id stringlengths 9 16 | title stringlengths 4 278 | abstract stringlengths 3 4.08k | cs.HC bool 2 classes | cs.CE bool 2 classes | cs.SD bool 2 classes | cs.SI bool 2 classes | cs.AI bool 2 classes | cs.IR bool 2 classes | cs.LG bool 2 classes | cs.RO bool 2 classes | cs.CL bool 2 classes | cs.IT bool 2 classes | cs.SY bool 2 classes | cs.CV bool 2 classes | cs.CR bool 2 classes | cs.CY bool 2 classes | cs.MA bool 2 classes | cs.NE bool 2 classes | cs.DB bool 2 classes | Other bool 2 classes | __index_level_0__ int64 0 541k |
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1307.5684 | Using a Dynamic Neural Field Model to Explore a Direct Collicular
Inhibition Account of Inhibition of Return | When the interval between a transient ash of light (a "cue") and a second visual response signal (a "target") exceeds at least 200ms, responding is slowest in the direction indicated by the first signal. This phenomenon is commonly referred to as inhibition of return (IOR). The dynamic neural field model (DNF) has proven to have broad explanatory power for IOR, effectively capturing many empirical results. Previous work has used a short-term depression (STD) implementation of IOR, but this approach fails to explain many behavioral phenomena observed in the literature. Here, we explore a variant model of IOR involving a combination of STD and delayed direct collicular inhibition. We demonstrate that this hybrid model can better reproduce established behavioural results. We use the results of this model to propose several experiments that would yield particularly valuable insight into the nature of the neurophysiological mechanisms underlying IOR. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 25,968 |
2108.04023 | DRINet: A Dual-Representation Iterative Learning Network for Point Cloud
Segmentation | We present a novel and flexible architecture for point cloud segmentation with dual-representation iterative learning. In point cloud processing, different representations have their own pros and cons. Thus, finding suitable ways to represent point cloud data structure while keeping its own internal physical property such as permutation and scale-invariant is a fundamental problem. Therefore, we propose our work, DRINet, which serves as the basic network structure for dual-representation learning with great flexibility at feature transferring and less computation cost, especially for large-scale point clouds. DRINet mainly consists of two modules called Sparse Point-Voxel Feature Extraction and Sparse Voxel-Point Feature Extraction. By utilizing these two modules iteratively, features can be propagated between two different representations. We further propose a novel multi-scale pooling layer for pointwise locality learning to improve context information propagation. Our network achieves state-of-the-art results for point cloud classification and segmentation tasks on several datasets while maintaining high runtime efficiency. For large-scale outdoor scenarios, our method outperforms state-of-the-art methods with a real-time inference speed of 62ms per frame. | false | false | false | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | 249,863 |
2009.11732 | A Unifying Review of Deep and Shallow Anomaly Detection | Deep learning approaches to anomaly detection have recently improved the state of the art in detection performance on complex datasets such as large collections of images or text. These results have sparked a renewed interest in the anomaly detection problem and led to the introduction of a great variety of new methods. With the emergence of numerous such methods, including approaches based on generative models, one-class classification, and reconstruction, there is a growing need to bring methods of this field into a systematic and unified perspective. In this review we aim to identify the common underlying principles as well as the assumptions that are often made implicitly by various methods. In particular, we draw connections between classic 'shallow' and novel deep approaches and show how this relation might cross-fertilize or extend both directions. We further provide an empirical assessment of major existing methods that is enriched by the use of recent explainability techniques, and present specific worked-through examples together with practical advice. Finally, we outline critical open challenges and identify specific paths for future research in anomaly detection. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 197,241 |
2402.19197 | Fine Structure-Aware Sampling: A New Sampling Training Scheme for
Pixel-Aligned Implicit Models in Single-View Human Reconstruction | Pixel-aligned implicit models, such as PIFu, PIFuHD, and ICON, are used for single-view clothed human reconstruction. These models need to be trained using a sampling training scheme. Existing sampling training schemes either fail to capture thin surfaces (e.g. ears, fingers) or cause noisy artefacts in reconstructed meshes. To address these problems, we introduce Fine Structured-Aware Sampling (FSS), a new sampling training scheme to train pixel-aligned implicit models for single-view human reconstruction. FSS resolves the aforementioned problems by proactively adapting to the thickness and complexity of surfaces. In addition, unlike existing sampling training schemes, FSS shows how normals of sample points can be capitalized in the training process to improve results. Lastly, to further improve the training process, FSS proposes a mesh thickness loss signal for pixel-aligned implicit models. It becomes computationally feasible to introduce this loss once a slight reworking of the pixel-aligned implicit function framework is carried out. Our results show that our methods significantly outperform SOTA methods qualitatively and quantitatively. Our code is publicly available at https://github.com/kcyt/FSS. | false | false | false | false | true | false | true | false | false | false | false | true | false | false | false | false | false | false | 433,714 |
