Minimal Information Research Articles

IMPLEMENTASI APLIKASI BANDUNG INTEGRATED MANPOWER MANAGEMENT APPLICATION (BIMMA) DALAM UPAYA MEMINIMALISIR PENGANGGURAN DI KOTA BANDUNG

This article was created to analyze government policies in overcoming the increase in unemployment in the city of Bandung. The policy is carried out through software and hardware policy innovations called the "Bandung Integrated Manpower Management Application". This application provides a solution in providing online jobs in reducing unemployment in the city of Bandung. The method used in this research is a quantitative method supported by the latest data. In addition, research was also carried out through previous research which was used as a reference regarding the Effectiveness of Online Job Exchange Services Through the BIMMA Application at the Bandung City Manpower Office. Based on the results of research and implementation that have been carried out by the government of Bandung City, this activity has not been carried out optimally due to minimal information to be accessed by persons with disabilities in Bandung City and also training that is not in line with the interests of persons with disabilities in participating in training held by the government

Do corporate credit spreads predict the real economy?

We evaluate whether corporate credit-spreads measures contain predictive information about the real U.S. economy in a comprehensive specification that includes financial sectors’ profitability, stock and bond market. We find that corporate credit spreads contain no predictive information about U.S. real GDP or consumption and that corporate credit spreads have minimal information about forthcoming recessions. In comparison, the financial sector’s profitability, the Treasury bond and stock market variables contain leading information about recessions, consumption and real GDP.

Novel linkage approach to join community-acquired and national data

BackgroundCommunity optometrists in Scotland have performed regular free-at-point-of-care eye examinations for all, for over 15 years. Eye examinations include retinal imaging but image storage is fragmented and they are not used for research. The Scottish Collaborative Optometry-Ophthalmology Network e-research project aimed to collect these images and create a repository linked to routinely collected healthcare data, supporting the development of pre-symptomatic diagnostic tools.MethodsAs the image record was usually separate from the patient record and contained minimal patient information, we developed an efficient matching algorithm using a combination of deterministic and probabilistic steps which minimised the risk of false positives, to facilitate national health record linkage. We visited two practices and assessed the data contained in their image device and Practice Management Systems. Practice activities were explored to understand the context of data collection processes. Iteratively, we tested a series of matching rules which captured a high proportion of true positive records compared to manual matches. The approach was validated by testing manual matching against automated steps in three further practices.ResultsA sequence of deterministic rules successfully matched 95% of records in the three test practices compared to manual matching. Adding two probabilistic rules to the algorithm successfully matched 99% of records.ConclusionsThe potential value of community-acquired retinal images can be harnessed only if they are linked to centrally-held healthcare care data. Despite the lack of interoperability between systems within optometry practices and inconsistent use of unique identifiers, data linkage is possible using robust, almost entirely automated processes.

Produktivitas kinerja Pegawai Negeri Sipil di Kota Bitung

This research was conducted at the Girian District Office, Bitung City with the aim of analyzing the productivity of civil servant performance from the aspects of Attitude, Ability and Work Spirit as well as analyzing what obstacles are faced in preparing or creating employee performance targets. The method used is a qualitative research method. The results of this study show that 1) in terms of attitude, the performance of district staff is good and they are able to communicate well with the community; 2) In terms of efficiency, the performance of district staff remains low; 3) The performance of district employees remains the worst in terms of work ethics. The factors hindering the performance of the staff of Gillian District Office in Bitung City are: 1) lack of motivation to work, which may affect community participation in community activities; 2) County employees do not receive training or development so that they do not know what type of work they will be required to do when appointed as county employees; 3) The average age of district employees is 50 years old; 4) Existing regulations change very quickly; 5) Technical bodies still provide minimal information on the latest regulations.

Clutter rejection in passive non-line-of-sight imaging via blind multispectral unmixing.

