Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiproduct Firms, Information and Loyalty

The information set of consumers who are uncertain about product attributes is a central ingredient in the analysis of marketing researchers and the strategies of marketing practitioners. Here, we suggest that the profile of a multiproduct firm is an important element in the information set about each one of its products. As an example of such profiles, Honda is known to produce gasoline-efficient cars, whereas Volvo is perceived to focus on safety. The model includes product differentiation and heterogeneous consumers who are uncertain about product attributes. Like previous studies, a consumer’s information set for a product includes noisy signals she obtains about the product’s attributes, and the parameters of her prior distribution. Unlike previous studies, her prior distribution of a product’s attributes depends on the firm’s profile (which is defined as the distribution of attributes across the firm’s products). We show that by revising the information set in this way, one can explain interesting empirical regularities concerning consumer behavior. For example, the consumer is loyal to a multiproduct firm even at times that it does not offer a product that matches her preferences better than competing firms. Our model also offers a parsimonious way of thinking about brand extension strategies and maps new channels of spillovers within a multiproduct firm. We estimate the model and test its implications using panel data on television viewing choices. The estimated model also allows for state dependence parameters and unobserved heterogeneity. Our structural estimates come from maximizing a simulated likelihood function and using importance sampling. The empirical results support the model and its implications. We find that the profile of a multiproduct firm is an important element in the information set of consumers. Finally, we illustrate the empirical bias in standard choice models that do not structure the information set as suggested here. ∗ We are grateful to Dmitri Byzalov for outstanding research assistance, and to Luis Cabral, Zvi Eckstein, Tulin Erdem, Barry Nalebuff, Ariel Pakes, Peter Reiss, Manuel Trajtenberg, and seminar participants at Econometrics in Tel-Aviv, Marketing Science 2001, Harvard, and Yale for helpful comments. † Soldiers Field Road, Boston, MA 02163. Phone: (617) 495-5082; Fax: (617) 495-0355; email: banand@hbs.edu. ‡ Faculty of Management, Tel Aviv University, Tel Aviv, Israel, 69978. Phone: 972-3-640-6311; Fax: 972-3-6405621; email: rroonn@post.tau. ac.il.
Why Assessment Will Never Work at Many Business Schools

On the long and arduous journey toward effective educational assessment, business schools have progressed in their ability to clearly state measurable learning goals and use direct measures of student learning. However, many schools are wrestling with the last stages of the journey—measuring present learning outcomes, implementing curricular/pedagogical changes, and then measuring postchange outcomes to determine if the implemented changes produced the desired effect. These last steps are particularly troublesome for a reason unrecognized in the assessment literature—inadequate statistical power caused primarily by the use of small student samples. Analyses presented here demonstrate that assessment efforts by smaller schools may never provide the statistical power required to obtain valid results in a reasonable time frame. Consequently, decisions on curricular and pedagogical change are too often based on inaccurate research findings. Rather than waste time and resources toward what essentially is a statistical dead end, an alternate approach is recommended: Schools should examine published pedagogical studies that use direct measures of learning with sufficient statistical power and utilize the findings to improve student learning.
Sleep-Wake Patterns during the Acute Phase after First-Ever Stroke

This study describes the pattern of day and night sleep and explores relationships between these patterns and sociodemographic and clinical factors as well as sleep environmental context and the patient's subjective sleep quality. Data from 110 patients with first-ever stroke was collected by structured interview surveys, medical record, and objective estimated sleep data from wrist actigraphy. The variability in estimated sleep is large. Half the patients slept either <6 hours or >8 hours per night, and 78% had more than nine awakenings per night. Men slept less than women, and patients sleeping at home had fewer awakenings than those who slept in hospital. It was estimated sleep during daytime in all, except 4, patients. Longer stay in hospital was related to more daytime sleep, and the subjective sleep quality correlated with estimated sleep time, wake time, and wake percentage.
Collaborative Recommendation with Multi-Criteria Ratings

Abstract. Recommendation systems utilize data analysis techniques to the problem of helping users find the items they would like. Example applications include the recommendation systems for movies, books, CDs and many others. As recommendation systems emerge as an independent research area, the rating structure plays a critical role in recent studies. Among many alternatives, the collaborative filtering algorithms are gen-erally accepted to be successful to estimate user ratings of unseen items and then to derive proper recommen-dations. In this paper, we extend the concept of single criterion ratings to multi-criteria ones, i.e., an item can be evaluated in many different aspects. For example, the goodness of a restaurant can be evaluated in terms of its food, decor, service and cost. Since there are usually conflicts among different criteria, the recommen-dation problem cannot be formulated as an optimization problem any more. Instead, we propose in this paper to use data query techniques to solve this multi-criteria recommendation problem. Empirical studies show that our approach is of both theoretical and practical values.
Structure determination of the silver carboxylate dimer [Ag(O2C20H39)]2, silver arachidate, using powder X-ray diffraction methods

