Reviewerly/exHarmony huggingface dataset:
CAENet: Efficient Multi-task Learning for Joint Semantic Segmentation and Depth Estimation

In this paper, we propose an efficient multi-task method, named Context-aware Attentive Enrichment Network (CAENet), to deal with the problem of real-time joint semantic segmentation and depth estimation. Building upon a light-weight encoder backbone, an efficient decoder is devised to fully leverage available information from multi-scale encoder features. In particular, a new Inception Residual Pooling (IRP) module is designed to efficiently extract contextual information from the high-level features with diverse receptive fields to improve semantic understanding ability. Then the context-aware features are enriched adaptively with spatial details from low-level features via a Light-weight Attentive Fusion (LAF) module using pseudo stereoscopic attention mechanism. These two modules are progressively used in a recursive manner to generate high-resolution shared features, which are further processed by task-specific heads to produce final outputs. Such network design effectively captures beneficial information for both semantic segmentation and depth estimation tasks while largely reducing the computational budget. Extensive experiments across multi-task benchmarks validate that CAENet achieves state-of-the-art performance with comparable inference speed against other real-time competing methods. Code is available at https://github.com/wlx-zju/CAENet .
Fast Convergence of Random Reshuffling Under Over-Parameterization and the Polyak-Łojasiewicz Condition

Modern machine learning models are often over-parameterized and as a result they can interpolate the training data. Under such a scenario, we study the convergence properties of a sampling-without-replacement variant of stochastic gradient descent (SGD) known as random reshuffling (RR). Unlike SGD that samples data with replacement at every iteration, RR chooses a random permutation of data at the beginning of each epoch and each iteration chooses the next sample from the permutation. For under-parameterized models, it has been shown RR can converge faster than SGD under certain assumptions. However, previous works do not show that RR outperforms SGD in over-parameterized settings except in some highly-restrictive scenarios. For the class of Polyak-Łojasiewicz (PL) functions, we show that RR can outperform SGD in over-parameterized settings when either one of the following holds: (i) the number of samples (n) is less than the product of the condition number ( $$\kappa $$ ) and the parameter ( $$\alpha $$ ) of a weak growth condition (WGC), or (ii) n is less than the parameter ( $$\rho $$ ) of a strong growth condition (SGC).
A Few Good Sentences: Content Selection for Abstractive Text Summarization

Abstractive text summarization has been of research interest for decades. Neural approaches, specifically recent transformer-based methods, have demonstrated promising performance in generating summaries with novel words and paraphrases. In spite of generating more fluent summaries, these approaches may yet show poor summary-worthy content selection. In these methods, the extractive content selection is majorly dependent on the reference summary with little to no focus on identifying the summary-worthy segments (SWORTS) in a reference-free setting. In this work, we leverage three metrics, namely, informativeness, relevance, and redundancy in selecting the SWORTS. We propose a novel topic-informed and reference-free method to rank the sentences in the source document based on their importance. We demonstrate the effectiveness of SWORTS selection in different settings such as fine-tuning, few-shot tuning, and zero-shot abstractive text summarization. We observe that self-training and cross-training a pre-trained model with SWORTS selected data shows competitive performance to the pre-trained model. Furthermore, a small amount of SWORTS selected data is sufficient for domain adaptation against fine-tuning on the entire training dataset with no content selection. In contrast to training a model on the source dataset with no content selection, we observe a significant reduction in the time required to train a model with SWORTS that further underlines the importance of content selection for training an abstractive text summarization model.
Improving Autoregressive NLP Tasks via Modular Linearized Attention

Various natural language processing (NLP) tasks necessitate models that are efficient and small based on their ultimate application at the edge or other resource-constrained environment. While prior research has reduced the size of these models, increasing computational efficiency without considerable performance impacts remains difficult, especially for autoregressive tasks. This paper proposes modular linearized attention (MLA), which combines multiple efficient attention mechanisms, including cosFormer [32], to maximize inference quality while achieving notable speedups. We validate this approach on several autoregressive NLP tasks, including speech-to-text neural machine translation (S2T NMT), speech-to-text simultaneous translation (SimulST), and autoregressive text-to-spectrogram, noting efficiency gains on TTS and competitive performance for NMT and SimulST during training and inference.
KL Regularized Normalization Framework for Low Resource Tasks

