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This paper investigates how investments in automation affect the gender pay gap. The evidence of the effects of automation on the labor market is growing; however, little is known about the implications of automation for the gender pay gap. The data used in this paper are from a matched employer–employee dataset incorporating detailed information on firms, their imports, and employee–level data for Estonian manufacturing and service employers for the period of 2006–2018. Through the use of the imports of automation goods as a proxy for the introduction of automation at the firm level, this paper estimates the effect of automation using simple Mincerian wage equations. The causality of the effect is further validated using propensity score matching (PSM). We find that introducing automation enlarges the gender pay gap, and PSM confirms that this also has a higher causal effect on the wages of male employees than female employees. The results imply that a higher representation of women in higher-paid positions does not guarantee a reduction in the gender pay gap in the presence of automation, and appropriate measures in education and retraining are needed to tackle the effect of automation on gender inequality. [ABSTRACT FROM AUTHOR]

Numerous real-world information networks form Heterogeneous Information Networks (HINs) with diverse objects and relations represented as nodes and edges in heterogeneous graphs. Similarity between nodes quantifies how closely two nodes resemble each other, mainly depending on the similarity of the nodes they are connected to, recursively. Users may be interested in only specific types of connections in the similarity definition, represented as meta-paths, i.e., a sequence of node and edge types. Existing Heterogeneous Graph Neural Network (HGNN)-based similarity search methods may accommodate meta-paths, but require retraining for different meta-paths. Conversely, existing path-based similarity search methods may switch flexibly between meta-paths but often suffer from lower accuracy, as they rely solely on path information. This paper proposes HetFS, a Fast Similarity method for ad-hoc queries with user-given meta-paths on Heterogeneous information networks. HetFS provides similarity results based on path information that satisfies the meta-path restriction, as well as node content. Extensive experiments demonstrate the effectiveness and efficiency of HetFS in addressing ad-hoc queries, outperforming state-of-the-art HGNNs and path-based approaches, and showing strong performance in downstream applications, including link prediction, node classification, and clustering. [ABSTRACT FROM AUTHOR]

Neural network model compression is very important to achieve model deployment based on the memory and storage available in different computing systems. Generally, the continuous drive for higher accuracy in these models increases their size and complexity, making it challenging to deploy them on resource-constrained computing environments. This article proposes various algorithms for model compression by exploiting weight characteristics and conducts an in-depth study of their performance. The algorithms involve manipulating exponents and mantissa in the floating-point representations of weights. In addition, we also present a retraining method that uses the proposed algorithms to further reduce the size of pre-trained models. The results presented in this article are mainly on BFloat16 floating-point format. The proposed weight manipulation algorithms save at least 20% of memory on state-of-the-art image classification models with very minor accuracy loss. This loss is bridged using the retraining method that saves at least 30% of memory, with potential memory savings of up to 43%. We compare the performance of the proposed methods against the state-of-the-art model compression techniques in terms of accuracy, memory savings, inference time, and energy. [ABSTRACT FROM AUTHOR]

Automatic transcription of large series of historical handwritten documents generally aims at allowing to search for textual information in these documents. However, automatic transcripts often lack the level of accuracy needed for reliable text indexing and search purposes. Probabilistic Indexing (PrIx) offers a unique alternative to raw transcripts. Since it needs training data to achieve good search performance, PrIx-based crowdsourcing techniques are introduced in this paper to gather the required data. In the proposed approach, PrIx confidence measures are used to drive a correction process in which users can amend errors and possibly add missing text. In a further step, corrected data are used to retrain the PrIx models. Results on five large series are reported which show consistent improvements after retraining. However, it can be argued whether the overall costs of the crowdsourcing operation pay off for the improvements, or perhaps it would have been more cost-effective to just start with a larger and cleaner amount of professionally produced training transcripts. [ABSTRACT FROM AUTHOR]

Understanding what is important and redundant within data can improve the modelling process of neural networks by reducing unnecessary model complexity, training time and memory storage. This information is however not always priorly available nor trivial to obtain from neural networks. There are existing feature selection methods which utilise the internal working of a neural network for selection, however further analysis and interpretation of the input features' significance is often limiting. We propose an approach that offers an extension that estimates the significance of features by analysing the gradient descent of a pairwise layer within a model. The changes that occur with the weights and gradients throughout training provide a profile that can be used to better understand the importance hierarchy between the features for ranking and feature selection. Additionally, this method is transferable to existing fully or partially trained models, which is beneficial for understanding existing or active models. The proposed approach is demonstrated empirically with a study which uses benchmark datasets from libraries such as MNIST and scikit-feat, as well as a simulated dataset and an applied real world dataset. This is verified with the ground truth where available, and if not, via a comparison of fundamental feature selection methods, which includes existing statistical based and embedded neural network based feature selection methods through the methodology of Reduce and Retrain. [ABSTRACT FROM AUTHOR]

An analogue of a neural network, the number of layers and the number of cells in which may be changed during its retraining is suggested in the paper. The main instrument for constructing such a network is extraction of maximal common properties of pairs of objects in the training set and of that ones used for retraining. The degree of coincidence of a recognized object with the one presented in the training set may be calculated using their maximal common properties. Computational complexities of such a network construction, recognition process and the network retraining are proved. A brief description of a similar network proposed by the author earlier for complex structured objects described using predicate calculus is presented. The analysis of comparison of computational complexity of a complex structured object recognition with various methods of their description is given. [ABSTRACT FROM AUTHOR]

A standard network pretrained on in-distribution (ID) samples could make high-confidence predictions on out-of-distribution (OOD) samples, leaving the possibility of failing to distinguish ID and OOD samples in the test phase. To address this over-confidence issue, the existing methods improve the OOD sensitivity from modeling perspectives, i.e., retraining it by modifying training processes or objective functions. In contrast, this paper proposes a simple but effective method, namely Weighted Non-IID Batching (WNB), by adjusting batch weights. WNB builds on a key observation: increasing the batch size can improve the OOD detection performance. This is because a smaller batch size may make its batch samples more likely to be treated as non-IID from the assumed ID, i.e., associated with an OOD. This causes a network to provide high-confidence predictions for all samples from the OOD. Accordingly, WNB applies a weight function to weight each batch according to the discrepancy between batch samples and the entire training ID dataset. Specifically, the weight function is derived by minimizing the generalization error bound. It ensures that the weight function assigns larger weights to batches with smaller discrepancies and makes a trade-off between ID classification and OOD detection performance. Experimental results show that incorporating WNB into state-of-the-art OOD detection methods can further improve their performance. [ABSTRACT FROM AUTHOR]