High-resolution powder X-ray diffraction and density functional plane wave pseudo-potential techniques have been used to obtain an optimized structural model of silver arachidate, [Ag(O2C(CH2)18CH3]2. The unit cell is triclinic, space group P-1 with cell dimensions of a = 4.1519(10) Å, b = 4.7055(10) Å, c = 53.555(4) Å, α = 89.473(15)°, β = 87.617(5)° and γ = 76.329(5)°. The structure is characterized by an 8-membered ring dimer of Ag atoms and carboxyl groups joined by four-member Ag–O rings with fully extended zigzag side chains, giving rise to one-dimensional chains along the b-axis.
Calibration of Housing Parameters for Underwater Stereo-Camera Rigs

When using perspective cameras underwater, the underwater housing with its glass interface between water and air causes the light rays to change their direction due to refraction. In applications where geometrical properties of images are exploited without explicitly modeling refraction, i.e. when using the perspective pinhole camera model, this leads to a systematical error. This error is depending on the housing configuration like distance between camera and glass interface and angle between glass interface normal and optical axis. In this paper, we analyze the calibration of those parameters using a camera model explicitly considering refraction. The goal is to determine those parameters without the need of handling a calibration target underwater, which is cumbersome, if not impossible.
When Blood Is Touched

The development of blood-compatible materials is reviewed. It grew from originally simplistic views of physical requirements such as surface charge and wettability, to endothelial cells seeded onto a biodegradable cast, and tissue engineering. In vitro findings grew from the discovery of one specific protein being adsorbed, to that of sequential protein adsorption with complex implications of platelet and white cell adhesion. The main challenge is still the production of small blood vessels (capillaries).
Kernel Snakes : Non-parametric Active Deformable Models

In this paper, we present a new non-parametric generalized formulation to statistical pressure snakes. We discuss the shortcomings of the traditional pressure snakes. We then introduce a new generic pressure model that alleviates these shortcomings, based on the Bayesian decision theory. Non-parametric techniques are used to obtain the statistical models that drive the snake. We discuss the advantages of using the proposed non-parametric model compared to other parametric techniques. Multi-colored-target tracking is used to demonstrate the performance of the proposed approach. Experimental results show enhanced, real-time performance.
Systolic array based architecture for DS fast motion estimation algorithm

The H.264/AVC video coding standard can achieve up to 50% more compression than its prior video coding standards. From the last two decade, developing systolic array architectures is one of the popular research for VLSI designers because of their inherent regularity and modularity. This paper introduce a 2-D systolic array architecture of diamond search (DS) block-matching algorithm. Until now, no one exiting architectures give the high throughput with 100% hardware utilization. By using, systolic array we have proposed a novel 2-D architecture for DS block-matching algorithm which overcome this problem at the expense of more hardware and power. This is also feasible for variable block size motion estimation and so, we can easily use this in new standards like H.264.
A study of the efficiency of pooling in haplotype estimation

MOTIVATION It has been claimed in the literature that pooling DNA samples is efficient in estimating haplotype frequencies. There is, however, no theoretical justification based on calculation of statistical efficiency. In fact, the limited evidence given so far is based on simulation studies with small numbers of loci. With rapid advance in technology, it is of interest to see if pooling is still efficient when the number of loci increases. METHODS Instead of resorting to simulation studies, we make use of asymptotic statistical theory to perform exact calculation of the efficiency of pooling relative to no pooling in the estimation of haplotype frequencies. As an intermediate step, we use the log-linear formulation of the haplotype probabilities and derive the asymptotic variance-covariance matrix of the maximum likelihood estimators of the canonical parameters of the log-linear model. RESULTS Based on our calculations under linkage equilibrium, pooling can suffer huge loss in efficiency relative to no pooling when there are more than three independent loci and the alleles are not rare. Pooling works better for rare alleles. In particular, if all the minor allele frequencies are 0.05, pooling maintains an advantage over no pooling until the number of independent loci reaches 6. High linkage disequilibrium effectively reduces the number of independent loci by ruling out certain haplotypes from occurring. Similar calculations of efficiency for the case of no pooling justify the common belief that it is not worthwhile to use molecular methods to resolve the phase ambiguity of individual genotype data. AVAILABILITY The R codes for the calculation are available at http://www.stat.nus.edu. sg/∼staxj/pooling CONTACT stakuka@nus.edu. sg.
Scaling laws for myelinated axons derived from an electrotonic core-conductor model.

