id stringlengths 9 16 | title stringlengths 4 278 | abstract stringlengths 3 4.08k | cs.HC bool 2 classes | cs.CE bool 2 classes | cs.SD bool 2 classes | cs.SI bool 2 classes | cs.AI bool 2 classes | cs.IR bool 2 classes | cs.LG bool 2 classes | cs.RO bool 2 classes | cs.CL bool 2 classes | cs.IT bool 2 classes | cs.SY bool 2 classes | cs.CV bool 2 classes | cs.CR bool 2 classes | cs.CY bool 2 classes | cs.MA bool 2 classes | cs.NE bool 2 classes | cs.DB bool 2 classes | Other bool 2 classes | __index_level_0__ int64 0 541k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1410.8577 | An Ensemble-based System for Microaneurysm Detection and Diabetic
Retinopathy Grading | Reliable microaneurysm detection in digital fundus images is still an open issue in medical image processing. We propose an ensemble-based framework to improve microaneurysm detection. Unlike the well-known approach of considering the output of multiple classifiers, we propose a combination of internal components of microaneurysm detectors, namely preprocessing methods and candidate extractors. We have evaluated our approach for microaneurysm detection in an online competition, where this algorithm is currently ranked as first and also on two other databases. Since microaneurysm detection is decisive in diabetic retinopathy grading, we also tested the proposed method for this task on the publicly available Messidor database, where a promising AUC 0.90 with 0.01 uncertainty is achieved in a 'DR/non-DR'-type classification based on the presence or absence of the microaneurysms. | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | false | false | 37,170 |
2312.06927 | WE economy: Potential of mutual aid distribution based on moral
responsibility and risk vulnerability | Reducing wealth inequality and disparity is a global challenge. The economic system is mainly divided into (1) gift and reciprocity, (2) power and redistribution, (3) market exchange, and (4) mutual aid without reciprocal obligations. The current inequality stems from a capitalist economy consisting of (2) and (3). To sublimate (1), which is the human economy, to (4), the concept of a "mixbiotic society" has been proposed in the philosophical realm. This is a society in which free and diverse individuals, "I," mix with each other, recognize their respective "fundamental incapability" and sublimate them into "WE" solidarity. The economy in this society must have moral responsibility as a coadventurer and consideration for vulnerability to risk. Therefore, I focus on two factors of mind perception: moral responsibility and risk vulnerability, and propose a novel model of wealth distribution following an econophysical approach. Specifically, I developed a joint-venture model, a redistribution model in the joint-venture model, and a "WE economy" model. A simulation comparison of a combination of the joint ventures and redistribution with the WE economies reveals that WE economies are effective in reducing inequality and resilient in normalizing wealth distribution as advantages, and susceptible to free riders as disadvantages. However, this disadvantage can be compensated for by fostering consensus and fellowship, and by complementing it with joint ventures. This study essentially presents the effectiveness of moral responsibility, the complementarity between the WE economy and the joint economy, and the direction of the economy toward reducing inequality. Future challenges are to develop the WE economy model based on real economic analysis and psychology, as well as to promote WE economy fieldwork for worker coops and platform cooperatives to realize a desirable mixbiotic society. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | 414,725 |
2207.06985 | ObjectBox: From Centers to Boxes for Anchor-Free Object Detection | We present ObjectBox, a novel single-stage anchor-free and highly generalizable object detection approach. As opposed to both existing anchor-based and anchor-free detectors, which are more biased toward specific object scales in their label assignments, we use only object center locations as positive samples and treat all objects equally in different feature levels regardless of the objects' sizes or shapes. Specifically, our label assignment strategy considers the object center locations as shape- and size-agnostic anchors in an anchor-free fashion, and allows learning to occur at all scales for every object. To support this, we define new regression targets as the distances from two corners of the center cell location to the four sides of the bounding box. Moreover, to handle scale-variant objects, we propose a tailored IoU loss to deal with boxes with different sizes. As a result, our proposed object detector does not need any dataset-dependent hyperparameters to be tuned across datasets. We evaluate our method on MS-COCO 2017 and PASCAL VOC 2012 datasets, and compare our results to state-of-the-art methods. We observe that ObjectBox performs favorably in comparison to prior works. Furthermore, we perform rigorous ablation experiments to evaluate different components of our method. Our code is available at: https://github.com/MohsenZand/ObjectBox. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 308,068 |
0911.4510 | Bigraphical models for protein and membrane interactions | We present a bigraphical framework suited for modeling biological systems both at protein level and at membrane level. We characterize formally bigraphs corresponding to biologically meaningful systems, and bigraphic rewriting rules representing biologically admissible interactions. At the protein level, these bigraphic reactive systems correspond exactly to systems of kappa-calculus. Membrane-level interactions are represented by just two general rules, whose application can be triggered by protein-level interactions in a well-de\"ined and precise way. This framework can be used to compare and merge models at different abstraction levels; in particular, higher-level (e.g. mobility) activities can be given a formal biological justification in terms of low-level (i.e., protein) interactions. As examples, we formalize in our framework the vesiculation and the phagocytosis processes. | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | 5,004 |
2208.10379 | Enhanced IoT Batteryless D2D Communications Using Reconfigurable
Intelligent Surfaces | Recent research on reconfigurable intelligent surfaces (RIS) suggests that the RIS panel, containing passive elements, enhances channel performance for the internet of things (IoT) systems by reflecting transmitted signals to the receiving nodes. This paper investigates RIS panel assisted-wireless network to instigate minimal base station (BS) transmit power in the form of energy harvesting for batteryless IoT sensors to maximize bits transmission in the significant multi-path environment, such as urban areas. Batteryless IoT sensors harvest energy through the RIS panel from external sources, such as from nearby BS radio frequency (RF) signal in the first optimal time frame, for a given time frame. The bits transmission among IoT sensors, followed by a device-to-device (D2D) communications protocol, is maximized using harvested energy in the final optimal time frame. The bits transmission is at least equal to the number of bits sampled by the IoT sensor. We formulate a non-convex mixed-integer non-linear problem to maximize the number of communicating bits subject to energy harvesting from BS RF signals, RIS panel energy consumption, and required time. We propose a robust solution by presenting an iterative algorithm. We perform extensive simulation results based on the 3GPP Urban Micro channel model to validate our model. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 314,041 |
2408.00337 | DistillGrasp: Integrating Features Correlation with Knowledge
Distillation for Depth Completion of Transparent Objects | Due to the visual properties of reflection and refraction, RGB-D cameras cannot accurately capture the depth of transparent objects, leading to incomplete depth maps. To fill in the missing points, recent studies tend to explore new visual features and design complex networks to reconstruct the depth, however, these approaches tremendously increase computation, and the correlation of different visual features remains a problem. To this end, we propose an efficient depth completion network named DistillGrasp which distillates knowledge from the teacher branch to the student branch. Specifically, in the teacher branch, we design a position correlation block (PCB) that leverages RGB images as the query and key to search for the corresponding values, guiding the model to establish correct correspondence between two features and transfer it to the transparent areas. For the student branch, we propose a consistent feature correlation module (CFCM) that retains the reliable regions of RGB images and depth maps respectively according to the consistency and adopts a CNN to capture the pairwise relationship for depth completion. To avoid the student branch only learning regional features from the teacher branch, we devise a distillation loss that not only considers the distance loss but also the object structure and edge information. Extensive experiments conducted on the ClearGrasp dataset manifest that our teacher network outperforms state-of-the-art methods in terms of accuracy and generalization, and the student network achieves competitive results with a higher speed of 48 FPS. In addition, the significant improvement in a real-world robotic grasping system illustrates the effectiveness and robustness of our proposed system. | false | false | false | false | false | true | false | false | false | false | false | true | false | false | false | false | false | false | 477,799 |
2010.03161 | Model-Free Non-Stationary RL: Near-Optimal Regret and Applications in
Multi-Agent RL and Inventory Control | We consider model-free reinforcement learning (RL) in non-stationary Markov decision processes. Both the reward functions and the state transition functions are allowed to vary arbitrarily over time as long as their cumulative variations do not exceed certain variation budgets. We propose Restarted Q-Learning with Upper Confidence Bounds (RestartQ-UCB), the first model-free algorithm for non-stationary RL, and show that it outperforms existing solutions in terms of dynamic regret. Specifically, RestartQ-UCB with Freedman-type bonus terms achieves a dynamic regret bound of $\widetilde{O}(S^{\frac{1}{3}} A^{\frac{1}{3}} \Delta^{\frac{1}{3}} H T^{\frac{2}{3}})$, where $S$ and $A$ are the numbers of states and actions, respectively, $\Delta>0$ is the variation budget, $H$ is the number of time steps per episode, and $T$ is the total number of time steps. We further present a parameter-free algorithm named Double-Restart Q-UCB that does not require prior knowledge of the variation budget. We show that our algorithms are \emph{nearly optimal} by establishing an information-theoretical lower bound of $\Omega(S^{\frac{1}{3}} A^{\frac{1}{3}} \Delta^{\frac{1}{3}} H^{\frac{2}{3}} T^{\frac{2}{3}})$, the first lower bound in non-stationary RL. Numerical experiments validate the advantages of RestartQ-UCB in terms of both cumulative rewards and computational efficiency. We demonstrate the power of our results in examples of multi-agent RL and inventory control across related products. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 199,304 |
2103.14357 | VDM-DA: Virtual Domain Modeling for Source Data-free Domain Adaptation | Domain adaptation aims to leverage a label-rich domain (the source domain) to help model learning in a label-scarce domain (the target domain). Most domain adaptation methods require the co-existence of source and target domain samples to reduce the distribution mismatch, however, access to the source domain samples may not always be feasible in the real world applications due to different problems (e.g., storage, transmission, and privacy issues). In this work, we deal with the source data-free unsupervised domain adaptation problem, and propose a novel approach referred to as Virtual Domain Modeling (VDM-DA). The virtual domain acts as a bridge between the source and target domains. On one hand, we generate virtual domain samples based on an approximated Gaussian Mixture Model (GMM) in the feature space with the pre-trained source model, such that the virtual domain maintains a similar distribution with the source domain without accessing to the original source data. On the other hand, we also design an effective distribution alignment method to reduce the distribution divergence between the virtual domain and the target domain by gradually improving the compactness of the target domain distribution through model learning. In this way, we successfully achieve the goal of distribution alignment between the source and target domains by training deep networks without accessing to the source domain data. We conduct extensive experiments on benchmark datasets for both 2D image-based and 3D point cloud-based cross-domain object recognition tasks, where the proposed method referred to Domain Adaptation with Virtual Domain Modeling (VDM-DA) achieves the state-of-the-art performances on all datasets. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 226,833 |
1910.06948 | Data-Driven Deep Learning of Partial Differential Equations in Modal
Space | We present a framework for recovering/approximating unknown time-dependent partial differential equation (PDE) using its solution data. Instead of identifying the terms in the underlying PDE, we seek to approximate the evolution operator of the underlying PDE numerically. The evolution operator of the PDE, defined in infinite-dimensional space, maps the solution from a current time to a future time and completely characterizes the solution evolution of the underlying unknown PDE. Our recovery strategy relies on approximation of the evolution operator in a properly defined modal space, i.e., generalized Fourier space, in order to reduce the problem to finite dimensions. The finite dimensional approximation is then accomplished by training a deep neural network structure, which is based on residual network (ResNet), using the given data. Error analysis is provided to illustrate the predictive accuracy of the proposed method. A set of examples of different types of PDEs, including inviscid Burgers' equation that develops discontinuity in its solution, are presented to demonstrate the effectiveness of the proposed method. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | true | false | true | 149,485 |
2207.14200 | CrAM: A Compression-Aware Minimizer | Deep neural networks (DNNs) often have to be compressed, via pruning and/or quantization, before they can be deployed in practical settings. In this work we propose a new compression-aware minimizer dubbed CrAM that modifies the optimization step in a principled way, in order to produce models whose local loss behavior is stable under compression operations such as pruning. Thus, dense models trained via CrAM should be compressible post-training, in a single step, without significant accuracy loss. Experimental results on standard benchmarks, such as residual networks for ImageNet classification and BERT models for language modelling, show that CrAM produces dense models that can be more accurate than the standard SGD/Adam-based baselines, but which are stable under weight pruning: specifically, we can prune models in one-shot to 70-80% sparsity with almost no accuracy loss, and to 90% with reasonable ($\sim 1\%$) accuracy loss, which is competitive with gradual compression methods. Additionally, CrAM can produce sparse models which perform well for transfer learning, and it also works for semi-structured 2:4 pruning patterns supported by GPU hardware. The code for reproducing the results is available at https://github.com/IST-DASLab/CrAM . | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 310,509 |
1708.06394 | Expressions for the Entropy of Binomial-Type Distributions | We develop a general method for computing logarithmic and log-gamma expectations of distributions. As a result, we derive series expansions and integral representations of the entropy for several fundamental distributions, including the Poisson, binomial, beta-binomial, negative binomial, and hypergeometric distributions. Our results also establish connections between the entropy functions and to the Riemann zeta function and its generalizations. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 79,311 |