Machine learning models often struggle to generalize accurately when tested on new class distributions that were not present in their training data. This is a significant challenge for real-world applications that require quick adaptation without the need for retraining. To address this issue, few-shot learning frameworks, which includes models such as Siamese Networks, have been proposed. Siamese Networks learn similarity between pairs of records through a metric that can be easily extended to new, unseen classes. However, these systems lack interpretability, which can hinder their use in certain applications. To address this, we propose a data-agnostic method to explain the outcomes of Siamese Networks in the context of few-shot learning. Our explanation method is based on a post-hoc perturbation-based procedure that evaluates the contribution of individual input features to the final outcome. As such, it falls under the category of post-hoc explanation methods. We present two variants, one that considers each input feature independently, and another that evaluates the interplay between features. Additionally, we propose two perturbation procedures to evaluate feature contributions. Qualitative and quantitative results demonstrate that our method is able to identify highly discriminant intra-class and inter-class characteristics, as well as predictive behaviors that lead to misclassification by relying on incorrect features. [ABSTRACT FROM AUTHOR]

The field of belief revision in logic is still in evolution and holds a variety of disparate approaches; a consequence of theoretical conjecture. As a probabilistic model of supra-classical, non-monotonic (SCNM) logic, the Boltzmann machine, offers an experimental gateway into the field. How does the Boltzmann network adapt to new information? Catastrophic forgetting is the default response to retraining in any neural network. We have moderated this irrational non-monotonicity by alterations in the Boltzmann learning algorithm. The spectrum of experimental belief change is limited by the availability of 'new' information, a pragmatic realization co-related to the property of Rational Monotonicity in the domain of SCNM logic. Recognizing this upper boundary of defeasible belief simplifies the task of experimentally exploring machine adaptation. A minority of belief revisions involve new, but unsurprising information, that is at least partially consistent with the previous learned beliefs. In these circumstances, the Boltzmann network incrementally adjusts the priority of model state exemplars in accordance with preference; the traditional approach in SCNM logic. However, in the majority of situations the new information will be surprisingly inconsistent with the previous beliefs. In these circumstances, the pre-order on model states stratified by preference, will not have sufficient granularity to represent the conflicting requirements of ranking based on compositional atomic typicality. This novel experimental finding has not previously been considered in the logical conjecture on Belief Revision. [ABSTRACT FROM AUTHOR]

Object detection is a crucial capability of autonomous agents for human–robot collaboration, as it facilitates the identification of the current processing state. In industrial scenarios, it is uncommon to have comprehensive knowledge of all the objects involved in a given task. Furthermore, training during deployment is not a viable option. Consequently, there is a need for a detector that is able to adapt to novel objects during deployment without the necessity of retraining or fine-tuning on novel data. To achieve this, we propose to exploit the ability of discriminative embeddings learned by an object re-identification model to generalize to unknown categories described by a few shots. To do so, we extract object crops with a class-agnostic detector and then compare the object features with the prototypes of the novel objects. Moreover, we demonstrate that the embedding is also effective for predicting regions of interest, which narrows the search space of the class-agnostic detector and, consequently, increases processing speed. The effectiveness of our approach is evaluated in an assembly scenario, wherein the majority of objects belong to categories distinct from those present in the training datasets. Our experiments demonstrate that, in this scenario, our approach outperforms the current best few-shot object-detection approach DE-ViT, which also does not perform fine-tuning on novel data, in terms of both detection capability and inference speed. [ABSTRACT FROM AUTHOR]

Spoken dialogue systems (SDS) heavily rely on dialogue state tracking (DST) for success. However, providing sufficient computational power for training proves challenging, given that DST involves tracking states from both user and system utterances. While machine learning approaches have improved DST, they have notable limitations. These approaches often overlook unseen slot values during training and use two separate modules to extract, generate, or match slot values, leading to high time and resource consumption. Moreover, learning and deducing relevant values for related slots remain understudied challenges. To address these gaps, this paper introduces UTMGAT-a Unified Transformer with Memory Encoder and Graph Attention Networks (GAT) for Multidomain DST. UTMGAT employs a BERT tokenizer to construct user utterances and a candidate sets vocabulary, reducing the need for constant retraining when dealing with unseen values. It utilizes a single transformer to gather dialogue context for slots and generate slot values, enhancing prediction accuracy while reducing memory and computation time. UTMGAT incorporates an embedding layer aggregator to filter out unnecessary values, identify required nodes for GAT, and establish relationships among relevant values associated with related slots. This approach simplifies graph representation and diminishes required computation power. The input to the GAT maintains equal size with batch sizes, generated through padding. Finally, we have experimentally evaluated our model against several models including LLM approaches over four popular datasets with our approach outperforming all competing models except two approaches on one dataset. [ABSTRACT FROM AUTHOR]

In absence of a closed form expression such as in the Heston model, the option pricing is computationally intensive when calibrating a model to market quotes. this article proposes an alternative to standard pricing methods based on physics-inspired neural networks (PINNs). A PINN integrates principles from physics into its learning process to enhance its efficiency in solving complex problems. In this article, the driving principle is the Feynman-Kac (FK) equation, which is a partial differential equation (PDE) governing the derivative price in the Heston model. We focus on the valuation of European options and show that PINNs constitute an efficient alternative for pricing options with various specifications and parameters without the need for retraining. [ABSTRACT FROM AUTHOR]

Combating fake news is a crucial endeavor, yet the complexity of the task requires multifaceted approaches that transcend singular technological solutions. Traditional fact-checking, often centralized and human-dependent, faces scalability and bias challenges. This paper introduces a novel blockchain-based framework that leverages the wisdom of the crowd for an authority-free, scalable, automated and reputation-driven fact-checking. Within this framework, stance detection acts as an automated means of opinion retrieval, while the Proof of Reputation consensus mechanism fosters an environment where reputable contributors have greater influence in shaping news credibility. Concurrently, the Hoeffding bound is used to allow the system to adapt to evolving contexts. In contrast to Machine Learning—based approaches, our framework limits the need for periodic retraining to update a model's frozen knowledge of the world. The experimental study conducted on real-world data demonstrates that the proposed framework offers a promising and efficient solution to combat the spread of fake news. [ABSTRACT FROM AUTHOR]

AbstractObjectiveDesignStudy sampleResultsConclusionTinnitus retraining therapy (TRT) has been widely used in tinnitus management. However, its efficacy is often assessed through subjective methods. Here, we aimed to assess potential neural changes following TRT using mismatch negativity (MMN).Chronic tinnitus (>6 months) patients participated in a six-month TRT program. We collected tinnitus psychoacoustic features and gathered the tinnitus handicap inventory (THI) before and after TRT. We also used a multi-featured paradigm, including frequency, intensity, duration, location and silent gap deviants, to elicit MMN response before and after TRT. Data were analyzed retrospectively.The study involved 26 chronic tinnitus patients.Post-TRT measurements showed that MMN amplitudes significantly increased for all deviant conditions (p ≤ .03). However, we did not find a significant difference in MMN latencies for all deviant conditions (p ≥ .13). The THI scores of the patients significantly decreased following the TRT program (p < 0.001). Our results reveal improved subjective tinnitus perception following the TRT program.These findings indicate that TRT might be a viable alternative in tinnitus management. The greater MMN amplitudes and improved subjective tinnitus perception raise the possibility that MMN can be a useful tool in tinnitus research and tinnitus patient follow-up. [ABSTRACT FROM AUTHOR]