2406.13144 | DialSim: A Real-Time Simulator for Evaluating Long-Term Multi-Party
Dialogue Understanding of Conversation Systems | Recent advancements in Large Language Models (LLMs) have significantly enhanced the capabilities of conversation systems, making them applicable to various fields (e.g., education). Despite their progress, the evaluation of the systems often overlooks the complexities of real-world conversations, such as real-time interactions, multi-party dialogues, and extended contextual dependencies. To bridge this gap, we introduce DialSim, a real-time dialogue simulator. In this simulator, a conversation system is assigned the role of a character from popular TV shows, requiring it to respond to spontaneous questions using past dialogue information and to distinguish between known and unknown information. Key features of DialSim include assessing the system's ability to respond within a reasonable time limit, handling long-term multi-party dialogues, and evaluating performance under randomized questioning with LongDialQA, a novel, high-quality question-answering dataset. Our experiments using DialSim reveal the strengths and weaknesses of the latest conversation systems, offering valuable insights for future advancements in conversational AI. DialSim is available at https://dialsim.github.io/. | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 465,713 |
1506.06442 | A Deep Memory-based Architecture for Sequence-to-Sequence Learning | We propose DEEPMEMORY, a novel deep architecture for sequence-to-sequence learning, which performs the task through a series of nonlinear transformations from the representation of the input sequence (e.g., a Chinese sentence) to the final output sequence (e.g., translation to English). Inspired by the recently proposed Neural Turing Machine (Graves et al., 2014), we store the intermediate representations in stacked layers of memories, and use read-write operations on the memories to realize the nonlinear transformations between the representations. The types of transformations are designed in advance but the parameters are learned from data. Through layer-by-layer transformations, DEEPMEMORY can model complicated relations between sequences necessary for applications such as machine translation between distant languages. The architecture can be trained with normal back-propagation on sequenceto-sequence data, and the learning can be easily scaled up to a large corpus. DEEPMEMORY is broad enough to subsume the state-of-the-art neural translation model in (Bahdanau et al., 2015) as its special case, while significantly improving upon the model with its deeper architecture. Remarkably, DEEPMEMORY, being purely neural network-based, can achieve performance comparable to the traditional phrase-based machine translation system Moses with a small vocabulary and a modest parameter size. | false | false | false | false | false | false | true | false | true | false | false | false | false | false | false | true | false | false | 44,420 |
2109.13486 | Exploring Teacher-Student Learning Approach for Multi-lingual
Speech-to-Intent Classification | End-to-end speech-to-intent classification has shown its advantage in harvesting information from both text and speech. In this paper, we study a technique to develop such an end-to-end system that supports multiple languages. To overcome the scarcity of multi-lingual speech corpus, we exploit knowledge from a pre-trained multi-lingual natural language processing model. Multi-lingual bidirectional encoder representations from transformers (mBERT) models are trained on multiple languages and hence expected to perform well in the multi-lingual scenario. In this work, we employ a teacher-student learning approach to sufficiently extract information from an mBERT model to train a multi-lingual speech model. In particular, we use synthesized speech generated from an English-Mandarin text corpus for analysis and training of a multi-lingual intent classification model. We also demonstrate that the teacher-student learning approach obtains an improved performance (91.02%) over the traditional end-to-end (89.40%) intent classification approach in a practical multi-lingual scenario. | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 257,653 |