A macroscopic cable equation, which describes the passive linear ("electrotonic") response of a myelinated axon, was previously derived from a segmented cable equation using Keller's two-space homogenization method [Basser, PJ, Med. and Biol. Comput. , 1993, Vol. 31, pp. S87-S92]. Here we use the space and length constants of this averaged cable equation to predict classical scaling laws that govern relationships among the inner and outer diameters of the axon's myelin sheath and the distance separating adjacent nodes of Ranvier. These laws are derived by maximizing the characteristic speed of an electrical disturbance along the axon, i.e., the ratio of the characteristic length and the characteristic time constants of the macroscopic cable, subject to the constraint that the nodal width is constant. Using this result, it is also possible to show that all myelinated axons are equally fault tolerant. No free parameters were used in this analysis; all variables and physical constants used in these calculations were taken from published experimental data.
Information integration for counter terrorism activities: requirements for context mediation

The National Research Council has noted that "[A]lthough there are many private and public databases that contain information potentially relevant to counterterrorism programs, they lack the necessary context definitions (i.e., metadata) and access tools to enable interoperation with other databases and the extraction of meaningful and timely information. " In this paper we present examples of these problems and a technology developed at MIT, called context mediation, which provides an approach for addressing these problems.
The Effective Potential of the N=0* Yang-Mills Theory

We study the \N=4 SYM theory with SU(N) gauge group in the large N limit, deformed by giving equal mass to the four adjoint fermions. With this modification, a potential is dynamically generated for the six scalars in the theory, \phi^i. We show that the resulting theory is stable (perturbatively in the 't Hooft coupling), and that there are some indications that =0 is the vacuum of the theory. Using the AdS/CFT correspondence, we compare the results to the corresponding supergravity computation, i.e. brane probing a deformed AdS_5 x S^5 background, and we find qualitative agreement.
Spectral Analysis of Access Rate Control in High Speed Networks

In this paper we study the performance trade-oo of access rate control in high speed networks by spectral analysis. The emphasis is placed on the interrelationship between access control queueing and network queueing, as aaected by degrees of control in response to diiering input power spectrum. Both deterministic and stochastic analyses are developed. While the simple deterministic analysis helps us to gain a good engineering insight into access rate control in high speed networks, the stochastic analysis provides us quantitative measures of the access rate control in random traac environment. Interesting observations are made between control performance and low-frequency input statistics. In the stochastic analysis we are able to derive the exact solutions of a network queue fed by a correlated source under the leaky-bucket access control.
Predicting Search User Examination with Visual Saliency

Predicting users' examination of search results is one of the key concerns in Web search related studies. With more and more heterogeneous components federated into search engine result pages (SERPs), it becomes difficult for traditional position-based models to accurately predict users' actual examination patterns. Therefore, a number of prior works investigate the connection between examination and users' explicit interaction behaviors (e.g.~click-through, mouse movement). Although these works gain much success in predicting users' examination behavior on SERPs, they require the collection of large scale user behavior data, which makes it impossible to predict examination behavior on newly-generated SERPs. To predict user examination on SERPs containing heterogenous components without user interaction information, we propose a new prediction model based on visual saliency map and page content features. Visual saliency, which is designed to measure the likelihood of a given area to attract human visual attention, is used to predict users' attention distribution on heterogenous search components. With an experimental search engine, we carefully design a user study in which users' examination behavior (eye movement) is recorded. Examination prediction results based on this collected data set demonstrate that visual saliency features significantly improve the performance of examination model in heterogeneous search environments. We also found that saliency features help predict internal examination behavior within vertical results.
Regularized Independent Component Analysis Regularization in Face Verification

