id stringlengths 9 16 | title stringlengths 4 278 | abstract stringlengths 3 4.08k | cs.HC bool 2 classes | cs.CE bool 2 classes | cs.SD bool 2 classes | cs.SI bool 2 classes | cs.AI bool 2 classes | cs.IR bool 2 classes | cs.LG bool 2 classes | cs.RO bool 2 classes | cs.CL bool 2 classes | cs.IT bool 2 classes | cs.SY bool 2 classes | cs.CV bool 2 classes | cs.CR bool 2 classes | cs.CY bool 2 classes | cs.MA bool 2 classes | cs.NE bool 2 classes | cs.DB bool 2 classes | Other bool 2 classes | __index_level_0__ int64 0 541k |
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2208.08017 | Towards Generating Robust, Fair, and Emotion-Aware Explanations for
Recommender Systems | As recommender systems become increasingly sophisticated and complex, they often suffer from lack of fairness and transparency. Providing robust and unbiased explanations for recommendations has been drawing more and more attention as it can help address these issues and improve trustworthiness and informativeness of recommender systems. However, despite the fact that such explanations are generated for humans who respond more strongly to messages with appropriate emotions, there is a lack of consideration for emotions when generating explanations for recommendations. Current explanation generation models are found to exaggerate certain emotions without accurately capturing the underlying tone or the meaning. In this paper, we propose a novel method based on a multi-head transformer, called Emotion-aware Transformer for Explainable Recommendation (EmoTER), to generate more robust, fair, and emotion-enhanced explanations. To measure the linguistic quality and emotion fairness of the generated explanations, we adopt both automatic text metrics and human perceptions for evaluation. Experiments on three widely-used benchmark datasets with multiple evaluation metrics demonstrate that EmoTER consistently outperforms the existing state-of-the-art explanation generation models in terms of text quality, explainability, and consideration for fairness to emotion distribution. Implementation of EmoTER will be released as an open-source toolkit to support further research. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 313,217 |
2501.05745 | Covariate Dependent Mixture of Bayesian Networks | Learning the structure of Bayesian networks from data provides insights into underlying processes and the causal relationships that generate the data, but its usefulness depends on the homogeneity of the data population, a condition often violated in real-world applications. In such cases, using a single network structure for inference can be misleading, as it may not capture sub-population differences. To address this, we propose a novel approach of modelling a mixture of Bayesian networks where component probabilities depend on individual characteristics. Our method identifies both network structures and demographic predictors of sub-population membership, aiding personalised interventions. We evaluate our method through simulations and a youth mental health case study, demonstrating its potential to improve tailored interventions in health, education, and social policy. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 523,706 |
2407.10315 | Order parameters and phase transitions of continual learning in deep
neural networks | Continual learning (CL) enables animals to learn new tasks without erasing prior knowledge. CL in artificial neural networks (NNs) is challenging due to catastrophic forgetting, where new learning degrades performance on older tasks. While various techniques exist to mitigate forgetting, theoretical insights into when and why CL fails in NNs are lacking. Here, we present a statistical-mechanics theory of CL in deep, wide NNs, which characterizes the network's input-output mapping as it learns a sequence of tasks. It gives rise to order parameters (OPs) that capture how task relations and network architecture influence forgetting and anterograde interference, as verified by numerical evaluations. For networks with a shared readout for all tasks (single-head CL), the relevant-feature and rule similarity between tasks, respectively measured by two OPs, are sufficient to predict a wide range of CL behaviors. In addition, the theory predicts that increasing the network depth can effectively reduce interference between tasks, thereby lowering forgetting. For networks with task-specific readouts (multi-head CL), the theory identifies a phase transition where CL performance shifts dramatically as tasks become less similar, as measured by another task-similarity OP. While forgetting is relatively mild compared to single-head CL across all tasks, sufficiently low similarity leads to catastrophic anterograde interference, where the network retains old tasks perfectly but completely fails to generalize new learning. Our results delineate important factors affecting CL performance and suggest strategies for mitigating forgetting. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 472,935 |
1903.05831 | SimpleDet: A Simple and Versatile Distributed Framework for Object
Detection and Instance Recognition | Object detection and instance recognition play a central role in many AI applications like autonomous driving, video surveillance and medical image analysis. However, training object detection models on large scale datasets remains computationally expensive and time consuming. This paper presents an efficient and open source object detection framework called SimpleDet which enables the training of state-of-the-art detection models on consumer grade hardware at large scale. SimpleDet supports up-to-date detection models with best practice. SimpleDet also supports distributed training with near linear scaling out of box. Codes, examples and documents of SimpleDet can be found at https://github.com/tusimple/simpledet . | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 124,247 |
2006.11852 | Labeling Explicit Discourse Relations using Pre-trained Language Models | Labeling explicit discourse relations is one of the most challenging sub-tasks of the shallow discourse parsing where the goal is to identify the discourse connectives and the boundaries of their arguments. The state-of-the-art models achieve slightly above 45% of F-score by using hand-crafted features. The current paper investigates the efficacy of the pre-trained language models in this task. We find that the pre-trained language models, when finetuned, are powerful enough to replace the linguistic features. We evaluate our model on PDTB 2.0 and report the state-of-the-art results in the extraction of the full relation. This is the first time when a model outperforms the knowledge intensive models without employing any linguistic features. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 183,393 |
1902.00577 | Robustness of Generalized Learning Vector Quantization Models against
Adversarial Attacks | Adversarial attacks and the development of (deep) neural networks robust against them are currently two widely researched topics. The robustness of Learning Vector Quantization (LVQ) models against adversarial attacks has however not yet been studied to the same extent. We therefore present an extensive evaluation of three LVQ models: Generalized LVQ, Generalized Matrix LVQ and Generalized Tangent LVQ. The evaluation suggests that both Generalized LVQ and Generalized Tangent LVQ have a high base robustness, on par with the current state-of-the-art in robust neural network methods. In contrast to this, Generalized Matrix LVQ shows a high susceptibility to adversarial attacks, scoring consistently behind all other models. Additionally, our numerical evaluation indicates that increasing the number of prototypes per class improves the robustness of the models. | false | false | false | false | true | false | true | false | false | false | false | true | false | false | false | false | false | false | 120,434 |
2103.06252 | Grasp Stability Analysis with Passive Reactions | [...] We argue that the traditional grasp modeling theory assumes a complexity that most robotic hands do not possess and is therefore of limited applicability to the robotic hands commonly used today. We discuss these limitations of the existing grasp modeling literature and set out to develop our own tools for the analysis of passive effects in robotic grasping. We show that problems of this kind are difficult to solve due to the non-convexity of the Maximum Dissipation Principle (MDP), which is part of the Coulomb friction law. We show that for planar grasps the MDP can be decomposed into a number of piecewise convex problems, which can be solved for efficiently. [...] Thus, we present the first polynomial runtime algorithm for the determination of passive stability of planar grasps. For the spacial case we [...] describe a convex relaxation of the Coulomb friction law and a successive hierarchical tightening approach that allows us to find solutions to the exact problem including the maximum dissipation principle. [...] The generality of our grasp model allows us to solve a wide variety of problems such as the computation of optimal actuator commands. This makes our framework a valuable tool for practical manipulation applications. Our work is relevant beyond robotic manipulation as it applies to the stability of any assembly of rigid bodies with frictional contacts, unilateral constraints and externally applied wrenches. Finally, we argue that with the advent of data-driven methods as well as the emergence of a new generation of highly sensorized hands there are opportunities for the application of the traditional grasp modeling theory to fields such as robotic in-hand manipulation through model-free reinforcement learning. We present a method that applies traditional grasp models to maintain quasi-static stability throughout a nominally model-free reinforcement learning task. [...] | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 224,240 |
2201.11059 | Generalization Error Bounds on Deep Learning with Markov Datasets | In this paper, we derive upper bounds on generalization errors for deep neural networks with Markov datasets. These bounds are developed based on Koltchinskii and Panchenko's approach for bounding the generalization error of combined classifiers with i.i.d. datasets. The development of new symmetrization inequalities in high-dimensional probability for Markov chains is a key element in our extension, where the spectral gap of the infinitesimal generator of the Markov chain plays a key parameter in these inequalities. We also propose a simple method to convert these bounds and other similar ones in traditional deep learning and machine learning to Bayesian counterparts for both i.i.d. and Markov datasets. Extensions to $m$-order homogeneous Markov chains such as AR and ARMA models and mixtures of several Markov data services are given. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 277,171 |
2004.13175 | A scoping review of transfer learning research on medical image analysis
using ImageNet | Objective: Employing transfer learning (TL) with convolutional neural networks (CNNs), well-trained on non-medical ImageNet dataset, has shown promising results for medical image analysis in recent years. We aimed to conduct a scoping review to identify these studies and summarize their characteristics in terms of the problem description, input, methodology, and outcome. Materials and Methods: To identify relevant studies, MEDLINE, IEEE, and ACM digital library were searched. Two investigators independently reviewed articles to determine eligibility and to extract data according to a study protocol defined a priori. Results: After screening of 8,421 articles, 102 met the inclusion criteria. Of 22 anatomical areas, eye (18%), breast (14%), and brain (12%) were the most commonly studied. Data augmentation was performed in 72% of fine-tuning TL studies versus 15% of the feature-extracting TL studies. Inception models were the most commonly used in breast related studies (50%), while VGGNet was the common in eye (44%), skin (50%) and tooth (57%) studies. AlexNet for brain (42%) and DenseNet for lung studies (38%) were the most frequently used models. Inception models were the most frequently used for studies that analyzed ultrasound (55%), endoscopy (57%), and skeletal system X-rays (57%). VGGNet was the most common for fundus (42%) and optical coherence tomography images (50%). AlexNet was the most frequent model for brain MRIs (36%) and breast X-Rays (50%). 35% of the studies compared their model with other well-trained CNN models and 33% of them provided visualization for interpretation. Discussion: This study identified the most prevalent tracks of implementation in the literature for data preparation, methodology selection and output evaluation for medical image analysis. Also, we identified several critical research gaps existing in the TL studies on medical image analysis. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 174,468 |
2305.08611 | GeNAS: Neural Architecture Search with Better Generalization | Neural Architecture Search (NAS) aims to automatically excavate the optimal network architecture with superior test performance. Recent neural architecture search (NAS) approaches rely on validation loss or accuracy to find the superior network for the target data. In this paper, we investigate a new neural architecture search measure for excavating architectures with better generalization. We demonstrate that the flatness of the loss surface can be a promising proxy for predicting the generalization capability of neural network architectures. We evaluate our proposed method on various search spaces, showing similar or even better performance compared to the state-of-the-art NAS methods. Notably, the resultant architecture found by flatness measure generalizes robustly to various shifts in data distribution (e.g. ImageNet-V2,-A,-O), as well as various tasks such as object detection and semantic segmentation. Code is available at https://github.com/clovaai/GeNAS. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 364,344 |
2412.00085 | Residual Attention Single-Head Vision Transformer Network for Rolling
Bearing Fault Diagnosis in Noisy Environments | Rolling bearings play a crucial role in industrial machinery, directly influencing equipment performance, durability, and safety. However, harsh operating conditions, such as high speeds and temperatures, often lead to bearing malfunctions, resulting in downtime, economic losses, and safety hazards. This paper proposes the Residual Attention Single-Head Vision Transformer Network (RA-SHViT-Net) for fault diagnosis in rolling bearings. Vibration signals are transformed from the time to frequency domain using the Fast Fourier Transform (FFT) before being processed by RA-SHViT-Net. The model employs the Single-Head Vision Transformer (SHViT) to capture local and global features, balancing computational efficiency and predictive accuracy. To enhance feature extraction, the Adaptive Hybrid Attention Block (AHAB) integrates channel and spatial attention mechanisms. The network architecture includes Depthwise Convolution, Single-Head Self-Attention, Residual Feed-Forward Networks (Res-FFN), and AHAB modules, ensuring robust feature representation and mitigating gradient vanishing issues. Evaluation on the Case Western Reserve University and Paderborn University datasets demonstrates the RA-SHViT-Net's superior accuracy and robustness in complex, noisy environments. Ablation studies further validate the contributions of individual components, establishing RA-SHViT-Net as an effective tool for early fault detection and classification, promoting efficient maintenance strategies in industrial settings. Keywords: rolling bearings, fault diagnosis, Vision Transformer, attention mechanism, noisy environments, Fast Fourier Transform (FFT) | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 512,483 |
2101.03888 | The Value of Information and Efficient Switching in Channel Selection | We consider a collection of statistically identical two-state continuous time Markov chains (channels). A controller continuously selects a channel with the view of maximizing infinite horizon average reward. A switching cost is paid upon channel changes. We consider two cases: full observation (all channels observed simultaneously) and partial observation (only the current channel observed). We analyze the difference in performance between these cases for various policies. For the partial observation case with two channels, or an infinite number of channels, we explicitly characterize an optimal threshold for two sensible policies which we name "call-gapping" and "cool-off". Our results present a qualitative view on the interaction of the number of channels, the available information, and the switching costs. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 215,018 |