2010.03855 | Dense Relational Image Captioning via Multi-task Triple-Stream Networks | We introduce dense relational captioning, a novel image captioning task which aims to generate multiple captions with respect to relational information between objects in a visual scene. Relational captioning provides explicit descriptions for each relationship between object combinations. This framework is advantageous in both diversity and amount of information, leading to a comprehensive image understanding based on relationships, e.g., relational proposal generation. For relational understanding between objects, the part-of-speech (POS; i.e., subject-object-predicate categories) can be a valuable prior information to guide the causal sequence of words in a caption. We enforce our framework to learn not only to generate captions but also to understand the POS of each word. To this end, we propose the multi-task triple-stream network (MTTSNet) which consists of three recurrent units responsible for each POS which is trained by jointly predicting the correct captions and POS for each word. In addition, we found that the performance of MTTSNet can be improved by modulating the object embeddings with an explicit relational module. We demonstrate that our proposed model can generate more diverse and richer captions, via extensive experimental analysis on large scale datasets and several metrics. Then, we present applications of our framework to holistic image captioning, scene graph generation, and retrieval tasks. | false | false | false | false | true | false | false | false | true | false | false | true | false | false | false | false | false | false | 199,548 |
2402.00856 | Towards Efficient Exact Optimization of Language Model Alignment | The alignment of language models with human preferences is vital for their application in real-world tasks. The problem is formulated as optimizing the model's policy to maximize the expected reward that reflects human preferences with minimal deviation from the initial policy. While considered as a straightforward solution, reinforcement learning (RL) suffers from high variance in policy updates, which impedes efficient policy improvement. Recently, direct preference optimization (DPO) was proposed to directly optimize the policy from preference data. However, we show that DPO derived based on the optimal solution of the problem leads to a compromised mean-seeking approximation of the optimal solution in practice. In this paper, we propose efficient exact optimization (EXO) of the alignment objective. EXO is guaranteed to optimize in the same direction as RL algorithms asymptotically for arbitrary policy parametrization. This leads to the same mode-seeking solution, while enables efficient optimization by circumventing the complexities of RL. We also compare our method to DPO with both theoretical and empirical analyses, and further demonstrate the advantages of our method over existing approaches on realistic human preference data. Code is available at https://github.com/haozheji/exact-optimization. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 425,751 |
1912.11189 | Computing the Number of Equivalent Classes on
$\mathcal{R}(s,n)/\mathcal{R}(k,n)$ | Affine equivalent classes of Boolean functions have many applications in modern cryptography and circuit design. Previous publications have shown that affine equivalence on the entire space of Boolean functions can be computed up to 10 variables, but not on the quotient Boolean function space modulo functions of different degrees. Computing the number of equivalent classes of cosets of Reed-Muller code $\mathcal{R}(1,n)$ is equivalent to classifying Boolean functions modulo linear functions, which can be computed only when $n\leq 7$. Based on the linear representation of the affine group $\mathcal{AGL}(n,2)$ on $\mathcal{R}(s,n)/\mathcal{R}(k,n)$, we obtain a useful counting formula to compute the number of equivalent classes. Instead of computing the conjugate classes and representatives directly in $\mathcal{AGL}(n,2)$, we reduce the computation complexity by introducing an isomorphic permutation group $P_n$ and performing the computation in $P_n$. With the proposed algorithm, the number of equivalent classes of cosets of $R(1,n)$ can be computed up to 10 variables. Furthermore, the number of equivalent classes on $\mathcal{R}(s,n)/\mathcal{R}(k,n)$ can also be computed when $-1\leq k< s\leq n\leq 10$, which is a major improvement and advancement comparing to previous methods. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 158,502 |
1912.12191 | Explain Your Move: Understanding Agent Actions Using Specific and
Relevant Feature Attribution | As deep reinforcement learning (RL) is applied to more tasks, there is a need to visualize and understand the behavior of learned agents. Saliency maps explain agent behavior by highlighting the features of the input state that are most relevant for the agent in taking an action. Existing perturbation-based approaches to compute saliency often highlight regions of the input that are not relevant to the action taken by the agent. Our proposed approach, SARFA (Specific and Relevant Feature Attribution), generates more focused saliency maps by balancing two aspects (specificity and relevance) that capture different desiderata of saliency. The first captures the impact of perturbation on the relative expected reward of the action to be explained. The second downweighs irrelevant features that alter the relative expected rewards of actions other than the action to be explained. We compare SARFA with existing approaches on agents trained to play board games (Chess and Go) and Atari games (Breakout, Pong and Space Invaders). We show through illustrative examples (Chess, Atari, Go), human studies (Chess), and automated evaluation methods (Chess) that SARFA generates saliency maps that are more interpretable for humans than existing approaches. For the code release and demo videos, see https://nikaashpuri.github.io/sarfa-saliency/. | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | false | false | 158,795 |
