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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2405.11524 | Simple-Sampling and Hard-Mixup with Prototypes to Rebalance Contrastive
Learning for Text Classification | Text classification is a crucial and fundamental task in natural language processing. Compared with the previous learning paradigm of pre-training and fine-tuning by cross entropy loss, the recently proposed supervised contrastive learning approach has received tremendous attention due to its powerful feature learning capability and robustness. Although several studies have incorporated this technique for text classification, some limitations remain. First, many text datasets are imbalanced, and the learning mechanism of supervised contrastive learning is sensitive to data imbalance, which may harm the model performance. Moreover, these models leverage separate classification branch with cross entropy and supervised contrastive learning branch without explicit mutual guidance. To this end, we propose a novel model named SharpReCL for imbalanced text classification tasks. First, we obtain the prototype vector of each class in the balanced classification branch to act as a representation of each class. Then, by further explicitly leveraging the prototype vectors, we construct a proper and sufficient target sample set with the same size for each class to perform the supervised contrastive learning procedure. The empirical results show the effectiveness of our model, which even outperforms popular large language models across several datasets. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 455,178 |
2306.09955 | Training shallow ReLU networks on noisy data using hinge loss: when do
we overfit and is it benign? | We study benign overfitting in two-layer ReLU networks trained using gradient descent and hinge loss on noisy data for binary classification. In particular, we consider linearly separable data for which a relatively small proportion of labels are corrupted or flipped. We identify conditions on the margin of the clean data that give rise to three distinct training outcomes: benign overfitting, in which zero loss is achieved and with high probability test data is classified correctly; overfitting, in which zero loss is achieved but test data is misclassified with probability lower bounded by a constant; and non-overfitting, in which clean points, but not corrupt points, achieve zero loss and again with high probability test data is classified correctly. Our analysis provides a fine-grained description of the dynamics of neurons throughout training and reveals two distinct phases: in the first phase clean points achieve close to zero loss, in the second phase clean points oscillate on the boundary of zero loss while corrupt points either converge towards zero loss or are eventually zeroed by the network. We prove these results using a combinatorial approach that involves bounding the number of clean versus corrupt updates across these phases of training. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 374,032 |
2204.01303 | GraFN: Semi-Supervised Node Classification on Graph with Few Labels via
Non-Parametric Distribution Assignment | Despite the success of Graph Neural Networks (GNNs) on various applications, GNNs encounter significant performance degradation when the amount of supervision signals, i.e., number of labeled nodes, is limited, which is expected as GNNs are trained solely based on the supervision obtained from the labeled nodes. On the other hand,recent self-supervised learning paradigm aims to train GNNs by solving pretext tasks that do not require any labeled nodes, and it has shown to even outperform GNNs trained with few labeled nodes. However, a major drawback of self-supervised methods is that they fall short of learning class discriminative node representations since no labeled information is utilized during training. To this end, we propose a novel semi-supervised method for graphs, GraFN, that leverages few labeled nodes to ensure nodes that belong to the same class to be grouped together, thereby achieving the best of both worlds of semi-supervised and self-supervised methods. Specifically, GraFN randomly samples support nodes from labeled nodes and anchor nodes from the entire graph. Then, it minimizes the difference between two predicted class distributions that are non-parametrically assigned by anchor-supports similarity from two differently augmented graphs. We experimentally show that GraFN surpasses both the semi-supervised and self-supervised methods in terms of node classification on real-world graphs. The source code for GraFN is available at https://github.com/Junseok0207/GraFN. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 289,565 |
2004.01791 | Open access institutional and news media tweet dataset for COVID-19
social science research | As COVID-19 quickly became one of the most concerned global crisis, the demand for data in academic research is also increasing. Currently, there are several open access Twitter datasets, but none of them is dedicated to the institutional and news media Twitter data collection, to fill this blank, we retrieved data from 69 institutional/news media Twitter accounts, 17 of them were related to government and international organizations, 52 of them were news media across North America, Europe and Asia. We believe our open access data can provide researchers more availability to conduct social science research. | false | false | false | true | false | false | false | false | false | false | false | false | false | true | false | false | false | true | 171,008 |
2212.04223 | Vicious Classifiers: Assessing Inference-time Data Reconstruction Risk
in Edge Computing | Privacy-preserving inference in edge computing paradigms encourages the users of machine-learning services to locally run a model on their private input and only share the models outputs for a target task with the server. We study how a vicious server can reconstruct the input data by observing only the models outputs while keeping the target accuracy very close to that of a honest server by jointly training a target model (to run at users' side) and an attack model for data reconstruction (to secretly use at servers' side). We present a new measure to assess the inference-time reconstruction risk. Evaluations on six benchmark datasets show the model's input can be approximately reconstructed from the outputs of a single inference. We propose a primary defense mechanism to distinguish vicious versus honest classifiers at inference time. By studying such a risk associated with emerging ML services our work has implications for enhancing privacy in edge computing. We discuss open challenges and directions for future studies and release our code as a benchmark for the community at https://github.com/mmalekzadeh/vicious-classifiers . | false | false | false | false | false | false | true | false | false | true | false | false | true | false | false | false | false | false | 335,368 |
2112.00054 | Task2Sim : Towards Effective Pre-training and Transfer from Synthetic
Data | Pre-training models on Imagenet or other massive datasets of real images has led to major advances in computer vision, albeit accompanied with shortcomings related to curation cost, privacy, usage rights, and ethical issues. In this paper, for the first time, we study the transferability of pre-trained models based on synthetic data generated by graphics simulators to downstream tasks from very different domains. In using such synthetic data for pre-training, we find that downstream performance on different tasks are favored by different configurations of simulation parameters (e.g. lighting, object pose, backgrounds, etc.), and that there is no one-size-fits-all solution. It is thus better to tailor synthetic pre-training data to a specific downstream task, for best performance. We introduce Task2Sim, a unified model mapping downstream task representations to optimal simulation parameters to generate synthetic pre-training data for them. Task2Sim learns this mapping by training to find the set of best parameters on a set of "seen" tasks. Once trained, it can then be used to predict best simulation parameters for novel "unseen" tasks in one shot, without requiring additional training. Given a budget in number of images per class, our extensive experiments with 20 diverse downstream tasks show Task2Sim's task-adaptive pre-training data results in significantly better downstream performance than non-adaptively choosing simulation parameters on both seen and unseen tasks. It is even competitive with pre-training on real images from Imagenet. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 269,020 |
2501.12799 | Int2Planner: An Intention-based Multi-modal Motion Planner for
Integrated Prediction and Planning | Motion planning is a critical module in autonomous driving, with the primary challenge of uncertainty caused by interactions with other participants. As most previous methods treat prediction and planning as separate tasks, it is difficult to model these interactions. Furthermore, since the route path navigates ego vehicles to a predefined destination, it provides relatively stable intentions for ego vehicles and helps constrain uncertainty. On this basis, we construct Int2Planner, an \textbf{Int}ention-based \textbf{Int}egrated motion \textbf{Planner} achieves multi-modal planning and prediction. Instead of static intention points, Int2Planner utilizes route intention points for ego vehicles and generates corresponding planning trajectories for each intention point to facilitate multi-modal planning. The experiments on the private dataset and the public nuPlan benchmark show the effectiveness of route intention points, and Int2Planner achieves state-of-the-art performance. We also deploy it in real-world vehicles and have conducted autonomous driving for hundreds of kilometers in urban areas. It further verifies that Int2Planner can continuously interact with the traffic environment. Code will be avaliable at https://github.com/cxlz/Int2Planner. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 526,442 |
2301.05688 | CANE: A Cascade-Control Approach for Network-Assisted Video QoE
Management | Prior efforts have shown that network-assisted schemes can improve the Quality-of-Experience (QoE) and QoE fairness when multiple video players compete for bandwidth. However, realizing network-assisted schemes in practice is challenging, as: i) the network has limited visibility into the client players' internal state and actions; ii) players' actions may nullify or negate the network's actions; and iii) the players' objectives might be conflicting. To address these challenges, we formulate network-assisted QoE optimization through a cascade control abstraction. This informs the design of CANE, a practical network-assisted QoE framework. CANE uses machine learning techniques to approximate each player's behavior as a black-box model and model predictive control to achieve a near-optimal solution. We evaluate CANE through realistic simulations and show that CANE improves multiplayer QoE fairness by ~50% compared to pure client-side adaptive bitrate algorithms and by ~20% compared to uniform traffic shaping. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | true | 340,420 |
2407.06298 | Multi-Label Plant Species Classification with Self-Supervised Vision
Transformers | We present a transfer learning approach using a self-supervised Vision Transformer (DINOv2) for the PlantCLEF 2024 competition, focusing on the multi-label plant species classification. Our method leverages both base and fine-tuned DINOv2 models to extract generalized feature embeddings. We train classifiers to predict multiple plant species within a single image using these rich embeddings. To address the computational challenges of the large-scale dataset, we employ Spark for distributed data processing, ensuring efficient memory management and processing across a cluster of workers. Our data processing pipeline transforms images into grids of tiles, classifying each tile, and aggregating these predictions into a consolidated set of probabilities. Our results demonstrate the efficacy of combining transfer learning with advanced data processing techniques for multi-label image classification tasks. Our code is available at https://github.com/dsgt-kaggle-clef/plantclef-2024. | false | false | false | false | false | true | true | false | false | false | false | true | false | false | false | false | false | false | 471,343 |
1801.07209 | MIMO Underlay Cognitive Radio: Optimized Power Allocation, Effective
Number of Transmit Antennas and Harvest-Transmit Tradeoff | In this paper, the performance of an underlay multiple-input multiple-output (MIMO) cognitive radio system is analytically studied. In particular, the secondary transmitter operates in a spatial multiplexing transmission mode, while a zero-forcing detector is employed at the secondary receiver. Additionally, the secondary system is interfered by single-antenna primary users (PUs). To enhance the performance of secondary transmission, optimal power allocation is performed at the secondary transmitter with a constraint on the maximum allowable outage threshold specified by the PUs. Further, the effective number of secondary transmit antennas is specified based on the optimal power allocation for an arbitrary MIMO scale. Also, a lower bound on the ergodic channel capacity of the secondary system is derived in a closed-form expression. Afterwards, the scenario of a massive MIMO secondary system is thoroughly analyzed and evaluated, where the harvesting-enabled secondary transmission is studied. The optimal power allocation, the effective number of secondary transmit antennas, the efficient tradeoff between transmit-and-harvest secondary antennas, and the average channel capacity of the secondary system are analytically presented. Finally, extensive numerical and simulation results corroborate the effectiveness of our analysis, while some useful engineering insights are provided. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 88,743 |
2111.11510 | Bootstrap Your Flow | Normalizing flows are flexible, parameterized distributions that can be used to approximate expectations from intractable distributions via importance sampling. However, current flow-based approaches are limited on challenging targets where they either suffer from mode seeking behaviour or high variance in the training loss, or rely on samples from the target distribution, which may not be available. To address these challenges, we combine flows with annealed importance sampling (AIS), while using the $\alpha$-divergence as our objective, in a novel training procedure, FAB (Flow AIS Bootstrap). Thereby, the flow and AIS improve each other in a bootstrapping manner. We demonstrate that FAB can be used to produce accurate approximations to complex target distributions, including Boltzmann distributions, in problems where previous flow-based methods fail. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 267,685 |
1810.10786 | Supervised Classification Methods for Flash X-ray single particle
diffraction Imaging | Current Flash X-ray single-particle diffraction Imaging (FXI) experiments, which operate on modern X-ray Free Electron Lasers (XFELs), can record millions of interpretable diffraction patterns from individual biomolecules per day. Due to the stochastic nature of the XFELs, those patterns will to a varying degree include scatterings from contaminated samples. Also, the heterogeneity of the sample biomolecules is unavoidable and complicates data processing. Reducing the data volumes and selecting high-quality single-molecule patterns are therefore critical steps in the experimental set-up. In this paper, we present two supervised template-based learning methods for classifying FXI patterns. Our Eigen-Image and Log-Likelihood classifier can find the best-matched template for a single-molecule pattern within a few milliseconds. It is also straightforward to parallelize them so as to fully match the XFEL repetition rate, thereby enabling processing at site. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 111,363 |
2408.10845 | CoVLA: Comprehensive Vision-Language-Action Dataset for Autonomous
