Miracle-dz/UniFAR-FTUs huggingface dataset:
Product Choice with Large Assortments: A Scalable Deep-Learning Model

Personalized marketing in retail requires a model to predict how different marketing actions affect product choices by individual customers. Large retailers often handle millions of transactions daily, involving thousands of products in hundreds of categories. Product choice models thus need to scale to large product assortments and customer bases, without extensive product attribute information. To address these challenges, we propose a custom deep neural network model. The model incorporates bottleneck layers to encode cross-product relationships, calibrates time-series filters to capture purchase dynamics for products with different interpurchase times, and relies on weight sharing between the products to improve convergence and scale to large assortments. The model applies to loyalty card transaction data without predefined categories or product attributes to predict customer-specific purchase probabilities in response to marketing actions. In a simulation, the proposed product choice model predicts purchase decisions better than baseline methods by adjusting the predicted probabilities for the effects of recent purchases and price discounts. The improved predictions lead to substantially higher revenue gains in a simulated coupon personalization problem. We verify predictive performance using transaction data from a large retailer with experimental variation in price discounts. This paper was accepted by Gui Liberali, Management Science Special Issue on Data-Driven Prescriptive Analytics.
High Dimensional Clustering

In this thesis, we study r-nets, a well known data structure in computational geometry. Specifically, we focus on cases where the data points are high dimensional. In high dimensional datasets, constructing r-nets is considered to be efficient when the runtime of the algorithm has a polynomial dependency on the dimension and a sub-quadratic dependency on the cardinality of the dataset. Towards this end, we relax the definition of r-nets and consider approximate solutions. Many problems can be reduced to finding approximate r-nets, from which a few are presented in this thesis. We devise algorithms that improve the run-time of approximating r-nets in high-dimensional spaces with `1 and `2 metrics from Õ(dn2−Θ( √ )) to Õ(dn+n2−α), where α = Ω( 1/3/log(1/ )). These algorithms are also used to improve a framework that provides approximate solutions to other high dimensional distance problems. Using this framework, several important related problems can also be solved efficiently (with the same runtime as the construction of the r-nets), e.g., (1 + )-approximate kth-nearest neighbor distance, (4 + )-approximate Min-Max clustering, (4 + )-approximate k-center clustering. In addition, we build an algorithm that (1 + )-approximates greedy permutations in time Õ((dn + n2−α) · log Φ) where Φ is the spread of the input. This algorithm is used to (2 + )-approximate k-center with the same time complexity.
Automatic segmentation of text into structured records

In this paper we present a method for automatically segmenting unformatted text records into structured elements. Several useful data sources today are human-generated as continuous text whereas convenient usage requires the data to be organized as structured records. A prime motivation is the warehouse address cleaning problem of transforming dirty addresses stored in large corporate databases as a single text field into subfields like “City” and “Street”. Existing tools rely on hand-tuned, domain-specific rule-based systems. We describe a tool DATAMOLD that learns to automatically extract structure when seeded with a small number of training examples. The tool enhances on Hidden Markov Models (HMM) to build a powerful probabilistic model that corroborates multiple sources of information including, the sequence of elements, their length distribution, distinguishing words from the vocabulary and an optional external data dictionary. Experiments on real-life datasets yielded accuracy of 90% on Asian addresses and 99% on US addresses. In contrast, existing information extraction methods based on rule-learning techniques yielded considerably lower accuracy.
Meshless Geometric Subdivision

Point-based surface processing has developed into an attractive alternative to mesh-based processing techniques for a number of geometric modeling applications. By working with point clouds directly, any processing is based on the given raw data and its underlying geometry rather than any arbitrary intermediate representations and generally artificial connectivity relations. We extend this principle into the area of subdivision surfaces by introducing the notion of meshless, or point cloud, geometric subdivision. Our meshless subdivision approach replaces the role of mesh connectivity with intrinsic point proximity thereby avoiding a number of limitations of widely-used mesh-based surface subdivision schemes. Apart from introducing this idea of meshless subdivision , we put forward a first intrinsic meshless subdivision scheme and present a new method for the computation of intrinsic means on euclidean manifolds.
A Markley Variables-based Attitude Estimation Method Using Optical Flow and a Star Vector for Spinning Spacecraft

