Miracle-dz/UniFAR-FTUs huggingface dataset:
Effective variations of peer instruction: The effects of peer discussions, committing to an answer, and reaching a consensus

Peer Instruction (PI) is a widely used student-centered pedagogy, but one that is used differently by different instructors. While all PI instructors survey their students with conceptual questions, some do not allow students to discuss with peers. We studied the effect of peer discussion by polling three groups of students (N = 86) twice on the same set of nine conceptual questions. The three groups differed in the tasks assigned between the first and second poll: the first group discussed, the second reflected in silence, and the third was distracted so they could neither reflect nor discuss. Comparing score changes between the first and second poll, we find minimal increases in the distraction condition (3%), sizable increases in the reflection condition (10%), and significantly larger increases in the peer discussion condition (21%). We also examined the effect of committing to an answer before peer discussion and reaching a consensus afterward. We compared a lecture-based control section to three var.
Small Time and Space Measurements of the Mean Rainfall Rate Made by a Gage Network and by a Dual-Polarization Radar.

Abstract Small time (1 to 15 min) and space (0.5 to 1. 5 km) measurements of the mean rainfall rate have been made within 10 km from a dual-polarization radar, during several widespread or convective precipitation events. At first, the time and space characteristic scales of the gage and of the radar data are evaluated in order to find the minimum time interval Δt and spatial resolution Δx for which the mean rainfall rate values have a large enough statistical significance. Then the quantitative comparisons am made between the mean rainfall rate RG deduced from the gage data and that deduced from the radar measurements either without (RZ) or With (RD) using the differential reflectivity ZDR values
Integrating Community Perspectives on Inclusion and Protection into IRB Structures

IRBs often face dueling values in research: their historically grounded mission to protect research participants from harm conflicts with more recent attention to the importance of including underrepresented groups in clinical studies. Friesen et al. (2023) capture this “protection-inclusion dilemma” in their target article. Briefly, they note that participants from underrepresented communities should “work alongside researchers to help develop appropriate approaches to promote inclusion” (Friesen et al. 2023). Here, we expand upon the importance of this involvement and provide recommendations for how IRBs can better engage with communities. To navigate the protectioninclusion dilemma, IRBs ought to prioritize the perspectives of the participants most impacted by their decisions.
Transformer Actor-Critic with Regularization: Automated Stock Trading using Reinforcement Learning

Recently, with the increasing interest in investments in financial stock markets, several methods have been proposed to automatically trade stocks and/or predict future stock prices using machine learning techniques, such as reinforcement learning (RL), LSTM, and transformers. Among them, RL has been applied to manage portfolio assets with a sequence of optimal actions. The most important factor in investing in stocks is the utilization of past stock price data. However, existing RL algorithms applied to stock markets do not consider past stock data when taking optimal actions, as RL is formulated based on the Markov decision process (MDP). To resolve this limitation, we propose Transformer Actor-Critic with Regularization (TACR) using decision transformer to train the model with the correlation of past MDP elements using an attention network. In addition, a critic network is added to improve the performance by updating the parameters based on the evaluation of an action. For an efficient learning method, we train our model using an offline RL algorithm through suboptimal trajectories. To prevent overestimating the value of actions and reduce learning time, we train TACR through a regularization technique with an added behavior cloning term. The experimental results using various stock market data show that TACR performs better than other state-of-the-art methods in terms of the Sharpe ratio and profit.
On Subset Selection of Multiple Humans To Improve Human-AI Team Accuracy

There are several classification tasks where neither the human nor the model is perfectly accurate. Some recent works, therefore, focus on the Human-AI team model, where the AI model’s probabilistic output is combined with the human-predicted class label. The combined decision is shown to consistently outperform the model’s or human’s accuracy alone. All the previous works, however, restrict to the setting where they consider a single human to combine with the AI model. Motivated by the crowdsourcing literature, which combines labels from multiple humans, we show that combining multiple human labels with the model’s probabilistic output can lead to significant improvement in accuracy. This paper further shows that while combining multiple humans helps, a naive combination of humans with AI model can lead to poor accuracy. Hence, there is a strong need for an intelligent strategy to select a subset of humans and combine their labels. To this end, we present an approach to merge the predicted labels from multiple humans with the model’s probabilistic output. We then provide an efficient algo-rithm to find the optimal subset of humans whose combined labels offer the most accurate output. Finally, we empirically demonstrate that the combined model outperforms the AI model or any human alone in terms of accuracy. Besides this, our subset selection al-gorithm and combination method outperforms the single human model and other naïve combination techniques.
Do As You Teach: A Multi-Teacher Approach to Self-Play in Deep Reinforcement Learning