Driving | Autonomous driving, particularly navigating complex and unanticipated scenarios, demands sophisticated reasoning and planning capabilities. While Multi-modal Large Language Models (MLLMs) offer a promising avenue for this, their use has been largely confined to understanding complex environmental contexts or generating high-level driving commands, with few studies extending their application to end-to-end path planning. A major research bottleneck is the lack of large-scale annotated datasets encompassing vision, language, and action. To address this issue, we propose CoVLA (Comprehensive Vision-Language-Action) Dataset, an extensive dataset comprising real-world driving videos spanning more than 80 hours. This dataset leverages a novel, scalable approach based on automated data processing and a caption generation pipeline to generate accurate driving trajectories paired with detailed natural language descriptions of driving environments and maneuvers. This approach utilizes raw in-vehicle sensor data, allowing it to surpass existing datasets in scale and annotation richness. Using CoVLA, we investigate the driving capabilities of MLLMs that can handle vision, language, and action in a variety of driving scenarios. Our results illustrate the strong proficiency of our model in generating coherent language and action outputs, emphasizing the potential of Vision-Language-Action (VLA) models in the field of autonomous driving. This dataset establishes a framework for robust, interpretable, and data-driven autonomous driving systems by providing a comprehensive platform for training and evaluating VLA models, contributing to safer and more reliable self-driving vehicles. The dataset is released for academic purpose. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 482,048 |
2407.14142 | Early Preparation Pays Off: New Classifier Pre-tuning for Class
Incremental Semantic Segmentation | Class incremental semantic segmentation aims to preserve old knowledge while learning new tasks, however, it is impeded by catastrophic forgetting and background shift issues. Prior works indicate the pivotal importance of initializing new classifiers and mainly focus on transferring knowledge from the background classifier or preparing classifiers for future classes, neglecting the flexibility and variance of new classifiers. In this paper, we propose a new classifier pre-tuning~(NeST) method applied before the formal training process, learning a transformation from old classifiers to generate new classifiers for initialization rather than directly tuning the parameters of new classifiers. Our method can make new classifiers align with the backbone and adapt to the new data, preventing drastic changes in the feature extractor when learning new classes. Besides, we design a strategy considering the cross-task class similarity to initialize matrices used in the transformation, helping achieve the stability-plasticity trade-off. Experiments on Pascal VOC 2012 and ADE20K datasets show that the proposed strategy can significantly improve the performance of previous methods. The code is available at \url{https://github.com/zhengyuan-xie/ECCV24_NeST}. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 474,668 |
1909.07918 | A Programming Framework for Differential Privacy with Accuracy
Concentration Bounds | Differential privacy offers a formal framework for reasoning about privacy and accuracy of computations on private data. It also offers a rich set of building blocks for constructing data analyses. When carefully calibrated, these analyses simultaneously guarantee privacy of the individuals contributing their data, and accuracy of their results for inferring useful properties about the population. The compositional nature of differential privacy has motivated the design and implementation of several programming languages aimed at helping a data analyst in programming differentially private analyses. However, most of the programming languages for differential privacy proposed so far provide support for reasoning about privacy but not for reasoning about the accuracy of data analyses. To overcome this limitation, in this work we present DPella, a programming framework providing data analysts with support for reasoning about privacy, accuracy and their trade-offs. The distinguishing feature of DPella is a novel component which statically tracks the accuracy of different data analyses. In order to make tighter accuracy estimations, this component leverages taint analysis for automatically inferring statistical independence of the different noise quantities added for guaranteeing privacy. We show the flexibility of our approach by not only implementing classical counting queries (e.g., CDFs) but also by analyzing hierarchical counting queries (like those done by Census Bureaus), where accuracy have different constraints per level and data analysts should figure out the best manner to calibrate privacy to meet the accuracy requirements. | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | true | true | 145,816 |
1805.08159 | Multi-Perspective Relevance Matching with Hierarchical ConvNets for
Social Media Search | Despite substantial interest in applications of neural networks to information retrieval, neural ranking models have only been applied to standard ad hoc retrieval tasks over web pages and newswire documents. This paper proposes MP-HCNN (Multi-Perspective Hierarchical Convolutional Neural Network) a novel neural ranking model specifically designed for ranking short social media posts. We identify document length, informal language, and heterogeneous relevance signals as features that distinguish documents in our domain, and present a model specifically designed with these characteristics in mind. Our model uses hierarchical convolutional layers to learn latent semantic soft-match relevance signals at the character, word, and phrase levels. A pooling-based similarity measurement layer integrates evidence from multiple types of matches between the query, the social media post, as well as URLs contained in the post. Extensive experiments using Twitter data from the TREC Microblog Tracks 2011--2014 show that our model significantly outperforms prior feature-based as well and existing neural ranking models. To our best knowledge, this paper presents the first substantial work tackling search over social media posts using neural ranking models. | false | false | false | false | false | true | false | false | true | false | false | false | false | false | false | false | false | false | 98,061 |
2409.17906 | Graph Reasoning with Large Language Models via Pseudo-code Prompting | Large language models (LLMs) have recently achieved remarkable success in various reasoning tasks in the field of natural language processing. This success of LLMs has also motivated their use in graph-related tasks. Among others, recent work has explored whether LLMs can solve graph problems such as counting the number of connected components of a graph or computing the shortest path distance between two nodes. Although LLMs possess preliminary graph reasoning abilities, they might still struggle to solve some seemingly simple problems. In this paper, we investigate whether prompting via pseudo-code instructions can improve the performance of LLMs in solving graph problems. Our experiments demonstrate that using pseudo-code instructions generally improves the performance of all considered LLMs. The graphs, pseudo-code prompts, and evaluation code are publicly available. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 492,021 |
2406.00024 | Embedding-Aligned Language Models | We propose a novel approach for training large language models (LLMs) to adhere to objectives defined within a latent embedding space. Our method leverages reinforcement learning (RL), treating a pre-trained LLM as an environment. Our embedding-aligned guided language (EAGLE) agent is trained to iteratively steer the LLM's generation towards optimal regions of the latent embedding space, w.r.t. some predefined criterion. We demonstrate the effectiveness of the EAGLE agent using the MovieLens 25M and Amazon Review datasets to surface content gaps that satisfy latent user demand. We also demonstrate the benefit of using an optimal design of a state-dependent action set to improve EAGLE's efficiency. Our work paves the way for controlled and grounded text generation using LLMs, ensuring consistency with domain-specific knowledge and data representations. | false | false | false | false | true | false | true | false | true | false | false | false | false | false | false | false | false | true | 459,662 |
2407.09187 | Enhancing Depressive Post Detection in Bangla: A Comparative Study of
TF-IDF, BERT and FastText Embeddings | Due to massive adoption of social media, detection of users' depression through social media analytics bears significant importance, particularly for underrepresented languages, such as Bangla. This study introduces a well-grounded approach to identify depressive social media posts in Bangla, by employing advanced natural language processing techniques. The dataset used in this work, annotated by domain experts, includes both depressive and non-depressive posts, ensuring high-quality data for model training and evaluation. To address the prevalent issue of class imbalance, we utilised random oversampling for the minority class, thereby enhancing the model's ability to accurately detect depressive posts. We explored various numerical representation techniques, including Term Frequency-Inverse Document Frequency (TF-IDF), Bidirectional Encoder Representations from Transformers (BERT) embedding and FastText embedding, by integrating them with a deep learning-based Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) model. The results obtained through extensive experimentation, indicate that the BERT approach performed better the others, achieving a F1-score of 84%. This indicates that BERT, in combination with the CNN-BiLSTM architecture, effectively recognises the nuances of Bangla texts relevant to depressive contents. Comparative analysis with the existing state-of-the-art methods demonstrates that our approach with BERT embedding performs better than others in terms of evaluation metrics and the reliability of dataset annotations. Our research significantly contribution to the development of reliable tools for detecting depressive posts in the Bangla language. By highlighting the efficacy of different embedding techniques and deep learning models, this study paves the way for improved mental health monitoring through social media platforms. | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 472,486 |
2502.12415 | Gaseous Object Detection | Object detection, a fundamental and challenging problem in computer vision, has experienced rapid development due to the effectiveness of deep learning. The current objects to be detected are mostly rigid solid substances with apparent and distinct visual characteristics. In this paper, we endeavor on a scarcely explored task named Gaseous Object Detection (GOD), which is undertaken to explore whether the object detection techniques can be extended from solid substances to gaseous substances. Nevertheless, the gas exhibits significantly different visual characteristics: 1) saliency deficiency, 2) arbitrary and ever-changing shapes, 3) lack of distinct boundaries. To facilitate the study on this challenging task, we construct a GOD-Video dataset comprising 600 videos (141,017 frames) that cover various attributes with multiple types of gases. A comprehensive benchmark is established based on this dataset, allowing for a rigorous evaluation of frame-level and video-level detectors. Deduced from the Gaussian dispersion model, the physics-inspired Voxel Shift Field (VSF) is designed to model geometric irregularities and ever-changing shapes in potential 3D space. By integrating VSF into Faster RCNN, the VSF RCNN serves as a simple but strong baseline for gaseous object detection. Our work aims to attract further research into this valuable albeit challenging area. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 534,855 |
1603.03793 | Training with Exploration Improves a Greedy Stack-LSTM Parser | We adapt the greedy Stack-LSTM dependency parser of Dyer et al. (2015) to support a training-with-exploration procedure using dynamic oracles(Goldberg and Nivre, 2013) instead of cross-entropy minimization. This form of training, which accounts for model predictions at training time rather than assuming an error-free action history, improves parsing accuracies for both English and Chinese, obtaining very strong results for both languages. We discuss some modifications needed in order to get training with exploration to work well for a probabilistic neural-network. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 53,154 |
2502.12377 | Alignment and Adversarial Robustness: Are More Human-Like Models More
Secure? | Representational alignment refers to the extent to which a model's internal representations mirror biological vision, offering insights into both neural similarity and functional correspondence. Recently, some more aligned models have demonstrated higher resiliency to adversarial examples, raising the question of whether more human-aligned models are inherently more secure. In this work, we conduct a large-scale empirical analysis to systematically investigate the relationship between representational alignment and adversarial robustness. We evaluate 118 models spanning diverse architectures and training paradigms, measuring their neural and behavioral alignment and engineering task performance across 106 benchmarks as well as their adversarial robustness via AutoAttack. Our findings reveal that while average alignment and robustness exhibit a weak overall correlation, specific alignment benchmarks serve as strong predictors of adversarial robustness, particularly those that measure selectivity towards texture or shape. These results suggest that different forms of alignment play distinct roles in model robustness, motivating further investigation into how alignment-driven approaches can be leveraged to build more secure and perceptually-grounded vision models. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 534,830 |
1505.05947 | A Pareto Front-Based Multiobjective Path Planning Algorithm | Path planning is one of the most vital elements of mobile robotics. With a priori knowledge of the environment, global path planning provides a collision-free route through the workspace. The global path plan can be calculated with a variety of informed search algorithms, most notably the A* search method, guaranteed to deliver a complete and optimal solution that minimizes the path cost. Path planning optimization typically looks to minimize the distance traversed from start to goal, yet many mobile robot applications call for additional path planning objectives, presenting a multiobjective optimization (MOO) problem. Past studies have applied genetic algorithms to MOO path planning problems, but these may have the disadvantages of computational complexity and suboptimal solutions. Alternatively, the algorithm in this paper approaches MOO path planning with the use of Pareto fronts, or finding non-dominated solutions. The algorithm presented incorporates Pareto optimality into every step of A* search, thus it is named A*-PO. Results of simulations show A*-PO outperformed several variations of the standard A* algorithm for MOO path planning. A planetary exploration rover case study was added to demonstrate the viability of A*-PO in a real-world application. | false | false | false | false | true | false | false | true | false | false | false | false | false | false | false | false | false | false | 43,369 |
2310.03789 | Grokking as a First Order Phase Transition in Two Layer Networks | A key property of deep neural networks (DNNs) is their ability to learn new features during training. This intriguing aspect of deep learning stands out most clearly in recently reported Grokking phenomena. While mainly reflected as a sudden increase in test accuracy, Grokking is also believed to be a beyond lazy-learning/Gaussian Process (GP) phenomenon involving feature learning. Here we apply a recent development in the theory of feature learning, the adaptive kernel approach, to two teacher-student models with cubic-polynomial and modular addition teachers. We provide analytical predictions on feature learning and Grokking properties of these models and demonstrate a mapping between Grokking and the theory of phase transitions. We show that after Grokking, the state of the DNN is analogous to the mixed phase following a first-order phase transition. In this mixed phase, the DNN generates useful internal representations of the teacher that are sharply distinct from those before the transition. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 397,426 |