1008.2005 | Approximation Analysis of Influence Spread in Social Networks | In the context of influence propagation in a social graph, we can identify three orthogonal dimensions - the number of seed nodes activated at the beginning (known as budget), the expected number of activated nodes at the end of the propagation (known as expected spread or coverage), and the time taken for the propagation. We can constrain one or two of these and try to optimize the third. In their seminal paper, Kempe et al. constrained the budget, left time unconstrained, and maximized the coverage: this problem is known as Influence Maximization. In this paper, we study alternative optimization problems which are naturally motivated by resource and time constraints on viral marketing campaigns. In the first problem, termed Minimum Target Set Selection (or MINTSS for short), a coverage threshold n is given and the task is to find the minimum size seed set such that by activating it, at least n nodes are eventually activated in the expected sense. In the second problem, termed MINTIME, a coverage threshold n and a budget threshold k are given, and the task is to find a seed set of size at most k such that by activating it, at least n nodes are activated, in the minimum possible time. Both these problems are NP-hard, which motivates our interest in their approximation. For MINTSS, we develop a simple greedy algorithm and show that it provides a bicriteria approximation. We also establish a generic hardness result suggesting that improving it is likely to be hard. For MINTIME, we show that even bicriteria and tricriteria approximations are hard under several conditions. However, if we allow the budget to be boosted by a logarithmic factor and allow the coverage to fall short, then the problem can be solved exactly in PTIME. Finally, we show the value of the approximation algorithms, by comparing them against various heuristics. | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | true | 7,253 |
1304.3856 | Proceedings of the Fourth Conference on Uncertainty in Artificial
Intelligence (1988) | This is the Proceedings of the Fourth Conference on Uncertainty in Artificial Intelligence, which was held in Minneapolis, MN, July 10-12, 1988 | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 23,946 |
1705.02894 | Geometric GAN | Generative Adversarial Nets (GANs) represent an important milestone for effective generative models, which has inspired numerous variants seemingly different from each other. One of the main contributions of this paper is to reveal a unified geometric structure in GAN and its variants. Specifically, we show that the adversarial generative model training can be decomposed into three geometric steps: separating hyperplane search, discriminator parameter update away from the separating hyperplane, and the generator update along the normal vector direction of the separating hyperplane. This geometric intuition reveals the limitations of the existing approaches and leads us to propose a new formulation called geometric GAN using SVM separating hyperplane that maximizes the margin. Our theoretical analysis shows that the geometric GAN converges to a Nash equilibrium between the discriminator and generator. In addition, extensive numerical results show that the superior performance of geometric GAN. | false | false | false | false | true | false | true | false | false | false | false | true | false | false | false | false | false | false | 73,079 |
2109.06050 | Few-Shot Cross-Lingual Stance Detection with Sentiment-Based
Pre-Training | The goal of stance detection is to determine the viewpoint expressed in a piece of text towards a target. These viewpoints or contexts are often expressed in many different languages depending on the user and the platform, which can be a local news outlet, a social media platform, a news forum, etc. Most research in stance detection, however, has been limited to working with a single language and on a few limited targets, with little work on cross-lingual stance detection. Moreover, non-English sources of labelled data are often scarce and present additional challenges. Recently, large multilingual language models have substantially improved the performance on many non-English tasks, especially such with limited numbers of examples. This highlights the importance of model pre-training and its ability to learn from few examples. In this paper, we present the most comprehensive study of cross-lingual stance detection to date: we experiment with 15 diverse datasets in 12 languages from 6 language families, and with 6 low-resource evaluation settings each. For our experiments, we build on pattern-exploiting training, proposing the addition of a novel label encoder to simplify the verbalisation procedure. We further propose sentiment-based generation of stance data for pre-training, which shows sizeable improvement of more than 6% F1 absolute in low-shot settings compared to several strong baselines. | false | false | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | 255,027 |
1804.04371 | Image Correction via Deep Reciprocating HDR Transformation | Image correction aims to adjust an input image into a visually pleasing one. Existing approaches are proposed mainly from the perspective of image pixel manipulation. They are not effective to recover the details in the under/over exposed regions. In this paper, we revisit the image formation procedure and notice that the missing details in these regions exist in the corresponding high dynamic range (HDR) data. These details are well perceived by the human eyes but diminished in the low dynamic range (LDR) domain because of the tone mapping process. Therefore, we formulate the image correction task as an HDR transformation process and propose a novel approach called Deep Reciprocating HDR Transformation (DRHT). Given an input LDR image, we first reconstruct the missing details in the HDR domain. We then perform tone mapping on the predicted HDR data to generate the output LDR image with the recovered details. To this end, we propose a united framework consisting of two CNNs for HDR reconstruction and tone mapping. They are integrated end-to-end for joint training and prediction. Experiments on the standard benchmarks demonstrate that the proposed method performs favorably against state-of-the-art image correction methods. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | true | 94,835 |
1306.4621 | English Character Recognition using Artificial Neural Network | This work focuses on development of a Offline Hand Written English Character Recognition algorithm based on Artificial Neural Network (ANN). The ANN implemented in this work has single output neuron which shows whether the tested character belongs to a particular cluster or not. The implementation is carried out completely in 'C' language. Ten sets of English alphabets (small-26, capital-26) were used to train the ANN and 5 sets of English alphabets were used to test the network. The characters were collected from different persons over duration of about 25 days. The algorithm was tested with 5 capital letters and 5 small letter sets. However, the result showed that the algorithm recognized English alphabet patterns with maximum accuracy of 92.59% and False Rejection Rate (FRR) of 0%. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | 25,322 |
1208.4662 | Automatic Segmentation of Fluorescence Lifetime Microscopy Images of
Cells Using Multi-Resolution Community Detection | We have developed an automatic method for segmenting fluorescence lifetime (FLT) imaging microscopy (FLIM) images of cells inspired by a multi-resolution community detection (MCD) based network segmentation method. The image processing problem is framed as identifying segments with respective average FLTs against a background in FLIM images. The proposed method segments a FLIM image for a given resolution of the network composed using image pixels as the nodes and similarity between the pixels as the edges. In the resulting segmentation, low network resolution leads to larger segments and high network resolution leads to smaller segments. Further, the mean-square error (MSE) in estimating the FLT segments in a FLIM image using the proposed method was found to be consistently decreasing with increasing resolution of the corresponding network. The proposed MCD method outperformed a popular spectral clustering based method in performing FLIM image segmentation. The spectral segmentation method introduced noisy segments in its output at high resolution. It was unable to offer a consistent decrease in MSE with increasing resolution. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 18,226 |
2311.01734 | Sculpting Holistic 3D Representation in Contrastive Language-Image-3D
Pre-training | Contrastive learning has emerged as a promising paradigm for 3D open-world understanding, i.e., aligning point cloud representation to image and text embedding space individually. In this paper, we introduce MixCon3D, a simple yet effective method aiming to sculpt holistic 3D representation in contrastive language-image-3D pre-training. In contrast to point cloud only, we develop the 3D object-level representation from complementary perspectives, e.g., multi-view rendered images with the point cloud. Then, MixCon3D performs language-3D contrastive learning, comprehensively depicting real-world 3D objects and bolstering text alignment. Additionally, we pioneer the first thorough investigation of various training recipes for the 3D contrastive learning paradigm, building a solid baseline with improved performance. Extensive experiments conducted on three representative benchmarks reveal that our method significantly improves over the baseline, surpassing the previous state-of-the-art performance on the challenging 1,156-category Objaverse-LVIS dataset by 5.7%. The versatility of MixCon3D is showcased in applications such as text-to-3D retrieval and point cloud captioning, further evidencing its efficacy in diverse scenarios. The code is available at https://github.com/UCSC-VLAA/MixCon3D. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 405,155 |
1907.06740 | Real-time Hair Segmentation and Recoloring on Mobile GPUs | We present a novel approach for neural network-based hair segmentation from a single camera input specifically designed for real-time, mobile application. Our relatively small neural network produces a high-quality hair segmentation mask that is well suited for AR effects, e.g. virtual hair recoloring. The proposed model achieves real-time inference speed on mobile GPUs (30-100+ FPS, depending on the device) with high accuracy. We also propose a very realistic hair recoloring scheme. Our method has been deployed in major AR application and is used by millions of users. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 138,691 |
2307.07869 | Custom DNN using Reward Modulated Inverted STDP Learning for Temporal
Pattern Recognition | Temporal spike recognition plays a crucial role in various domains, including anomaly detection, keyword spotting and neuroscience. This paper presents a novel algorithm for efficient temporal spike pattern recognition on sparse event series data. The algorithm leverages a combination of reward-modulatory behavior, Hebbian and anti-Hebbian based learning methods to identify patterns in dynamic datasets with short intervals of training. The algorithm begins with a preprocessing step, where the input data is rationalized and translated to a feature-rich yet sparse spike time series data. Next, a linear feed forward spiking neural network processes this data to identify a trained pattern. Finally, the next layer performs a weighted check to ensure the correct pattern has been detected.To evaluate the performance of the proposed algorithm, it was trained on a complex dataset containing spoken digits with spike information and its output compared to state-of-the-art. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | true | false | false | 379,570 |
1703.02883 | Memory Enriched Big Bang Big Crunch Optimization Algorithm for Data
Clustering | Cluster analysis plays an important role in decision making process for many knowledge-based systems. There exist a wide variety of different approaches for clustering applications including the heuristic techniques, probabilistic models, and traditional hierarchical algorithms. In this paper, a novel heuristic approach based on big bang-big crunch algorithm is proposed for clustering problems. The proposed method not only takes advantage of heuristic nature to alleviate typical clustering algorithms such as k-means, but it also benefits from the memory based scheme as compared to its similar heuristic techniques. Furthermore, the performance of the proposed algorithm is investigated based on several benchmark test functions as well as on the well-known datasets. The experimental results show the significant superiority of the proposed method over the similar algorithms. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 69,634 |
2308.04223 | Real-Time Progressive Learning: Accumulate Knowledge from Control with
Neural-Network-Based Selective Memory | Memory, as the basis of learning, determines the storage, update and forgetting of knowledge and further determines the efficiency of learning. Featured with the mechanism of memory, a radial basis function neural network based learning control scheme named real-time progressive learning (RTPL) is proposed to learn the unknown dynamics of the system with guaranteed stability and closed-loop performance. Instead of the Lyapunov-based weight update law of conventional neural network learning control (NNLC), which mainly concentrates on stability and control performance, RTPL employs the selective memory recursive least squares (SMRLS) algorithm to update the weights of the neural network and achieves the following merits: 1) improved learning speed without filtering, 2) robustness to hyperparameter setting of neural networks, 3) good generalization ability, i.e., reuse of learned knowledge in different tasks, and 4) guaranteed learning performance under parameter perturbation. Moreover, RTPL realizes continuous accumulation of knowledge as a result of its reasonably allocated memory while NNLC may gradually forget knowledge that it has learned. Corresponding theoretical analysis and simulation studies demonstrate the effectiveness of RTPL. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | true | false | false | 384,333 |
2410.03676 | A quest through interconnected datasets: lessons from highly-cited
ICASSP papers | As audio machine learning outcomes are deployed in societally impactful applications, it is important to have a sense of the quality and origins of the data used. Noticing that being explicit about this sense is not trivially rewarded in academic publishing in applied machine learning domains, and neither is included in typical applied machine learning curricula, we present a study into dataset usage connected to the top-5 cited papers at the International Conference on Acoustics, Speech, and Signal Processing (ICASSP). In this, we conduct thorough depth-first analyses towards origins of used datasets, often leading to searches that had to go beyond what was reported in official papers, and ending into unclear or entangled origins. Especially in the current pull towards larger, and possibly generative AI models, awareness of the need for accountability on data provenance is increasing. With this, we call on the community to not only focus on engineering larger models, but create more room and reward for explicitizing the foundations on which such models should be built. | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 494,897 |
2006.06997 | Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow
in Phase Retrieval | Despite the widespread use of gradient-based algorithms for optimizing high-dimensional non-convex functions, understanding their ability of finding good minima instead of being trapped in spurious ones remains to a large extent an open problem. Here we focus on gradient flow dynamics for phase retrieval from random measurements. When the ratio of the number of measurements over the input dimension is small the dynamics remains trapped in spurious minima with large basins of attraction. We find analytically that above a critical ratio those critical points become unstable developing a negative direction toward the signal. By numerical experiments we show that in this regime the gradient flow algorithm is not trapped; it drifts away from the spurious critical points along the unstable direction and succeeds in finding the global minimum. Using tools from statistical physics we characterize this phenomenon, which is related to a BBP-type transition in the Hessian of the spurious minima. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 181,644 |
