JohnVitz/NLP_Assignment_3_part_3 · Datasets at Hugging Face

2409.08211

Graph Laplacian-based Bayesian Multi-fidelity Modeling

We present a novel probabilistic approach for generating multi-fidelity data while accounting for errors inherent in both low- and high-fidelity data. In this approach a graph Laplacian constructed from the low-fidelity data is used to define a multivariate Gaussian prior density for the coordinates of the true data points. In addition, few high-fidelity data points are used to construct a conjugate likelihood term. Thereafter, Bayes rule is applied to derive an explicit expression for the posterior density which is also multivariate Gaussian. The maximum \textit{a posteriori} (MAP) estimate of this density is selected to be the optimal multi-fidelity estimate. It is shown that the MAP estimate and the covariance of the posterior density can be determined through the solution of linear systems of equations. Thereafter, two methods, one based on spectral truncation and another based on a low-rank approximation, are developed to solve these equations efficiently. The multi-fidelity approach is tested on a variety of problems in solid and fluid mechanics with data that represents vectors of quantities of interest and discretized spatial fields in one and two dimensions. The results demonstrate that by utilizing a small fraction of high-fidelity data, the multi-fidelity approach can significantly improve the accuracy of a large collection of low-fidelity data points.

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true

false

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false

487,810

2406.13060

Scale-Translation Equivariant Network for Oceanic Internal Solitary Wave Localization

Internal solitary waves (ISWs) are gravity waves that are often observed in the interior ocean rather than the surface. They hold significant importance due to their capacity to carry substantial energy, thus influence pollutant transport, oil platform operations, submarine navigation, etc. Researchers have studied ISWs through optical images, synthetic aperture radar (SAR) images, and altimeter data from remote sensing instruments. However, cloud cover in optical remote sensing images variably obscures ground information, leading to blurred or missing surface observations. As such, this paper aims at altimeter-based machine learning solutions to automatically locate ISWs. The challenges, however, lie in the following two aspects: 1) the altimeter data has low resolution, which requires a strong machine learner; 2) labeling data is extremely labor-intensive, leading to very limited data for training. In recent years, the grand progress of deep learning demonstrates strong learning capacity given abundant data. Besides, more recent studies on efficient learning and self-supervised learning laid solid foundations to tackle the aforementioned challenges. In this paper, we propose to inject prior knowledge to achieve a strong and efficient learner. Specifically, intrinsic patterns in altimetry data are efficiently captured using a scale-translation equivariant convolutional neural network (ST-ECNN). By considering inherent symmetries in neural network design, ST-ECNN achieves higher efficiency and better performance than baseline models. Furthermore, we also introduce prior knowledge from massive unsupervised data to enhance our solution using the SimCLR framework for pre-training. Our final solution achieves an overall better performance than baselines on our handcrafted altimetry dataset. Data and codes are available at https://github.com/ZhangWan-byte/Internal_Solitary_Wave_Localization .

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true

false

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false

465,674

2406.17618

Towards Building an End-to-End Multilingual Automatic Lyrics Transcription Model

Multilingual automatic lyrics transcription (ALT) is a challenging task due to the limited availability of labelled data and the challenges introduced by singing, compared to multilingual automatic speech recognition. Although some multilingual singing datasets have been released recently, English continues to dominate these collections. Multilingual ALT remains underexplored due to the scale of data and annotation quality. In this paper, we aim to create a multilingual ALT system with available datasets. Inspired by architectures that have been proven effective for English ALT, we adapt these techniques to the multilingual scenario by expanding the target vocabulary set. We then evaluate the performance of the multilingual model in comparison to its monolingual counterparts. Additionally, we explore various conditioning methods to incorporate language information into the model. We apply analysis by language and combine it with the language classification performance. Our findings reveal that the multilingual model performs consistently better than the monolingual models trained on the language subsets. Furthermore, we demonstrate that incorporating language information significantly enhances performance.

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false

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false

467,648

1602.00554

Graph-based Predictable Feature Analysis

We propose graph-based predictable feature analysis (GPFA), a new method for unsupervised learning of predictable features from high-dimensional time series, where high predictability is understood very generically as low variance in the distribution of the next data point given the previous ones. We show how this measure of predictability can be understood in terms of graph embedding as well as how it relates to the information-theoretic measure of predictive information in special cases. We confirm the effectiveness of GPFA on different datasets, comparing it to three existing algorithms with similar objectives---namely slow feature analysis, forecastable component analysis, and predictable feature analysis---to which GPFA shows very competitive results.

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true

false

51,596

2401.11810

Generalization and Informativeness of Conformal Prediction

The safe integration of machine learning modules in decision-making processes hinges on their ability to quantify uncertainty. A popular technique to achieve this goal is conformal prediction (CP), which transforms an arbitrary base predictor into a set predictor with coverage guarantees. While CP certifies the predicted set to contain the target quantity with a user-defined tolerance, it does not provide control over the average size of the predicted sets, i.e., over the informativeness of the prediction. In this work, a theoretical connection is established between the generalization properties of the base predictor and the informativeness of the resulting CP prediction sets. To this end, an upper bound is derived on the expected size of the CP set predictor that builds on generalization error bounds for the base predictor. The derived upper bound provides insights into the dependence of the average size of the CP set predictor on the amount of calibration data, the target reliability, and the generalization performance of the base predictor. The theoretical insights are validated using simple numerical regression and classification tasks.

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423,157

1001.4413

Structure and Behaviour of Virtual Organisation Breeding Environments

This paper provides an outline of a formal approach that we are developing for modelling Virtual Organisations (VOs) and their Breeding Environments (VBEs). We propose different levels of representation for the functional structures and processes that VBEs and VOs involve, which are independent of the specificities of the infrastructures (organisational and technical) that support the functioning of VBEs. This allows us to reason about properties of tasks performed within VBEs and services provided through VOs without committing to the way in which they are implemented.

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true

false

true

5,519

2112.05142

HairCLIP: Design Your Hair by Text and Reference Image

Hair editing is an interesting and challenging problem in computer vision and graphics. Many existing methods require well-drawn sketches or masks as conditional inputs for editing, however these interactions are neither straightforward nor efficient. In order to free users from the tedious interaction process, this paper proposes a new hair editing interaction mode, which enables manipulating hair attributes individually or jointly based on the texts or reference images provided by users. For this purpose, we encode the image and text conditions in a shared embedding space and propose a unified hair editing framework by leveraging the powerful image text representation capability of the Contrastive Language-Image Pre-Training (CLIP) model. With the carefully designed network structures and loss functions, our framework can perform high-quality hair editing in a disentangled manner. Extensive experiments demonstrate the superiority of our approach in terms of manipulation accuracy, visual realism of editing results, and irrelevant attribute preservation. Project repo is https://github.com/wty-ustc/HairCLIP.

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true

false

true

270,750

2403.17936

ConvoFusion: Multi-Modal Conversational Diffusion for Co-Speech Gesture Synthesis

Gestures play a key role in human communication. Recent methods for co-speech gesture generation, while managing to generate beat-aligned motions, struggle generating gestures that are semantically aligned with the utterance. Compared to beat gestures that align naturally to the audio signal, semantically coherent gestures require modeling the complex interactions between the language and human motion, and can be controlled by focusing on certain words. Therefore, we present ConvoFusion, a diffusion-based approach for multi-modal gesture synthesis, which can not only generate gestures based on multi-modal speech inputs, but can also facilitate controllability in gesture synthesis. Our method proposes two guidance objectives that allow the users to modulate the impact of different conditioning modalities (e.g. audio vs text) as well as to choose certain words to be emphasized during gesturing. Our method is versatile in that it can be trained either for generating monologue gestures or even the conversational gestures. To further advance the research on multi-party interactive gestures, the DnD Group Gesture dataset is released, which contains 6 hours of gesture data showing 5 people interacting with one another. We compare our method with several recent works and demonstrate effectiveness of our method on a variety of tasks. We urge the reader to watch our supplementary video at our website.

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true

false

441,705

2302.08007

With Shared Microexponents, A Little Shifting Goes a Long Way

This paper introduces Block Data Representations (BDR), a framework for exploring and evaluating a wide spectrum of narrow-precision formats for deep learning. It enables comparison of popular quantization standards, and through BDR, new formats based on shared microexponents (MX) are identified, which outperform other state-of-the-art quantization approaches, including narrow-precision floating-point and block floating-point. MX utilizes multiple levels of quantization scaling with ultra-fine scaling factors based on shared microexponents in the hardware. The effectiveness of MX is demonstrated on real-world models including large-scale generative pretraining and inferencing, and production-scale recommendation systems.

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false

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true

345,898

2311.12291

Instance-aware 3D Semantic Segmentation powered by Shape Generators and Classifiers

Existing 3D semantic segmentation methods rely on point-wise or voxel-wise feature descriptors to output segmentation predictions. However, these descriptors are often supervised at point or voxel level, leading to segmentation models that can behave poorly at instance-level. In this paper, we proposed a novel instance-aware approach for 3D semantic segmentation. Our method combines several geometry processing tasks supervised at instance-level to promote the consistency of the learned feature representation. Specifically, our methods use shape generators and shape classifiers to perform shape reconstruction and classification tasks for each shape instance. This enforces the feature representation to faithfully encode both structural and local shape information, with an awareness of shape instances. In the experiments, our method significantly outperform existing approaches in 3D semantic segmentation on several public benchmarks, such as Waymo Open Dataset, SemanticKITTI and ScanNetV2.

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false

409,276

2407.06770

Pretraining-finetuning Framework for Efficient Co-design: A Case Study on Quadruped Robot Parkour

In nature, animals with exceptional locomotion abilities, such as cougars, often possess asymmetric fore and hind legs. This observation inspired us: could optimizing the leg length of quadruped robots endow them with similar locomotive capabilities? In this paper, we propose an approach that co-optimizes the mechanical structure and control policy to boost the locomotive prowess of quadruped robots. Specifically, we introduce a novel pretraining-finetuning framework, which not only guarantees optimal control strategies for each mechanical candidate but also ensures time efficiency. Additionally, we have devised an innovative training method for our pretraining network, integrating spatial domain randomization with regularization methods, markedly improving the network's generalizability. Our experimental results indicate that the proposed pretraining-finetuning framework significantly enhances the overall co-design performance with less time consumption. Moreover, the co-design strategy substantially exceeds the conventional method of independently optimizing control strategies, further improving the robot's locomotive performance and providing an innovative approach to enhancing the extreme parkour capabilities of quadruped robots.

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false

471,525

2205.09510

An Introduction to Quantum Machine Learning for Engineers

In the current noisy intermediate-scale quantum (NISQ) era, quantum machine learning is emerging as a dominant paradigm to program gate-based quantum computers. In quantum machine learning, the gates of a quantum circuit are parametrized, and the parameters are tuned via classical optimization based on data and on measurements of the outputs of the circuit. Parametrized quantum circuits (PQCs) can efficiently address combinatorial optimization problems, implement probabilistic generative models, and carry out inference (classification and regression). This monograph provides a self-contained introduction to quantum machine learning for an audience of engineers with a background in probability and linear algebra. It first describes the necessary background, concepts, and tools necessary to describe quantum operations and measurements. Then, it covers parametrized quantum circuits, the variational quantum eigensolver, as well as unsupervised and supervised quantum machine learning formulations.

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297,294

2410.22062

Bayesian Quantum Neural Network for Renewable-Rich Power Flow with Training Efficiency and Generalization Capability Improvements

This paper addresses the challenges of power flow calculation in large scale power systems with high renewable penetration, focusing on computational efficiency and generalization. Traditional methods, while accurate, struggle with scalability for large power systems. Existing data driven deep learning approaches, despite their speed, require extensive training data and lacks generalization capability in face of unseen scenarios, such as uncertainties of power flow caused by renewables. To overcome these limitations, we propose a novel power flow calculation model based on Bayesian Quantum Neural Networks (BQNNs). This model leverages quantum computing's ability to improve the training efficiency. The BQNN is trained using Bayesian methods, enabling it to update its understanding of renewable energy uncertainties dynamically, improving generalization to unseen data. Additionally, we introduce two evaluation metrics: effective dimension for model complexity and generalization error bound to assess the model's performance in unseen scenarios. Our approach demonstrates improved training efficiency and better generalization capability, making it as an effective tool for future steady-state power system analysis.