2201.10899 | Speeding up Heterogeneous Federated Learning with Sequentially Trained
Superclients | Federated Learning (FL) allows training machine learning models in privacy-constrained scenarios by enabling the cooperation of edge devices without requiring local data sharing. This approach raises several challenges due to the different statistical distribution of the local datasets and the clients' computational heterogeneity. In particular, the presence of highly non-i.i.d. data severely impairs both the performance of the trained neural network and its convergence rate, increasing the number of communication rounds requested to reach a performance comparable to that of the centralized scenario. As a solution, we propose FedSeq, a novel framework leveraging the sequential training of subgroups of heterogeneous clients, i.e. superclients, to emulate the centralized paradigm in a privacy-compliant way. Given a fixed budget of communication rounds, we show that FedSeq outperforms or match several state-of-the-art federated algorithms in terms of final performance and speed of convergence. Finally, our method can be easily integrated with other approaches available in the literature. Empirical results show that combining existing algorithms with FedSeq further improves its final performance and convergence speed. We test our method on CIFAR-10 and CIFAR-100 and prove its effectiveness in both i.i.d. and non-i.i.d. scenarios. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 277,132 |
2311.04161 | Breaking the Heavy-Tailed Noise Barrier in Stochastic Optimization
Problems | We consider stochastic optimization problems with heavy-tailed noise with structured density. For such problems, we show that it is possible to get faster rates of convergence than $\mathcal{O}(K^{-2(\alpha - 1)/\alpha})$, when the stochastic gradients have finite moments of order $\alpha \in (1, 2]$. In particular, our analysis allows the noise norm to have an unbounded expectation. To achieve these results, we stabilize stochastic gradients, using smoothed medians of means. We prove that the resulting estimates have negligible bias and controllable variance. This allows us to carefully incorporate them into clipped-SGD and clipped-SSTM and derive new high-probability complexity bounds in the considered setup. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 406,122 |
2405.12523 | Single Image Unlearning: Efficient Machine Unlearning in Multimodal
Large Language Models | Machine unlearning empowers individuals with the `right to be forgotten' by removing their private or sensitive information encoded in machine learning models. However, it remains uncertain whether MU can be effectively applied to Multimodal Large Language Models (MLLMs), particularly in scenarios of forgetting the leaked visual data of concepts. To overcome the challenge, we propose an efficient method, Single Image Unlearning (SIU), to unlearn the visual recognition of a concept by fine-tuning a single associated image for few steps. SIU consists of two key aspects: (i) Constructing Multifaceted fine-tuning data. We introduce four targets, based on which we construct fine-tuning data for the concepts to be forgotten; (ii) Jointly training loss. To synchronously forget the visual recognition of concepts and preserve the utility of MLLMs, we fine-tune MLLMs through a novel Dual Masked KL-divergence Loss combined with Cross Entropy loss. Alongside our method, we establish MMUBench, a new benchmark for MU in MLLMs and introduce a collection of metrics for its evaluation. Experimental results on MMUBench show that SIU completely surpasses the performance of existing methods. Furthermore, we surprisingly find that SIU can avoid invasive membership inference attacks and jailbreak attacks. To the best of our knowledge, we are the first to explore MU in MLLMs. We will release the code and benchmark in the near future. | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | false | false | 455,561 |
2401.05176 | Convergences and Divergences between Automatic Assessment and Human
Evaluation: Insights from Comparing ChatGPT-Generated Translation and Neural
Machine Translation | Large language models have demonstrated parallel and even superior translation performance compared to neural machine translation (NMT) systems. However, existing comparative studies between them mainly rely on automated metrics, raising questions into the feasibility of these metrics and their alignment with human judgment. The present study investigates the convergences and divergences between automated metrics and human evaluation in assessing the quality of machine translation from ChatGPT and three NMT systems. To perform automatic assessment, four automated metrics are employed, while human evaluation incorporates the DQF-MQM error typology and six rubrics. Notably, automatic assessment and human evaluation converge in measuring formal fidelity (e.g., error rates), but diverge when evaluating semantic and pragmatic fidelity, with automated metrics failing to capture the improvement of ChatGPT's translation brought by prompt engineering. These results underscore the indispensable role of human judgment in evaluating the performance of advanced translation tools at the current stage. | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 420,669 |