Passive non-line-of-sight imaging methods that utilize scattered light to "look around corners" are often hindered by unwanted sources that overwhelm the weaker desired signal. Recent approaches to mitigate these "clutter" sources have exploited dependencies in the spectral content, or color, of the scattered light. A particularly successful method utilized blind source separation methods to isolate the desired imaging signal with minimal prior information. This current paper quantifies the efficacy of several preconditioning and unmixing algorithms when blind source separation methods are employed for passive multispectral non-line-of-sight imaging. Using an OLED television monitor as the source of both the desired signals and clutter, we conducted multiple controlled experiments to test these methods under a variety of scene conditions. We conclude that the preconditioner is a vital component as it greatly decreases the power and correlation of the clutter. Additionally, the choice of unmixing algorithm significantly impacts the reconstruction quality. By optimizing these two components, we find that effective image retrieval can be obtained even when the clutter signals are as much as 670 times stronger than the desired image.

Aftershock forecasts based on incomplete earthquake catalogues: ETASI model application to the 2023 SE Türkiye earthquake sequence

SUMMARY The epidemic-type aftershock sequence (ETAS) model is the state-of-the-art approach for modelling short-term earthquake clustering and is preferable for short-term aftershock forecasting. However, due to the large variability of different earthquake sequences, the model parameters must be adjusted to the local seismicity for accurate forecasting. Such an adjustment based on the first aftershocks is hampered by the incompleteness of earthquake catalogues after a mainshock, which can be explained by a blind period of the seismic networks after each earthquake, during which smaller events with lower magnitudes cannot be detected. Assuming a constant blind time, direct relationships based only on this additional parameter can be established between the actual seismicity rate and magnitude distributions and those that can be detected. The ETAS-incomplete (ETASI) model uses these relationships to estimate the true ETAS parameters and the catalogue incompleteness jointly. In this study, we apply the ETASI model to the SE Türkiye earthquake sequence, consisting of a doublet of M 7.7 and M 7.6 earthquakes that occurred within less than half a day of each other on 6 February 2023. We show that the ETASI model can explain the catalogue incompleteness and fits the observed earthquake numbers and magnitudes well. A pseudo-prospective forecasting experiment shows that the daily number of detectable m ≥ 2 can be well predicted based on minimal and incomplete information from early aftershocks. However, the maximum magnitude (Mmax ) of the next day’s aftershocks would have been overestimated due to the highly variable b value within the sequence. Instead, using the regional b value estimated for 2000–2022 would have well predicted the observed Mmax values.

Patient satisfaction with the management of refractory and unexplained chronic cough in Canada: Results from a national survey.

Chronic cough (persisting for ≥8 weeks) is a common disorder affecting approximately 5 to 10% of adults worldwide that is sometimes refractory to treatment (refractory chronic cough [RCC]) or has no identifiable cause (unexplained chronic cough [UCC]). There is minimal information on the patient's experience of RCC/UCC in Canada. The aim of this study was to evaluate the patient journey and perceptions related to RCC/UCC management in Canada. Our exploratory study included Canadians in the Leger Opinion Panel and focused on individuals with RCC or UCC. Key entry criteria were: age ≥18 years, cough on most days for ≥8 weeks, no smoking within 1 year, no serious respiratory disease or lung cancer, and not taking angiotensin-converting enzyme inhibitors. Individuals who met entry criteria were invited to complete an approximately 30-minute online survey with questions on demographic characteristics, healthcare professional (HCP) interactions, diagnosis of underlying conditions, current treatments, and satisfaction with HCPs and chronic cough therapies. A total of 49,076 individuals completed the chronic cough screening questionnaire (July 30, 2021 to September 1, 2021): 1,620 (3.3%) met entry criteria for RCC or UCC, and 1,046 (2.1%) completed the online survey (mean age of 45 years, 61% female). Most respondents (58%) reported their chronic cough was managed by a general practitioner (GP). Forty-four percent of respondents did not have a diagnosis of an underlying condition for their cough. Breathing tests (39%) and chest imaging (34%) were the most common diagnostic tests. Cough suppressants (18%) were the most frequent current treatment. Respondents were moderately satisfied with their HCPs, but more than half considered their treatment ineffective and 34% had considered no longer seeking medical attention because of a lack of treatment success. Individuals with RCC/UCC in Canada are largely unsatisfied with the effectiveness of treatment. Additional HCP education and new treatment options are needed to improve patient satisfaction.

