Sampling Paradigm Research Articles

Dynamics of second-language learners' semantic memory networks: Evidence from a snowball sampling paradigm.

This article provides an analysis of structural changes in second-language (L2)-based semantic memory networks-graphs composed of L2 words as nodes and semantic relations between them as edges, during L2 learning. We used snowball sampling paradigm to create individual semantic networks of participants divided into two groups differing in L2 learning time and then compare their structural characteristics cross-sectionally. The results showed that as L2 learning progresses, semantic memory networks tend to become more connected (by increasing the average node degree), more efficient (by decreasing the average shortest path length), less fragmented (by decreasing the modularity), less centralized (by decreasing the centralization), less dense (by decreasing the density), and no more "small-worlded" (by similar average clustering coefficients and small-world indices). The findings provide quantitative evidence of how the duration of L2 learning shapes the structure of L2-based semantic memory networks generated in the snowball sampling paradigm. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A deployable film method to enable replicable sampling of low-abundance environmental microbiomes

Urbanizing global populations spend over 90% of their time indoors where microbiome abundance and diversity are low. Chronic exposure to microbiomes with low abundance and diversity have demonstrated negative long-term impacts on human health. Sequencing-based analyses of environmental nucleic acids are critical to understanding the impact of the indoor microbiome on human health, however low DNA yields indoors, alongside sample collection and processing inconsistencies, currently challenge study replicability. This study presents a comparative assessment of a novel, passive, easily replicable sampling strategy using polydimethylsiloxane (PDMS) sheets alongside a representative swab-based collection protocol. Deployable, customizable PDMS films designed for whole-sample insertion into standardized extraction kits demonstrated 43% higher DNA yields per sample, and 76% higher yields per cm2 of sampler over swab-based protocols. These results indicate that this accessible, scalable method enables sufficient DNA collection to comprehensively evaluate indoor microbiome exposures and potential human health impacts using smaller, more space efficient samplers, representing an attractive alternative to swab-based collection. In addition, this process reduces the manual steps required for microbiome sampling which could address inter-study variability, transform the current microbiome sampling paradigm, and ultimately benefit the replicability and accessibility of microbiome exposure studies.

Denoising and Augmented Negative Sampling for Collaborative Filtering

Negative sampling plays a crucial role in implicit-feedback-based collaborative filtering, where it leverages massive unlabeled data to generate negative signals for guiding supervised learning. The current state-of-the-art approaches focus on utilizing hard negative samples that contain more information to establish a better decision boundary. To strike a balance between efficiency and effectiveness, most existing methods adopt a two-pass approach: in the first pass, a fixed number of unobserved items are sampled using a simple static distribution, while, in the second pass, a more sophisticated negative sampling strategy is employed to select the final negative items. However, selecting negative samples solely from the original items in a dataset is inherently restricted due to the limited available choices, and thus may not be able to effectively contrast positive samples. In this paper, we empirically validate this observation through meticulously designed experiments and identify three major limitations of existing solutions: ambiguous trap, information discrimination, and false negative samples. Our response to such limitations is to introduce “denoised” and “augmented” negative samples that may not exist in the original dataset. This direction renders a few substantial technical challenges. First, constructing augmented negative samples may introduce excessive noise that eventually distorts the decision boundary. Second, the scarcity of supervision signals hampers the denoising process. To this end, we introduce a novel generic denoising and augmented negative sampling (DANS) paradigm and provide a concrete instantiation. First, we disentangle the hard and easy factors of negative items. Then, we regulate the augmentation of easy factors by carefully considering the direction and magnitude. Next, we propose a reverse attention mechanism to learn a user’s negative preference, which allows us to perform a dimension-level denoising procedure on hard factors. Finally, we design an advanced negative sampling strategy to identify the final negative samples, taking into account both the score function used in existing methods and a novel metric called synthesization gain. Through extensive experiments on real-world datasets, we demonstrate that our method substantially outperforms state-of-the-art baselines. Our code is publicly available at https://github.com/Asa9aoTK/ANS-Recbole.

Neural Network With a Preference Sampling Paradigm for Imbalanced Data Classification.