Background: During inpatient rehabilitation, physical therapists (PTs) often need to manually advance patients' limbs, adding physical burden to PTs and impacting gait retraining quality. Different electromechanical devices alleviate this burden by assisting a patient's limb advancement and supporting their body weight. However, they are less ideal for neuromuscular engagement when patients no longer need body weight support but continue to require assistance with limb advancement as they recover. The objective of this study was to determine the feasibility of using a hip flexion exosuit to aid paretic limb advancement during inpatient rehabilitation post-stroke. Methods: Fourteen individuals post-stroke received three to seven 1-hour walking sessions with the exosuit over one to two weeks in addition to standard care of inpatient rehabilitation. The exosuit assistance was either triggered by PTs or based on gait events detected by body-worn sensors. We evaluated clinical (distance, speed) and spatiotemporal (cadence, stride length, swing time symmetry) gait measures with and without exosuit assistance during 2-minute and 10-meter walk tests. Sessions were grouped by the assistance required from the PTs (limb advancement and balance support, balance support only, or none) without exosuit assistance. Results: PTs successfully operated the exosuit in 97% of sessions, of which 70% assistance timing was PT-triggered to accommodate atypical gait. Exosuit assistance eliminated the need for manual limb advancement from PTs. In sessions with participants requiring limb advancement and balance support, the average distance and cadence during 2-minute walk test increased with exosuit assistance by 2.2 ± 3.1 m and 3.4 ± 1.9 steps/min, respectively (p < 0.017). In sessions with participants requiring balance support only, the average speed during 10-meter walk test increased with exosuit by 0.07 ± 0.12 m/s (p = 0.042). Clinical and spatiotemporal measures of independent ambulators were similar with and without exosuit (p > 0.339). Conclusions: We incorporated a unilateral hip flexion exosuit into inpatient stroke rehabilitation in individuals with varying levels of impairments. The exosuit assistance removed the burden of manual limb advancement from the PTs and resulted in improved gait measures in some conditions. Future work will understand how to optimize controller and assistance profiles for this population. [ABSTRACT FROM AUTHOR]

With the high proportion of distributed energy resource (DER) access in the distributed network, the tie-line power should be controlled and smoothed to minimize power flow fluctuations due to the uncertainty of DER. In this paper, a stochastic model predictive control (SMPC) method is proposed for tie-line power smoothing using a novel data-driven linear power flow (LPF) model that enhances efficiency by updating parameters online instead of retraining. The scenario method is then employed to simplify the objective function and chance constraints. The stability of the proposed model is demonstrated theoretically, and the performance analysis indicates positive results. In the one-day case study, the mean relative error is only 1.1%, with upper and lower quartiles of 1.4% and 0.2%, respectively, which demonstrates the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

We propose MatUp, an upsampling filter for material super‐resolution. Our method takes as input a low‐resolution SVBRDF and upscales its maps so that their rendering under various lighting conditions fits upsampled renderings inferred in the radiance domain with pre‐trained RGB upsamplers. We formulate our local filter as a compact Multilayer Perceptron (MLP), which acts on a small window of the input SVBRDF and is optimized using a data‐fitting loss defined over upsampled radiance at various locations. This optimization is entirely performed at the scale of a single, independent material. Doing so, MatUp leverages the reconstruction capabilities acquired over large collections of natural images by pre‐trained RGB models and provides regularization over self‐similar structures. In particular, our light‐weight neural filter avoids retraining complex architectures from scratch or accessing any large collection of low/high resolution material pairs – which do not actually exist at the scale RGB upsamplers are trained with. As a result, MatUp provides fine and coherent details in the upscaled material maps, as shown in the extensive evaluation we provide. [ABSTRACT FROM AUTHOR]

Recent advancements in Scene Text Visual Question Answering (Text-VQA) employ autoregressive Transformers, showing improved performance with larger models and pre-training datasets. Although various pruning frameworks exist to simplify Transformers, many are integrated into the time-consuming training process. Researchers have recently explored post-training pruning techniques, which separate pruning from training and reduce time consumption. Some methods use gradient-based importance scores that rely on labeled data, while others offer retraining-free algorithms that quickly enhance pruned model accuracy. This paper proposes a novel gradient-based importance score that only necessitates raw, unlabeled data for post-training structured autoregressive Transformer pruning. Additionally, we introduce a Retraining Strategy (ReSt) for efficient performance restoration of pruned models of arbitrary sizes. We evaluate our approach on TextVQA and ST-VQA datasets using TAP, TAP†† and SaL‡-Base where all utilize autoregressive Transformers. On TAP and TAP†† , our pruning approach achieves up to 60% reduction in size with less than a 2.4% accuracy drop and the proposed ReSt retraining approach takes only 3 to 34 min, comparable to existing retraining-free techniques. On SaL‡-Base , the proposed method achieves up to 50% parameter reduction with less than 2.9% accuracy drop requiring only 1.19 h of retraining using the proposed ReSt approach. The code is publicly accessible at https://github.com/soonchangAI/LFPR. • We study a label-free importance score for structured pruning of autoregressive Transformers. • We propose an adaptive retraining approach for pruned Transformer models of varying sizes. • Our pruned model achieve up to 60% reduction in size with only ¡2.4% drop in accuracy. [ABSTRACT FROM AUTHOR]

Objectives: Limited research exists that outlines the predictive relevance of the treatment components of prolonged exposure (PE) for post‐traumatic stress disorder (PTSD) on PTSD and depression symptom outcomes. The goal of the present study was to investigate relations between participant completion of breathing retraining, in vivo exposure, and imaginal exposure exercises and symptom outcomes. Methods: A total of 58 participants completed a trial of PE as part of a larger trial on peer involvement and treatment adherence. Diagnostic and self‐report measures were completed throughout treatment. Participants also recorded weekly completion of breathing retraining, in vivo exposure, and imaginal exposure exercises. Pearson correlations and hierarchical regression analyses were used to investigate relations between average weekly treatment component completion and treatment outcomes, controlling for relevant variables. Results: Although breathing retraining and in vivo exposures were associated with PTSD outcomes in the correlational findings, use of breathing retraining, in vivo exposures, and imaginal exposures were not reliably associated with PTSD symptom outcomes when controlling for other variables in the regression analysis. However, when investigating changes in comorbid symptoms of depression, greater use of breathing retraining was associated with decreased symptoms of depression at posttreatment. Conclusions: Present findings demonstrate the differential relations between participation in various PE treatment components and posttreatment symptom outcomes. The importance of breathing retraining in addressing comorbid depressive symptoms is discussed, with emphasis on potentially increasing relaxation and positive activities more broadly to encourage further treatment benefits. [ABSTRACT FROM AUTHOR]