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false

503,480

1807.11113

Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Images

Convolutional neural networks have led to significant breakthroughs in the domain of medical image analysis. However, the task of breast cancer segmentation in whole-slide images (WSIs) is still underexplored. WSIs are large histopathological images with extremely high resolution. Constrained by the hardware and field of view, using high-magnification patches can slow down the inference process and using low-magnification patches can cause the loss of information. In this paper, we aim to achieve two seemingly conflicting goals for breast cancer segmentation: accurate and fast prediction. We propose a simple yet efficient framework Reinforced Auto-Zoom Net (RAZN) to tackle this task. Motivated by the zoom-in operation of a pathologist using a digital microscope, RAZN learns a policy network to decide whether zooming is required in a given region of interest. Because the zoom-in action is selective, RAZN is robust to unbalanced and noisy ground truth labels and can efficiently reduce overfitting. We evaluate our method on a public breast cancer dataset. RAZN outperforms both single-scale and multi-scale baseline approaches, achieving better accuracy at low inference cost.

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104,103

1406.4619

A Generalized Markov-Chain Modelling Approach to $(1,\lambda)$-ES Linear Optimization: Technical Report

Several recent publications investigated Markov-chain modelling of linear optimization by a $(1,\lambda)$-ES, considering both unconstrained and linearly constrained optimization, and both constant and varying step size. All of them assume normality of the involved random steps, and while this is consistent with a black-box scenario, information on the function to be optimized (e.g. separability) may be exploited by the use of another distribution. The objective of our contribution is to complement previous studies realized with normal steps, and to give sufficient conditions on the distribution of the random steps for the success of a constant step-size $(1,\lambda)$-ES on the simple problem of a linear function with a linear constraint. The decomposition of a multidimensional distribution into its marginals and the copula combining them is applied to the new distributional assumptions, particular attention being paid to distributions with Archimedean copulas.

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false

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true

33,953

1812.00174

Stochastic Training of Residual Networks: a Differential Equation Viewpoint

During the last few years, significant attention has been paid to the stochastic training of artificial neural networks, which is known as an effective regularization approach that helps improve the generalization capability of trained models. In this work, the method of modified equations is applied to show that the residual network and its variants with noise injection can be regarded as weak approximations of stochastic differential equations. Such observations enable us to bridge the stochastic training processes with the optimal control of backward Kolmogorov's equations. This not only offers a novel perspective on the effects of regularization from the loss landscape viewpoint but also sheds light on the design of more reliable and efficient stochastic training strategies. As an example, we propose a new way to utilize Bernoulli dropout within the plain residual network architecture and conduct experiments on a real-world image classification task to substantiate our theoretical findings.

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true

false

115,181

2204.04817

Effective Mutation Rate Adaptation through Group Elite Selection

Evolutionary algorithms are sensitive to the mutation rate (MR); no single value of this parameter works well across domains. Self-adaptive MR approaches have been proposed but they tend to be brittle: Sometimes they decay the MR to zero, thus halting evolution. To make self-adaptive MR robust, this paper introduces the Group Elite Selection of Mutation Rates (GESMR) algorithm. GESMR co-evolves a population of solutions and a population of MRs, such that each MR is assigned to a group of solutions. The resulting best mutational change in the group, instead of average mutational change, is used for MR selection during evolution, thus avoiding the vanishing MR problem. With the same number of function evaluations and with almost no overhead, GESMR converges faster and to better solutions than previous approaches on a wide range of continuous test optimization problems. GESMR also scales well to high-dimensional neuroevolution for supervised image-classification tasks and for reinforcement learning control tasks. Remarkably, GESMR produces MRs that are optimal in the long-term, as demonstrated through a comprehensive look-ahead grid search. Thus, GESMR and its theoretical and empirical analysis demonstrate how self-adaptation can be harnessed to improve performance in several applications of evolutionary computation.

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true

false

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false

290,798

2110.11384

Decentralised Person Re-Identification with Selective Knowledge Aggregation

Existing person re-identification (Re-ID) methods mostly follow a centralised learning paradigm which shares all training data to a collection for model learning. This paradigm is limited when data from different sources cannot be shared due to privacy concerns. To resolve this problem, two recent works have introduced decentralised (federated) Re-ID learning for constructing a globally generalised model (server)without any direct access to local training data nor shared data across different source domains (clients). However, these methods are poor on how to adapt the generalised model to maximise its performance on individual client domain Re-ID tasks having different Re-ID label spaces, due to a lack of understanding of data heterogeneity across domains. We call this poor 'model personalisation'. In this work, we present a new Selective Knowledge Aggregation approach to decentralised person Re-ID to optimise the trade-off between model personalisation and generalisation. Specifically, we incorporate attentive normalisation into the normalisation layers in a deep ReID model and propose to learn local normalisation layers specific to each domain, which are decoupled from the global model aggregation in federated Re-ID learning. This helps to preserve model personalisation knowledge on each local client domain and learn instance-specific information. Further, we introduce a dual local normalisation mechanism to learn generalised normalisation layers in each local model, which are then transmitted to the global model for central aggregation. This facilitates selective knowledge aggregation on the server to construct a global generalised model for out-of-the-box deployment on unseen novel domains. Extensive experiments on eight person Re-ID datasets show that the proposed approach to decentralised Re-ID significantly outperforms the state-of-the-art decentralised methods.

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false

262,458

2002.07890

Informative Path Planning for Mobile Sensing with Reinforcement Learning

Large-scale spatial data such as air quality, thermal conditions and location signatures play a vital role in a variety of applications. Collecting such data manually can be tedious and labour intensive. With the advancement of robotic technologies, it is feasible to automate such tasks using mobile robots with sensing and navigation capabilities. However, due to limited battery lifetime and scarcity of charging stations, it is important to plan paths for the robots that maximize the utility of data collection, also known as the informative path planning (IPP) problem. In this paper, we propose a novel IPP algorithm using reinforcement learning (RL). A constrained exploration and exploitation strategy is designed to address the unique challenges of IPP, and is shown to have fast convergence and better optimality than a classical reinforcement learning approach. Extensive experiments using real-world measurement data demonstrate that the proposed algorithm outperforms state-of-the-art algorithms in most test cases. Interestingly, unlike existing solutions that have to be re-executed when any input parameter changes, our RL-based solution allows a degree of transferability across different problem instances.

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true

false

164,594

2502.01235

One-step full gradient suffices for low-rank fine-tuning, provably and efficiently

This paper studies how to improve the performance of Low-Rank Adaption (LoRA) as guided by our theoretical analysis. Our first set of theoretical results show that for random initialization and linear models, \textit{i)} LoRA will align to the certain singular subspace of one-step gradient of full fine-tuning; \textit{ii)} preconditioners improve convergence in the high-rank case. These insights motivate us to focus on preconditioned LoRA using a specific spectral initialization strategy for aligning with certain subspaces. For both linear and nonlinear models, we prove that alignment and generalization guarantees can be directly achieved at initialization, and the subsequent linear convergence can be also built. Our analysis leads to the \emph{LoRA-One} algorithm (using \emph{One}-step gradient and preconditioning), a theoretically grounded algorithm that achieves significant empirical improvement over vanilla LoRA and its variants on several benchmarks. Our theoretical analysis, based on decoupling the learning dynamics and characterizing how spectral initialization contributes to feature learning, may be of independent interest for understanding matrix sensing and deep learning theory. The source code can be found in the https://github.com/YuanheZ/LoRA-One.

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true

false

true

false

529,764

1201.4897

Adaptive Systems with Closed-loop Reference Models: Stability, Robustness and Transient Performance

This paper explores the properties of adaptive systems with closed-loop reference models. Using additional design freedom available in closed-loop reference models, we design new adaptive controllers that are (a) stable, and (b) have improved transient properties. Numerical studies that complement theoretical derivations are also reported.

false

true

false

13,935

2303.12016

Automatic evaluation of herding behavior in towed fishing gear using end-to-end training of CNN and attention-based networks

This paper considers the automatic classification of herding behavior in the cluttered low-visibility environment that typically surrounds towed fishing gear. The paper compares three convolutional and attention-based deep action recognition network architectures trained end-to-end on a small set of video sequences captured by a remotely controlled camera and classified by an expert in fishing technology. The sequences depict a scene in front of a fishing trawl where the conventional herding mechanism has been replaced by directed laser light. The goal is to detect the presence of a fish in the sequence and classify whether or not the fish reacts to the lasers. A two-stream CNN model, a CNN-transformer hybrid, and a pure transformer model were trained end-to-end to achieve 63%, 54%, and 60% 10-fold classification accuracy on the three-class task when compared to the human expert. Inspection of the activation maps learned by the three networks raises questions about the attributes of the sequences the models may be learning, specifically whether changes in viewpoint introduced by human camera operators that affect the position of laser lines in the video frames may interfere with the classification. This underlines the importance of careful experimental design when capturing scientific data for automatic end-to-end evaluation and the usefulness of inspecting the trained models.

false

true

false

353,103

2012.05825

Semi-supervised novelty detection using ensembles with regularized disagreement

Deep neural networks often predict samples with high confidence even when they come from unseen classes and should instead be flagged for expert evaluation. Current novelty detection algorithms cannot reliably identify such near OOD points unless they have access to labeled data that is similar to these novel samples. In this paper, we develop a new ensemble-based procedure for semi-supervised novelty detection (SSND) that successfully leverages a mixture of unlabeled ID and novel-class samples to achieve good detection performance. In particular, we show how to achieve disagreement only on OOD data using early stopping regularization. While we prove this fact for a simple data distribution, our extensive experiments suggest that it holds true for more complex scenarios: our approach significantly outperforms state-of-the-art SSND methods on standard image data sets (SVHN/CIFAR-10/CIFAR-100) and medical image data sets with only a negligible increase in computation cost.

false

true

false

210,905

1306.5793

A State-Space Approach for Optimal Traffic Monitoring via Network Flow Sampling

The robustness and integrity of IP networks require efficient tools for traffic monitoring and analysis, which scale well with traffic volume and network size. We address the problem of optimal large-scale flow monitoring of computer networks under resource constraints. We propose a stochastic optimization framework where traffic measurements are done by exploiting the spatial (across network links) and temporal relationship of traffic flows. Specifically, given the network topology, the state-space characterization of network flows and sampling constraints at each monitoring station, we seek an optimal packet sampling strategy that yields the best traffic volume estimation for all flows of the network. The optimal sampling design is the result of a concave minimization problem; then, Kalman filtering is employed to yield a sequence of traffic estimates for each network flow. We evaluate our algorithm using real-world Internet2 data.

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true

false

true

25,432

2203.01827

Democratic Governance and International Research Collaboration: A Longitudinal Analysis of the Global Science Network

The democracy-science relationship has traditionally been examined through philosophical conjecture and single country case studies. There remains limited global scale empirical research on the topic. This study explores country level factors related to the dynamics of the global scientific research collaboration network, focusing on structural associations between democratic governance and the strength of international research collaboration ties. This study combines longitudinal data on 170 countries between 2008 and 2017 from the Varieties of Democracy Institute, World Bank Indicators, Scopus, and Web of Science bibliometric data. Methods of analysis include descriptive network analysis, temporal exponential random graph models (TERGM), and valued exponential random graph models (VERGM). The results suggest positive significant effects of democratic governance on the formation and strength of international research collaboration ties, as well as homophily between countries with similar levels of democratic governance. The results also show the importance of exogenous factors, such as GDP, population size, and geographical distance, as well as endogenous network factors including preferential attachment and transitivity.

