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In phase I trials, effectively treating patients and minimizing the chance of exposing them to subtherapeutic and overly toxic doses are clinicians' top priority. Motived by this practical consideration, we propose Bayesian optimal interval (BOIN) designs to find the maximum tolerated dose and to minimize the probability of inappropriate dose assignments for patients. We show, both theoretically and numerically, that the BOIN design not only has superior finite and large sample properties but also can be easily implemented in a simple way similar to the traditional '3+3' design. Compared with the well-known continual reassessment method, the BOIN design yields comparable average performance to select the maximum tolerated dose but has a substantially lower risk of assigning patients to subtherapeutic and overly toxic doses. We apply the BOIN design to two cancer clinical trials.

Though attribute reduction defined by neighborhood decision error rate can improve the classification performance of neighborhood classifier via deleting redundant attributes, such reduction does not take the variations of classification results into account. To fill this gap, a multi-criterion based attribute reduction is proposed, which considers both neighborhood decision error rate and neighborhood decision consistency. The neighborhood decision consistency is used to measure the variations of classification results if attributes change. Following the novel attribute reduction, a heuristic algorithm is also designed to derive reduct which aims to obtain less error rate and higher consistency simultaneously. The experimental results on 10 UCI data sets show that the multi-criterion based reduction can not only improve the decision consistencies without decreasing the classification accuracies significantly, but also bring us more stable reducts. This study suggests new trends concerning criteria and constraints in attribute reduction.

Sustaining cooperation among unrelated individuals is a fundamental challenge in biology and the social sciences. In human society, this problem can be solved by establishing incentive institutions that reward cooperators and punish free-riders. Most of the previous studies have focused on which incentives promote cooperation best. However, a higher cooperation level does not always imply higher group fitness, and only incentives that lead to higher fitness can survive in social evolution. In this paper, we compare the efficiencies of three types of institutional incentives, namely, reward, punishment, and a mixture of reward and punishment, by analysing the group fitness at the stable equilibria of evolutionary dynamics. We find that the optimal institutional incentive is sensitive to decision errors. When there is no error, a mixture of reward and punishment can lead to high levels of cooperation and fitness. However, for intermediate and large errors, reward performs best, and one should avoid punishment. The failure of punishment is caused by two reasons. First, punishment cannot maintain a high cooperation level. Second, punishing defectors almost always reduces the group fitness. Our findings highlight the role of reward in human cooperation. In an uncertain world, the institutional reward is not only effective but also efficient.

Mouse-tracking is regarded as a powerful technique to investigate latent cognitive and emotional states. However, drawing inferences from this manifold data source carries the risk of several pitfalls, especially when using aggregated data rather than single-trial trajectories. Researchers might reach wrong conclusions because averages lump together two distinct contributions that speak towards fundamentally different mechanisms underlying between-condition differences: influences from online-processing during action execution and influences from incomplete decision processes. Here, we propose a simple method to assess these factors, thus allowing us to probe whether process-pure interpretations are appropriate. By applying this method to data from 12 published experiments on ideomotor action control, we show that the interpretation of previous results changes when dissociating online processing from decision and initiation errors. Researchers using mouse-tracking to investigate cognition and emotion are therefore well advised to conduct detailed trial-by-trial analyses, particularly when they test for direct leakage of ongoing processing into movement trajectories.

Influential economic approaches as random utility models assume a monotonic relation between choice frequencies and “strength of preference,” in line with widespread evidence from the cognitive sciences, which also document an inverse relation to response times. However, for economic decisions under risk, these effects are largely untested, because models used to fit data assume them. Further, the dimension underlying strength of preference remains unclear in economics, with candidates including payoff-irrelevant numerical magnitudes. We provide a systematic, out-of-sample empirical validation of these relations (both for choices and response times) relying on both a new experimental design and simulations.