Background: Running retraining is commonly used in the management of medial tibial stress syndrome (MTSS) but evidence for its effectiveness is lacking. The primary aim of this study is to determine if the addition of running retraining to best standard care is beneficial in the management of runners with MTSS. Methods: This study is an assessor‐blinded and participant‐blinded, parallel‐group, randomised controlled trial. The trial will recruit 64 participants aged between 18 and 45 years, with a clinical diagnosis of MTSS that has affected their running participation for at least four weeks. Participants will be randomised to receive best standard care (control) or running retraining and best standard care (intervention group) over an 8‐week period. Best standard care will consist of load management advice, symptom management advice, footwear advice and a strengthening program. Running retraining will consist of a cue to reduce running step length. Outcomes will be measured at weeks 1, 2, 4 and 8. The primary outcome measure will be the University of Wisconsin Running Injury and Recovery Index at week 4. Secondary outcome measures include: (i) Exercise Induced Leg Pain Questionnaire—British Version, (ii) global rating of change scale, (iii) worst pain experienced during a run, (iv) weekly run volume, (v) reactive strength index score, (vi) single leg hop test, (vii) soleus single leg maximum voluntary isometric contraction, (viii) gastrocnemius single leg maximum voluntary isometric contraction, (ix) single leg plantar flexor endurance test, (x) running step length, and (xi) running step rate. Data will be analysed using the intention‐to‐treat principle. Discussion: This randomised controlled trial will evaluate if reducing running step length provides additional benefit to best standard care in the management of runners with MTSS over an 8‐week period. Trial registration: Australian New Zealand Clinical Trials Registry: ACTRN12624000230550. [ABSTRACT FROM AUTHOR]

This paper explores the use of transfer learning techniques to develop robust speech emotion recognition (SER) models capable of handling noise in real-world environments. Two SER frameworks have been proposed in this work: Framework-1 is a two-stage framework that involves retraining pretrained networks on clean data in the first stage followed by fine-tuning the network further with noisy data in the second stage, while Framework-2 directly retrains pretrained networks on multi-conditioned noisy data. To create multi-conditioned data, we have used both natural noise recordings and trance music under a single augmentation framework. Three pre-trained models (AlexNet, GoogleNet, VGG19) are evaluated on two datasets (IEMOCAP and IITKGP-SEHSC) using bottleneck features and quantized bottleneck features (only in the test phase) for noise mitigation. The experiments involve retraining the last one or two layers or using an SVM classifier on the bottleneck features. The results reveal that GoogleNet and VGG19 outperform AlexNet, and fine-tuning the final two layers of these models achieves the highest accuracy. Additionally, quantized bottleneck features further improve performance. Most importantly, Framework-2 consistently outperforms Framework-1 in most cases. While comparisons with existing work are challenging due to widely varying experimental settings in related works, the findings demonstrate competitive performance. A major novelty in this work lies in the variety of SNR conditions explored and the use of trance music for creating multi-conditioned noisy data. [ABSTRACT FROM AUTHOR]

Distinguishing a natural scene from artwork is a simple task for the human visual system, but a challenging one for machines due to the wide range of psychovisual features, illumination gamuts, and varied interpretations of glossiness. While state-of-the-art image quality metrics quantify overall visual quality to a remarkable degree of accuracy, quantification of 'glossiness' of a scene to represent its naturalness is still an emerging area. The rapid growth of deep learning methods and CNN-based architectures inspired us to explore the fusion of best performing CNN architectures in this paper for an image dataset specifically created to replicate unnaturalness of artwork or portrait-like images. Performance of four CNN networks was tested using transfer learning, selective retraining and optimizing initial learning rates on a dataset of about 8.5k images created to represent various degrees of glossiness. A fusion framework was then proposed using the top two architectures. In terms of eleven levels of naturalness (0 to 10), both quantitative and qualitative evaluation of the fusion frameworks was conducted. The framework resulting from fusion of GoogleNet and VGG16, referred to as GoogleVGG Fusion in this paper, is found to reach accuracies comparable to individual networks but with nearly half the computational cost. The proposed GoogleVGG Fusion model achieved an accuracy of 87.86% with the labelled scores and a Spearman's Rank Correlation (SROCC) of 0.9794. As expected, the accuracy of the proposed framework with the subjective scores in comparison with non–deep learning (DL) & DL-based methods is remarkably better. [ABSTRACT FROM AUTHOR]

The effects of some widely abused doping substances such as anabolic androgenic steroids (AAS) on performance are well documented, particularly in the short term, and the use of these substances is banned by various sporting authorities, with athletes sanctioned from competing for up to 4 years. However, controversy exists on whether residual physiological effects of some doping practices could persist even years after discontinuation, granting unfair advantages to athletes long after sanctions have been served. Particularly, in support of the so-called muscle memory theory, growing evidence in both animals and humans suggests that AAS administration could exert long-term effects at the muscle level, notably a higher number of myonuclei. This effect could enhance retraining/muscle remodeling capacity long after AAS cessation, thus supposing an advantage for doped athletes even +4 years after doping practices have been discontinued. If confirmed, the persistence of physiological improvements resulting from past doping practices raises serious ethical concerns in the sports field and opens the door to lifelong sanctions. [ABSTRACT FROM AUTHOR]

The article deals with the problem of the involvement of academic staff (AS) of universities in the system of continuing education. The subject of the study is the possibilities and problems of supplementary vocational education (SVE) for academic staff of Russian universities. Based on the materials of statistics and analysis of documents (university development programs, regulations on advanced training), the objective aspects of the functioning of the university system of AS supplementary vocational education have been studied. The subjective aspects of obtaining supplementary vocational education are revealed on the basis of materials from semi-structured interviews with AS of universities of the Ural Federal District. The motivation for obtaining SVE, the needs for specific content and types of training, financing, and organizational conditions are analyzed. The key problems of the academic segment of the SVE system are revealed: reduction of the state financing of the costs of universities for the training of AS; commercialization of the university SVE system; the lack of guaranteed quality and diversity of SVE programs for AS; unsatisfactory organizational conditions for SVE of AS; inflexibility of the continuing education system in terms of taking into account the results of AS training. In a practical sense, the identified problems are directions for changes in the state and university policy of professional development of the AS in the context of the implementation of higher education development programs

Reservoir structure optimization of echo state networks (ESN) is an important enabler for improving network performance. In this regard, pruning provides an effective means to optimize reservoir structure by removing redundant components in the network. Existing studies achieve reservoir pruning by removing insignificant neuronal connections. However, such processing causes the optimized neurons to still remain in the reservoir and thus hinder network inference by participating in computations, leading to suboptimal utilization of pruning benefits by the network. To solve this problem, this paper proposes an adaptive pruning algorithm for ESN within the detrended multiple cross-correlation (DMC2) framework, i.e., DMAP. On the whole, it contains two main functional parts: DMC2 measure of reservoir neurons and reservoir pruning. Specifically, the former is used to quantify the correlation among neurons. Based on this, the latter can remove neurons with high correlation from the reservoir completely, and finally obtain the optimal network structure by retraining the output weights. Experiment results show that DMAP-ESN outperforms its competitors in nonlinear approximation capability and reservoir stability. [ABSTRACT FROM AUTHOR]