Markley variables have advantages of slow variation, easy numerical integration and high precision in describing the attitude of spinning spacecraft. Previous attitude estimation methods based on Markley variables for spinning spacecraft usually employ a sun vector from the sun sensor, a magnetic vector from the magnetometer, or the angular rate from the gyro as the measurement. This paper proposes a Markley variables-based attitude estimation method using optical flow and a star vector from a star sensor as the measurement, where optical flow provides rate information and the star vector provides direction information. This method can estimate the direction of the spin axis and spin angular rate very well by using only one star sensor. In addition, the star sensor has higher accuracy than the traditional sun sensor and magnetometer, and the star sensor can also replace the gyro in case the gyro is out of action. The impact factors of this method are also analysed, which include spin angular rate, spin axis orientation and spacecraft moment of inertia tensor error.
Primary cells derived from Tuberous Sclerosis Complex patients show autophagy alteration in the haploinsufficiency state

Abstract Tuberous sclerosis complex (TSC) is an autosomal dominant cancer predisposition disorder caused by heterozygous mutations in TSC1 or TSC2 genes and characterized by mTORC1 hyperactivation. TSC-associated tumors develop after loss of heterozygosity mutations and their treatment involves the use of mTORC1 inhibitors. We aimed to evaluate cellular processes regulated by mTORC1 in TSC cells with different mutations before tumor development. Flow cytometry analyses were performed to evaluate cell viability, cell cycle and autophagy in non-tumor primary TSC cells with different heterozygous mutations and in control cells without TSC mutations, before and after treatment with rapamycin (mTORC1 inhibitor). We did not observe differences in cell viability and cell cycle between the cell groups. However, autophagy was reduced in mutated cells. After rapamycin treatment, mutated cells showed a significant increase in the autophagy process (p=0.039). We did not observe differences between cells with distinct TSC mutations. Our main finding is the alteration of autophagy in non-tumor TSC cells. Previous studies in literature found autophagy alterations in tumor TSC cells or knock-out animal models. We showed that autophagy could be an important mechanism that leads to TSC tumor formation in the haploinsufficiency state. This result could guide future studies in this field.
UC Merced Proceedings of the Annual Meeting of the Cognitive Science Society

Efﬁcient codes have been shown to perform well in image and audio classiﬁcation tasks, but the impact of sparsity—and indeed the entire notion of efﬁcient coding—has not yet been well explored in the context of human movements. This paper tests several coding approaches on a movement classiﬁ-cation task and ﬁnds that efﬁcient codes for kinematic (joint angle) data perform well for classifying many different types of movements. In particular, the best classiﬁcation method relied on a sparse coding algorithm combined with a codebook that was tuned to kinematic movement data. The other approaches tested here—sparse coding with a random codebook, and "dense" coding using PCA—provide interesting baseline results and allow us to investigate why sparse codes appear to work well.
Multiuser Detection

From the Publisher: The development of multiuser detection techniques is one of the most important recent advances in communications technology. This self-contained and comprehensive book sets out the basic details of multiuser detection, starting with simple examples and progressing to state-of-the-art applications. The only prerequisites assumed are undergraduate-level probability, linear algebra, and digital communications. The book contains over 240 exercises and will be a suitable textbook for electrical engineering students. It will also be an ideal self-study guide for practicing engineers, as well as a valuable reference volume for researchers in communications, information theory, and signal processing.
FLUID: A Common Model for Semantic Structural Graph Summaries Based on Equivalence Relations

Summarization is a widespread method to handle very large graphs. The task of structural graph summarization is to compute a concise while at the same time meaningful synopsis of the key structural information of the graph. As there are many tasks regarding what information is to be summarized from a graph, there is no single concept or model of graph summaries. We have studied existing structural graph summaries for large (semantic) graphs. Despite the different purposes and concepts followed by the existing graph summaries, we found common patterns in the captured graph structures. We abstract from these patterns and provide for the first time a formally defined common model FLUID (FLexible graph sUmmarIes for Data graphs) that allows to flexibly define structural graph summaries. FLUID allows to quickly define, adapt, and compare different graph summaries for different purposes and datasets. To this end, FLUID provides features of structural summarization based on equivalence relations such as distinction of types and properties, direction of edges, bisimulation, and inference. We conduct a detailed complexity analysis of the features provided by FLUID. We show that graph summaries defined with FLUID can be computed in the worst case in $\mathcal{O}(n^2)$ w.r. t. $n$ the number of edges in the data graph. However, we analyzed large-scale graphs obtained from the web with billions of edges and found indications that the average complexity is bounded by $\Theta(n)$. Finally, we provide a formal declarative language for FLUID, which allows application developers to quickly define and modify their graph summaries. The language can be used to build any of the existing structural graph summaries found in the literature and beyond. In addition, FLUID can be extended by new summarization features by defining further equivalence relations over graph structures.
A Simple and Effective Closed Test for Chinese Word Segmentation Based on Sequence Labeling