1809.02393 | Improving Neural Question Generation using Answer Separation | Neural question generation (NQG) is the task of generating a question from a given passage with deep neural networks. Previous NQG models suffer from a problem that a significant proportion of the generated questions include words in the question target, resulting in the generation of unintended questions. In this paper, we propose answer-separated seq2seq, which better utilizes the information from both the passage and the target answer. By replacing the target answer in the original passage with a special token, our model learns to identify which interrogative word should be used. We also propose a new module termed keyword-net, which helps the model better capture the key information in the target answer and generate an appropriate question. Experimental results demonstrate that our answer separation method significantly reduces the number of improper questions which include answers. Consequently, our model significantly outperforms previous state-of-the-art NQG models. | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | true | false | false | 107,042 |
2501.00830 | LLM+AL: Bridging Large Language Models and Action Languages for Complex
Reasoning about Actions | Large Language Models (LLMs) have made significant strides in various intelligent tasks but still struggle with complex action reasoning tasks that require systematic search. To address this limitation, we propose a method that bridges the natural language understanding capabilities of LLMs with the symbolic reasoning strengths of action languages. Our approach, termed "LLM+AL," leverages the LLM's strengths in semantic parsing and commonsense knowledge generation alongside the action language's proficiency in automated reasoning based on encoded knowledge. We compare LLM+AL against state-of-the-art LLMs, including ChatGPT-4, Claude 3 Opus, Gemini Ultra 1.0, and o1-preview, using benchmarks for complex reasoning about actions. Our findings indicate that, although all methods exhibit errors, LLM+AL, with relatively minimal human corrections, consistently leads to correct answers, whereas standalone LLMs fail to improve even with human feedback. LLM+AL also contributes to automated generation of action languages. | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 521,817 |
2110.14503 | Simple data balancing achieves competitive worst-group-accuracy | We study the problem of learning classifiers that perform well across (known or unknown) groups of data. After observing that common worst-group-accuracy datasets suffer from substantial imbalances, we set out to compare state-of-the-art methods to simple balancing of classes and groups by either subsampling or reweighting data. Our results show that these data balancing baselines achieve state-of-the-art-accuracy, while being faster to train and requiring no additional hyper-parameters. In addition, we highlight that access to group information is most critical for model selection purposes, and not so much during training. All in all, our findings beg closer examination of benchmarks and methods for research in worst-group-accuracy optimization. | false | false | false | false | true | false | true | false | false | false | false | false | true | false | false | false | false | false | 263,552 |
2008.08316 | Data-Independent Structured Pruning of Neural Networks via Coresets | Model compression is crucial for deployment of neural networks on devices with limited computational and memory resources. Many different methods show comparable accuracy of the compressed model and similar compression rates. However, the majority of the compression methods are based on heuristics and offer no worst-case guarantees on the trade-off between the compression rate and the approximation error for an arbitrarily new sample. We propose the first efficient structured pruning algorithm with a provable trade-off between its compression rate and the approximation error for any future test sample. Our method is based on the coreset framework and it approximates the output of a layer of neurons/filters by a coreset of neurons/filters in the previous layer and discards the rest. We apply this framework in a layer-by-layer fashion from the bottom to the top. Unlike previous works, our coreset is data independent, meaning that it provably guarantees the accuracy of the function for any input $x\in \mathbb{R}^d$, including an adversarial one. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 192,386 |
2312.04879 | HC-Ref: Hierarchical Constrained Refinement for Robust Adversarial
Training of GNNs | Recent studies have shown that attackers can catastrophically reduce the performance of GNNs by maliciously modifying the graph structure or node features on the graph. Adversarial training, which has been shown to be one of the most effective defense mechanisms against adversarial attacks in computer vision, holds great promise for enhancing the robustness of GNNs. There is limited research on defending against attacks by performing adversarial training on graphs, and it is crucial to delve deeper into this approach to optimize its effectiveness. Therefore, based on robust adversarial training on graphs, we propose a hierarchical constraint refinement framework (HC-Ref) that enhances the anti-perturbation capabilities of GNNs and downstream classifiers separately, ultimately leading to improved robustness. We propose corresponding adversarial regularization terms that are conducive to adaptively narrowing the domain gap between the normal part and the perturbation part according to the characteristics of different layers, promoting the smoothness of the predicted distribution of both parts. Moreover, existing research on graph robust adversarial training primarily concentrates on training from the standpoint of node feature perturbations and seldom takes into account alterations in the graph structure. This limitation makes it challenging to prevent attacks based on topological changes in the graph. This paper generates adversarial examples by utilizing graph structure perturbations, offering an effective approach to defend against attack methods that are based on topological changes. Extensive experiments on two real-world graph benchmarks show that HC-Ref successfully resists various attacks and has better node classification performance compared to several baseline methods. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 413,870 |
2207.14145 | A Probabilistic Framework for Estimating the Risk of Pedestrian-Vehicle
Conflicts at Intersections | Pedestrian safety has become an important research topic among various studies due to the increased number of pedestrian-involved crashes. To evaluate pedestrian safety proactively, surrogate safety measures (SSMs) have been widely used in traffic conflict-based studies as they do not require historical crashes as inputs. However, most existing SSMs were developed based on the assumption that road users would maintain constant velocity and direction. Risk estimations based on this assumption are less unstable, more likely to be exaggerated, and unable to capture the evasive maneuvers of drivers. Considering the limitations among existing SSMs, this study proposes a probabilistic framework for estimating the risk of pedestrian-vehicle conflicts at intersections. The proposed framework loosen restrictions of constant speed by predicting trajectories using a Gaussian Process Regression and accounts for the different possible driver maneuvers with a Random Forest model. Real-world LiDAR data collected at an intersection was used to evaluate the performance of the proposed framework. The newly developed framework is able to identify all pedestrian-vehicle conflicts. Compared to the Time-to-Collision, the proposed framework provides a more stable risk estimation and captures the evasive maneuvers of vehicles. Moreover, the proposed framework does not require expensive computation resources, which makes it an ideal choice for real-time proactive pedestrian safety solutions at intersections. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 310,495 |
2007.11975 | Online Boosting with Bandit Feedback | We consider the problem of online boosting for regression tasks, when only limited information is available to the learner. We give an efficient regret minimization method that has two implications: an online boosting algorithm with noisy multi-point bandit feedback, and a new projection-free online convex optimization algorithm with stochastic gradient, that improves state-of-the-art guarantees in terms of efficiency. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 188,691 |
2001.00474 | The Algebraic Theory of Fractional Jumps | In this paper we start by briefly surveying the theory of Fractional Jumps and transitive projective maps. Then, we give an efficient construction of a fractional jump of a projective map and we extend the compound generator construction for the Inversive Congruential Generator to Fractional jump sequences. In addition, we provide new results on the absolute jump index, on projectively primitive polynomials, and on the explicit description of fractional jump generators. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 159,224 |
2012.13779 | Towards sample-efficient episodic control with DAC-ML | The sample-inefficiency problem in Artificial Intelligence refers to the inability of current Deep Reinforcement Learning models to optimize action policies within a small number of episodes. Recent studies have tried to overcome this limitation by adding memory systems and architectural biases to improve learning speed, such as in Episodic Reinforcement Learning. However, despite achieving incremental improvements, their performance is still not comparable to how humans learn behavioral policies. In this paper, we capitalize on the design principles of the Distributed Adaptive Control (DAC) theory of mind and brain to build a novel cognitive architecture (DAC-ML) that, by incorporating a hippocampus-inspired sequential memory system, can rapidly converge to effective action policies that maximize reward acquisition in a challenging foraging task. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 213,328 |
1509.03870 | The USFD Spoken Language Translation System for IWSLT 2014 | The University of Sheffield (USFD) participated in the International Workshop for Spoken Language Translation (IWSLT) in 2014. In this paper, we will introduce the USFD SLT system for IWSLT. Automatic speech recognition (ASR) is achieved by two multi-pass deep neural network systems with adaptation and rescoring techniques. Machine translation (MT) is achieved by a phrase-based system. The USFD primary system incorporates state-of-the-art ASR and MT techniques and gives a BLEU score of 23.45 and 14.75 on the English-to-French and English-to-German speech-to-text translation task with the IWSLT 2014 data. The USFD contrastive systems explore the integration of ASR and MT by using a quality estimation system to rescore the ASR outputs, optimising towards better translation. This gives a further 0.54 and 0.26 BLEU improvement respectively on the IWSLT 2012 and 2014 evaluation data. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 46,870 |
1807.01069 | Adversarial Robustness Toolbox v1.0.0 | Adversarial Robustness Toolbox (ART) is a Python library supporting developers and researchers in defending Machine Learning models (Deep Neural Networks, Gradient Boosted Decision Trees, Support Vector Machines, Random Forests, Logistic Regression, Gaussian Processes, Decision Trees, Scikit-learn Pipelines, etc.) against adversarial threats and helps making AI systems more secure and trustworthy. Machine Learning models are vulnerable to adversarial examples, which are inputs (images, texts, tabular data, etc.) deliberately modified to produce a desired response by the Machine Learning model. ART provides the tools to build and deploy defences and test them with adversarial attacks. Defending Machine Learning models involves certifying and verifying model robustness and model hardening with approaches such as pre-processing inputs, augmenting training data with adversarial samples, and leveraging runtime detection methods to flag any inputs that might have been modified by an adversary. The attacks implemented in ART allow creating adversarial attacks against Machine Learning models which is required to test defenses with state-of-the-art threat models. Supported Machine Learning Libraries include TensorFlow (v1 and v2), Keras, PyTorch, MXNet, Scikit-learn, XGBoost, LightGBM, CatBoost, and GPy. The source code of ART is released with MIT license at https://github.com/IBM/adversarial-robustness-toolbox. The release includes code examples, notebooks with tutorials and documentation (http://adversarial-robustness-toolbox.readthedocs.io). | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 101,982 |
2111.11952 | Leveraging Selective Prediction for Reliable Image Geolocation | Reliable image geolocation is crucial for several applications, ranging from social media geo-tagging to fake news detection. State-of-the-art geolocation methods surpass human performance on the task of geolocation estimation from images. However, no method assesses the suitability of an image for this task, which results in unreliable and erroneous estimations for images containing no geolocation clues. In this paper, we define the task of image localizability, i.e. suitability of an image for geolocation, and propose a selective prediction methodology to address the task. In particular, we propose two novel selection functions that leverage the output probability distributions of geolocation models to infer localizability at different scales. Our selection functions are benchmarked against the most widely used selective prediction baselines, outperforming them in all cases. By abstaining from predicting non-localizable images, we improve geolocation accuracy from 27.8% to 70.5% at the city-scale, and thus make current geolocation models reliable for real-world applications. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | true | 267,825 |
2206.00381 | The statistical nature of h-index of a network node | Evaluating the importance of a network node is a crucial task in network science and graph data mining. H-index is a popular centrality measure for this task, however, there is still a lack of its interpretation from a rigorous statistical aspect. Here we show the statistical nature of h-index from the perspective of order statistics, and we obtain a new family of centrality indices by generalizing the h-index along this direction. The theoretical and empirical evidences show that such a statistical interpretation enables us to obtain a general and versatile framework for quantifying the importance of a network node. Under this framework, many new centrality indices can be derived and some of which can be more accurate and robust than h-index. We believe that this research opens up new avenues for developing more effective indices for node importance quantification from a viewpoint that still remains unexplored. | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 300,102 |
1802.03043 | PoTrojan: powerful neural-level trojan designs in deep learning models | With the popularity of deep learning (DL), artificial intelligence (AI) has been applied in many areas of human life. Neural network or artificial neural network (NN), the main technique behind DL, has been extensively studied to facilitate computer vision and natural language recognition. However, the more we rely on information technology, the more vulnerable we are. That is, malicious NNs could bring huge threat in the so-called coming AI era. In this paper, for the first time in the literature, we propose a novel approach to design and insert powerful neural-level trojans or PoTrojan in pre-trained NN models. Most of the time, PoTrojans remain inactive, not affecting the normal functions of their host NN models. PoTrojans could only be triggered in very rare conditions. Once activated, however, the PoTrojans could cause the host NN models to malfunction, either falsely predicting or classifying, which is a significant threat to human society of the AI era. We would explain the principles of PoTrojans and the easiness of designing and inserting them in pre-trained deep learning models. PoTrojans doesn't modify the existing architecture or parameters of the pre-trained models, without re-training. Hence, the proposed method is very efficient. | false | false | false | false | false | false | true | false | false | false | false | false | true | false | false | false | false | false | 89,882 |
