of aphid-infested and control plants for both KS4202 and SD76R. The results of this research provide insights to better understand the tolerance response in KS4202 and ultimately will result in improved management options for this important insect pest. iv ACKNOWLEDGMENTS I want to express my deepest gratitude to my advisors Dr. Thomas Hunt and Dr. providing me an excellent environment to perform my research. I also want to thank …
Computational Integration of Homolog and Pathway Gene Module Expression Reveals General Stemness Signatures

The stemness hypothesis states that all stem cells use common mechanisms to regulate self-renewal and multi-lineage potential. However, gene expression meta-analyses at the single gene level have failed to identify a significant number of genes selectively expressed by a broad range of stem cell types. We hypothesized that stemness may be regulated by modules of homologs. While the expression of any single gene within a module may vary from one stem cell type to the next, it is possible that the expression of the module as a whole is required so that the expression of different, yet functionally-synonymous, homologs is needed in different stem cells. Thus, we developed a computational method to test for stem cell-specific gene expression patterns from a comprehensive collection of 49 murine datasets covering 12 different stem cell types. We identified 40 individual genes and 224 stemness modules with reproducible and specific up-regulation across multiple stem cell types. The stemness modules included families regulating chromatin remodeling, DNA repair, and Wnt signaling. Strikingly, the majority of modules represent evolutionarily related homologs. Moreover, a score based on the discovered modules could accurately distinguish stem cell-like populations from other cell types in both normal and cancer tissues. This scoring system revealed that both mouse and human metastatic populations exhibit higher stemness indices than non-metastatic populations, providing further evidence for a stem cell-driven component underlying the transformation to metastatic disease.
Reduction of Short-Channel Effects in FinFET

An application of FinFET Technology has opened new development in Nano-technology. Simulations show that FinFET structure should be scalable down to 10 nm. Formation of ultra thin fin enables suppressed short channel effects. It is an attractive successor to the single gate MOSFET by merit of its superior electrostatic properties and comparative case of manufacturability process. Inventing new device is always essential to improve the circuit performance; the total steps are more than usual MOSFET process, but the cost of material is smaller. Since it is more compact, using FinFET is economical. The leakage current due to DIBL was well suppressed and the roll-off of a FinFET is well controlled.
Statistical optimisation of cell growth and carotenoid production by rhodotorula mucilaginosa

Sequential statistical methods were used to maximise carotenoid production by a strain of Rhodotorula mucilaginosa, isolated from the Brazilian ecosystem. Initially, a factorial 25–1 experimental design was used, and the variables were pH and the levels of glucose, yeast extract, MgSO4.7H2O and KH2PO4. The nitrogen source (yeast extract) was the most important variable in enhancing carotenoid production; MgSO4.7H2O and KH2PO4 had a negative influence. The initial pH had no significant effect on carotenoid and cell productions. We further investigated the effects of glucose and yeast extract effects, using a second-order central composite design (CCD) to optimise carotenoid production, which was adequately approximated with a full quadratic equation obtained from a two-factor-2-level design. The analysis of quadratic surfaces showed that after 5 days of cultivation at 25 °C, the maximum carotenoid concentration (745 µg l-1) was obtained with 15 g l-1 of yeast extract and 20 g l-1 of glucose. The maximum carotenoid production (152 µg g-1) was obtained with 5 g l-1 yeast extract and 10 g l-1 glucose. Carotenoid formation was more sensitive to changes in yeast extract concentration than to changes in glucose concentration. Maximum cell production was achieved with 15–17 g l-1 of yeast extract and 15–20 g l-1 of glucose.
Less Is More: Latent Learning Is Maximized by Shorter Training Sessions in Auditory Perceptual Learning

Background The time course and outcome of perceptual learning can be affected by the length and distribution of practice, but the training regimen parameters that govern these effects have received little systematic study in the auditory domain. We asked whether there was a minimum requirement on the number of trials within a training session for learning to occur, whether there was a maximum limit beyond which additional trials became ineffective, and whether multiple training sessions provided benefit over a single session. Methodology/Principal Findings We investigated the efficacy of different regimens that varied in the distribution of practice across training sessions and in the overall amount of practice received on a frequency discrimination task. While learning was relatively robust to variations in regimen, the group with the shortest training sessions (∼8 min) had significantly faster learning in early stages of training than groups with longer sessions. In later stages, the group with the longest training sessions (>1 hr) showed slower learning than the other groups, suggesting overtraining. Between-session improvements were inversely correlated with performance; they were largest at the start of training and reduced as training progressed. In a second experiment we found no additional longer-term improvement in performance, retention, or transfer of learning for a group that trained over 4 sessions (∼4 hr in total) relative to a group that trained for a single session (∼1 hr). However, the mechanisms of learning differed; the single-session group continued to improve in the days following cessation of training, whereas the multi-session group showed no further improvement once training had ceased. Conclusions/Significance Shorter training sessions were advantageous because they allowed for more latent, between-session and post-training learning to emerge. These findings suggest that efficient regimens should use short training sessions, and optimized spacing between sessions.
VCZRP: Enhancing Performance of Mobile Ad Hoc Networks Using Virtual Coordinates