Business Continuity in South Africa During COVID-19 Pandemic: Digital Technology Perspective

South Africa, like many other countries globally, implemented a lockdown in an attempt to contain the spread of the coronavirus which has had an adverse effect on business operations and sustainability. Minimal information is available concerning the impact of digital technology utilisation on business continuity during such a pandemic. Therefore, this study aimed to determine the impact of digital technology on business continuity during this period at a South African Testing, Inspection and Certification (TIC) company. A cross-sectional study was conducted among 59 employees and data were collected using an online anonymous questionnaire for the different types of digital technology used pre-pandemic and during the pandemic, as well as its impact on interactions and work expectations. Results revealed that the company used virtual meetings, cloud storage, electronic job systems, telecommunications, messaging applications, business intelligence solutions, big data analytics, and online training. There were moderate positive relationships found between the types of technology and business continuity during the pandemic. Areas for continual improvement were reported as network connectivity, communication, and training. It is recommended that post-COVID-19, companies need to continually evaluate the use of digital technology to further reduce the time spent by employees on manual processes thereby improving productivity to provide better customer service and enhance competitive advantage.

Hypopituitarism—A rare manifestation in Joubert syndrome: about 4 cases

<p>Joubert syndrome (JS) is a complex medical condition characterized by a pathognomonic midbrain-hindbrain malformation visible on brain imaging, which is known as the “molar tooth sign” (MTS). The presence of the MTS in the brain is the defining diagnostic criterion for JS. Individuals with JS commonly exhibit a developmental delay, hypotonia, and abnormal eye movements. In addition, neonatal breathing dysregulation is observed in about half of the cases. Midline brain defects associated with JS can lead to pituitary hormone abnormalities, thereby manifesting as multiple pituitary insufficiencies in the neonatal period, such as hypoglycemia and, in male patients, a micropenis with undescended testes. Although JS is a well-researched genetic condition, there is minimal information on the endocrinological aspects of JS. This manuscript aims to emphasize the spectrum of endocrinologic findings in JS through the retrospective evaluation of four cases characterized by combined pituitary dysfunctions, including secondary hypothyroidism, growth hormone deficiency, and panhypopituitarism. Highlighting these treatable aspects of JS is crucial, as continuous endocrinological monitoring can positively impact a patient's well-being, particularly in managing secondary adrenal and thyroid insufficiencies.</p>

Why Privacy-Preserving Protocols Are Sometimes Not Enough: A Case Study of the Brisbane Toll Collection Infrastructure

The use of Electronic Toll Collection (ETC) systems is on the rise, as these systems have a significant impact on reducing operational costs. Toll service providers (TSPs) access various information, including drivers' IDs and monthly toll fees, to bill drivers. While this is legitimate, such information could be misused for other purposes violating drivers' privacy, most prominent, to infer drivers' movement patterns. To this end, privacy-preserving ETC (PPETC) schemes have been designed to minimize the amount of information leaked while still allowing drivers to be charged. We demonstrate that merely applying such PPETC schemes to current ETC infrastructures may not ensure privacy. This is due to the (inevitable) minimal information leakage, such as monthly toll fees, which can potentially result in a privacy breach when combined with additional background information, such as road maps and statistical data. To show this, we provide a counterexample using the case study of Brisbane's ETC system. We present two attacks: the first, being a variant of the presence disclosure attack, tries to disclose the toll stations visited by a driver during a billing period as well as the frequency of visits. The second, being a stronger attack, aims to discover cycles of toll stations (e.g., the ones passed during a commute from home to work and back) and their frequencies. We evaluate the success rates of our attacks using real parameters and statistics from Brisbane's ETC system. In one scenario, the success rate of our toll station disclosure attack can be as high as 94%. This scenario affects about 61% of drivers. In the same scenario, our cycle disclosure attack can achieve a success rate of 51%. It is remarkable that these high success rates can be achieved by only using minimal information as input, which is, e.g., available to a driver's payment service provider or bank, and by following very simple attack strategies without exploiting optimizations. As a further contribution, we analyze how the choice of various parameters, such as the set of toll rates, the number of toll stations, and the billing period length, impact a driver's privacy level regarding our attacks.