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true

false

true

283,523

2402.09384

Persuasion, Delegation, and Private Information in Algorithm-Assisted Decisions

A principal designs an algorithm that generates a publicly observable prediction of a binary state. She must decide whether to act directly based on the prediction or to delegate the decision to an agent with private information but potential misalignment. We study the optimal design of the prediction algorithm and the delegation rule in such environments. Three key findings emerge: (1) Delegation is optimal if and only if the principal would make the same binary decision as the agent had she observed the agent's information. (2) Providing the most informative algorithm may be suboptimal even if the principal can act on the algorithm's prediction. Instead, the optimal algorithm may provide more information about one state and restrict information about the other. (3) Well-intentioned policies aiming to provide more information, such as keeping a "human-in-the-loop" or requiring maximal prediction accuracy, could strictly worsen decision quality compared to systems with no human or no algorithmic assistance. These findings predict the underperformance of human-machine collaborations if no measures are taken to mitigate common preference misalignment between algorithms and human decision-makers.

true

false

true

false

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false

true

429,495

2409.17730

Autoregressive Generation Strategies for Top-K Sequential Recommendations

The goal of modern sequential recommender systems is often formulated in terms of next-item prediction. In this paper, we explore the applicability of generative transformer-based models for the Top-K sequential recommendation task, where the goal is to predict items a user is likely to interact with in the "near future". We explore commonly used autoregressive generation strategies, including greedy decoding, beam search, and temperature sampling, to evaluate their performance for the Top-K sequential recommendation task. In addition, we propose novel Reciprocal Rank Aggregation (RRA) and Relevance Aggregation (RA) generation strategies based on multi-sequence generation with temperature sampling and subsequent aggregation. Experiments on diverse datasets give valuable insights regarding commonly used strategies' applicability and show that suggested approaches improve performance on longer time horizons compared to widely-used Top-K prediction approach and single-sequence autoregressive generation strategies.

false

true

false

491,957

2305.16323

Detecting Concept Drift for the reliability prediction of Software Defects using Instance Interpretation

In the context of Just-In-Time Software Defect Prediction (JIT-SDP), Concept drift (CD) can occur due to changes in the software development process, the complexity of the software, or changes in user behavior that may affect the stability of the JIT-SDP model over time. Additionally, the challenge of class imbalance in JIT-SDP data poses a potential risk to the accuracy of CD detection methods if rebalancing is implemented. This issue has not been explored to the best of our knowledge. Furthermore, methods to check the stability of JIT-SDP models over time by considering labeled evaluation data have been proposed. However, it should be noted that future data labels may not always be available promptly. We aim to develop a reliable JIT-SDP model using CD point detection directly by identifying changes in the interpretation of unlabeled simplified and resampled data. To evaluate our approach, we first obtained baseline methods based on model performance monitoring to identify CD points on labeled data. We then compared the output of the proposed methods with baseline methods based on performance monitoring of threshold-dependent and threshold-independent criteria using well-known performance measures in CD detection methods, such as accuracy, MDR, MTD, MTFA, and MTR. We also utilize the Friedman statistical test to assess the effectiveness of our approach. As a result, our proposed methods show higher compatibility with baseline methods based on threshold-independent criteria when applied to rebalanced data, and with baseline methods based on threshold-dependent criteria when applied to simple data.

false

true

false

true

368,021

2010.06778

Google Crowdsourced Speech Corpora and Related Open-Source Resources for Low-Resource Languages and Dialects: An Overview

This paper presents an overview of a program designed to address the growing need for developing freely available speech resources for under-represented languages. At present we have released 38 datasets for building text-to-speech and automatic speech recognition applications for languages and dialects of South and Southeast Asia, Africa, Europe and South America. The paper describes the methodology used for developing such corpora and presents some of our findings that could benefit under-represented language communities.

false

true

false

200,593

2003.09301

Distributed and Democratized Learning: Philosophy and Research Challenges

Due to the availability of huge amounts of data and processing abilities, current artificial intelligence (AI) systems are effective in solving complex tasks. However, despite the success of AI in different areas, the problem of designing AI systems that can truly mimic human cognitive capabilities such as artificial general intelligence, remains largely open. Consequently, many emerging cross-device AI applications will require a transition from traditional centralized learning systems towards large-scale distributed AI systems that can collaboratively perform multiple complex learning tasks. In this paper, we propose a novel design philosophy called democratized learning (Dem-AI) whose goal is to build large-scale distributed learning systems that rely on the self-organization of distributed learning agents that are well-connected, but limited in learning capabilities. Correspondingly, inspired by the societal groups of humans, the specialized groups of learning agents in the proposed Dem-AI system are self-organized in a hierarchical structure to collectively perform learning tasks more efficiently. As such, the Dem-AI learning system can evolve and regulate itself based on the underlying duality of two processes which we call specialized and generalized processes. In this regard, we present a reference design as a guideline to realize future Dem-AI systems, inspired by various interdisciplinary fields. Accordingly, we introduce four underlying mechanisms in the design such as plasticity-stability transition mechanism, self-organizing hierarchical structuring, specialized learning, and generalization. Finally, we establish possible extensions and new challenges for the existing learning approaches to provide better scalable, flexible, and more powerful learning systems with the new setting of Dem-AI.

false

true

false

true

false

169,011

2406.19573

On Counterfactual Interventions in Vector Autoregressive Models

Counterfactual reasoning allows us to explore hypothetical scenarios in order to explain the impacts of our decisions. However, addressing such inquires is impossible without establishing the appropriate mathematical framework. In this work, we introduce the problem of counterfactual reasoning in the context of vector autoregressive (VAR) processes. We also formulate the inference of a causal model as a joint regression task where for inference we use both data with and without interventions. After learning the model, we exploit linearity of the VAR model to make exact predictions about the effects of counterfactual interventions. Furthermore, we quantify the total causal effects of past counterfactual interventions. The source code for this project is freely available at https://github.com/KurtButler/counterfactual_interventions.

false

true

false

468,471

1910.07344

Conditional Invertible Flow for Point Cloud Generation

This paper focuses on a novel generative approach for 3D point clouds that makes use of invertible flow-based models. The main idea of the method is to treat a point cloud as a probability density in 3D space that is modeled using a cloud-specific neural network. To capture the similarity between point clouds we rely on parameter sharing among networks, with each cloud having only a small embedding vector that defines it. We use invertible flows networks to generate the individual point clouds, and to regularize the embedding vectors. We evaluate the generative capabilities of the model both in qualitative and quantitative manner.

false

true

false

true

false

149,582

1201.4999

A study of distributed QoS adapter in large-scale wireless networks

Considering the comfortably establishing ad hoc networks, the use of this type of network is increasing day to day. On the other side, it is predicted that using multimedia applications will be more public in these network. As it is known, in contrary to best-effort flows, the transmission of multimedia flows in any network need support from QoS. However, the wireless ad hoc networks are severely affected by bandwidth, and establishing a QoS in these networks face problems. In this paper, we have proposed a thoroughly distributed algorithm to support the QoS in ad hoc networks. This algorithm guarantees the QoS of the real-time applications vis-a-vis each other and best-effort flows as well. The algorithm suggested in this paper dynamically regulates the Contention Window of the flows and serves the flows in terms of their requests QoS choosing the smallest CW in every node. This algorithm also uses the fixed and/or less stationary nodes for the transmission of real-time flows by increasing the QoS of the multimedia flows. This algorithm is preferred because it prioritizes the flows that are of the same class but have not obtained favorite QoS compared to other flows of the same class in addition to classifying the flows in the network and offering better services to the classes of higher priority. All this occur without the controlled packets forwarding and resource reserving and freeing method. We have proved the correctness of this algorithm using Markov's mathematical model.

false

true

false

true

13,941

2106.02930

Spectral Temporal Graph Neural Network for Trajectory Prediction

An effective understanding of the contextual environment and accurate motion forecasting of surrounding agents is crucial for the development of autonomous vehicles and social mobile robots. This task is challenging since the behavior of an autonomous agent is not only affected by its own intention, but also by the static environment and surrounding dynamically interacting agents. Previous works focused on utilizing the spatial and temporal information in time domain while not sufficiently taking advantage of the cues in frequency domain. To this end, we propose a Spectral Temporal Graph Neural Network (SpecTGNN), which can capture inter-agent correlations and temporal dependency simultaneously in frequency domain in addition to time domain. SpecTGNN operates on both an agent graph with dynamic state information and an environment graph with the features extracted from context images in two streams. The model integrates graph Fourier transform, spectral graph convolution and temporal gated convolution to encode history information and forecast future trajectories. Moreover, we incorporate a multi-head spatio-temporal attention mechanism to mitigate the effect of error propagation in a long time horizon. We demonstrate the performance of SpecTGNN on two public trajectory prediction benchmark datasets, which achieves state-of-the-art performance in terms of prediction accuracy.

false

true

false

true

false

true

false

239,094

1711.02168

On the Monetary Loss Due to Passive and Active Attacks on MIMO Smart Grid Communications

We consider multiple source nodes (consumers) communicating wirelessly their energy demands to the meter data-management system (MDMS) over the subarea gateway(s). We quantify the impacts of passive and active security attacks on the wireless communications system's reliability and security as well as the energy-demand estimation-error cost in dollars paid by the utility. We adopt a multiple-input multiple-output multi-antenna-eavesdropper (MIMOME) wiretap channel model. To secure the MIMO wireless communication system, the legitimate nodes generate artificial noise (AN) vectors to mitigate the effect of the passive eavesdropping attacks. In addition, we propose a redundant design where multiple gateways are assumed to coexist in each subarea to forward the consumers' energy-demand messages. We quantify the redundant designs impact on the communication reliability between the consumers and the MDMS and on the energy-demand estimation-error cost.

false

true

false

true

84,013

2001.00975

Privacy in Data Service Composition

In modern information systems different information features, about the same individual, are often collected and managed by autonomous data collection services that may have different privacy policies. Answering many end-users' legitimate queries requires the integration of data from multiple such services. However, data integration is often hindered by the lack of a trusted entity, often called a mediator, with which the services can share their data and delegate the enforcement of their privacy policies. In this paper, we propose a flexible privacy-preserving data integration approach for answering data integration queries without the need for a trusted mediator. In our approach, services are allowed to enforce their privacy policies locally. The mediator is considered to be untrusted, and only has access to encrypted information to allow it to link data subjects across the different services. Services, by virtue of a new privacy requirement, dubbed k-Protection, limiting privacy leaks, cannot infer information about the data held by each other. End-users, in turn, have access to privacy-sanitized data only. We evaluated our approach using an example and a real dataset from the healthcare application domain. The results are promising from both the privacy preservation and the performance perspectives.

false

true

false

true

159,361

1507.01763

Matrices Whose Inversions are Tridiagonal, Band or Block-Tridiagonal and Their Relationship with the Covariance Matrices of a Random Markov Processes (Fields)

The article discusses the matrices of the three forms whose inversions are: tridiagonal matrix, banded matrix or block-tridiagonal matrix and their relationships with the covariance matrices of measurements of ordinary (simple) Markov Random Processes (MRP), multiconnected MRP and vector MRP respectively. Such covariance matrices are frequently occurring in the problems of optimal filtering, extrapolation and interpolation of MRP and Markov Random Fields (MRF). It is shown, that the structure of these three forms of matrices has the same form, but the matrix elements in the first case are scalar quantities; in the second case matrix elements representing a product of vectors of dimension m; and in the third case, the off-diagonal elements are the product of matrices and vectors of dimension m. The properties of such matrices were investigated and a simple formulas of their inversion was founded. Also computational efficiency in the storage and inverse of such matrices have been considered. To illustrate the acquired results an example of the covariance matrix inversions of two-dimensional MRP is given.

false

true

false

44,904

1707.05537

Beyond Forward Shortcuts: Fully Convolutional Master-Slave Networks (MSNets) with Backward Skip Connections for Semantic Segmentation