2105.09679 | Improved Neuronal Ensemble Inference with Generative Model and MCMC | Neuronal ensemble inference is a significant problem in the study of biological neural networks. Various methods have been proposed for ensemble inference from experimental data of neuronal activity. Among them, Bayesian inference approach with generative model was proposed recently. However, this method requires large computational cost for appropriate inference. In this work, we give an improved Bayesian inference algorithm by modifying update rule in Markov chain Monte Carlo method and introducing the idea of simulated annealing for hyperparameter control. We compare the performance of ensemble inference between our algorithm and the original one, and discuss the advantage of our method. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 236,139 |
2105.13191 | Deep Learning Techniques for Compressive Sensing-Based Reconstruction
and Inference -- A Ubiquitous Systems Perspective | Compressive sensing (CS) is a mathematically elegant tool for reducing the sampling rate, potentially bringing context-awareness to a wider range of devices. Nevertheless, practical issues with the sampling and reconstruction algorithms prevent further proliferation of CS in real world domains, especially among heterogeneous ubiquitous devices. Deep learning (DL) naturally complements CS for adapting the sampling matrix, reconstructing the signal, and learning form the compressed samples. While the CS-DL integration has received substantial research interest recently, it has not yet been thoroughly surveyed, nor has the light been shed on practical issues towards bringing the CS-DL to real world implementations in the ubicomp domain. In this paper we identify main possible ways in which CS and DL can interplay, extract key ideas for making CS-DL efficient, identify major trends in CS-DL research space, and derive guidelines for future evolution of CS-DL within the ubicomp domain. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 237,230 |
0911.1386 | Machine Learning: When and Where the Horses Went Astray? | Machine Learning is usually defined as a subfield of AI, which is busy with information extraction from raw data sets. Despite of its common acceptance and widespread recognition, this definition is wrong and groundless. Meaningful information does not belong to the data that bear it. It belongs to the observers of the data and it is a shared agreement and a convention among them. Therefore, this private information cannot be extracted from the data by any means. Therefore, all further attempts of Machine Learning apologists to justify their funny business are inappropriate. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 4,889 |
2411.09896 | Revealing the Evolution of Order in Materials Microstructures Using
Multi-Modal Computer Vision | The development of high-performance materials for microelectronics, energy storage, and extreme environments depends on our ability to describe and direct property-defining microstructural order. Our present understanding is typically derived from laborious manual analysis of imaging and spectroscopy data, which is difficult to scale, challenging to reproduce, and lacks the ability to reveal latent associations needed for mechanistic models. Here, we demonstrate a multi-modal machine learning (ML) approach to describe order from electron microscopy analysis of the complex oxide La$_{1-x}$Sr$_x$FeO$_3$. We construct a hybrid pipeline based on fully and semi-supervised classification, allowing us to evaluate both the characteristics of each data modality and the value each modality adds to the ensemble. We observe distinct differences in the performance of uni- and multi-modal models, from which we draw general lessons in describing crystal order using computer vision. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 508,410 |
1604.04154 | Robust Control Framework for Time-Varying Power-Sharing among
Distributed Energy Resources | One of the most important challenges facing an electric grid is to incorporate renewables and distributed energy resources (DERs) to the grid. Because of the associated uncertainties in power generations and peak power demands, opportunities for improving the functioning and reliability of the grid lie in the design of an efficient, yet pragmatic distributed control framework with guaranteed robustness margins. This paper addresses the problem of output voltage regulation for multiple DC-DC converters connected to a grid, and prescribes a robust scheme for sharing power among different sources. More precisely, we develop a control architecture where, unlike most standard control frameworks, the desired power ratios appear as reference signals to individual converter systems, and not as internal parameters of the system of parallel converters. This makes the proposed approach suited for scenarios when the desired power ratios vary rapidly with time. Additionally, the proposed control framework is suitable to both centralized and decentralized implementations, i.e., the same control architecture can be employed for voltage regulation irrespective of the availability of common load-current (or power) measurement, without the need to modify controller parameters. The control design is obtained using robust optimal-control framework. Case studies presented show the enhanced performance of prescribed optimal controllers for voltage regulation and power sharing. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 54,604 |
2210.17367 | Analysis and Detection of Singing Techniques in Repertoires of J-POP
Solo Singers | In this paper, we focus on singing techniques within the scope of music information retrieval research. We investigate how singers use singing techniques using real-world recordings of famous solo singers in Japanese popular music songs (J-POP). First, we built a new dataset of singing techniques. The dataset consists of 168 commercial J-POP songs, and each song is annotated using various singing techniques with timestamps and vocal pitch contours. We also present descriptive statistics of singing techniques on the dataset to clarify what and how often singing techniques appear. We further explored the difficulty of the automatic detection of singing techniques using previously proposed machine learning techniques. In the detection, we also investigate the effectiveness of auxiliary information (i.e., pitch and distribution of label duration), not only providing the baseline. The best result achieves 40.4% at macro-average F-measure on nine-way multi-class detection. We provide the annotation of the dataset and its detail on the appendix website 0 . | false | false | true | false | false | true | false | false | false | false | false | false | false | false | false | false | false | true | 327,660 |
2401.18085 | Motion Guidance: Diffusion-Based Image Editing with Differentiable
Motion Estimators | Diffusion models are capable of generating impressive images conditioned on text descriptions, and extensions of these models allow users to edit images at a relatively coarse scale. However, the ability to precisely edit the layout, position, pose, and shape of objects in images with diffusion models is still difficult. To this end, we propose motion guidance, a zero-shot technique that allows a user to specify dense, complex motion fields that indicate where each pixel in an image should move. Motion guidance works by steering the diffusion sampling process with the gradients through an off-the-shelf optical flow network. Specifically, we design a guidance loss that encourages the sample to have the desired motion, as estimated by a flow network, while also being visually similar to the source image. By simultaneously sampling from a diffusion model and guiding the sample to have low guidance loss, we can obtain a motion-edited image. We demonstrate that our technique works on complex motions and produces high quality edits of real and generated images. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 425,426 |
2412.16691 | From Correlation to Causation: Understanding Climate Change through
Causal Analysis and LLM Interpretations | This research presents a three-step causal inference framework that integrates correlation analysis, machine learning-based causality discovery, and LLM-driven interpretations to identify socioeconomic factors influencing carbon emissions and contributing to climate change. The approach begins with identifying correlations, progresses to causal analysis, and enhances decision making through LLM-generated inquiries about the context of climate change. The proposed framework offers adaptable solutions that support data-driven policy-making and strategic decision-making in climate-related contexts, uncovering causal relationships within the climate change domain. | false | false | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | 519,637 |
2109.02791 | Safety-Critical Learning of Robot Control with Temporal Logic
Specifications | Reinforcement learning (RL) is a promising approach. However, success is limited to real-world applications, because ensuring safe exploration and facilitating adequate exploitation is a challenge for controlling robotic systems with unknown models and measurement uncertainties. The learning problem becomes even more difficult for complex tasks over continuous state-action. In this paper, we propose a learning-based robotic control framework consisting of several aspects: (1) we leverage Linear Temporal Logic (LTL) to express complex tasks over infinite horizons that are translated to a novel automaton structure; (2) we detail an innovative reward scheme for LTL satisfaction with a probabilistic guarantee. Then, by applying a reward shaping technique, we develop a modular policy-gradient architecture exploiting the benefits of the automaton structure to decompose overall tasks and enhance the performance of learned controllers; (3) by incorporating Gaussian Processes (GPs) to estimate the uncertain dynamic systems, we synthesize a model-based safe exploration during the learning process using Exponential Control Barrier Functions (ECBFs) that generalize systems with high-order relative degrees; (4) to further improve the efficiency of exploration, we utilize the properties of LTL automata and ECBFs to propose a safe guiding process. Finally, we demonstrate the effectiveness of the framework via several robotic environments. We show an ECBF-based modular deep RL algorithm that achieves near-perfect success rates and safety guarding with high probability confidence during training. | false | false | false | false | false | false | true | true | false | false | false | false | false | false | false | false | false | true | 253,856 |
2107.02447 | Complete weight enumerators for several classes of two-weight and
three-weight linear codes | In this paper, for an odd prime $p$, by extending Li et al.'s construction \cite{CL2016}, several classes of two-weight and three-weight linear codes over the finite field $\mathbb{F}_p$ are constructed from a defining set, and then their complete weight enumerators are determined by using Weil sums. Furthermore, we show that some examples of these codes are optimal or almost optimal with respect to the Griesmer bound. Our results generalize the corresponding results in \cite{CL2016, GJ2019}. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 244,826 |
2501.13624 | QMamba: Post-Training Quantization for Vision State Space Models | State Space Models (SSMs), as key components of Mamaba, have gained increasing attention for vision models recently, thanks to their efficient long sequence modeling capability. Given the computational cost of deploying SSMs on resource-limited edge devices, Post-Training Quantization (PTQ) is a technique with the potential for efficient deployment of SSMs. In this work, we propose QMamba, one of the first PTQ frameworks to our knowledge, designed for vision SSMs based on the analysis of the activation distributions in SSMs. We reveal that the distribution of discrete parameters exhibits long-tailed skewness and the distribution of the hidden state sequence exhibits highly dynamic variations. Correspondingly, we design Long-tailed Skewness Quantization (LtSQ) to quantize discrete parameters and Temporal Group Quantization (TGQ) to quantize hidden states, which reduces the quantization errors. Extensive experiments demonstrate that QMamba outperforms advanced PTQ methods on vision models across multiple model sizes and architectures. Notably, QMamba surpasses existing methods by 21.0% on ImageNet classification with 4-bit activations. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 526,761 |
1808.00525 | The impact of imbalanced training data on machine learning for author
name disambiguation | In supervised machine learning for author name disambiguation, negative training data are often dominantly larger than positive training data. This paper examines how the ratios of negative to positive training data can affect the performance of machine learning algorithms to disambiguate author names in bibliographic records. On multiple labeled datasets, three classifiers - Logistic Regression, Na\"ive Bayes, and Random Forest - are trained through representative features such as coauthor names, and title words extracted from the same training data but with various positive-negative training data ratios. Results show that increasing negative training data can improve disambiguation performance but with a few percent of performance gains and sometimes degrade it. Logistic Regression and Na\"ive Bayes learn optimal disambiguation models even with a base ratio (1:1) of positive and negative training data. Also, the performance improvement by Random Forest tends to quickly saturate roughly after 1:10 ~ 1:15. These findings imply that contrary to the common practice using all training data, name disambiguation algorithms can be trained using part of negative training data without degrading much disambiguation performance while increasing computational efficiency. This study calls for more attention from author name disambiguation scholars to methods for machine learning from imbalanced data. | false | false | false | false | false | true | true | false | true | false | false | false | false | false | false | false | false | true | 104,405 |
1905.13125 | Seeker: Real-Time Interactive Search | This paper introduces Seeker, a system that allows users to interactively refine search rankings in real time, through feedback in the form of likes and dislikes. When searching online, users may not know how to accurately describe their product of choice in words. An alternative approach is to search an embedding space, allowing the user to query using a representation of the item (like a tune for a song, or a picture for an object). However, this approach requires the user to possess an example representation of their desired item. Additionally, most current search systems do not allow the user to dynamically adapt the results with further feedback. On the other hand, users often have a mental picture of the desired item and are able to answer ordinal questions of the form: "Is this item similar to what you have in mind?" With this assumption, our algorithm allows for users to provide sequential feedback on search results to adapt the search feed. We show that our proposed approach works well both qualitatively and quantitatively. Unlike most previous representation-based search systems, we can quantify the quality of our algorithm by evaluating humans-in-the-loop experiments. | false | false | false | false | false | true | true | false | false | false | false | false | false | false | false | false | false | false | 132,994 |
2402.00065 | A technical note for the 91-clauses SAT resolution with Indirect QAOA
based approach | This paper addresses the resolution of the 3-SAT problem using a QAOA-like approach. The chosen principle involves modeling the solution ranks of the 3-SAT problem, which, in this particular case, directly represent a solution. This results in a highly compact circuit with few gates, enabling the modeling of large-sized 3-SAT problems. Numerical experimentation demonstrates that the approach can solve instances composed of 91 clauses and 20 variables with an implementation based on Qiskit. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 425,466 |