1610.08127 | Fast Bayesian Non-Negative Matrix Factorisation and Tri-Factorisation | We present a fast variational Bayesian algorithm for performing non-negative matrix factorisation and tri-factorisation. We show that our approach achieves faster convergence per iteration and timestep (wall-clock) than Gibbs sampling and non-probabilistic approaches, and do not require additional samples to estimate the posterior. We show that in particular for matrix tri-factorisation convergence is difficult, but our variational Bayesian approach offers a fast solution, allowing the tri-factorisation approach to be used more effectively. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | true | 62,890 |
2311.03223 | CarbonFish -- A Bistable Underactuated Compliant Fish Robot capable of
High Frequency Undulation | The Hair Clip Mechanism HCM represents an innovative in plane prestressed bistable mechanism, as delineated in our preceding studies, devised to augment the functional prowess of soft robotics. When juxtaposed with conventional soft and compliant robotic systems, HCMs exhibit pronounced rigidity, augmented mobility, reproducible repeatability, and an effective design and fabrication paradigm. In this research, we investigate the feasibility of utilizing carbon fiber reinforced plastic CFRP as the foundational material for an HCM based fish robot, herein referred to as CarbonFish. Our objective centers on realizing high frequency undulatory motion, thereby laying the groundwork for accelerated aquatic locomotion in subsequent models. We proffer an exhaustive design and fabrication schema underpinned by mathematical principles. Preliminary evaluations of our single actuated CarbonFish have evidenced an undulation frequency approaching 10 Hz, suggesting its potential to outperform other biologically inspired aquatic entities as well as real fish. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 405,764 |
2307.13372 | Submodular Reinforcement Learning | In reinforcement learning (RL), rewards of states are typically considered additive, and following the Markov assumption, they are $\textit{independent}$ of states visited previously. In many important applications, such as coverage control, experiment design and informative path planning, rewards naturally have diminishing returns, i.e., their value decreases in light of similar states visited previously. To tackle this, we propose $\textit{submodular RL}$ (SubRL), a paradigm which seeks to optimize more general, non-additive (and history-dependent) rewards modelled via submodular set functions which capture diminishing returns. Unfortunately, in general, even in tabular settings, we show that the resulting optimization problem is hard to approximate. On the other hand, motivated by the success of greedy algorithms in classical submodular optimization, we propose SubPO, a simple policy gradient-based algorithm for SubRL that handles non-additive rewards by greedily maximizing marginal gains. Indeed, under some assumptions on the underlying Markov Decision Process (MDP), SubPO recovers optimal constant factor approximations of submodular bandits. Moreover, we derive a natural policy gradient approach for locally optimizing SubRL instances even in large state- and action- spaces. We showcase the versatility of our approach by applying SubPO to several applications, such as biodiversity monitoring, Bayesian experiment design, informative path planning, and coverage maximization. Our results demonstrate sample efficiency, as well as scalability to high-dimensional state-action spaces. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 381,564 |
2110.03205 | Evolutionary Computation-Assisted Brainwriting for Large-Scale Online
Ideation | Brainstorming is an effective technique for offline ideation although the number of participants able to join an ideation session and suggest ideas is limited. To increase the diversity and quality of the ideas suggested, many participants with various backgrounds should be able to join the session. We have devised an evolutionary computation-assisted brainwriting method for large-scale online ideation. In this method, participants not only suggest ideas but also evaluate ideas previously suggested by other participants. The evaluation results are used in the evolutionary computation to identify good ideas to which the participants can be exposed via a brainwriting-like interface. We compared the performance of the proposed method with that of a simple online brainwriting method for large-scale online ideation with more than 30 participants. The proposed method enhanced robustness of idea quality improvement due to preferentially exposing the participants to good ideas. | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | 259,410 |
2005.04081 | Geometric graphs from data to aid classification tasks with graph
convolutional networks | Traditional classification tasks learn to assign samples to given classes based solely on sample features. This paradigm is evolving to include other sources of information, such as known relations between samples. Here we show that, even if additional relational information is not available in the data set, one can improve classification by constructing geometric graphs from the features themselves, and using them within a Graph Convolutional Network. The improvement in classification accuracy is maximized by graphs that capture sample similarity with relatively low edge density. We show that such feature-derived graphs increase the alignment of the data to the ground truth while improving class separation. We also demonstrate that the graphs can be made more efficient using spectral sparsification, which reduces the number of edges while still improving classification performance. We illustrate our findings using synthetic and real-world data sets from various scientific domains. | false | false | false | true | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 176,351 |
2203.07345 | Federated Cycling (FedCy): Semi-supervised Federated Learning of
Surgical Phases | Recent advancements in deep learning methods bring computer-assistance a step closer to fulfilling promises of safer surgical procedures. However, the generalizability of such methods is often dependent on training on diverse datasets from multiple medical institutions, which is a restrictive requirement considering the sensitive nature of medical data. Recently proposed collaborative learning methods such as Federated Learning (FL) allow for training on remote datasets without the need to explicitly share data. Even so, data annotation still represents a bottleneck, particularly in medicine and surgery where clinical expertise is often required. With these constraints in mind, we propose FedCy, a federated semi-supervised learning (FSSL) method that combines FL and self-supervised learning to exploit a decentralized dataset of both labeled and unlabeled videos, thereby improving performance on the task of surgical phase recognition. By leveraging temporal patterns in the labeled data, FedCy helps guide unsupervised training on unlabeled data towards learning task-specific features for phase recognition. We demonstrate significant performance gains over state-of-the-art FSSL methods on the task of automatic recognition of surgical phases using a newly collected multi-institutional dataset of laparoscopic cholecystectomy videos. Furthermore, we demonstrate that our approach also learns more generalizable features when tested on data from an unseen domain. | false | false | false | false | true | false | true | false | false | false | false | true | false | false | false | false | false | false | 285,400 |
1907.04096 | Efficient Pose Selection for Interactive Camera Calibration | The choice of poses for camera calibration with planar patterns is only rarely considered - yet the calibration precision heavily depends on it. This work presents a pose selection method that finds a compact and robust set of calibration poses and is suitable for interactive calibration. Consequently, singular poses that would lead to an unreliable solution are avoided explicitly, while poses reducing the uncertainty of the calibration are favoured. For this, we use uncertainty propagation. Our method takes advantage of a self-identifying calibration pattern to track the camera pose in real-time. This allows to iteratively guide the user to the target poses, until the desired quality level is reached. Therefore, only a sparse set of key-frames is needed for calibration. The method is evaluated on separate training and testing sets, as well as on synthetic data. Our approach performs better than comparable solutions while requiring 30% less calibration frames. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 138,020 |
2307.15273 | Recovering high-quality FODs from a reduced number of diffusion-weighted
images using a model-driven deep learning architecture | Fibre orientation distribution (FOD) reconstruction using deep learning has the potential to produce accurate FODs from a reduced number of diffusion-weighted images (DWIs), decreasing total imaging time. Diffusion acquisition invariant representations of the DWI signals are typically used as input to these methods to ensure that they can be applied flexibly to data with different b-vectors and b-values; however, this means the network cannot condition its output directly on the DWI signal. In this work, we propose a spherical deconvolution network, a model-driven deep learning FOD reconstruction architecture, that ensures intermediate and output FODs produced by the network are consistent with the input DWI signals. Furthermore, we implement a fixel classification penalty within our loss function, encouraging the network to produce FODs that can subsequently be segmented into the correct number of fixels and improve downstream fixel-based analysis. Our results show that the model-based deep learning architecture achieves competitive performance compared to a state-of-the-art FOD super-resolution network, FOD-Net. Moreover, we show that the fixel classification penalty can be tuned to offer improved performance with respect to metrics that rely on accurately segmented of FODs. Our code is publicly available at https://github.com/Jbartlett6/SDNet . | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 382,209 |
1505.01354 | Exploiting Known Interference as Green Signal Power for Downlink
Beamforming Optimization | We propose a data-aided transmit beamforming scheme for the multi-user multiple-input-single-output (MISO) downlink channel. While conventional beamforming schemes aim at the minimization of the transmit power subject to suppressing interference to guarantee quality of service (QoS) constraints, here we use the knowledge of both data and channel state information (CSI) at the transmitter to exploit, rather than suppress, constructive interference. More specifically, we design a new precoding scheme for the MISO downlink that minimizes the transmit power for generic phase shift keying (PSK) modulated signals. The proposed precoder reduces the transmit power compared to conventional schemes, by adapting the QoS constraints to accommodate constructive interference as a source of useful signal power. By exploiting the power of constructively interfering symbols, the proposed scheme achieves the required QoS at lower transmit power. We extend this concept to the signal to interference plus noise ratio (SINR) balancing problem, where higher SINR values compared to the conventional SINR balancing optimization are achieved for given transmit power budgets. In addition, we derive equivalent virtual multicast formulations for both optimizations, both of which provide insights of the optimal solution and facilitate the design of a more efficient solver. Finally, we propose a robust beamforming technique to deal with imperfect CSI, that also reduces the transmit power over conventional techniques, while guaranteeing the required QoS. Our simulation and analysis show significant power savings for small scale MISO downlink channels with the proposed data-aided optimization compared to conventional beamforming optimization. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 42,831 |
1604.02032 | 3-D Hand Pose Estimation from Kinect's Point Cloud Using Appearance
Matching | We present a novel appearance-based approach for pose estimation of a human hand using the point clouds provided by the low-cost Microsoft Kinect sensor. Both the free-hand case, in which the hand is isolated from the surrounding environment, and the hand-object case, in which the different types of interactions are classified, have been considered. The hand-object case is clearly the most challenging task having to deal with multiple tracks. The approach proposed here belongs to the class of partial pose estimation where the estimated pose in a frame is used for the initialization of the next one. The pose estimation is obtained by applying a modified version of the Iterative Closest Point (ICP) algorithm to synthetic models to obtain the rigid transformation that aligns each model with respect to the input data. The proposed framework uses a "pure" point cloud as provided by the Kinect sensor without any other information such as RGB values or normal vector components. For this reason, the proposed method can also be applied to data obtained from other types of depth sensor, or RGB-D camera. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 54,280 |
2401.00689 | Large language model for Bible sentiment analysis: Sermon on the Mount | The revolution of natural language processing via large language models has motivated its use in multidisciplinary areas that include social sciences and humanities and more specifically, comparative religion. Sentiment analysis provides a mechanism to study the emotions expressed in text. Recently, sentiment analysis has been used to study and compare translations of the Bhagavad Gita, which is a fundamental and sacred Hindu text. In this study, we use sentiment analysis for studying selected chapters of the Bible. These chapters are known as the Sermon on the Mount. We utilize a pre-trained language model for sentiment analysis by reviewing five translations of the Sermon on the Mount, which include the King James version, the New International Version, the New Revised Standard Version, the Lamsa Version, and the Basic English Version. We provide a chapter-by-chapter and verse-by-verse comparison using sentiment and semantic analysis and review the major sentiments expressed. Our results highlight the varying sentiments across the chapters and verses. We found that the vocabulary of the respective translations is significantly different. We detected different levels of humour, optimism, and empathy in the respective chapters that were used by Jesus to deliver his message. | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 419,073 |
2105.08949 | Multi-Contrast MRI Super-Resolution via a Multi-Stage Integration
Network | Super-resolution (SR) plays a crucial role in improving the image quality of magnetic resonance imaging (MRI). MRI produces multi-contrast images and can provide a clear display of soft tissues. However, current super-resolution methods only employ a single contrast, or use a simple multi-contrast fusion mechanism, ignoring the rich relations among different contrasts, which are valuable for improving SR. In this work, we propose a multi-stage integration network (i.e., MINet) for multi-contrast MRI SR, which explicitly models the dependencies between multi-contrast images at different stages to guide image SR. In particular, our MINet first learns a hierarchical feature representation from multiple convolutional stages for each of different-contrast image. Subsequently, we introduce a multi-stage integration module to mine the comprehensive relations between the representations of the multi-contrast images. Specifically, the module matches each representation with all other features, which are integrated in terms of their similarities to obtain an enriched representation. Extensive experiments on fastMRI and real-world clinical datasets demonstrate that 1) our MINet outperforms state-of-the-art multi-contrast SR methods in terms of various metrics and 2) our multi-stage integration module is able to excavate complex interactions among multi-contrast features at different stages, leading to improved target-image quality. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 235,917 |
2211.15616 | Weight Predictor Network with Feature Selection for Small Sample Tabular
Biomedical Data | Tabular biomedical data is often high-dimensional but with a very small number of samples. Although recent work showed that well-regularised simple neural networks could outperform more sophisticated architectures on tabular data, they are still prone to overfitting on tiny datasets with many potentially irrelevant features. To combat these issues, we propose Weight Predictor Network with Feature Selection (WPFS) for learning neural networks from high-dimensional and small sample data by reducing the number of learnable parameters and simultaneously performing feature selection. In addition to the classification network, WPFS uses two small auxiliary networks that together output the weights of the first layer of the classification model. We evaluate on nine real-world biomedical datasets and demonstrate that WPFS outperforms other standard as well as more recent methods typically applied to tabular data. Furthermore, we investigate the proposed feature selection mechanism and show that it improves performance while providing useful insights into the learning task. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 333,339 |