Comparison of qSOFA, MEDS, and APACHE II Scores in Early Identification of Sepsis for Patients with 28 days Mortality and ICU Admission: A cross-sectional Study

Introduction: Sepsis is a life-threatening infection that results in organ dysfunction due to an increased pathogen load, necessitating urgent intervention. There is a gap in clinicians’ ability to identify septic patients at high-risk with poor outcomes, highlighting the need for validated predictive scores for early intervention, favourable outcomes, and prompt recovery. Aim: To validate the predictive capacity of the Sequential Organ Failure Assessment (qSOFA), Mortality in Emergency Department Sepsis (MEDS), and Acute Physiology and Chronic Health Evaluation (APACHE 2) scores in patients with 28- day mortality and in Intensive Care Unit (ICU) patients due to sepsis. Materials and Methods: This cross-sectional study was conducted on 150 septic patients at the Department of Emergency Medicine, Sri Ramachandra Institute of Higher Education and Research in Chennai, India, between June and December 2022. Parameters assessed included Respiratory Rate (RR), Systolic Blood Pressure (SBP), Mean Arterial Pressure (MAP), temperature, White Blood Cell (WBC) count, platelet count, bilirubin, and creatinine. Descriptive analysis of age, gender, source, RR, GCS, SBP, qSOFA, MEDS, APACHE 2 in 28-day mortality, and ICU patients. Positive correlation and good predictivity of predictive scores (qSOFA, MEDS, APACHE 2) were analysed using Spearman’s Rank Correlation Coefficient (SRCCRs) statistical test in 28-day mortality and ICU patients. Results: A total of 150 septic patients (male: female-93:57) with an average age of 57.07±14.4 years were included. Urosepsis was the most common (n=51), followed by respiratory sepsis (n=48). Of these, 96 patients were admitted to the ICU, and 54 patients experienced 28-day mortality. The average and median values of RR were 27.1±7.64 breaths/minute (b/m) and 26 b/m, respectively. SBP values were 106.13±30.47 mmHg and 110 mmHg, respectively. Diastolic Blood Pressure (DBP) values were 66±16.51 mmHg and 60 mmHg, respectively. The average and median values of GCS were 12.75±3.92 and 15, respectively. The average for qSOFA was 29.1±0.025, with a median of 28; for MEDS, the average was 7.99±5.89, with a median of 7; and for APACHE 2, the average was 16.74±9.64, with a median of 15. Spearman’s Rank Correlation Coefficient (SRCCRs) demonstrated a strong positive correlation and good predictive validity between qSOFA, MEDS, and APACHE 2 scores in 28- day mortality and ICU patients (<0.001). Receiver Operating Characteristic Curve (ROC) analysis indicated good predictive validity for qSOFA in 28-day mortality and ICU patients. Conclusion: qSOFA exhibited a positive correlation and good predictive validity compared to MEDS and APACHE 2 in both 28-day mortality and ICU patients (<0.001). This study highlights the utility and applicability of qSOFA at the bedside for initial triage, as it can be quickly employed with minimal information.