1910.03515 | Designing Trustworthy AI: A Human-Machine Teaming Framework to Guide
Development | Artificial intelligence (AI) holds great promise to empower us with knowledge and augment our effectiveness. We can -- and must -- ensure that we keep humans safe and in control, particularly with regard to government and public sector applications that affect broad populations. How can AI development teams harness the power of AI systems and design them to be valuable to humans? Diverse teams are needed to build trustworthy artificial intelligent systems, and those teams need to coalesce around a shared set of ethics. There are many discussions in the AI field about ethics and trust, but there are few frameworks available for people to use as guidance when creating these systems. The Human-Machine Teaming (HMT) Framework for Designing Ethical AI Experiences described in this paper, when used with a set of technical ethics, will guide AI development teams to create AI systems that are accountable, de-risked, respectful, secure, honest, and usable. To support the team's efforts, activities to understand people's needs and concerns will be introduced along with the themes to support the team's efforts. For example, usability testing can help determine if the audience understands how the AI system works and complies with the HMT Framework. The HMT Framework is based on reviews of existing ethical codes and best practices in human-computer interaction and software development. Human-machine teams are strongest when human users can trust AI systems to behave as expected, safely, securely, and understandably. Using the HMT Framework to design trustworthy AI systems will provide support to teams in identifying potential issues ahead of time and making great experiences for humans. | true | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 148,515 |
2412.15211 | Generative Multiview Relighting for 3D Reconstruction under Extreme
Illumination Variation | Reconstructing the geometry and appearance of objects from photographs taken in different environments is difficult as the illumination and therefore the object appearance vary across captured images. This is particularly challenging for more specular objects whose appearance strongly depends on the viewing direction. Some prior approaches model appearance variation across images using a per-image embedding vector, while others use physically-based rendering to recover the materials and per-image illumination. Such approaches fail at faithfully recovering view-dependent appearance given significant variation in input illumination and tend to produce mostly diffuse results. We present an approach that reconstructs objects from images taken under different illuminations by first relighting the images under a single reference illumination with a multiview relighting diffusion model and then reconstructing the object's geometry and appearance with a radiance field architecture that is robust to the small remaining inconsistencies among the relit images. We validate our proposed approach on both synthetic and real datasets and demonstrate that it greatly outperforms existing techniques at reconstructing high-fidelity appearance from images taken under extreme illumination variation. Moreover, our approach is particularly effective at recovering view-dependent "shiny" appearance which cannot be reconstructed by prior methods. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 518,985 |
2412.12626 | Improving the Transferability of 3D Point Cloud Attack via
Spectral-aware Admix and Optimization Designs | Deep learning models for point clouds have shown to be vulnerable to adversarial attacks, which have received increasing attention in various safety-critical applications such as autonomous driving, robotics, and surveillance. Existing 3D attackers generally design various attack strategies in the white-box setting, requiring the prior knowledge of 3D model details. However, real-world 3D applications are in the black-box setting, where we can only acquire the outputs of the target classifier. Although few recent works try to explore the black-box attack, they still achieve limited attack success rates (ASR). To alleviate this issue, this paper focuses on attacking the 3D models in a transfer-based black-box setting, where we first carefully design adversarial examples in a white-box surrogate model and then transfer them to attack other black-box victim models. Specifically, we propose a novel Spectral-aware Admix with Augmented Optimization method (SAAO) to improve the adversarial transferability. In particular, since traditional Admix strategy are deployed in the 2D domain that adds pixel-wise images for perturbing, we can not directly follow it to merge point clouds in coordinate domain as it will destroy the geometric shapes. Therefore, we design spectral-aware fusion that performs Graph Fourier Transform (GFT) to get spectral features of the point clouds and add them in the spectral domain. Afterward, we run a few steps with spectral-aware weighted Admix to select better optimization paths as well as to adjust corresponding learning weights. At last, we run more steps to generate adversarial spectral feature along the optimization path and perform Inverse-GFT on the adversarial spectral feature to obtain the adversarial example in the data domain. Experiments show that our SAAO achieves better transferability compared to existing 3D attack methods. | false | false | false | false | false | false | false | false | false | false | false | true | true | false | false | false | false | false | 517,953 |
2209.09318 | Guarding a Non-Maneuverable Translating Line with an Attached Defender | In this paper we consider a target-guarding differential game where the defender must protect a linearly translating line-segment by intercepting an attacker who tries to reach it. In contrast to common target-guarding problems, we assume that the defender is attached to the target and moves along with it. This assumption affects the defenders' maximum speed in inertial frame, which depends on the target's direction of motion. Zero-sum differential game of degree for both the attacker-win and defender-win scenarios are studied, where the payoff is defined to be the distance between the two agents at the time of game termination. We derive the equilibrium strategies and the Value function by leveraging the solution for the infinite-length target scenario. The zero-level set of this Value function provides the barrier surface that divides the state space into defender-win and attacker-win regions. We present simulation results to demonstrate the theoretical results. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 318,457 |
1608.06754 | Resource Allocation in Dynamic TDD Heterogeneous Networks under Mixed
Traffic | Recently, Dynamic Time Division Duplex (TDD) has been proposed to handle the asymmetry of traffic demand between DownLink (DL) and UpLink (UL) in Heterogeneous Networks (HetNets). However, for mixed traffic consisting of best effort traffic and soft Quality of Service (QoS) traffic, the resource allocation problem has not been adequately studied in Dynamic TDD HetNets. In this paper, we focus on such problem in a two-tier HetNet with co-channel deployment of one Macro cell Base Station (MBS) and multiple Small cell Base Stations (SBSs) in hotspots. Different from existing work, we introduce low power almost blank subframes to alleviate MBS-to-SBS interference which is inherent in TDD operation. To tackle the resource allocation problem, we propose a two-step strategy. First, from the view point of base stations, we propose a transmission protocol and perform time resource allocation by formulating and solving a network capacity maximization problem under DL/UL traffic demands. Second, from the view point of User Equipments (UEs), we formulate their resource allocation as a Network Utility Maximization (NUM) problem. An efficient iterative algorithm is proposed to solve the NUM problem. Simulations show the advantage of the proposed algorithm in terms of network throughput and UE QoS satisfaction level. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | true | 60,155 |
1805.04246 | Convex Programming Based Spectral Clustering | Clustering is a fundamental task in data analysis, and spectral clustering has been recognized as a promising approach to it. Given a graph describing the relationship between data, spectral clustering explores the underlying cluster structure in two stages. The first stage embeds the nodes of the graph in real space, and the second stage groups the embedded nodes into several clusters. The use of the $k$-means method in the grouping stage is currently standard practice. We present a spectral clustering algorithm that uses convex programming in the grouping stage and study how well it works. This algorithm is designed based on the following observation. If a graph is well-clustered, then the nodes with the largest degree in each cluster can be found by computing an enclosing ellipsoid of the nodes embedded in real space, and the clusters can be identified by using those nodes. We show that, for well-clustered graphs, the algorithm can find clusters of nodes with minimal conductance. We also give an experimental assessment of the algorithm's performance. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 97,204 |
2501.19353 | Do Large Multimodal Models Solve Caption Generation for Scientific
Figures? Lessons Learned from SciCap Challenge 2023 | Since the SciCap datasets launch in 2021, the research community has made significant progress in generating captions for scientific figures in scholarly articles. In 2023, the first SciCap Challenge took place, inviting global teams to use an expanded SciCap dataset to develop models for captioning diverse figure types across various academic fields. At the same time, text generation models advanced quickly, with many powerful pre-trained large multimodal models (LMMs) emerging that showed impressive capabilities in various vision-and-language tasks. This paper presents an overview of the first SciCap Challenge and details the performance of various models on its data, capturing a snapshot of the fields state. We found that professional editors overwhelmingly preferred figure captions generated by GPT-4V over those from all other models and even the original captions written by authors. Following this key finding, we conducted detailed analyses to answer this question: Have advanced LMMs solved the task of generating captions for scientific figures? | false | false | false | false | true | false | false | false | true | false | false | true | false | false | false | false | false | false | 529,129 |
2306.15321 | Multi-Dimensional Refinement Graph Convolutional Network with Robust
Decouple Loss for Fine-Grained Skeleton-Based Action Recognition | Graph convolutional networks have been widely used in skeleton-based action recognition. However, existing approaches are limited in fine-grained action recognition due to the similarity of inter-class data. Moreover, the noisy data from pose extraction increases the challenge of fine-grained recognition. In this work, we propose a flexible attention block called Channel-Variable Spatial-Temporal Attention (CVSTA) to enhance the discriminative power of spatial-temporal joints and obtain a more compact intra-class feature distribution. Based on CVSTA, we construct a Multi-Dimensional Refinement Graph Convolutional Network (MDR-GCN), which can improve the discrimination among channel-, joint- and frame-level features for fine-grained actions. Furthermore, we propose a Robust Decouple Loss (RDL), which significantly boosts the effect of the CVSTA and reduces the impact of noise. The proposed method combining MDR-GCN with RDL outperforms the known state-of-the-art skeleton-based approaches on fine-grained datasets, FineGym99 and FSD-10, and also on the coarse dataset NTU-RGB+D X-view version. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 375,982 |
1908.09470 | Local Graph Stability in Exponential Family Random Graph Models | Exponential family Random Graph Models (ERGMs) can be viewed as expressing a probability distribution on graphs arising from the action of competing social forces that make ties more or less likely, depending on the state of the rest of the graph. Such forces often lead to a complex pattern of dependence among edges, with non-trivial large-scale structures emerging from relatively simple local mechanisms. While this provides a powerful tool for probing macro-micro connections, much remains to be understood about how local forces shape global outcomes. One simple question of this type is that of the conditions needed for social forces to stabilize a particular structure. We refer to this property as local stability and seek a general means of identifying the set of parameters under which a target graph is locally stable with respect to a set of alternatives. Here, we provide a complete characterization of the region of the parameter space inducing local stability, showing it to be the interior of a convex cone whose faces can be derived from the change-scores of the sufficient statistics vis-a-vis the alternative structures. As we show, local stability is a necessary but not sufficient condition for more general notions of stability, the latter of which can be explored more efficiently by using the ``stable cone'' within the parameter space as a starting point. In addition, we show how local stability can be used to determine whether a fitted model implies that an observed structure would be expected to arise primarily from the action of social forces, versus by merit of the model permitting a large number of high probability structures, of which the observed structure is one. We also use our approach to identify the dyads within a given structure that are the least stable, and hence predicted to have the highest probability of changing over time. | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | true | 142,857 |
2204.09710 | Complete identification of complex salt geometries from inaccurate
migrated subsurface offset gathers using deep learning | Delimiting salt inclusions from migrated images is a time-consuming activity that relies on highly human-curated analysis and is subject to interpretation errors or limitations of the methods available. We propose to use migrated images produced from an inaccurate velocity model (with a reasonable approximation of sediment velocity, but without salt inclusions) to predict the correct salt inclusions shape using a Convolutional Neural Network (CNN). Our approach relies on subsurface Common Image Gathers to focus the sediments' reflections around the zero offset and to spread the energy of salt reflections over large offsets. Using synthetic data, we trained a U-Net to use common-offset subsurface images as input channels for the CNN and the correct salt-masks as network output. The network learned to predict the salt inclusions masks with high accuracy; moreover, it also performed well when applied to synthetic benchmark data sets that were not previously introduced. Our training process tuned the U-Net to successfully learn the shape of complex salt bodies from partially focused subsurface offset images. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 292,527 |
1901.07702 | Exploring Uncertainty in Conditional Multi-Modal Retrieval Systems | We cast visual retrieval as a regression problem by posing triplet loss as a regression loss. This enables epistemic uncertainty estimation using dropout as a Bayesian approximation framework in retrieval. Accordingly, Monte Carlo (MC) sampling is leveraged to boost retrieval performance. Our approach is evaluated on two applications: person re-identification and autonomous car driving. Comparable state-of-the-art results are achieved on multiple datasets for the former application. We leverage the Honda driving dataset (HDD) for autonomous car driving application. It provides multiple modalities and similarity notions for ego-motion action understanding. Hence, we present a multi-modal conditional retrieval network. It disentangles embeddings into separate representations to encode different similarities. This form of joint learning eliminates the need to train multiple independent networks without any performance degradation. Quantitative evaluation highlights our approach competence, achieving 6% improvement in a highly uncertain environment. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 119,277 |