Summary In this work, a regularized Independent Component Analysis (coined as RICA) is proposed in face verification. RICA attempts to generate a linearly independent feature representation with minimal within-class variance, leading to better data discrimination. In RICA, information of correlation coefficients between image data is employed to form a Laplacian matrix. This matrix measures the degree of deviation of a data point from its nearby/ adjacent points. In other words, local discriminative features of data could be disclosured through the Laplacian matrix. As the name suggests, independent component analysis (ICA) is adopted as feature descriptor in this approach. Since there are two different architectures of ICA (ICA I and ICA II), RICA is implemented on these two types of feature extractor and the proposed techniques are known as RICA_ICA I and RICA_ICA II, respectively. The efficiency of RICA is assessed based on three face datasets, namely (1) Facial Recognition Technology (FERET), (2) CMU Pose, Illumination, and Expression (CMU PIE) and (3) Face Recognition Grand Challenge (FRGC).
Mining Techniques for Clinical Expert System and Predicting and Treating Lung Cancer with Big Data

Cancer is the very most important cause of death for both Men and Women. The early apprehension and treatment of cancer can be helpful in curing the disease completely. So the obligation of techniques to detect the state of cancer nodule in early stage increasing. A disease that is commonly misdiagnosed is Lung Cancer. Earlier diagnosis of Lung Cancer saves massive lives, flaw which may lead to other harsh problems causing sudden fatal end detection and diagnosis of the disease. Its treat rate and prediction depends mainly on the early detection and diagnosis sudden fatal end. Data mining have found numerous applications in business and methodical domain. Valuable information can be discovered from application of data mining techniques in healthcare system. In this study, we momentarily examine the potential use of classification based data mining techniques in healthcare system. Big data collects the huge amounts of healthcare data set and survey many data mining approaches for predicting the likelihood of patients getting a Lung Cancer disease. Aim of the paper is to suggest a model for early detection and correct diagnosis of the disease using which will help the doctor in saving the life of the patient.
Controlling software cost using fuzzy Quality based EVM

Earned Value Management (EVM) is the most recognized tool for monitoring and controlling project performance. Its ultimate goal is to provide reliable early warning signals about the cost and schedule performance of a project. However, EVM has its limitations in monitoring and controlling software project activities. The lack of quality performance indicator and the inadequacy in incorporating the impact of the software fuzzy nature, volatile requirements, and unplanned reworks in its forecasting model are among these limitations. This paper proposed a fuzzy quality-based EVM model integrating cost, schedule, and quality for a given software project. The primary contribution of the paper is the introduction of the Quality Performance Index (QPI), which is expected to enhance the accuracy of cost performance monitoring and controlling.
Sand casting precision technology based on non-occupying coating technology

The current status of foundry technology is introduced. A suggestion of improving sand casting precision to realize precision casting by using rapid prototyping technology and non-occupying coating technology is put forward. Combining with making brake drum of sand casting mold, the key technology is analyzed. The advantages are pointed out by comparing with the common.
Image Processing Technique to Count the Number of Logs in a Timber Truck

This paper summarises the results of using image processing technique to get information about the load of timber trucks before their arrival using digital images or geo tagged images. Once the images are captured and sent to sawmill by drivers from forest, we can predict their arrival time using geo tagged coordinates, count the number of (timber) logs piled up in a truck, identify their type and calculate their diameter. With this information we can schedule and prioritise the inflow and unloading of trucks in the light of production schedules and raw material stocks available at the sawmill yard. It is important to keep all the actors in a supply chain integrated coordinated, so that optimal working routines can be reached in the sawmill yard.
Backward Registration-Based Aspect Ratio Similarity for Image Retargeting Quality Assessment

During the past few years, there have been various kinds of content-aware image retargeting operators proposed for image resizing. However, the lack of effective objective retargeting quality assessment metrics limits the further development of image retargeting techniques. Different from traditional image quality assessment (IQA) metrics, the quality degradation during image retargeting is caused by artificial retargeting modifications, and the difficulty for image retargeting quality assessment (IRQA) lies in the alternation of the image resolution and content, which makes it impossible to directly evaluate the quality degradation like traditional IQA. In this paper, we interpret the image retargeting in a unified framework of resampling grid generation and forward resampling. We show that the geometric change estimation is an efficient way to clarify the relationship between the images. We formulate the geometric change estimation as a backward registration problem with Markov random field and provide an effective solution. The geometric change aims to provide the evidence about how the original image is resized into the target image. Under the guidance of the geometric change, we develop a novel aspect ratio similarity (ARS) metric to evaluate the visual quality of retargeted images by exploiting the local block changes with a visual importance pooling strategy. Experimental results on the publicly available MIT RetargetMe and CUHK data sets demonstrate that the proposed ARS can predict more accurate visual quality of retargeted images compared with the state-of-the-art IRQA metrics.