Bias Scores Research Articles

Predicting coastal wave conditions: A simple machine learning approach

Accurate and reliable nearshore wave predictions are highly valuable for a range of marine activities, including coastal engineering and maritime transport. However, in nearshore locations, predicting wave properties is challenging due to complex shallow water processes, requiring local wave models. This article develops an alternative data-driven framework to predict wave parameters (e.g. significant wave height) through the extension of wave buoy datasets using a trained Gaussian process regression (GPR — a supervised machine learning method). We present an easy-to-implement workflow, where the extensive range of input parameters (from ECMWF’s (1) ERA5 reanalysis and (2) IFS forecast global wave model, ≈50km resolution) drives the development of GPR models. At five contrasting locations around the United Kingdom’s coastline, the GPR models produce wave predictions (forecast and hindcast) with low bias scores and strong correlations with observations. When compared to the global (ERA5 reanalysis) and a benchmark shelf-scale (Atlantic-European North West Shelf reanalysis; AENWS, 1.5−3.0km resolution) model, the GPR hindcasts reduced significant wave height (Hs) root-mean-squared error (RMSE) from 0.46 m (ERA5) and 0.21 m (AENWS) to 0.16 m (GPR). For the average zero-crossing wave period (Tz) RMSE reduced from 1.46 s (ERA5) and 1.15 s (AENWS) to 0.58 s (GPR). Because our approach uses publicly available global data, it can be implemented at any historic or active buoy location. We provide proof of concept for an online forecast and hindcast tool which has the potential to improve accessibility to coastal wave predictions for many marine stakeholders.

Enhanced related-key differential neural distinguishers for SIMON and SIMECK block ciphers

At CRYPTO 2019, Gohr pioneered the application of deep learning to differential cryptanalysis and successfully attacked the 11-round NSA block cipher Speck32/64 with a 7-round and an 8-round single-key differential neural distinguisher. Subsequently, Lu et al. (DOI 10.1093/comjnl/bxac195) presented the improved related-key differential neural distinguishers against the SIMON and SIMECK. Following this work, we provide a framework to construct the enhanced related-key differential neural distinguisher for SIMON and SIMECK. In order to select input differences efficiently, we introduce a method that leverages weighted bias scores to approximate the suitability of various input differences. Building on the principles of the basic related-key differential neural distinguisher, we further propose an improved scheme to construct the enhanced related-key differential neural distinguisher by utilizing two input differences, and obtain superior accuracy than Lu et al. for both SIMON and SIMECK. Specifically, our meticulous selection of input differences yields significant accuracy improvements of 3% and 1.9% for the 12-round and 13-round basic related-key differential neural distinguishers of SIMON32/64. Moreover, our enhanced related-key differential neural distinguishers surpass the basic related-key differential neural distinguishers. For 13-round SIMON32/64, 13-round SIMON48/96, and 14-round SIMON64/128, the accuracy of their related-key differential neural distinguishers increases from 0.545, 0.650, and 0.580 to 0.567, 0.696, and 0.618, respectively. For 15-round SIMECK32/64, 19-round SIMECK48/96, and 22-round SIMECK64/128, the accuracy of their neural distinguishers is improved from 0.547, 0.516, and 0.519 to 0.568, 0.523, and 0.526, respectively.

A common factor underlying individual differences in confirmation bias

When they are asked to test a given hypothesis, individuals tend to be biased towards confirming evidence. This phenomenon has been documented on different cognitive components: information search, weighing of evidence, and memory recall. However, the interpretation of these observations has been debated, and it remains unclear whether they truly reflect a confirmation bias (as opposed to e.g., a bias towards positive information). In the present study we aimed at evaluating whether these biases might be subtended by a common factor. We adapted three classic experimental paradigms on hypothesis testing (Wason selection task, 2-4-6 task, and interviewee task) and examined the relation between these biases using an individual differences approach. Participants (N = 200) completed a total of nine behavioral tasks, in which each component of confirmation bias was measured in each of the three experimental paradigms. Correlations and factor analyses within a multitrait–multimethod framework indicated greater convergence of bias scores within each component across paradigms, than within experimental paradigms. This suggests that a common factor underlies the different measurements of confirmation bias across experimental paradigms, at least to some extent. In these paradigms, thus, biases towards confirming evidence may truly reflect a confirmation bias.

Enhancing Bias Assessment for Complex Term Groups in Language Embedding Models: Quantitative Comparison of Methods.