Ad hoc networks are wireless networks where mobile nodes relay on each other to keep network connected without help of pre-existing infrastructure or centralized control. These are characterized by dynamic topology caused by node mobility, multihop wireless connectivity and limited bandwidth, and potentially large scale makes routing as challenging problem in these systems. The key challenge in the design of ad hoc networks is the development of dynamic routing protocols that can efficiently find routes between two communicating nodes. To address these various routing protocols are existing like on- demand routing, geographical routing. These protocols require availability of location information and suffer from poor routing performance and severe dead end problems. Existing, Virtual Coordinate Systems perform routing without location information using virtual coordinate vector at each node which is calculated using predefine landmark nodes. But these systems have the problem of updating the virtual coordinates because of frequent topological changes causing considerable overhead. Proposed system uses virtual coordinate system with ZRP (Zone Routing Protocol) aspects. Also, by using VC Id instead of virtual coordinate vector for node updations can reduce wastage of network bandwidth.
Discovering Gender Discrimination in Project Funding

The selection of projects for funding can hide discriminatory decisions. We present a case study investigating gender discrimination in a dataset of scientific research proposals submitted to an Italian national call. The method for the analysis relies on a data mining classification strategy that is inspired by a legal methodology for proving evidence of social discrimination against protected-by-law groups.
What determines a feeling's position in affective space? A case for appraisal

The location of verbally reported feelings in a three-dimensional affective space is determined by the results of appraisal processes that elicit the respective states. One group of participants rated their evaluation of 59 pictures from the International Affective Picture System (IAPS) on a profile of nine appraisal criteria. Another group rated their affective reactions to the same pictures on the classic dimensions of affective meaning (valence, arousal, potency). The ratings on the affect dimensions correlate differentially with specific appraisal ratings. These results can be interpreted as showing that the reactions to the IAPS pictures are predictably produced through appraisal of picture content. The relevance of the findings for emotion induction paradigms and for emotion theory in general is discussed.
Analytical Tractability of Hexagonal Network Model With Random User Location

Explicit derivation of interferences in hexagonal wireless networks has been widely considered intractable and requires extensive computations with system level simulations. In this paper, we fundamentally tackle this problem and explicitly evaluate the downlink interference-to-signal ratio (ISR) for any mobile location m in a hexagonal wireless network, whether composed of omni-directional or tri-sectorized sites. The explicit formula of ISR is a very convergent series on m and involves the use of Gauss hypergeometric and Hurwitz Riemann zeta functions. Besides, we establish simple identities that well approximate this convergent series and turn out quite useful compared to other approximations in literature. The derived expression of ISR is easily extended to any frequency reuse pattern. Moreover, it is also exploited in the derivation of an explicit form of SINR distribution for any arbitrary distribution of mobile user locations, reflecting the spatial traffic density in the network. Knowing explicitly about interferences and SINR distribution is very useful information in capacity and coverage planning of wireless cellular networks and particularly for macro-cells' layer that forms almost a regular point pattern.
Spectral Gaps for Spin Systems: Some Non-convex Phase Examples

We prove that a convex phase may be perturbed into a non-convex phase preserving the spectral gap properties of the unbounded spin system with nearest neighbour interaction associated to this potential. The proof is based on Helffer's method that reduces the spectral properties of the unbounded spin system to some uniform spectral gap of the one-dimensional phase. We then make use of Hardy's criterion for Poincarr inequalities on the real line to construct our examples.
Latent Dirichlet Allocation based multilevel classification

Information processing and knowledge extraction are the two key factors for mining technique. Many models were proposed and implemented successfully on the available information over internet. Automatic Categorization is a machine learning approach which is important for the information processing. In this paper an attempt is made to propose a multilevel classification model using Latent Dirichlet Allocation (LDA) approach. Though the existence of Latent Dirichlet Allocation (LDA) is observed in the literature but a modified model for multilevel classification is presented which is independent of any language. In order to achieve such model many existing proposals were considered like PLSI, which uses the Exceptional Maximization (EM) method only to train the latent classes. The iterative process of Latent Dirichlet Allocation (LDA), which yields the multilevel classification of the corpus. Topic Modeling is used to discover the hidden things that pervade the collection to annotate the documents according to the new topics.
A root-MUSIC algorithm for non circular sources