The recent development of generative Artificial Intelligence (AI) tools capable of generating images from text sequences is creating opportunities in many disciplines. Architecture is no exception, and text-to-image generators can be used in graphic representation. However, in most cases, they are not capable of generating architectural drawings following the graphic style of specific authors. Very recently, AI tools offer users the possibility of partial retraining using a small set of images. This opens the possibility of developing custom text-to-image generators with AI. For example, it would be possible to recreate the way a particular author represents architectures. In this article, we explore the potential of these custom generators using the works of Sebastiano Serlio as a case study. The results show that custom AI generators can capture Serlio's style, opening the research field to in-depth studies on the idiosyncratic modes of graphic expression in architectures throughout history. In this article, we explore the potential of these custom generators using the works of Sebastiano Serlio as a case study. The results show that custom AI generators can capture Serlio's style, opening the research field to in-depth studies on the idiosyncratic modes of graphic expression in architecture throughout history. [ABSTRACT FROM AUTHOR]

Introduction Running-related injuries are prevalent in the military and are often related to physical fitness test training. Non-rearfoot striking while running is known to increase the risk of Achilles tendon injuries because of the high eccentric energy absorption by the elastic components of the planarflexor muscle-tendon complex. However, there is limited evidence to suggest benefits of converting runners with Achilles tendon pain to use a rearfoot strike. Methods This is a case series of two active-duty Service members with chronic, running-related Achilles tendon pain that utilized a natural non-rearfoot strike pattern. Both patients were trained to utilize a rearfoot strike while running through the use of real-time visual feedback from wearable sensors. Results The trained rearfoot strike pattern was retained for over one month after the intervention, and both patients reported improvements in pain and self-reported function. Conclusions This case series demonstrated the clinical utility of converting two non-rearfoot strike runners to a rearfoot strike pattern to decrease eccentric demands on the plantarflexors and reduce Achilles tendon pain while running. [ABSTRACT FROM AUTHOR]

In recent years, image semantic segmentation technology has developed rapidly, but image annotation usually requires a significant amount of human and financial resources, especially for remote sensing image annotation, which can be expensive and sometimes even unaffordable. To address this issue, this paper integrates the idea of curriculum learning into the self-training method and screens reliable pseudo-labels through computing image-level confidence, significantly reducing the confirmation error problem. Furthermore, the semi-supervised model in this paper combines implicit semantic enhancement with strong data augmentation, which can reduce the coupling between the teacher model and the student model's prediction distribution and enhance the model's robustness. Finally, the proposed semi-supervised method is experimentally verified using the ISPRS competition dataset and compared with existing state-of-the-art (SOTA) methods. Experimental results show that the proposed semi-supervised segmentation method achieves higher segmentation accuracy compared to self-training methods. Moreover, despite not using iterative training to simplify the training process, the proposed method still yields satisfactory segmentation results. [ABSTRACT FROM AUTHOR]

Vertical federated learning (VFL) revolutionizes privacy-preserved collaboration for small businesses that have distinct but complementary feature sets. However, as the scope of VFL expands, the constant entering and leaving of participants and the subsequent exercise of the "right to be forgotten" pose a great challenge in practice. The question of how to efficiently erase one's contribution from the shared model remains largely unexplored in the context of VFL. In this article, we introduce a vertical federated unlearning framework, which integrates model checkpointing techniques with a hybrid, first-order optimization technique. The core concept is to reduce backpropagation time and improve convergence/generalization by combining the advantages of the existing optimizers. We provide in-depth theoretical analysis and time complexity to illustrate the effectiveness of the proposed design. We conduct extensive experiments on six public datasets and demonstrate that our method could achieve up to 6.3× speedup compared to the baseline, with negligible influence on the original learning task. [ABSTRACT FROM AUTHOR]

Although deep-learning methods can achieve human-level performance in boundary detection, their improvements mostly rely on larger models and specific datasets, leading to significant computational power consumption. As a fundamental low-level vision task, a single model with fewer parameters to achieve cross-dataset boundary detection merits further investigation. In this study, a lightweight universal boundary detection method was developed based on convolution and a transformer. The network is called a "transformer with difference convolutional network" (TDCN), which implies the introduction of a difference convolutional network rather than a pure transformer. The TDCN structure consists of three parts: convolution, transformer, and head function. First, a convolution network fused with edge operators is used to extract multiscale difference features. These pixel difference features are then fed to the hierarchical transformer as tokens. Considering the intrinsic characteristics of the boundary detection task, a new boundary-aware self-attention structure was designed in the transformer to provide inductive bias. By incorporating the proposed attention loss function, it introduces the direction of the boundary as strongly supervised information to improve the detection ability of the model. Finally, several head functions with multiscale feature inputs were trained using a bidirectional additive strategy. In the experiments, the proposed method achieved competitive performance on multiple public datasets with fewer model parameters. A single model was obtained to realize universal prediction even for different datasets without retraining, demonstrating the effectiveness of the method. The code is available at https://github.com/neulmc/TDCN. [ABSTRACT FROM AUTHOR]

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Foot pronation is a prevalent condition known to contribute to a range of lower extremity injuries. Numerous interventions have been employed to address this issue, many of which are expensive and necessitate specific facilities. Gait retraining has been suggested as a promising intervention for modifying foot pronation, offering the advantage of being accessible and independent of additional materials or specific time. We aimed to systematically review the literature on the effect of gait retraining on foot pronation. We searched four databases including PubMed, Web of Science, Scopus and Embase from their inception through 20 June 2023. The Downs and Black appraisal scale was applied to assess quality of included studies. Two reviewers screened studies to identify studies reporting the effect of different methods of gait-retraining on foot pronation. Outcomes of interest were rearfoot eversion, foot pronation, and foot arch. Two authors separately extracted data from included studies. Data of interest were study design, intervention, variable, sample size and sex, tools, age, height, weight, body mass index, running experience, and weekly distance of running. Mean differences and 95% confidence intervals (CI) were calculated with random effects model in RevMan version 5.4. Fifteen studies with a total of 295 participants were included. The results of the meta-analysis showed that changing step width does not have a significant effect on peak rearfoot eversion. The results of the meta-analysis showed that changing step width does not have a significant effect on peak rearfoot eversion. Results of single studies indicated that reducing foot progression angle (MD 2.1, 95% CI 0.62, 3.58), lateralizing COP (MD -3.3, 95% CI -4.88, -1.72) can effectively reduce foot pronation. Overall, this study suggests that gait retraining may be a promising intervention for reducing foot pronation; Most of the included studies demonstrated significant improvements in foot pronation following gait retraining. Changing center of pressure, foot progression angle and forefoot strike training appeared to yield more favorable outcomes. However, further research is needed to fully understand its effectiveness and long-term benefits. [ABSTRACT FROM AUTHOR]