Artificial intelligence (AI) is rapidly being adopted to build products and aid in the decision-making process across industries. However, AI systems have been shown to exhibit and even amplify biases, causing a growing concern among people worldwide. Thus, investigating methods of measuring and mitigating bias within these AI-powered tools is necessary. In natural language processing applications, the word embedding association test (WEAT) is a popular method of measuring bias in input embeddings, a common area of measure bias in AI. However, certain limitations of the WEAT have been identified (ie, their nonrobust measure of bias and their reliance on predefined and limited groups of words or sentences), which may lead to inadequate measurements and evaluations of bias. Thus, this study takes a new approach at modifying this popular measure of bias, with a focus on making it more robust and applicable in other domains. In this study, we introduce the SD-WEAT, which is a modified version of the WEAT that uses the SD of multiple permutations of the WEATs to calculate bias in input embeddings. With the SD-WEAT, we evaluated the biases and stability of several language embedding models, including Global Vectors for Word Representation (GloVe), Word2Vec, and bidirectional encoder representations from transformers (BERT). This method produces results comparable to those of the WEAT, with strong correlations between the methods' bias scores or effect sizes (r=0.786) and P values (r=0.776), while addressing some of its largest limitations. More specifically, the SD-WEAT is more accessible, as it removes the need to predefine attribute groups, and because the SD-WEAT measures bias over multiple runs rather than one, it reduces the impact of outliers and sample size. Furthermore, the SD-WEAT was found to be more consistent and reliable than its predecessor. Thus, the SD-WEAT shows promise for robustly measuring bias in the input embeddings fed to AI language models.

Unboxing Occupational Bias: Debiasing LLMs with U.S. Labor Data

Large Language Models (LLMs) are prone to inheriting and amplifying societal biases embedded within their training data, potentially reinforcing harmful stereotypes related to gender, occupation, and other sensitive categories. This issue becomes particularly problematic as biased LLMs can have far-reaching consequences, leading to unfair practices and exacerbating social inequalities across various domains, such as recruitment, online content moderation, or even the criminal justice system. Although prior research has focused on detecting bias in LLMs using specialized datasets designed to highlight intrinsic biases, there has been a notable lack of investigation into how these findings correlate with authoritative datasets, such as those from the U.S. National Bureau of Labor Statistics (NBLS). To address this gap, we conduct empirical research that evaluates LLMs in a “bias-out-of-the-box” setting, analyzing how the generated outputs compare with the distributions found in NBLS data. Furthermore, we propose a straightforward yet effective debiasing mechanism that directly incorporates NBLS instances to mitigate bias within LLMs. Our study spans seven different LLMs, including instructable, base, and mixture-of-expert models, and reveals significant levels of bias that are often overlooked by existing bias detection techniques. Importantly, our debiasing method, which does not rely on external datasets, demonstrates a substantial reduction in bias scores, highlighting the efficacy of our approach in creating fairer and more reliable LLMs.

Regional gender bias and year predict gender representation on civil trial teams.

There are documented gender disparities in the legal field. We examined whether gender representation on civil trial teams varied on the basis of (a) the degree of regional gender bias "in the air" and (b) time. We hypothesized that women were underrepresented both on trial teams and in leadership roles within those teams. We predicted that these gender disparities were exacerbated in regions with stronger regional gender bias and that these gender disparities attenuated over time. We coded attorney gender and case outcomes in real civil trials (N = 655). We created regional implicit and explicit gender bias scores based on the year and region of the case using Project Implicit data. Finally, we used order-constrained inference and Bayesian modeling to identify the best-performing models. Overall, women represented only 17% of attorneys at trial and 13% in leadership roles-indicating vast gender disparities. Gender disparities on teams and in leadership roles were more extreme in regions with high (vs. low) regional gender bias (teams: Bayes factor [BF] = 9,182; leadership: BF = 91,667) and improved over time (teams: BF = 6,420; leadership: BF = 3,495). Gender alone best predicted the likelihood of serving in a leadership role (BF = 1,197,397). Female attorneys were grossly underrepresented on civil trial teams. Gender representation on teams, but not leadership roles, has improved slightly over time. Culture may also contribute; women were less represented on trial teams in regions with greater gender bias in the air-particularly in leadership roles. Despite these slight improvements in representation on trial teams over time and in low-bias regions, gender disparities in leadership roles persist over time and levels of regional bias. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Emotion-specific recognition biases and how they relate to emotion-specific recognition accuracy, family and child demographic factors, and social behaviour