Most data in real life are characterized by imbalance problems. One of the classic models for dealing with imbalanced data is neural networks. However, the data imbalance problem often causes the neural network to display negative class preference behavior. Using an undersampling strategy to reconstruct a balanced dataset is one of the methods to alleviate the data imbalance problem. However, most existing undersampling methods focus more on the data or aim to preserve the overall structural characteristics of the negative class through potential energy estimation, while the problems of gradient inundation and insufficient empirical representation of positive samples have not been well considered. Therefore, a new paradigm for solving the data imbalance problem is proposed. Specifically, to solve the problem of gradient inundation, an informative undersampling strategy is derived from the performance degradation and used to restore the ability of neural networks to work under imbalanced data. In addition, to alleviate the problem of insufficient empirical representation of positive samples, a boundary expansion strategy with linear interpolation and the prediction consistency constraint is considered. We tested the proposed paradigm on 34 imbalanced datasets with imbalance ratios ranging from 16.90 to 100.14. The test results show that our paradigm obtained the best area under the receiver operating characteristic curve (AUC) on 26 datasets.

Integrating Mobile and Fixed-Site Black Carbon Measurements to Bridge Spatiotemporal Gaps in Urban Air Quality.

Urban air pollution can vary sharply in space and time. However, few monitoring strategies can concurrently resolve spatial and temporal variation at fine scales. Here, we present a new measurement-driven spatiotemporal modeling approach that transcends the individual limitations of two complementary sampling paradigms: mobile monitoring and fixed-site sensor networks. We develop, validate, and apply this model to predict black carbon (BC) using data from an intensive, 100-day field study in West Oakland, CA. Our spatiotemporal model exploits coherent spatial patterns derived from a multipollutant mobile monitoring campaign to fill spatial gaps in time-complete BC data from a low-cost sensor network. Our model performs well in reconstructing patterns at fine spatial and temporal resolution (30 m, 15 min), demonstrating strong out-of-sample correlations for both mobile (Pearson's R ∼ 0.77) and fixed-site measurements (R ∼ 0.95) while revealing features that are not effectively captured by a single monitoring approach in isolation. The model reveals sharp concentration gradients near major emission sources while capturing their temporal variability, offering valuable insights into pollution sources and dynamics.

BosonSampling.jl: A Julia package for quantum multi-photon interferometry

We present a free open source package for high performance simulation and numerical investigation of boson samplers and, more generally, multi-photon interferometry. Our package is written in Julia, allowing C-like performance with easy notations and fast, high-level coding. Underlying building blocks can easily be modified without complicated low-level language modifications. We present a great variety of routines for tasks related to boson sampling, such as statistical tools, optimization methods and classical samplers. Special emphasis is put on validation of experiments, where we present novel algorithms. This package goes beyond the boson sampling paradigm, allowing for the investigation of new interferometric behaviours such as bosonic bunching.

The “bubbles”-study: validation of ultra-short scales for the assessment of positive mental health, life satisfaction, and perceived social support

Abstract Aim The positive dimension of mental health should be considered to assess the complete state of a person’s mental health and to protect it. Very brief valid screening instruments implemented in general mental health programs and in therapeutic setting are supportive tools hereby. In the present work, we aimed to develop such tools. Subject and methods In two studies (Study 1: N = 1,004; Study 2: N = 1,000) on representative German population samples, we developed and validated three ultra-short scales – the “bubbles” – that consist of only one item per construct based on Positive Mental Health Scale (PMH-Scale; Lukat et al. 2016), Satisfaction with Life Scale (SWLS; Diener et al. 1985), and Social Support Questionnaire (F-SozU K-6; Lin et al. 2019) for the assessment of the positive mental health dimension. The bubbles are designed in a graphical/visual format. Results The results of Study 1 and Study 2 revealed that the bubbles are valid instruments that fit the original instruments on the factor level; the bubbles mostly replicated their association pattern with demographic variables, and with variables that belong to the negative and the positive dimension of mental health. Conclusions The shortness of the bubbles prevents participants’ fatigue and motivation decrease. Thus, due to their time- and cost-efficiency, the bubbles can be used as brief screening tools in research (e.g., large-scale studies, longitudinal studies, experience sampling paradigms) and in praxis (e.g., mental health programs). Future research should validate the bubbles in clinical context and cross-national.