2106.04372 | Segmentation and ABCD rule extraction for skin tumors classification | During the last years, computer vision-based diagnosis systems have been widely used in several hospitals and dermatology clinics, aiming at the early detection of malignant melanoma tumor, which is among the most frequent types of skin cancer. In this work, we present an automated diagnosis system based on the ABCD rule used in clinical diagnosis in order to discriminate benign from malignant skin lesions. First, to reduce the influence of small structures, a preprocessing step based on morphological and fast marching schemes is used. In the second step, an unsupervised approach for lesion segmentation is proposed. Iterative thresholding is applied to initialize level set automatically. As the detection of an automated border is an important step for the correctness of subsequent phases in the computerized melanoma recognition systems, we compare its accuracy with growcut and mean shift algorithms, and discuss how these results may influence in the following steps: the feature extraction and the final lesion classification. Relying on visual diagnosis four features: Asymmetry (A), Border (B), Color (C) and Diversity (D) are computed and used to construct a classification module based on artificial neural network for the recognition of malignant melanoma. This framework has been tested on a dermoscopic database [16] of 320 images. The classification results show an increasing true detection rate and a decreasing false positive rate. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 239,691 |
1905.04368 | Digital Passport: A Novel Technological Strategy for Intellectual
Property Protection of Convolutional Neural Networks | In order to prevent deep neural networks from being infringed by unauthorized parties, we propose a generic solution which embeds a designated digital passport into a network, and subsequently, either paralyzes the network functionalities for unauthorized usages or maintain its functionalities in the presence of a verified passport. Such a desired network behavior is successfully demonstrated in a number of implementation schemes, which provide reliable, preventive and timely protections against tens of thousands of fake-passport deceptions. Extensive experiments also show that the deep neural network performance under unauthorized usages deteriorate significantly (e.g. with 33% to 82% reductions of CIFAR10 classification accuracies), while networks endorsed with valid passports remain intact. | false | false | false | false | false | false | true | false | false | false | false | true | true | false | false | false | false | false | 130,439 |
2204.02969 | Holistic Fault Detection and Diagnosis System in Imbalanced, Scarce,
Multi-Domain (ISMD) Data Setting for Component-Level Prognostics and Health
Management (PHM) | In the current Industrial 4.0 revolution, Prognostics and Health Management (PHM) is an emerging field of research. The difficulty of obtaining data from electromechanical systems in an industrial setting increases proportionally with the scale and accessibility of the automated industry, resulting in a less interpolated PHM system. To put it another way, the development of an accurate PHM system for each industrial system necessitates a unique dataset acquired under specified conditions. In most circumstances, obtaining this one-of-a-kind dataset is difficult, and the resulting dataset has a significant imbalance, a lack of certain useful information, and multi-domain knowledge. To address this, this paper provides a fault detection and diagnosis system that evaluates and pre-processes Imbalanced, Scarce, Multi-Domain (ISMD) data acquired from an industrial robot utilizing Signal Processing (SP) techniques and Deep Learning-based (DL) domain knowledge transfer. The domain knowledge transfer is used to produce a synthetic dataset with a high interpolation rate that contains all the useful information about each domain. For domain knowledge transfer and data generation, Continuous Wavelet Transform (CWT) with Generative Adversarial Network (GAN) was used, as well as Convolutional Neural Network (CNN) to test the suggested methodology using transfer learning and categorize several faults. The proposed methodology was tested on a real experimental bench that included an industrial robot created by Hyundai Robotics Co. This development resulted in a satisfactory resolution with 99.7% (highest) classification accuracy achieved by transfer learning on several CNN benchmark models. | false | false | false | false | false | false | false | true | false | false | true | false | false | false | false | false | false | false | 290,144 |
2307.12134 | Modality Confidence Aware Training for Robust End-to-End Spoken Language
Understanding | End-to-end (E2E) spoken language understanding (SLU) systems that generate a semantic parse from speech have become more promising recently. This approach uses a single model that utilizes audio and text representations from pre-trained speech recognition models (ASR), and outperforms traditional pipeline SLU systems in on-device streaming scenarios. However, E2E SLU systems still show weakness when text representation quality is low due to ASR transcription errors. To overcome this issue, we propose a novel E2E SLU system that enhances robustness to ASR errors by fusing audio and text representations based on the estimated modality confidence of ASR hypotheses. We introduce two novel techniques: 1) an effective method to encode the quality of ASR hypotheses and 2) an effective approach to integrate them into E2E SLU models. We show accuracy improvements on STOP dataset and share the analysis to demonstrate the effectiveness of our approach. | false | false | true | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 381,148 |
2406.02559 | ShadowRefiner: Towards Mask-free Shadow Removal via Fast Fourier
Transformer | Shadow-affected images often exhibit pronounced spatial discrepancies in color and illumination, consequently degrading various vision applications including object detection and segmentation systems. To effectively eliminate shadows in real-world images while preserving intricate details and producing visually compelling outcomes, we introduce a mask-free Shadow Removal and Refinement network (ShadowRefiner) via Fast Fourier Transformer. Specifically, the Shadow Removal module in our method aims to establish effective mappings between shadow-affected and shadow-free images via spatial and frequency representation learning. To mitigate the pixel misalignment and further improve the image quality, we propose a novel Fast-Fourier Attention based Transformer (FFAT) architecture, where an innovative attention mechanism is designed for meticulous refinement. Our method wins the championship in the Perceptual Track and achieves the second best performance in the Fidelity Track of NTIRE 2024 Image Shadow Removal Challenge. Besides, comprehensive experiment result also demonstrate the compelling effectiveness of our proposed method. The code is publicly available: https://github.com/movingforward100/Shadow_R. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 460,828 |
2308.07123 | An Outlook into the Future of Egocentric Vision | What will the future be? We wonder! In this survey, we explore the gap between current research in egocentric vision and the ever-anticipated future, where wearable computing, with outward facing cameras and digital overlays, is expected to be integrated in our every day lives. To understand this gap, the article starts by envisaging the future through character-based stories, showcasing through examples the limitations of current technology. We then provide a mapping between this future and previously defined research tasks. For each task, we survey its seminal works, current state-of-the-art methodologies and available datasets, then reflect on shortcomings that limit its applicability to future research. Note that this survey focuses on software models for egocentric vision, independent of any specific hardware. The paper concludes with recommendations for areas of immediate explorations so as to unlock our path to the future always-on, personalised and life-enhancing egocentric vision. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 385,398 |
2001.09876 | The POLAR Framework: Polar Opposites Enable Interpretability of
Pre-Trained Word Embeddings | We introduce POLAR - a framework that adds interpretability to pre-trained word embeddings via the adoption of semantic differentials. Semantic differentials are a psychometric construct for measuring the semantics of a word by analysing its position on a scale between two polar opposites (e.g., cold -- hot, soft -- hard). The core idea of our approach is to transform existing, pre-trained word embeddings via semantic differentials to a new "polar" space with interpretable dimensions defined by such polar opposites. Our framework also allows for selecting the most discriminative dimensions from a set of polar dimensions provided by an oracle, i.e., an external source. We demonstrate the effectiveness of our framework by deploying it to various downstream tasks, in which our interpretable word embeddings achieve a performance that is comparable to the original word embeddings. We also show that the interpretable dimensions selected by our framework align with human judgement. Together, these results demonstrate that interpretability can be added to word embeddings without compromising performance. Our work is relevant for researchers and engineers interested in interpreting pre-trained word embeddings. | false | false | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | 161,686 |
2303.06550 | Spatial Correspondence between Graph Neural Network-Segmented Images | Graph neural networks (GNNs) have been proposed for medical image segmentation, by predicting anatomical structures represented by graphs of vertices and edges. One such type of graph is predefined with fixed size and connectivity to represent a reference of anatomical regions of interest, thus known as templates. This work explores the potentials in these GNNs with common topology for establishing spatial correspondence, implicitly maintained during segmenting two or more images. With an example application of registering local vertebral sub-regions found in CT images, our experimental results showed that the GNN-based segmentation is capable of accurate and reliable localization of the same interventionally interesting structures between images, not limited to the segmentation classes. The reported average target registration errors of 2.2$\pm$1.3 mm and 2.7$\pm$1.4 mm, for aligning holdout test images with a reference and for aligning two test images, respectively, were by a considerable margin lower than those from the tested non-learning and learning-based registration algorithms. Further ablation studies assess the contributions towards the registration performance, from individual components in the originally segmentation-purposed network and its training algorithm. The results highlight that the proposed segmentation-in-lieu-of-registration approach shares methodological similarities with existing registration methods, such as the use of displacement smoothness constraint and point distance minimization albeit on non-grid graphs, which interestingly yielded benefits for both segmentation and registration. We, therefore, conclude that the template-based GNN segmentation can effectively establish spatial correspondence in our application, without any other dedicated registration algorithms. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 350,891 |
2206.11072 | AlphaMLDigger: A Novel Machine Learning Solution to Explore Excess
Return on Investment | How to quickly and automatically mine effective information and serve investment decisions has attracted more and more attention from academia and industry. And new challenges have arisen with the global pandemic. This paper proposes a two-phase AlphaMLDigger that effectively finds excessive returns in a highly fluctuated market. In phase 1, a deep sequential natural language processing (NLP) model is proposed to transfer Sina Microblog blogs to market sentiment. In phase 2, the predicted market sentiment is combined with social network indicator features and stock market history features to predict the stock movements with different Machine Learning models and optimizers. The results show that the ensemble models achieve an accuracy of 0.984 and significantly outperform the baseline model. In addition, we find that COVID-19 brings data shift to China's stock market. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 304,144 |
2006.09655 | Fairness-Oriented Semi-Chaotic Genetic Algorithm-Based Channel
Assignment Technique for Nodes Starvation Problem in Wireless Mesh Network | Multi-Radio Multi-Channel Wireless Mesh Networks (WMNs) have emerged as a scalable, reliable, and agile wireless network that supports many types of innovative technologies such as the Internet of Things (IoT) and vehicular networks. Due to the limited number of orthogonal channels, interference between channels adversely affects the fair distribution of bandwidth among mesh clients, causing node starvation in terms of insufficient bandwidth, which impedes the adoption of WMN as an efficient access technology. Therefore, a fair channel assignment is crucial for the mesh clients to utilize the available resources. However, the node starvation problem due to unfair channel distribution has been vastly overlooked during channel assignment by the extant research. Instead, existing channel assignment algorithms either reduce the total network interference or maximize the total network throughput, which neither guarantees a fair distribution of the channels nor eliminates node starvation. To this end, the Fairness-Oriented Semi-Chaotic Genetic Algorithm-Based Channel Assignment Technique (FA-SCGA-CAA) was proposed in this paper for Nodes Starvation Problem in Wireless Mesh Networks. FA-SCGA-CAA optimizes fairness based on multiple-criterion using a modified version of the Genetic Algorithm (GA). The modification includes proposing a semi-chaotic technique for creating the primary chromosome with powerful genes. Such a chromosome was used to create a strong population that directs the search towards the global minima in an effective and efficient way. The outcome is a nonlinear fairness oriented fitness function that aims at maximizing the link fairness while minimizing the link interference. Comparison with related work shows that the proposed FA_SCGA_CAA reduced the potential nodes starvation by 22% and improved network capacity utilization by 23%. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | true | 182,615 |
2211.16750 | Score-based Continuous-time Discrete Diffusion Models | Score-based modeling through stochastic differential equations (SDEs) has provided a new perspective on diffusion models, and demonstrated superior performance on continuous data. However, the gradient of the log-likelihood function, i.e., the score function, is not properly defined for discrete spaces. This makes it non-trivial to adapt \textcolor{\cdiff}{the score-based modeling} to categorical data. In this paper, we extend diffusion models to discrete variables by introducing a stochastic jump process where the reverse process denoises via a continuous-time Markov chain. This formulation admits an analytical simulation during backward sampling. To learn the reverse process, we extend score matching to general categorical data and show that an unbiased estimator can be obtained via simple matching of the conditional marginal distributions. We demonstrate the effectiveness of the proposed method on a set of synthetic and real-world music and image benchmarks. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 333,735 |
1908.07422 | Human Gait Symmetry Assessment using a Depth Camera and Mirrors | This paper proposes a reliable approach for human gait symmetry assessment using a depth camera and two mirrors. The input of our system is a sequence of 3D point clouds which are formed from a setup including a Time-of-Flight (ToF) depth camera and two mirrors. A cylindrical histogram is estimated for describing the posture in each point cloud. The sequence of such histograms is then separated into two sequences of sub-histograms representing two half-bodies. A cross-correlation technique is finally applied to provide values describing gait symmetry indices. The evaluation was performed on 9 different gait types to demonstrate the ability of our approach in assessing gait symmetry. A comparison between our system and related methods, that employ different input data types, is also provided. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 142,290 |