ABSTRACT The errors young children make when recognising others’ emotions may be systematic over-identification biases and may partially explain the challenges some have socially. These biases and associations may be differential by emotion. In a sample of 871 ethnically and racially diverse preschool-aged children (i.e. 33–68 months; 49% Hispanic/Latine, 52% Children of Colour), emotion recognition was assessed, and scores for accuracy and bias were calculated by emotion (i.e. anger, sad, happy, calm, and fear). Child and family characteristics and teacher-reported social behaviour were also collected. Multilevel structural equation modelling revealed emotion-specific recognition accuracies varied between 36 and 65% whereas biases varied between 4 and 13%. Anger was the strongest bias followed by sad, happy, fear, and calm, in contrast to the pattern for accuracy – happy, sad, angry, fear, and calm. More variance was explained in emotion-specific recognition accuracies by child and family characteristics – 7–38% – than biases – 3–7%. Negatively-valanced emotion recognition biases associated with positively-valanced accuracies, and positively-valued emotion recognition biases associated with negatively-valued accuracies. Biases did not have meaningful associations with social behaviour. This study highlights that children’s emotion recognition errors may partially be systematic, but future studies are needed to understand the underlying cognitive mechanisms.

Randomized Controlled Trials Studying Nonoperative Treatments of Osteoarthritis Often Use Misleading and Uninformative Control Groups: A Systematic Review.

Because there are no known treatments that alter the natural course of the pathophysiology of osteoarthritis, nonoperative treatment needs to be compared with known effective treatments that seek to mitigate symptoms or with similarly invasive inert (placebo) treatments to determine effectiveness. Comparing a treatment to an uninformative control group may inappropriately legitimize and support the use of potentially ineffective treatments. We therefore investigated the prevalence of inappropriate control groups in musculoskeletal research and asked whether these are associated with reporting a positive treatment effect. We systematically reviewed randomized trials of nonoperative treatments of osteoarthritis and asked: (1) What proportion of randomized trials use uninformative control groups (defined as a treatment less invasive than the tested treatment, or a treatment that might possibly not outperform placebo but is not acknowledged as such)? (2) Is the use of uninformative control groups independently associated with reporting a positive treatment effect (defined as p < 0.05 in favor of the intervention, or as making a recommendation favoring the intervention over the control treatment)? In a systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched PubMed, Cochrane, and Embase up to September 2023 for randomized controlled trials published between 2020 to 2022 that compared one or more nonoperative treatments for the symptoms of osteoarthritis. We excluded studies that contained a surgical treatment group. We identified 103 trials that met eligibility criteria, with a total of 15,491 patients. The risk of bias was high in 60% (n = 62) of trials using the Cochrane Risk of Bias Tool, version 2. Although the high risk of bias in the included studies is concerning, it does not invalidate our design; instead, it highlights that some studies may use flawed methods to recommend treatments with unproven effectiveness beyond nonspecific effects because the kinds of bias observed would tend to increase the apparent benefit of the treatment(s) being evaluated. We used logistic regression to test the association of uninformative control groups with a positive treatment effect, accounting for potential confounders such as conflict of interest and study bias using the Cochrane Risk of Bias score. The use of uninformative control groups (treatments less invasive than the tested treatment, or treatments that might not outperform placebo but are not acknowledged as such) was found in 46% (47 of 103) of included studies. After accounting for potential confounding, there was no association between reporting positive treatment effects and the use of an uninformative control group. Studies with a low risk of bias had a lower likelihood of reporting a positive treatment effect (OR 0.2 [95% confidence interval 0.05 to 0.9]; p = 0.04, model pseudo R2 = 0.21). The finding that recent studies that mimic high-level evidence often use uninformative control groups that do not adequately account for nonspecific effects (perceived treatment benefits unrelated to a treatment's direct physiological effects) points to a high risk of legitimizing ineffective treatments. This raises the ethical imperative for patients, clinicians, journal peer reviewers, and journal editors to hold researchers to the standard of an adequate, informative control group. Awareness and risk of bias checklists might help patients and clinicians forgo new treatments based on seemingly high-level evidence that may carry only iatrogenic, financial, and psychological harm (false hope, in particular). Level I, therapeutic study.