ACM Reference Format: Chaitanya Kharyal, Tanmay Sinha, Sai KrishnaGottipati, FatemehAbdollahi, Srijita Das, and Matthew E. Taylor. 2023. Do As You Teach: A Multi-Teacher Approach to Self-Play in Deep Reinforcement Learning: Extended Abstract. In Proc. of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023), London, United Kingdom, May 29 – June 2, 2023, IFAAMAS, 3 pages.
Analyzing the Sensitivity to Policy-Value Decoupling in Deep Reinforcement Learning Generalization

Existence of policy-value representation asymmetry negatively affects the generalization capability of traditional actor-critic architectures that use a shared representation of policy and value. Fully decoupled/separated networks for policy and value avoid overfitting by addressing this representation asymmetry. However, using two separate networks introduces increased computational overhead. Recent work has also shown that partial separation can achieve the same level of generalization in most tasks while reducing this computational overhead. Thus, the questions arise: Do we really need two separate networks? Is there any particular scenario where only full separation works? Does increasing the degree of separation in a partially separated network help in generalization? In this work, we attempt to analyze the generalization performance vis-a-vis the extent of decoupling of the policy and value networks. We compare four different degrees of network separation, namely: fully shared, early separation, late separation, and full separation on the RL generalization benchmark Procgen, a suite of 16 procedurally-generated environments. We show that unless the environment has a distinct or explicit source of value estimation, partial late separation can easily capture the necessary policy-value representation asymmetry and achieve better generalization performance in unseen scenarios, however, early separation fails to produce good results.
Longitudinal Data Analysis Using Generalized Linear Model with Missing Responses

Longitudinal studies rely on data collected at several occasions from a set of selected individuals. The purpose of these studies is to use a regression-type model to express a response variable as a function of explanatory variables, or covariates. In this thesis, we use marginal models for the analysis of such data, which, coupled with the method of estimating equations, provide estimators of the main regression parameter. When some of the responses are missing or there is error in the recorded covariates, the original estimating equation may be biased. We use techniques available in the literature to modify it and regain the unbiasedness property. We prove the asymptotic normality of the regression estimator obtained under these more realistic circumstances, and provide theoretical and numerical examples to illustrate this approach.
Online Coalitional Skill Formation

Efficiently allocating heterogeneous tasks to agents that arrive dynamically and have diverse skills is a central problem in multi-agent systems called online task allocation . In many cases, a single agent does not meet the skill levels required by a particular task, which incentivizes the agents to form coalitions for handling it. In this paper, we propose a new framework, termed as online coalitional skill formation ( OCSF ), for handling online task allocation via coalition formation, where tasks require different skills for being successfully fulfilled, and each agent has different levels at mastering each skill. The goal of the organizer is therefore to assign agents that arrive online to a coalition responsible for performing some task, so as to optimally approach the desired skill levels of all tasks. Focusing on the case in which the set of possible mastering levels for each skill is discrete , we suggest different assignment algorithms based on the knowledge the organizer has on the arriving agents. When agents arrive i.i. d. according to some unknown distribution, we propose a greedy and adaptive scheme that assigns an agent to a task, proving a tight bound on the system’s performance. If the distribution is known , we devise a novel correlation to Constrained Markov Decision Processes whose goal is maximizing the rate at which agents are assigned to each task while respecting their requirements. We then construct a non-adaptive approach that terminates when all the tasks’ requirements are met. Finally, if the distribution is unknown , we provide two algorithms that learn it online . We have fully implemented the algorithms, showing that in many cases a higher diversity in skills may yield poor assignments.
Resolving Nonstationary Spectral Information in Wind Speed Time Series Using the Hilbert-Huang Transform

Abstract This work is motivated by the observation that large-amplitude wind fluctuations on temporal scales of 1–10 h present challenges for the power management of large offshore wind farms. Wind fluctuations on these scales are analyzed at a meteorological measurement mast in the Danish North Sea using a 4-yr time series of 10-min wind speed observations. An adaptive spectral analysis method called the Hilbert–Huang transform is chosen for the analysis, because the nonstationarity of time series of wind speed observations means that they are not well described by a global spectral analysis method such as the Fourier transform. The Hilbert–Huang transform is a local method based on a nonparametric and empirical decomposition of the data followed by calculation of instantaneous amplitudes and frequencies using the Hilbert transform. The Hilbert–Huang transformed 4-yr time series is averaged and summarized to show climatological patterns in the relationship between wind variability and time of day. First,.
Flow past a moderately heated horizontal cylinder at low Reynolds number

Flow past a heated two-dimensional cylinder in the laminar regime is investigated. A hybrid finite element–finite volume method is used to solve Navier–Stokes and energy equations. The vortex shedding frequency defined nondimensionally as Strouhal number is observed to be increasing with heating. Vortex shedding behind heated cylinder exhibits asymmetry in wake region due to stable and unstable density stratification below and above the cylinder, respectively. A negative lift is generated due to heating, in an otherwise symmetric zero time-averaged lift configuration, due to skewness in vortex shedding towards the direction of buoyancy. We also note that the heating brings down the drag for the cases considered here. The thermal energy transport due to convection, defined by nondimensional Nusselt number is found to increase only marginally with cylinder heating.
Energy and exergy analyses of a new solar air heater with circular-type turbulators having different relief angles

In this study, energy and exergy analyses of a new SAHs with circular-type turbulators are performed. Experiments are conducted for 0.0023, 0.0033, 0.0044 and 0.0055 kg/s, respectively. While the energy efficiency values are obtained between 28.6% and 79.5%, the exergy efficiency values are calculated between 8.1% and 42.4%. Type II, which has 45° obstacle relief angle and 0.0055 kg/s mass flow rate, shows the highest energy and exergy efficiencies among all the considered cases. The lowest energy and exergy efficiencies are obtained in Type I case, which has no obstacles and has a 0.0023 kg/s mass flow rate. According to the uncertainty analyses that are made, uncertainties of energy efficiencies are obtained between 0.53% and 1.06% whereas uncertainties for exergy efficiencies are varied between 0.27% and 5.4% for all the investigated cases. In addition, the obtained energy and exergy efficiency results are compared with results given in the literature.
Online Influence Maximization under Decreasing Cascade Model

We study online influence maximization (OIM) under a new model of decreasing cascade (DC). This model is a generalization of the independent cascade (IC) model by considering the common phenomenon of market saturation. In DC, the chance of an influence attempt being successful reduces with previous failures. The effect is neglected by previous OIM works under IC and linear threshold models. We propose the DC-UCB algorithm to solve this problem, which achieves a regret bound of the same order as the state-of-the-art works on the IC model. Extensive experiments on both synthetic and real datasets show the effectiveness of our algorithm.
Segmentation of ECG from Surface EMG Using DWT and EMD: A Comparison Study

The electrocardiographic (ECG) signal is a major artifact during recording the surface electromyography (SEMG). Removal of this artifact is one of the important tasks before SEMG analysis for biomedical goals. In this paper, the application of discrete wavelet transform (DWT) and empirical mode decomposition (EMD) for elimination of ECG artifact from SEMG is investigated. The focus of this research is to reach the optimized number of decomposed levels using mean power frequency (MPF) by both techniques. In order to implement the proposed methods, ten simulated and three real ECG contaminated SEMG signals have been tested. Signal-to-noise ratio (SNR) and mean square error (MSE) between the filtered and the pure signals are applied as the performance indexes of this research. The obtained results suggest both techniques could remove ECG artifact from SEMG signals fair enough, however, DWT performs much better and faster in real data.
Assimilation of Simulated Wind Lidar Data with a Kalman Filter

Abstract The object of this paper is to present some results obtained with an extended Kalman fitter (EKF). First, a discussion is given of the way that the EKF has been implemented and tested for a global nondivergent barotropic model spectrally truncated at T21. In the present paper, the assimilation experiments focused solely on the time evolution of the forecast error covariances that are influenced by two factors: 1) their time integration performed here with the tangent linear model obtained from a linearization around the true trajectory and 2) the accuracy and distribution of the observations. Data from a simulated radiosonde network have been assimilated over a 24-h period. The results show that even though no model error has been considered, there can be a substantial forecast error growth, especially in regions where the flow is unstable and no data are available. The error growth is attributed to instability processes that are embedded within the complex flow configuration around which the non.
Learning to Coordinate from Offline Datasets with Uncoordinated Behavior Policies

In offline multi-agent reinforcement learning (RL), multiple agents must learn to coordinate from previously collected datasets. Like the single-agent case, we must handle the distribution shift issue from the datasets. Most importantly, we also need to deal with possible miscoordination in the datasets, collected by some unco-ordinated behavior policies. To address this, we propose a novel offline multi-agent RL method using counterfactual sample-average approximation with subteam masking. Specifically, we compute the best-response policy for each agent using sample-average approximation. For the miscoordination issue, we use counterfactual mechanism and subteam masking to reason about the agents’ contributions to the team. Based on this, each agent learns to coordinate from the uncoordinated datasets. Empirically, we evaluate our method in two benchmark domains: a continuous multi-agent MuJoCo control domain, and a challenging cooperation environment Starcraft II domain. Our experimental results confirm that our approach can achieve significantly better performance than several state-of-the-art methods. The source code is available at:
CoRaL: Continual Representation Learning for Overcoming Catastrophic Forgetting