2108.00778 | Analysing digital in-memory computing for advanced finFET node | Digital In-memory computing improves energy efficiency and throughput of a data-intensive process, which incur memory thrashing and, resulting multiple same memory accesses in a von Neumann architecture. Digital in-memory computing involves accessing multiple SRAM cells simultaneously, which may result in a bit flip when not timed critically. Therefore we discuss the transient voltage characteristics of the bitlines during an SRAM compute. To improve the packaging density and also avoid MOSFET down-scaling issues, we use a 7-nm predictive PDK which uses a finFET node. The finFET process has discrete fins and a lower Voltage supply, which makes the design of in-memory compute SRAM difficult. In this paper, we design a 6T SRAM cell in 7-nm finFET node and compare its SNMs with a UMC 28nm node implementation. Further, we design and simulate the rest of the SRAM peripherals, and in-memory computation for an advanced finFET node. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | true | 248,830 |
1910.08270 | Learning to Answer Subjective, Specific Product-Related Queries using
Customer Reviews by Adversarial Domain Adaptation | Online customer reviews on large-scale e-commerce websites, represent a rich and varied source of opinion data, often providing subjective qualitative assessments of product usage that can help potential customers to discover features that meet their personal needs and preferences. Thus they have the potential to automatically answer specific queries about products, and to address the problems of answer starvation and answer augmentation on associated consumer Q & A forums, by providing good answer alternatives. In this work, we explore several recently successful neural approaches to modeling sentence pairs, that could better learn the relationship between questions and ground truth answers, and thus help infer reviews that can best answer a question or augment a given answer. In particular, we hypothesize that our adversarial domain adaptation-based approach, due to its ability to additionally learn domain-invariant features from a large number of unlabeled, unpaired question-review samples, would perform better than our proposed baselines, at answering specific, subjective product-related queries using reviews. We validate this hypothesis using a small gold standard dataset of question-review pairs evaluated by human experts, significantly surpassing our chosen baselines. Moreover, our approach, using no labeled question-review sentence pair data for training, gives performance at par with another method utilizing labeled question-review samples for the same task. | false | false | false | false | false | true | true | false | true | false | false | false | false | false | false | false | false | false | 149,827 |
1304.1138 | Refinement and Coarsening of Bayesian Networks | In almost all situation assessment problems, it is useful to dynamically contract and expand the states under consideration as assessment proceeds. Contraction is most often used to combine similar events or low probability events together in order to reduce computation. Expansion is most often used to make distinctions of interest which have significant probability in order to improve the quality of the assessment. Although other uncertainty calculi, notably Dempster-Shafer [Shafer, 1976], have addressed these operations, there has not yet been any approach of refining and coarsening state spaces for the Bayesian Network technology. This paper presents two operations for refining and coarsening the state space in Bayesian Networks. We also discuss their practical implications for knowledge acquisition. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 23,491 |
1404.6955 | Probabilistic graphs using coupled random variables | Neural network design has utilized flexible nonlinear processes which can mimic biological systems, but has suffered from a lack of traceability in the resulting network. Graphical probabilistic models ground network design in probabilistic reasoning, but the restrictions reduce the expressive capability of each node making network designs complex. The ability to model coupled random variables using the calculus of nonextensive statistical mechanics provides a neural node design incorporating nonlinear coupling between input states while maintaining the rigor of probabilistic reasoning. A generalization of Bayes rule using the coupled product enables a single node to model correlation between hundreds of random variables. A coupled Markov random field is designed for the inferencing and classification of UCI's MLR 'Multiple Features Data Set' such that thousands of linear correlation parameters can be replaced with a single coupling parameter with just a (3%, 4%) percent reduction in (classification, inference) performance. | false | false | false | false | false | false | true | false | false | true | false | false | false | false | false | true | false | false | 32,649 |
2203.01825 | What Makes Transfer Learning Work For Medical Images: Feature Reuse &
Other Factors | Transfer learning is a standard technique to transfer knowledge from one domain to another. For applications in medical imaging, transfer from ImageNet has become the de-facto approach, despite differences in the tasks and image characteristics between the domains. However, it is unclear what factors determine whether - and to what extent - transfer learning to the medical domain is useful. The long-standing assumption that features from the source domain get reused has recently been called into question. Through a series of experiments on several medical image benchmark datasets, we explore the relationship between transfer learning, data size, the capacity and inductive bias of the model, as well as the distance between the source and target domain. Our findings suggest that transfer learning is beneficial in most cases, and we characterize the important role feature reuse plays in its success. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 283,521 |
2210.05831 | Social-Group-Agnostic Word Embedding Debiasing via the Stereotype
Content Model | Existing word embedding debiasing methods require social-group-specific word pairs (e.g., "man"-"woman") for each social attribute (e.g., gender), which cannot be used to mitigate bias for other social groups, making these methods impractical or costly to incorporate understudied social groups in debiasing. We propose that the Stereotype Content Model (SCM), a theoretical framework developed in social psychology for understanding the content of stereotypes, which structures stereotype content along two psychological dimensions - "warmth" and "competence" - can help debiasing efforts to become social-group-agnostic by capturing the underlying connection between bias and stereotypes. Using only pairs of terms for warmth (e.g., "genuine"-"fake") and competence (e.g.,"smart"-"stupid"), we perform debiasing with established methods and find that, across gender, race, and age, SCM-based debiasing performs comparably to group-specific debiasing | false | false | false | false | true | false | true | false | true | false | false | false | false | false | false | false | false | false | 323,015 |
1905.00397 | Fast AutoAugment | Data augmentation is an essential technique for improving generalization ability of deep learning models. Recently, AutoAugment has been proposed as an algorithm to automatically search for augmentation policies from a dataset and has significantly enhanced performances on many image recognition tasks. However, its search method requires thousands of GPU hours even for a relatively small dataset. In this paper, we propose an algorithm called Fast AutoAugment that finds effective augmentation policies via a more efficient search strategy based on density matching. In comparison to AutoAugment, the proposed algorithm speeds up the search time by orders of magnitude while achieves comparable performances on image recognition tasks with various models and datasets including CIFAR-10, CIFAR-100, SVHN, and ImageNet. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 129,455 |
1806.04410 | Is India's Unique Identification Number a legally valid identification? | A legally valid identification document allows impartial arbitration of the identification of individuals. It protects individuals from a violation of their dignity, justice, liberty and equality. It protects the nation from a destruction of its republic, democratic, sovereign status. In order to test the ability of an identification document to establish impartial identification of individuals, it must be evaluated for its ability to establish identity, undertake identification and build confidence to impartial, reliable and valid identification. The processes of issuing, using and validating identification documents alter the ability of the document to establish identity, undertake identification and build confidence to impartial and valid identification. These processes alter the ability of the document to serve as proof of identity, proof of address, proof of being a resident, or even the proof of existence of a person. We examine the ability of the UID number to serve as an identification document with the ability to impartially arbitrate the identification of individuals and serve as proof of identity, address, and demonstrate existence of a person. We evaluate the implications of the continued use UID system on our ability to undertake legally valid identification ensure integrity of the identity and address databases across the world. | false | false | false | true | false | false | false | false | false | false | false | false | true | true | false | false | false | false | 100,222 |
2311.18355 | Enabling Robots to Identify Missing Steps in Robot Tasks for Guided
Learning from Demonstration | Learning from Demonstration (LfD) systems are commonly used to teach robots new tasks by generating a set of skills from user-provided demonstrations. These skills can then be sequenced by planning algorithms to execute complex tasks. However, LfD systems typically require a full demonstration of the entire task, even when parts of it are already known to the robot. This limitation comes from the system's inability to recognize which sub-tasks are already familiar, leading to a repetitive and burdensome demonstration process for users. In this paper, we introduce a new method for guided demonstrations that reduces this burden, by helping the robot to identify which parts of the task it already knows, considering the overall task goal and the robot's existing skills. In particular, through a combinatorial search, the method finds the smallest necessary change in the initial task conditions that allows the robot to solve the task with its current knowledge. This state is referred to as the excuse state. The human demonstrator is then only required to teach how to reach the excuse state (missing sub-task), rather than demonstrating the entire task. Empirical results and a pilot user study show that our method reduces demonstration time by 61% and decreases the size of demonstrations by 72%. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 411,654 |
1807.03117 | Deep Semantic Segmentation in an AUV for Online Posidonia Oceanica
Meadows identification | Recent studies have shown evidence of a significant decline of the Posidonia oceanica (P.O.) meadows on a global scale. The monitoring and mapping of these meadows are fundamental tools for measuring their status. We present an approach based on a deep neural network to automatically perform a high-precision semantic segmentation of P.O. meadows in sea-floor images, offering several improvements over the state of the art techniques. Our network demonstrates outstanding performance over diverse test sets, reaching a precision of 96.57% and an accuracy of 96.81%, surpassing the reliability of labelling the images manually. Also, the network is implemented in an Autonomous Underwater Vehicle (AUV), performing an online P.O. segmentation, which will be used to generate real-time semantic coverage maps. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 102,432 |
2502.11426 | Verti-Bench: A General and Scalable Off-Road Mobility Benchmark for
Vertically Challenging Terrain | Recent advancement in off-road autonomy has shown promises in deploying autonomous mobile robots in outdoor off-road environments. Encouraging results have been reported from both simulated and real-world experiments. However, unlike evaluating off-road perception tasks on static datasets, benchmarking off-road mobility still faces significant challenges due to a variety of factors, including variations in vehicle platforms and terrain properties. Furthermore, different vehicle-terrain interactions need to be unfolded during mobility evaluation, which requires the mobility systems to interact with the environments instead of comparing against a pre-collected dataset. In this paper, we present Verti-Bench, a mobility benchmark that focuses on extremely rugged, vertically challenging off-road environments. 100 unique off-road environments and 1000 distinct navigation tasks with millions of off-road terrain properties, including a variety of geometry and semantics, rigid and deformable surfaces, and large natural obstacles, provide standardized and objective evaluation in high-fidelity multi-physics simulation. Verti-Bench is also scalable to various vehicle platforms with different scales and actuation mechanisms. We also provide datasets from expert demonstration, random exploration, failure cases (rolling over and getting stuck), as well as a gym-like interface for reinforcement learning. We use Verti-Bench to benchmark ten off-road mobility systems, present our findings, and identify future off-road mobility research directions. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 534,359 |
1801.03406 | DeepSeek: Content Based Image Search & Retrieval | Most of the internet today is composed of digital media that includes videos and images. With pixels becoming the currency in which most transactions happen on the internet, it is becoming increasingly important to have a way of browsing through this ocean of information with relative ease. YouTube has 400 hours of video uploaded every minute and many million images are browsed on Instagram, Facebook, etc. Inspired by recent advances in the field of deep learning and success that it has gained on various problems like image captioning and, machine translation , word2vec , skip thoughts, etc, we present DeepSeek a natural language processing based deep learning model that allows users to enter a description of the kind of images that they want to search, and in response the system retrieves all the images that semantically and contextually relate to the query. Two approaches are described in the following sections. | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | 88,086 |
2412.13794 | MATCHED: Multimodal Authorship-Attribution To Combat Human Trafficking
in Escort-Advertisement Data | Human trafficking (HT) remains a critical issue, with traffickers increasingly leveraging online escort advertisements (ads) to advertise victims anonymously. Existing detection methods, including Authorship Attribution (AA), often center on text-based analyses and neglect the multimodal nature of online escort ads, which typically pair text with images. To address this gap, we introduce MATCHED, a multimodal dataset of 27,619 unique text descriptions and 55,115 unique images collected from the Backpage escort platform across seven U.S. cities in four geographical regions. Our study extensively benchmarks text-only, vision-only, and multimodal baselines for vendor identification and verification tasks, employing multitask (joint) training objectives that achieve superior classification and retrieval performance on in-distribution and out-of-distribution (OOD) datasets. Integrating multimodal features further enhances this performance, capturing complementary patterns across text and images. While text remains the dominant modality, visual data adds stylistic cues that enrich model performance. Moreover, text-image alignment strategies like CLIP and BLIP2 struggle due to low semantic overlap and vague connections between the modalities of escort ads, with end-to-end multimodal training proving more robust. Our findings emphasize the potential of multimodal AA (MAA) to combat HT, providing LEAs with robust tools to link ads and disrupt trafficking networks. | false | false | false | false | true | false | false | false | true | false | false | false | false | true | false | false | false | false | 518,453 |
2009.03679 | Proximity full-text searches of frequently occurring words with a
response time guarantee | Full-text search engines are important tools for information retrieval. In a proximity full-text search, a document is relevant if it contains query terms near each other, especially if the query terms are frequently occurring words. For each word in the text, we use additional indexes to store information about nearby words at distances from the given word of less than or equal to MaxDistance, which is a parameter. A search algorithm for the case when the query consists of high-frequently used words is discussed. In addition, we present results of experiments with different values of MaxDistance to evaluate the search speed dependence on the value of MaxDistance. These results show that the average time of the query execution with our indexes is 94.7-45.9 times (depending on the value of MaxDistance) less than that with standard inverted files when queries that contain high-frequently occurring words are evaluated. This is a pre-print of a contribution published in Pinelas S., Kim A., Vlasov V. (eds) Mathematical Analysis With Applications. CONCORD-90 2018. Springer Proceedings in Mathematics & Statistics, vol 318, published by Springer, Cham. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-42176-2_37 | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | 194,860 |
2302.10755 | Federated Gradient Matching Pursuit | Traditional machine learning techniques require centralizing all training data on one server or data hub. Due to the development of communication technologies and a huge amount of decentralized data on many clients, collaborative machine learning has become the main interest while providing privacy-preserving frameworks. In particular, federated learning (FL) provides such a solution to learn a shared model while keeping training data at local clients. On the other hand, in a wide range of machine learning and signal processing applications, the desired solution naturally has a certain structure that can be framed as sparsity with respect to a certain dictionary. This problem can be formulated as an optimization problem with sparsity constraints and solving it efficiently has been one of the primary research topics in the traditional centralized setting. In this paper, we propose a novel algorithmic framework, federated gradient matching pursuit (FedGradMP), to solve the sparsity constrained minimization problem in the FL setting. We also generalize our algorithms to accommodate various practical FL scenarios when only a subset of clients participate per round, when the local model estimation at clients could be inexact, or when the model parameters are sparse with respect to general dictionaries. Our theoretical analysis shows the linear convergence of the proposed algorithms. A variety of numerical experiments are conducted to demonstrate the great potential of the proposed framework -- fast convergence both in communication rounds and computation time for many important scenarios without sophisticated parameter tuning. | false | false | false | false | false | false | true | false | false | true | false | false | false | false | false | false | false | true | 346,927 |
2305.09691 | Evaluation Strategy of Time-series Anomaly Detection with Decay Function | Recent algorithms of time-series anomaly detection have been evaluated by applying a Point Adjustment (PA) protocol. However, the PA protocol has a problem of overestimating the performance of the detection algorithms because it only depends on the number of detected abnormal segments and their size. We propose a novel evaluation protocol called the Point-Adjusted protocol with decay function (PAdf) to evaluate the time-series anomaly detection algorithm by reflecting the following ideal requirements: detect anomalies quickly and accurately without false alarms. This paper theoretically and experimentally shows that the PAdf protocol solves the over- and under-estimation problems of existing protocols such as PA and PA\%K. By conducting re-evaluations of SOTA models in benchmark datasets, we show that the PA protocol only focuses on finding many anomalous segments, whereas the score of the PAdf protocol considers not only finding many segments but also detecting anomalies quickly without delay. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 364,741 |
2305.15426 | Transcending Grids: Point Clouds and Surface Representations Powering
Neurological Processing | In healthcare, accurately classifying medical images is vital, but conventional methods often hinge on medical data with a consistent grid structure, which may restrict their overall performance. Recent medical research has been focused on tweaking the architectures to attain better performance without giving due consideration to the representation of data. In this paper, we present a novel approach for transforming grid based data into its higher dimensional representations, leveraging unstructured point cloud data structures. We first generate a sparse point cloud from an image by integrating pixel color information as spatial coordinates. Next, we construct a hypersurface composed of points based on the image dimensions, with each smooth section within this hypersurface symbolizing a specific pixel location. Polygonal face construction is achieved using an adjacency tensor. Finally, a dense point cloud is generated by densely sampling the constructed hypersurface, with a focus on regions of higher detail. The effectiveness of our approach is demonstrated on a publicly accessible brain tumor dataset, achieving significant improvements over existing classification techniques. This methodology allows the extraction of intricate details from the original image, opening up new possibilities for advanced image analysis and processing tasks. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 367,621 |
cs/0206008 | Computer modeling of feelings and emotions: a quantitative neural
network model of the feeling-of-knowing | The first quantitative neural network model of feelings and emotions is proposed on the base of available data on their neuroscience and evolutionary biology nature, and on a neural network human memory model which admits distinct description of conscious and unconscious mental processes in a time dependent manner. As an example, proposed model is applied to quantitative description of the feeling of knowing. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | false | false | 537,605 |
2403.06011 | Reinforcement Learning Paycheck Optimization for Multivariate Financial
Goals | We study paycheck optimization, which examines how to allocate income in order to achieve several competing financial goals. For paycheck optimization, a quantitative methodology is missing, due to a lack of a suitable problem formulation. To deal with this issue, we formulate the problem as a utility maximization problem. The proposed formulation is able to (i) unify different financial goals; (ii) incorporate user preferences regarding the goals; (iii) handle stochastic interest rates. The proposed formulation also facilitates an end-to-end reinforcement learning solution, which is implemented on a variety of problem settings. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 436,252 |
2201.06463 | Bayesian Calibration of Imperfect Computer Models using Physics-Informed
Priors | We introduce a computational efficient data-driven framework suitable for quantifying the uncertainty in physical parameters and model formulation of computer models, represented by differential equations. We construct physics-informed priors, which are multi-output GP priors that encode the model's structure in the covariance function. This is extended into a fully Bayesian framework that quantifies the uncertainty of physical parameters and model predictions. Since physical models often are imperfect descriptions of the real process, we allow the model to deviate from the observed data by considering a discrepancy function. For inference, Hamiltonian Monte Carlo is used. Further, approximations for big data are developed that reduce the computational complexity from $\mathcal{O}(N^3)$ to $\mathcal{O}(N\cdot m^2),$ where $m \ll N.$ Our approach is demonstrated in simulation and real data case studies where the physics are described by time-dependent ODEs describe (cardiovascular models) and space-time dependent PDEs (heat equation). In the studies, it is shown that our modelling framework can recover the true parameters of the physical models in cases where 1) the reality is more complex than our modelling choice and 2) the data acquisition process is biased while also producing accurate predictions. Furthermore, it is demonstrated that our approach is computationally faster than traditional Bayesian calibration methods. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 275,733 |
2407.09504 | AI-Based Copyright Detection Of An Image In a Video Using Degree Of
Similarity And Image Hashing | The expanse of information available over the internet makes it difficult to identify whether a specific work is a replica or a duplication of a protected work, especially if we talk about visual representations. Strategies are planned to identify the utilization of the copyrighted image in a report. Still, we want to resolve the issue of involving a copyrighted image in a video and a calculation that could recognize the degree of similarity of the copyrighted picture utilized in the video, even for the pieces of the video that are not featured a lot and in the end perform characterization errands on those edges. Machine learning (ML) and artificial intelligence (AI) are vital to address this problem. Numerous associations have been creating different calculations to screen the identification of copyrighted work. This work means concentrating on those calculations, recognizing designs inside the information, and fabricating a more reasonable model for copyrighted image classification and detection. We have used different algorithms like- Image Processing, Convolutional Neural Networks (CNN), Image hashing, etc. Keywords- Copyright, Artificial Intelligence(AI), Copyrighted Image, Convolutional Neural Network(CNN), Image processing, Degree of similarity, Image Hashing. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | true | 472,600 |
2502.10993 | RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via
Margin-aware Preference Optimization | Large language models (LLMs) have achieved impressive performance but face high computational costs and latency, limiting their deployment in resource-constrained settings. In contrast, small-scale LLMs (SLMs) are more efficient yet struggle to capture evolving real-world knowledge. Retrieval-augmented generation (RAG) helps by integrating external knowledge, but imperfect retrieval can introduce distracting noise that misleads SLMs. We propose RoseRAG, a robust RAG framework for SLMs via Margin-aware Preference Optimization. RoseRAG employs multi-turn prompting for detailed reasoning, rejection sampling for high-quality explanations, and contrastive preference selection to refine responses by maximizing the likelihood gap between preferred and non-preferred outputs. By integrating these components into a margin-aware optimization process, RoseRAG robustly enhances the accuracy and reliability of SLMs for RAG applications. Extensive experiments on three open-domain question answering benchmarks indicate that our innovative RoseRAG surpasses state-of-the-art baselines significantly. | false | false | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | 534,135 |
2303.10126 | IRGen: Generative Modeling for Image Retrieval | While generative modeling has become prevalent across numerous research fields, its integration into the realm of image retrieval remains largely unexplored and underjustified. In this paper, we present a novel methodology, reframing image retrieval as a variant of generative modeling and employing a sequence-to-sequence model. This approach is harmoniously aligned with the current trend towards unification in research, presenting a cohesive framework that allows for end-to-end differentiable searching. This, in turn, facilitates superior performance via direct optimization techniques. The development of our model, dubbed IRGen, addresses the critical technical challenge of converting an image into a concise sequence of semantic units, which is pivotal for enabling efficient and effective search. Extensive experiments demonstrate that our model achieves state-of-the-art performance on three widely-used image retrieval benchmarks as well as two million-scale datasets, yielding significant improvement compared to prior competitive retrieval methods. In addition, the notable surge in precision scores facilitated by generative modeling presents the potential to bypass the reranking phase, which is traditionally indispensable in practical retrieval workflows. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 352,314 |
2309.13079 | MiChao-HuaFen 1.0: A Specialized Pre-trained Corpus Dataset for
Domain-specific Large Models | With the advancement of deep learning technologies, general-purpose large models such as GPT-4 have demonstrated exceptional capabilities across various domains. Nevertheless, there remains a demand for high-quality, domain-specific outputs in areas like healthcare, law, and finance. This paper first evaluates the existing large models for specialized domains and discusses their limitations. To cater to the specific needs of certain domains, we introduce the ``MiChao-HuaFen 1.0'' pre-trained corpus dataset, tailored for the news and governmental sectors. The dataset, sourced from publicly available internet data from 2022, underwent multiple rounds of cleansing and processing to ensure high quality and reliable origins, with provisions for consistent and stable updates. This dataset not only supports the pre-training of large models for Chinese vertical domains but also aids in propelling deep learning research and applications in related fields. | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 394,049 |
2110.15606 | Improving Camouflaged Object Detection with the Uncertainty of
Pseudo-edge Labels | This paper focuses on camouflaged object detection (COD), which is a task to detect objects hidden in the background. Most of the current COD models aim to highlight the target object directly while outputting ambiguous camouflaged boundaries. On the other hand, the performance of the models considering edge information is not yet satisfactory. To this end, we propose a new framework that makes full use of multiple visual cues, i.e., saliency as well as edges, to refine the predicted camouflaged map. This framework consists of three key components, i.e., a pseudo-edge generator, a pseudo-map generator, and an uncertainty-aware refinement module. In particular, the pseudo-edge generator estimates the boundary that outputs the pseudo-edge label, and the conventional COD method serves as the pseudo-map generator that outputs the pseudo-map label. Then, we propose an uncertainty-based module to reduce the uncertainty and noise of such two pseudo labels, which takes both pseudo labels as input and outputs an edge-accurate camouflaged map. Experiments on various COD datasets demonstrate the effectiveness of our method with superior performance to the existing state-of-the-art methods. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 263,934 |
2501.17771 | 2SSP: A Two-Stage Framework for Structured Pruning of LLMs | We propose a novel Two-Stage framework for Structured Pruning (2SSP) for pruning Large Language Models (LLMs), which combines two different strategies of pruning, namely Width and Depth Pruning. The first stage (Width Pruning) removes entire neurons, hence their corresponding rows and columns, aiming to preserve the connectivity among the pruned structures in the intermediate state of the Feed-Forward Networks in each Transformer block. This is done based on an importance score measuring the impact of each neuron over the output magnitude. The second stage (Depth Pruning), instead, removes entire Attention submodules. This is done by applying an iterative process that removes the Attention submodules with the minimum impact on a given metric of interest (in our case, perplexity). We also propose a novel mechanism to balance the sparsity rate of the two stages w.r.t. to the desired global sparsity. We test 2SSP on four LLM families and three sparsity rates (25\%, 37.5\%, and 50\%), measuring the resulting perplexity over three language modeling datasets as well as the performance over six downstream tasks. Our method consistently outperforms five state-of-the-art competitors over three language modeling and six downstream tasks, with an up to two-order-of-magnitude gain in terms of pruning time. The code is available at available at \url{https://github.com/FabrizioSandri/2SSP}. | false | false | false | false | true | false | true | false | true | false | false | false | false | false | false | false | false | false | 528,447 |
0911.4416 | Land cover classification using fuzzy rules and aggregation of
contextual information through evidence theory | Land cover classification using multispectral satellite image is a very challenging task with numerous practical applications. We propose a multi-stage classifier that involves fuzzy rule extraction from the training data and then generation of a possibilistic label vector for each pixel using the fuzzy rule base. To exploit the spatial correlation of land cover types we propose four different information aggregation methods which use the possibilistic class label of a pixel and those of its eight spatial neighbors for making the final classification decision. Three of the aggregation methods use Dempster-Shafer theory of evidence while the remaining one is modeled after the fuzzy k-NN rule. The proposed methods are tested with two benchmark seven channel satellite images and the results are found to be quite satisfactory. They are also compared with a Markov random field (MRF) model-based contextual classification method and found to perform consistently better. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | true | false | false | 5,001 |
1910.02610 | Multi-hop Question Answering via Reasoning Chains | Multi-hop question answering requires models to gather information from different parts of a text to answer a question. Most current approaches learn to address this task in an end-to-end way with neural networks, without maintaining an explicit representation of the reasoning process. We propose a method to extract a discrete reasoning chain over the text, which consists of a series of sentences leading to the answer. We then feed the extracted chains to a BERT-based QA model to do final answer prediction. Critically, we do not rely on gold annotated chains or "supporting facts:" at training time, we derive pseudogold reasoning chains using heuristics based on named entity recognition and coreference resolution. Nor do we rely on these annotations at test time, as our model learns to extract chains from raw text alone. We test our approach on two recently proposed large multi-hop question answering datasets: WikiHop and HotpotQA, and achieve state-of-art performance on WikiHop and strong performance on HotpotQA. Our analysis shows the properties of chains that are crucial for high performance: in particular, modeling extraction sequentially is important, as is dealing with each candidate sentence in a context-aware way. Furthermore, human evaluation shows that our extracted chains allow humans to give answers with high confidence, indicating that these are a strong intermediate abstraction for this task. | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 148,296 |
1302.6703 | Compressive Sensing for Spread Spectrum Receivers | With the advent of ubiquitous computing there are two design parameters of wireless communication devices that become very important power: efficiency and production cost. Compressive sensing enables the receiver in such devices to sample below the Shannon-Nyquist sampling rate, which may lead to a decrease in the two design parameters. This paper investigates the use of Compressive Sensing (CS) in a general Code Division Multiple Access (CDMA) receiver. We show that when using spread spectrum codes in the signal domain, the CS measurement matrix may be simplified. This measurement scheme, named Compressive Spread Spectrum (CSS), allows for a simple, effective receiver design. Furthermore, we numerically evaluate the proposed receiver in terms of bit error rate under different signal to noise ratio conditions and compare it with other receiver structures. These numerical experiments show that though the bit error rate performance is degraded by the subsampling in the CS-enabled receivers, this may be remedied by including quantization in the receiver model. We also study the computational complexity of the proposed receiver design under different sparsity and measurement ratios. Our work shows that it is possible to subsample a CDMA signal using CSS and that in one example the CSS receiver outperforms the classical receiver. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 22,404 |
2109.02763 | Binaural SoundNet: Predicting Semantics, Depth and Motion with Binaural
Sounds | Humans can robustly recognize and localize objects by using visual and/or auditory cues. While machines are able to do the same with visual data already, less work has been done with sounds. This work develops an approach for scene understanding purely based on binaural sounds. The considered tasks include predicting the semantic masks of sound-making objects, the motion of sound-making objects, and the depth map of the scene. To this aim, we propose a novel sensor setup and record a new audio-visual dataset of street scenes with eight professional binaural microphones and a 360-degree camera. The co-existence of visual and audio cues is leveraged for supervision transfer. In particular, we employ a cross-modal distillation framework that consists of multiple vision teacher methods and a sound student method -- the student method is trained to generate the same results as the teacher methods do. This way, the auditory system can be trained without using human annotations. To further boost the performance, we propose another novel auxiliary task, coined Spatial Sound Super-Resolution, to increase the directional resolution of sounds. We then formulate the four tasks into one end-to-end trainable multi-tasking network aiming to boost the overall performance. Experimental results show that 1) our method achieves good results for all four tasks, 2) the four tasks are mutually beneficial -- training them together achieves the best performance, 3) the number and orientation of microphones are both important, and 4) features learned from the standard spectrogram and features obtained by the classic signal processing pipeline are complementary for auditory perception tasks. The data and code are released. | false | false | true | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 253,845 |
2405.02008 | DiffMap: Enhancing Map Segmentation with Map Prior Using Diffusion Model | Constructing high-definition (HD) maps is a crucial requirement for enabling autonomous driving. In recent years, several map segmentation algorithms have been developed to address this need, leveraging advancements in Bird's-Eye View (BEV) perception. However, existing models still encounter challenges in producing realistic and consistent semantic map layouts. One prominent issue is the limited utilization of structured priors inherent in map segmentation masks. In light of this, we propose DiffMap, a novel approach specifically designed to model the structured priors of map segmentation masks using latent diffusion model. By incorporating this technique, the performance of existing semantic segmentation methods can be significantly enhanced and certain structural errors present in the segmentation outputs can be effectively rectified. Notably, the proposed module can be seamlessly integrated into any map segmentation model, thereby augmenting its capability to accurately delineate semantic information. Furthermore, through extensive visualization analysis, our model demonstrates superior proficiency in generating results that more accurately reflect real-world map layouts, further validating its efficacy in improving the quality of the generated maps. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 451,592 |
1605.09465 | Submodularity in Input Node Selection for Networked Systems | Networked systems are systems of interconnected components, in which the dynamics of each component are influenced by the behavior of neighboring components. Examples of networked systems include biological networks, critical infrastructures such as power grids, transportation systems, and the Internet, and social networks. The growing importance of such systems has led to an interest in control of networks to ensure performance, stability, robustness, and resilience. A widely-studied method for controlling networked systems is to directly control a subset of input nodes, which then steer the remaining nodes to their desired states. This article presents submodular optimization approaches for input node selection in networked systems. Submodularity is a property of set functions that enables the development of computationally tractable algorithms with provable optimality bounds. For a variety of physically relevant systems, the physical dynamics have submodular structures that can be exploited to develop efficient input selection algorithms. This article will describe these structures and the resulting algorithms, as well as discuss open problems. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 56,567 |
2001.03525 | Counterexample: scale-free networked graphs with invariable diameter and
density feature | Here, we propose a class of scale-free networks $G(t;m)$ with some intriguing properties, which can not be simultaneously held by all the theoretical models with power-law degree distribution in the existing literature, including (i) average degrees $\langle k\rangle$ of all the generated networks are no longer a constant in the limit of large graph size, implying that they are not sparse but dense, (ii) power-law parameters $\gamma$ of these networks are precisely calculated equal to $2$, as well (iii) their diameters $D$ are all an invariant in the growth process of models. While our models have deterministic structure with clustering coefficients equivalent to zero, we might be able to obtain various candidates with nonzero clustering coefficient based on original networks using some reasonable approaches, for instance, randomly adding some new edges under the premise of keeping the three important properties above unchanged. In addition, we study trapping problem on networks $G(t;m)$ and then obtain closed-form solution to mean hitting time $\langle \mathcal{H}\rangle_{t}$. As opposed to other previous models, our results show an unexpected phenomenon that the analytic value for $\langle \mathcal{H}\rangle_{t}$ is approximately close to the logarithm of vertex number of networks $G(t;m)$. From the theoretical point of view, these networked models considered here can be thought of as counterexamples for most of the published models obeying power-law distribution in current study. | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 160,003 |
cs/0008014 | Aspects of Pattern-Matching in Data-Oriented Parsing | Data-Oriented Parsing (dop) ranks among the best parsing schemes, pairing state-of-the art parsing accuracy to the psycholinguistic insight that larger chunks of syntactic structures are relevant grammatical and probabilistic units. Parsing with the dop-model, however, seems to involve a lot of CPU cycles and a considerable amount of double work, brought on by the concept of multiple derivations, which is necessary for probabilistic processing, but which is not convincingly related to a proper linguistic backbone. It is however possible to re-interpret the dop-model as a pattern-matching model, which tries to maximize the size of the substructures that construct the parse, rather than the probability of the parse. By emphasizing this memory-based aspect of the dop-model, it is possible to do away with multiple derivations, opening up possibilities for efficient Viterbi-style optimizations, while still retaining acceptable parsing accuracy through enhanced context-sensitivity. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 537,183 |
1604.06416 | Computationally Efficient Calculations of Target Performance of the
Normalized Matched Filter Detector for Hydrocoustic Signals | Detection of hydroacoustic transmissions is a key enabling technology in applications such as depth measurements, detection of objects, and undersea mapping. To cope with the long channel delay spread and the low signal-to-noise ratio, hydroacoustic signals are constructed with a large time-bandwidth product, $N$. A promising detector for hydroacoustic signals is the normalized matched filter (NMF). For the NMF, the detection threshold depends only on $N$, thereby obviating the need to estimate the characteristics of the sea ambient noise which are time-varying and hard to estimate. While previous works analyzed the characteristics of the normalized matched filter (NMF), for hydroacoustic signals with large $N$ values the expressions available are computationally complicated to evaluate. Specifically for hydroacoustic signals of large $N$ values, this paper presents approximations for the probability distribution of the NMF. These approximations are found extremely accurate in numerical simulations. We also outline a computationally efficient method to calculate the receiver operating characteristic (ROC) which is required to determine the detection threshold. Results from an experiment conducted in the Mediterranean sea at depth of 900~m agree with the analysis. | false | true | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 54,934 |
2412.07378 | A Spectral Framework for Tracking Communities in Evolving Networks | Discovering and tracking communities in time-varying networks is an important task in network science, motivated by applications in fields ranging from neuroscience to sociology. In this work, we characterize the celebrated family of spectral methods for static clustering in terms of the low-rank approximation of high-dimensional node embeddings. From this perspective, it becomes natural to view the evolving community detection problem as one of subspace tracking on the Grassmann manifold. While the resulting optimization problem is nonconvex, we adopt a block majorize-minimize Riemannian optimization scheme to learn the Grassmann geodesic which best fits the data. Our framework generalizes any static spectral community detection approach and leads to algorithms achieving favorable performance on synthetic and real temporal networks, including those that are weighted, signed, directed, mixed-membership, multiview, hierarchical, cocommunity-structured, bipartite, or some combination thereof. We demonstrate how to specifically cast a wide variety of methods into our framework, and demonstrate greatly improved dynamic community detection results in all cases. | false | false | false | true | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 515,640 |
2410.21264 | LARP: Tokenizing Videos with a Learned Autoregressive Generative Prior | We present LARP, a novel video tokenizer designed to overcome limitations in current video tokenization methods for autoregressive (AR) generative models. Unlike traditional patchwise tokenizers that directly encode local visual patches into discrete tokens, LARP introduces a holistic tokenization scheme that gathers information from the visual content using a set of learned holistic queries. This design allows LARP to capture more global and semantic representations, rather than being limited to local patch-level information. Furthermore, it offers flexibility by supporting an arbitrary number of discrete tokens, enabling adaptive and efficient tokenization based on the specific requirements of the task. To align the discrete token space with downstream AR generation tasks, LARP integrates a lightweight AR transformer as a training-time prior model that predicts the next token on its discrete latent space. By incorporating the prior model during training, LARP learns a latent space that is not only optimized for video reconstruction but is also structured in a way that is more conducive to autoregressive generation. Moreover, this process defines a sequential order for the discrete tokens, progressively pushing them toward an optimal configuration during training, ensuring smoother and more accurate AR generation at inference time. Comprehensive experiments demonstrate LARP's strong performance, achieving state-of-the-art FVD on the UCF101 class-conditional video generation benchmark. LARP enhances the compatibility of AR models with videos and opens up the potential to build unified high-fidelity multimodal large language models (MLLMs). | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | false | false | 503,154 |