2404.05866 | GeniL: A Multilingual Dataset on Generalizing Language | Generative language models are transforming our digital ecosystem, but they often inherit societal biases, for instance stereotypes associating certain attributes with specific identity groups. While whether and how these biases are mitigated may depend on the specific use cases, being able to effectively detect instances of stereotype perpetuation is a crucial first step. Current methods to assess presence of stereotypes in generated language rely on simple template or co-occurrence based measures, without accounting for the variety of sentential contexts they manifest in. We argue that understanding the sentential context is crucial for detecting instances of generalization. We distinguish two types of generalizations: (1) language that merely mentions the presence of a generalization ("people think the French are very rude"), and (2) language that reinforces such a generalization ("as French they must be rude"), from non-generalizing context ("My French friends think I am rude"). For meaningful stereotype evaluations, we need to reliably distinguish such instances of generalizations. We introduce the new task of detecting generalization in language, and build GeniL, a multilingual dataset of over 50K sentences from 9 languages (English, Arabic, Bengali, Spanish, French, Hindi, Indonesian, Malay, and Portuguese) annotated for instances of generalizations. We demonstrate that the likelihood of a co-occurrence being an instance of generalization is usually low, and varies across different languages, identity groups, and attributes. We build classifiers to detect generalization in language with an overall PR-AUC of 58.7, with varying degrees of performance across languages. Our research provides data and tools to enable a nuanced understanding of stereotype perpetuation, a crucial step towards more inclusive and responsible language technologies. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 445,235 |
2407.06902 | Learning From Crowdsourced Noisy Labels: A Signal Processing Perspective | One of the primary catalysts fueling advances in artificial intelligence (AI) and machine learning (ML) is the availability of massive, curated datasets. A commonly used technique to curate such massive datasets is crowdsourcing, where data are dispatched to multiple annotators. The annotator-produced labels are then fused to serve downstream learning and inference tasks. This annotation process often creates noisy labels due to various reasons, such as the limited expertise, or unreliability of annotators, among others. Therefore, a core objective in crowdsourcing is to develop methods that effectively mitigate the negative impact of such label noise on learning tasks. This feature article introduces advances in learning from noisy crowdsourced labels. The focus is on key crowdsourcing models and their methodological treatments, from classical statistical models to recent deep learning-based approaches, emphasizing analytical insights and algorithmic developments. In particular, this article reviews the connections between signal processing (SP) theory and methods, such as identifiability of tensor and nonnegative matrix factorization, and novel, principled solutions of longstanding challenges in crowdsourcing -- showing how SP perspectives drive the advancements of this field. Furthermore, this article touches upon emerging topics that are critical for developing cutting-edge AI/ML systems, such as crowdsourcing in reinforcement learning with human feedback (RLHF) and direct preference optimization (DPO) that are key techniques for fine-tuning large language models (LLMs). | true | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 471,571 |
2108.07323 | Clustering augmented Self-Supervised Learning: Anapplication to Land
Cover Mapping | Collecting large annotated datasets in Remote Sensing is often expensive and thus can become a major obstacle for training advanced machine learning models. Common techniques of addressing this issue, based on the underlying idea of pre-training the Deep Neural Networks (DNN) on freely available large datasets, cannot be used for Remote Sensing due to the unavailability of such large-scale labeled datasets and the heterogeneity of data sources caused by the varying spatial and spectral resolution of different sensors. Self-supervised learning is an alternative approach that learns feature representation from unlabeled images without using any human annotations. In this paper, we introduce a new method for land cover mapping by using a clustering based pretext task for self-supervised learning. We demonstrate the effectiveness of the method on two societally relevant applications from the aspect of segmentation performance, discriminative feature representation learning and the underlying cluster structure. We also show the effectiveness of the active sampling using the clusters obtained from our method in improving the mapping accuracy given a limited budget of annotating. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 250,880 |
1707.02633 | Controlling Linguistic Style Aspects in Neural Language Generation | Most work on neural natural language generation (NNLG) focus on controlling the content of the generated text. We experiment with controlling several stylistic aspects of the generated text, in addition to its content. The method is based on conditioned RNN language model, where the desired content as well as the stylistic parameters serve as conditioning contexts. We demonstrate the approach on the movie reviews domain and show that it is successful in generating coherent sentences corresponding to the required linguistic style and content. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 76,734 |
2502.00264 | Beyond the Permutation Symmetry of Transformers: The Role of Rotation
for Model Fusion | Symmetry in the parameter space of deep neural networks (DNNs) has proven beneficial for various deep learning applications. A well-known example is the permutation symmetry in Multi-Layer Perceptrons (MLPs), where permuting the rows of weight matrices in one layer and applying the inverse permutation to adjacent layers yields a functionally equivalent model. While permutation symmetry fully characterizes the equivalence set for MLPs, its discrete nature limits its utility for transformers. In this paper, we introduce rotation symmetry, a novel form of parameter space symmetry for transformers that generalizes permutation symmetry by rotating parameter matrices in self-attention layers. Unlike permutation symmetry, rotation symmetry operates in a continuous domain, thereby significantly expanding the equivalence set for transformers. Based on this property, we propose a theoretically optimal parameter matching algorithm as a plug-and-play module to enhance model fusion. We evaluate our approach using pre-trained transformers across diverse natural language and vision tasks. Experimental results demonstrate that our rotation symmetry-based matching algorithm substantially improves model fusion, highlighting the potential of parameter space symmetry to facilitate model fusion. Our code is available on https://github.com/zhengzaiyi/RotationSymmetry. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 529,282 |
2009.12015 | Influence of segmentation accuracy in structural MR head scans on
electric field computation for TMS and tES | In several diagnosis and therapy procedures based on electrostimulation effect, the internal physical quantity related to the stimulation is the induced electric field. To estimate the induced electric field in an individual human model, the segmentation of anatomical imaging, such as (magnetic resonance image (MRI) scans, of the corresponding body parts into tissues is required. Then, electrical properties associated with different annotated tissues are assigned to the digital model to generate a volume conductor. An open question is how segmentation accuracy of different tissues would influence the distribution of the induced electric field. In this study, we applied parametric segmentation of different tissues to exploit the segmentation of available MRI to generate different quality of head models using deep learning neural network architecture, named ForkNet. Then, the induced electric field are compared to assess the effect of model segmentation variations. Computational results indicate that the influence of segmentation error is tissue-dependent. In brain, sensitivity to segmentation accuracy is relatively high in cerebrospinal fluid (CSF), moderate in gray matter (GM) and low in white matter for transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES). A CSF segmentation accuracy reduction of 10% in terms of Dice coefficient (DC) lead to decrease up to 4% in normalized induced electric field in both applications. However, a GM segmentation accuracy reduction of 5.6% DC leads to increase of normalized induced electric field up to 6%. Opposite trend of electric field variation was found between CSF and GM for both TMS and tES. The finding obtained here would be useful to quantify potential uncertainty of computational results. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 197,314 |
2207.13410 | Post-Train Adaptive MobileNet for Fast Anti-Spoofing | Many applications require high accuracy of neural networks as well as low latency and user data privacy guaranty. Face anti-spoofing is one of such tasks. However, a single model might not give the best results for different device performance categories, while training multiple models is time consuming. In this work we present Post-Train Adaptive (PTA) block. Such a block is simple in structure and offers a drop-in replacement for the MobileNetV2 Inverted Residual block. The PTA block has multiple branches with different computation costs. The branch to execute can be selected on-demand and at runtime; thus, offering different inference times and configuration capability for multiple device tiers. Crucially, the model is trained once and can be easily reconfigured after training, even directly on a mobile device. In addition, the proposed approach shows substantially better overall performance in comparison to the original MobileNetV2 as tested on CelebA-Spoof dataset. Different PTA block configurations are sampled at training time, which also decreases overall wall-clock time needed to train the model. While we present computational results for the anti-spoofing problem, the MobileNetV2 with PTA blocks is applicable to any problem solvable with convolutional neural networks, which makes the results presented practically significant. | false | false | false | false | false | false | true | false | false | false | false | true | true | false | false | false | false | false | 310,299 |
2205.14326 | Adaptive Activation Network For Low Resource Multilingual Speech
Recognition | Low resource automatic speech recognition (ASR) is a useful but thorny task, since deep learning ASR models usually need huge amounts of training data. The existing models mostly established a bottleneck (BN) layer by pre-training on a large source language, and transferring to the low resource target language. In this work, we introduced an adaptive activation network to the upper layers of ASR model, and applied different activation functions to different languages. We also proposed two approaches to train the model: (1) cross-lingual learning, replacing the activation function from source language to target language, (2) multilingual learning, jointly training the Connectionist Temporal Classification (CTC) loss of each language and the relevance of different languages. Our experiments on IARPA Babel datasets demonstrated that our approaches outperform the from-scratch training and traditional bottleneck feature based methods. In addition, combining the cross-lingual learning and multilingual learning together could further improve the performance of multilingual speech recognition. | false | false | true | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 299,316 |
1808.09572 | Cycle-of-Learning for Autonomous Systems from Human Interaction | We discuss different types of human-robot interaction paradigms in the context of training end-to-end reinforcement learning algorithms. We provide a taxonomy to categorize the types of human interaction and present our Cycle-of-Learning framework for autonomous systems that combines different human-interaction modalities with reinforcement learning. Two key concepts provided by our Cycle-of-Learning framework are how it handles the integration of the different human-interaction modalities (demonstration, intervention, and evaluation) and how to define the switching criteria between them. | true | false | false | false | true | false | false | true | false | false | false | false | false | false | false | false | false | false | 106,221 |
2501.04239 | Dynamic Localisation of Spatial-Temporal Graph Neural Network | Spatial-temporal data, fundamental to many intelligent applications, reveals dependencies indicating causal links between present measurements at specific locations and historical data at the same or other locations. Within this context, adaptive spatial-temporal graph neural networks (ASTGNNs) have emerged as valuable tools for modelling these dependencies, especially through a data-driven approach rather than pre-defined spatial graphs. While this approach offers higher accuracy, it presents increased computational demands. Addressing this challenge, this paper delves into the concept of localisation within ASTGNNs, introducing an innovative perspective that spatial dependencies should be dynamically evolving over time. We introduce \textit{DynAGS}, a localised ASTGNN framework aimed at maximising efficiency and accuracy in distributed deployment. This framework integrates dynamic localisation, time-evolving spatial graphs, and personalised localisation, all orchestrated around the Dynamic Graph Generator, a light-weighted central module leveraging cross attention. The central module can integrate historical information in a node-independent manner to enhance the feature representation of nodes at the current moment. This improved feature representation is then used to generate a dynamic sparse graph without the need for costly data exchanges, and it supports personalised localisation. Performance assessments across two core ASTGNN architectures and nine real-world datasets from various applications reveal that \textit{DynAGS} outshines current benchmarks, underscoring that the dynamic modelling of spatial dependencies can drastically improve model expressibility, flexibility, and system efficiency, especially in distributed settings. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 523,144 |
2310.11465 | BaitBuster-Bangla: A Comprehensive Dataset for Clickbait Detection in
Bangla with Multi-Feature and Multi-Modal Analysis | This study presents a large multi-modal Bangla YouTube clickbait dataset consisting of 253,070 data points collected through an automated process using the YouTube API and Python web automation frameworks. The dataset contains 18 diverse features categorized into metadata, primary content, engagement statistics, and labels for individual videos from 58 Bangla YouTube channels. A rigorous preprocessing step has been applied to denoise, deduplicate, and remove bias from the features, ensuring unbiased and reliable analysis. As the largest and most robust clickbait corpus in Bangla to date, this dataset provides significant value for natural language processing and data science researchers seeking to advance modeling of clickbait phenomena in low-resource languages. Its multi-modal nature allows for comprehensive analyses of clickbait across content, user interactions, and linguistic dimensions to develop more sophisticated detection methods with cross-linguistic applications. | false | false | false | false | true | false | true | false | true | false | false | false | false | false | false | false | false | false | 400,650 |