Transformer-based models, exemplified by GPT-3, ChatGPT, and GPT-4, have recently garnered considerable attention in both academia and industry due to their promising performance in general language tasks. Nevertheless, these models typically involve computationally encoding processes, and in some cases, decoding processes as well, both of which are fundamentally large-scale matrix multiplication. These operations bring the inevitable challenges of massive computation resources and huge memory footprint, usually requiring at least 1 0 23 FLOPs and hundreds of gigabytes, respectively. A common method to address this issue is to reduce the computational and memory requirements by applying layerwise quantization to the transformer, replacing the usual fp32 data type with a low-bit equivalent. Unfortunately, this method often leads to decreased model accuracy and necessitates time-consuming retraining. Such retraining not only requires fine-tuning skills but also substantial computational resources, posing challenges for users. To specifically tackle these issues, we propose BCT, a framework of blockwise compression for transformers without retraining, aiming to facilitate model deployment. Unlike layerwise compression methods, BCT achieves finer compression of the entire transformer by operating blockwise. This method mitigates data distribution deviation caused by quantization, eliminating the requirement for retraining. BCT effectively compresses all components of the model, including but not limited to the embedding, matrix multiplication, GELU, Softmax, layer normalization, and intermediate results. In a case study, an efficient model is compressed by BCT achieving up to 7.988x compression. Subsequently, we also evaluate it on several General Language Understanding Evaluation (GLUE) datasets. Experimental results on the majority of GLUE benchmark demonstrate the effectiveness of our method, as BCT achieves less than a 0.9% degradation in accuracy compared to the more than a 1% degradation seen with other methods providing similar or inferior compression ratios. [ABSTRACT FROM AUTHOR]

Guided by information from RGB images, depth completion methods rebuild the dense depth from sparse depth input. However, the varying densities of valid pixels in sparse depth maps pose a significant challenge to the robustness of the completion model. To improve the robustness of depth completion, we propose a two-stage model called Semantic Aggregated Depth Completion (SADC) in this paper, comprising a coarse-grained completion stage and a fine-grained completion stage. In the coarse-grained completion stage, the Semantic Extraction Network (SEN) extracts RGB features and sends them to the Dynamic Semantic Aggregation (DSA) to predict the local semantic relationship (LSR) matrix. DSA aggregates the valid information based on the LSR matrix iteratively, resulting in coarse-grained completion results. In the fine-grained completion stage, SADC uses the Semantic Guidance Network (SGN) and Semantic Guidance Fusion (SGF) modules to refine the dense depth features from coarse-grained completion results by RGB features in multi-level and predict fine-grained completion results. We validate our method on NYU-v2 and KITTI with different valid pixel densities. The results demonstrate that SADC performs best results on benchmark tests and exhibits robustness to different densities without retraining. [ABSTRACT FROM AUTHOR]

Excessive loads at lower limb joints can lead to pain and degenerative diseases. Altering joint loads with muscle coordination retraining might help to treat or prevent clinical symptoms in a non-invasive way. Knowing how much muscle coordination retraining can reduce joint loads and which muscles have the biggest impact on joint loads is crucial for personalized gait retraining. We introduced a simulation framework to quantify the potential of muscle coordination retraining to reduce joint loads for an individuum. Furthermore, the proposed framework enables to pinpoint muscles, which alterations have the highest likelihood to reduce joint loads. Simulations were performed based on three-dimensional motion capture data of five healthy adolescents (femoral torsion 10°–29°, tibial torsion 19°–38°) and five patients with idiopathic torsional deformities at the femur and/or tibia (femoral torsion 18°–52°, tibial torsion 3°–50°). For each participant, a musculoskeletal model was modified to match the femoral and tibial geometry obtained from magnetic resonance images. Each participant's model and the corresponding motion capture data were used as input for a Monte Carlo analysis to investigate how different muscle coordination strategies influence joint loads. OpenSim was used to run 10,000 simulations for each participant. Root-mean-square of muscle forces and peak joint contact forces were compared between simulations. Depending on the participant, altering muscle coordination led to a maximum reduction in hip, knee, patellofemoral and ankle joint loads between 5 and 18%, 4% and 45%, 16% and 36%, and 2% and 6%, respectively. In some but not all participants reducing joint loads at one joint increased joint loads at other joints. The required alteration in muscle forces to achieve a reduction in joint loads showed a large variability between participants. The potential of muscle coordination retraining to reduce joint loads depends on the person's musculoskeletal geometry and gait pattern and therefore showed a large variability between participants, which highlights the usefulness and importance of the proposed framework to personalize gait retraining. [ABSTRACT FROM AUTHOR]

Background: Patellofemoral Pain (PFP) is one of the main injuries in runners. Consistent evidence support strengthening programs to modulate symptoms, however, few studies investigated the effects of gait retraining programs. Objective: To investigate the effects of two different two-week partially supervised gait retraining programs on pain, function, and lower limb kinematics of runners with PFP. Methods: Randomized controlled trial. Thirty runners were allocated to gait retraining groups focusing on impact (n = 10) or cadence (n = 10), or to a control group (n = 10). Impact group received guidance to reduce tibial acceleration by 50%, while cadence group was asked to increase cadence by 7.5–10%. The control group did not receive any intervention. Usual and running pain, knee function, and lower limb kinematics (contralateral pelvic drop, hip adduction, knee flexion, ankle dorsiflexion, tibia inclination, and foot inclination) were evaluated before (T0), immediately after the intervention (T2), and six months after the protocol (T24). Results: A significant group x time interaction was found for running pain (p = 0.010) and knee function (p = 0.019). Both programs had greater improvements in running pain compared to no intervention at T24 (Impact x Control—mean difference (MD) −3.2, 95% CI −5.1 to −1.3, p = 0.001; Cadence x Control—MD −2.9, 95% CI −4.8 to −1.0, p = 0.002). Participants of the impact group had greater improvements in knee function compared to no intervention at T2 (Impact x Control–MD 10.8, 95% CI 1.0 to 20.6, p = 0.027). No between-group differences in usual pain and lower limb kinematics were found (p>0.05). Conclusion: Compared to no intervention, both programs were more effective in improving running pain six months after the protocol. The program focused on impact was more effective in improving knee function immediately after the intervention. Clinical trial registry number: RBR-8yb47v [ABSTRACT FROM AUTHOR]

Military service members (SMs) demonstrate high rates of patellofemoral chondral defects (PFCDs) that are difficult to diagnosis and, if untreated, result in a cascade of events eventually leading to osteoarthritis. Running is an essential occupational task for SMs; however, there is little evidence regarding techniques to maintain running ability in individuals with cartilage defects. The purpose of this case series was to assess the clinical application of foot strike run retraining in patients with PFCDs. This case series included two active duty U.S. Marine Corps SMs who presented to outpatient physical therapy with PFCD, diagnosed via MRI. Both patients attended eight foot strike run retraining sessions. Running mechanics and patient-reported outcomes were recorded pre-training, post-training, and at a 1-month follow-up visit. Both patients successfully converted their strike pattern from a rearfoot to a non-rearfoot strike pattern with training and retained this strategy at 1-month follow-up. Post-intervention, both patients demonstrated increased running tolerance, and improvements in Numeric Pain Rating Scale and Lower Extremity Functional Scale scores. Biomechanical analysis showed that both patients demonstrated a 63% to 70% reduction in average and peak vertical ground reaction force loading rates post-treatment. Modification of foot strike pattern from rear to non-rearfoot strike during running for individuals with PFCD can reduce the magnitude of impact loading, which potentially limits disease progression. These findings suggest that foot strike run retraining may be a feasible strategy to reduce pain and improve function in SMs with PFCD who are required to run for occupational responsibilities. [ABSTRACT FROM AUTHOR]

