Belief Evolution Network-based Probability Transformation and Fusion

Smets proposes the Pignistic Probability Transformation (PPT) as the decision layer in the Transferable Belief Model (TBM), which argues when there is no more information, we have to make a decision using a Probability Mass Function (PMF). In this paper, the Belief Evolution Network (BEN) and the full causality function are proposed by introducing causality in Hierarchical Hypothesis Space (HHS). Based on BEN, we interpret the PPT from an information fusion view and propose a new Probability Transformation (PT) method called Full Causality Probability Transformation (FCPT), which has better performance under Bi-Criteria evaluation. Besides, we heuristically propose a new probability fusion method based on FCPT. Compared with Dempster Rule of Combination (DRC), the proposed method has more reasonable result when fusing same evidence

Quantum belief function

The belief function in Dempster Shafer evidence theory can express more information than the traditional Bayesian distribution. It is widely used in approximate reasoning, decision-making and information fusion. However, its power exponential explosion characteristics leads to the extremely high computational complexity when handling large amounts of elements in classic computers. In order to solve the problem, we encode the basic belief assignment (BBA) into quantum states, which makes each qubit correspond to control an element. Besides the high efficiency, this quantum expression is very conducive to measure the similarity between two BBAs, and the measuring quantum algorithm we come up with has exponential acceleration theoretically compared to the corresponding classical algorithm. In addition, we simulate our quantum version of BBA on Qiskit platform, which ensures the rationality of our algorithm experimentally. We believe our results will shed some light on utilizing the characteristic of quantum computation to handle belief function more conveniently

Analyzing Customer Needs of Product Ecosystems Using Online Product Reviews

It is necessary to analyze customer needs of a product ecosystem in order to increase customer satisfaction and user experience, which will, in turn, enhance its business strategy and profits. However, it is often time-consuming and challenging to identify and analyze customer needs of product ecosystems using traditional methods due to numerous products and services as well as their interdependence within the product ecosystem. In this paper, we analyzed customer needs of a product ecosystem by capitalizing on online product reviews of multiple products and services of the Amazon product ecosystem with machine learning techniques. First, we filtered the noise involved in the reviews using a fastText method to categorize the reviews into informative and uninformative regarding customer needs. Second, we extracted various customer needs related topics using a latent Dirichlet allocation technique. Third, we conducted sentiment analysis using a valence aware dictionary and sentiment reasoner method, which not only predicted the sentiment of the reviews, but also its intensity. Based on the first three steps, we classified customer needs using an analytical Kano model dynamically. The case study of Amazon product ecosystem showed the potential of the proposed method.https://deepblue.lib.umich.edu/bitstream/2027.42/153962/1/ANALYZING CUSTOMER NEEDS OF PRODUCT ECOSYSTEMS USING ONLINE PRODUCT REVIEWS.pdfDescription of ANALYZING CUSTOMER NEEDS OF PRODUCT ECOSYSTEMS USING ONLINE PRODUCT REVIEWS.pdf : Main articl

PDCD1 genes may protect against extraocular manifestations in Chinese Han patients with Vogt-Koyanagi-Harada syndrome

Purpose: To analyze the potential association of programmed cell death 1 (PDCD1) with Vogt-Koyanagi-Harada (VKH) syndrome in a Chinese Han population. Methods: Three single nucleotide polymorphism (SNPs), PD-1.3G/A, PD-1.5C/T, and PD-1.6G/A, were genotyped in 247 VKH patients and 289 age-, sex-, and ethnically-matched healthy controls using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) assay. The associations of genotypes and alleles with VKH syndrome were analyzed. Results: All genotype distributions in healthy controls were in Hardy-Weinberg equilibrium. The genotype and allele frequencies of PD-1.3, PD-1.5, and PD-1.6 were not different between patients with VKH syndrome and healthy controls. No significant difference was observed according to the status of human leukocyte antigen (HLA)-DR4 and HLA-DRw53. Compared to the controls, lower frequencies of the PD-1.5C genotype and allele frequencies were observed in VKH patients with extraocular findings. Conclusions: PD-1.3 and PD-1.6 polymorphisms are not associated with the susceptibility to VKH syndrome in the Chinese Han population. However, PD-1.5 may be negatively associated with the occurrence of extraocular manifestations of VKH syndrome

A Machine Learning Approach to Customer Needs Analysis for Product Ecosystems

Creating product ecosystems has been one of the strategic ways to enhance user experience and business advantages. Among many, customer needs analysis for product ecosystems is one of the most challenging tasks in creating a successful product ecosystem from both the perspectives of marketing research and product development. In this paper, we propose a machine-learning approach to customer needs analysis for product ecosystems by examining a large amount of online user-generated product reviews within a product ecosystem. First, we filtered out uninformative reviews from the informative reviews using a fastText technique. Then, we extract a variety of topics with regard to customer needs using a topic modeling technique named latent Dirichlet allocation. In addition, we applied a rule-based sentiment analysis method to predict not only the sentiment of the reviews but also their sentiment intensity values. Finally, we categorized customer needs related to different topics extracted using an analytic Kano model based on the dissatisfaction-satisfaction pair from the sentiment analysis. A case example of the Amazon product ecosystem was used to illustrate the potential and feasibility of the proposed method.https://deepblue.lib.umich.edu/bitstream/2027.42/153965/1/A Machine Learning Approach to Customer Needs Analysis for Product Ecosystems.pd

Genomic mosaicism due to homoeologous exchange generates extensive phenotypic diversity in nascent allopolyploids

Allopolyploidy is an important process in plant speciation, yet newly formed allopolyploid species typically suffer from extreme genetic bottlenecks. One escape from this impasse might be homoeologous meiotic pairing, during which homoeologous exchanges (HEs) generate phenotypically variable progeny. However, the immediate genome-wide patterns and resulting phenotypic diversity generated by HEs remain largely unknown. Here, we analyzed the genome composition of 202 phenotyped euploid segmental allopolyploid individuals from the 4th selfed generation following chromosomal doubling of reciprocal F1 hybrids of crosses between rice subspecies, using whole genome sequencing. We describe rampant occurrence of HEs that, by overcoming incompatibility or conferring superiority of hetero-cytonuclear interactions, generate extensive and individualized genomic mosaicism across the analyzed tetraploids. We show that the resulting homoeolog copy number alteration in tetraploids affects known-function genes and their complex genetic interactions, in the process creating extraordinary phenotypic diversity at the population level following a single initial hybridization. Our results illuminate the immediate genomic landscapes possible in a tetraploid genomic environment, and underscore HE as an important mechanism that fuels rapid phenotypic diversification accompanying the initial stages of allopolyploid evolution

Advantage of Hole Stimulus in Rivalry Competition

Mounting psychophysical evidence suggests that early visual computations are sensitive to the topological properties of stimuli, such as the determination of whether the object has a hole or not. Previous studies have demonstrated that the hole feature took some advantages during conscious perception. In this study, we investigate whether there exists a privileged processing for hole stimuli during unconscious perception. By applying a continuous flash suppression paradigm, the target was gradually introduced to one eye to compete against a flashed full contrast Mondrian pattern which was presented to the other eye. This method ensured that the target image was suppressed during the initial perceptual period. We compared the initial suppressed duration between the stimuli with and without the hole feature and found that hole stimuli required less time than no-hole stimuli to gain dominance against the identical suppression noise. These results suggest the hole feature could be processed in the absence of awareness, and there exists a privileged detection of hole stimuli during suppressed phase in the interocular rivalry

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe