Low Complexity Condition Research Articles

A characterisation of linear repetitivity for cut and project sets with general polytopal windows

The cut and project method is a central construction in the theory of Aperiodic Order for generating quasicrystals with pure point diffraction. Linear repetitivity (LR) is a form of ideal regularity of aperiodic patterns. Recently, Koivusalo and the present author characterised LR for cut and project sets with convex polytopal windows whose supporting hyperplanes are commensurate with the lattice, the weak homogeneity property. For such cut and project sets, we show that LR is equivalent to two properties. One is a low complexity condition, which may be determined from the cut and project data by calculating the ranks of the intersections of the projection of the lattice to the internal space with the subspaces parallel to the supporting hyperplanes of the window. The second condition is that the projection of the lattice to the internal space is Diophantine (or ‘badly approximable’), which loosely speaking means that the lattice points in the total space stay far from the physical space, relative to their norm. We review then extend these results to non-convex and disconnected polytopal windows, as well as windows with polytopal partitions producing cut and project sets of labelled points. Moreover, we obtain a complete characterisation of LR in the fully general case, where weak homogeneity is not assumed. Here, the Diophantine property must be replaced with an inhomogeneous analogue. We show that cut and project schemes with internal space isomorphic to Rn⊕G⊕Zr, for G finite Abelian, can, up to MLD equivalence, be reduced to ones with internal space Rn, so our results also cover cut and project sets of this form, such as the (generalised) Penrose tilings.

A high‐performance and lightweight framework for real‐time facial expression recognition

AbstractFacial expression recognition technology has become a powerful tool for conveying human emotions and intentions and is widely used in areas such as assisted driving and intelligent medical care. Due to the limited computing power of current hardware devices and the real‐time requirements of application scenarios, this paper proposes a high‐performance and lightweight framework for real‐time facial expression recognition framework to solve the problem of real‐time completion of expression recognition tasks under low hardware costs. To address these issues, this paper first designs a RepVGG and mobileNetV2 dual‐channel structure in the feature extraction. It is then input into the MobileViT Block for global feature modelling. Finally, the position vector of the capsule network is used to replace the output of the global pooling, preserving the spatial relationship of the salient features and enhancing the classification effect. Compared with the mainstream facial expression recognition algorithm that cannot get good classification results under low complexity conditions, the model has a significant accuracy improvement while ensuring lightweight. With only 294.60M FLOPS and 0.95M parameters, it achieved an accuracy of 97.53% on the KDEF dataset and 85.56% on the RAF‐DB, demonstrating the advanced nature of the algorithm.

Fecal Microbiota Transplantation as a Cancer Therapeutic.

For decades, cancer research and treatment focused on the cellular level, viewing cancer as a genetic disease of cell transformation. In the era of chemotherapy and radiotherapy, studies from the second half of the 19th century suggesting an association between the microbiota and cancer were almost neglected. The main focus of the field was limited to identification of specific viruses and bacteria that may serve as direct carcinogens leading to the recognition of 7 viruses (i.e., human papillomavirus, hepatitis B virus, and Kaposi sarcoma-associated herpesvirus) and 1 bacterium (Helicobacter pylori) as human carcinogens by the International Agency for Research on Cancer (https://monographs.iarc.who.int/agents-classified-by-the-iarc/). Shortly after the publication of the first draft of the human genome project in February 2001, the Nobel laureate microbiologist Joshua Lederberg raised the question: "Is human identity all in the genes?" It took more than a decade later and the development of multiomic techniques to confirm that his answer "each one of us is a small ecological community" was correct (Lederberg J. Keynote Address: Beyond the Genome. Brooklyn Law Rev 67). This ecological notion became relevant to cancer prevention, prediction, and treatment following the immunotherapy revolution and the understanding of the metabolic and immunologic roles of the microbiota in health and disease. Recently, the microbiota was recognized as an emerging hallmark of cancer following a large body of research showing its role in tumorigenesis, treatment efficacy and toxicity, and initial data regarding the role of microbial modulation in cancer therapy (Cancer Discov 2022;12(1):31-46). In the current review, we will focus on the role of fecal microbiota transplantation, the first microbial modulation technique that is used mainly in low-complexity conditions such as recurrent Clostridium difficile infections (Aliment Pharmacol Ther 2017;46(5):479-493), as a possible cancer therapeutic. However, to better understand the suggested roles of fecal microbiota transplantation in medical oncology, we first need to understand cancer as an ecological niche and the role of the microbiota in tumorigenesis and cancer treatment, specifically immunotherapy.

Damselfish face climate change: Impact of temperature and habitat structure on agonistic behavior.

Oceans absorb a huge part of the atmospheric heat, leading to the rise in water temperature. Reefs are among the most affected ecosystems, where the complex behavioral repertoire of fishes is usually an indicator of environmental impacts. Here, we examined whether temperature (28 and 34°C) and habitat complexity (high and low) interact to affect the agonistic behavior (mirror test) of the dusky damselfish (Stegastes fuscus), a key species in Brazilian reefs because of its gardening capacity and territorial behavior. Higher temperatures altered basal behavior in both high and low-complexity conditions. Fish kept at 28°C under the high-complexity condition were more aggressive than those at a higher temperature (34°C) and in a low-complexity condition, which also exhibited lower dispersion. Our data show that changes in behavior of coral reef fish is associated to fluctuations in environmental conditions. Thus, it is important to implement management or conservation strategies that could mitigate global change effects.

Low-Complexity Joint Range and Doppler FMCW Radar Algorithm Based on Number of Targets.

A low-complexity joint range and Doppler frequency-modulated continuous wave (FMCW) radar algorithm based on the number of targets is proposed in this paper. This paper introduces two low-complexity FMCW radar algorithms, that is, region of interest (ROI)-based and partial discrete Fourier transform (DFT)-based algorithms. We find the low-complexity condition of each algorithm by analyzing the complexity of these algorithms. From this analysis, it is found that the number of targets is an important factor in determining complexity. Based on this result, the proposed algorithm selects a low-complexity algorithm between two algorithms depending the estimated number of targets and thus achieves lower complexity compared two low-complexity algorithms introduced. The experimental results using real FMCW radar systems show that the proposed algorithm works well in a real environment. Moreover, central process unit time and count of float pointing are shown as a measure of complexity.

Decomposition of mean-field Gibbs distributions into product measures

We show that under a low complexity condition on the gradient of a Hamiltonian, Gibbs distributions on the Boolean hypercube are approximate mixtures of product measures whose probability vectors are critical points of an associated mean-field functional. This extends a previous work by the first author. As an application, we demonstrate how this framework helps characterize both Ising models satisfying a mean-field condition and the conditional distributions which arise in the emerging theory of nonlinear large deviations, both in the dense case and in the polynomially-sparse case.

Weighted Acquisition of UWB Signals Based on Energy Detection

Due to the low complexity and cost characteristics of ultra-wideband (UWB) systems, a weighted acquisition algorithm based on energy detection is proposed in this paper. This method is divided into two steps to acquire the direct path (DP) component. Firstly, weighted energy detection is applied to determine which energy block the DP lies in by generalized likelihood ratio test (GLRT). A sub-optimal weighted vector is obtained, by which the closed form of detection performance is proposed. In the second step, the precise position of DP within the detected energy block is obtained by the statistical characteristics of the channel energy distributions. Key parameters that affect acquisition performance are studied by analytical and numerical methods. Simulations and experiments are carried out for performance and complexity comparison with traditional ones. The results show that weighted acquisition achieves better performance under relative low complexity conditions.

The Impact of Expert Testimony on Jurors' Decisions: Gender of the Expert and Testimony Complexity1

The present study investigated whether people used the gender of an expert witness as a heuristic cue to evaluate the evidence presented by the expert. Specifically, the gender of the expert and the complexity of the expert's testimony (low, high) were varied systematically within a simulated civil trial involving an antitrust price-fixing agreement. It was expected that the male expert would be more persuasive than the female expert, but only when the testimony presented was complex. As predicted, this interaction was revealed across a range of dependent measures. Somewhat unexpected was the finding of a female expert advantage in the low-complexity condition. The implications of these findings are discussed.

A Research Note on the Effects of Gender and Task Complexity on an Audit Judgment

This study examines the effects of gender and task complexity on the accuracy of audit judgments. Because research in cognitive psychology and marketing suggests that females may be more accurate decision makers in complex decision tasks, we hypothesize that there will be a significant interaction between gender and task complexity on the accuracy of an audit judgment. A 2×2 full factorial experiment (males/females by high-/low-task complexity) was carried out. The number and consistency of cues was manipulated to create the high- and low-complexity conditions. Participants were required to judge whether an inventory balance was fairly presented based on case material that contained a material misstatement in the inventory account balance. The results support the hypothesis.

Redundancy gain in discrimination of colored lights: effects of complexity and eccentricity.

The present study investigated the redundancy gain in discrimination of colored lights. Subjects were requested to discriminate a target from 34 nontarget lights differing from the target in luminance, hue, or both, and RTs were recorded. Experimental variables were the complexity of stimulus pattern and the eccentricity of the target in visual angle. The 35 lights were arranged regularly on an imaginary grid in the low-complexity condition and were arranged randomly in the high-complexity condition. The eccentricity of target was varied from 0 degree to 16 degrees. Only when the stimulus pattern was highly complex and eccentricity was large, the combination of luminance and hue made the subject's discrimination faster than when luminance or hue individually operated as a cue for discrimination. That is, a redundant target effect was found only for a highly complex stimulus pattern and in peripheral vision.

Age-Related Effects on Temporal Strategies to Speed Motor Performance

The purpose of ihis study was to compare the effects of a single practice session on performance strategies used by young and older adults to decrease movement time (MT) while maintaining accuracy of an aiming task. Five young and 5 older adult males practiced until each accumulated 500 target hits in each of two complexity conditions as defined by Fitts’s law. Participants decreased MT with practice; however, older adults had longer MTs than the young, particularly in the high-complexity condition. With practice in the low-complexity condition, participants decreased absolute acceleration and deceleration times but maintained the relative amount of MT devoted to temporal phases. In contrast, with practice in the high-complexity condition, participants decreased absolute deceleration and dwell time and changed the temporal structure. Results suggest that older adults can decrease MT with practice and that the performance strategies adopted to speed performance are more a function of task complexity than age.

Mechanisms governing organizational performance in complex decision-making environments

The present experiment tested level of organizational performance as a function of perceived self-efficacy, task complexity, assigned and self-set goals, and analytic strategies in managerial decision-making in a stimulated organization. Organizational performance and analytic strategies were assessed across 16 trials and self-reactive influences operating through perceived selfefficacy, self-set goals, and acceptance of assigned goals were assessed at several points in the simulation. Assigned challenging goals had a positive effect on level of performance in the low complexity condition but not in the condition of high organizational complexity. The latter finding was explained in terms of the temporal and social complexity of the linkages between managerial effort and collective accomplishment and the multifaceted nature of goal-setting in complex social environments. The self-reactive influences had comparable effects on managerial performance across the differing levels of goal assignment and organizational complexity. In path analyses, perceived self-efficacy was positively related to effective use of analytic strategies for discovering optimal managerial rules and level of personal goals. Both perceived self-efficacy and analytic strategies contributed to managerial success in raising organizational performance. Self-set goals also contributed to performance in the earlier trials of the simulation but not in the later trials, following a change in task demands which reduced the generalizability of prior standards.

A Study of the Relationship between Decision Model Naturalness and Performance

Two objectives in the design of decision support systems (DSS) are to improve decision-making performance and to use DSS modeling forms that are natural, that is, to adopt modeling paradigms that are congruent with decision makers' conceptual models of decision tasks. By accomplishing the latter objective, a DSS should enjoy better conceptual ease of use and face validity. However, past research finds that DSS deemed natural for a task by decision makers, DSS designers, and researchers alike, often do not improve (or even hinder) performance; the inverse also occurs. Further decision-making behavior seems quite sensitive to minor task differences. How reliably are decision model natural ness and performance related? This study utilizes the bootstrapping paradigm of psychological research to help answer this question. In assessing the naturalness and performance of differing model paradigms over time and across levels of task complexity, no single, systematic pattern emerges. But the results suggest that naturalness and performance are differentially sensitive to task contingencies. For example, while relative performance is stable over time only in the low complexity condition, relative naturalness is stable over time only int the intermediate complexity condition. One implication of the results is that conceptual ease of use may be an unreliable predictor of a DSS's effect on performance. DSS mechanisms may help decision makers better analyze model naturalness and performance.