Recent deep CNNs contain forward shortcut connections; i.e. skip connections from low to high layers. Reusing features from lower layers that have higher resolution (location information) benefit higher layers to recover lost details and mitigate information degradation. However, during inference the lower layers do not know about high layer features, although they contain contextual high semantics that benefit low layers to adaptively extract informative features for later layers. In this paper, we study the influence of backward skip connections which are in the opposite direction to forward shortcuts, i.e. paths from high layers to low layers. To achieve this -- which indeed runs counter to the nature of feed-forward networks -- we propose a new fully convolutional model that consists of a pair of networks. A `Slave' network is dedicated to provide the backward connections from its top layers to the `Master' network's bottom layers. The Master network is used to produce the final label predictions. In our experiments we validate the proposed FCN model on ADE20K (ImageNet scene parsing), PASCAL-Context, and PASCAL VOC 2011 datasets.

false

true

false

77,254

1906.05505

Scalable Community Detection over Geo-Social Network

We consider a community finding problem called Co-located Community Detection (CCD) over geo-social networks, which retrieves communities that satisfy both high structural tightness and spatial closeness constraints. To provide a solution that benefits from existing studies on community detection, we decouple the spatial constraint from graph structural constraint and propose a uniform CCD framework which gives users the freedom to choose customized measurements for social cohesiveness (e.g., $k$-core or $k$-truss). For the spatial closeness constraint, we apply the bounded radius spatial constraint and develop an exact algorithm together with effective pruning rules. To further improve the efficiency and make our framework scale to a very large scale of data, we propose a near-linear time approximation algorithm with a constant approximation ratio ($\sqrt{2}$). We conduct extensive experiments on both synthetic and real-world datasets to demonstrate the efficiency and effectiveness of our algorithms.

false

true

false

true

false

135,049

1906.02738

Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading

Although neural conversation models are effective in learning how to produce fluent responses, their primary challenge lies in knowing what to say to make the conversation contentful and non-vacuous. We present a new end-to-end approach to contentful neural conversation that jointly models response generation and on-demand machine reading. The key idea is to provide the conversation model with relevant long-form text on the fly as a source of external knowledge. The model performs QA-style reading comprehension on this text in response to each conversational turn, thereby allowing for more focused integration of external knowledge than has been possible in prior approaches. To support further research on knowledge-grounded conversation, we introduce a new large-scale conversation dataset grounded in external web pages (2.8M turns, 7.4M sentences of grounding). Both human evaluation and automated metrics show that our approach results in more contentful responses compared to a variety of previous methods, improving both the informativeness and diversity of generated output.

false

true

false

true

false

true

false

134,160

2011.04062

MLAS: Metric Learning on Attributed Sequences

Distance metric learning has attracted much attention in recent years, where the goal is to learn a distance metric based on user feedback. Conventional approaches to metric learning mainly focus on learning the Mahalanobis distance metric on data attributes. Recent research on metric learning has been extended to sequential data, where we only have structural information in the sequences, but no attribute is available. However, real-world applications often involve attributed sequence data (e.g., clickstreams), where each instance consists of not only a set of attributes (e.g., user session context) but also a sequence of categorical items (e.g., user actions). In this paper, we study the problem of metric learning on attributed sequences. Unlike previous work on metric learning, we now need to go beyond the Mahalanobis distance metric in the attribute feature space while also incorporating the structural information in sequences. We propose a deep learning framework, called MLAS (Metric Learning on Attributed Sequences), to learn a distance metric that effectively measures dissimilarities between attributed sequences. Empirical results on real-world datasets demonstrate that the proposed MLAS framework significantly improves the performance of metric learning compared to state-of-the-art methods on attributed sequences.

false

true

false

205,445

1405.0354

Map-Reduce Parallelization of Motif Discovery

Motif discovery is one of the most challenging problems in bioinformatics today. DNA sequence motifs are becoming increasingly important in analysis of gene regulation. Motifs are short, recurring patterns in DNA that have a biological function. For example, they indicate binding sites for Transcription Factors (TFs) and nucleases. There are a number of Motif Discovery algorithms that run sequentially. The sequential nature stops these algorithms from being parallelized. HOMER is one such Motif discovery tool, that we have decided to use to overcome this limitation. To overcome this limitation, we propose a new methodology for Motif Discovery, using HOMER, that parallelizes the task. Parallelized version can potentially yield better scalability and performance. To achieve this, we have decided to use sub-sampling and the Map Reduce model. At each Map node, a sub-sampled version of the input DNA sequences is used as input to HOMER. Subsampling at each map node is performed with different parameters to ensure that no two HOMER instances receive identical inputs. The output of the map phase and the input of the reduce phase is a list of Motifs discovered using the sub-sampled sequences. The reduce phase calculates the mode, most frequent Motifs, and outputs them as the final discovered Motifs. We found marginal speed gains with this model of execution and substantial amount of quality loss in discovered Motifs.

false

true

false

true

32,760

2112.11030

Maxwell: a hardware and software highly integrated compute-storage system

The compute-storage framework is responsible for data storage and processing, and acts as the digital chassis of all upper-level businesses. The performance of the framework affects the business's processing throughput, latency, jitter, and etc., and also determines the theoretical performance upper bound that the business can achieve. In financial applications, the compute-storage framework must have high reliability and high throughput, but with low latency as well as low jitter characteristics. For some scenarios such as hot-spot account update, the performance of the compute-storage framework even surfaces to become a server performance bottleneck of the whole business system. In this paper, we study the hot-spot account issue faced by Alipay and present our exciting solution to this problem by developing a new compute-storage system, called Maxwell. Maxwell is a distributed compute-storage system with integrated hardware and software optimizations. Maxwell does not rely on any specific hardware (e.g. GPUs or FPGAs). Instead, it takes deep advantage of computer components' characteristics, such as disk, network, operating system and CPU, and aims to emit the ultimate performance of both hardware and software. In comparison with the existing hot-spot account updating solutions deployed online, Maxwell achieves three orders of magnitude performance improvement for end-to-end evaluation. Meanwhile, Maxwell also demonstrates remarkable performance gains in other related businesses of Ant Group.

false

true

272,590

2106.03171

Feature-based Style Randomization for Domain Generalization

As a recent noticeable topic, domain generalization (DG) aims to first learn a generic model on multiple source domains and then directly generalize to an arbitrary unseen target domain without any additional adaption. In previous DG models, by generating virtual data to supplement observed source domains, the data augmentation based methods have shown its effectiveness. To simulate the possible unseen domains, most of them enrich the diversity of original data via image-level style transformation. However, we argue that the potential styles are hard to be exhaustively illustrated and fully augmented due to the limited referred styles, leading the diversity could not be always guaranteed. Unlike image-level augmentation, we in this paper develop a simple yet effective feature-based style randomization module to achieve feature-level augmentation, which can produce random styles via integrating random noise into the original style. Compared with existing image-level augmentation, our feature-level augmentation favors a more goal-oriented and sample-diverse way. Furthermore, to sufficiently explore the efficacy of the proposed module, we design a novel progressive training strategy to enable all parameters of the network to be fully trained. Extensive experiments on three standard benchmark datasets, i.e., PACS, VLCS and Office-Home, highlight the superiority of our method compared to the state-of-the-art methods.

false

true

false

239,209

2311.00176

ChipNeMo: Domain-Adapted LLMs for Chip Design

ChipNeMo aims to explore the applications of large language models (LLMs) for industrial chip design. Instead of directly deploying off-the-shelf commercial or open-source LLMs, we instead adopt the following domain adaptation techniques: domain-adaptive tokenization, domain-adaptive continued pretraining, model alignment with domain-specific instructions, and domain-adapted retrieval models. We evaluate these methods on three selected LLM applications for chip design: an engineering assistant chatbot, EDA script generation, and bug summarization and analysis. Our evaluations demonstrate that domain-adaptive pretraining of language models, can lead to superior performance in domain related downstream tasks compared to their base LLaMA2 counterparts, without degradations in generic capabilities. In particular, our largest model, ChipNeMo-70B, outperforms the highly capable GPT-4 on two of our use cases, namely engineering assistant chatbot and EDA scripts generation, while exhibiting competitive performance on bug summarization and analysis. These results underscore the potential of domain-specific customization for enhancing the effectiveness of large language models in specialized applications.

false

true

false

404,533

1612.07921

Understanding Non-optical Remote-sensed Images: Needs, Challenges and Ways Forward

Non-optical remote-sensed images are going to be used more often in man- aging disaster, crime and precision agriculture. With more small satellites and unmanned air vehicles planning to carry radar and hyperspectral image sensors there is going to be an abundance of such data in the recent future. Understanding these data in real-time will be crucial in attaining some of the important sustain- able development goals. Processing non-optical images is, in many ways, different from that of optical images. Most of the recent advances in the domain of image understanding has been using optical images. In this article we shall explain the needs for image understanding in non-optical domain and the typical challenges. Then we shall describe the existing approaches and how we can move from there to the desired goal of a reliable real-time image understanding system.

false

true

false

66,005

2110.03775

Proposing a System Level Machine Learning Hybrid Architecture and Approach for a Comprehensive Autism Spectrum Disorder Diagnosis

Autism Spectrum Disorder (ASD) is a severe neuropsychiatric disorder that affects intellectual development, social behavior, and facial features, and the number of cases is still significantly increasing. Due to the variety of symptoms ASD displays, the diagnosis process remains challenging, with numerous misdiagnoses as well as lengthy and expensive diagnoses. Fortunately, if ASD is diagnosed and treated early, then the patient will have a much higher chance of developing normally. For an ASD diagnosis, machine learning algorithms can analyze both social behavior and facial features accurately and efficiently, providing an ASD diagnosis in a drastically shorter amount of time than through current clinical diagnosis processes. Therefore, we propose to develop a hybrid architecture fully utilizing both social behavior and facial feature data to improve the accuracy of diagnosing ASD. We first developed a Linear Support Vector Machine for the social behavior based module, which analyzes Autism Diagnostic Observation Schedule (ADOS) social behavior data. For the facial feature based module, a DenseNet model was utilized to analyze facial feature image data. Finally, we implemented our hybrid model by incorporating different features of the Support Vector Machine and the DenseNet into one model. Our results show that the highest accuracy of 87% for ASD diagnosis has been achieved by our proposed hybrid model. The pros and cons of each module will be discussed in this paper.

false

true

false

259,626

2312.10457

Semantic-Aware Autoregressive Image Modeling for Visual Representation Learning

The development of autoregressive modeling (AM) in computer vision lags behind natural language processing (NLP) in self-supervised pre-training. This is mainly caused by the challenge that images are not sequential signals and lack a natural order when applying autoregressive modeling. In this study, inspired by human beings' way of grasping an image, i.e., focusing on the main object first, we present a semantic-aware autoregressive image modeling (SemAIM) method to tackle this challenge. The key insight of SemAIM is to autoregressive model images from the semantic patches to the less semantic patches. To this end, we first calculate a semantic-aware permutation of patches according to their feature similarities and then perform the autoregression procedure based on the permutation. In addition, considering that the raw pixels of patches are low-level signals and are not ideal prediction targets for learning high-level semantic representation, we also explore utilizing the patch features as the prediction targets. Extensive experiments are conducted on a broad range of downstream tasks, including image classification, object detection, and instance/semantic segmentation, to evaluate the performance of SemAIM. The results demonstrate SemAIM achieves state-of-the-art performance compared with other self-supervised methods. Specifically, with ViT-B, SemAIM achieves 84.1% top-1 accuracy for fine-tuning on ImageNet, 51.3% AP and 45.4% AP for object detection and instance segmentation on COCO, which outperforms the vanilla MAE by 0.5%, 1.0%, and 0.5%, respectively.

false

true

false

416,176

2201.01293

A Transformer-Based Siamese Network for Change Detection

This paper presents a transformer-based Siamese network architecture (abbreviated by ChangeFormer) for Change Detection (CD) from a pair of co-registered remote sensing images. Different from recent CD frameworks, which are based on fully convolutional networks (ConvNets), the proposed method unifies hierarchically structured transformer encoder with Multi-Layer Perception (MLP) decoder in a Siamese network architecture to efficiently render multi-scale long-range details required for accurate CD. Experiments on two CD datasets show that the proposed end-to-end trainable ChangeFormer architecture achieves better CD performance than previous counterparts. Our code is available at https://github.com/wgcban/ChangeFormer.

