On Determining Minimal Spectrally Arbitrary Patterns

In this paper we present a new family of minimal spectrally arbitrary patterns which allow for arbitrary spectrum by using the Nilpotent-Jacobian method. The novel approach here is that we use the Intermediate Value Theorem to avoid finding an explicit nilpotent realization of the new minimal spectrally arbitrary patterns.Comment: 8 page

Maximizing The Impact Of Improvement Efforts On Customer Satisfaction

When a customer satisfaction survey consists of a large number of attributes (questionnaire items), determination of critical attributes that would make the biggest impact on customers’ overall satisfaction could be important, but very tedious and time-consuming process. Even though the critical attributes are identified, the improvement efforts toward these attributes are often misdirected and wasted because of the mismatch between the improvement efforts and the critical needs of the affected customer group. This paper introduces a method with which improvement efforts can be tailored to the needs of the customer group who could bring the most impactful influence on improving customer satisfaction. For the critical attribute considered, the percentage of customers who assigned a specific satisfaction rating is obtained, and the cumulative percentages of customers are examined and the target group of customers to whom the improvement efforts would be tailored is identified. The piecewise linear approximation method is also discussed to estimate the non-linear relationship of the attribute, which also may help determine the target customer group. The overall shape of the piecewise function and the slopes at the line segments may be used in determining which attributes are satisfaction-maintaining or satisfaction-enhancing, and where and how the improvement efforts should be focused in order to maximize the effectiveness of the improvement efforts

RecurSeed and EdgePredictMix: Single-stage Learning is Sufficient for Weakly-Supervised Semantic Segmentation

Although weakly-supervised semantic segmentation using only image-level labels (WSSS-IL) is potentially useful, its low performance and implementation complexity still limit its application. The main causes are (a) non-detection and (b) false-detection phenomena: (a) The class activation maps refined from existing WSSS-IL methods still only represent partial regions for large-scale objects, and (b) for small-scale objects, over-activation causes them to deviate from the object edges. We propose RecurSeed which alternately reduces non and false-detections through recursive iterations, thereby implicitly finding an optimal junction that minimizes both errors. We also propose a novel data augmentation (DA) approach called EdgePredictMix, which further expresses an object's edge by utilizing the probability difference information between adjacent pixels in combining the segmentation results, thereby compensating for the shortcomings when applying the existing DA methods to WSSS. We achieved new state-of-the-art performances on both the PASCAL VOC 2012 and MS COCO 2014 benchmarks (VOC val 74.4%, COCO val 46.4%). The code is available at https://github.com/OFRIN/RecurSeed_and_EdgePredictMix

Incidental finding of a Sertoli-Leydig cell tumor in a postmenopausal woman with complex endometrial hyperplasia

Sertoli-Leydig cell tumors (SLCTs) arise from the non-germ cell component of the ovary and typically present in young women with evidence of hyperandrogenism such as precocious puberty, amenorrhea, hirsutism and virilization. It is very rare accounting for less than 0.2% of all ovarian tumors, and because of the rarity, no standardized treatment approach has reached a consensus. The prognosis is generally good with complete reversion of symptoms after surgery, although some cases have been reported to be malignant. Recently the need for DICER1 mutations testing in paediatric patients has been emphasized for the surveillance of possible synchronous tumors and affected family members. Authors present here a case of Sertoli-Leydig cell tumor incidentally found while performing a hysterectomy with bilateral salpingo-oophorectomy in a postmenopausal woman with endometrial hyperplasia that caused intractable vaginal bleeding

Unit Roots in Economic and Financial Time Series: A Re-Evaluation based on Enlightened Judgement

This paper re-evaluates the key past results of unit root test, emphasizing that the use of a conventional level of significance is not in general optimal due to the test having low power. The optimal levels for popular unit root tests, chosen using the line of enlightened judgement under a symmetric loss function, are found to be much higher than conventional ones. We also propose simple calibration rules for the optimal level of significance for a range of unit root tests based on asymptotic local power. At the optimal levels, many time series in the extended Nelson-Plosser data set are judged to be trend-stationary, including real income variables, employment variables and money stock. We also find nearly all real exchange rates covered in the Elliott-Pesavento study to be stationary at the optimal levels, which lends strong support for the purchasing power parity. Additionally, most of the real interest rates covered in the Rapach-Weber study are found to be stationary