1506.06221 | Ranking the Importance Level of Intermediaries to a Criminal using a
Reliance Measure | Recent research on finding important intermediate nodes in a network suspected to contain criminal activity is highly dependent on network centrality values. Betweenness centrality, for example, is widely used to rank the nodes that act as brokers in the shortest paths connecting all source and all the end nodes in a network. However both the shortest path node betweenness and the linearly scaled betweenness can only show rankings for all the nodes in a network. In this paper we explore the mathematical concept of pair-dependency on intermediate nodes, adapting the concept to criminal relationships and introducing a new source-intermediate reliance measure. To illustrate our measure, we apply it to rank the nodes in the Enron email dataset and the Noordin Top Terrorist networks. We compare the reliance ranking with Google PageRank, Markov centrality as well as betweenness centrality and show that a criminal investigation using the reliance measure, will lead to a different prioritisation in terms of possible people to investigate. While the ranking for the Noordin Top terrorist network nodes yields more extreme differences than for the Enron email transaction network, in the latter the reliance values for the set of finance managers immediately identified another employee convicted of money laundering. | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 44,397 |
2401.10748 | Fast gradient-free activation maximization for neurons in spiking neural
networks | Elements of neural networks, both biological and artificial, can be described by their selectivity for specific cognitive features. Understanding these features is important for understanding the inner workings of neural networks. For a living system, such as a neuron, whose response to a stimulus is unknown and not differentiable, the only way to reveal these features is through a feedback loop that exposes it to a large set of different stimuli. The properties of these stimuli should be varied iteratively in order to maximize the neuronal response. To utilize this feedback loop for a biological neural network, it is important to run it quickly and efficiently in order to reach the stimuli that maximizes certain neurons' activation with the least number of iterations possible. Here we present a framework with an efficient design for such a loop. We successfully tested it on an artificial spiking neural network (SNN), which is a model that simulates the asynchronous spiking activity of neurons in living brains. Our optimization method for activation maximization is based on the low-rank Tensor Train decomposition of the discrete activation function. The optimization space is the latent parameter space of images generated by SN-GAN or VQ-VAE generative models. To our knowledge, this is the first time that effective AM has been applied to SNNs. We track changes in the optimal stimuli for artificial neurons during training and show that highly selective neurons can form already in the early epochs of training and in the early layers of a convolutional spiking network. This formation of refined optimal stimuli is associated with an increase in classification accuracy. Some neurons, especially in the deeper layers, may gradually change the concepts they are selective for during learning, potentially explaining their importance for model performance. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | true | false | false | 422,757 |
2107.01477 | Byzantine-robust Federated Learning through Spatial-temporal Analysis of
Local Model Updates | Federated Learning (FL) enables multiple distributed clients (e.g., mobile devices) to collaboratively train a centralized model while keeping the training data locally on the client. Compared to traditional centralized machine learning, FL offers many favorable features such as offloading operations which would usually be performed by a central server and reducing risks of serious privacy leakage. However, Byzantine clients that send incorrect or disruptive updates due to system failures or adversarial attacks may disturb the joint learning process, consequently degrading the performance of the resulting model. In this paper, we propose to mitigate these failures and attacks from a spatial-temporal perspective. Specifically, we use a clustering-based method to detect and exclude incorrect updates by leveraging their geometric properties in the parameter space. Moreover, to further handle malicious clients with time-varying behaviors, we propose to adaptively adjust the learning rate according to momentum-based update speculation. Extensive experiments on 4 public datasets demonstrate that our algorithm achieves enhanced robustness comparing to existing methods under both cross-silo and cross-device FL settings with faulty/malicious clients. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 244,497 |