Exploring Buddhism Values at Sari Temple: An Investigation in a Buddhist Tourism Destination

Religious tourism has now become one of the favorite destinations for tourists. However, there are still many temples that are not widely known by tourists. Even though many temples, such as Sari Temple, have a certain charm, such as the shape of the temple which is like a house complete with doors and windows. However, there is still minimal information regarding the history and meaning of the temple reliefs. This research was carried out at the Sari temple which is located in Uang Istimewa Yogyakarta. This research aims to analyze the Buddhist values at Sari Temple based on the history of this temple. This research uses a qualitative research design where the research object is Sari Temple itself. There are three techniques used to collect data, namely interviews, literature study, and documentation. The data collected was then analyzed qualitatively through four stages, namely data collection, data reduction, data presentation, and verification or drawing conclusions. The results of the research show that there are three Buddhist values that can be found based on the history of Sari temple, namely samma sankappa (right thoughts) , samma kamanta (right action) and samma vaca (right speech). Each of these Buddhist values has moral values in it.

Construction of prediction model of severe acute pancreatitis based on serum soluble T cell immunogloblulin and mucin domain-containing protein 3

To investigate the predictive value of the model based on soluble T cell immunogloblulin and mucin domain-containing protein 3 (sTIM3) for the progression of severe acute pancreatitis (SAP) in patients with acute pancreatitis (AP). A retrospective cohort study was conducted. The AP patients admitted to Changzhou First People's Hospital and Changzhou Second People's Hospital from June 1, 2020 to June 30, 2022 were enrolled. Mild AP (MAP) and moderately severe AP (MSAP) patients were classified as non-SAP group, and SAP patients were classified as SAP group according to the progression of AP patients during hospitalization. The basic data, blood biological indicators, serum sTIM3 level, bedside index for severity in acute pancreatitis (BISAP), acute physiology and chronic health evaluation II (APACHE II) score, modified computed tomography severity index (MCTSI) score within 48 hours of admission, and prognosis indicators were collected. Multivariate Logistic regression analysis was conducted to analyze the risk factors of the progression of SAP in patients with AP during hospitalization. Based on the results of multivariate analysis and the best parameters selected based on the minimal Akaike information criterion (AIC), the SAP prediction model based on sTIM3 was constructed. The receive operator characteristic curve (ROC curve) was plotted to analyze the predictive efficacy of the model. A total of 99 AP patients were enrolled, 80 patients in the non-SAP group and 19 patients in the SAP group. Compared with the non-SAP group, body mass index (BMI), drinking history ratio, heart rate (HR), respiration rate (RR), white blood cell count (WBC), red blood cell count (RBC), C-reactive protein (CRP), alanine aminotransferase (ALT), serum creatinine (SCr), procalcitonin (PCT), interleukin-6 (IL-6), sTIM3, BISAP score, APACHE II score and MCTSI score were significantly increased, and pulse oxygen saturation (SpO2), direct bilirubin (DBil) and IL-10 were significantly decreased. The length of intensive care unit (ICU) stay and total length of hospital stay of patients in the SAP group were significantly longer than those in the non-SAP group [length of ICU stay (days): 1.0 (0, 1.5) vs. 0 (0, 0), total length of hospital stay (days): 17.11±9.39 vs. 8.40±3.08, both P < 0.01]. Multivariate Logistic regression analysis showed that HR [odds ratio (OR) = 1.059, 95% confidence interval (95%CI) was 1.010-1.110, P = 0.017], DBil (OR = 0.981, 95%CI was 0.950-0.997, P = 0.043), and sTIM3 (OR = 1.002, 95%CI was 1.001-1.003, P = 0.027) were independent risk factors for predicting the progression of SAP in patients with AP, and the SAP prediction model based on sTIM3 was constructed: Logit(P) = -14.602+0.187×BMI+0.057×HR+0.006×CRP-0.020×DBil+0.002×sTIM3. ROC curve analysis showed that among the aforementioned single factor quantitative indicators, IL-6 was the most effective in predicting the progression of AP patients to SAP during hospitalization, but the predictive performance of prediction model based on the sTIM3 was significantly better than IL-6 [area under the ROC curve (AUC) and 95%CI: 0.957 (0.913-1.000) vs. 0.902 (0.845-0.958), P < 0.05]. The model based on serum sTIM3 demonstrated good predictive value for the progression of SAP in patients with AP.