2303.08387 | Learning to Place Unseen Objects Stably using a Large-scale Simulation | Object placement is a fundamental task for robots, yet it remains challenging for partially observed objects. Existing methods for object placement have limitations, such as the requirement for a complete 3D model of the object or the inability to handle complex shapes and novel objects that restrict the applicability of robots in the real world. Herein, we focus on addressing the Unseen Object Placement (UOP}=) problem. We tackled the UOP problem using two methods: (1) UOP-Sim, a large-scale dataset to accommodate various shapes and novel objects, and (2) UOP-Net, a point cloud segmentation-based approach that directly detects the most stable plane from partial point clouds. Our UOP approach enables robots to place objects stably, even when the object's shape and properties are not fully known, thus providing a promising solution for object placement in various environments. We verify our approach through simulation and real-world robot experiments, demonstrating state-of-the-art performance for placing single-view and partial objects. Robot demos, codes, and dataset are available at https://gistailab.github.io/uop/ | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 351,624 |
2405.07764 | LGDE: Local Graph-based Dictionary Expansion | We present Local Graph-based Dictionary Expansion (LGDE), a method for data-driven discovery of the semantic neighbourhood of words using tools from manifold learning and network science. At the heart of LGDE lies the creation of a word similarity graph from the geometry of word embeddings followed by local community detection based on graph diffusion. The diffusion in the local graph manifold allows the exploration of the complex nonlinear geometry of word embeddings to capture word similarities based on paths of semantic association, over and above direct pairwise similarities. Exploiting such semantic neighbourhoods enables the expansion of dictionaries of pre-selected keywords, an important step for tasks in information retrieval, such as database queries and online data collection. We validate LGDE on two user-generated English-language corpora and show that LGDE enriches the list of keywords with improved performance relative to methods based on direct word similarities or co-occurrences. We further demonstrate our method through a real-world use case from communication science, where LGDE is evaluated quantitatively on the expansion of a conspiracy-related dictionary from online data collected and analysed by domain experts. Our empirical results and expert user assessment indicate that LGDE expands the seed dictionary with more useful keywords due to the manifold-learning-based similarity network. | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 453,846 |
2203.15162 | A Distribution Evolutionary Algorithm for the Graph Coloring Problem | Graph coloring is a challenging combinatorial optimization problem with a wide range of applications. In this paper, a distribution evolutionary algorithm based on a population of probability model (DEA-PPM) is developed to address it efficiently. Unlike existing estimation of distribution algorithms where a probability model is updated by generated solutions, DEA-PPM employs a distribution population based on a novel probability model, and an orthogonal exploration strategy is introduced to search the distribution space with the assistance of an refinement strategy. By sampling the distribution population, efficient search in the solution space is realized based on a tabu search process. Meanwhile, DEA-PPM introduces an iterative vertex removal strategy to improve the efficiency of $k$-coloring, and an inherited initialization strategy is implemented to address the chromatic problem well. The cooperative evolution of the distribution population and the solution population leads to a good balance between exploration and exploitation. Numerical results demonstrate that the DEA-PPM of small population size is competitive to the state-of-the-art metaheuristics.utes to its competitiveness to the state-of-the-art metaheuristics. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | 288,263 |
1412.6483 | Temperature diagnostics of the solar atmosphere using SunPy | The solar atmosphere is a hot (about 1MK), magnetised plasma of great interest to physicists. There have been many previous studies of the temperature of the Sun's atmosphere (Plowman2012, Wit2012, Hannah2012, Aschwanden2013, etc.). Almost all of these studies use the SolarSoft software package written in the commercial Interactive Data Language (IDL), which has been the standard language for solar physics. The SunPy project aims to provide an open-source library for solar physics. This work presents (to the authors' knowledge) the first study of its type to use SunPy rather than SolarSoft. This work uses SunPy to process multi-wavelength solar observations made by the Atmospheric Imaging Assembly (AIA) instrument aboard the Solar Dynamics Observatory (SDO) and produce temperature maps of the Sun's atmosphere. The method uses SunPy's utilities for querying databases of solar events, downloading solar image data, storing and processing images as spatially aware Map objects, and tracking solar features as the Sun rotates. An essential consideration in developing this software is computational efficiency due to the large amount of data collected by AIA/SDO, and in anticipating new solar missions which will result in even larger sets of data. An overview of the method and implementation is given, along with tests involving synthetic data and examples of results using real data for various regions in the Sun's atmosphere. | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 38,638 |
2403.08680 | Towards the THz Networks in the 6G Era | This commentary dedicates to envision what role THz is going to play in the coming human-centric 6G era. Three distinct THz network types including outdoor, indoor, and body area networks are discussed, with an emphasis on their capabilities in human body detection. Synthesizing these networks will unlock a bunch of fascinating applications across industrial, biomedical and entertainment fields, significantly enhancing the quality of human life. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 437,425 |
2111.11177 | Deep Learning for Beam-Management: State-of-the-Art, Opportunities and
Challenges | Benefiting from huge bandwidth resources, millimeter-wave (mmWave) communications provide one of the most promising technologies for next-generation wireless networks. To compensate for the high pathloss of mmWave signals, large-scale antenna arrays are required both at the base stations and user equipment to establish directional beamforming, where beam-management is adopted to acquire and track the optimal beam pair having the maximum received power. Naturally, narrow beams are required for achieving high beamforming gain, but they impose enormous training overhead and high sensitivity to blockages. As a remedy, deep learning (DL) may be harnessed for beam-management. First, the current state-of-the-art is reviewed, followed by the associated challenges and future research opportunities. We conclude by highlighting the associated DL design insights and novel beam-management mechanisms. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 267,577 |
1708.04429 | Smart Meter Privacy via the Trapdoor Channel | A battery charging policy that provides privacy guarantees for smart meter systems with finite capacity battery is proposed. For this policy an upper bound on the information leakage rate is provided. The upper bound applies for general random processes modelling the energy consumption of the user. It is shown that the average energy consumption of the user determines the information leakage rate to the utility provider. The upper bound is shown to be tight by deriving the probability law of a random process achieving the bound. | false | false | false | false | false | false | false | false | false | true | true | false | false | false | false | false | false | false | 78,945 |
2207.07868 | CLOSE: Curriculum Learning On the Sharing Extent Towards Better One-shot
NAS | One-shot Neural Architecture Search (NAS) has been widely used to discover architectures due to its efficiency. However, previous studies reveal that one-shot performance estimations of architectures might not be well correlated with their performances in stand-alone training because of the excessive sharing of operation parameters (i.e., large sharing extent) between architectures. Thus, recent methods construct even more over-parameterized supernets to reduce the sharing extent. But these improved methods introduce a large number of extra parameters and thus cause an undesirable trade-off between the training costs and the ranking quality. To alleviate the above issues, we propose to apply Curriculum Learning On Sharing Extent (CLOSE) to train the supernet both efficiently and effectively. Specifically, we train the supernet with a large sharing extent (an easier curriculum) at the beginning and gradually decrease the sharing extent of the supernet (a harder curriculum). To support this training strategy, we design a novel supernet (CLOSENet) that decouples the parameters from operations to realize a flexible sharing scheme and adjustable sharing extent. Extensive experiments demonstrate that CLOSE can obtain a better ranking quality across different computational budget constraints than other one-shot supernets, and is able to discover superior architectures when combined with various search strategies. Code is available at https://github.com/walkerning/aw_nas. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 308,349 |
1210.4850 | Markov Determinantal Point Processes | A determinantal point process (DPP) is a random process useful for modeling the combinatorial problem of subset selection. In particular, DPPs encourage a random subset Y to contain a diverse set of items selected from a base set Y. For example, we might use a DPP to display a set of news headlines that are relevant to a user's interests while covering a variety of topics. Suppose, however, that we are asked to sequentially select multiple diverse sets of items, for example, displaying new headlines day-by-day. We might want these sets to be diverse not just individually but also through time, offering headlines today that are unlike the ones shown yesterday. In this paper, we construct a Markov DPP (M-DPP) that models a sequence of random sets {Yt}. The proposed M-DPP defines a stationary process that maintains DPP margins. Crucially, the induced union process Zt = Yt u Yt-1 is also marginally DPP-distributed. Jointly, these properties imply that the sequence of random sets are encouraged to be diverse both at a given time step as well as across time steps. We describe an exact, efficient sampling procedure, and a method for incrementally learning a quality measure over items in the base set Y based on external preferences. We apply the M-DPP to the task of sequentially displaying diverse and relevant news articles to a user with topic preferences. | false | false | false | false | false | true | true | false | false | false | false | false | false | false | false | false | false | false | 19,177 |
2401.05392 | AT-2FF: Adaptive Type-2 Fuzzy Filter for De-noising Images Corrupted
with Salt-and-Pepper | Noise is inevitably common in digital images, leading to visual image deterioration. Therefore, a suitable filtering method is required to lessen the noise while preserving the image features (edges, corners, etc.). This paper presents the efficient type-2 fuzzy weighted mean filter with an adaptive threshold to remove the SAP noise. The present filter has two primary steps: The first stage categorizes images as lightly, medium, and heavily corrupted based on an adaptive threshold by comparing the M-ALD of processed pixels with the upper and lower MF of the type-2 fuzzy identifier. The second stage eliminates corrupted pixels by computing the appropriate weight using GMF with the mean and variance of the uncorrupted pixels in the filter window. Simulation results vividly show that the obtained denoised images preserve image features, i.e., edges, corners, and other sharp structures, compared with different filtering methods. | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | false | false | 420,753 |
2501.07102 | AdaCS: Adaptive Normalization for Enhanced Code-Switching ASR | Intra-sentential code-switching (CS) refers to the alternation between languages that happens within a single utterance and is a significant challenge for Automatic Speech Recognition (ASR) systems. For example, when a Vietnamese speaker uses foreign proper names or specialized terms within their speech. ASR systems often struggle to accurately transcribe intra-sentential CS due to their training on monolingual data and the unpredictable nature of CS. This issue is even more pronounced for low-resource languages, where limited data availability hinders the development of robust models. In this study, we propose AdaCS, a normalization model integrates an adaptive bias attention module (BAM) into encoder-decoder network. This novel approach provides a robust solution to CS ASR in unseen domains, thereby significantly enhancing our contribution to the field. By utilizing BAM to both identify and normalize CS phrases, AdaCS enhances its adaptive capabilities with a biased list of words provided during inference. Our method demonstrates impressive performance and the ability to handle unseen CS phrases across various domains. Experiments show that AdaCS outperforms previous state-of-the-art method on Vietnamese CS ASR normalization by considerable WER reduction of 56.2% and 36.8% on the two proposed test sets. | false | false | true | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 524,265 |
1512.01569 | Social networks, happiness and health: from sentiment analysis to a
multidimensional indicator of subjective well-being | This paper applies a novel technique of opinion analysis over social media data with the aim of proposing a new indicator of perceived and subjective well-being. This new index, namely SWBI, examines several dimension of individual and social life. The indicator has been compared to some other existing indexes of well-being and health conditions in Italy: the BES (Benessere Equo Sostenibile), the incidence rate of influenza and the abundance of PM10 in urban environments. SWBI is a daily measure available at province level. BES data, currently available only for 2013 and 2014, are annual and available at regional level. Flu data are weekly and distributed as regional data and PM10 are collected daily for different cities. Due to the fact that the time scale and space granularity of the different indexes varies, we apply a novel statistical technique to discover nowcasting features and the classical latent analysis to study the relationships among them. A preliminary analysis suggest that the environmental and health conditions anticipate several dimensions of the perception of well-being as measured by SWBI. Moreover, the set of indicators included in the BES represent a latent dimension of well-being which shares similarities with the latent dimension represented by SWBI. | false | false | false | true | false | false | false | false | false | false | false | false | false | true | false | false | false | false | 49,828 |
1909.06628 | Temporal FiLM: Capturing Long-Range Sequence Dependencies with
Feature-Wise Modulations | Learning representations that accurately capture long-range dependencies in sequential inputs -- including text, audio, and genomic data -- is a key problem in deep learning. Feed-forward convolutional models capture only feature interactions within finite receptive fields while recurrent architectures can be slow and difficult to train due to vanishing gradients. Here, we propose Temporal Feature-Wise Linear Modulation (TFiLM) -- a novel architectural component inspired by adaptive batch normalization and its extensions -- that uses a recurrent neural network to alter the activations of a convolutional model. This approach expands the receptive field of convolutional sequence models with minimal computational overhead. Empirically, we find that TFiLM significantly improves the learning speed and accuracy of feed-forward neural networks on a range of generative and discriminative learning tasks, including text classification and audio super-resolution | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 145,427 |
2406.06422 | Notes on Various Errors and Jacobian Derivations for SLAM | This paper delves into critical concepts and meticulous calculations pertinent to Simultaneous Localization and Mapping (SLAM), with a focus on error analysis and Jacobian matrices. We introduce various types of errors commonly encountered in SLAM, including reprojection error, photometric error, relative pose error, and line reprojection error, alongside their mathematical formulations. The fundamental role of error as the discrepancy between observed and predicted values in SLAM optimization is examined, emphasizing non-linear least squares methods for optimization. We provide a detailed analysis of: - Reprojection Error: Including Jacobian calculations for camera poses and map points, highlighting both theoretical underpinnings and practical consequences. - Photometric Error: Addressing errors from image intensity variations, essential for direct method-based SLAM. - Relative Pose Error: Discussing its significance in pose graph optimization, especially in loop closure scenarios. The paper also presents extensive derivations of Jacobian matrices for various SLAM components such as camera poses, map points, and motion parameters. We explore the application of Lie theory to optimize rotation representations and transformations, improving computational efficiency. Specific software implementations are referenced, offering practical insights into the real-world application of these theories in SLAM systems. Additionally, advanced topics such as line reprojection errors and IMU measurement errors are explored, discussing their impact on SLAM accuracy and performance. This comprehensive examination aims to enhance understanding and implementation of error analysis and Jacobian derivation in SLAM, contributing to more accurate and efficient state estimation in complex environments. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 462,566 |