Objective: The goal of this article is to compile relevant literature that proposes that perhaps the abdominal drawing-in maneuver (ADIM), also known as the “abdominal hollowing maneuver” (AHM), should not be applied as a general rule during the core training. Methods and Materials: Based on current evidence, this paper evaluates whether general use of this ADIM is beneficial for the core training. In this regard, we have searched for related scientific evidence in PubMed and Google Scholar. The keywords used were: abdominal hollowing maneuver, abdominal drawing-in maneuver, lumbo-pelvic stability, core stability. Results: The literature shows us that the application of ADIM during core strengthening exercises does not meet the requirements for isolated TrA-specific retraining, can increase Inter-Rectus Distance, may be incompatible with postural stability goals, and could interfere the Rectus Abdominis input impairing the lumbo-pelvic stability during high-load exercises. Conclusion: It can be concluded that if the goal is to train core stability and strength with high-load scenarios we should not use the ADIM. [ABSTRACT FROM AUTHOR]

Generally, classification models have closed nature, and they are constrained by the number of classes in the training data. Hence, classifying "unknown" - OOD (out-of-distribution) - samples is challenging, especially in the so called "open set" problem. We propose and investigate different solutions - single and combined algorithms - to tackle this task, where we use and expand a K-classifier to be able to identify K+1 classes. They do not require any retraining or modification on the K-classifier architecture. We show their strengths when avoiding type I or type II errors is fundamental. We also present a mathematical representation for the task to estimate the K+1 classification accuracy, and an inequality that defines its boundaries. Additionally, we introduce a formula to calculate the exact K+1 classification accuracy. [ABSTRACT FROM AUTHOR]

Background: Gait retraining is a common therapeutic intervention that can alter gait characteristics to reduce knee loading in knee osteoarthritis populations. It can be enhanced when combined with biofeedback that provides real-time information about the users' gait, either directly (i.e. knee moment feedback) or indirectly (i.e. gait pattern feedback). However, it is unknown which types of biofeedback are more effective at reducing knee loading, and also how the changes in gait affect pain during different activities of daily living. Therefore, this study aims to evaluate the acute (6 weeks of training) and chronic (1 month post training) effects of biofeedback based on personalised gait patterns to reduce knee loading and pain in people with knee osteoarthritis, as well as examine if more than one session of knee moment feedback is needed to optimise the gait patterns. Methods: This is a parallel group, randomised controlled trial in a symptomatic knee osteoarthritis population in which participants will be randomised into either a knee moment biofeedback group (n = 20), a gait pattern biofeedback group (n = 20) or a control group (n = 10). Supervised training sessions will be carried out weekly for six continuous weeks, with real-time biofeedback provided using marker-based motion capture and an instrumented treadmill. Baseline, post-intervention and 1-month follow-up assessments will be performed to measure knee loading parameters, gait pattern parameters, muscle activation, knee pain and functional ability. Discussion: This study will identify the optimal gait patterns for participants' gait retraining and compare the effectiveness of gait pattern biofeedback to a control group in reducing knee loading and index knee pain. Additionally, this study will explore how many sessions are needed to identify the optimal gait pattern with knee moment feedback. Results will be disseminated in future peer-reviewed journal articles, conference presentations and internet media to a wide audience of clinicians, physiotherapists, researchers and individuals with knee osteoarthritis. Trial registration: This study was retrospectively registered under the International Standard Randomised Controlled Trial Number registry on 7th March 2023 (ISRCTN28045513). [ABSTRACT FROM AUTHOR]

Purpose: To compare the effect of three differently spaced retraining schedules (1-day, 2-day, and 1-week intervals) on the acquisition of basic arthroscopic skills and skill retention after 3 months. Methods: Thirty orthopaedic residents without arthroscopic experience were enrolled in a double-blind, randomised, parallel-controlled trial. Spaced retaining schedules were divided into massed training and retraining phases. Participants were required to obtain perfect scores in all tasks on the simulator in the massed training phase, followed by a pretest to evaluate the training effect. During the retraining phase, participants were randomly assigned to Groups A (1-day interval), B (2-day interval) or C (1-week interval). A posttest was used to evaluate the effect of different retraining patterns. Follow-up evaluations were conducted at 1 week, 1 month and 3 months after the completion of spaced retraining schedules to measure skill retention. One-way ANOVA and paired-sample t tests were used for statistical analysis. Results: Significant between-group differences in diagnostic arthroscopy (137.0 ± 24.8 vs. 140.1 ± 21.3 vs. 175.3 ± 27.4 s, P(A−C) = 0.005, P(B−C) = 0.010) and loose body removal (193.1 ± 33.9 vs. 182.0 ± 32.1 vs. 228.7 ± 42.9 s, P(B−C) = 0.025) completion times were observed. No significant differences were found in other posttest metrics. An assessment of skill retention after the 3-month follow-up (Evaluation 3) showed significant differences in diagnostic arthroscopy completion time (202.5 ± 53.3 vs. 172.0 ± 27.2 vs. 225.5 ± 42.1 s, P(B−C) = 0.026). No significant differences were found in other Evaluation 3 metrics. Conclusion: The 2-day retraining schedule was the most effective for the acquisition and retention of basic arthroscopic skills and could be integrated into arthroscopic skills curricula. After a 3-month follow-up, residents who followed this schedule showed better skill retention than those who followed the 1-week interval schedule. Level of evidence: Level I. [ABSTRACT FROM AUTHOR]

Summary: Intensive sleep retraining (ISR) is a brief behavioural treatment for sleep onset insomnia, administered in just a single overnight treatment session. This systematic review evaluates existing trials about the efficacy of intensive sleep retraining for treating insomnia, to inform whether there is enough evidence to recommend its use for clinical practice. A systematic literature search was conducted across three databases, yielding 108 results. Of these studies, three were deemed suitable for inclusion in this review. The included studies consistently reported significant reductions in insomnia symptoms following intensive sleep retraining, particularly decreases in sleep diary‐derived sleep latency and increases in total sleep time. Based on these inconclusive but promising findings, a research agenda is proffered to test intensive sleep retraining as a treatment for insomnia. Large randomised controlled trials are needed to elucidate the potential benefits of intensive sleep retraining for different populations with insomnia, as are mechanistic trials to test which components underlie its seemingly therapeutic effects. Since more practical modalities of intensive sleep retraining administration have been developed, such trials are more feasible to conduct now than ever before. [ABSTRACT FROM AUTHOR]