We present in this paper a new direction finding algorithm for non circular sources that is based on polynomial rooting. Due to the non circularity characteristics of the impinging sources, the proposed method is able to handle more sources than sensors. By using a polynomial rooting instead of a searching technique the method is limited to linear uniformly spaced arrays. However, polynomial rooting reduces significantly computation cost and enhances resolution power. Computer simulations are used to show the performance of the algorithm.
Experimental comparison of observers for tool position estimation of industrial robots

This paper investigates methods for tool position estimation of industrial robots. It is assumed that the motor angular position and the tool acceleration are measured. The considered observers are different versions of the extended Kalman filter as well as a deterministic observer. A method for tuning the observers is suggested and the robustness of the methods is investigated. The observers are evaluated experimentally on a commercial industrial robot.
A portable and extensible performance model for stream-processing of XPath queries.

AbstractXML has become a standard for document storage and interchange and its convenientsyntax improves the interoperability of many applications. The computational complexityand practical cost of XPath queries can vary dramatically so its unconstrained use leadsto unpredictable space and time costs. Stream-processing of XPath queries mitigates thisproblem by restricting the query language fragment and allows simpler implementations bystack automata with no limit on the XML document size.Wa have designed an accurate performance model for such algorithms. It collects staticinformation about the XML document to statically predict the memory consumption of aquery to within a few percent. The model is portable, adapts to any document structure andscales to 1GiB documents and beyond. In addition to predictable performance, user interac-tion with the system can lead to optimizations by constraining the search for sub-documents.Existing literature on this problem has not addressed all the technical problems involvedand our model is to our knowledge the most complete of its kind.
Shunt Active Filtering in Smart Grid Distributed Generation Systems

This is the report for our mini-project assignment in the course "TET4190 Power Electronics for Renewable Energy" at NTNU, where we were to explore how distributed resources can be used for power quality (PQ) improvement in presence of nonlinear loads, preventing them from spreading around in the grid. The tasks became:Trying to design shunt active filter to compensate the harmonic current in a smart distributed converter system across the distribution level of power grid. Using Matlab simulation and Simpower to simulate the power system and shunt active filter equipment, to check and improve the design and relative parameter. 10/1/2012 Supervised by Phd student Mehdi Karbalaye Zadeh
Purinergic regulation of the epithelial Na+ channel

1. The epithelial Na+ channel (ENaC) is a major conductive pathway that transports Na+ across the apical membrane of the distal nephron, the respiratory tract, the distal colon and the ducts of exocrine glands. The ENaC is regulated by hormonal and humoral factors, including extracellular nucleotides that are available from the epithelial cells themselves.
Fuzzy-Bayesian network approach to genre-based recommender systems

The World Wide Web has created a new media for mass marketing that can also be highly customized to online customers' needs and expectations. Recommender Systems (RS) play an important role in this area. Here, we aim to establish a genre-based collaborative RS to automatically suggest and rank a list of appropriate items (movies) to a user based on the user profile and the past voting patterns of other users with similar tastes. The contribution of this paper is using genre based information in a hybrid fuzzy-Bayesian network collaborative RS. The interest to the different genres is computed based on a hybrid user model. The similarity of like-minded users according to the fuzzy distance and also Pearson correlation coefficient is involved in a Bayesian network.
Molecular motors one at a time: FIONA to the rescue

Processive molecular motors act as intracellular transporters of a broad range of cargoes varying from organelles to messenger RNAs. Due to the nanometre range movements and complex dynamics of these motors, highly specialized tools are required to study them, in particular at the single-molecule level. Such tools are what physicists are providing for understanding these biological systems. Fluorescence based real-time localization techniques, with 1 nm spatial resolution and down to 1 ms temporal resolution (FIONA: fluorescence imaging with one-nanometre accuracy), and their applications to a group of molecular motors (myosin V, myosin VI, kinesin, and dynein) are the topics of this paper. In addition to the well established in vitro studies, the recent applications of these techniques to the much more challenging, but also more informative, in vivo realm will be discussed.
Pedestrian tracking in infrared from moving vehicles

The automatic detection and tracking of pedestrians in imagery constitute important and challenging problems both in computer vision and driver assistance systems. We address these problems for the case of a forward looking monocular infrared camera under strong vehicle induced camera motion. An integrated detection & tracking strategy is introduced based on a state-of-the-art feature based object detector originally developed for images in the visual spectrum. The proposed pedestrian detection algorithm can be applied to both infrared and visual imagery. We show the difficulties arising from the specifics of infrared data under strong camera motion and how to tackle these problems by replacing common motion models like the Kalman filter by a feature matching approach.