2102.08354 | Topological Deep Learning: Classification Neural Networks | Topological deep learning is a formalism that is aimed at introducing topological language to deep learning for the purpose of utilizing the minimal mathematical structures to formalize problems that arise in a generic deep learning problem. This is the first of a sequence of articles with the purpose of introducing and studying this formalism. In this article, we define and study the classification problem in machine learning in a topological setting. Using this topological framework, we show when the classification problem is possible or not possible in the context of neural networks. Finally, we demonstrate how our topological setting immediately illuminates aspects of this problem that are not as readily apparent using traditional tools. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 220,421 |
2212.07035 | MA-GCL: Model Augmentation Tricks for Graph Contrastive Learning | Contrastive learning (CL), which can extract the information shared between different contrastive views, has become a popular paradigm for vision representation learning. Inspired by the success in computer vision, recent work introduces CL into graph modeling, dubbed as graph contrastive learning (GCL). However, generating contrastive views in graphs is more challenging than that in images, since we have little prior knowledge on how to significantly augment a graph without changing its labels. We argue that typical data augmentation techniques (e.g., edge dropping) in GCL cannot generate diverse enough contrastive views to filter out noises. Moreover, previous GCL methods employ two view encoders with exactly the same neural architecture and tied parameters, which further harms the diversity of augmented views. To address this limitation, we propose a novel paradigm named model augmented GCL (MA-GCL), which will focus on manipulating the architectures of view encoders instead of perturbing graph inputs. Specifically, we present three easy-to-implement model augmentation tricks for GCL, namely asymmetric, random and shuffling, which can respectively help alleviate high- frequency noises, enrich training instances and bring safer augmentations. All three tricks are compatible with typical data augmentations. Experimental results show that MA-GCL can achieve state-of-the-art performance on node classification benchmarks by applying the three tricks on a simple base model. Extensive studies also validate our motivation and the effectiveness of each trick. (Code, data and appendix are available at https://github.com/GXM1141/MA-GCL. ) | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 336,279 |
2209.03116 | A New Method for the High-Precision Assessment of Tumor Changes in
Response to Treatment | Imaging demonstrates that preclinical and human tumors are heterogeneous, i.e. a single tumor can exhibit multiple regions that behave differently during both normal development and also in response to treatment. The large variations observed in control group tumors can obscure detection of significant therapeutic effects due to the ambiguity in attributing causes of change. This can hinder development of effective therapies due to limitations in experimental design, rather than due to therapeutic failure. An improved method to model biological variation and heterogeneity in imaging signals is described. Specifically, Linear Poisson modelling (LPM) evaluates changes in apparent diffusion co-efficient (ADC) before and 72 hours after radiotherapy, in two xenograft models of colorectal cancer. The statistical significance of measured changes are compared to those attainable using a conventional t-test analysis on basic ADC distribution parameters. When LPMs were applied to treated tumors, the LPMs detected highly significant changes. The analyses were significant for all tumors, equating to a gain in power of 4 fold (i.e. equivelent to having a sample size 16 times larger), compared with the conventional approach. In contrast, highly significant changes are only detected at a cohort level using t-tests, restricting their potential use within personalised medicine and increasing the number of animals required during testing. Furthermore, LPM enabled the relative volumes of responding and non-responding tissue to be estimated for each xenograft model. Leave-one-out analysis of the treated xenografts provided quality control and identified potential outliers, raising confidence in LPM data at clinically relevant sample sizes. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 316,405 |
1205.1813 | Graph spectra and the detectability of community structure in networks | We study networks that display community structure -- groups of nodes within which connections are unusually dense. Using methods from random matrix theory, we calculate the spectra of such networks in the limit of large size, and hence demonstrate the presence of a phase transition in matrix methods for community detection, such as the popular modularity maximization method. The transition separates a regime in which such methods successfully detect the community structure from one in which the structure is present but is not detected. By comparing these results with recent analyses of maximum-likelihood methods we are able to show that spectral modularity maximization is an optimal detection method in the sense that no other method will succeed in the regime where the modularity method fails. | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 15,861 |