Scaling and Masking: A New Paradigm of Data Sampling for Image and Video Quality Assessment

Quality assessment of images and videos emphasizes both local details and global semantics, whereas general data sampling methods (e.g., resizing, cropping or grid-based fragment) fail to catch them simultaneously. To address the deficiency, current approaches have to adopt multi-branch models and take as input the multi-resolution data, which burdens the model complexity. In this work, instead of stacking up models, a more elegant data sampling method (named as SAMA, scaling and masking) is explored, which compacts both the local and global content in a regular input size. The basic idea is to scale the data into a pyramid first, and reduce the pyramid into a regular data dimension with a masking strategy. Benefiting from the spatial and temporal redundancy in images and videos, the processed data maintains the multi-scale characteristics with a regular input size, thus can be processed by a single-branch model. We verify the sampling method in image and video quality assessment. Experiments show that our sampling method can improve the performance of current single-branch models significantly, and achieves competitive performance to the multi-branch models without extra model complexity. The source code will be available at https://github.com/Sissuire/SAMA.

The "bubbles"-study: Validation of ultra-short scales for the assessment of depression, anxiety and stress symptoms.

Depression, anxiety and stress symptoms cause substantial psychological and economic burdens around the globe. To mitigate the negative consequences, the negative symptoms should be identified at an early stage. Therefore, the implementation of very brief valid screening tools in mental health prevention programs and in therapeutic settings is advantageous. In two studies on representative German population samples, we developed and validated three ultra-short scales-the "bubbles"-that consist of only one item based on the Depression Anxiety Stress 21 subscales (DASS-21) for the assessment of depression, anxiety and stress symptoms. The results of Study 1 (N = 1,001) and Study 2 (N = 894) revealed that the bubbles are valid instruments that fit the DASS-21 subscales on the factor level. Moreover, the bubbles replicated the association pattern of the DASS-21 subscales with demographic variables, and with variables that belong to the negative and the positive dimension of mental health. Thus, due to their time- and cost-efficiency, the bubbles can be used as brief screening tools in research (e.g., large-scale studies, longitudinal studies, experience sampling paradigms) and in praxis. Their shortness can prevent fatigue, motivation decrease, and participants' drop-out.

Force modulation: A behavioural marker of mind-wandering.

It is known that the Metronome Response Task (MRT)-one of the most used mind-wandering sampling paradigms, struggles to differentiate between spontaneous mind-wandering (wherein one's attention is uncontrollably shifted away from the task at hand) and deliberate mind-wandering (wherein one's attention is purposefully shifted away). Thus, we endeavoured to design and test a new mind-wandering measure, called the In Sync Task (IST), that can achieve such differentiation more readily. Unlike the MRT, which involves having participants click in sync (using a mouse) with rhythmically presented, auditory monotones, the IST requires participants to (1) click in sync with tone triplets that increase incrementally in loudness and (2) modulate their clicking force to the presented tone's loudness. Here, we measured (1) participants' variabilities in their rhythmic response times (as is the MRT) and (2) their consistency in appropriately modulating their clicking force. Across two separate samples of university students (n = 119 and n = 121) collected between June 2022 to February 2023, we showed performance differences between the mind-wandering subtypes. Specifically, participants were better able to modulate their clicking force during epochs of spontaneous, compared with deliberate, mind-wandering, whereas the MRT was unable to differentiate between these conditions. In sum, we show that there appear to be greater costs to performance when one deliberately mind-wanders, as opposed to spontaneously mind-wandering.

Reducing the In band Network Telemetry overhead through the spatial sampling: Theory and experimental results

In band Network Telemetry (INT) allows for a high accurate monitoring of the network status, at the cost of an increased overhead, due to the insertion of telemetry information in the packets carrying the user data. In this paper we present a novel approach to reduce the overhead due to the realization of network monitoring solutions based on the use of INT. In particular we introduce the concept of spatial sampling as a means to reduce the amount of telemetry data to collect. The idea is to exploit the spatial correlations arising among the different INT data and imposed by the network topology, to reduce the amount of telemetry values to collect and reconstruct the unseen ones. We further introduce SPAN, a sampling and recovering strategy that works according to the spatial sampling paradigm. The integration of SPAN in a programmable network is discussed and an extensive performance evaluation is performed. The results show that it is possible to reduce the overhead due to INT data collection of 65%, while achieving a high accurate reconstruction of the network status.

Eelbrain, a Python toolkit for time-continuous analysis with temporal response functions.

Even though human experience unfolds continuously in time, it is not strictly linear; instead, it entails cascading processes building hierarchical cognitive structures. For instance, during speech perception, humans transform a continuously varying acoustic signal into phonemes, words, and meaning, and these levels all have distinct but interdependent temporal structures. Time-lagged regression using temporal response functions (TRFs) has recently emerged as a promising tool for disentangling electrophysiological brain responses related to such complex models of perception. Here, we introduce the Eelbrain Python toolkit, which makes this kind of analysis easy and accessible. We demonstrate its use, using continuous speech as a sample paradigm, with a freely available EEG dataset of audiobook listening. A companion GitHub repository provides the complete source code for the analysis, from raw data to group-level statistics. More generally, we advocate a hypothesis-driven approach in which the experimenter specifies a hierarchy of time-continuous representations that are hypothesized to have contributed to brain responses, and uses those as predictor variables for the electrophysiological signal. This is analogous to a multiple regression problem, but with the addition of a time dimension. TRF analysis decomposes the brain signal into distinct responses associated with the different predictor variables by estimating a multivariate TRF (mTRF), quantifying the influence of each predictor on brain responses as a function of time(-lags). This allows asking two questions about the predictor variables: (1) Is there a significant neural representation corresponding to this predictor variable? And if so, (2) what are the temporal characteristics of the neural response associated with it? Thus, different predictor variables can be systematically combined and evaluated to jointly model neural processing at multiple hierarchical levels. We discuss applications of this approach, including the potential for linking algorithmic/representational theories at different cognitive levels to brain responses through computational models with appropriate linking hypotheses.

Dynamic multi-objective workflow scheduling for combined resources in cloud

Cloud resource providers offer idle resources to users as spot instances. The price of the instances changes with market supply and demand, and the dynamic price can have a significant impact on workflow scheduling. In this work, we use a combination of spot and on-demand instances as the foundation cloud resource and characterize the dynamic workflow scheduling problem as a dynamic multi-objective optimization problem (DMOP), where the dynamics originate from the dynamic price of spot instances. The scheduling solution is found by considering three objectives: maximizing the reliability of the instances while minimizing the makespan and cost. In addition, we provide an enhanced MOEAD algorithm called MOEA/D-URDI that combines diversity introduction and uniform random sampling, where the uniform random sampling paradigm is used to generate the initial weight vector. The dynamic multi-objective optimization evolutionary algorithm DMOEA/D-URDI is then created by combining the method with a dynamic optimization framework. Our technique beats existing algorithms, according to experimental data based on dynamic benchmark sets and three well-known scientific procedures in terms of metrics on dynamic benchmark sets and better ensures reliability in scheduling scientific workflows while reducing makespan and cost.

A temporal shift reconstruction network for compressive video sensing

AbstractCompressive sensing provides a promising sampling paradigm for video acquisition for resource‐limited sensor applications. However, the reconstruction of original video signals from sub‐sampled measurements is still a great challenge. To exploit the temporal redundancies within videos during the recovery, previous works tend to perform alignment on initial reconstructions, which are too coarse to provide accurate motion estimations. To solve this problem, the authors propose a novel reconstruction network, named TSRN, for compressive video sensing. Specifically, the authors utilise a number of stacked temporal shift reconstruction blocks (TSRBs) to enhance the initial reconstruction progressively. Each TSRB could learn the temporal structures by exchanging information with last and next time step, and no additional computations is imposed on the network compared to regular 2D convolutions due to the high efficiency of temporal shift operations. After the enhancement, a bidirectional alignment module to build accurate temporal dependencies directly with the help of optical flows is employed. Different from previous methods that only extract supplementary information from the key frames, the proposed alignment module can receive temporal information from the whole video sequence via bidirectional propagations, thus yielding better performance. Experimental results verify the superiority of the proposed method over other state‐of‐the‐art approaches quantitatively and qualitatively.

