Dynamic word problems

We give a number of new lower bounds in the cell probe model<br />with logarithmic cell size, which entails the same bounds on the random access computer with logarithmic word size and unit cost operations. We study the signed prefix sum problem: given a string of length n of zeroes and signed ones, compute the sum of its ith prefix during updates. We show a<br />lower bound of Omega(log n/log log n) time per operations, even if the prefix sums are bounded by log n/log log n during all updates. We also show that if the update time is bounded by the product of the worst-case update time and the<br />answer to the query, then the update time must be Omega(sqrt(log n/ log log n)).<br /> These results allow us to prove lower bounds for a variety of seemingly unrelated<br />dynamic problems. We give a lower bound for the dynamic planar point location in monotone subdivisions of <br />Omega(log n/ log log n) per operation. We give<br />a lower bound for the dynamic transitive closure problem on upward planar graphs with one source and one sink of <br />Omega(log n/(log logn)^2) per operation. We give a lower bound of Omega(sqrt(log n/log log n)) for the dynamic membership problem of any Dyck language with two or more letters. This implies the same<br />lower bound for the dynamic word problem for the free group with k generators. We also give lower bounds for the dynamic prefix majority and prefix equality problems.

Optimal Bounds for the Predecessor Problem and Related Problems

With the proliferation of mobile computing technologies, location based services have been identified as one of the most promising target application. We classify mobile information service domains based on feature characteristics of the information sources and different patterns of mobile information access. By carefully examining the service requirements, we identify the dynamic data management problem that must be addressed for effective location based services in mobile environments. We then devise a general architecture and cost model for servicing both location independent and location dependent data. Based on the architecture and cost model, we propose a set of dynamic data management strategies that employs judicious caching, proactive server pushing and neighborhood replication to reduce service cost and improve response time under changing user mobility and access patterns. Detail behavior analysis helps us in precisely capturing when and how to apply these strategies. Simulation results suggest that different strategies are effective for different types of data in response to different patterns of movement and information access.

Sensory profiles of 13 coffee samples from the Huila Region, Colombia were evaluated using two different sensory panels: a highly trained descriptive sensory panel and a group of Q‐certified coffee cuppers. The trained panel consisted of six descriptive panelists who developed a lexicon to evaluate and then test the coffee samples. Four “cuppers” scored the same samples based on the Specialty Coffee Association of America “cupping protocol.” In addition, cuppers generated tasting notes to characterize the different coffee samples. Data indicated little overlap between the two methods and a low relationship between the two different sets of terms. Moreover, tasting notes by cuppers indicate lack of agreement on the terms used to describe samples, with only four terms used by more than two assessors to describe a single coffee product out of a total of 59 terms used by the cuppers. The results indicate that the cupping method and sensory descriptive methods provide different information that cannot be used as an alternative to each other when describing coffee products. Instead, the results suggest that the two types of data may be used synergistically to evaluate the quality and the sensory properties to better characterize coffee samples.Practical ApplicationsThis research shows that “expert” coffee cupper data and trained sensory panel data cannot be used interchangeably. Thus, for research purposes sensory panel information is necessary for tracking changes in sensory properties.

This paper is devoted to analysis and visualization of multidimensional data in problems of computational fluid dynamics (CFD). Multidimensional data are considered as the result of parametrical search and optimizing analysis. For such types of multidimensional data, analysis is aimed to find some hidden dependencies of specific parameters. This is the main difference of the proposed approach from the general approach of data analysis, which is usually applied to solve problems of classification and clusterization. A rough approximate approach is proposed for data processing. This approach includes data visualization in the space of principal components and data approximation by geometrical primitives (in particular, planes). An example of practical application of the proposed approach is given. The possibility of elastic maps application to multidimensional data in question is discussed.

Comparative genomic hybridization (CGH) can detect chromosomal deletions and amplifications of tumors, and various laboratories and public databases have accumulated a large number of CGH data, providing the opportunity to analyze the molecular mechanism of tumorigenesis in the whole genome. Tree models are generally used to study the history of biological formation and evolution in the field of bioinformatics, and evolutionary relationships between species are usually represented using phylogenetic tree. Tree models are also powerful bioinformatics tools to analyze CGH data and explore carcinogenesis. Two common tree models, the branching tree and the distanced-based tree, as well as their basic principles, methods are introduced detailedly, several technical problems in construction of tree models are discussed, and their applications in cancer research are reviewed systematically in this paper. As a generalization of single path linear model, tree models can more accurately conclude multigene, multistep, multipathway process of tumorigenesis, exploring the molecular mechanism of tumorigenesis from different angels. Apart from CGH data, tree models can be used to analyze various types of data, including high-resolution data (e.g., array-CGH data).

Structural semantics for polymorphic data types (preliminary report)

We consider centrally controlled multi-location systems, which coordinate their replenishment strategies through the use of transshipments. In a dynamic deterministic demand environment the problem is characterized by several locations, each of which has known demand for a single product for each period in a given finite horizon. We consider replenishment, transshipment and inventory holding costs at each location, where the first two have location-dependent fixed, as well as linear components, and the third is linear and identical to all locations. We prove that the resulting dynamic transshipment problem is NP-hard, identify a special structure which is satisfied by an optimal solution and develop, based on this structure, an exponential time algorithm to solve the problem optimally. In addition, we develop a heuristic algorithm, based on partitioning the time horizon, which is capable of solving larger instances than the optimal solution. Our computational tests demonstrate that the heuristic performs extremely well.