This paper presents a detailed performance evaluation of a proposed Orthogonal Time Frequency Space (OTFS) system for Integrated Sensing and Communications (ISAC) in doubly dispersive wireless channels, characterized by both delay and Doppler spreads. The system is benchmarked against conventional Orthogonal Frequency Division Multiplexing (OFDM) schemes with Linear Minimum Mean Square Error (LMMSE) and Minimum Mean Square Error Decision Feedback Equalizer (MMSE-DFE) receivers. Through extensive simulations, the paper assesses Bit Error Rate (BER) and throughput performance under various Signal-to-Noise Ratios (SNRs), channel estimation error percentages, and multipath conditions. Results indicate that the proposed OTFS system is highly suitable for ISAC scenarios due to its delay-Doppler domain resilience and robustness to mobility, delivering superior BER performance, e.g., 1.25×10−5 at 20 dB SNR with 0% estimation error, compared to 1.10×10−3 for OFDM-LMMSE. It also sustains 64 Mbps throughput under ideal conditions, though it shows sensitivity under severe estimation errors and rich multipath. In contrast, OFDM with LMMSE demonstrates smaller performance variation, maintaining over 61 Mbps throughput even at 100% estimation error and 15 scattered path components. These results suggest that OTFS is an effective waveform for ISAC when accurate channel estimation is available, while the corresponding OFDM with MMSE-DFE remains a robust fallback in highly uncertain environments.

A cell constantly receives signals and takes different fates accordingly. Given the uncertainty rendered by signal transduction noise, a cell may incorrectly perceive these signals. It may mistakenly behave as if there is a signal, although there is none, or may miss the presence of a signal that actually exists. In this paper, we consider a signaling system with two outputs, and introduce and develop methods to model and compute key cell decision-making parameters based on the two outputs and in response to the input signal. In the considered system, the tumor necrosis factor (TNF) regulates the two transcription factors, the nuclear factor κB (NFκB) and the activating transcription factor-2 (ATF-2). These two system outputs are involved in important physiological functions such as cell death and survival, viral replication, and pathological conditions, such as autoimmune diseases and different types of cancer. Using the introduced methods, we compute and show what the decision thresholds are, based on the single-cell measured concentration levels of NFκB and ATF-2. We also define and compute the decision error probabilities, i.e., false alarm and miss probabilities, based on the concentration levels of the two outputs. By considering the joint response of the two outputs of the signaling system, one can learn more about complex cellular decision-making processes, the corresponding decision error rates, and their possible involvement in the development of some pathological conditions.

Speaker Recognition is a vital application of speech processing. Speaker Recognition performs a task of authenticating or recognizing a speaker based on the unique features captured which characterize the speaker. Characteristics or features which are unique to an individual such as fundamental frequency, speaking style, pitch, and duration are used as distinguishing components of the human speech signal. Exploring these characteristics for various applications with an attempt to implement a robust speaker recognition system has been the impetus behind the research in this domain. This paper makes an attempt to present the available Feature Extraction and Recognition techniques with their merits and demerits. It also discusses the pre-emphasis stage of the speaker recognition system. The standard databases available for speaker recognition along with the criterion for their selection are also reviewed. The paper presents an overview of various toolkits and performance parameters of Automatic Speaker Recognition System.

There is substantial evidence that risky decision-making involves a stochastic error process. The literature has adopted different approaches to address this issue, however, risk preferences are not uniquely identified by the most popular methods; decision error is not predicted to monotonically decrease with risk aversion. This paper reports the results of an experiment that elicits risk preferences to identify risk averse individuals and evaluates the frequency the stochastically dominant of two lotteries is chosen. Risk averse subjects exhibit a strong preference for dominant lotteries. More importantly, violations are consistent with stochastic decision error that decreases with risk aversion.

From the viewpoint of rough granular computing, neighborhood decision error rate-based attribute reduction aims to improve the classification performance of the neighborhood classifier. Nevertheless, for imbalanced data which can be seen everywhere in real-world applications, such reduction does not pay much attention to the classification results of samples in minority class. Therefore, a new strategy to attribute reduction is proposed, which is embedded with preprocessing of the imbalanced data. First, the widely accepted SMOTE algorithm and K-means algorithm are used for oversampling and undersampling, respectively. Second, the neighborhood decision error rate-based attribute reduction is designed for those updated data. Finally, the neighborhood classifier can be tested with the attributes in reducts. The experimental results on some UCI and PROMISE data sets show that our approach is superior to the traditional attribute reduction based on the evaluations of F-measure and G-mean. Therefore, the contribution of this paper is to construct the attribute reduction strategy for imbalanced data, which can select useful attributes for improving the classification performance in such data.