Modern technology has been changing the manner work is performed in many jobs, leading to wiping out of some jobs and new job creation. Individuals who have lost employment as a result of automation process application at their job positions face the need to change their jobs or professions. The study aims to identify the most viable options for occupational retraining, for transition from occupations with high automation risk as opposed to those of low automation risk, based on the workers' existing skills, knowledge and experience. The Occupational Information Network database, which classifies and defines various professions, as well as the findings of C.B. Frey and M.A. Osborne, determining the degree of automation risk for each profession, were used for this purpose. The agglomerative method of hierarchical clustering was used to determine the similarity between occupations, in terms of the skills needed to perform them. In this manner, 21 clusters were identified in the population of 579 different occupations. The retraining options were explored within the same industry, in order to enable utilization of employee knowledge and experience. Potential retraining options arising from skill similarity between the occupations were identified for only 33 occupations, within 15 industries. This study findings represent an auxiliary tool in the search for retraining options for workers at risk of losing employment due to their job automation. [ABSTRACT FROM AUTHOR]

Background: Aging is a normal biological process, and its dynamic changes are beyond the control of human beings. The role of depression, fear, and anxiety in old age is preoccupied with unhappy thoughts, and loneliness leads to early deterioration in their cognitive performance which makes it difficult to perform daily activities and hence affects their quality of life. Aim: The present study was done to assess the effectiveness of cognitive retraining in elderly depressives and its effect on their quality of life. Materials and Methods: The study was done on a sample of 20 depressive elderly patients with an age range above 60 years diagnosed with ICD-10 criteria. The study was pre- and post-intervention, and the sampling method was purposive. Firstly to screen handedness, Annet’s hand preference battery was used, to assess the depression in elderly, the geriatric depression scale was done, and for attention and memory, Digit Span Test (WAIS-R) and Wechsler Memory Scale (WMS) were administered, respectively. WHO-QOL BREF was done to know the quality of life of the patient. After preassessment, cognitive retraining tasks were introduced to the patients. A total of 15 sessions were taken within three months of follow‑up and after that to evaluate the efficacy of an outcome of the cognitive retraining, post-assessment was done. Results: Improvement was noticed in elderly depressive patients in the area of cognitive domain, i.e., attention, recognition, shifting, fixedness, recall, and planning which showed improvement in quality of life and daily activities. Conclusion: In the remediation of cognitive deficits, cognitive retraining plays a very important role, and it helps the elderly population to live their life healthily and hence enhance their coping skills. [ABSTRACT FROM AUTHOR]

Introduction: Tobacco smoking is a leading cause of preventable death worldwide. The perinatal period provides a unique opportunity for intervention, as many smokers quit smoking during pregnancy but relapse postpartum. Novel relapse prevention interventions that reduce the burden of treatment attendance in this population are needed. Attentional retraining (AR) has been shown to reduce attentional biases toward smoking-related stimuli, a cognitive process implicated in smoking, AR has not been applied to perinatal smokers, and the effect of AR on craving and smoking is not clear. The goal of this study was to evaluate the delivery of AR for smoking cues in perinatal smokers utilizing a mobile intervention. Methods: This pilot study utilized Ecological Momentary Assessment (EMA) methodology delivered on a mobile device to examine the relapse process and evaluate the utility of AR in former smokers attempting to remain abstinent postpartum. AR (or Control Training) was administered to abstinent smokers (N = 17) for up to 2 weeks both before and after delivery. Results: All 17 participants completed the study. There was evidence that AR reduced attentional bias in the AR group (vs. Controls). There was no evidence that AR reduced craving. An exploratory analysis revealed that there was no evidence that AR reduced smoking during the study period. Discussion: AR using EMA methodology via a mobile device is feasible in perinatal smokers. Further research using larger samples is required to evaluate the utility of mobile AR in reducing craving and smoking. [ABSTRACT FROM AUTHOR]

Background: Cognitive retraining or remediation approaches dispense high levels of stimulation and new learning tasks, leading to an increased neural connections, which facilitate rapid recovery in patients with neurological as well as psychiatric conditions. Objectives: The current study aimed to investigate the effect of cognitive retraining (CR) in depressive disorders. We assigned 40 patients with mild to moderate depression to two sample groups, with 20 participants each: CR alone and CR with medicine. A 6-week CR module was delivered, and participants' scores on measures such as the Beck Depression Inventory-II, Metacognition Questionnaire 30, World Health Organization Quality of Life- Brief, and Global Assessment of Functioning were compared. Results: Analysis using Stata/IC version 16 included descriptive statistics, paired and independent t-tests, analysis of covariance, and propensity score matching. Cohen's d was computed to determine the effect size. Within-group analysis revealed statistically significant differences in pre-post scores of the outcome measures (p <.05) and large effect size (d = 3.41; d = 3.60) in both groups. The difference in scores of outcome measures between the groups was not significant (p >.05) even when covariates were controlled, or nearest neighbor match analysis was carried out. CR is effective in alleviating symptoms and dysfunctional metacognitive beliefs in addition to enhancing functioning and quality of life. Conclusions: CR-based interventions may be essential mental health services owing to growing research in psychotherapy via virtual modes such as tele- and video-conferencing. These interventions can substantiate both prevention and remedy. [ABSTRACT FROM AUTHOR]

Word retraining programs have been shown to improve naming ability post-stroke and in progressive aphasias. Here, we investigated benefits for a 22-year-old Danish man (DJ), whose difficulties followed brain damage from heavy alcohol misuse. Using a multiple baseline-across-behaviours design (target behaviour: retrieval of word list items), DJ completed a 4-week "Look, Listen, Repeat" program on a computer. Ninety personally relevant target words were selected to create three matched lists. List 1 was trained for 10 sessions over 2 weeks, followed by 9 sessions for List 2 over 2 weeks, while the third list remained untrained. Naming performance was evaluated at baseline, during the intervention, and at 1 and 4 months post-training. Naming improved following each intervention block (p <.001), with only one data point overlapping between the baseline and treatment phases for trained items. Untrained words remained unchanged (p = 1.00), with 50% of data points non-overlapping across baseline to treatment phases. Performance was maintained over time, and appeared to generalize, with DJ naming more trained objects in their natural setting (85%) than untrained items (64%). While more evidence is needed, brief (20-minute), intensive (5-day/week) word retraining programs may assist word retrieval for people with brain damage associated with alcohol misuse. [ABSTRACT FROM AUTHOR]

The public sector necessitates the development of public service managers' competencies. However, this need has received far too little attention, particularly in terms of stakeholder management and service delivery. The case study method was used to interview seven public managers from the Buffalo City Metropolitan Municipality in the Eastern Cape, South Africa. Thematic analysis revealed the following findings: insufficient education and training in public institutions, insufficient capacity building, and an insufficient comprehensive professional development mandate to address these issues. The study recommends that the government strengthen the capacity of managers in various departments in order to improve professional development and service delivery. [ABSTRACT FROM AUTHOR]