0904.2585 | Interference Relay Channels - Part I: Transmission Rates | We analyze the performance of a system composed of two interfering point-to-point links where the transmitters can exploit a common relay to improve their individual transmission rate. When the relay uses the amplify-and-forward protocol we prove that it is not always optimal (in some sense defined later on) to exploit all the relay transmit power and derive the corresponding optimal amplification factor. For the case of the decode-and-forward protocol, already investigated in [1], we show that this protocol, through the cooperation degree between each transmitter and the relay, is the only one that naturally introduces a game between the transmitters. For the estimate-and-forward protocol, we derive two rate regions for the general case of discrete interference relay channels (IRCs) and specialize these results to obtain the Gaussian case; these regions correspond to two compression schemes at the relay, having different resolution levels. These schemes are compared analytically in some special cases. All the results mentioned are illustrated by simulations, given in this part, and exploited to study power allocation games in multi-band IRCs in the second part of this two-part paper. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 3,551 |
1505.03257 | Optimal linear estimation under unknown nonlinear transform | Linear regression studies the problem of estimating a model parameter $\beta^* \in \mathbb{R}^p$, from $n$ observations $\{(y_i,\mathbf{x}_i)\}_{i=1}^n$ from linear model $y_i = \langle \mathbf{x}_i,\beta^* \rangle + \epsilon_i$. We consider a significant generalization in which the relationship between $\langle \mathbf{x}_i,\beta^* \rangle$ and $y_i$ is noisy, quantized to a single bit, potentially nonlinear, noninvertible, as well as unknown. This model is known as the single-index model in statistics, and, among other things, it represents a significant generalization of one-bit compressed sensing. We propose a novel spectral-based estimation procedure and show that we can recover $\beta^*$ in settings (i.e., classes of link function $f$) where previous algorithms fail. In general, our algorithm requires only very mild restrictions on the (unknown) functional relationship between $y_i$ and $\langle \mathbf{x}_i,\beta^* \rangle$. We also consider the high dimensional setting where $\beta^*$ is sparse ,and introduce a two-stage nonconvex framework that addresses estimation challenges in high dimensional regimes where $p \gg n$. For a broad class of link functions between $\langle \mathbf{x}_i,\beta^* \rangle$ and $y_i$, we establish minimax lower bounds that demonstrate the optimality of our estimators in both the classical and high dimensional regimes. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 43,060 |
2412.19469 | Knowledge Graph-Based Multi-Agent Path Planning in Dynamic Environments
using WAITR | This paper addresses the challenge of multi-agent path planning for efficient data collection in dynamic, uncertain environments, exemplified by autonomous underwater vehicles (AUVs) navigating the Gulf of Mexico. Traditional greedy algorithms, though computationally efficient, often fall short in long-term planning due to their short-sighted nature, missing crucial data collection opportunities and increasing exposure to hazards. To address these limitations, we introduce WAITR (Weighted Aggregate Inter-Temporal Reward), a novel path-planning framework that integrates a knowledge graph with pathlet-based planning, segmenting the environment into dynamic, speed-adjusted sub-regions (pathlets). This structure enables coordinated, adaptive planning, as agents can operate within time-bound regions while dynamically responding to environmental changes. WAITR's cumulative scoring mechanism balances immediate data collection with long-term optimization of Points of Interest (POIs), ensuring safer navigation and comprehensive data coverage. Experimental results show that WAITR substantially improves POI coverage and reduces exposure to hazards, achieving up to 27.1\% greater event coverage than traditional greedy methods. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | 520,854 |
2212.01735 | Neural Fourier Filter Bank | We present a novel method to provide efficient and highly detailed reconstructions. Inspired by wavelets, we learn a neural field that decompose the signal both spatially and frequency-wise. We follow the recent grid-based paradigm for spatial decomposition, but unlike existing work, encourage specific frequencies to be stored in each grid via Fourier features encodings. We then apply a multi-layer perceptron with sine activations, taking these Fourier encoded features in at appropriate layers so that higher-frequency components are accumulated on top of lower-frequency components sequentially, which we sum up to form the final output. We demonstrate that our method outperforms the state of the art regarding model compactness and convergence speed on multiple tasks: 2D image fitting, 3D shape reconstruction, and neural radiance fields. Our code is available at https://github.com/ubc-vision/NFFB. | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | false | true | 334,549 |
1701.04934 | Investigating the Application of Common-Sense Knowledge-Base for
Identifying Term Obfuscation in Adversarial Communication | Word obfuscation or substitution means replacing one word with another word in a sentence to conceal the textual content or communication. Word obfuscation is used in adversarial communication by terrorist or criminals for conveying their messages without getting red-flagged by security and intelligence agencies intercepting or scanning messages (such as emails and telephone conversations). ConceptNet is a freely available semantic network represented as a directed graph consisting of nodes as concepts and edges as assertions of common sense about these concepts. We present a solution approach exploiting vast amount of semantic knowledge in ConceptNet for addressing the technically challenging problem of word substitution in adversarial communication. We frame the given problem as a textual reasoning and context inference task and utilize ConceptNet's natural-language-processing tool-kit for determining word substitution. We use ConceptNet to compute the conceptual similarity between any two given terms and define a Mean Average Conceptual Similarity (MACS) metric to identify out-of-context terms. The test-bed to evaluate our proposed approach consists of Enron email dataset (having over 600000 emails generated by 158 employees of Enron Corporation) and Brown corpus (totaling about a million words drawn from a wide variety of sources). We implement word substitution techniques used by previous researches to generate a test dataset. We conduct a series of experiments consisting of word substitution methods used in the past to evaluate our approach. Experimental results reveal that the proposed approach is effective. | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | 66,913 |
2406.08863 | Self-supervised Graph Neural Network for Mechanical CAD Retrieval | CAD (Computer-Aided Design) plays a crucial role in mechanical industry, where large numbers of similar-shaped CAD parts are often created. Efficiently reusing these parts is key to reducing design and production costs for enterprises. Retrieval systems are vital for achieving CAD reuse, but the complex shapes of CAD models are difficult to accurately describe using text or keywords, making traditional retrieval methods ineffective. While existing representation learning approaches have been developed for CAD, manually labeling similar samples in these methods is expensive. Additionally, CAD models' unique parameterized data structure presents challenges for applying existing 3D shape representation learning techniques directly. In this work, we propose GC-CAD, a self-supervised contrastive graph neural network-based method for mechanical CAD retrieval that directly models parameterized CAD raw files. GC-CAD consists of two key modules: structure-aware representation learning and contrastive graph learning framework. The method leverages graph neural networks to extract both geometric and topological information from CAD models, generating feature representations. We then introduce a simple yet effective contrastive graph learning framework approach, enabling the model to train without manual labels and generate retrieval-ready representations. Experimental results on four datasets including human evaluation demonstrate that the proposed method achieves significant accuracy improvements and up to 100 times efficiency improvement over the baseline methods. | false | false | false | false | true | true | false | false | false | false | false | true | false | false | false | false | false | false | 463,667 |
2311.04922 | Are cascade dialogue state tracking models speaking out of turn in
spoken dialogues? | In Task-Oriented Dialogue (TOD) systems, correctly updating the system's understanding of the user's needs is key to a smooth interaction. Traditionally TOD systems are composed of several modules that interact with one another. While each of these components is the focus of active research communities, their behavior in interaction can be overlooked. This paper proposes a comprehensive analysis of the errors of state of the art systems in complex settings such as Dialogue State Tracking which highly depends on the dialogue context. Based on spoken MultiWoz, we identify that errors on non-categorical slots' values are essential to address in order to bridge the gap between spoken and chat-based dialogue systems. We explore potential solutions to improve transcriptions and help dialogue state tracking generative models correct such errors. | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 406,399 |
1809.08566 | Query Understanding via Entity Attribute Identification | Understanding searchers' queries is an essential component of semantic search systems. In many cases, search queries involve specific attributes of an entity in a knowledge base (KB), which can be further used to find query answers. In this study, we aim to move forward the understanding of queries by identifying their related entity attributes from a knowledge base. To this end, we introduce the task of entity attribute identification and propose two methods to address it: (i) a model based on Markov Random Field, and (ii) a learning to rank model. We develop a human annotated test collection and show that our proposed methods can bring significant improvements over the baseline methods. | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | 108,535 |
1007.3105 | A Selection Region Based Routing Protocol for Random Mobile ad hoc
Networks | We propose a selection region based multi-hop routing protocol for random mobile ad hoc networks, where the selection region is defined by two parameters: a reference distance and a selection angle. At each hop, a relay is chosen as the nearest node to the transmitter that is located within the selection region. By assuming that the relay nodes are randomly placed, we derive an upper bound for the optimum reference distance to maximize the expected density of progress and investigate the relationship between the optimum selection angle and the optimum reference distance. We also note that the optimized expected density of progress scales as $\Theta(\sqrt{\lambda})$, which matches the prior results in the literature. Compared with the spatial-reuse multi-hop protocol in \cite{Baccelli:Aloha} recently proposed by Baccelli \emph{et al.}, in our new protocol the amount of nodes involved and the calculation complexity for each relay selection are reduced significantly, which is attractive for energy-limited wireless ad hoc networks (e.g., wireless sensor networks). | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 7,071 |
2011.01181 | QMUL-SDS @ SardiStance: Leveraging Network Interactions to Boost
Performance on Stance Detection using Knowledge Graphs | This paper presents our submission to the SardiStance 2020 shared task, describing the architecture used for Task A and Task B. While our submission for Task A did not exceed the baseline, retraining our model using all the training tweets, showed promising results leading to (f-avg 0.601) using bidirectional LSTM with BERT multilingual embedding for Task A. For our submission for Task B, we ranked 6th (f-avg 0.709). With further investigation, our best experimented settings increased performance from (f-avg 0.573) to (f-avg 0.733) with same architecture and parameter settings and after only incorporating social interaction features -- highlighting the impact of social interaction on the model's performance. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 204,516 |
2409.08522 | MAPX: An explainable model-agnostic framework for the detection of false
information on social media networks | The automated detection of false information has become a fundamental task in combating the spread of "fake news" on online social media networks (OSMN) as it reduces the need for manual discernment by individuals. In the literature, leveraging various content or context features of OSMN documents have been found useful. However, most of the existing detection models often utilise these features in isolation without regard to the temporal and dynamic changes oft-seen in reality, thus, limiting the robustness of the models. Furthermore, there has been little to no consideration of the impact of the quality of documents' features on the trustworthiness of the final prediction. In this paper, we introduce a novel model-agnostic framework, called MAPX, which allows evidence based aggregation of predictions from existing models in an explainable manner. Indeed, the developed aggregation method is adaptive, dynamic and considers the quality of OSMN document features. Further, we perform extensive experiments on benchmarked fake news datasets to demonstrate the effectiveness of MAPX using various real-world data quality scenarios. Our empirical results show that the proposed framework consistently outperforms all state-of-the-art models evaluated. For reproducibility, a demo of MAPX is available at \href{https://github.com/SCondran/MAPX_framework}{this link} | false | false | false | true | false | false | true | false | true | false | false | false | false | false | false | false | false | false | 487,944 |
2208.08351 | Minimum Cost Adaptive Submodular Cover | Adaptive submodularity is a fundamental concept in stochastic optimization, with numerous applications such as sensor placement, hypothesis identification and viral marketing. We consider the problem of minimum cost cover of adaptive-submodular functions, and provide a $4(1+\ln Q)$-approximation algorithm, where $Q$ is the goal value. In fact, we consider a significantly more general objective of minimizing the $p^{th}$ moment of the coverage cost, and show that our algorithm simultaneously achieves a $(p+1)^{p+1}\cdot (\ln Q+1)^p$ approximation guarantee for all $p\ge 1$. All our approximation ratios are best possible up to constant factors (assuming $P\ne NP$). Moreover, our results also extend to the setting where one wants to cover {\em multiple} adaptive-submodular functions. Finally, we evaluate the empirical performance of our algorithm on instances of hypothesis identification. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 313,348 |