A multifaceted approach to detect gender biases in Natural Language Generation

Recent advances in generative models have skyrocketed the popularity of conversational chatbots and have revolutionized the way we interact with artificial intelligence. At the same time, research has shown that machine learning models can unconsciously reflect and amplify human biases. This is particularly dangerous for generative models given the huge popularity of such technologies. Specifically, a fundamental source of bias of such technologies is the resources on which the models are trained. To address this issue, this paper proposes a methodology to analyze intrinsic gender bias in Natural Language Generation (NLG). Some works already propose metrics and approaches to measure bias in the Natural Language processing field. However, there is a lack of standard methodology to measure gender bias in NLG. Therefore, adapting the Bias Score approach, our proposal involves three sequential stages applied to individual texts to detect intrinsic gender bias on NLG effectively. Those steps are as follows: (i) word scoring; (ii) word filtering; and (iii) generative-word analysis. This methodology is applied to recent datasets and pre-trained models widely used for the generation of text with common sense. In particular, this paper analyzes the potential gender bias in CommonGen and C2Gen datasets and the SimpleNLG and T5 models. The results show the ability of the proposed methodology to detect gender bias in word distributions, presenting a strong correlation with the words typically associated with a specific gender. Results indicate that both tested datasets are intrinsically gender-biased, and therefore, tested models fine-tuned with those datasets also are.

A 3D-Var assimilation scheme for vertical velocity with CMA-MESO v5.0

Abstract. Certain vertical motions associated with meso-microscale systems are favorable for convection development and maintenance. Correct initialization of updraft motions is thus significant in convective precipitation forecasts. A three-dimensional variational-based vertical velocity (w) assimilation scheme has been developed within the high-resolution (3 km) CMA-MESO (the Mesoscale Weather Numerical Forecast System of the China Meteorological Administration) model. This scheme utilizes the adiabatic Richardson equation as the observation operator for w, enabling the update of horizontal winds and mass fields of the model's background. The tangent linear and adjoint operators are subsequently developed and undergo an accuracy check. A single-point w observation assimilation experiment reveals that the observational information is effectively spread both horizontally and vertically. Specifically, the assimilation of w contributes to the generation of horizontal wind convergence at lower model levels and divergence at higher model levels, thereby adjusting the locations of convection occurrence. The impact of assimilating w on the forecast is then examined through a series of continuous 10 d runs. Further assimilation of w, in addition to the assimilation of conventional and radial wind data, significantly improves the forecast accuracy of precipitation, resulting in higher FSS (fractions skill score) values and higher ETS (equitable threat score) skills at higher thresholds (5 and 20 mm h−1). However, it should be noted that further assimilation of w can potentially lead to some false precipitation, resulting in slightly lower ETS values at lower thresholds (1 mm h−1) and a neutral impact on BIAS (bias score) skills. An individual case study conducted within the batch experiments reveals that assimilating w has a beneficial impact on the enhancement of vertical motion across different layers of the model, facilitating the transport of moisture from lower to middle–high model levels, thereby leading to an improvement in forecast skills.

A radiant shift: Attention-embedded CNNs for accurate solar irradiance forecasting and prediction from sky images

The continuous increase in solar power integration with energy systems can be attributed to the push for cleaner energy use globally, highlighting the importance of accurate solar forecasts. Conventional prediction methods, although valuable, often fall short of delivering the precision required for dynamic energy management systems due to their inability to effectively capture the intricate relationships inherent in solar irradiance variations. This research addresses this limitation by introducing a novel approach that harnesses the potential of Attention-embedded Convolutional Neural Networks (ATT_CNN) to forecast Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), and Diffuse Horizontal Irradiance (DHI) for intra-hour solar forecasting utilizing sequences of sky images. The main contribution of this study is the integration of attention mechanisms into the CNN architecture, strategically designed to enhance their efficacy in predicting GHI, DNI, and DHI by fostering an adaptive focus on sky image features. First, we use SRRL dataset with GHI, DNI, and DHI features as predicting labels, then the Mean Bias Error (MBE), Root Mean Square Error (RMSE), and Forecasting RMSE skill score (FSS) are used to evaluate the model's performance. This study utilized six lead times and four sequence lengths, based on this the best combination is: Sequence length: 4 with Minutes: 20. This combination provides a balanced and optimal performance with low RMSE (62.75 W/m2), low MBE (2.71 W/m2), and a high FSS (38.81), indicating good accuracy, minimal bias, and high skill score. Furthermore, when compared with state-of-the-art models, the proposed model yielded superior results. This advancement holds profound implications for the optimization of solar power utilization within mainstream energy systems, further underscoring the significance of cutting-edge deep learning (DL) techniques in advancing sustainable energy technologies. The findings of this study indicate that integrating attention mechanisms is essential to enhance the accuracy and reliability of forecasts, and using longer sequences of images can further improve forecasting performance.