Attention Constraints and Learning in Categories

Many decision makers are thought to economize on attention by processing information at the simpler level of a category. We directly test whether such category focus reflects an adaptive response to attention constraints, in five preregistered experiments using an information sampling paradigm with mouse tracking. Consistent with rational principles, participants focus more on category-level information when individual differences are small, when the category contains more members, and when time constraints are more severe. Participants are sensitive to the statistical structure of the category even when it must be learned from experience, and they respond to a latent shift in this structure. Beliefs about category members tend to cluster together more when category focus is high—a key element of rational inattention. However, this is counteracted by greater weight placed on salient and idiosyncratic information when the category is large. Our results broadly substantiate influential theories of categorical thinking, giving us a clearer view on the drivers and consequences of inattention. This paper was accepted by Marie Claire Villeval, behavioral economics and decision analysis. Funding: This work was supported by the Office of Naval Research (N00014-21-1-2170), the Pershing Square Fund for Research on the Foundations of Human Behavior, and an NWO Vidi grant (VI.Vidi.201.059). Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4803 .

Sampling by Difference as a Method of Applying the Sampling Kantorovich Model in Digital Image Processing

In this paper, the connections between the Sampling Kantorovich model and the sampling process are highlighted and exploited. Based on the theoretical framework of the Sampling Kantorovich operators, a sampling paradigm, here named Sampling Kantorovich by Difference (SKD), is introduced. In line of principle, SKD allows for overcoming the technical limitation due to the fact that the resolution of a signal/image is strictly connected with the size of the used sensors. We analyze the paradigm in the case of a simulated super resolution type problem. The same mathematical model, being extendable to other signal reconstruction procedures, suggests a theoretical way for new technical solutions in the sampling procedures.

Optimization of Sleep classification in Patients With Serious Mental Illness Using Accelerometer and ECG Data From a Wearable Patch

AbstractIntroductionSleep is an important behavioral biomarker for patients with serious mental illness (SMI). The ability to accurately quantify sleep in a real-world setting could thus provide insight into patient well-being. In this study, patients in a sleep lab wore a patch that is part of a digital medicine system (aripiprazole with sensor (AS)) designed to provide objective records of medication ingestion. The patch provided accelerometer and electrocardiogram (ECG) data; polysomnography (PSG) data was collected to be used as the gold standard for sleep stage classification. The accelerometer and ECG data were used to build machine learning classification models to distinguish periods of wake from periods of sleep. To optimize these models for a real-world environment, different data sampling paradigms and methodologies were explored, and resultant model performances were analyzed.MethodsData was collected for a total of 220 nights, across 73 unique subjects—42 subjects had a diagnosed SMI (schizophrenia, bipolar disorder I, or major depressive disorder) and 31 subjects were healthy volunteers. PSG data, which provides a sleep stage designation at 30-second intervals, was combined into 5-minute windows, labeled as either “Sleep” or “Wake” based on which class comprised the majority of the 30-second intervals within the window. Accelerometer and ECG features were derived for each 5-minute window. Models were trained with three learning methodologies: a light gradient boosting machine (LGBM), a conditional random field (CRF), and a long short-term memory (LSTM) network. Model performance was tested with the full complement of accelerometer and ECG data, as well as down-sampled subsets of data. Additionally, ECG data from the PSG system was incorporated to test the effect of other ECG sampling strategies.ResultsCRF models produced the best classification performance (AUC = 0.91) with the full patch dataset. Down-sampling to include less than half of the accelerometer data did begin to degrade the specificity of the model. Down-sampling to include less frequent ECG collection did not have a significant effect on model performance; however, changing the sampling paradigm to continuous ECG collection from a block sampling paradigm did lead to more robust classification of when a patient was awake.ConclusionsAccurately recording sleep in a logistically simple way can provide insights into the well-being of SMI patients. Combining these insights with the objective medication ingestion records provided by AS would be of great value to SMI patients, as well as their caregivers and physicians. This research explores what amount of sensor data is required to accurately quantify sleep and some of the machine learning strategies that can ameliorate data limitations, providing guidance for the optimization of digital device design.FundingOtsuka Pharmaceutical Development & Commercialization, Inc.