Humans have the ability to acquire, retain and transfer knowledge over their lifespan. For intelligent agents to achieve fluent longitudinal interaction, they need to continually retain, refine and acquire new knowledge. However, current learning approaches, in particular Deep Neural Networks, are prone to catastrophic forgetting, a phenomenon where the network forgets its past representation as the data distribution changes. To address this challenge, in this work, we propose CoRaL, a novel continual learning framework that considers the past response of the network when learning a new task. CoRaL comprises a Representation Learning module that learns representations that are robust to distribution shifts and a Knowledge Distillation module that encourages the network to retain past knowledge. The Representation Learning module is a Siamese Network setup that maximizes the similarity between two augmented versions of the input. The Knowledge Distillation module buffers past inputs and penalizes divergence between past and current network output. We evaluated CoRaL on three challenging Continual Learning scenarios across four datasets. The results suggest that CoRaL outperformed all evaluated state-of-the-art methods, achieving the highest accuracy and lowest forgetting. Finally, we conducted extensive ablation studies to highlight the importance of the proposed modules in addressing catastrophic forgetting.
Strategic Play By Resource-Bounded Agents in Security Games

Many studies have shown that humans are"predictably irrational": they do not act in a fully rational way, but their deviations from rational behavior are quite systematic. Our goal is to see the extent to which we can explain and justify these deviations as the outcome of rational but resource-bounded agents doing as well as they can, given their limitations. We focus on the well-studied ranger-poacher game, where rangers are trying to protect a number of sites from poaching. We capture the computational limitations by modeling the poacher and the ranger as probabilistic finite automata (PFAs). We show that, with sufficiently large memory, PFAs learn to play the Nash equilibrium (NE) strategies of the game and achieve the NE utility. However, if we restrict the memory, we get more"human-like"behaviors, such as probability matching (i.e., visiting sites in proportion to the probability of a rhino being there), and avoiding sites where there was a bad outcome (e.g., the poacher was caught by the ranger), that we also observed in experiments conducted on Amazon Mechanical Turk. Interestingly, we find that adding human-like behaviors such as probability matching and overweighting significant events (like getting caught) actually improves performance, showing that this seemingly irrational behavior can be quite rational.
New variable step size LMS adaptive filtering algorithm and its performance analysis

The problem of step size selection for adaptive least mean squares(LMS) filtering algorithm is studied.A new variable step size LMS algorithm with improved hyperbolic tangent function is presented,which is derived by the extensive review about some existing LMS algorithms.The selective rule of step size parameters is discussed,and the performance analysis such as convergence,anti-interference and the steady-state error of the algorithm are also given.The algorithm can not only obtain the good properties of the fast convergence and the tracking speed,but also achieve lower misadjustment.Theoretical analysis and simulation results show that the proposed variable step size LMS algorithm has better steady-state performance than that of the existing algorithms.
Differentially Private Network Data Collection for Influence Maximization

When designing interventions in public health, development, and education, decision makers rely on social network data to target a small number of people, capitalizing on peer effects and social contagion to bring about the most welfare benefits to the population. Developing new methods that are privacy-preserving for network data collection and targeted interventions is critical for designing sustainable public health and development interventions on social networks. In a similar vein, social media platforms rely on network data and information from past diffusions to organize their ad campaign and improve the efficacy of targeted advertising. Ensuring that these network operations do not violate users’ privacy is critical to the sustainability of social media platforms and their ad economies. We study privacy guarantees for influence maximization algorithms when the social network is unknown, and the inputs are samples of prior influence cascades that are collected at random. Building on recent results that address seeding with costly network information, our privacy-preserving algorithms introduce randomization in the collected data or the algorithm output, and can bound each node’s (or group of nodes’) privacy loss in deciding whether or not their data should be included in the algorithm input. We provide theoretical guarantees of the seeding performance with a limited sample size subject to differential privacy budgets in both central and local privacy regimes. Simulations on empirical network datasets reveal the diminishing value of network information with decreasing privacy budget in both regimes, as well as additional nuances of post-processing in the local regime.
Adaptation of A-to-I RNA editing in bacteria, fungi, and animals

COPYRIGHT © 2023 Duan, Li and Cai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Adaptation of A-to-I RNA editing in bacteria, fungi, and animals