In many Chinese text processing tasks, Chinese word segmentation is a vital and required step. Various methods have been proposed to address this problem using machine learning algorithm in previous studies. In order to achieve high performance, many studies used external resources and combined with various machine learning algorithms to help segmentation. The goal of this paper is to construct a simple and effective Chinese word segmentation tool without external resources, that is, a closed test for Chinese word segmentation. We use training data to construct a vocabulary to combine maximum matching word segmentation results with sequence labeling methods including hidden Markov model (HMM) and conditional random fields (CRF). The major idea is to provide machine learning algorithm with ambiguity information via forward and backward maximum matching as well as unknown word information via vocabulary masking. The experimental results show that maximum matching and vocabulary masking can significantly improve the performance of HMM segmentation (F-measure: 0.812 → 0.948 → 0.953). Meanwhile, combining maximum matching with CRF achieves a performance with 0.953 and is improved to 0.963 via vocabulary masking.
Facial Expression Analysis for Emotion and Behavior of Online Learner and Framework for Content Adaptation: A Survey

In online learning, learners don’t have a teacher to watch the facial expression of the learner while learning. We recognize the facial expression of online learners through the Webcam and we identify the emotion and behavior of the learner while learning. We can analyze the learner with respect to their learning style and performance of learning. With the help of object detection techniques, we can detect the facial expression of the learner and classify the learner according to the facial expression while learning. After identifying the facial expression of the learner if learner expression is negative then we can adapt content accordingly to generate interest with the help of content adaptation.
Feature-based cut detection with automatic threshold selection

Our approach is based on feature tracking where cuts are detected when only few features can be tracked across frame pairs. This unique criterion was found sufficient to detect most cuts in most videos even in the case of short term transitions. Furthermore, we propose a method to stabilize the differences and automatically identify a global threshold to achieve a high detection rate. For the TRECVID06 submitted runs, we tested the algorithm under the following conditions: • CU-UO-All-Runtype:1 corresponds to the Precision set as determined by automatic threshold selection; • CU-UO-All-Runtype:2 corresponds to the F1 set as determined by automatic threshold selection; • CU-UO-All-Runtype:3 corresponds to the Recall set as determined by automatic threshold selection; • The other runs correspond to various manually selected threshold values. One important aspect of our system resides in the user control ability over the type of acceptable errors; according to this, the system will select automatically the most appropriate threshold to minimize the specified type of error (false negatives versus false positives). From the experiments, this component works as expected even though our results were less effective than expected. The exact reason for the exhibited performance is still to be determined currently being investigated.
Auto-generation by interaction of weak eddies

For channel flow, we explore how the interaction of weak eddies produces additional eddies by means of auto-generation. This is done by DNS of two eddies with different initial strengths, initial sizes and initial stream-wise spacing between them. The numerical procedure followed is similar to Zhou et al[1]. The two eddies merge into a single stronger eddy when a larger upstream and a smaller downstream eddy are placed within a certain initial stream-wise separation distance. Subsequently, the resulting stronger eddy is observed to auto-generate new eddies. The non-merging cases with small initial stream wise separation also auto-generate. The auto-generation is characterized by a rapid lift-up of an initial eddy, which blocks the incoming flow and leads to shear- layer roll-up and formation of a new eddy. The same sequence of events is observed in a fully developed turbulent boundary layer[2].
Improving the Accessibility of Mobile OCR Apps Via Interactive Modalities

Mobile optical character recognition (OCR) apps have come of age. Many blind individuals use them on a daily basis. The usability of such tools, however, is limited by the requirement that a good picture of the text to be read must be taken, something that is difﬁcult to do without sight. Some mobile OCR apps already implement auto-shot and guidance mechanisms to facilitate this task. In this paper, we describe two experiments with blind participants, who tested these two interactive mechanisms on a customized iPhone implementation. These experiments bring to light a number of interesting aspects of accessing a printed document without sight, and enable a comparative analysis of the available interaction modalities.
Planes Parallel to Image Plane 3 D − Texture Mapping Rendered Image Trilinear Interpolation Compositing