Shame on me for needing you: A multistudy examination of links between receiving interpersonal emotion regulation and experiencing shame.

Recent theory and research have drawn attention to interpersonal dimensions of emotion regulation. Yet, few empirical investigations of the outcomes of interpersonal emotion regulation have been conducted. We propose that one negative affective outcome of received interpersonal emotion regulation of conceptual and practical interest is shame. In the present series of studies, participants from six disparate samples reported on experiences of receiving interpersonal emotion regulation using autobiographical recall and ecological sampling paradigms (total analyzed n = 1,868; total analyzed k = 2,515 instances of receiving interpersonal emotion regulation). We sought to quantify the frequency and distinctiveness of shame as an outcome of receiving interpersonal emotion regulation. We used an exploratory-confirmatory approach to identify robust and generalizable correlates of shame. We considered individual (e.g., trait external shame-proneness), situational (e.g., desire for regulation), relational (e.g., perceived closeness with the provider), and interaction-specific (e.g., perceptions of provider hostility) variables. Our results indicate that it is not uncommon for people to experience receiving interpersonal emotion regulation as shame-inducing, and these perceptions are distinct from their evaluations of the overall helpfulness of the interaction. An internal mini meta-analysis showed that the strongest correlates of shame across studies and samples were individual differences in nonacceptance of negative emotions and expressive suppression and interaction-specific ratings of provider responsiveness and hostility. We discuss the conceptual, methodological, and practical implications of these findings for studying interpersonal emotion regulation and shame. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The Effect of Situational Experiment Conditions on Hasty Decision Making in the 'Beads Task'.

'Jumping to Conclusions', or hasty decision making, is widely studied within clinical and computational psychology. It is typically investigated using the 'beads task', a sequential information sampling paradigm, and defining one or two draws as jumping to conclusion. Situational experimental conditions, e.g., group vs. individual testing, abstract vs. cover story, show-up fee or course credit, frequently vary between studies. Little effort has been dedicated to investigating the potential effects of demand characteristics on hasty decision making. We explored this in four samples of participants (n = 336), in different situational experiment conditions, with two distinct variations of the beads task. An abstract 'Draws to Decision' (DtD) variant, and a cover story combined DtD and probabilistic inferences variant. Situational conditions did not have a significant effect on overall DtD for either variant. However, when using 'extreme scores' (DtD of 1 or 1 to 2) as a measure of hasty decision making, situational conditions had an effect for the abstract variant, with individual testing having the fewest hasty decision makers (DtD1: Mann-Whitney U = 2137.5, p = 0.02; DtD1-2: Mann-Whitney U = 2017.5, p < 0.01), but not for the cover story variant. Our results suggest that the abstract variant is more susceptible to test conditions, especially if a categorisation is used to classify hasty decisions. This does not imply that the cover story variant is better suited to capturing jumping to conclusions behaviour, but highlights the importance of mirroring the situational conditions between different samples. We recommend that testing conditions should be fully disclosed.

Spectral Gap-Based Seismic Survey Design

Seismic imaging in challenging sedimentary basins and reservoirs requires acquiring, processing, and imaging very large volumes of data (tens of terabytes). To reduce the cost of acquisition and the time from acquiring the data to producing a subsurface image, novel acquisition systems based on compressive sensing (CS), low-rank matrix recovery (LRMR), and randomized sampling have been developed and implemented. These approaches allow practitioners to achieve dense wavefield reconstruction from a substantially reduced number of field samples. However, designing acquisition surveys suited for this new sampling paradigm remains a critical and challenging role in oil, gas, and geothermal exploration. Typical random designs studied in the LRMR and CS literature are difficult to achieve by standard industry hardware. For practical purposes, a compromise between stochastic and realizable samples is needed. In this article, we propose a deterministic and computationally cheap tool to alleviate randomized acquisition design, prior to survey deployment and large-scale optimization. We consider universal and deterministic matrix completion results in the context of seismology, where a bipartite graph representation of the source–receiver layout allows for the respective spectral gap (SG) to act as a quality metric for wavefield reconstruction. We provide realistic scenarios to demonstrate the utility of the SG as a flexible tool that can be incorporated into existing survey design workflows for successful seismic data acquisition via low-rank and sparse signal recovery.