2108.08800 | EqGNN: Equalized Node Opportunity in Graphs | Graph neural networks (GNNs), has been widely used for supervised learning tasks in graphs reaching state-of-the-art results. However, little work was dedicated to creating unbiased GNNs, i.e., where the classification is uncorrelated with sensitive attributes, such as race or gender. Some ignore the sensitive attributes or optimize for the criteria of statistical parity for fairness. However, it has been shown that neither approaches ensure fairness, but rather cripple the utility of the prediction task. In this work, we present a GNN framework that allows optimizing representations for the notion of Equalized Odds fairness criteria. The architecture is composed of three components: (1) a GNN classifier predicting the utility class, (2) a sampler learning the distribution of the sensitive attributes of the nodes given their labels. It generates samples fed into a (3) discriminator that discriminates between true and sampled sensitive attributes using a novel "permutation loss" function. Using these components, we train a model to neglect information regarding the sensitive attribute only with respect to its label. To the best of our knowledge, we are the first to optimize GNNs for the equalized odds criteria. We evaluate our classifier over several graph datasets and sensitive attributes and show our algorithm reaches state-of-the-art results. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 251,390 |
2302.05224 | Shared Situational Awareness with V2X Communication and Set-membership
Estimation | The ability to perceive and comprehend a traffic situation and to estimate the state of the vehicles and road-users in the surrounding of the ego-vehicle is known as situational awareness. Situational awareness for a heavy-duty autonomous vehicle is a critical part of the automation platform and depends on the ego-vehicle's field-of-view. But when it comes to the urban scenario, the field-of-view of the ego-vehicle is likely to be affected by occlusion and blind spots caused by infrastructure, moving vehicles, and parked vehicles. This paper proposes a framework to improve situational awareness using set-membership estimation and Vehicle-to-Everything (V2X) communication. This framework provides safety guarantees and can adapt to dynamically changing scenarios, and is integrated into an existing complex autonomous platform. A detailed description of the framework implementation and real-time results are illustrated in this paper. | false | false | false | false | false | false | false | true | false | false | true | false | false | false | false | false | false | false | 344,974 |
cs/0702033 | Bounds on ordered codes and orthogonal arrays | We derive new estimates of the size of codes and orthogonal arrays in the ordered Hamming space (the Niederreiter-Rosenbloom-Tsfasman space). We also show that the eigenvalues of the ordered Hamming scheme, the association scheme that describes the combinatorics of the space, are given by the multivariable Krawtchouk polynomials, and establish some of their properties. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 540,139 |
2202.02900 | A Passivity Based Framework for Safe Physical Human Robot Interaction | In this paper, the problem of making a safe compliant contact between a human and an assistive robot is considered. Users with disabilities have a need to utilize their assistive robots for physical human-robot interaction (PHRI) during certain activities of daily living (ADLs). Specifically, we propose a hybrid force/velocity/attitude control for a PHRI system based on measurements from a 6-axis force/torque sensor mounted on the robot wrist. While automatically aligning the end-effector surface with the unknown environmental (human) surface, a desired commanded force is applied in the normal direction while following desired velocity commands in the tangential directions. A Lyapunov based stability analysis is provided to prove both convergence as well as passivity of the interaction to ensure both performance and safety. Simulation as well as experimental results verify the performance and robustness of the proposed hybrid controller in the presence of dynamic uncertainties as well as safe physical human-robot interactions for a kinematically redundant robotic manipulator. | false | false | false | false | false | false | false | true | false | false | true | false | false | false | false | false | false | false | 278,993 |
1910.00768 | Contextual Local Explanation for Black Box Classifiers | We introduce a new model-agnostic explanation technique which explains the prediction of any classifier called CLE. CLE gives an faithful and interpretable explanation to the prediction, by approximating the model locally using an interpretable model. We demonstrate the flexibility of CLE by explaining different models for text, tabular and image classification, and the fidelity of it by doing simulated user experiments. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 147,766 |
1609.09152 | Fusing Similarity Models with Markov Chains for Sparse Sequential
Recommendation | Predicting personalized sequential behavior is a key task for recommender systems. In order to predict user actions such as the next product to purchase, movie to watch, or place to visit, it is essential to take into account both long-term user preferences and sequential patterns (i.e., short-term dynamics). Matrix Factorization and Markov Chain methods have emerged as two separate but powerful paradigms for modeling the two respectively. Combining these ideas has led to unified methods that accommodate long- and short-term dynamics simultaneously by modeling pairwise user-item and item-item interactions. In spite of the success of such methods for tackling dense data, they are challenged by sparsity issues, which are prevalent in real-world datasets. In recent years, similarity-based methods have been proposed for (sequentially-unaware) item recommendation with promising results on sparse datasets. In this paper, we propose to fuse such methods with Markov Chains to make personalized sequential recommendations. We evaluate our method, Fossil, on a variety of large, real-world datasets. We show quantitatively that Fossil outperforms alternative algorithms, especially on sparse datasets, and qualitatively that it captures personalized dynamics and is able to make meaningful recommendations. | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | 61,675 |
2502.11470 | Optimized detection of cyber-attacks on IoT networks via hybrid deep
learning models | The rapid expansion of Internet of Things (IoT) devices has increased the risk of cyber-attacks, making effective detection essential for securing IoT networks. This work introduces a novel approach combining Self-Organizing Maps (SOMs), Deep Belief Networks (DBNs), and Autoencoders to detect known and previously unseen attack patterns. A comprehensive evaluation using simulated and real-world traffic data is conducted, with models optimized via Particle Swarm Optimization (PSO). The system achieves an accuracy of up to 99.99% and Matthews Correlation Coefficient (MCC) values exceeding 99.50%. Experiments on NSL-KDD, UNSW-NB15, and CICIoT2023 confirm the model's strong performance across diverse attack types. These findings suggest that the proposed method enhances IoT security by identifying emerging threats and adapting to evolving attack strategies. | false | false | false | false | true | false | false | false | false | false | false | false | true | false | false | false | false | false | 534,392 |
2205.13445 | Mutual Information Divergence: A Unified Metric for Multimodal
Generative Models | Text-to-image generation and image captioning are recently emerged as a new experimental paradigm to assess machine intelligence. They predict continuous quantity accompanied by their sampling techniques in the generation, making evaluation complicated and intractable to get marginal distributions. Based on a recent trend that multimodal generative evaluations exploit a vison-and-language pre-trained model, we propose the negative Gaussian cross-mutual information using the CLIP features as a unified metric, coined by Mutual Information Divergence (MID). To validate, we extensively compare it with competing metrics using carefully-generated or human-annotated judgments in text-to-image generation and image captioning tasks. The proposed MID significantly outperforms the competitive methods by having consistency across benchmarks, sample parsimony, and robustness toward the exploited CLIP model. We look forward to seeing the underrepresented implications of the Gaussian cross-mutual information in multimodal representation learning and the future works based on this novel proposition. | false | false | false | false | true | false | true | false | true | true | false | true | false | false | false | false | false | false | 298,937 |
2402.10769 | Distillation Enhanced Generative Retrieval | Generative retrieval is a promising new paradigm in text retrieval that generates identifier strings of relevant passages as the retrieval target. This paradigm leverages powerful generative language models, distinct from traditional sparse or dense retrieval methods. In this work, we identify a viable direction to further enhance generative retrieval via distillation and propose a feasible framework, named DGR. DGR utilizes sophisticated ranking models, such as the cross-encoder, in a teacher role to supply a passage rank list, which captures the varying relevance degrees of passages instead of binary hard labels; subsequently, DGR employs a specially designed distilled RankNet loss to optimize the generative retrieval model, considering the passage rank order provided by the teacher model as labels. This framework only requires an additional distillation step to enhance current generative retrieval systems and does not add any burden to the inference stage. We conduct experiments on four public datasets, and the results indicate that DGR achieves state-of-the-art performance among the generative retrieval methods. Additionally, DGR demonstrates exceptional robustness and generalizability with various teacher models and distillation losses. | false | false | false | false | true | true | false | false | true | false | false | false | false | false | false | false | false | false | 430,107 |
2005.02137 | Label Propagation Adaptive Resonance Theory for Semi-supervised
Continuous Learning | Semi-supervised learning and continuous learning are fundamental paradigms for human-level intelligence. To deal with real-world problems where labels are rarely given and the opportunity to access the same data is limited, it is necessary to apply these two paradigms in a joined fashion. In this paper, we propose Label Propagation Adaptive Resonance Theory (LPART) for semi-supervised continuous learning. LPART uses an online label propagation mechanism to perform classification and gradually improves its accuracy as the observed data accumulates. We evaluated the proposed model on visual (MNIST, SVHN, CIFAR-10) and audio (NSynth) datasets by adjusting the ratio of the labeled and unlabeled data. The accuracies are much higher when both labeled and unlabeled data are used, demonstrating the significant advantage of LPART in environments where the data labels are scarce. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 175,776 |
2302.03106 | Efficient and Flexible Topic Modeling using Pretrained Embeddings and
Bag of Sentences | Pre-trained language models have led to a new state-of-the-art in many NLP tasks. However, for topic modeling, statistical generative models such as LDA are still prevalent, which do not easily allow incorporating contextual word vectors. They might yield topics that do not align well with human judgment. In this work, we propose a novel topic modeling and inference algorithm. We suggest a bag of sentences (BoS) approach using sentences as the unit of analysis. We leverage pre-trained sentence embeddings by combining generative process models and clustering. We derive a fast inference algorithm based on expectation maximization, hard assignments, and an annealing process. The evaluation shows that our method yields state-of-the art results with relatively little computational demands. Our method is also more flexible compared to prior works leveraging word embeddings, since it provides the possibility to customize topic-document distributions using priors. Code and data is at \url{https://github.com/JohnTailor/BertSenClu}. | false | false | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | 344,218 |
2402.04632 | GSN: Generalisable Segmentation in Neural Radiance Field | Traditional Radiance Field (RF) representations capture details of a specific scene and must be trained afresh on each scene. Semantic feature fields have been added to RFs to facilitate several segmentation tasks. Generalised RF representations learn the principles of view interpolation. A generalised RF can render new views of an unknown and untrained scene, given a few views. We present a way to distil feature fields into the generalised GNT representation. Our GSN representation generates new views of unseen scenes on the fly along with consistent, per-pixel semantic features. This enables multi-view segmentation of arbitrary new scenes. We show different semantic features being distilled into generalised RFs. Our multi-view segmentation results are on par with methods that use traditional RFs. GSN closes the gap between standard and generalisable RF methods significantly. Project Page: https://vinayak-vg.github.io/GSN/ | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | true | 427,538 |
2106.01483 | Multiscale Domain Adaptive YOLO for Cross-Domain Object Detection | The area of domain adaptation has been instrumental in addressing the domain shift problem encountered by many applications. This problem arises due to the difference between the distributions of source data used for training in comparison with target data used during realistic testing scenarios. In this paper, we introduce a novel MultiScale Domain Adaptive YOLO (MS-DAYOLO) framework that employs multiple domain adaptation paths and corresponding domain classifiers at different scales of the recently introduced YOLOv4 object detector to generate domain-invariant features. We train and test our proposed method using popular datasets. Our experiments show significant improvements in object detection performance when training YOLOv4 using the proposed MS-DAYOLO and when tested on target data representing challenging weather conditions for autonomous driving applications. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 238,507 |
2306.07975 | Fundamental connections between utility theories of wealth and
information theory | We establish fundamental connections between utility theories of wealth from the economic sciences and information-theoretic quantities. In particular, we introduce operational tasks based on betting where both gambler and bookmaker have access to side information, or betting tasks with double side information for short. In order to characterise these operational tasks we introduce new conditional R\'enyi divergences, and explore some of their properties. Furthermore, we introduce an utility theory of wealth ratios, and operationally interpret there the two-parameter $(q,r)$ generalised mutual information measure recently introduced by V. M. Ili\'c and I. V. Djordjevi\'c; it quantifies the advantage provided by side information in betting tasks for utility theories of wealth ratios. Moreover, we show that the Ili\'c-Djordjevi\'c conditional entropy satisfies a type of generalised chain rule, which generalises that of Arimoto-R\'enyi. Finally, we address the implications of these results on the quantum resource theories of informative measurements and non-constant channels. Altogether, these results further help strengthening the bridge between the theory of expected utility from the economic sciences and Shannon's theory of information. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 373,230 |
1411.3285 | Amoeba Techniques for Shape and Texture Analysis | Morphological amoebas are image-adaptive structuring elements for morphological and other local image filters introduced by Lerallut et al. Their construction is based on combining spatial distance with contrast information into an image-dependent metric. Amoeba filters show interesting parallels to image filtering methods based on partial differential equations (PDEs), which can be confirmed by asymptotic equivalence results. In computing amoebas, graph structures are generated that hold information about local image texture. This paper reviews and summarises the work of the author and his coauthors on morphological amoebas, particularly their relations to PDE filters and texture analysis. It presents some extensions and points out directions for future investigation on the subject. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 37,485 |