2407.03255 | How Similar Are Elected Politicians and Their Constituents? Quantitative
Evidence From Online Social Networks | How similar are politicians to those who vote for them? This is a critical question at the heart of democratic representation and particularly relevant at times when political dissatisfaction and populism are on the rise. To answer this question we compare the online discourse of elected politicians and their constituents. We collect a two and a half years (September 2020 - February 2023) constituency-level dataset for USA and UK that includes: (i) the Twitter timelines (5.6 Million tweets) of elected political representatives (595 UK Members of Parliament and 433 USA Representatives), (ii) the Nextdoor posts (21.8 Million posts) of the constituency (98.4% USA and 91.5% UK constituencies). We find that elected politicians tend to be equally similar to their constituents in terms of content and style regardless of whether a constituency elects a right or left-wing politician. The size of the electoral victory and the level of income of a constituency shows a nuanced picture. The narrower the electoral victory, the more similar the style and the more dissimilar the content is. The lower the income of a constituency, the more similar the content is. In terms of style, poorer constituencies tend to have a more similar sentiment and more dissimilar psychological text traits (i.e. measured with LIWC categories). | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 470,091 |
1501.06115 | Constrained Extreme Learning Machines: A Study on Classification Cases | Extreme learning machine (ELM) is an extremely fast learning method and has a powerful performance for pattern recognition tasks proven by enormous researches and engineers. However, its good generalization ability is built on large numbers of hidden neurons, which is not beneficial to real time response in the test process. In this paper, we proposed new ways, named "constrained extreme learning machines" (CELMs), to randomly select hidden neurons based on sample distribution. Compared to completely random selection of hidden nodes in ELM, the CELMs randomly select hidden nodes from the constrained vector space containing some basic combinations of original sample vectors. The experimental results show that the CELMs have better generalization ability than traditional ELM, SVM and some other related methods. Additionally, the CELMs have a similar fast learning speed as ELM. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | true | false | false | 39,572 |
0903.1945 | Hessian and concavity of mutual information, differential entropy, and
entropy power in linear vector Gaussian channels | Within the framework of linear vector Gaussian channels with arbitrary signaling, closed-form expressions for the Jacobian of the minimum mean square error and Fisher information matrices with respect to arbitrary parameters of the system are calculated in this paper. Capitalizing on prior research where the minimum mean square error and Fisher information matrices were linked to information-theoretic quantities through differentiation, closed-form expressions for the Hessian of the mutual information and the differential entropy are derived. These expressions are then used to assess the concavity properties of mutual information and differential entropy under different channel conditions and also to derive a multivariate version of the entropy power inequality due to Costa. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 3,329 |
1802.07756 | Determining the best classifier for predicting the value of a boolean
field on a blood donor database using genetic algorithms | Motivation: Thanks to digitization, we often have access to large databases, consisting of various fields of information, ranging from numbers to texts and even boolean values. Such databases lend themselves especially well to machine learning, classification and big data analysis tasks. We are able to train classifiers, using already existing data and use them for predicting the values of a certain field, given that we have information regarding the other fields. Most specifically, in this study, we look at the Electronic Health Records (EHRs) that are compiled by hospitals. These EHRs are convenient means of accessing data of individual patients, but there processing as a whole still remains a task. However, EHRs that are composed of coherent, well-tabulated structures lend themselves quite well to the application to machine language, via the usage of classifiers. In this study, we look at a Blood Transfusion Service Center Data Set (Data taken from the Blood Transfusion Service Center in Hsin-Chu City in Taiwan). We used scikit-learn machine learning in python. From Support Vector Machines(SVM), we use Support Vector Classification(SVC), from the linear model we import Perceptron. We also used the K.neighborsclassifier and the decision tree classifiers. Furthermore, we use the TPOT library to find an optimized pipeline using genetic algorithms. Using the above classifiers, we score each one of them using k fold cross-validation. Contact: ritabratamaiti@hiretrex.com GitHub Repository: https://github.com/ritabratamaiti/Blooddonorprediction | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 90,945 |
2409.05556 | SciAgents: Automating scientific discovery through multi-agent
intelligent graph reasoning | A key challenge in artificial intelligence is the creation of systems capable of autonomously advancing scientific understanding by exploring novel domains, identifying complex patterns, and uncovering previously unseen connections in vast scientific data. In this work, we present SciAgents, an approach that leverages three core concepts: (1) the use of large-scale ontological knowledge graphs to organize and interconnect diverse scientific concepts, (2) a suite of large language models (LLMs) and data retrieval tools, and (3) multi-agent systems with in-situ learning capabilities. Applied to biologically inspired materials, SciAgents reveals hidden interdisciplinary relationships that were previously considered unrelated, achieving a scale, precision, and exploratory power that surpasses traditional human-driven research methods. The framework autonomously generates and refines research hypotheses, elucidating underlying mechanisms, design principles, and unexpected material properties. By integrating these capabilities in a modular fashion, the intelligent system yields material discoveries, critique and improve existing hypotheses, retrieve up-to-date data about existing research, and highlights their strengths and limitations. Our case studies demonstrate scalable capabilities to combine generative AI, ontological representations, and multi-agent modeling, harnessing a `swarm of intelligence' similar to biological systems. This provides new avenues for materials discovery and accelerates the development of advanced materials by unlocking Nature's design principles. | false | false | false | false | true | false | true | false | true | false | false | false | false | false | false | false | false | false | 486,817 |
2006.04721 | Modeling Discourse Structure for Document-level Neural Machine
Translation | Recently, document-level neural machine translation (NMT) has become a hot topic in the community of machine translation. Despite its success, most of existing studies ignored the discourse structure information of the input document to be translated, which has shown effective in other tasks. In this paper, we propose to improve document-level NMT with the aid of discourse structure information. Our encoder is based on a hierarchical attention network (HAN). Specifically, we first parse the input document to obtain its discourse structure. Then, we introduce a Transformer-based path encoder to embed the discourse structure information of each word. Finally, we combine the discourse structure information with the word embedding before it is fed into the encoder. Experimental results on the English-to-German dataset show that our model can significantly outperform both Transformer and Transformer+HAN. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 180,796 |
2108.11042 | Localization Uncertainty-Based Attention for Object Detection | Object detection has been applied in a wide variety of real world scenarios, so detection algorithms must provide confidence in the results to ensure that appropriate decisions can be made based on their results. Accordingly, several studies have investigated the probabilistic confidence of bounding box regression. However, such approaches have been restricted to anchor-based detectors, which use box confidence values as additional screening scores during non-maximum suppression (NMS) procedures. In this paper, we propose a more efficient uncertainty-aware dense detector (UADET) that predicts four-directional localization uncertainties via Gaussian modeling. Furthermore, a simple uncertainty attention module (UAM) that exploits box confidence maps is proposed to improve performance through feature refinement. Experiments using the MS COCO benchmark show that our UADET consistently surpasses baseline FCOS, and that our best model, ResNext-64x4d-101-DCN, obtains a single model, single-scale AP of 48.3% on COCO test-dev, thus achieving the state-of-the-art among various object detectors. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 252,073 |
1301.3601 | Statistical Analysis of Self-Organizing Networks with Biased Cell
Association and Interference Avoidance | In this work, we assess the viability of heterogeneous networks composed of legacy macrocells which are underlaid with self-organizing picocells. Aiming to improve coverage, cell-edge throughput and overall system capacity, self-organizing solutions, such as range expansion bias, almost blank subframe and distributed antenna systems are considered. Herein, stochastic geometry is used to model network deployments, while higher-order statistics through the cumulants concept is utilized to characterize the probability distribution of the received power and aggregate interference at the user of interest. A compre- hensive analytical framework is introduced to evaluate the performance of such self-organizing networks in terms of outage probability and average channel capacity with respect to the tagged receiver. To conduct our studies, we consider a shadowed fading channel model incorporating log-normal shadowing and Nakagami-m fading. Results show that the analytical framework matches well with numerical results obtained from Monte Carlo simulations. We also observed that by simply using almost blank subframes the aggregate interference at the tagged receiver is reduced by about 12dB. Although more elaborated interference control techniques such as, downlink bitmap and distributed antennas systems become needed, when the density of picocells in the underlaid tier gets high. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | true | 21,120 |
2011.12981 | Capacity Region of Two-users Weak Gaussian Interference Channel | Computing capacity of Gaussian Interference Channel (GIC) is complex since knowledge of input distributions is needed to find the mutual information terms in closed forms, which should be optimized over input distributions and associated resource allocation. The optimum solution may require dividing the available resources among several GIC (each called a "constituent region", hereafter) and apply time-sharing among them. The current article focuses on a single constituent region (meaning the constraints on resources are all satisfied with equality) for a 2-users weak GIC. It is shown that, by relying on a different, intuitively straightforward, interpretation of the underlying optimization problem, one can determine the encoding/decoding strategies in the process of computing the optimum solution. This is based on gradually moving along the boundary of the capacity region in infinitesimal steps, where the solution for the end point in each step is optimized relying on the solution at the step's starting point. This approach enables proving Gaussian distribution is optimum over the entire boundary, and also allows finding simple closed form solutions describing different parts of the capacity region. The solution for each constituent 2-users GIC coincides with the optimum solution to the Han Kobayashi (HK) system of constraints with i.i.d. (scalar) Gaussian inputs. Although the article is focused on 2-users weak GIC, the proof for optimality of Gaussian distribution is independent of the values of cross gains, and thereby is universally applicable to strong, mixed and Z interference channels, as well as to GIC with more than two users. In addition, the procedure for the construction of boundary is applicable for arbitrary cross gain values, by re-deriving various conditions that have been established assuming cross gains being less than one. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 208,331 |
1608.07775 | Hierarchical Attention Model for Improved Machine Comprehension of
Spoken Content | Multimedia or spoken content presents more attractive information than plain text content, but the former is more difficult to display on a screen and be selected by a user. As a result, accessing large collections of the former is much more difficult and time-consuming than the latter for humans. It's therefore highly attractive to develop machines which can automatically understand spoken content and summarize the key information for humans to browse over. In this endeavor, a new task of machine comprehension of spoken content was proposed recently. The initial goal was defined as the listening comprehension test of TOEFL, a challenging academic English examination for English learners whose native languages are not English. An Attention-based Multi-hop Recurrent Neural Network (AMRNN) architecture was also proposed for this task, which considered only the sequential relationship within the speech utterances. In this paper, we propose a new Hierarchical Attention Model (HAM), which constructs multi-hopped attention mechanism over tree-structured rather than sequential representations for the utterances. Improved comprehension performance robust with respect to ASR errors were obtained. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 60,268 |
2311.06922 | A Survey on Socially Aware Robot Navigation: Taxonomy and Future
Challenges | Socially aware robot navigation is gaining popularity with the increase in delivery and assistive robots. The research is further fueled by a need for socially aware navigation skills in autonomous vehicles to move safely and appropriately in spaces shared with humans. Although most of these are ground robots, drones are also entering the field. In this paper, we present a literature survey of the works on socially aware robot navigation in the past 10 years. We propose four different faceted taxonomies to navigate the literature and examine the field from four different perspectives. Through the taxonomic review, we discuss the current research directions and the extending scope of applications in various domains. Further, we put forward a list of current research opportunities and present a discussion on possible future challenges that are likely to emerge in the field. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 407,131 |
2102.09808 | Improving Anytime Prediction with Parallel Cascaded Networks and a
Temporal-Difference Loss | Although deep feedforward neural networks share some characteristics with the primate visual system, a key distinction is their dynamics. Deep nets typically operate in serial stages wherein each layer completes its computation before processing begins in subsequent layers. In contrast, biological systems have cascaded dynamics: information propagates from neurons at all layers in parallel but transmission occurs gradually over time, leading to speed-accuracy trade offs even in feedforward architectures. We explore the consequences of biologically inspired parallel hardware by constructing cascaded ResNets in which each residual block has propagation delays but all blocks update in parallel in a stateful manner. Because information transmitted through skip connections avoids delays, the functional depth of the architecture increases over time, yielding anytime predictions that improve with internal-processing time. We introduce a temporal-difference training loss that achieves a strictly superior speed-accuracy profile over standard losses and enables the cascaded architecture to outperform state-of-the-art anytime-prediction methods. The cascaded architecture has intriguing properties, including: it classifies typical instances more rapidly than atypical instances; it is more robust to both persistent and transient noise than is a conventional ResNet; and its time-varying output trace provides a signal that can be exploited to improve information processing and inference. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 220,902 |