First paragraph: Why do industrial agricultural operations continue to displace smaller family farms in spite of their continued pollution of the natural environment and degradation of rural communi­ties? Large-scale, specialized agricultural operations, such as concentrated animal feeding operations (or CAFOs), persist because they have an economic advantage over smaller, diversified farming opera­tions. They have higher ecological and social costs but lower economic costs. This economic advan­tage is commonly referred to as economies of scale. In economic theory, there are two types of economies of scale. Internal economies of scale refer to differences in the costs of production associated with different sizes of production units. In animal agriculture, “scale” refers to the number of hogs, poultry, milk cows, or beef cattle in a single farming operation or production unit. In field crop and pasture-based animal production, scale refers to the acres of land in a single production unit. External economies of scale, on the other hand, refer to differences such as the costs of fertilizer or feed, or the cost of complying with government regulations, for different sizes of management units. Management units may include one or more production units under single man­agement or control (Ross, 2022). A single farm or production unit may comprise multiple parcels of land, but a farm management unit may comprise multiple farms that are managed as a single economic entity or unit. . . .

This research aims to prove whether the decreasing trend in tax avoidance can increase company value and vice versa. Company value as a dependent variable is proxied using Tobin’s Q formula, tax avoidance as an independent variable is proxied using the GAAP ETR formula, and firm size as a moderating variable is proxied using the natural logarithm of total assets. The type of data used is secondary archival data in the form of financial report data of non-financial companies for the period 2020 to 2022 that are listed on the Indonesia Stock Exchange. The sample obtained was 705 financial reports and the data was then analyzed using structured equation modeling. The results of the research show that tax avoidance has a significant positive effect on the company value of energy sector companies from 2020 to 2022 at an error rate of 5%, and firm size can moderate tax avoidance so that it has a significant negative effect on the company value of basic material sector companies from 2020 to 2022 at an error rate of 10%.

Deep learning has been successfully applied to various types of classification and prediction tasks including images, text, and tabular data, however interpretability of deep learning models still remains challenges, which becomes a limiting factor for further applications.Most of current interpretable models are limited to how to interpret the deep learning models. In this paper, we propose a simple but effective novel Local Interpretable model-agnostic Visualizations and Explanations (LIVE) algorithm through combining decision tree and deep learning models. The proposed LIVE algorithm is validated through 4 different data sets with different types of deep learning models including convolutional neural network (CNN) and long short-term memory models (LSTM). Our experiment results show that 1) LIVE algorithm could generally consistently help improve the deep learning models by 0.02 0.03 in AUC; 2) Our LIVE algorithm is a weakly supervised localization tool for region of interest identification; 3) Our LIVE algorithm could be applied to different deep learning models and different types of data.

Detecting phishing websites has been noted as complex and dynamic problem area because of the subjective considerations and ambiguities of detection mechanism. We propose a novel approach that uses Ripple-down Rule (RDR) to acquire knowledge from human experts with the modified RDR model-generating algorithm (Induct RDR), which applies machine-learning approach. The modified algorithm considers two different data types (numeric and nominal) and also applies information theory from decision tree learning algorithms. Our experimental results showed the proposing approach can help to deduct the cost of solving over-generalization and over-fitting problems of machine learning approach. Three models were included in comparison: RDR with machine learning and human knowledge, RDR machine learning only and J48 machine learning only. The result shows the improvements in prediction accuracy of the knowledge acquired by machine learning.

This paper presents the architecture and field-programmable gate array (FPGA) implementation of a 32-bit central processing unit (CPU) dedicated to programmable logic controllers (PLCs). The CPU instruction set directly matches the instructions of the IEC 61131-3 standard Instruction List (IL) language. The designed IL processor is capable of handling 1-bit, 8-bit, 16-bit, and 32-bit data types. Apart from integer arithmetic operations, the IL processor also performs single precision floating-point operations included in the IEC 61131-3 IL language specification. It also directly performs instructions with a parenthesis modifier for all supported data types and facilitates the fast execution of a code containing Boolean expressions. This paper also presents the performance evaluation results of the developed IL processor. Although the IL processor executed the test algorithms substantially faster than industrial PLCs (e.g., Siemens S7-1200 and Fanux VersaMax), it turned out to be significantly slower than high-performance programmable automation controllers (PACs).

Nowadays, the representation of many real word problems needs to use some type of relational model. As a consequence, information used by a wide range of systems has been stored in multi relational tables. However, from a data mining point of view, it has been a problem, since most of the traditional data mining algorithms have not been originally proposed to handle this type of data without discarding relationship information. Aiming to ameliorate this problem, we propose a hierarchical approach for handling relational data. In this approach the relational data is converted into a hierarchical structure (the main table as the root and the relations as the nodes). This hierarchical way to represent relational data can be used either for classification or clustering purposes. In this paper, we will use it in clustering algorithms. In order to do so, we propose a hierarchical distance metric to compute the similarity between the tables. In the empirical analysis, we will apply the proposed approach in two well-known clustering algorithms (k-means and agglomerative hierarchical). Finally, this paper also compares the effectiveness of our approach with one existing relational approach.

Microarray data classification has been typically seen as a difficult challenge for machine learning researchers mainly due to its high dimension in feature while sample size is small. However, this type of data presents other complications such as overlapping between classes, dataset shift, class imbalance, non-linearity, or features extracted under extremely different distributions. This paper intends to analyze in depth the theoretical complexity of several popular binary datasets, by making use of complexity measures, and then connecting it with the empirical results obtained by four widely-used classifiers. Two different situations are covered: datasets with only training set and datasets originally divided into training and test sets. In both cases it is demonstrated that there exists a correlation between the complexity measures and the actual error rates, which can facilitate in the future how to deal with a given dataset. Finally, we present a case study on Prostate dataset, improving the test classification accuracy from 53% to 97%.