2312.14751 | Hazards from Increasingly Accessible Fine-Tuning of Downloadable
Foundation Models | Public release of the weights of pretrained foundation models, otherwise known as downloadable access \citep{solaiman_gradient_2023}, enables fine-tuning without the prohibitive expense of pretraining. Our work argues that increasingly accessible fine-tuning of downloadable models may increase hazards. First, we highlight research to improve the accessibility of fine-tuning. We split our discussion into research that A) reduces the computational cost of fine-tuning and B) improves the ability to share that cost across more actors. Second, we argue that increasingly accessible fine-tuning methods may increase hazard through facilitating malicious use and making oversight of models with potentially dangerous capabilities more difficult. Third, we discuss potential mitigatory measures, as well as benefits of more accessible fine-tuning. Given substantial remaining uncertainty about hazards, we conclude by emphasizing the urgent need for the development of mitigations. | false | false | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | 417,740 |
2006.03680 | Evaluating the Disentanglement of Deep Generative Models through
Manifold Topology | Learning disentangled representations is regarded as a fundamental task for improving the generalization, robustness, and interpretability of generative models. However, measuring disentanglement has been challenging and inconsistent, often dependent on an ad-hoc external model or specific to a certain dataset. To address this, we present a method for quantifying disentanglement that only uses the generative model, by measuring the topological similarity of conditional submanifolds in the learned representation. This method showcases both unsupervised and supervised variants. To illustrate the effectiveness and applicability of our method, we empirically evaluate several state-of-the-art models across multiple datasets. We find that our method ranks models similarly to existing methods. We make ourcode publicly available at https://github.com/stanfordmlgroup/disentanglement. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 180,394 |
2111.03009 | Computation of Input Disturbance Sets for Constrained Output
Reachability | Linear models with additive unknown-but-bounded input disturbances are extensively used to model uncertainty in robust control systems design. Typically, the disturbance set is either assumed to be known a priori or estimated from data through set-membership identification. However, the problem of computing a suitable input disturbance set in case the set of possible output values is assigned a priori has received relatively little attention. This problem arises in many contexts, such as in supervisory control, actuator design, decentralized control, and others. In this paper, we propose a method to compute input disturbance sets (and the corresponding set of states) such that the resulting set of outputs matches as closely as possible a given set of outputs, while additionally satisfying strict (inner or outer) inclusion constraints. We formulate the problem as an optimization problem by relying on the concept of robust invariance. The effectiveness of the approach is demonstrated in numerical examples that illustrate how to solve safe reference set and input-constraint set computation problems. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 265,026 |
1211.1250 | Detection-Directed Sparse Estimation using Bayesian Hypothesis Test and
Belief Propagation | In this paper, we propose a sparse recovery algorithm called detection-directed (DD) sparse estimation using Bayesian hypothesis test (BHT) and belief propagation (BP). In this framework, we consider the use of sparse-binary sensing matrices which has the tree-like property and the sampled-message approach for the implementation of BP. The key idea behind the proposed algorithm is that the recovery takes DD-estimation structure consisting of two parts: support detection and signal value estimation. BP and BHT perform the support detection, and an MMSE estimator finds the signal values using the detected support set. The proposed algorithm provides noise-robustness against measurement noise beyond the conventional MAP approach, as well as a solution to remove quantization effect by the sampled-message based BP independently of memory size for the message sampling. We explain how the proposed algorithm can have the aforementioned characteristics via exemplary discussion. In addition, our experiments validate such superiority of the proposed algorithm, compared to recent algorithms under noisy setup. Interestingly the experimental results show that performance of the proposed algorithm approaches that of the oracle estimator as SNR becomes higher. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 19,589 |
2309.15065 | Language-EXtended Indoor SLAM (LEXIS): A Versatile System for Real-time
