Neural Information Processing Systems http://nips.cc/

Fuzzy implication functions are one of the most important operators used in the fuzzy logic framework. While their flexible definition allows for diverse families with distinct properties, this variety needs a deeper theoretical understanding of their structural relationships. In this work, we focus on the study of construction methods, which employ different techniques to generate new fuzzy implication functions from existing ones. Particularly, we generalize the $F$-chain-based construction, recently introduced by Mesiar et al. to extend a method for constructing aggregation functions to the context of fuzzy implication functions. Our generalization employs collections of fuzzy implication functions rather than single ones, and uses two different increasing functions instead of a unique $F$-chain. We analyze property preservation under this construction and establish sufficient conditions. Furthermore, we demonstrate that our generalized $F$-chain-based construction is a unifying framework for several existing methods. In particular, we show that various construction techniques, such as contraposition, aggregation, and generalized vertical/horizontal threshold methods, can be reformulated within our approach. This reveals structural similarities between seemingly distinct construction strategies and provides a cohesive perspective on fuzzy implication construction methods.

For many call centers, customer satisfaction (CSAT) is a key performance indicator (KPI). However, only a fraction of customers take the CSAT survey after the call, leading to a biased and inaccurate average CSAT value, and missed opportunities for coaching, follow-up, and rectification. Therefore, call centers can benefit from a model predicting customer satisfaction on calls where the customer did not complete the survey. Given that CSAT is a closely monitored KPI, it is critical to minimize any bias in the average predicted CSAT (pCSAT). In this paper, we introduce a method such that predicted CSAT (pCSAT) scores accurately replicate the distribution of survey CSAT responses for every call center with sufficient data in a live production environment. The method can be applied to many multiclass classification problems to improve the class balance and minimize its changes upon model updates.

Ordinal regression seeks class label predictions when the penalty incurred for mistakes increases according to an ordering over the labels. The absolute error is a canonical example. Many existing methods for this task reduce to binary classification problems and employ surrogate losses, such as the hinge loss. We instead derive uniquely defined surrogate ordinal regression loss functions by seeking the predictor that is robust to the worst-case approximations of training data labels, subject to matching certain provided training data statistics. We demonstrate the advantages of our approach over other surrogate losses based on hinge loss approximations using UCI ordinal prediction tasks.

White blood cells (WBCs) are the most diverse cell types observed in the healing process of injured skeletal muscles. In the course of healing, WBCs exhibit dynamic cellular response and undergo multiple protein expression changes. The progress of healing can be analyzed by quantifying the number of WBCs or the amount of specific proteins in light microscopic images obtained at different time points after injury. In this paper, we propose an automated quantifying and analysis framework to analyze WBCs using light microscopic images of uninjured and injured muscles. The proposed framework is based on the Localized Iterative Otsu's threshold method with muscle edge detection and region of interest extraction. Compared with the threshold methods used in ImageJ, the LI Otsu's threshold method has high resistance to background area and achieves better accuracy. The CD68-positive cell results are presented for demonstrating the effectiveness of the proposed work.

Ordinal regression (OR, or called ordinal classification) is the classification of ordinal data in which the underlying target variable is labeled from a categorical ordinal scale that is considered to be equipped with a natural ordinal relation for the underlying explanatory variable, as formalized in Section 2.1. The ordinal scale is typically formed as a graded summary of objective indicators like age groups {'0-9', '10-19',..., '90-99', '100-'} or graded evaluation of subjectivity like human rating {'excellent', 'good', 'average', 'bad', 'terrible'}. OR techniques are employed in a variety of practical applications, for example, age estimation (Niu et al., 2016; Cao et al., 2020), information retrieval (Liu, 2011), movie rating (Yu et al., 2006), and questionnaire survey (Bürkner and Vuorre, 2019). Threshold methods are popular for OR problems as a simple way to capture the ordinal relation of ordinal data, and have been studied vigorously in machine learning research (Shashua and Levin, 2003; Lin and Li, 2006; Chu and Keerthi, 2007; Lin and Li, 2012; Li and Lin, 2007; Pedregosa et al., 2017; Yamasaki, 2023). Those methods learn a one-dimensional transformation (1DT) of the observation of the explanatory variable so that an observation with a larger class label tends to have a larger 1DT value; they then assign a label prediction to the learned 1DT according to the rank of an interval to which the 1DT belongs among intervals on the real line separated by threshold parameters.