1910.03009 | Improving Neural Machine Translation Robustness via Data Augmentation:
Beyond Back Translation | Neural Machine Translation (NMT) models have been proved strong when translating clean texts, but they are very sensitive to noise in the input. Improving NMT models robustness can be seen as a form of "domain" adaption to noise. The recently created Machine Translation on Noisy Text task corpus provides noisy-clean parallel data for a few language pairs, but this data is very limited in size and diversity. The state-of-the-art approaches are heavily dependent on large volumes of back-translated data. This paper has two main contributions: Firstly, we propose new data augmentation methods to extend limited noisy data and further improve NMT robustness to noise while keeping the models small. Secondly, we explore the effect of utilizing noise from external data in the form of speech transcripts and show that it could help robustness. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 148,383 |
2104.04329 | Learning Position and Target Consistency for Memory-based Video Object
Segmentation | This paper studies the problem of semi-supervised video object segmentation(VOS). Multiple works have shown that memory-based approaches can be effective for video object segmentation. They are mostly based on pixel-level matching, both spatially and temporally. The main shortcoming of memory-based approaches is that they do not take into account the sequential order among frames and do not exploit object-level knowledge from the target. To address this limitation, we propose to Learn position and target Consistency framework for Memory-based video object segmentation, termed as LCM. It applies the memory mechanism to retrieve pixels globally, and meanwhile learns position consistency for more reliable segmentation. The learned location response promotes a better discrimination between target and distractors. Besides, LCM introduces an object-level relationship from the target to maintain target consistency, making LCM more robust to error drifting. Experiments show that our LCM achieves state-of-the-art performance on both DAVIS and Youtube-VOS benchmark. And we rank the 1st in the DAVIS 2020 challenge semi-supervised VOS task. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 229,361 |
2210.14457 | Implicit Identity Leakage: The Stumbling Block to Improving Deepfake
Detection Generalization | In this paper, we analyse the generalization ability of binary classifiers for the task of deepfake detection. We find that the stumbling block to their generalization is caused by the unexpected learned identity representation on images. Termed as the Implicit Identity Leakage, this phenomenon has been qualitatively and quantitatively verified among various DNNs. Furthermore, based on such understanding, we propose a simple yet effective method named the ID-unaware Deepfake Detection Model to reduce the influence of this phenomenon. Extensive experimental results demonstrate that our method outperforms the state-of-the-art in both in-dataset and cross-dataset evaluation. The code is available at https://github.com/megvii-research/CADDM. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 326,552 |
2210.07373 | Mind the Labels: Describing Relations in Knowledge Graphs With
Pretrained Models | Pretrained language models (PLMs) for data-to-text (D2T) generation can use human-readable data labels such as column headings, keys, or relation names to generalize to out-of-domain examples. However, the models are well-known in producing semantically inaccurate outputs if these labels are ambiguous or incomplete, which is often the case in D2T datasets. In this paper, we expose this issue on the task of descibing a relation between two entities. For our experiments, we collect a novel dataset for verbalizing a diverse set of 1,522 unique relations from three large-scale knowledge graphs (Wikidata, DBPedia, YAGO). We find that although PLMs for D2T generation expectedly fail on unclear cases, models trained with a large variety of relation labels are surprisingly robust in verbalizing novel, unseen relations. We argue that using data with a diverse set of clear and meaningful labels is key to training D2T generation systems capable of generalizing to novel domains. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 323,675 |
2406.02597 | CoNO: Complex Neural Operator for Continous Dynamical Physical Systems | Neural operators extend data-driven models to map between infinite-dimensional functional spaces. While these operators perform effectively in either the time or frequency domain, their performance may be limited when applied to non-stationary spatial or temporal signals whose frequency characteristics change with time. Here, we introduce Complex Neural Operator (CoNO) that parameterizes the integral kernel using Fractional Fourier Transform (FrFT), better representing non-stationary signals in a complex-valued domain. Theoretically, we prove the universal approximation capability of CoNO. We perform an extensive empirical evaluation of CoNO on seven challenging partial differential equations (PDEs), including regular grids, structured meshes, and point clouds. Empirically, CoNO consistently attains state-of-the-art performance, showcasing an average relative gain of 10.9%. Further, CoNO exhibits superior performance, outperforming all other models in additional tasks such as zero-shot super-resolution and robustness to noise. CoNO also exhibits the ability to learn from small amounts of data -- giving the same performance as the next best model with just 60% of the training data. Altogether, CoNO presents a robust and superior model for modeling continuous dynamical systems, providing a fillip to scientific machine learning. | false | false | false | false | true | false | true | false | false | false | false | true | false | false | false | true | false | false | 460,850 |
2109.11667 | Performance Improvement of Dimmable OFDM-Visible Light Communication
using Subcarrier Index Modulation and Reed Solomon Encoding | In this paper, we propose a new subcarrier index modulation scheme for orthogonal frequency division multiplexing (OFDM), which incorporates the Reed-Solomon (RS) encoding in a visible light communication (VLC) system. In this scheme, the incoming bits are first encoded using the RS encoder, then a set of symbols in the resulting RS codeword are punctured, and the remaining symbols are modulated and mapped onto the OFDM subcarriers. This system is referred to as RS-OFDM-IM. Unlike the traditional subcarrier index modulation (SIM) schemes, the proposed scheme operates based on conveying extra information by inactivating the selected subcarriers, which facilitates simultaneous clipping noise reduction and spectral efficiency enhancement in OFDM-VLC. The bit error rate (BER) and throughput of the proposed technique is theoretically and numerically analyzed. The simulation results show the superiority of the proposed technique as compared to the coded DCO-OFDM without SIM and the classical SIM in OFDM-VLC in a system with practical clipping conditions. | false | false | false | false | false | false | false | false | false | true | true | false | false | false | false | false | false | false | 257,014 |
2403.16016 | Fill in the ____ (a Diffusion-based Image Inpainting Pipeline) | Image inpainting is the process of taking an image and generating lost or intentionally occluded portions. Inpainting has countless applications including restoring previously damaged pictures, restoring the quality of images that have been degraded due to compression, and removing unwanted objects/text. Modern inpainting techniques have shown remarkable ability in generating sensible completions for images with mask occlusions. In our paper, an overview of the progress of inpainting techniques will be provided, along with identifying current leading approaches, focusing on their strengths and weaknesses. A critical gap in these existing models will be addressed, focusing on the ability to prompt and control what exactly is generated. We will additionally justify why we think this is the natural next progressive step that inpainting models must take, and provide multiple approaches to implementing this functionality. Finally, we will evaluate the results of our approaches by qualitatively checking whether they generate high-quality images that correctly inpaint regions with the objects that they are instructed to produce. | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | false | false | 440,835 |
1411.5458 | Liquid State Machine with Dendritically Enhanced Readout for Low-power,
Neuromorphic VLSI Implementations | In this paper, we describe a new neuro-inspired, hardware-friendly readout stage for the liquid state machine (LSM), a popular model for reservoir computing. Compared to the parallel perceptron architecture trained by the p-delta algorithm, which is the state of the art in terms of performance of readout stages, our readout architecture and learning algorithm can attain better performance with significantly less synaptic resources making it attractive for VLSI implementation. Inspired by the nonlinear properties of dendrites in biological neurons, our readout stage incorporates neurons having multiple dendrites with a lumped nonlinearity. The number of synaptic connections on each branch is significantly lower than the total number of connections from the liquid neurons and the learning algorithm tries to find the best 'combination' of input connections on each branch to reduce the error. Hence, the learning involves network rewiring (NRW) of the readout network similar to structural plasticity observed in its biological counterparts. We show that compared to a single perceptron using analog weights, this architecture for the readout can attain, even by using the same number of binary valued synapses, up to 3.3 times less error for a two-class spike train classification problem and 2.4 times less error for an input rate approximation task. Even with 60 times larger synapses, a group of 60 parallel perceptrons cannot attain the performance of the proposed dendritically enhanced readout. An additional advantage of this method for hardware implementations is that the 'choice' of connectivity can be easily implemented exploiting address event representation (AER) protocols commonly used in current neuromorphic systems where the connection matrix is stored in memory. Also, due to the use of binary synapses, our proposed method is more robust against statistical variations. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | true | 37,742 |
2408.16986 | AdaptVision: Dynamic Input Scaling in MLLMs for Versatile Scene
Understanding | Over the past few years, the advancement of Multimodal Large Language Models (MLLMs) has captured the wide interest of researchers, leading to numerous innovations to enhance MLLMs' comprehension. In this paper, we present AdaptVision, a multimodal large language model specifically designed to dynamically process input images at varying resolutions. We hypothesize that the requisite number of visual tokens for the model is contingent upon both the resolution and content of the input image. Generally, natural images with a lower information density can be effectively interpreted by the model using fewer visual tokens at reduced resolutions. In contrast, images containing textual content, such as documents with rich text, necessitate a higher number of visual tokens for accurate text interpretation due to their higher information density. Building on this insight, we devise a dynamic image partitioning module that adjusts the number of visual tokens according to the size and aspect ratio of images. This method mitigates distortion effects that arise from resizing images to a uniform resolution and dynamically optimizing the visual tokens input to the LLMs. Our model is capable of processing images with resolutions up to $1008\times 1008$. Extensive experiments across various datasets demonstrate that our method achieves impressive performance in handling vision-language tasks in both natural and text-related scenes. The source code and dataset are now publicly available at \url{https://github.com/harrytea/AdaptVision}. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 484,533 |
2306.02031 | DOS: Diverse Outlier Sampling for Out-of-Distribution Detection | Modern neural networks are known to give overconfident prediction for out-of-distribution inputs when deployed in the open world. It is common practice to leverage a surrogate outlier dataset to regularize the model during training, and recent studies emphasize the role of uncertainty in designing the sampling strategy for outlier dataset. However, the OOD samples selected solely based on predictive uncertainty can be biased towards certain types, which may fail to capture the full outlier distribution. In this work, we empirically show that diversity is critical in sampling outliers for OOD detection performance. Motivated by the observation, we propose a straightforward and novel sampling strategy named DOS (Diverse Outlier Sampling) to select diverse and informative outliers. Specifically, we cluster the normalized features at each iteration, and the most informative outlier from each cluster is selected for model training with absent category loss. With DOS, the sampled outliers efficiently shape a globally compact decision boundary between ID and OOD data. Extensive experiments demonstrate the superiority of DOS, reducing the average FPR95 by up to 25.79% on CIFAR-100 with TI-300K. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 370,730 |
2002.03757 | Distributed Learning with Dependent Samples | This paper focuses on learning rate analysis of distributed kernel ridge regression for strong mixing sequences. Using a recently developed integral operator approach and a classical covariance inequality for Banach-valued strong mixing sequences, we succeed in deriving optimal learning rate for distributed kernel ridge regression. As a byproduct, we also deduce a sufficient condition for the mixing property to guarantee the optimal learning rates for kernel ridge regression. Our results extend the applicable range of distributed learning from i.i.d. samples to non-i.i.d. sequences. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 163,393 |
1210.4888 | Local Structure Discovery in Bayesian Networks | Learning a Bayesian network structure from data is an NP-hard problem and thus exact algorithms are feasible only for small data sets. Therefore, network structures for larger networks are usually learned with various heuristics. Another approach to scaling up the structure learning is local learning. In local learning, the modeler has one or more target variables that are of special interest; he wants to learn the structure near the target variables and is not interested in the rest of the variables. In this paper, we present a score-based local learning algorithm called SLL. We conjecture that our algorithm is theoretically sound in the sense that it is optimal in the limit of large sample size. Empirical results suggest that SLL is competitive when compared to the constraint-based HITON algorithm. We also study the prospects of constructing the network structure for the whole node set based on local results by presenting two algorithms and comparing them to several heuristics. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 19,213 |
1610.06449 | Exploiting inter-image similarity and ensemble of extreme learners for
fixation prediction using deep features | This paper presents a novel fixation prediction and saliency modeling framework based on inter-image similarities and ensemble of Extreme Learning Machines (ELM). The proposed framework is inspired by two observations, 1) the contextual information of a scene along with low-level visual cues modulates attention, 2) the influence of scene memorability on eye movement patterns caused by the resemblance of a scene to a former visual experience. Motivated by such observations, we develop a framework that estimates the saliency of a given image using an ensemble of extreme learners, each trained on an image similar to the input image. That is, after retrieving a set of similar images for a given image, a saliency predictor is learnt from each of the images in the retrieved image set using an ELM, resulting in an ensemble. The saliency of the given image is then measured in terms of the mean of predicted saliency value by the ensemble's members. | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | false | false | 62,647 |