false

true

false

274,207

2310.08019

Robust 1-bit Compressed Sensing with Iterative Hard Thresholding

In 1-bit compressed sensing, the aim is to estimate a $k$-sparse unit vector $x\in S^{n-1}$ within an $\epsilon$ error (in $\ell_2$) from minimal number of linear measurements that are quantized to just their signs, i.e., from measurements of the form $y = \mathrm{Sign}(\langle a, x\rangle).$ In this paper, we study a noisy version where a fraction of the measurements can be flipped, potentially by an adversary. In particular, we analyze the Binary Iterative Hard Thresholding (BIHT) algorithm, a proximal gradient descent on a properly defined loss function used for 1-bit compressed sensing, in this noisy setting. It is known from recent results that, with $\tilde{O}(\frac{k}{\epsilon})$ noiseless measurements, BIHT provides an estimate within $\epsilon$ error. This result is optimal and universal, meaning one set of measurements work for all sparse vectors. In this paper, we show that BIHT also provides better results than all known methods for the noisy setting. We show that when up to $\tau$-fraction of the sign measurements are incorrect (adversarial error), with the same number of measurements as before, BIHT agnostically provides an estimate of $x$ within an $\tilde{O}(\epsilon+\tau)$ error, maintaining the universality of measurements. This establishes stability of iterative hard thresholding in the presence of measurement error. To obtain the result, we use the restricted approximate invertibility of Gaussian matrices, as well as a tight analysis of the high-dimensional geometry of the adversarially corrupted measurements.

false

true

false

true

false

true

399,221

2409.17929

The Lou Dataset -- Exploring the Impact of Gender-Fair Language in German Text Classification

Gender-fair language, an evolving German linguistic variation, fosters inclusion by addressing all genders or using neutral forms. Nevertheless, there is a significant lack of resources to assess the impact of this linguistic shift on classification using language models (LMs), which are probably not trained on such variations. To address this gap, we present Lou, the first dataset featuring high-quality reformulations for German text classification covering seven tasks, like stance detection and toxicity classification. Evaluating 16 mono- and multi-lingual LMs on Lou shows that gender-fair language substantially impacts predictions by flipping labels, reducing certainty, and altering attention patterns. However, existing evaluations remain valid, as LM rankings of original and reformulated instances do not significantly differ. While we offer initial insights on the effect on German text classification, the findings likely apply to other languages, as consistent patterns were observed in multi-lingual and English LMs.

false

true

false

492,032

2310.13022

Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding

The recent success of large pre-trained language models (PLMs) heavily hinges on massive labeled data, which typically produces inferior performance in low-resource scenarios. To remedy this dilemma, we study self-training as one of the predominant semi-supervised learning (SSL) approaches, which utilizes large-scale unlabeled data to generate synthetic examples. However, too many noisy labels will hurt the model performance, and the self-training procedure requires multiple training iterations making it more expensive if all the model parameters of the PLM are updated. This paper presents UPET, a novel Uncertainty-aware Parameter-Efficient self-Training framework to effectively and efficiently address the labeled data scarcity issue. Specifically, we incorporate Monte Carlo (MC) dropout in Bayesian neural network (BNN) to perform uncertainty estimation for the teacher model and then judiciously select reliable pseudo-labeled examples based on confidence and certainty. During the student training, we introduce multiple parameter-efficient learning (PEL) paradigms that allow the optimization of only a small percentage of parameters. We also propose a novel Easy-Hard Contrastive Tuning to enhance the robustness and generalization. Extensive experiments over multiple downstream tasks demonstrate that UPET achieves a substantial improvement in terms of performance and efficiency. Our codes and data are released at https: //github.com/wjn1996/UPET.

false

true

false

true

false

true

false

401,262

1402.5874

Predictive Interval Models for Non-parametric Regression

Having a regression model, we are interested in finding two-sided intervals that are guaranteed to contain at least a desired proportion of the conditional distribution of the response variable given a specific combination of predictors. We name such intervals predictive intervals. This work presents a new method to find two-sided predictive intervals for non-parametric least squares regression without the homoscedasticity assumption. Our predictive intervals are built by using tolerance intervals on prediction errors in the query point's neighborhood. We proposed a predictive interval model test and we also used it as a constraint in our hyper-parameter tuning algorithm. This gives an algorithm that finds the smallest reliable predictive intervals for a given dataset. We also introduce a measure for comparing different interval prediction methods yielding intervals having different size and coverage. These experiments show that our methods are more reliable, effective and precise than other interval prediction methods.

false

true

false

31,123

1301.6749

Inference in Multiply Sectioned Bayesian Networks with Extended Shafer-Shenoy and Lazy Propagation

As Bayesian networks are applied to larger and more complex problem domains, search for flexible modeling and more efficient inference methods is an ongoing effort. Multiply sectioned Bayesian networks (MSBNs) extend the HUGIN inference for Bayesian networks into a coherent framework for flexible modeling and distributed inference.Lazy propagation extends the Shafer-Shenoy and HUGIN inference methods with reduced space complexity. We apply the Shafer-Shenoy and lazy propagation to inference in MSBNs. The combination of the MSBN framework and lazy propagation provides a better framework for modeling and inference in very large domains. It retains the modeling flexibility of MSBNs and reduces the runtime space complexity, allowing exact inference in much larger domains given the same computational resources.

false

true

false

21,542

2106.08650

Shuffle Transformer with Feature Alignment for Video Face Parsing

This is a short technical report introducing the solution of the Team TCParser for Short-video Face Parsing Track of The 3rd Person in Context (PIC) Workshop and Challenge at CVPR 2021. In this paper, we introduce a strong backbone which is cross-window based Shuffle Transformer for presenting accurate face parsing representation. To further obtain the finer segmentation results, especially on the edges, we introduce a Feature Alignment Aggregation (FAA) module. It can effectively relieve the feature misalignment issue caused by multi-resolution feature aggregation. Benefiting from the stronger backbone and better feature aggregation, the proposed method achieves 86.9519% score in the Short-video Face Parsing track of the 3rd Person in Context (PIC) Workshop and Challenge, ranked the first place.

false

true

false

241,373

2409.07787

Stable Language Model Pre-training by Reducing Embedding Variability

Stable pre-training is essential for achieving better-performing language models. However, tracking pre-training stability by calculating gradient variance at every step is impractical due to the significant computational costs. We explore Token Embedding Variability (TEV) as a simple and efficient proxy for assessing pre-training stability in language models with pre-layer normalization, given that shallower layers are more prone to gradient explosion (section 2.2). Moreover, we propose Multi-head Low-Rank Attention (MLRA) as an architecture to alleviate such instability by limiting the exponential growth of output embedding variance, thereby preventing the gradient explosion (section 3.2). Empirical results on GPT-2 with MLRA demonstrate increased stability and lower perplexity, particularly in deeper models.

false

true

false

487,664

2104.08850

On Training Sketch Recognizers for New Domains

Sketch recognition algorithms are engineered and evaluated using publicly available datasets contributed by the sketch recognition community over the years. While existing datasets contain sketches of a limited set of generic objects, each new domain inevitably requires collecting new data for training domain specific recognizers. This gives rise to two fundamental concerns: First, will the data collection protocol yield ecologically valid data? Second, will the amount of collected data suffice to train sufficiently accurate classifiers? In this paper, we draw attention to these two concerns. We show that the ecological validity of the data collection protocol and the ability to accommodate small datasets are significant factors impacting recognizer accuracy in realistic scenarios. More specifically, using sketch-based gaming as a use case, we show that deep learning methods, as well as more traditional methods, suffer significantly from dataset shift. Furthermore, we demonstrate that in realistic scenarios where data is scarce and expensive, standard measures taken for adapting deep learners to small datasets fall short of comparing favorably with alternatives. Although transfer learning, and extensive data augmentation help deep learners, they still perform significantly worse compared to standard setups (e.g., SVMs and GBMs with standard feature representations). We pose learning from small datasets as a key problem for the deep sketch recognition field, one which has been ignored in the bulk of the existing literature.

false

true

false

true

false

231,013

2405.19366

ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text

The utilization of deep learning on electrocardiogram (ECG) analysis has brought the advanced accuracy and efficiency of cardiac healthcare diagnostics. By leveraging the capabilities of deep learning in semantic understanding, especially in feature extraction and representation learning, this study introduces a new multimodal contrastive pretaining framework that aims to improve the quality and robustness of learned representations of 12-lead ECG signals. Our framework comprises two key components, including Cardio Query Assistant (CQA) and ECG Semantics Integrator(ESI). CQA integrates a retrieval-augmented generation (RAG) pipeline to leverage large language models (LLMs) and external medical knowledge to generate detailed textual descriptions of ECGs. The generated text is enriched with information about demographics and waveform patterns. ESI integrates both contrastive and captioning loss to pretrain ECG encoders for enhanced representations. We validate our approach through various downstream tasks, including arrhythmia detection and ECG-based subject identification. Our experimental results demonstrate substantial improvements over strong baselines in these tasks. These baselines encompass supervised and self-supervised learning methods, as well as prior multimodal pretraining approaches.

false

true

false

458,873

2412.10651

LAN: Learning to Adapt Noise for Image Denoising

Removing noise from images, a.k.a image denoising, can be a very challenging task since the type and amount of noise can greatly vary for each image due to many factors including a camera model and capturing environments. While there have been striking improvements in image denoising with the emergence of advanced deep learning architectures and real-world datasets, recent denoising networks struggle to maintain performance on images with noise that has not been seen during training. One typical approach to address the challenge would be to adapt a denoising network to new noise distribution. Instead, in this work, we shift our focus to adapting the input noise itself, rather than adapting a network. Thus, we keep a pretrained network frozen, and adapt an input noise to capture the fine-grained deviations. As such, we propose a new denoising algorithm, dubbed Learning-to-Adapt-Noise (LAN), where a learnable noise offset is directly added to a given noisy image to bring a given input noise closer towards the noise distribution a denoising network is trained to handle. Consequently, the proposed framework exhibits performance improvement on images with unseen noise, displaying the potential of the proposed research direction. The code is available at https://github.com/chjinny/LAN

false

true

false

true

false

517,034

1602.02944

Fast phase retrieval for high dimensions: A block-based approach

This paper addresses fundamental scaling issues that hinder phase retrieval (PR) in high dimensions. We show that, if the measurement matrix can be put into a generalized block-diagonal form, a large PR problem can be solved on separate blocks, at the cost of a few extra global measurements to merge the partial results. We illustrate this principle using two distinct PR methods, and discuss different design trade-offs. Experimental results indicate that this block-based PR framework can reduce computational cost and memory requirements by several orders of magnitude.

false

true

false

51,937

2007.10015

CobotGear: Interaction with Collaborative Robots using Wearable Optical Motion Capturing Systems

In industrial applications, complex tasks require human collaboration since the robot doesn't have enough dexterity. However, the robots are still implemented as tools and not as collaborative intelligent systems. To ensure safety in the human-robot collaboration, we introduce a system that presents a new method that integrates low-cost wearable mocap, and an improved collision avoidance algorithm based on the artificial potential fields. Wearable optical motion capturing allows to track the human hand position with high accuracy and low latency on large working areas. To increase the efficiency of the proposed algorithm, two obstacle types are discriminated according to their collision probability. A preliminary experiment was performed to analyze the algorithm behavior and to select the best values for the obstacle's threshold angle $\theta_{OBS}$, and for the avoidance threshold distance $d_{AT}$. The second experiment was carried out to evaluate the system performance with $d_{AT}$ = 0.2 m and $\theta_{OBS}$ = 45 degrees. The third experiment evaluated the system in a real collaborative task. The results demonstrate the robust performance of the robotic arm generating smooth collision-free trajectories. The proposed technology will allow consumer robots to safely collaborate with humans in cluttered environments, e.g., factories, kitchens, living rooms, and restaurants.