Bloodstain pattern analysis plays a crucial role in crime scene investigations by providing valuable information through the study of unique blood patterns. Conventional image analysis methods, like Thresholding and Contrast, impose stringent requirements on the image background and is labor-intensive in the context of droplet image segmentation. The Segment Anything Model (SAM), a recently proposed method for extensive image recognition, is yet to be adequately assessed for its accuracy and efficiency on bloodstain image segmentation. This paper explores the application of pre-trained SAM and fine-tuned SAM on bloodstain image segmentation with diverse image backgrounds. Experiment results indicate that both pre-trained and fine-tuned SAM perform the bloodstain image segmentation task with satisfactory accuracy and efficiency, while fine-tuned SAM achieves an overall 2.2\% accuracy improvement than pre-trained SAM and 4.70\% acceleration in terms of speed for image recognition. Analysis of factors that influence bloodstain recognition is carried out. This research demonstrates the potential application of SAM on bloodstain image segmentation, showcasing the effectiveness of Artificial Intelligence application in criminology research. We release all code and demos at \url{https://github.com/Zdong104/Bloodstain_Analysis_Ai_Tool}

In recent years, transformer-based language representation models (LRMs) have achieved state-of-the-art results on difficult natural language understanding problems, such as question answering and text summarization. As these models are integrated into real-world applications, evaluating their ability to make rational decisions is an important research agenda, with practical ramifications. This article investigates LRMs' rational decision-making ability through a carefully designed set of decision-making benchmarks and experiments. Inspired by classic work in cognitive science, we model the decision-making problem as a bet. We then investigate an LRM's ability to choose outcomes that have optimal, or at minimum, positive expected gain. Through a robust body of experiments on four established LRMs, we show that a model is only able to `think in bets' if it is first fine-tuned on bet questions with an identical structure. Modifying the bet question's structure, while still retaining its fundamental characteristics, decreases an LRM's performance by more than 25\%, on average, although absolute performance remains well above random. LRMs are also found to be more rational when selecting outcomes with non-negative expected gain, rather than optimal or strictly positive expected gain. Our results suggest that LRMs could potentially be applied to tasks that rely on cognitive decision-making skills, but that more research is necessary before they can robustly make rational decisions.

The threshold of the image is an important part of the image segmentation process. To apply a binary threshold on the image we should make the image in the desire condition where the binary threshold works properly. The meaning of the condition here is that when we do threshold, we want that the image condition should in some intensity level to segment. For example, if have an image A and then this will give a better threshold result. Color-spaces is a process to break a color image into different components of images.

We study a distributed estimation problem in which two remotely located agents, Alice and Bob, observe an unlimited number of i.i.d. samples corresponding to different parts of a random vector. Alice can send $k$ bits on average to Bob, who in turn wants to estimate the cross-correlation matrix between the two parts of the vector. In the case where the agents observe jointly Gaussian scalar random variables with an unknown correlation $\rho$, we obtain two constructive and simple unbiased estimators attaining a variance of $\frac{1-\rho^2}{2k\ln 2}$, which coincides with a known but non-constructive random coding result of Zhang and Berger. We extend our approach to the vector Gaussian case, which has not been treated before, and construct an estimator that is uniformly better than the scalar estimator applied separately to each of the correlations. We then show that the Gaussian performance can essentially be attained even when the distribution is completely unknown. This in particular implies that in the general problem of distributed correlation estimation, the variance can decay at least as $O(1/k)$ with the number of transmitted bits. This behavior is however not tight: we give an example of a rich family of distributions where a slightly modified estimator attains a variance of $2^{-\Omega(k)}$.