1303.3100 | Ergodic Interference Alignment with Delayed Feedback | We propose new ergodic interference alignment techniques for $K$-user interference channels with delayed feedback. Two delayed feedback scenarios are considered -- delayed channel information at transmitter (CIT) and delayed output feedback. It is proved that the proposed techniques achieve total $2K/(K+2)$ DoF which is higher than that by the retrospective interference alignment for the delayed feedback scenarios. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 22,891 |
1002.2456 | The Permutation Groups and the Equivalence of Cyclic and Quasi-Cyclic
Codes | We give the class of finite groups which arise as the permutation groups of cyclic codes over finite fields. Furthermore, we extend the results of Brand and Huffman et al. and we find the properties of the set of permutations by which two cyclic codes of length p^r can be equivalent. We also find the set of permutations by which two quasi-cyclic codes can be equivalent. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 5,690 |
2410.06800 | Efficient Weight-Space Laplace-Gaussian Filtering and Smoothing for
Sequential Deep Learning | Efficiently learning a sequence of related tasks, such as in continual learning, poses a significant challenge for neural nets due to the delicate trade-off between catastrophic forgetting and loss of plasticity. We address this challenge with a grounded framework for sequentially learning related tasks based on Bayesian inference. Specifically, we treat the model's parameters as a nonlinear Gaussian state-space model and perform efficient inference using Gaussian filtering and smoothing. This general formalism subsumes existing continual learning approaches, while also offering a clearer conceptual understanding of its components. Leveraging Laplace approximations during filtering, we construct Gaussian posterior measures on the weight space of a neural network for each task. We use it as an efficient regularizer by exploiting the structure of the generalized Gauss-Newton matrix (GGN) to construct diagonal plus low-rank approximations. The dynamics model allows targeted control of the learning process and the incorporation of domain-specific knowledge, such as modeling the type of shift between tasks. Additionally, using Bayesian approximate smoothing can enhance the performance of task-specific models without needing to re-access any data. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 496,351 |
2210.17393 | Probabilistic Decomposition Transformer for Time Series Forecasting | Time series forecasting is crucial for many fields, such as disaster warning, weather prediction, and energy consumption. The Transformer-based models are considered to have revolutionized the field of sequence modeling. However, the complex temporal patterns of the time series hinder the model from mining reliable temporal dependencies. Furthermore, the autoregressive form of the Transformer introduces cumulative errors in the inference step. In this paper, we propose the probabilistic decomposition Transformer model that combines the Transformer with a conditional generative model, which provides hierarchical and interpretable probabilistic forecasts for intricate time series. The Transformer is employed to learn temporal patterns and implement primary probabilistic forecasts, while the conditional generative model is used to achieve non-autoregressive hierarchical probabilistic forecasts by introducing latent space feature representations. In addition, the conditional generative model reconstructs typical features of the series, such as seasonality and trend terms, from probability distributions in the latent space to enable complex pattern separation and provide interpretable forecasts. Extensive experiments on several datasets demonstrate the effectiveness and robustness of the proposed model, indicating that it compares favorably with the state of the art. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 327,667 |
2102.05838 | Common Information Belief based Dynamic Programs for Stochastic Zero-sum
Games with Competing Teams | Decentralized team problems where players have asymmetric information about the state of the underlying stochastic system have been actively studied, but \emph{games} between such teams are less understood. We consider a general model of zero-sum stochastic games between two competing teams. This model subsumes many previously considered team and zero-sum game models. For this general model, we provide bounds on the upper (min-max) and lower (max-min) values of the game. Furthermore, if the upper and lower values of the game are identical (i.e., if the game has a \emph{value}), our bounds coincide with the value of the game. Our bounds are obtained using two dynamic programs based on a sufficient statistic known as the common information belief (CIB). We also identify certain information structures in which only the minimizing team controls the evolution of the CIB. In these cases, we show that one of our CIB based dynamic programs can be used to find the min-max strategy (in addition to the min-max value). We propose an approximate dynamic programming approach for computing the values (and the strategy when applicable) and illustrate our results with the help of an example. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | true | false | false | false | 219,560 |
2203.11261 | Healthy Twitter discussions? Time will tell | Studying misinformation and how to deal with unhealthy behaviours within online discussions has recently become an important field of research within social studies. With the rapid development of social media, and the increasing amount of available information and sources, rigorous manual analysis of such discourses has become unfeasible. Many approaches tackle the issue by studying the semantic and syntactic properties of discussions following a supervised approach, for example using natural language processing on a dataset labeled for abusive, fake or bot-generated content. Solutions based on the existence of a ground truth are limited to those domains which may have ground truth. However, within the context of misinformation, it may be difficult or even impossible to assign labels to instances. In this context, we consider the use of temporal dynamic patterns as an indicator of discussion health. Working in a domain for which ground truth was unavailable at the time (early COVID-19 pandemic discussions) we explore the characterization of discussions based on the the volume and time of contributions. First we explore the types of discussions in an unsupervised manner, and then characterize these types using the concept of ephemerality, which we formalize. In the end, we discuss the potential use of our ephemerality definition for labeling online discourses based on how desirable, healthy and constructive they are. | false | false | false | true | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 286,850 |
2110.11558 | MHAttnSurv: Multi-Head Attention for Survival Prediction Using
Whole-Slide Pathology Images | In pathology, whole-slide images (WSI) based survival prediction has attracted increasing interest. However, given the large size of WSIs and the lack of pathologist annotations, extracting the prognostic information from WSIs remains a challenging task. Previous studies have used multiple instance learning approaches to combine the information from multiple randomly sampled patches, but different visual patterns may contribute differently to prognosis prediction. In this study, we developed a multi-head attention approach to focus on various parts of a tumor slide, for more comprehensive information extraction from WSIs. We evaluated our approach on four cancer types from The Cancer Genome Atlas database. Our model achieved an average c-index of 0.640, outperforming two existing state-of-the-art approaches for WSI-based survival prediction, which have an average c-index of 0.603 and 0.619 on these datasets. Visualization of our attention maps reveals each attention head focuses synergistically on different morphological patterns. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 262,524 |
1811.00683 | Quasi-random sampling for multivariate distributions via generative
neural networks | Generative moment matching networks (GMMNs) are introduced for generating quasi-random samples from multivariate models with any underlying copula in order to compute estimates under variance reduction. So far, quasi-random sampling for multivariate distributions required a careful design, exploiting specific properties (such as conditional distributions) of the implied parametric copula or the underlying quasi-Monte Carlo (QMC) point set, and was only tractable for a small number of models. Utilizing GMMNs allows one to construct quasi-random samples for a much larger variety of multivariate distributions without such restrictions, including empirical ones from real data with dependence structures not well captured by parametric copulas. Once trained on pseudo-random samples from a parametric model or on real data, these neural networks only require a multivariate standard uniform randomized QMC point set as input and are thus fast in estimating expectations of interest under dependence with variance reduction. Numerical examples are considered to demonstrate the approach, including applications inspired by risk management practice. All results are reproducible with the demos GMMN_QMC_paper, GMMN_QMC_data and GMMN_QMC_timings as part of the R package gnn. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 112,160 |
2409.14057 | Co-occurrence is not Factual Association in Language Models | Pretrained language models can encode a large amount of knowledge and utilize it for various reasoning tasks, yet they can still struggle to learn novel factual knowledge effectively from finetuning on limited textual demonstrations. In this work, we show that the reason for this deficiency is that language models are biased to learn word co-occurrence statistics instead of true factual associations. We identify the differences between two forms of knowledge representation in language models: knowledge in the form of co-occurrence statistics is encoded in the middle layers of the transformer model and does not generalize well to reasoning scenarios beyond simple question answering, while true factual associations are encoded in the lower layers and can be freely utilized in various reasoning tasks. Based on these observations, we propose two strategies to improve the learning of factual associations in language models. We show that training on text with implicit rather than explicit factual associations can force the model to learn factual associations instead of co-occurrence statistics, significantly improving the generalization of newly learned knowledge. We also propose a simple training method to actively forget the learned co-occurrence statistics, which unblocks and enhances the learning of factual associations when training on plain narrative text. On both synthetic and real-world corpora, the two proposed strategies improve the generalization of the knowledge learned during finetuning to reasoning scenarios such as indirect and multi-hop question answering. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 490,302 |
2305.11862 | Reducing Sequence Length by Predicting Edit Operations with Large
Language Models | Large Language Models (LLMs) have demonstrated remarkable performance in various tasks and gained significant attention. LLMs are also used for local sequence transduction tasks, including grammatical error correction (GEC) and formality style transfer, where most tokens in a source text are kept unchanged. However, the models that generate all target tokens in such tasks have a tendency to simply copy the input text as is, without making needed changes, because the difference between input and output texts is minimal in the training data. This is also inefficient because the computational cost grows quadratically with the target sequence length with Transformer. This paper proposes predicting edit spans for the source text for local sequence transduction tasks. Representing an edit span with a position of the source text and corrected tokens, we can reduce the length of the target sequence and the computational cost for inference. We apply instruction tuning for LLMs on the supervision data of edit spans. Experiments show that the proposed method achieves comparable performance to the baseline in four tasks, paraphrasing, formality style transfer, GEC, and text simplification, despite reducing the length of the target text by as small as 21%. Furthermore, we report that the task-specific fine-tuning with the proposed method achieved state-of-the-art performance in the four tasks. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 365,726 |
2411.00476 | PlanScope: Learning to Plan Within Decision Scope Does Matter | In the context of autonomous driving, learning-based methods have been promising for the development of planning modules. During the training process of planning modules, directly minimizing the discrepancy between expert-driving logs and planning output is widely deployed. In general, driving logs consist of suddenly appearing obstacles or swiftly changing traffic signals, which typically necessitate swift and nuanced adjustments in driving maneuvers. Concurrently, future trajectories of the vehicles exhibit their long-term decisions, such as adhering to a reference lane or circumventing stationary obstacles. Due to the unpredictable influence of future events in driving logs, reasoning bias could be naturally introduced to learning based planning modules, which leads to a possible degradation of driving performance. To address this issue, we identify the decisions and their corresponding time horizons, and characterize a so-called decision scope by retaining decisions within derivable horizons only, to mitigate the effect of irrational behaviors caused by unpredictable events. This framework employs wavelet transformation based log preprocessing with an effective loss computation approach, rendering the planning model only sensitive to valuable decisions at the current state. Since frequency domain characteristics are extracted in conjunction with time domain features by wavelets, decision information across various frequency bands within the corresponding time horizon can be suitably captured. Furthermore, to achieve valuable decision learning, this framework leverages a transformer based decoder that incrementally generates the detailed profiles of future decisions over multiple steps. Our experiments demonstrate that our proposed method outperforms baselines in terms of driving scores with closed-loop evaluations on the nuPlan dataset. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 504,614 |
2305.07593 | Unconditionally Secure Access Control Encryption | Access control encryption (ACE) enforces, through a sanitizer as the mediator, that only legitimate sender-receiver pairs can communicate, without the sanitizer knowing the communication metadata, including its sender and recipient identity, the policy over them, and the underlying plaintext. Any illegitimate transmission is indistinguishable from pure noise. Existing works focused on computational security and require trapdoor functions and possibly other heavyweight primitives. We present the first ACE scheme with information-theoretic security (unconditionally against unbounded adversaries). Our novel randomization techniques over matrices realize sanitization (traditionally via homomorphism over a fixed randomness space) such that the secret message in the hidden message subspace remains intact if and only if there is no illegitimate transmission. | false | false | false | false | false | false | false | false | false | true | false | false | true | false | false | false | false | false | 363,955 |
2209.04927 | Operational and Economy-Wide Impacts of Compound Cyberattacks and