false

true

false

188,140

1907.04650

Hardware/Software Co-Exploration of Neural Architectures

We propose a novel hardware and software co-exploration framework for efficient neural architecture search (NAS). Different from existing hardware-aware NAS which assumes a fixed hardware design and explores the neural architecture search space only, our framework simultaneously explores both the architecture search space and the hardware design space to identify the best neural architecture and hardware pairs that maximize both test accuracy and hardware efficiency. Such a practice greatly opens up the design freedom and pushes forward the Pareto frontier between hardware efficiency and test accuracy for better design tradeoffs. The framework iteratively performs a two-level (fast and slow) exploration. Without lengthy training, the fast exploration can effectively fine-tune hyperparameters and prune inferior architectures in terms of hardware specifications, which significantly accelerates the NAS process. Then, the slow exploration trains candidates on a validation set and updates a controller using the reinforcement learning to maximize the expected accuracy together with the hardware efficiency. Experiments on ImageNet show that our co-exploration NAS can find the neural architectures and associated hardware design with the same accuracy, 35.24% higher throughput, 54.05% higher energy efficiency and 136x reduced search time, compared with the state-of-the-art hardware-aware NAS.

false

true

false

true

false

138,162

2210.05177

Make Sharpness-Aware Minimization Stronger: A Sparsified Perturbation Approach

Deep neural networks often suffer from poor generalization caused by complex and non-convex loss landscapes. One of the popular solutions is Sharpness-Aware Minimization (SAM), which smooths the loss landscape via minimizing the maximized change of training loss when adding a perturbation to the weight. However, we find the indiscriminate perturbation of SAM on all parameters is suboptimal, which also results in excessive computation, i.e., double the overhead of common optimizers like Stochastic Gradient Descent (SGD). In this paper, we propose an efficient and effective training scheme coined as Sparse SAM (SSAM), which achieves sparse perturbation by a binary mask. To obtain the sparse mask, we provide two solutions which are based onFisher information and dynamic sparse training, respectively. In addition, we theoretically prove that SSAM can converge at the same rate as SAM, i.e., $O(\log T/\sqrt{T})$. Sparse SAM not only has the potential for training acceleration but also smooths the loss landscape effectively. Extensive experimental results on CIFAR10, CIFAR100, and ImageNet-1K confirm the superior efficiency of our method to SAM, and the performance is preserved or even better with a perturbation of merely 50% sparsity. Code is availiable at https://github.com/Mi-Peng/Sparse-Sharpness-Aware-Minimization.

false

true

false

true

false

true

false

322,738

2309.08511

Generalised Diffusion Probabilistic Scale-Spaces

Diffusion probabilistic models excel at sampling new images from learned distributions. Originally motivated by drift-diffusion concepts from physics, they apply image perturbations such as noise and blur in a forward process that results in a tractable probability distribution. A corresponding learned reverse process generates images and can be conditioned on side information, which leads to a wide variety of practical applications. Most of the research focus currently lies on practice-oriented extensions. In contrast, the theoretical background remains largely unexplored, in particular the relations to drift-diffusion. In order to shed light on these connections to classical image filtering, we propose a generalised scale-space theory for diffusion probabilistic models. Moreover, we show conceptual and empirical connections to diffusion and osmosis filters.

false

true

false

true

false

392,198

2001.11382

Intweetive Text Summarization

The amount of user generated contents from various social medias allows analyst to handle a wide view of conversations on several topics related to their business. Nevertheless keeping up-to-date with this amount of information is not humanly feasible. Automatic Summarization then provides an interesting mean to digest the dynamics and the mass volume of contents. In this paper, we address the issue of tweets summarization which remains scarcely explored. We propose to automatically generated summaries of Micro-Blogs conversations dealing with public figures E-Reputation. These summaries are generated using key-word queries or sample tweet and offer a focused view of the whole Micro-Blog network. Since state-of-the-art is lacking on this point we conduct and evaluate our experiments over the multilingual CLEF RepLab Topic-Detection dataset according to an experimental evaluation process.

false

true

false

true

false

162,056

2405.19029

Convex neural network synthesis for robustness in the 1-norm

With neural networks being used to control safety-critical systems, they increasingly have to be both accurate (in the sense of matching inputs to outputs) and robust. However, these two properties are often at odds with each other and a trade-off has to be navigated. To address this issue, this paper proposes a method to generate an approximation of a neural network which is certifiably more robust. Crucially, the method is fully convex and posed as a semi-definite programme. An application to robustifying model predictive control is used to demonstrate the results. The aim of this work is to introduce a method to navigate the neural network robustness/accuracy trade-off.

false

true

false

true

false

458,719

1710.10427

DevRank: Mining Influential Developers In Github

As the social coding is becoming increasingly popular, understanding the influence of developers can benefit various applications, such as advertisement for new projects and innovations. However, most existing works have focused only on ranking influential nodes in non-weighted and homogeneous networks, which are not able to transfer proper importance scores to the real important node. To rank developers in Github, we define developer's influence on the capacity of attracting attention which can be measured by the number of followers obtained in the future. We further defined a new method, DevRank, which ranks the developers by influence propagation through heterogeneous network constructed according to user behaviors, including "commit" and "follow". Our experiment compares the performance between DevRank and some other link analysis algorithms, the results have shown that DevRank can improve the ranking accuracy.

false

true

false

true

83,381

2001.01095

High-Dimensional Independence Testing via Maximum and Average Distance Correlations

This paper introduces and investigates the utilization of maximum and average distance correlations for multivariate independence testing. We characterize their consistency properties in high-dimensional settings with respect to the number of marginally dependent dimensions, assess the advantages of each test statistic, examine their respective null distributions, and present a fast chi-square-based testing procedure. The resulting tests are non-parametric and applicable to both Euclidean distance and the Gaussian kernel as the underlying metric. To better understand the practical use cases of the proposed tests, we evaluate the empirical performance of the maximum distance correlation, average distance correlation, and the original distance correlation across various multivariate dependence scenarios, as well as conduct a real data experiment to test the presence of various cancer types and peptide levels in human plasma.

false

true

false

159,403

1807.05653

Learning and Matching Multi-View Descriptors for Registration of Point Clouds

Critical to the registration of point clouds is the establishment of a set of accurate correspondences between points in 3D space. The correspondence problem is generally addressed by the design of discriminative 3D local descriptors on the one hand, and the development of robust matching strategies on the other hand. In this work, we first propose a multi-view local descriptor, which is learned from the images of multiple views, for the description of 3D keypoints. Then, we develop a robust matching approach, aiming at rejecting outlier matches based on the efficient inference via belief propagation on the defined graphical model. We have demonstrated the boost of our approaches to registration on the public scanning and multi-view stereo datasets. The superior performance has been verified by the intensive comparisons against a variety of descriptors and matching methods.

false

true

false

102,971

2401.13505

Generative Human Motion Stylization in Latent Space

Human motion stylization aims to revise the style of an input motion while keeping its content unaltered. Unlike existing works that operate directly in pose space, we leverage the latent space of pretrained autoencoders as a more expressive and robust representation for motion extraction and infusion. Building upon this, we present a novel generative model that produces diverse stylization results of a single motion (latent) code. During training, a motion code is decomposed into two coding components: a deterministic content code, and a probabilistic style code adhering to a prior distribution; then a generator massages the random combination of content and style codes to reconstruct the corresponding motion codes. Our approach is versatile, allowing the learning of probabilistic style space from either style labeled or unlabeled motions, providing notable flexibility in stylization as well. In inference, users can opt to stylize a motion using style cues from a reference motion or a label. Even in the absence of explicit style input, our model facilitates novel re-stylization by sampling from the unconditional style prior distribution. Experimental results show that our proposed stylization models, despite their lightweight design, outperform the state-of-the-art in style reenactment, content preservation, and generalization across various applications and settings. Project Page: https://murrol.github.io/GenMoStyle

false

true

false

423,749

2409.17213

Plurals: A System for Guiding LLMs Via Simulated Social Ensembles

Recent debates raised concerns that language models may favor certain viewpoints. But what if the solution is not to aim for a 'view from nowhere' but rather to leverage different viewpoints? We introduce Plurals, a system and Python library for pluralistic AI deliberation. Plurals consists of Agents (LLMs, optionally with personas) which deliberate within customizable Structures, with Moderators overseeing deliberation. Plurals is a generator of simulated social ensembles. Plurals integrates with government datasets to create nationally representative personas, includes deliberation templates inspired by deliberative democracy, and allows users to customize both information-sharing structures and deliberation behavior within Structures. Six case studies demonstrate fidelity to theoretical constructs and efficacy. Three randomized experiments show simulated focus groups produced output resonant with an online sample of the relevant audiences (chosen over zero-shot generation in 75% of trials). Plurals is both a paradigm and a concrete system for pluralistic AI. The Plurals library is available at https://github.com/josh-ashkinaze/plurals and will be continually updated.

true

false

true

false

true

false

true

false

491,705

2111.13138

TunBERT: Pretrained Contextualized Text Representation for Tunisian Dialect

Pretrained contextualized text representation models learn an effective representation of a natural language to make it machine understandable. After the breakthrough of the attention mechanism, a new generation of pretrained models have been proposed achieving good performances since the introduction of the Transformer. Bidirectional Encoder Representations from Transformers (BERT) has become the state-of-the-art model for language understanding. Despite their success, most of the available models have been trained on Indo-European languages however similar research for under-represented languages and dialects remains sparse. In this paper, we investigate the feasibility of training monolingual Transformer-based language models for under represented languages, with a specific focus on the Tunisian dialect. We evaluate our language model on sentiment analysis task, dialect identification task and reading comprehension question-answering task. We show that the use of noisy web crawled data instead of structured data (Wikipedia, articles, etc.) is more convenient for such non-standardized language. Moreover, results indicate that a relatively small web crawled dataset leads to performances that are as good as those obtained using larger datasets. Finally, our best performing TunBERT model reaches or improves the state-of-the-art in all three downstream tasks. We release the TunBERT pretrained model and the datasets used for fine-tuning.

false

true

false

true

false

268,198

1501.04058

Power Control and Soft Topology Adaptations in Multihop Cellular Networks with Multi-Point Connectivity

The LTE standards account for the use of relays to enhance coverage near the cell edge. In a traditional topology, a mobile can either establish a direct link to the base station (BS) or a link to the relay, but not both. In this paper, we consider the benefit of multipoint connectivity in allowing User Equipment (UEs) to split their transmit power over simultaneous links to the BS and the relay, in effect transmitting two parallel flows. We model decisions by the UEs as to: (i) which point of access to attach to (either a relay or a relay and the BS or only the BS); and (ii) how to allocate transmit power over these links so as to maximize their total rate. We show that this flexibility in the selection of points of access leads to substantial network capacity increase against when nodes operate in a fixed network topology. Individual adaptations by UEs, in terms of both point of access and transmit power, are interdependent due to interference and to the possibility of over-loading of the backhaul links. We show that these decisions can converge without any explicit cooperation and derive a closed-form expression for the transmit power levels.

false

true

false

39,324

1302.1020

Block-to-Block Distribution Matching

In this work, binary block-to-block distribution matching is considered. m independent and uniformly distributed bits are mapped to n output bits resembling a target product distribution. A rate R is called achieved by a sequence of encoder-decoder pairs, if for m,n to infinity, (1) m/n approaches R, (2) the informational divergence per bit of the output distribution and the target distribution goes to zero, and (3) the probability of erroneous decoding goes to zero. It is shown that the maximum achievable rate is equal to the entropy of the target distribution. A practical encoder-decoder pair is constructed that provably achieves the maximum rate in the limit. Numerical results illustrate that the suggested system operates close to the limits with reasonable complexity. The key idea is to internally use a fixed-to-variable length matcher and to compensate underflow by random mapping and to cast an error when overflow occurs.