2009.02111 | A nested genetic algorithm strategy for the optimal plastic design of
frames | An innovative strategy for the optimal design of planar frames able to resist to seismic excitations is here proposed. The procedure is based on genetic algorithms (GA) which are performed according to a nested structure suitable to be implemented in parallel computing on several devices. In particular, this solution foresees two nested genetic algorithms. The first one, named "External GA", seeks, among a predefined list of profiles, the size of the structural elements of the frame which correspond to the most performing solution associated to the highest value of an appropriate fitness function. The latter function takes into account, among other considerations, of the seismic safety factor and the failure mode which are calculated by means of the second algorithm, named "Internal GA". The details of the proposed procedure are provided and applications to the seismic design of two frames of different size are described. | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 194,469 |
2208.12104 | Algorithmic Differentiation for Automated Modeling of Machine Learned
Force Fields | Reconstructing force fields (FFs) from atomistic simulation data is a challenge since accurate data can be highly expensive. Here, machine learning (ML) models can help to be data economic as they can be successfully constrained using the underlying symmetry and conservation laws of physics. However, so far, every descriptor newly proposed for an ML model has required a cumbersome and mathematically tedious remodeling. We therefore propose using modern techniques from algorithmic differentiation within the ML modeling process -- effectively enabling the usage of novel descriptors or models fully automatically at an order of magnitude higher computational efficiency. This paradigmatic approach enables not only a versatile usage of novel representations and the efficient computation of larger systems -- all of high value to the FF community -- but also the simple inclusion of further physical knowledge such as higher-order information (e.g. Hessians, more complex partial differential equations constraints etc.), even beyond the presented FF domain. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 314,626 |
1902.04202 | De-identification without losing faces | Training of deep learning models for computer vision requires large image or video datasets from real world. Often, in collecting such datasets, we need to protect the privacy of the people captured in the images or videos, while still preserve the useful attributes such as facial expressions. In this work, we describe a new face de-identification method that can preserve essential facial attributes in the faces while concealing the identities. Our method takes advantage of the recent advances in face attribute transfer models, while maintaining a high visual quality. Instead of changing factors of the original faces or synthesizing faces completely, our method use a trained facial attribute transfer model to map non-identity related facial attributes to the face of donors, who are a small number (usually 2 to 3) of consented subjects. Using the donors' faces ensures that the natural appearance of the synthesized faces, while ensuring the identity of the synthesized faces are changed. On the other hand, the FATM blends the donors' facial attributes to those of the original faces to diversify the appearance of the synthesized faces. Experimental results on several sets of images and videos demonstrate the effectiveness of our face de-ID algorithm. | false | false | false | false | false | false | true | false | false | false | false | true | false | true | false | false | false | false | 121,284 |
2011.07035 | Continual Learning with Deep Artificial Neurons | Neurons in real brains are enormously complex computational units. Among other things, they're responsible for transforming inbound electro-chemical vectors into outbound action potentials, updating the strengths of intermediate synapses, regulating their own internal states, and modulating the behavior of other nearby neurons. One could argue that these cells are the only things exhibiting any semblance of real intelligence. It is odd, therefore, that the machine learning community has, for so long, relied upon the assumption that this complexity can be reduced to a simple sum and fire operation. We ask, might there be some benefit to substantially increasing the computational power of individual neurons in artificial systems? To answer this question, we introduce Deep Artificial Neurons (DANs), which are themselves realized as deep neural networks. Conceptually, we embed DANs inside each node of a traditional neural network, and we connect these neurons at multiple synaptic sites, thereby vectorizing the connections between pairs of cells. We demonstrate that it is possible to meta-learn a single parameter vector, which we dub a neuronal phenotype, shared by all DANs in the network, which facilitates a meta-objective during deployment. Here, we isolate continual learning as our meta-objective, and we show that a suitable neuronal phenotype can endow a single network with an innate ability to update its synapses with minimal forgetting, using standard backpropagation, without experience replay, nor separate wake/sleep phases. We demonstrate this ability on sequential non-linear regression tasks. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 206,422 |
2304.00983 | Modelling Maritime SAR Effective Sweep Widths for Helicopters in VDM | Search and Rescue (SAR) is searching for and providing help to people in danger. In the UK, SAR teams are typically charities with limited resources, and SAR missions are time critical. Search managers need to objectively decide which search assets (e.g. helicopter vs drone) would be better. A key metric in the SAR community is effective sweep width (W), which provides a single measure for a search asset's ability to detect a specific object in specific environmental conditions. Tables of W for different search assets are provided in various manuals, such as the International Aeronautical and Maritime SAR (IAMSAR) Manual. However, these tables take years of expensive testing and experience to produce, and no such tables exist for drones. This paper uses the Vienna Development Method (VDM) to build an initial model of W for a known case (helicopters at sea) with a view to predicting W tables for drones. The model computes W for various search object sizes, helicopter altitude and visibility. The results for the model are quite different from the published tables, which shows that the abstraction level is not yet correct, however it produced useful insights and directions for the next steps. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | true | 355,884 |
1604.07625 | Mutual Transformation of Information and Knowledge | Information and knowledge are transformable into each other. Information transformation into knowledge by the example of rule generation from OWL (Web Ontology Language) ontology has been shown during the development of the SWES (Semantic Web Expert System). The SWES is expected as an expert system for searching OWL ontologies from the Web, generating rules from the found ontologies and supplementing the SWES knowledge base with these rules. The purpose of this paper is to show knowledge transformation into information by the example of ontology generation from rules. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 55,114 |
1512.04376 | Limits on the Capacity of In-Band Full Duplex Communication in Uplink
Cellular Networks | Simultaneous co-channel transmission and reception, denoted as in-band full duplex (FD) communication, has been promoted as an attractive solution to improve the spectral efficiency of cellular networks. However, in addition to the self-interference problem, cross-mode interference (i.e., between uplink and downlink) imposes a major obstacle for the deployment of FD communication in cellular networks. More specifically, the downlink to uplink interference represents the performance bottleneck for FD operation due to the uplink limited transmission power and venerable operation when compared to the downlink counterpart. While the positive impact of FD communication to the downlink performance has been proved in the literature, its effect on the uplink transmission has been neglected. This paper focuses on the effect of downlink interference on the uplink transmission in FD cellular networks in order to see whether FD communication is beneficial for the uplink transmission or not, and if yes for which type of network. To quantify the expected performance gains, we derive a closed form expression of the maximum achievable uplink capacity in FD cellular networks. In contrast to the downlink capacity which always improves with FD communication, our results show that the uplink performance may improve or degrade depending on the associated network parameters. Particularly, we show that the intensity of base stations (BSs) has a more prominent effect on the uplink performance than their transmission power. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | true | 50,126 |
2005.14553 | Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation
for Semantic Nighttime Image Segmentation | We address the problem of semantic nighttime image segmentation and improve the state-of-the-art, by adapting daytime models to nighttime without using nighttime annotations. Moreover, we design a new evaluation framework to address the substantial uncertainty of semantics in nighttime images. Our central contributions are: 1) a curriculum framework to gradually adapt semantic segmentation models from day to night through progressively darker times of day, exploiting cross-time-of-day correspondences between daytime images from a reference map and dark images to guide the label inference in the dark domains; 2) a novel uncertainty-aware annotation and evaluation framework and metric for semantic segmentation, including image regions beyond human recognition capability in the evaluation in a principled fashion; 3) the Dark Zurich dataset, comprising 2416 unlabeled nighttime and 2920 unlabeled twilight images with correspondences to their daytime counterparts plus a set of 201 nighttime images with fine pixel-level annotations created with our protocol, which serves as a first benchmark for our novel evaluation. Experiments show that our map-guided curriculum adaptation significantly outperforms state-of-the-art methods on nighttime sets both for standard metrics and our uncertainty-aware metric. Furthermore, our uncertainty-aware evaluation reveals that selective invalidation of predictions can improve results on data with ambiguous content such as our benchmark and profit safety-oriented applications involving invalid inputs. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 179,300 |
2001.09386 | Generating Representative Headlines for News Stories | Millions of news articles are published online every day, which can be overwhelming for readers to follow. Grouping articles that are reporting the same event into news stories is a common way of assisting readers in their news consumption. However, it remains a challenging research problem to efficiently and effectively generate a representative headline for each story. Automatic summarization of a document set has been studied for decades, while few studies have focused on generating representative headlines for a set of articles. Unlike summaries, which aim to capture most information with least redundancy, headlines aim to capture information jointly shared by the story articles in short length, and exclude information that is too specific to each individual article. In this work, we study the problem of generating representative headlines for news stories. We develop a distant supervision approach to train large-scale generation models without any human annotation. This approach centers on two technical components. First, we propose a multi-level pre-training framework that incorporates massive unlabeled corpus with different quality-vs.-quantity balance at different levels. We show that models trained within this framework outperform those trained with pure human curated corpus. Second, we propose a novel self-voting-based article attention layer to extract salient information shared by multiple articles. We show that models that incorporate this layer are robust to potential noises in news stories and outperform existing baselines with or without noises. We can further enhance our model by incorporating human labels, and we show our distant supervision approach significantly reduces the demand on labeled data. | false | false | false | false | true | true | false | false | true | false | false | false | false | false | false | false | false | false | 161,554 |
1901.09557 | Out-of-Sample Testing for GANs | We propose a new method to evaluate GANs, namely EvalGAN. EvalGAN relies on a test set to directly measure the reconstruction quality in the original sample space (no auxiliary networks are necessary), and it also computes the (log)likelihood for the reconstructed samples in the test set. Further, EvalGAN is agnostic to the GAN algorithm and the dataset. We decided to test it on three state-of-the-art GANs over the well-known CIFAR-10 and CelebA datasets. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 119,783 |
2404.13880 | Regional Style and Color Transfer | This paper presents a novel contribution to the field of regional style transfer. Existing methods often suffer from the drawback of applying style homogeneously across the entire image, leading to stylistic inconsistencies or foreground object twisted when applied to image with foreground elements such as person figures. To address this limitation, we propose a new approach that leverages a segmentation network to precisely isolate foreground objects within the input image. Subsequently, style transfer is applied exclusively to the background region. The isolated foreground objects are then carefully reintegrated into the style-transferred background. To enhance the visual coherence between foreground and background, a color transfer step is employed on the foreground elements prior to their rein-corporation. Finally, we utilize feathering techniques to achieve a seamless amalgamation of foreground and background, resulting in a visually unified and aesthetically pleasing final composition. Extensive evaluations demonstrate that our proposed approach yields significantly more natural stylistic transformations compared to conventional methods. | false | false | false | false | true | false | true | false | false | false | false | true | false | false | false | false | false | false | 448,484 |
2501.15273 | Into the Void: Mapping the Unseen Gaps in High Dimensional Data | We present a comprehensive pipeline, augmented by a visual analytics system named ``GapMiner'', that is aimed at exploring and exploiting untapped opportunities within the empty areas of high-dimensional datasets. Our approach begins with an initial dataset and then uses a novel Empty Space Search Algorithm (ESA) to identify the center points of these uncharted voids, which are regarded as reservoirs containing potentially valuable novel configurations. Initially, this process is guided by user interactions facilitated by GapMiner. GapMiner visualizes the Empty Space Configurations (ESC) identified by the search within the context of the data, enabling domain experts to explore and adjust ESCs using a linked parallel-coordinate display. These interactions enhance the dataset and contribute to the iterative training of a connected deep neural network (DNN). As the DNN trains, it gradually assumes the task of identifying high-potential ESCs, diminishing the need for direct user involvement. Ultimately, once the DNN achieves adequate accuracy, it autonomously guides the exploration of optimal configurations by predicting performance and refining configurations, using a combination of gradient ascent and improved empty-space searches. Domain users were actively engaged throughout the development of our system. Our findings demonstrate that our methodology consistently produces substantially superior novel configurations compared to conventional randomization-based methods. We illustrate the effectiveness of our method through several case studies addressing various objectives, including parameter optimization, adversarial learning, and reinforcement learning. | true | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 527,480 |