Large pretrained models, such as Bert, GPT, and Wav2Vec, have demonstrated great potential for learning representations that are transferable to a wide variety of downstream tasks. It is difficult to obtain a large quantity of supervised data due to the limite d availability of resources and time. In light of this, a significant amount of research has been conducted in the area of adopting large pretrained datasets for diverse downstream tasks via fine tuning, linear probing, or prompt tuning in low resource settings. Normalization techniques are essential for accelerating training and improving the generalization of deep neural networks and have been successfully used in a wide variety of applications. A lot of normalization techniques have been proposed but the success of normalization in low resource downstream NLP and speech tasks is limited. One of the reasons is the inability to capture expressiveness by re-scaling parameters of normalization. We propose Kullback-Leibler(KL) Regularized normalization (KL-Norm) which make the normalized data well behaved and helps in better generalization as it reduces over-fitting, generalises well on out of domain distributions and removes irrelevant biases and features with negligible increase in model parameters and memory overheads. Detailed experimental evaluation on multiple low resource NLP and speech tasks, demonstrates the superior performance of KL-Norm as compared to other popular normalization and regularization techniques.
Generality-Training of a Classifier for Improved Calibration in Unseen Contexts

Abstract Artificial neural networks tend to output class probabilities that are miscalibrated, i . e ., their reported uncertainty is not a very good indicator of how much we should trust the model. Consequently, methods have been developed to improve the model’s predictive uncertainty, both during training and post-hoc. Even if the model is calibrated on the domain used in training, it typically becomes over-confident when applied on slightly different target domains, e.g. due to perturbations or shifts in the data. The model can be recalibrated for a fixed list of target domains, but its performance can still be poor on unseen target domains. To address this issue, we propose a generality-training procedure that learns a modified head for the neural network to achieve better calibration generalization to new domains while retaining calibration performance on the given domains. This generality-head is trained on multiple domains using a new objective function with increased emphasis on the calibration loss compared to cross-entropy. Such training results in a more general model in the sense of not only better calibration but also better accuracy on unseen domains, as we demonstrate experimentally on multiple datasets. The code and supplementary for the paper is available ( https://github.com/bsl-traveller/CaliGen.git ).
Exploring the Training Robustness of Distributional Reinforcement Learning Against Noisy State Observations

In real scenarios, state observations that an agent observes may contain measurement errors or adversarial noises, misleading the agent to take suboptimal actions or even collapse while training. In this paper, we study the training robustness of distributional Reinforcement Learning (RL), a class of state-of-the-art methods that estimate the whole distribution, as opposed to only the expectation, of the total return. Firstly, we validate the contraction of distributional Bellman operators in the State-Noisy Markov Decision Process (SN-MDP), a typical tabular case that incorporates both random and adversarial state observation noises. In the noisy setting with function approximation, we then analyze the vulnerability of least squared loss in expectation-based RL with either linear or nonlinear function approximation. By contrast, we theoretically characterize the bounded gradient norm of distributional RL loss based on the categorical parameterization equipped with the Kullback-Leibler (KL) divergence. The resulting stable gradients while the optimization in distributional RL accounts for its better training robustness against state observation noises. Finally, extensive experiments on the suite of environments verified that distributional RL is less vulnerable against both random and adversarial noisy state observations compared with its expectation-based counterpar (Code is available in https://github.com/datake/RobustDistRL . The extended version of the paper is in https://arxiv.org/abs/2109.08776 .).
Informed Priors for Knowledge Integration in Trajectory Prediction

Informed learning approaches explicitly integrate prior knowledge into learning systems, which can reduce data needs and increase robustness. However, existing work typically aims to integrate formal scientific knowledge by directly pruning the problem space, which is infeasible for more intuitive world and expert knowledge, or requires specific architecture changes and knowledge representations. We propose a probabilistic informed learning approach to integrate prior world and expert knowledge without these requirements. Our approach repurposes continual learning methods to operationalize Baye’s rule for informed learning and to enable probabilistic and multi-modal predictions. We exemplify our proposal in an application to two state-of-the-art trajectory predictors for autonomous driving. This safety-critical domain is subject to an overwhelming variety of rare scenarios requiring robust and accurate predictions. We evaluate our models on a public benchmark dataset and demonstrate that our approach outperforms non-informed and informed learning baselines. Notably, we can compete with a conventional baseline, even using only half as many observations of the training dataset.
On the Good Behaviour of Extremely Randomized Trees in Random Forest-Distance Computation