DMMG: Dual Min-Max Games for Self-Supervised Skeleton-Based Action Recognition.

In this work, we propose a new Dual Min-Max Games (DMMG) based self-supervised skeleton action recognition method by augmenting unlabeled data in a contrastive learning framework. Our DMMG consists of a viewpoint variation min-max game and an edge perturbation min-max game. These two min-max games adopt an adversarial paradigm to perform data augmentation on the skeleton sequences and graph-structured body joints, respectively. Our viewpoint variation min-max game focuses on constructing various hard contrastive pairs by generating skeleton sequences from various viewpoints. These hard contrastive pairs help our model learn representative action features, thus facilitating model transfer to downstream tasks. Moreover, our edge perturbation min-max game specializes in building diverse hard contrastive samples through perturbing connectivity strength among graph-based body joints. The connectivity-strength varying contrastive pairs enable the model to capture minimal sufficient information of different actions, such as representative gestures for an action while preventing the model from overfitting. By fully exploiting the proposed DMMG, we can generate sufficient challenging contrastive pairs and thus achieve discriminative action feature representations from unlabeled skeleton data in a self-supervised manner. Extensive experiments demonstrate that our method achieves superior results under various evaluation protocols on widely-used NTU-RGB+D, NTU120-RGB+D and PKU-MMD datasets.

Semi-supervised machine learning approach for reaction stoichiometry and kinetic model identification using spectral data from flow reactors

The semi-supervised machine learning approach is an integrated calibration-free modelling framework for identifying reaction systems from spectral data using minimal prior information and it is validated with experimental data obtained in a micro-reactor.

Graph Exploration for Effective Multiagent Q-Learning.

This article proposes an exploration technique for multiagent reinforcement learning (MARL) with graph-based communication among agents. We assume that the individual rewards received by the agents are independent of the actions by the other agents, while their policies are coupled. In the proposed framework, neighboring agents collaborate to estimate the uncertainty about the state-action space in order to execute more efficient explorative behavior. Different from existing works, the proposed algorithm does not require counting mechanisms and can be applied to continuous-state environments without requiring complex conversion techniques. Moreover, the proposed scheme allows agents to communicate in a fully decentralized manner with minimal information exchange. And for continuous-state scenarios, each agent needs to exchange only a single parameter vector. The performance of the algorithm is verified with theoretical results for discrete-state scenarios and with experiments for the continuous ones.

Low-Dimensional Gradient Helps Out-of-Distribution Detection.

Detecting out-of-distribution (OOD) samples is essential for ensuring the reliability of deep neural networks (DNNs) in real-world scenarios. While previous research has predominantly investigated the disparity between in-distribution (ID) and OOD data through forward information analysis, the discrepancy in parameter gradients during the backward process of DNNs has received insufficient attention. Existing studies on gradient disparities mainly focus on the utilization of gradient norms, neglecting the wealth of information embedded in gradient directions. To bridge this gap, in this paper, we conduct a comprehensive investigation into leveraging the entirety of gradient information for OOD detection. The primary challenge arises from the high dimensionality of gradients due to the large number of network parameters. To solve this problem, we propose performing linear dimension reduction on the gradient using a designated subspace that comprises principal components. This innovative technique enables us to obtain a low-dimensional representation of the gradient with minimal information loss. Subsequently, by integrating the reduced gradient with various existing detection score functions, our approach demonstrates superior performance across a wide range of detection tasks. For instance, on the ImageNet benchmark with ResNet50 model, our method achieves an average reduction of 11.15% in the false positive rate at 95% recall (FPR95) compared to the current state-of-the-art approach. The code is at https://github.com/Wuyingwen/Low-dimensional-Gradient-Helps-OOD-Detection.