false

true

false

21,774

2204.09404

A Probabilistic Time-Evolving Approach to Scanpath Prediction

Human visual attention is a complex phenomenon that has been studied for decades. Within it, the particular problem of scanpath prediction poses a challenge, particularly due to the inter- and intra-observer variability, among other reasons. Besides, most existing approaches to scanpath prediction have focused on optimizing the prediction of a gaze point given the previous ones. In this work, we present a probabilistic time-evolving approach to scanpath prediction, based on Bayesian deep learning. We optimize our model using a novel spatio-temporal loss function based on a combination of Kullback-Leibler divergence and dynamic time warping, jointly considering the spatial and temporal dimensions of scanpaths. Our scanpath prediction framework yields results that outperform those of current state-of-the-art approaches, and are almost on par with the human baseline, suggesting that our model is able to generate scanpaths whose behavior closely resembles those of the real ones.

true

false

true

false

true

false

292,425

2406.06427

Notes on Kalman Filter (KF, EKF, ESKF, IEKF, IESKF)

The Kalman Filter (KF) is a powerful mathematical tool widely used for state estimation in various domains, including Simultaneous Localization and Mapping (SLAM). This paper presents an in-depth introduction to the Kalman Filter and explores its several extensions: the Extended Kalman Filter (EKF), the Error-State Kalman Filter (ESKF), the Iterated Extended Kalman Filter (IEKF), and the Iterated Error-State Kalman Filter (IESKF). Each variant is meticulously examined, with detailed derivations of their mathematical formulations and discussions on their respective advantages and limitations. By providing a comprehensive overview of these techniques, this paper aims to offer valuable insights into their applications in SLAM and enhance the understanding of state estimation methodologies in complex environments.

false

true

false

462,570

1608.03990

Machine Learning-augmented Predictive Modeling of Turbulent Separated Flows over Airfoils

A modeling paradigm is developed to augment predictive models of turbulence by effectively utilizing limited data generated from physical experiments. The key components of our approach involve inverse modeling to infer the spatial distribution of model discrepancies, and, machine learning to reconstruct discrepancy information from a large number of inverse problems into corrective model forms. We apply the methodology to turbulent flows over airfoils involving flow separation. Model augmentations are developed for the Spalart Allmaras (SA) model using adjoint-based full field inference on experimentally measured lift coefficient data. When these model forms are reconstructed using neural networks (NN) and embedded within a standard solver, we show that much improved predictions in lift can be obtained for geometries and flow conditions that were not used to train the model. The NN-augmented SA model also predicts surface pressures extremely well. Portability of this approach is demonstrated by confirming that predictive improvements are preserved when the augmentation is embedded in a different commercial finite-element solver. The broader vision is that by incorporating data that can reveal the form of the innate model discrepancy, the applicability of data-driven turbulence models can be extended to more general flows.

false

true

false

59,759

2410.22619

Efficient Feature Extraction and Classification Architecture for MRI-Based Brain Tumor Detection

Uncontrolled cell division in the brain is what gives rise to brain tumors. If the tumor size increases by more than half, there is little hope for the patient's recovery. This emphasizes the need of rapid and precise brain tumor diagnosis. When it comes to analyzing, diagnosing, and planning therapy for brain tumors, MRI imaging plays a crucial role. A brain tumor's development history is crucial information for doctors to have. When it comes to distinguishing between human soft tissues, MRI scans are superior. In order to get reliable classification results from MRI scans quickly, deep learning is one of the most practical methods. Early human illness diagnosis has been demonstrated to be more accurate when deep learning methods are used. In the case of diagnosing a brain tumor, when even a little misdiagnosis might have serious consequences, accuracy is especially important. Disclosure of brain tumors in medical images is still a difficult task. Brain MRIs are notoriously imprecise in revealing the presence or absence of tumors. Using MRI scans of the brain, a Convolutional Neural Network (CNN) was trained to identify the presence of a tumor in this research. Results from the CNN model showed an accuracy of 99.17%. The CNN model's characteristics were also retrieved. In order to evaluate the CNN model's capability for processing images, we applied the features via the following machine learning models: KNN, Logistic regression, SVM, Random Forest, Naive Bayes, and Perception. CNN and machine learning models were also evaluated using the standard metrics of Precision, Recall, Specificity, and F1 score. The significance of the doctor's diagnosis enhanced the accuracy of the CNN model's assistance in identifying the existence of tumor and treating the patient.

false

true

false

true

false

503,694

2407.01960

Zero-Shot Video Restoration and Enhancement Using Pre-Trained Image Diffusion Model

Diffusion-based zero-shot image restoration and enhancement models have achieved great success in various tasks of image restoration and enhancement. However, directly applying them to video restoration and enhancement results in severe temporal flickering artifacts. In this paper, we propose the first framework for zero-shot video restoration and enhancement based on the pre-trained image diffusion model. By replacing the spatial self-attention layer with the proposed short-long-range (SLR) temporal attention layer, the pre-trained image diffusion model can take advantage of the temporal correlation between frames. We further propose temporal consistency guidance, spatial-temporal noise sharing, and an early stopping sampling strategy to improve temporally consistent sampling. Our method is a plug-and-play module that can be inserted into any diffusion-based image restoration or enhancement methods to further improve their performance. Experimental results demonstrate the superiority of our proposed method. Our code is available at https://github.com/cao-cong/ZVRD.

false

true

false

true

false

469,529

cs/0406001

Side-Information Coding with Turbo Codes and its Application to Quantum Key Distribution

Turbo coding is a powerful class of forward error correcting codes, which can achieve performances close to the Shannon limit. The turbo principle can be applied to the problem of side-information source coding, and we investigate here its application to the reconciliation problem occurring in a continuous-variable quantum key distribution protocol.

false

true

false

true

false

538,226

1801.08676

Neural Algebra of Classifiers

The world is fundamentally compositional, so it is natural to think of visual recognition as the recognition of basic visually primitives that are composed according to well-defined rules. This strategy allows us to recognize unseen complex concepts from simple visual primitives. However, the current trend in visual recognition follows a data greedy approach where huge amounts of data are required to learn models for any desired visual concept. In this paper, we build on the compositionality principle and develop an "algebra" to compose classifiers for complex visual concepts. To this end, we learn neural network modules to perform boolean algebra operations on simple visual classifiers. Since these modules form a complete functional set, a classifier for any complex visual concept defined as a boolean expression of primitives can be obtained by recursively applying the learned modules, even if we do not have a single training sample. As our experiments show, using such a framework, we can compose classifiers for complex visual concepts outperforming standard baselines on two well-known visual recognition benchmarks. Finally, we present a qualitative analysis of our method and its properties.

false

true

false

true

false

88,985

2408.12629

Data-Free Class Incremental Gesture Recognition via Synthetic Feature Sampling

Data-Free Class Incremental Learning (DFCIL) aims to enable models to continuously learn new classes while retraining knowledge of old classes, even when the training data for old classes is unavailable. Although explored primarily with image datasets by researchers, this study focuses on investigating DFCIL for skeleton-based gesture classification due to its significant real-world implications, particularly considering the growing prevalence of VR/AR headsets where gestures serve as the primary means of control and interaction. In this work, we made an intriguing observation: skeleton models trained with base classes(even very limited) demonstrate strong generalization capabilities to unseen classes without requiring additional training. Building on this insight, we developed Synthetic Feature Replay (SFR) that can sample synthetic features from class prototypes to replay for old classes and augment for new classes (under a few-shot setting). Our proposed method showcases significant advancements over the state-of-the-art, achieving up to 15% enhancements in mean accuracy across all steps and largely mitigating the accuracy imbalance between base classes and new classes.

false

true

false

true

false

482,819

2111.15339

RadioWeaves for Extreme Spatial Multiplexing in Indoor Environments

With the advances in virtual and augmented reality, gaming applications, and entertainment, certain indoor scenarios will require vastly higher capacity than what can be delivered by 5G. In this paper, we focus on massive MIMO for indoor environments. We provide a case study of the distributed deployment of the antenna elements over the walls of a room and not restricting the antenna separation to be half the wavelength. This is a new paradigm of massive MIMO antenna deployment, introduced under the name RadioWeaves. We investigate different antenna deployment scenarios in line of sight communication. We observe that the RadioWeaves deployment can spatially separate users much better than a conventional co-located deployment, which outweighs the losses caused by grating lobes and thus saves a lot on transmit power. Through simulations, we show that the RadioWeaves technology can provide high rates to multiple users by spending very little power at the transmitter compared to a co-located deployment.

false

true

false

268,891

1809.08422

Medical Knowledge Embedding Based on Recursive Neural Network for Multi-Disease Diagnosis

The representation of knowledge based on first-order logic captures the richness of natural language and supports multiple probabilistic inference models. Although symbolic representation enables quantitative reasoning with statistical probability, it is difficult to utilize with machine learning models as they perform numerical operations. In contrast, knowledge embedding (i.e., high-dimensional and continuous vectors) is a feasible approach to complex reasoning that can not only retain the semantic information of knowledge but also establish the quantifiable relationship among them. In this paper, we propose recursive neural knowledge network (RNKN), which combines medical knowledge based on first-order logic with recursive neural network for multi-disease diagnosis. After RNKN is efficiently trained from manually annotated Chinese Electronic Medical Records (CEMRs), diagnosis-oriented knowledge embeddings and weight matrixes are learned. Experimental results verify that the diagnostic accuracy of RNKN is superior to that of some classical machine learning models and Markov logic network (MLN). The results also demonstrate that the more explicit the evidence extracted from CEMRs is, the better is the performance achieved. RNKN gradually exhibits the interpretation of knowledge embeddings as the number of training epochs increases.

false

true

false

108,504

1904.09636

Model Compression with Multi-Task Knowledge Distillation for Web-scale Question Answering System

Deep pre-training and fine-tuning models (like BERT, OpenAI GPT) have demonstrated excellent results in question answering areas. However, due to the sheer amount of model parameters, the inference speed of these models is very slow. How to apply these complex models to real business scenarios becomes a challenging but practical problem. Previous works often leverage model compression approaches to resolve this problem. However, these methods usually induce information loss during the model compression procedure, leading to incomparable results between compressed model and the original model. To tackle this challenge, we propose a Multi-task Knowledge Distillation Model (MKDM for short) for web-scale Question Answering system, by distilling knowledge from multiple teacher models to a light-weight student model. In this way, more generalized knowledge can be transferred. The experiment results show that our method can significantly outperform the baseline methods and even achieve comparable results with the original teacher models, along with significant speedup of model inference.

false

true

false

128,436

2401.13652

Graph-Informed Neural Networks for Sparse Grid-Based Discontinuity Detectors

In this paper, we present a novel approach for detecting the discontinuity interfaces of a discontinuous function. This approach leverages Graph-Informed Neural Networks (GINNs) and sparse grids to address discontinuity detection also in domains of dimension larger than 3. GINNs, trained to identify troubled points on sparse grids, exploit graph structures built on the grids to achieve efficient and accurate discontinuity detection performances. We also introduce a recursive algorithm for general sparse grid-based detectors, characterized by convergence properties and easy applicability. Numerical experiments on functions with dimensions n = 2 and n = 4 demonstrate the efficiency and robust generalization of GINNs in detecting discontinuity interfaces. Notably, the trained GINNs offer portability and versatility, allowing integration into various algorithms and sharing among users.

false

true

false

true

false

true

423,806

2107.07610

Self-Supervised Contrastive Learning with Adversarial Perturbations for Defending Word Substitution-based Attacks

In this paper, we present an approach to improve the robustness of BERT language models against word substitution-based adversarial attacks by leveraging adversarial perturbations for self-supervised contrastive learning. We create a word-level adversarial attack generating hard positives on-the-fly as adversarial examples during contrastive learning. In contrast to previous works, our method improves model robustness without using any labeled data. Experimental results show that our method improves robustness of BERT against four different word substitution-based adversarial attacks, and combining our method with adversarial training gives higher robustness than adversarial training alone. As our method improves the robustness of BERT purely with unlabeled data, it opens up the possibility of using large text datasets to train robust language models against word substitution-based adversarial attacks.