Originally introduced in the context of supervised classification, ensembles of Extremely Randomized Trees (ERT) have shown to provide surprisingly effective models also in unsupervised settings, e.g., for anomaly detection (via Isolation Forests) and for distance computation. In this paper, we focus on this latter application of ERT, namely in the context of Random Forest (RF) distance computation. We aim at narrowing the gap between the established empirical evidence of the good behaviour of ERT and the still limited theoretical understanding of their (somehow) surprisingly good performance when compared to more involved methodologies. Our main contribution is the following: we assume the existence of a proper representation on a given domain, i.e., a vectorial representation of the objects which satisfies the Compactness Hypothesis formulated by Arkadev and Braverman in 1967. Under such a hypothesis, given the “true” distance between two objects, we show how to derive a bound on the approximation guaranteed by two main RF-distances obtained by employing ensembles of ERTs, with respect to such “true” distance. In other words, we show that there exists a constant c such that if two objects are $$\epsilon $$ -close in the true distance, then with high probability they are $$(c \cdot \epsilon )$$ -close in the RF-distances computed with ERT forests.
Multiple-Source Adaptation Using Variational Rényi Bound Optimization

Multiple Source Adaptation (MSA) is a problem that involves identifying a predictor which minimizes the error for the target domain while utilizing the predictors from the source domains. In practice, the source domains typically exhibit varying probability distributions across the input space and are unknown to the learner. Consequently, accurate probability estimates are essential for effectively addressing the MSA problem. To this end, variation inference is an attractive approach that aims to approximate probability densities. Traditionally, it is done by maximizing a lower bound for the likelihood of the observed data (evidence), i.e. maximizing the Evidence Lower BOund (ELBO). Recently, researchers have proposed optimizing the Variational Rényi bound (VR) instead of ELBO, which can be biased or difficult to approximate due to high variance. To address these issues, we propose a new upper bound called Variational Rényi Log Upper bound (VRLU). Unlike existing VR bounds, the VRLU bound maintains the upper bound property when using the Monte Carlo (MC) approximation. Additionally, we introduce the Variational Rényi Sandwich (VRS) method, which jointly optimizes an upper and a lower bound, resulting in a more accurate density estimate. Following this, we apply the VRS density estimate to the MSA problem. We show, both theoretically and empirically, that using VRS estimators provides tighter error bounds and improved performance, compared to leading MSA methods.
On the Distributional Convergence of Temporal Difference Learning

Temporal Difference (TD) learning is one of the most simple but efficient algorithms for policy evaluation in reinforcement learning. Although the finite-time convergence results of the TD algorithm are abundant now, the distributional convergence has still been blank. This paper shows that TD with constant step size simulates Markov chains converging to some stationary distribution under both i.i.d. and Markov chain observation models. We prove that TD enjoys the geometric distributional convergence rate and show how the step size affects the expectation and covariance of the stationary distribution. All assumptions used in our paper are mild and common in the TD community. Our proved results indicate a tradeoff between the convergence speed and accuracy for TD. Based on our theoretical findings, we explain why the Jacobi preconditioner can accelerate the TD algorithms.
Learning Hierarchical Planning-Based Policies from Offline Data

Hierarchical policy architectures incorporating some planning component into the top-level have shown superior performance and generalization in agent navigation tasks. Cost or safety reasons may, however, prevent training in an online (RL) fashion with continuous environment interaction. We therefore propose HORIBLe-VRN, an algorithm to learn a hierarchical policy with a top-level planning-based module from pre-collected data. A key challenge is to deal with the unknown, latent high-level (HL) actions. Our algorithm features an EM-style hierarchical imitation learning stage, incorporating HL action inference, and a subsequent offline RL refinement stage for the top-level policy. We empirically evaluate HORIBLe-VRN in a long horizon, sparse reward agent navigation task, investigating performance, generalization capabilities, and robustness with respect to sub-optimal demonstration data.
An experimental test of the Big-Fish-Little-Pond Effect using an immersive virtual reality classroom

Abstract Academic self-concept plays a central role in successful learning and is substantially shaped by social comparisons. Research on the so-called Big-Fish-Little-Pond Effect (BFLPE) has yielded a highly robust and generalizable pattern of negative effects of higher class/school average achievement on students’ self-concept when controlling for individual achievement. However, most BFLPE studies have not provided information about the causes behind or the mechanisms underlying the proposed effects. To address this, we used a fully immersive virtual reality (IVR) classroom to experimentally test the extent to which students recognized performance-related classroom behavior as implicit social comparison information and how these perceptions explained differences in students’ self-concepts. Participants ( N = 381 sixth-grade students) experienced an authentic yet standardized IVR teaching situation with virtual classmates who exhibited different performance levels (operationalized as 20% vs. 35% vs. 65% vs. 80% of classmates raising their hands). Hand-raising behavior had a significant positive effect on students’ perceptions of the class’ performance level ( d 20% vs. 65% = 0.60; d 20% vs. 80% = 1.24). In line with the BFLPE, results showed a negative effect of higher performing classmates on students’ situational self-concept ( d 20% vs. 80% = 0.30). Students’ perceptions of the class’ performance level fully explained the effect of classmates’ hand-raising behavior on students’ situational self-concept. The study’s experimental approach provided new insights into the emergence of social comparison effects in the classroom, highlighting the major role of students’ perceptions of their classmates’ performance-related behavior, and moreover demonstrated the general potential of using IVR in classroom research.
Artificial Intelligence and Multiple Sclerosis: Up-to-Date Review