The Efficacy of Physical Fitness Training on Dance Injury: A Systematic Review.

Greater levels of physical fitness have been linked to improved dance performance and decreased injury incidence. The aim was to review the efficacy of physical fitness training on dance injury. The electronic databases CINAHL, Cochrane Library, PubMed, Web of Science, MEDLINE, China National Knowledge Infrastructure were used to search peer-reviewed published articles in English or Chinese. Studies were scored using Strength of the Evidence for a Conclusion and a risk bias checklist. 10 studies met the inclusion criteria from an initial 2450 publications. These studies offered physical fitness training for professional (n = 3) and pre-professional dancers (n = 7), participant sample size ranged between 5 to 62, ages from 11 to 27 years, and most participants were females. Assessment scores were classified as Fair (n = 1), Limited (n = 7), and Expert Opinion Only (n = 2) and risk of bias scores ranged from 22.7-68.2 %. After physical fitness training, 80 % of studies reported significant benefits in injury rate, the time between injuries, pain intensity, pain severity, missed dance activities and injury count. This review suggests that physical fitness training could have a beneficial effect on injury incidence in dance. The evidence is limited by the current study methodologies.

Generalized zero-shot action recognition through reservation-based gate and semantic-enhanced contrastive learning

Generalized zero-shot action recognition (GZSAR) aims to classify actions from both the classes seen in the training phase and unseen classes for which no samples are available. Since all training samples are derived from seen classes, conducting classification directly in a combined space that encompasses both seen and unseen classes would introduce a competition between the predicted scores of seen and unseen classes, potentially resulting in misclassification of unseen test samples as seen ones. Besides, existing generative methods rely solely on the provided class-level semantic features and overlook the exploration of interrelations among the semantic features, thereby limiting the quality of the generated features. In this paper, we tackle GZSAR through a novel method known as reservation-based gate and semantic-enhanced contrastive learning (RGSCL). We introduce a reserved classifier and optimize it with constructed fictive samples to learn the reservation-based gate which avoids the competition and alleviates the impact of biased classification scores towards seen classes. Further, we propose to conduct contrastive learning based on the hypersphere-based enhanced semantic features, aiming to ensure the generated features maintain a consistent relationship with corresponding semantic features, thereby improving the comprehension of the generator to the semantic interrelations. RGSCL exhibits strong compatibility with existing generative GZSAR methods. Extensive experimental results on three datasets of both conventional zero-shot and generalized zero-shot settings showcase the effectiveness of the proposed RGSCL.

Transferability of a Bayesian Belief Network across diverse agricultural catchments using high-frequency hydrochemistry and land management data

Biogeochemical catchment models are often developed for a single catchment and, as a result, often generalize poorly beyond this. Evaluating their transferability is an important step in improving their predictive power and application range. We assess the transferability of a recently developed Bayesian Belief Network (BBN) that simulated monthly stream phosphorus (P) concentrations in a poorly-drained grassland catchment through application to three further catchments with different hydrological regimes and agricultural land uses. In all catchments, flow and turbidity were measured sub-hourly from 2009 to 2016 and supplemented with 400–500 soil P test measurements. In addition to a previously parameterized BBN, five further model structures were implemented to incorporate in a stepwise way: in-stream P removal using expert elicitation, additional groundwater P stores and delivery, and the presence or absence of septic tank treatment, and, in one case, Sewage Treatment Works. Model performance was tested through comparison of predicted and observed total reactive P (TRP) concentrations and percentage bias (PBIAS). The original BBN accurately simulated the absolute values of observed flow and TRP concentrations in the poorly and moderately drained catchments (albeit with poor apparent percentage bias scores; 76 % ≤ PBIAS≤94 %) irrespective of the dominant land use, but performed less well in the groundwater-dominated catchments. However, including groundwater total dissolved P (TDP) and Sewage Treatment Works (STWs) inputs, and in-stream P uptake improved model performance (−5 % ≤ PBIAS≤18 %). A sensitivity analysis identified redundant variables further helping to streamline the model applications. An enhanced BBN model capable for wider application and generalisation resulted.

A New Post-Processing Method for Improving Track and Rainfall Ensemble Forecasts for Typhoons over Eastern China

This paper proposes a new post-processing method for model data in order to improve typhoon track and rainfall forecasts. The model data used in the article include low-resolution ensemble forecasts and high-resolution forecasts. The entire improvement method contains the following three steps. The first step is to correct the typhoon track forecast: three ensemble member optimization methods are applied to the low-resolution ensemble forecasts, and then the best optimization method is selected with the principle of the smallest average distance error. The results of rainfall forecasts show that the corrected rainfall forecast performs better than the original forecasts. The second step is to derive the high-resolution probability rainfall forecast: the neighborhood method is applied to the deterministic high-resolution rainfall forecast. The last step is to correct the typhoon rainfall forecast: the low- and high-resolution forecasts are blended using the probability-matching method with two different schemes. The results show that the forecasts of the two schemes perform better than the original forecast under all rainfall thresholds and all forecast lead times. In terms of bias score, a rain forecast from one scheme corrects the rainfall deviation from observation better for light and moderate rainfall, whereas a rain forecast from another scheme corrects the rainfall deviation better for heavy and torrential rainfall. The better performance of corrected rain forecasts in the case of Typhoon Lekima and Rumbia over eastern China is demonstrated.

Social anxiety and attentional bias to negative emotional information: the relationship and intervention

BackgroundAccording to the cognitive behavioral model of social anxiety, attentional bias to negative emotional information causes and maintains anxiety. The goal of attentional bias modification (ABM) is to reduce anxiety by reducing attention bias to negative emotional information.MethodWe used questionnaires and experiments to explore the improvement effect of ABM training on social anxiety in college students. In Study 1, we used dot-probe tasks to investigate the attentional bias to negative emotional information and the relationship with social anxiety severity in college students. In Study 2, college students with high social anxiety were divided into two groups: attentional bias modification training task group (ABM) and attention control condition task group (ACC). The ABM group received a continuous intervention for 10 days to observe changes in social anxiety levels and attentional bias scores in the pretest and posttest stages.ResultsThe results showed that the correlation of attentional bias to negative emotional information and social anxiety severity was significant. Meanwhile, the high social anxiety participants responded more quickly to negative emotional information. After the intervention, social anxiety levels and attentional bias scores of the training group were significantly reduced.ConclusionsThe results showed that attentional bias modification training can reduce attentional bias to negative emotional information in college students with social anxiety and effectively improve their social anxiety.

The Ambiguous Cue Task: Measurement reliability of an experimental paradigm for the assessment of interpretation bias and associations with mental health

Interpretation biases in the processing of ambiguous affective information are assumed to play an important role in the onset and maintenance of emotional disorders. Reports of low reliability for experimental measures of cognitive biases have called into question previous findings on the association of these measures with markers of mental health and demonstrated the need to systematically evaluate measurement reliability for measures of cognitive biases. We evaluated reliability and correlations with self-report measures of mental health for interpretation bias scores derived from the Ambiguous Cue Task (ACT), an experimental paradigm for the assessment of approach-avoidance behavior towards ambiguous affective stimuli. For a non-clinical sample, the measurement of an interpretation bias with the ACT showed high internal consistency (rSB = .91 – .96, N = 354) and acceptable 2-week test–retest correlations (rPearson = .61 – .65, n = 109). Correlations between the ACT interpretation bias scores and mental health-related self-report measures of personality and well-being were generally small (r ≤ |.11|) and statistically not significant when correcting for multiple comparisons. These findings suggest that in non-clinical populations, individual differences in the interpretation of ambiguous affective information as assessed with the ACT do not show a clear association with self-report markers of mental health. However, in allowing for a highly reliable measurement of interpretation bias, the ACT provides a valuable tool for studies considering potentially small effect sizes in non-clinical populations by studying bigger samples as well as for work on clinical populations, for which potentially greater effects can be expected.

Improvement in the Forecasting of Low Visibility over Guizhou, China, Based on a Multi-Variable Deep Learning Model

High-quality visibility forecasting benefits traffic transportation safety, public services, and tourism. For a more accurate forecast of the visibility in the Guizhou region of China, we constructed several visibility forecasting models via progressive refinements in different compositions of input observational variables and the adoption of the Unet architecture to perform hourly visibility forecasts with lead times ranging from 0 to 72 h over Guizhou, China. Three Unet-based visibility forecasting models were constructed according to different inputs of meteorological variables. The model training via multiple observational variables and visibility forecasts of a high-spatiotemporal-resolution numerical weather prediction model (China Meteorological Administration, Guangdong, CMA-GD) produced a higher threat score (TS), which led to substantial improvements for different thresholds of visibility compared to CMA-GD. However, the Unet-based models had a larger bias score (BS) than the CMA-GD model. By introducing the U2net architecture, there was a further improvement in the TS of the model by approximately a factor of two compared to the Unet model, along with a significant reduction in the BS, which enhanced the stability of the model forecast. In particular, the U2net-based model performed the best in terms of the TS below the visibility threshold of 200 m, with a more than eightfold increase over the CMA-GD model. Furthermore, the U2net-based model had some improvements in the TS, BS, and RMSE (root-mean-square error) compared to the LSTM_Attention model. The spatial distribution of the TS showed that the U2net-based model performed better at the model grid scale of 3 km than at the scale of individual weather stations. In summary, the visibility forecasting model based on the U2net algorithm, multiple observational variables, and visibility data from the CMA-GD model performed the best. The compositions of input observational variables were the key factor in improving the deep learning model’s forecasting capability, and these improvements could improve the value of forecasts and support the socioeconomic needs of sectors reliant on visibility forecasting.

Treatment expectations and clinical outcomes following repetitive transcranial magnetic stimulation for treatment-resistant depression

BackgroundPatient expectations, including both positive (placebo) and negative (nocebo) effects, influence treatment outcomes, yet their impact on acute repetitive transcranial magnetic stimulation (rTMS) for treatment-resistant depression (TRD) is unclear. MethodsIn this single-center retrospective chart review, 208 TRD patients completed the Stanford Expectation of Treatment Scale (SETS) before starting open-label rTMS treatment. Patients were offered two excitatory rTMS protocols (deep TMS or intermittent theta-burst stimulation), which stimulated the left dorsolateral prefrontal cortex. A minimum of 20 once daily treatments were provided, delivered over 4–6 weeks. Primary outcomes were 1) remission, measured by a post-treatment score of <8 on the Hamilton Depression Rating Scale (HAMD-17), and 2) premature discontinuation. The change in HAMD-17 scores over time was used as a secondary outcome. Physicians were blinded to SETS scores. Logistic and linear regression, adjusting for covariates, assessed SETS and HAMD-17 relationships. ResultsOf 208 patients, 177 had baseline and covariate data available. The mean positivity bias score (positive expectancy minus negative expectancy subscale averages) was 0.48 ± 2.21, indicating the cohort was neutral regarding the expectations of their treatment on average. Higher positive expectancy scores were significantly associated with greater odds of remission (OR = 1.90, p = 0.003) and greater reduction in HAMD-17 scores (β = 1.30, p = 0.005) at the end of acute treatment, after adjusting for covariates. Negative expectancy was not associated with decreased odds of remission (p = 0.2) or treatment discontinuation (p = 0.8). ConclusionsHigher pre-treatment positive expectations were associated with greater remission rates with open-label rTMS in a naturalistic cohort of patients with TRD.

Estudio de teleconexión entre variables hidrológicas y climatológicas en una cuenca de cabecera del Rio Maipo para la aplicación de modelos de pronóstico

This study conducts a teleconnection analysis of the seasonal streamflow during the dry season (winter and summer) at the Olivares River basin, a headwater of the Maipo River basin, with traditional climate indices (Antarctic Oscillation, Niño1+2, and Niño3.4), new indices obtained from sea surface temperature (SST) anomaly spatial fields, and in situ hydrometeorological variables from the previous season to identify potential predictors for implementing seasonal streamflow forecast models in the study area. To illustrate the potential of the predictors identified, we fit multiple linear regression models (MLRM) for seasonal streamflow forecast for 0- and 3-month lead times. The forecasts are validated using the leave-1-year-out cross-validation (LOOCV) approach and performance metrics such as the Pearson correlation coefficient (R), BIAS, Nash-Sutcliffe efficiency (NSE), and continuous rank probability skill score (CRPSS). Results show a good performance of the forecast model for cross-validation with R and NSE values ranging from 0.55 to 0.95 and from 0.28 to 0.88 for 0- and 3-month lead times during the dry season. This early implementation provides good perspectives for implementing probabilistic seasonal streamflow forecasting models, which can provide a powerful output to develop robust water management strategies to tackle water scarcity in the study area.