1705.01576 | Fourth-order Tensors with Multidimensional Discrete Transforms | The big data era is swamping areas including data analysis, machine/deep learning, signal processing, statistics, scientific computing, and cloud computing. The multidimensional feature and huge volume of big data put urgent requirements to the development of multilinear modeling tools and efficient algorithms. In this paper, we build a novel multilinear tensor space that supports useful algorithms such as SVD and QR, while generalizing the matrix space to fourth-order tensors was believed to be challenging. Specifically, given any multidimensional discrete transform, we show that fourth-order tensors are bilinear operators on a space of matrices. First, we take a transform-based approach to construct a new tensor space by defining a new multiplication operation and tensor products, and accordingly the analogous concepts: identity, inverse, transpose, linear combinations, and orthogonality. Secondly, we define the $\mathcal{L}$-SVD for fourth-order tensors and present an efficient algorithm, where the tensor case requires a stronger condition for unique decomposition than the matrix case. Thirdly, we define the tensor $\mathcal{L}$-QR decomposition and propose a Householder QR algorithm to avoid the catastrophic cancellation problem associated with the conventional Gram-Schmidt process. Finally, we validate our schemes on video compression and one-shot face recognition. For video compression, compared with the existing tSVD, the proposed $\mathcal{L}$-SVD achieves $3\sim 10$dB gains in RSE, while the running time is reduced by about $50\%$ and $87.5\%$, respectively. For one-shot face recognition, the recognition rate is increased by about $10\% \sim 20\%$. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | true | 72,859 |
1504.07159 | Combining Local Appearance and Holistic View: Dual-Source Deep Neural
Networks for Human Pose Estimation | We propose a new learning-based method for estimating 2D human pose from a single image, using Dual-Source Deep Convolutional Neural Networks (DS-CNN). Recently, many methods have been developed to estimate human pose by using pose priors that are estimated from physiologically inspired graphical models or learned from a holistic perspective. In this paper, we propose to integrate both the local (body) part appearance and the holistic view of each local part for more accurate human pose estimation. Specifically, the proposed DS-CNN takes a set of image patches (category-independent object proposals for training and multi-scale sliding windows for testing) as the input and then learns the appearance of each local part by considering their holistic views in the full body. Using DS-CNN, we achieve both joint detection, which determines whether an image patch contains a body joint, and joint localization, which finds the exact location of the joint in the image patch. Finally, we develop an algorithm to combine these joint detection/localization results from all the image patches for estimating the human pose. The experimental results show the effectiveness of the proposed method by comparing to the state-of-the-art human-pose estimation methods based on pose priors that are estimated from physiologically inspired graphical models or learned from a holistic perspective. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 42,506 |
2212.06313 | Metaheuristic-based Energy-aware Image Compression for Mobile App
Development | The JPEG standard is widely used in different image processing applications. One of the main components of the JPEG standard is the quantisation table (QT) since it plays a vital role in the image properties such as image quality and file size. In recent years, several efforts based on population-based metaheuristic (PBMH) algorithms have been performed to find the proper QT(s) for a specific image, although they do not take into consideration the user opinion in advance. Take an android developer as an example, who prefers a small-size image, while the optimisation process results in a high-quality image, leading to a huge file size. Another pitfall of the current works is a lack of comprehensive coverage, meaning that the QT(s) can not provide all possible combinations of file size and quality. Therefore, this paper aims to propose three distinct contributions. First, to include the user opinion in the compression process, the file size of the output image can be controlled by a user in advance. To this end, we propose a novel objective function for population-based JPEG image compression. Second, to tackle the lack of comprehensive coverage, we suggest a novel representation. Our proposed representation can not only provide more comprehensive coverage but also find the proper value for the quality factor for a specific image without any background knowledge. Both changes in representation and objective function are independent of the search strategies and can be used with any type of population-based metaheuristic (PBMH) algorithm. Therefore, as the third contribution, we also provide a comprehensive benchmark on 22 state-of-the-art and recently-introduced PBMH algorithms. Our extensive experiments on different benchmark images and in terms of different criteria show that our novel formulation for JPEG image compression can work effectively. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | 336,068 |
2105.11134 | One2Set: Generating Diverse Keyphrases as a Set | Recently, the sequence-to-sequence models have made remarkable progress on the task of keyphrase generation (KG) by concatenating multiple keyphrases in a predefined order as a target sequence during training. However, the keyphrases are inherently an unordered set rather than an ordered sequence. Imposing a predefined order will introduce wrong bias during training, which can highly penalize shifts in the order between keyphrases. In this work, we propose a new training paradigm One2Set without predefining an order to concatenate the keyphrases. To fit this paradigm, we propose a novel model that utilizes a fixed set of learned control codes as conditions to generate a set of keyphrases in parallel. To solve the problem that there is no correspondence between each prediction and target during training, we propose a $K$-step target assignment mechanism via bipartite matching, which greatly increases the diversity and reduces the duplication ratio of generated keyphrases. The experimental results on multiple benchmarks demonstrate that our approach significantly outperforms the state-of-the-art methods. | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 236,609 |
2010.01047 | Relaxing the Constraints on Predictive Coding Models | Predictive coding is an influential theory of cortical function which posits that the principal computation the brain performs, which underlies both perception and learning, is the minimization of prediction errors. While motivated by high-level notions of variational inference, detailed neurophysiological models of cortical microcircuits which can implements its computations have been developed. Moreover, under certain conditions, predictive coding has been shown to approximate the backpropagation of error algorithm, and thus provides a relatively biologically plausible credit-assignment mechanism for training deep networks. However, standard implementations of the algorithm still involve potentially neurally implausible features such as identical forward and backward weights, backward nonlinear derivatives, and 1-1 error unit connectivity. In this paper, we show that these features are not integral to the algorithm and can be removed either directly or through learning additional sets of parameters with Hebbian update rules without noticeable harm to learning performance. Our work thus relaxes current constraints on potential microcircuit designs and hopefully opens up new regions of the design-space for neuromorphic implementations of predictive coding. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 198,494 |
2312.05471 | Fine-Grained Analysis of Team Collaborative Dialogue | Natural language analysis of human collaborative chat dialogues is an understudied domain with many unique challenges: a large number of dialogue act labels, underspecified and dynamic tasks, interleaved topics, and long-range contextual dependence. While prior work has studied broad metrics of team dialogue and associated performance using methods such as LSA, there has been little effort in generating fine-grained descriptions of team dynamics and individual performance from dialogue. We describe initial work towards developing an explainable analytics tool in the software development domain using Slack chats mined from our organization, including generation of a novel, hierarchical labeling scheme; design of descriptive metrics based on the frequency of occurrence of dialogue acts; and initial results using a transformer + CRF architecture to incorporate long-range context. | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 414,101 |
1602.05682 | Audio Recording Device Identification Based on Deep Learning | In this paper we present a research on identification of audio recording devices from background noise, thus providing a method for forensics. The audio signal is the sum of speech signal and noise signal. Usually, people pay more attention to speech signal, because it carries the information to deliver. So a great amount of researches have been dedicated to getting higher Signal-Noise-Ratio (SNR). There are many speech enhancement algorithms to improve the quality of the speech, which can be seen as reducing the noise. However, noises can be regarded as the intrinsic fingerprint traces of an audio recording device. These digital traces can be characterized and identified by new machine learning techniques. Therefore, in our research, we use the noise as the intrinsic features. As for the identification, multiple classifiers of deep learning methods are used and compared. The identification result shows that the method of getting feature vector from the noise of each device and identifying them with deep learning techniques is viable, and well-preformed. | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 52,283 |
2405.11280 | Joint Analysis of Single-Cell Data across Cohorts with Missing
Modalities | Joint analysis of multi-omic single-cell data across cohorts has significantly enhanced the comprehensive analysis of cellular processes. However, most of the existing approaches for this purpose require access to samples with complete modality availability, which is impractical in many real-world scenarios. In this paper, we propose (Single-Cell Cross-Cohort Cross-Category) integration, a novel framework that learns unified cell representations under domain shift without requiring full-modality reference samples. Our generative approach learns rich cross-modal and cross-domain relationships that enable imputation of these missing modalities. Through experiments on real-world multi-omic datasets, we demonstrate that offers a robust solution to single-cell tasks such as cell type clustering, cell type classification, and feature imputation. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 455,074 |
2411.01757 | Mitigating Spurious Correlations via Disagreement Probability | Models trained with empirical risk minimization (ERM) are prone to be biased towards spurious correlations between target labels and bias attributes, which leads to poor performance on data groups lacking spurious correlations. It is particularly challenging to address this problem when access to bias labels is not permitted. To mitigate the effect of spurious correlations without bias labels, we first introduce a novel training objective designed to robustly enhance model performance across all data samples, irrespective of the presence of spurious correlations. From this objective, we then derive a debiasing method, Disagreement Probability based Resampling for debiasing (DPR), which does not require bias labels. DPR leverages the disagreement between the target label and the prediction of a biased model to identify bias-conflicting samples-those without spurious correlations-and upsamples them according to the disagreement probability. Empirical evaluations on multiple benchmarks demonstrate that DPR achieves state-of-the-art performance over existing baselines that do not use bias labels. Furthermore, we provide a theoretical analysis that details how DPR reduces dependency on spurious correlations. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 505,208 |
2404.03386 | SENSOR: Imitate Third-Person Expert's Behaviors via Active Sensoring | In many real-world visual Imitation Learning (IL) scenarios, there is a misalignment between the agent's and the expert's perspectives, which might lead to the failure of imitation. Previous methods have generally solved this problem by domain alignment, which incurs extra computation and storage costs, and these methods fail to handle the \textit{hard cases} where the viewpoint gap is too large. To alleviate the above problems, we introduce active sensoring in the visual IL setting and propose a model-based SENSory imitatOR (SENSOR) to automatically change the agent's perspective to match the expert's. SENSOR jointly learns a world model to capture the dynamics of latent states, a sensor policy to control the camera, and a motor policy to control the agent. Experiments on visual locomotion tasks show that SENSOR can efficiently simulate the expert's perspective and strategy, and outperforms most baseline methods. | false | false | false | false | true | false | true | true | false | false | false | false | false | false | false | false | false | false | 444,236 |
2110.06741 | Dynamical Wasserstein Barycenters for Time-series Modeling | Many time series can be modeled as a sequence of segments representing high-level discrete states, such as running and walking in a human activity application. Flexible models should describe the system state and observations in stationary "pure-state" periods as well as transition periods between adjacent segments, such as a gradual slowdown between running and walking. However, most prior work assumes instantaneous transitions between pure discrete states. We propose a dynamical Wasserstein barycentric (DWB) model that estimates the system state over time as well as the data-generating distributions of pure states in an unsupervised manner. Our model assumes each pure state generates data from a multivariate normal distribution, and characterizes transitions between states via displacement-interpolation specified by the Wasserstein barycenter. The system state is represented by a barycentric weight vector which evolves over time via a random walk on the simplex. Parameter learning leverages the natural Riemannian geometry of Gaussian distributions under the Wasserstein distance, which leads to improved convergence speeds. Experiments on several human activity datasets show that our proposed DWB model accurately learns the generating distribution of pure states while improving state estimation for transition periods compared to the commonly used linear interpolation mixture models. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 260,728 |
1607.02952 | Are human interactivity times lognormal? | In this paper, we are analyzing the interactivity time, defined as the duration between two consecutive tasks such as sending emails, collecting friends and followers and writing comments in online social networks (OSNs). The distributions of these times are heavy tailed and often described by a power-law distribution. However, power-law distributions usually only fit the heavy tail of empirical data and ignore the information in the smaller value range. Here, we argue that the durations between writing emails or comments, adding friends and receiving followers are likely to follow a lognormal distribution. We discuss the similarities between power-law and lognormal distributions, show that binning of data can deform a lognormal to a power-law distribution and propose an explanation for the appearance of lognormal interactivity times. The historical debate of similarities between lognormal and power-law distributions is reviewed by illustrating the resemblance of measurements in this paper with the historical problem of income and city size distributions. | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 58,444 |
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