1412.6127 | Multi-User Diversity with Optimal Power Allocation in Spectrum Sharing
under Average Interference Power Constraint | In this paper, we investigate the performance of multi-user diversity (MUD) with optimal power allocation (OPA) in spectrum sharing (SS) under average interference power (AIP) constraint. In particular, OPA through average transmit power constraint in conjunction with the AIP constraint is assumed to maximize the ergodic secondary capacity. The solution of this problem requires the calculation of two Lagrange multipliers instead of one as obtained for the peak interference power (PIP) constraint and calculated using the well known water-filling algorithm. To this end, an algorithm based on bisection method is devised in order to calculate both Lagrange multipliers iteratively. Moreover, Rayleigh and Nakagami-$m$ fading channels with one and multiple primary users are considered to derive the required end-to-end SNR analysis. Numerical results are depicted to corroborate our performance analysis and compare it with the PIP case highlighting hence, the impact of the AIP constraint compared to the PIP constraint application. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 38,575 |
1706.02766 | Evolutionary Multitasking for Multiobjective Continuous Optimization:
Benchmark Problems, Performance Metrics and Baseline Results | In this report, we suggest nine test problems for multi-task multi-objective optimization (MTMOO), each of which consists of two multiobjective optimization tasks that need to be solved simultaneously. The relationship between tasks varies between different test problems, which would be helpful to have a comprehensive evaluation of the MO-MFO algorithms. It is expected that the proposed test problems will germinate progress the field of the MTMOO research. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | 75,028 |
1812.10071 | Coupled Recurrent Network (CRN) | Many semantic video analysis tasks can benefit from multiple, heterogenous signals. For example, in addition to the original RGB input sequences, sequences of optical flow are usually used to boost the performance of human action recognition in videos. To learn from these heterogenous input sources, existing methods reply on two-stream architectural designs that contain independent, parallel streams of Recurrent Neural Networks (RNNs). However, two-stream RNNs do not fully exploit the reciprocal information contained in the multiple signals, let alone exploit it in a recurrent manner. To this end, we propose in this paper a novel recurrent architecture, termed Coupled Recurrent Network (CRN), to deal with multiple input sources. In CRN, the parallel streams of RNNs are coupled together. Key design of CRN is a Recurrent Interpretation Block (RIB) that supports learning of reciprocal feature representations from multiple signals in a recurrent manner. Different from RNNs which stack the training loss at each time step or the last time step, we propose an effective and efficient training strategy for CRN. Experiments show the efficacy of the proposed CRN. In particular, we achieve the new state of the art on the benchmark datasets of human action recognition and multi-person pose estimation. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 117,286 |
2010.09323 | Multi-view Subspace Clustering Networks with Local and Global Graph
Information | This study investigates the problem of multi-view subspace clustering, the goal of which is to explore the underlying grouping structure of data collected from different fields or measurements. Since data do not always comply with the linear subspace models in many real-world applications, most existing multi-view subspace clustering methods that based on the shallow linear subspace models may fail in practice. Furthermore, underlying graph information of multi-view data is always ignored in most existing multi-view subspace clustering methods. To address aforementioned limitations, we proposed the novel multi-view subspace clustering networks with local and global graph information, termed MSCNLG, in this paper. Specifically, autoencoder networks are employed on multiple views to achieve latent smooth representations that are suitable for the linear assumption. Simultaneously, by integrating fused multi-view graph information into self-expressive layers, the proposed MSCNLG obtains the common shared multi-view subspace representation, which can be used to get clustering results by employing the standard spectral clustering algorithm. As an end-to-end trainable framework, the proposed method fully investigates the valuable information of multiple views. Comprehensive experiments on six benchmark datasets validate the effectiveness and superiority of the proposed MSCNLG. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 201,495 |
1903.10713 | Multiscale CNN based Deep Metric Learning for Bioacoustic
Classification: Overcoming Training Data Scarcity Using Dynamic Triplet Loss | This paper proposes multiscale convolutional neural network (CNN)-based deep metric learning for bioacoustic classification, under low training data conditions. The proposed CNN is characterized by the utilization of four different filter sizes at each level to analyze input feature maps. This multiscale nature helps in describing different bioacoustic events effectively: smaller filters help in learning the finer details of bioacoustic events, whereas, larger filters help in analyzing a larger context leading to global details. A dynamic triplet loss is employed in the proposed CNN architecture to learn a transformation from the input space to the embedding space, where classification is performed. The triplet loss helps in learning this transformation by analyzing three examples, referred to as triplets, at a time where intra-class distance is minimized while maximizing the inter-class separation by a dynamically increasing margin. The number of possible triplets increases cubically with the dataset size, making triplet loss more suitable than the softmax cross-entropy loss in low training data conditions. Experiments on three different publicly available datasets show that the proposed framework performs better than existing bioacoustic classification frameworks. Experimental results also confirm the superiority of the triplet loss over the cross-entropy loss in low training data conditions | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 125,349 |
2206.01207 | RACA: Relation-Aware Credit Assignment for Ad-Hoc Cooperation in
Multi-Agent Deep Reinforcement Learning | In recent years, reinforcement learning has faced several challenges in the multi-agent domain, such as the credit assignment issue. Value function factorization emerges as a promising way to handle the credit assignment issue under the centralized training with decentralized execution (CTDE) paradigm. However, existing value function factorization methods cannot deal with ad-hoc cooperation, that is, adapting to new configurations of teammates at test time. Specifically, these methods do not explicitly utilize the relationship between agents and cannot adapt to different sizes of inputs. To address these limitations, we propose a novel method, called Relation-Aware Credit Assignment (RACA), which achieves zero-shot generalization in ad-hoc cooperation scenarios. RACA takes advantage of a graph-based relation encoder to encode the topological structure between agents. Furthermore, RACA utilizes an attention-based observation abstraction mechanism that can generalize to an arbitrary number of teammates with a fixed number of parameters. Experiments demonstrate that our method outperforms baseline methods on the StarCraftII micromanagement benchmark and ad-hoc cooperation scenarios. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 300,388 |
1804.06992 | Infrared and Visible Image Fusion using a Deep Learning Framework | In recent years, deep learning has become a very active research tool which is used in many image processing fields. In this paper, we propose an effective image fusion method using a deep learning framework to generate a single image which contains all the features from infrared and visible images. First, the source images are decomposed into base parts and detail content. Then the base parts are fused by weighted-averaging. For the detail content, we use a deep learning network to extract multi-layer features. Using these features, we use l_1-norm and weighted-average strategy to generate several candidates of the fused detail content. Once we get these candidates, the max selection strategy is used to get final fused detail content. Finally, the fused image will be reconstructed by combining the fused base part and detail content. The experimental results demonstrate that our proposed method achieves state-of-the-art performance in both objective assessment and visual quality. The Code of our fusion method is available at https://github.com/hli1221/imagefusion_deeplearning | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 95,424 |
0902.0392 | Tree Exploration for Bayesian RL Exploration | Research in reinforcement learning has produced algorithms for optimal decision making under uncertainty that fall within two main types. The first employs a Bayesian framework, where optimality improves with increased computational time. This is because the resulting planning task takes the form of a dynamic programming problem on a belief tree with an infinite number of states. The second type employs relatively simple algorithm which are shown to suffer small regret within a distribution-free framework. This paper presents a lower bound and a high probability upper bound on the optimal value function for the nodes in the Bayesian belief tree, which are analogous to similar bounds in POMDPs. The bounds are then used to create more efficient strategies for exploring the tree. The resulting algorithms are compared with the distribution-free algorithm UCB1, as well as a simpler baseline algorithm on multi-armed bandit problems. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 3,098 |
2502.01776 | Sparse VideoGen: Accelerating Video Diffusion Transformers with
Spatial-Temporal Sparsity | Diffusion Transformers (DiTs) dominate video generation but their high computational cost severely limits real-world applicability, usually requiring tens of minutes to generate a few seconds of video even on high-performance GPUs. This inefficiency primarily arises from the quadratic computational complexity of 3D Full Attention with respect to the context length. In this paper, we propose a training-free framework termed Sparse VideoGen (SVG) that leverages the inherent sparsity in 3D Full Attention to boost inference efficiency. We reveal that the attention heads can be dynamically classified into two groups depending on distinct sparse patterns: (1) Spatial Head, where only spatially-related tokens within each frame dominate the attention output, and (2) Temporal Head, where only temporally-related tokens across different frames dominate. Based on this insight, SVG proposes an online profiling strategy to capture the dynamic sparse patterns and predicts the type of attention head. Combined with a novel hardware-efficient tensor layout transformation and customized kernel implementations, SVG achieves up to 2.28x and 2.33x end-to-end speedup on CogVideoX-v1.5 and HunyuanVideo, respectively, while preserving generation quality. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 530,026 |
1711.09265 | Predictive Learning: Using Future Representation Learning Variantial
Autoencoder for Human Action Prediction | The unsupervised Pretraining method has been widely used in aiding human action recognition. However, existing methods focus on reconstructing the already present frames rather than generating frames which happen in future.In this paper, We propose an improved Variantial Autoencoder model to extract the features with a high connection to the coming scenarios, also known as Predictive Learning. Our framework lists as following: two steam 3D-convolution neural networks are used to extract both spatial and temporal information as latent variables. Then a resample method is introduced to create new normal distribution probabilistic latent variables and finally, the deconvolution neural network will use these latent variables generate next frames. Through this possess, we train the model to focus more on how to generate the future and thus it will extract the future high connected features. In the experiment stage, A large number of experiments on UT and UCF101 datasets reveal that future generation aids Prediction does improve the performance. Moreover, the Future Representation Learning Network reach a higher score than other methods when in half observation. This means that Future Representation Learning is better than the traditional Representation Learning and other state- of-the-art methods in solving the human action prediction problems to some extends. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 85,366 |
2306.14169 | A Web-based Mpox Skin Lesion Detection System Using State-of-the-art
Deep Learning Models Considering Racial Diversity | The recent 'Mpox' outbreak, formerly known as 'Monkeypox', has become a significant public health concern and has spread to over 110 countries globally. The challenge of clinically diagnosing mpox early on is due, in part, to its similarity to other types of rashes. Computer-aided screening tools have been proven valuable in cases where Polymerase Chain Reaction (PCR) based diagnosis is not immediately available. Deep learning methods are powerful in learning complex data representations, but their efficacy largely depends on adequate training data. To address this challenge, we present the "Mpox Skin Lesion Dataset Version 2.0 (MSLD v2.0)" as a follow-up to the previously released openly accessible dataset, one of the first datasets containing mpox lesion images. This dataset contains images of patients with mpox and five other non-mpox classes (chickenpox, measles, hand-foot-mouth disease, cowpox, and healthy). We benchmark the performance of several state-of-the-art deep learning models, including VGG16, ResNet50, DenseNet121, MobileNetV2, EfficientNetB3, InceptionV3, and Xception, to classify mpox and other infectious skin diseases. In order to reduce the impact of racial bias, we utilize a color space data augmentation method to increase skin color variability during training. Additionally, by leveraging transfer learning implemented with pre-trained weights generated from the HAM10000 dataset, an extensive collection of pigmented skin lesion images, we achieved the best overall accuracy of $83.59\pm2.11\%$. Finally, the developed models are incorporated within a prototype web application to analyze uploaded skin images by a user and determine whether a subject is a suspected mpox patient. | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | false | false | 375,579 |
1610.05426 | Critical Transitions in Public Opinion: A Case Study of American
Presidential Election | At the tipping point, it is known that small incident can trigger dramatic societal shift. Getting early-warning signals for such changes are valuable to avoid detrimental outcomes such as riots or collapses of nations. However, it is notoriously hard to capture the processes of such transitions in the real-world. Here, we demonstrate the occurrence of a major shift in public opinion in the form of political support. Instead of simple swapping of ruling parties, we study the regime shift of a party popularity based on its attractiveness by examining the American presidential elections during 1980-2012. A single irreversible transition is detected in 1991. Once a transition happens, recovery to the original level of attractiveness does not bring popularity of the political party back. Remarkably, this transition is corroborated by tell-tale early-warning signature of critical slowing down. Our approach is applicable to shifts in public attitude within any social system. | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 62,508 |
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