Extreme Weather Events on Electric Power Networks | The growing frequencies of extreme weather events and cyberattacks give rise to a novel threat where a malicious cyber actor aims to disrupt stressed components of critical infrastructure systems immediately before, during, or shortly after an extreme weather event. In this paper, we initiate the study of Compound Cyber-Physical Threats and develop a two-stage framework for the analysis of operational disruptions in electric power networks and economy-wide impacts under three scenarios: a Heatwave, a Cyberattack, and a Compound scenario when the Cyberattack is timed with the Heatwave. In the first stage, we use a bilevel optimization problem to represent the adversarial rationale of a cyberattacker in the upper level. In the lower level, we model disruptions in the electric power network using an optimal power flow model. In the second stage, we couple the disruption of electricity supply with a Computable General Equilibrium model to elucidate the impacts on all economic sectors. For the New York Independent System Operator, we find that a 9% demand increase in a Heatwave may not lead to unserved load. The Cyberattack can lead to 4% of unserved electric load in Long Island, while the Compound scenario can increase unserved electric load in Long Island to 13% and affect almost 600,000 customers. Our results show that the activity of state and local government enterprises can decrease by 30% in the Compound scenario. We conclude that the vulnerability of federal, state, and local government enterprises to electricity disruptions can affect a broad range of populations. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 316,926 |
2006.10553 | Artificial Musical Intelligence: A Survey | Computers have been used to analyze and create music since they were first introduced in the 1950s and 1960s. Beginning in the late 1990s, the rise of the Internet and large scale platforms for music recommendation and retrieval have made music an increasingly prevalent domain of machine learning and artificial intelligence research. While still nascent, several different approaches have been employed to tackle what may broadly be referred to as "musical intelligence." This article provides a definition of musical intelligence, introduces a taxonomy of its constituent components, and surveys the wide range of AI methods that can be, and have been, brought to bear in its pursuit, with a particular emphasis on machine learning methods. | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | false | true | 182,926 |
2208.03523 | Generalizing Downsampling from Regular Data to Graphs | Downsampling produces coarsened, multi-resolution representations of data and it is used, for example, to produce lossy compression and visualization of large images, reduce computational costs, and boost deep neural representation learning. Unfortunately, due to their lack of a regular structure, there is still no consensus on how downsampling should apply to graphs and linked data. Indeed reductions in graph data are still needed for the goals described above, but reduction mechanisms do not have the same focus on preserving topological structures and properties, while allowing for resolution-tuning, as is the case in regular data downsampling. In this paper, we take a step in this direction, introducing a unifying interpretation of downsampling in regular and graph data. In particular, we define a graph coarsening mechanism which is a graph-structured counterpart of controllable equispaced coarsening mechanisms in regular data. We prove theoretical guarantees for distortion bounds on path lengths, as well as the ability to preserve key topological properties in the coarsened graphs. We leverage these concepts to define a graph pooling mechanism that we empirically assess in graph classification tasks, providing a greedy algorithm that allows efficient parallel implementation on GPUs, and showing that it compares favorably against pooling methods in literature. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 311,808 |
2012.08266 | *-CFQ: Analyzing the Scalability of Machine Learning on a Compositional
Task | We present *-CFQ ("star-CFQ"): a suite of large-scale datasets of varying scope based on the CFQ semantic parsing benchmark, designed for principled investigation of the scalability of machine learning systems in a realistic compositional task setting. Using this suite, we conduct a series of experiments investigating the ability of Transformers to benefit from increased training size under conditions of fixed computational cost. We show that compositional generalization remains a challenge at all training sizes, and we show that increasing the scope of natural language leads to consistently higher error rates, which are only partially offset by increased training data. We further show that while additional training data from a related domain improves the accuracy in data-starved situations, this improvement is limited and diminishes as the distance from the related domain to the target domain increases. | false | false | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | 211,723 |
2310.20621 | Deepfake detection by exploiting surface anomalies: the SurFake approach | The ever-increasing use of synthetically generated content in different sectors of our everyday life, one for all media information, poses a strong need for deepfake detection tools in order to avoid the proliferation of altered messages. The process to identify manipulated content, in particular images and videos, is basically performed by looking for the presence of some inconsistencies and/or anomalies specifically due to the fake generation process. Different techniques exist in the scientific literature that exploit diverse ad-hoc features in order to highlight possible modifications. In this paper, we propose to investigate how deepfake creation can impact on the characteristics that the whole scene had at the time of the acquisition. In particular, when an image (video) is captured the overall geometry of the scene (e.g. surfaces) and the acquisition process (e.g. illumination) determine a univocal environment that is directly represented by the image pixel values; all these intrinsic relations are possibly changed by the deepfake generation process. By resorting to the analysis of the characteristics of the surfaces depicted in the image it is possible to obtain a descriptor usable to train a CNN for deepfake detection: we refer to such an approach as SurFake. Experimental results carried out on the FF++ dataset for different kinds of deepfake forgeries and diverse deep learning models confirm that such a feature can be adopted to discriminate between pristine and altered images; furthermore, experiments witness that it can also be combined with visual data to provide a certain improvement in terms of detection accuracy. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 404,460 |
1702.01711 | Q-WordNet PPV: Simple, Robust and (almost) Unsupervised Generation of
Polarity Lexicons for Multiple Languages | This paper presents a simple, robust and (almost) unsupervised dictionary-based method, qwn-ppv (Q-WordNet as Personalized PageRanking Vector) to automatically generate polarity lexicons. We show that qwn-ppv outperforms other automatically generated lexicons for the four extrinsic evaluations presented here. It also shows very competitive and robust results with respect to manually annotated ones. Results suggest that no single lexicon is best for every task and dataset and that the intrinsic evaluation of polarity lexicons is not a good performance indicator on a Sentiment Analysis task. The qwn-ppv method allows to easily create quality polarity lexicons whenever no domain-based annotated corpora are available for a given language. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 67,853 |
2402.13818 | Beyond Hate Speech: NLP's Challenges and Opportunities in Uncovering
Dehumanizing Language | Dehumanization, characterized as a subtle yet harmful manifestation of hate speech, involves denying individuals of their human qualities and often results in violence against marginalized groups. Despite significant progress in Natural Language Processing across various domains, its application in detecting dehumanizing language is limited, largely due to the scarcity of publicly available annotated data for this domain. This paper evaluates the performance of cutting-edge NLP models, including GPT-4, GPT-3.5, and LLAMA-2, in identifying dehumanizing language. Our findings reveal that while these models demonstrate potential, achieving a 70\% accuracy rate in distinguishing dehumanizing language from broader hate speech, they also display biases. They are over-sensitive in classifying other forms of hate speech as dehumanization for a specific subset of target groups, while more frequently failing to identify clear cases of dehumanization for other target groups. Moreover, leveraging one of the best-performing models, we automatically annotated a larger dataset for training more accessible models. However, our findings indicate that these models currently do not meet the high-quality data generation threshold necessary for this task. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 431,425 |
2403.15037 | Implementation of Firm-Dispatchable Generation in South Africa | South Africa is currently facing a critical situation in its power generation landscape, which is plagued by frequent power outages and the need to move from fossil fuels to renewable energy sources. This period emphasizes the importance of having firm-dispatchable power to balance out the intermittent nature of wind and solar energy sources. The paper proposes to repurpose old coal-fired power plants to generate firm-dispatchable energy in line with the principles of a Just Transition. Eskom's coal plants are approaching the end of their economic life, and their declining energy availability factor is becoming a challenge in meeting the country's energy needs. The study suggests that a comprehensive strategy that integrates wind, solar, and firm-dispatchable power can be cost-effective and reliable compared to the traditional coal-based approach or the nuclear alternative. The study emphasizes the necessity of a 25-year plan that would invest in flexible and modular dispatchable generation. It also highlights the strategic location of this generating capacity, including repurposing decommissioned coal plant sites. The proposed model integrates private investment, adheres to established best practices, and emphasizes adaptability to changing demand dynamics. The study provides a roadmap for enabling firm-dispatchable capacity for South Africa's energy transition, emphasizing economic prudence, environmental sustainability, and alignment with the principles of the Just Transition program. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 440,363 |
0907.4385 | The Cost of Stability in Coalitional Games | A key question in cooperative game theory is that of coalitional stability, usually captured by the notion of the \emph{core}--the set of outcomes such that no subgroup of players has an incentive to deviate. However, some coalitional games have empty cores, and any outcome in such a game is unstable. In this paper, we investigate the possibility of stabilizing a coalitional game by using external payments. We consider a scenario where an external party, which is interested in having the players work together, offers a supplemental payment to the grand coalition (or, more generally, a particular coalition structure). This payment is conditional on players not deviating from their coalition(s). The sum of this payment plus the actual gains of the coalition(s) may then be divided among the agents so as to promote stability. We define the \emph{cost of stability (CoS)} as the minimal external payment that stabilizes the game. We provide general bounds on the cost of stability in several classes of games, and explore its algorithmic properties. To develop a better intuition for the concepts we introduce, we provide a detailed algorithmic study of the cost of stability in weighted voting games, a simple but expressive class of games which can model decision-making in political bodies, and cooperation in multiagent settings. Finally, we extend our model and results to games with coalition structures. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | true | 4,157 |
2002.04518 | Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement
Learning | Off-policy evaluation of sequential decision policies from observational data is necessary in applications of batch reinforcement learning such as education and healthcare. In such settings, however, unobserved variables confound observed actions, rendering exact evaluation of new policies impossible, i.e., unidentifiable. We develop a robust approach that estimates sharp bounds on the (unidentifiable) value of a given policy in an infinite-horizon problem given data from another policy with unobserved confounding, subject to a sensitivity model. We consider stationary or baseline unobserved confounding and compute bounds by optimizing over the set of all stationary state-occupancy ratios that agree with a new partially identified estimating equation and the sensitivity model. We prove convergence to the sharp bounds as we collect more confounded data. Although checking set membership is a linear program, the support function is given by a difficult nonconvex optimization problem. We develop approximations based on nonconvex projected gradient descent and demonstrate the resulting bounds empirically. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 163,630 |
2308.08461 | CDR: Conservative Doubly Robust Learning for Debiased Recommendation | In recommendation systems (RS), user behavior data is observational rather than experimental, resulting in widespread bias in the data. Consequently, tackling bias has emerged as a major challenge in the field of recommendation systems. Recently, Doubly Robust Learning (DR) has gained significant attention due to its remarkable performance and robust properties. However, our experimental findings indicate that existing DR methods are severely impacted by the presence of so-called Poisonous Imputation, where the imputation significantly deviates from the truth and becomes counterproductive. To address this issue, this work proposes Conservative Doubly Robust strategy (CDR) which filters imputations by scrutinizing their mean and variance. Theoretical analyses show that CDR offers reduced variance and improved tail bounds.In addition, our experimental investigations illustrate that CDR significantly enhances performance and can indeed reduce the frequency of poisonous imputation. | false | false | false | false | false | true | true | false | false | false | false | false | false | false | false | false | false | false | 385,911 |
1912.04134 | Estimation of Muscle Fascicle Orientation in Ultrasonic Images | We compare four different algorithms for automatically estimating the muscle fascicle angle from ultrasonic images: the vesselness filter, the Radon transform, the projection profile method and the gray level cooccurence matrix (GLCM). The algorithm results are compared to ground truth data generated by three different experts on 425 image frames from two videos recorded during different types of motion. The best agreement with the ground truth data was achieved by a combination of pre-processing with a vesselness filter and measuring the angle with the projection profile method. The robustness of the estimation is increased by applying the algorithms to subregions with high gradients and performing a LOESS fit through these estimates. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 156,770 |
2306.02208 | Tight Regret Bounds for Single-pass Streaming Multi-armed Bandits | Regret minimization in streaming multi-armed bandits (MABs) has been studied extensively in recent years. In the single-pass setting with $K$ arms and $T$ trials, a regret lower bound of $\Omega(T^{2/3})$ has been proved for any algorithm with $o(K)$ memory (Maiti et al. [NeurIPS'21]; Agarwal at al. [COLT'22]). On the other hand, however, the previous best regret upper bound is still $O(K^{1/3} T^{2/3}\log^{1/3}(T))$, which is achieved by the streaming implementation of the simple uniform exploration. The $O(K^{1/3}\log^{1/3}(T))$ gap leaves the open question of the tight regret bound in the single-pass MABs with sublinear arm memory. In this paper, we answer this open problem and complete the picture of regret minimization in single-pass streaming MABs. We first improve the regret lower bound to $\Omega(K^{1/3}T^{2/3})$ for algorithms with $o(K)$ memory, which matches the uniform exploration regret up to a logarithm factor in $T$. We then show that the $\log^{1/3}(T)$ factor is not necessary, and we can achieve $O(K^{1/3}T^{2/3})$ regret by finding an $\varepsilon$-best arm and committing to it in the rest of the trials. For regret minimization with high constant probability, we can apply the single-memory $\varepsilon$-best arm algorithms in Jin et al. [ICML'21] to obtain the optimal bound. Furthermore, for the expected regret minimization, we design an algorithm with a single-arm memory that achieves $O(K^{1/3} T^{2/3}\log(K))$ regret, and an algorithm with $O(\log^{*}(n))$-memory with the optimal $O(K^{1/3} T^{2/3})$ regret following the $\varepsilon$-best arm algorithm in Assadi and Wang [STOC'20]. We further tested the empirical performances of our algorithms. The simulation results show that the proposed algorithms consistently outperform the benchmark uniform exploration algorithm by a large margin, and on occasion, reduce the regret by up to 70%. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 370,799 |
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