Line integral convolution (LIC) is an effective technique for visualizing vector fields. The application of LIC to 3D flow fields has yet been limited by difficulties to efficiently display and animate the resulting 3D–images. Texture–based volume rendering allows interactive visualization and manipulation of 3D–LIC textures. In order to ensure the comprehensive and convenient exploration of flow fields, we suggest interactive functionality including transfer functions and different clipping mechanisms. Thereby, we efficiently substitute the calculation of LIC based on sparse noise textures and show the convenient visual access of interior structures. Further on, we introduce two approaches for animating static 3D–flow fields without the computational expense and the immense memory requirements for pre–computed 3D–textures and without loss of interactivity. This is achieved by using a single 3D–LIC texture and a set of time surfaces as clipping geometries. In our first approach we use the clipping geometry to pre–compute a special 3D–LIC texture that can be animated by time–dependent color tables. Our second approach uses time volumes to actually clip the 3D–LIC volume interactively during rasterization. Additionally, several examples demonstrate the value of our strategy in practice.
The Regulation of Water Infrastructure in Italy: Evolution and Effects

Water infrastructure regulatory systems are occasionally characterised by the combination of selected features of alternative models of regulation, such as public ownership, discretionary regulation, and franchise regulation. Issues arise regarding why these combined or 'hybrid' systems appear, and what level of performance they achieve. An instance of such 'hybrid' regulatory system is the one resulting from the implementation of the 1994 water reform in Italy, which largely resulted in the allocation of water franchise to local government fully or majority-owned water firms subject to price regulation in a two-tier regulatory system. The analysis shows that the water regulatory system in Italy was affected by both the behaviour of sub-national governments and incumbent water firms during the implementation process as well as the regulatory system statutory design. The performance of the water regulatory system is relatively disappointing in various dimensions. This case study suggests some theoretical and practical implications on the implementation and the effects of regulatory reforms which set up 'hybrid' regulatory systems.
is a

The last few years have seen much progress in proving “non-approximability results” for well-known NP-hard optimization problems. As we know, the breakthrough has come by the application of results from probabilistic proof checking. It is an area that seems to continue to surprise: since the connection was discovered in 1991 (Feige et. al. [21]), not only have non-approximability results emerged for a wide range of problems, but the factors shown hard steadily increase. Today, tight results are known for central problems like Max-Clique and Min-Set-Cover. (That is, the approximation algorithms we have for these problems can be shown to be the best possible). Such results also seem to be in sight for Chrom-Num. These are remarkable things, especially in the light of our knowledge of just five years ago. And meanwhile we continue to make progress on the Max-SNP front, where both the algorithms and the non-approximability results are improving, so perhaps there is more to come. This (short) survey will try to explain how the best known results are being derived. There is here a story that I believe is interesting and yet, although of course building on previous work, can be told in a a relatively self-contained way while still illustrating the central themes. It is characterized by the finding and exploitation of ever deepening “interactions” between proof checking and approximation, and the building of proof systems of unusual but powerful nature which not only have impressive consequences in approximation but may be of interest in their own right.
Semi-automatic acquisition and labelling of image data using SOMs

Application of neural networks for real world object recognition suffers from the need to acquire large quantities of labelled image data. We propose a solution that acquires images from a domain at random and structures the data in two steps: Data driven mechanisms extract windows of interest, which are clustered by a SOM. Regions of the SOM in which objects form clusters serve as "suggestions" for categories. An interactive visualisation of the SOM combined with distance measures allows the user to determine classes and build training sets. By this means, large labelled data sets for a neural classifier can be easily generated.
Business and Economics

In today’s business world, computers are connected to the broadband wide area network (WAN), allowing businesses to integrate industrial value chains and provide customers with various value-added services. Consequently, the virtual community of a public service network can be created and marketing channels in e-generation can be established to provide customers with relevant information or commodities and services through the use of high-speed transmission systems and interactive delivery. However, if e-commerce operators are not fully aware of the characteristics of e-generation consumers, if website operators lack the necessary competitive edge, or if the Internet-based transaction mechanism is not secure, consumers may not be attracted to or continue their interest in online shopping. In the Internet world of e-commerce, it is difficult to fully grasp the changes in customer demands along with the gradual opening of information on the web. To be profitable, it is more important to obtain the value created by loyal customers for businesses in addition to a wide range of online visitors. By integrating the analyses of e-commerce with 244 questionnaires and validating the data with statistical analysis methods, this study provides e-commerce business owners with suggestions on marketing strategies and proposes future directions for subsequent research based on the research findings.