Matrix Factorization For Augmented Deep Learning Model Generation

This paper aims to address two significant challenges of Deep Learning(DL) model generation, high computational cost involved during the training phase and data interpretability from high dimensional data. The computational cost is curbed using centralized as well as distributed matrix factorization technique known as CUR decomposition. CUR decomposition has the advantage of reducing the dimensions of the dataset without data transformation and minimal information loss. Though Singular Value De-composition(SVD) is the best matrix decomposition technique with least reconstruction error, it transforms the data and hence the original data cannot be interpreted from the decomposed matrices while applying DL technique. So CUR decomposition is leveraged to reduce the number of features in high-dimensional data while preserving the essential information. Extensive experimental analysis is conducted to evaluate and compare the performance of CUR and SVD techniques based on reconstruction error and time complexity in centralized as well as distributed setting. The results exhibit that CUR outperforms SVD in terms of computational efficiency, especially in a distributed setting where the decomposition time for CUR was a fraction compared to SVD. At the same time DL models generated from this reduced dataset exhibit comparable accuracy with the original high dimensional dataset as well as reduced dataset obtained through SVD. This work also enhances the accessibility of the CUR algorithm to users, for which an application is developed that enables users to execute the algorithm on any dataset. The user-friendly interface of the application facilitates the upload of datasets, configuration of desired decomposition rank, and execution of the algorithm with a single click.

The language of power: Interpersonal perceptions of sense of power, dominance, and prestige based on word usage

We examined whether people can accurately judge the experienced power, dominance, and prestige of others based on short texts, and what linguistic cues are related to these hierarchy-related variables. Past research suggests that personality is reflected in language, but hierarchy—another important feature in human relationships—has not been fully considered. In two studies with a zero-acquaintance setting, judges ( N1/2 = 105/202) read self-descriptions of speakers ( N1/2 = 200/200) and completed peer ratings of speakers’ power, dominance, and prestige. Speakers completed the same scales as self-ratings. We found substantial associations between the hierarchy concepts and several word categories (e.g., sense of power was negatively related to tentative words in self- and peer reports). For power and prestige, judges and speakers used the same linguistic cues for their assessments. Further, judges converged strongly in their assessments and showed high self-other agreement for all variables. We conclude that social hierarchy concepts are enacted in language and can be perceived from minimal verbal information. The findings show the importance of distinguishing between various hierarchy concepts when analyzing language correlates and have implications for testing power theories with linguistic material and the understanding of perceptions regarding hierarchy differences and following downstream consequences.

The Role of Attractiveness in Gendered Sexual Response Patterns

ABSTRACT Previously documented sexual response patterns of gender-specificity among gynephilic men and gender-nonspecificity among gynephilic women could be explained by women responding more strongly to non-gendered aspects of sexual stimuli. Cues of attractiveness are known determinants of sexual decision-making, yet have not been directly tested as determinants of sexual response. The current study investigated the role of attractiveness cues in explaining gender-based patterns of sexual response. Thirty-one gynephilic men and 60 androphilic women were presented slideshows of images depicting individual nude men and women that were pre-rated in a pilot study as either attractive or unattractive. The men and women were posed with legs spread and aroused genitals displayed prominently. Images were isolated against a white background and included minimal contextual information. Three sexual responses – genital arousal (via photoplethysmographs), self-reported arousal, and visual attention (via eye-tracking) – were recorded continuously. Across all three response modalities, men’s and women’s responses were stronger for the attractive versus unattractive images and for their preferred versus non-preferred gender. For men’s arousal and women’s self-reported arousal, the effect of attractiveness was stronger for their preferred versus non-preferred gender. Thus, both men and women demonstrated preference-specific patterns of sexual response. Gender cues had the strongest effect on men’s visual attention, whereas attractiveness cues had the strongest effect on women’s visual attention. Findings establish the importance of target attractiveness in arousal to sexual stimuli and add to mounting evidence that androphilic women’s sexual responses are sensitive to gender, but may be more sensitive to non-gendered features of sexual stimuli.