false

true

false

246,471

2112.03002

GraphPrompt: Graph-Based Prompt Templates for Biomedical Synonym Prediction

In the expansion of biomedical dataset, the same category may be labeled with different terms, thus being tedious and onerous to curate these terms. Therefore, automatically mapping synonymous terms onto the ontologies is desirable, which we name as biomedical synonym prediction task. Unlike biomedical concept normalization (BCN), no clues from context can be used to enhance synonym prediction, making it essential to extract graph features from ontology. We introduce an expert-curated dataset OBO-syn encompassing 70 different types of concepts and 2 million curated concept-term pairs for evaluating synonym prediction methods. We find BCN methods perform weakly on this task for not making full use of graph information. Therefore, we propose GraphPrompt, a prompt-based learning approach that creates prompt templates according to the graphs. GraphPrompt obtained 37.2\% and 28.5\% improvement on zero-shot and few-shot settings respectively, indicating the effectiveness of these graph-based prompt templates. We envision that our method GraphPrompt and OBO-syn dataset can be broadly applied to graph-based NLP tasks, and serve as the basis for analyzing diverse and accumulating biomedical data. All the data and codes are avalible at: https://github.com/HanwenXuTHU/GraphPrompt

false

true

false

true

false

270,044

1506.06418

Extreme Extraction: Only One Hour per Relation

Information Extraction (IE) aims to automatically generate a large knowledge base from natural language text, but progress remains slow. Supervised learning requires copious human annotation, while unsupervised and weakly supervised approaches do not deliver competitive accuracy. As a result, most fielded applications of IE, as well as the leading TAC-KBP systems, rely on significant amounts of manual engineering. Even "Extreme" methods, such as those reported in Freedman et al. 2011, require about 10 hours of expert labor per relation. This paper shows how to reduce that effort by an order of magnitude. We present a novel system, InstaRead, that streamlines authoring with an ensemble of methods: 1) encoding extraction rules in an expressive and compositional representation, 2) guiding the user to promising rules based on corpus statistics and mined resources, and 3) introducing a new interactive development cycle that provides immediate feedback --- even on large datasets. Experiments show that experts can create quality extractors in under an hour and even NLP novices can author good extractors. These extractors equal or outperform ones obtained by comparably supervised and state-of-the-art distantly supervised approaches.

false

true

false

true

false

44,417

2310.08072

Training Generative Question-Answering on Synthetic Data Obtained from an Instruct-tuned Model

This paper presents a simple and cost-effective method for synthesizing data to train question-answering systems. For training, fine-tuning GPT models is a common practice in resource-rich languages like English, however, it becomes challenging for non-English languages due to the scarcity of sufficient question-answer (QA) pairs. Existing approaches use question and answer generators trained on human-authored QA pairs, which involves substantial human expenses. In contrast, we use an instruct-tuned model to generate QA pairs in a zero-shot or few-shot manner. We conduct experiments to compare various strategies for obtaining QA pairs from the instruct-tuned model. The results demonstrate that a model trained on our proposed synthetic data achieves comparable performance to a model trained on manually curated datasets, without incurring human costs.

false

true

false

399,248

2210.12529

On-Demand Sampling: Learning Optimally from Multiple Distributions

Social and real-world considerations such as robustness, fairness, social welfare and multi-agent tradeoffs have given rise to multi-distribution learning paradigms, such as collaborative learning, group distributionally robust optimization, and fair federated learning. In each of these settings, a learner seeks to uniformly minimize its expected loss over $n$ predefined data distributions, while using as few samples as possible. In this paper, we establish the optimal sample complexity of these learning paradigms and give algorithms that meet this sample complexity. Importantly, our sample complexity bounds for multi-distribution learning exceed that of learning a single distribution by only an additive factor of $n \log(n) / \epsilon^2$. This improves upon the best known sample complexity bounds for fair federated learning by Mohri et al. and collaborative learning by Nguyen and Zakynthinou by multiplicative factors of $n$ and $\log(n)/\epsilon^3$, respectively. We also provide the first sample complexity bounds for the group DRO objective of Sagawa et al. To guarantee these optimal sample complexity bounds, our algorithms learn to sample from data distributions on demand. Our algorithm design and analysis are enabled by our extensions of online learning techniques for solving stochastic zero-sum games. In particular, we contribute stochastic variants of no-regret dynamics that can trade off between players' differing sampling costs.

false

true

false

true

false

325,784

1312.4564

Adaptive Stochastic Alternating Direction Method of Multipliers

The Alternating Direction Method of Multipliers (ADMM) has been studied for years. The traditional ADMM algorithm needs to compute, at each iteration, an (empirical) expected loss function on all training examples, resulting in a computational complexity proportional to the number of training examples. To reduce the time complexity, stochastic ADMM algorithms were proposed to replace the expected function with a random loss function associated with one uniformly drawn example plus a Bregman divergence. The Bregman divergence, however, is derived from a simple second order proximal function, the half squared norm, which could be a suboptimal choice. In this paper, we present a new family of stochastic ADMM algorithms with optimal second order proximal functions, which produce a new family of adaptive subgradient methods. We theoretically prove that their regret bounds are as good as the bounds which could be achieved by the best proximal function that can be chosen in hindsight. Encouraging empirical results on a variety of real-world datasets confirm the effectiveness and efficiency of the proposed algorithms.

false

true

false

29,147

2012.03322

A Pseudo-labelling Auto-Encoder for unsupervised image classification

In this paper, we introduce a unique variant of the denoising Auto-Encoder and combine it with the perceptual loss to classify images in an unsupervised manner. The proposed method, called Pseudo Labelling, consists of first applying a randomly sampled set of data augmentation transformations to each training image. As a result, each initial image can be considered as a pseudo-label to its corresponding augmented ones. Then, an Auto-Encoder is used to learn the mapping between each set of the augmented images and its corresponding pseudo-label. Furthermore, the perceptual loss is employed to take into consideration the existing dependencies between the pixels in the same neighbourhood of an image. This combination encourages the encoder to output richer encodings that are highly informative of the input's class. Consequently, the Auto-Encoder's performance on unsupervised image classification is improved in terms of stability, accuracy and consistency across all tested datasets. Previous state-of-the-art accuracy on the MNIST, CIFAR-10 and SVHN datasets is improved by 0.3\%, 3.11\% and 9.21\% respectively.

false

true

false

true

false

210,065

1402.3384

A Minimax Distortion View of Differentially Private Query Release

We consider the problem of differentially private query release through a synthetic database approach. Departing from the existing approaches that require the query set to be specified in advance, we advocate to devise query-set independent mechanisms, with an ambitious goal of providing accurate answers, while meeting the privacy constraints, for all queries in a general query class. Specifically, a differentially private mechanism is constructed to "encode" rich stochastic structure into the synthetic database, and "customized" companion estimators are then derived to provide accurate answers by making use of all available information, including the mechanism (which is public information) and the query functions. Accordingly, the distortion under the best of this kind of mechanisms at the worst-case query in a general query class, so called the minimax distortion, provides a fundamental characterization of differentially private query release. For the general class of statistical queries, we prove that with the squared-error distortion measure, the minimax distortion is $O(1/n)$ by deriving asymptotically tight upper and lower bounds in the regime that the database size $n$ goes to infinity. The upper bound is achievable by a mechanism $\mathcal{E}$ and its corresponding companion estimators, which points directly to the feasibility of the proposed approach in large databases. We further evaluate the mechanism $\mathcal{E}$ and the companion estimators through experiments on real datasets from Netflix and Facebook. Experimental results show improvement over the state-of-art MWEM algorithm and verify the scaling behavior $O(1/n)$ of the minimax distortion.

false

true

false

true

false

true

false

30,870

2104.01294

Representations of Language Varieties Are Reliable Given Corpus Similarity Measures

This paper measures similarity both within and between 84 language varieties across nine languages. These corpora are drawn from digital sources (the web and tweets), allowing us to evaluate whether such geo-referenced corpora are reliable for modelling linguistic variation. The basic idea is that, if each source adequately represents a single underlying language variety, then the similarity between these sources should be stable across all languages and countries. The paper shows that there is a consistent agreement between these sources using frequency-based corpus similarity measures. This provides further evidence that digital geo-referenced corpora consistently represent local language varieties.

false

true

false

228,295

2006.05301

VAEs in the Presence of Missing Data

Real world datasets often contain entries with missing elements e.g. in a medical dataset, a patient is unlikely to have taken all possible diagnostic tests. Variational Autoencoders (VAEs) are popular generative models often used for unsupervised learning. Despite their widespread use it is unclear how best to apply VAEs to datasets with missing data. We develop a novel latent variable model of a corruption process which generates missing data, and derive a corresponding tractable evidence lower bound (ELBO). Our model is straightforward to implement, can handle both missing completely at random (MCAR) and missing not at random (MNAR) data, scales to high dimensional inputs and gives both the VAE encoder and decoder principled access to indicator variables for whether a data element is missing or not. On the MNIST and SVHN datasets we demonstrate improved marginal log-likelihood of observed data and better missing data imputation, compared to existing approaches.

false

true

false

181,016

2106.15179

Wrong Colored Vermeer: Color-Symmetric Image Distortion

Color symmetry implies that the colors of geometrical objects are assigned according to their symmetry properties. It is defined by associating the elements of the symmetry group with a color permutation. I use this concept for generative art and apply symmetry-consistent color distortions to images of paintings by Johannes Vermeer. The color permutations are realized as mappings of the HSV color space onto itself.

false

true

false

true

243,639

1907.10388

Higher-Order Function Networks for Learning Composable 3D Object Representations

We present a new approach to 3D object representation where a neural network encodes the geometry of an object directly into the weights and biases of a second 'mapping' network. This mapping network can be used to reconstruct an object by applying its encoded transformation to points randomly sampled from a simple geometric space, such as the unit sphere. We study the effectiveness of our method through various experiments on subsets of the ShapeNet dataset. We find that the proposed approach can reconstruct encoded objects with accuracy equal to or exceeding state-of-the-art methods with orders of magnitude fewer parameters. Our smallest mapping network has only about 7000 parameters and shows reconstruction quality on par with state-of-the-art object decoder architectures with millions of parameters. Further experiments on feature mixing through the composition of learned functions show that the encoding captures a meaningful subspace of objects.

false

true

false

true

false

139,606

1503.04964

Energy Sharing for Multiple Sensor Nodes with Finite Buffers

We consider the problem of finding optimal energy sharing policies that maximize the network performance of a system comprising of multiple sensor nodes and a single energy harvesting (EH) source. Sensor nodes periodically sense the random field and generate data, which is stored in the corresponding data queues. The EH source harnesses energy from ambient energy sources and the generated energy is stored in an energy buffer. Sensor nodes receive energy for data transmission from the EH source. The EH source has to efficiently share the stored energy among the nodes in order to minimize the long-run average delay in data transmission. We formulate the problem of energy sharing between the nodes in the framework of average cost infinite-horizon Markov decision processes (MDPs). We develop efficient energy sharing algorithms, namely Q-learning algorithm with exploration mechanisms based on the $\epsilon$-greedy method as well as upper confidence bound (UCB). We extend these algorithms by incorporating state and action space aggregation to tackle state-action space explosion in the MDP. We also develop a cross entropy based method that incorporates policy parameterization in order to find near optimal energy sharing policies. Through simulations, we show that our algorithms yield energy sharing policies that outperform the heuristic greedy method.

false

true

false

true

41,199

2306.07180

Diffusion Models for Black-Box Optimization

The goal of offline black-box optimization (BBO) is to optimize an expensive black-box function using a fixed dataset of function evaluations. Prior works consider forward approaches that learn surrogates to the black-box function and inverse approaches that directly map function values to corresponding points in the input domain of the black-box function. These approaches are limited by the quality of the offline dataset and the difficulty in learning one-to-many mappings in high dimensions, respectively. We propose Denoising Diffusion Optimization Models (DDOM), a new inverse approach for offline black-box optimization based on diffusion models. Given an offline dataset, DDOM learns a conditional generative model over the domain of the black-box function conditioned on the function values. We investigate several design choices in DDOM, such as re-weighting the dataset to focus on high function values and the use of classifier-free guidance at test-time to enable generalization to function values that can even exceed the dataset maxima. Empirically, we conduct experiments on the Design-Bench benchmark and show that DDOM achieves results competitive with state-of-the-art baselines.

false

true

false

372,912