Multiple sclerosis (MS) remains a challenging neurological disorder for the clinician in terms of diagnosis and management. The growing integration of AI-based algorithms in healthcare offers a golden opportunity for clinicians and patients with MS. AI models are based on statistical analyses of large quantities of data from patients including "demographics, genetics, clinical and radiological presentation." These approaches are promising in the quest for greater diagnostic accuracy, tailored management plans, and better prognostication of disease. The use of AI in multiple sclerosis represents a paradigm shift in disease management. With ongoing advancements in AI technologies and the increasing availability of large-scale datasets, the potential for further innovation is immense. As AI continues to evolve, its integration into clinical practice will play a vital role in improving diagnostics, optimizing treatment strategies, and enhancing patient outcomes for MS. This review is about conducting a literature review to identify relevant studies on AI applications in MS. Only peer-reviewed studies published in the last four years have been selected. Data related to AI techniques, advancements, and implications are extracted. Through data analysis, key themes and tendencies are identified. The review presents a cohesive synthesis of the current state of AI and MS, highlighting potential implications and new advancements.
The Analysis of Students' Learning Difficulties in Online Learning Activities during COVID-19 Pandemic

This study aims to describe of students’ difficulty in online learning during the Covid -19 pandemic. The research was conducted in SD KEMALA BHAYANGKARI 1 oadag utara by applying descriptive qualitative. Instrument of the research included questionnaire (Guttman scale) and interview. The respondents of the research consist of 9 students. The result of the research shows 1) the average of questionnaire was 68.83 (difficult category) and 2) based the interview, the difficulty in online learning during the Covid - 19 pandemic which included students did not have handphone, the internet network is not smooth, and internet quota was not adequate.
Transfer Function Interpolation and Extrapolation: A fast AC analysis method for evolutionary circuit design

Automatic design of analog circuits is an important application and research field of evolutionary computation. Although evolutionary circuit design has important exploration value, its massive computing resource consumption seriously hinders related research. Most computing resources are used for fitness evaluation (candidate circuit verification). As the most commonly used circuit verification method, AC analysis has been widely applied to evolutionary circuit design and has become the central part of fitness evaluation. In order to improve the speed of fitness evaluation, thereby reducing the computing resource consumption of evolutionary circuit design, this paper presented a fast AC analysis method called Transfer Function Interpolation and Extrapolation (TFIE). In this method, the transfer function acts as a bridge between the circuit and its frequency response. The computation cost of AC analysis can be reduced through fitting and substitution. Experimental results show that TFIE can multiply the speed of AC analysis, significantly reducing the time consumption of evolutionary circuit design, and its precision is sufficient for practical design tasks. Furthermore, the proposed method can analyze some actual circuits, obtaining results with quasi-SPICE-level accuracy. In other words, this paper provides an effective method to reduce the time consumption of fitness evaluation and evolutionary circuit design.
Rethinking two-dimensional camera motion estimation assessment for digital video stabilization: A camera motion field-based metric

Digital video stabilization aims to remove camera motion jitters through software implementation. The first step of the classical video stabilization methodology is called camera motion estimation, which is usually performed using only RGB frames of the unstable video. Despite recent advances in camera motion estimation strategies, methods classified as two-dimensional are still not properly evaluated, even though it is well known that motion estimation is a crucial step for classical approaches to video stabilization. The main purpose of this work is to draw attention to two-dimensional camera motion estimation assessment and reinforce its importance on video stabilization progress. We proposed a new approach to perform this evaluation using camera motion fields in a pixel-by-pixel comparison and demonstrated through experimental results that our metrics are reliable for diverse scenarios comparing them to image similarity metrics. In addition, we showed and analyzed the results of our metrics for a global and a local method of camera motion estimation. We believe that our assessment and study presented in this work is an important starting point for a more rigorous analysis of this task. In addition, this can be a foundation for coming 2D camera motion estimation methods based on deep learning.