Visual Scene Understanding | Versatile and adaptive semantic understanding would enable autonomous systems to comprehend and interact with their surroundings. Existing fixed-class models limit the adaptability of indoor mobile and assistive autonomous systems. In this work, we introduce LEXIS, a real-time indoor Simultaneous Localization and Mapping (SLAM) system that harnesses the open-vocabulary nature of Large Language Models (LLMs) to create a unified approach to scene understanding and place recognition. The approach first builds a topological SLAM graph of the environment (using visual-inertial odometry) and embeds Contrastive Language-Image Pretraining (CLIP) features in the graph nodes. We use this representation for flexible room classification and segmentation, serving as a basis for room-centric place recognition. This allows loop closure searches to be directed towards semantically relevant places. Our proposed system is evaluated using both public, simulated data and real-world data, covering office and home environments. It successfully categorizes rooms with varying layouts and dimensions and outperforms the state-of-the-art (SOTA). For place recognition and trajectory estimation tasks we achieve equivalent performance to the SOTA, all also utilizing the same pre-trained model. Lastly, we demonstrate the system's potential for planning. | false | false | false | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | 394,836 |
1904.02795 | Generalized Lazy Search for Robot Motion Planning: Interleaving Search
and Edge Evaluation via Event-based Toggles | Lazy search algorithms can efficiently solve problems where edge evaluation is the bottleneck in computation, as is the case for robotic motion planning. The optimal algorithm in this class, LazySP, lazily restricts edge evaluation to only the shortest path. Doing so comes at the expense of search effort, i.e., LazySP must recompute the search tree every time an edge is found to be invalid. This becomes prohibitively expensive when dealing with large graphs or highly cluttered environments. Our key insight is the need to balance both edge evaluation and search effort to minimize the total planning time. Our contribution is two-fold. First, we propose a framework, Generalized Lazy Search (GLS), that seamlessly toggles between search and evaluation to prevent wasted efforts. We show that for a choice of toggle, GLS is provably more efficient than LazySP. Second, we leverage prior experience of edge probabilities to derive GLS policies that minimize expected planning time. We show that GLS equipped with such priors significantly outperforms competitive baselines for many simulated environments in R2, SE(2) and 7-DoF manipulation. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 126,525 |
2208.05142 | Plug-and-Play Model-Agnostic Counterfactual Policy Synthesis for Deep
Reinforcement Learning based Recommendation | Recent advances in recommender systems have proved the potential of Reinforcement Learning (RL) to handle the dynamic evolution processes between users and recommender systems. However, learning to train an optimal RL agent is generally impractical with commonly sparse user feedback data in the context of recommender systems. To circumvent the lack of interaction of current RL-based recommender systems, we propose to learn a general Model-Agnostic Counterfactual Synthesis (MACS) Policy for counterfactual user interaction data augmentation. The counterfactual synthesis policy aims to synthesise counterfactual states while preserving significant information in the original state relevant to the user's interests, building upon two different training approaches we designed: learning with expert demonstrations and joint training. As a result, the synthesis of each counterfactual data is based on the current recommendation agent's interaction with the environment to adapt to users' dynamic interests. We integrate the proposed policy Deep Deterministic Policy Gradient (DDPG), Soft Actor Critic (SAC) and Twin Delayed DDPG in an adaptive pipeline with a recommendation agent that can generate counterfactual data to improve the performance of recommendation. The empirical results on both online simulation and offline datasets demonstrate the effectiveness and generalisation of our counterfactual synthesis policy and verify that it improves the performance of RL recommendation agents. | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | 312,329 |
2207.08224 | Learning with Recoverable Forgetting | Life-long learning aims at learning a sequence of tasks without forgetting the previously acquired knowledge. However, the involved training data may not be life-long legitimate due to privacy or copyright reasons. In practical scenarios, for instance, the model owner may wish to enable or disable the knowledge of specific tasks or specific samples from time to time. Such flexible control over knowledge transfer, unfortunately, has been largely overlooked in previous incremental or decremental learning methods, even at a problem-setup level. In this paper, we explore a novel learning scheme, termed as Learning wIth Recoverable Forgetting (LIRF), that explicitly handles the task- or sample-specific knowledge removal and recovery. Specifically, LIRF brings in two innovative schemes, namely knowledge deposit and withdrawal, which allow for isolating user-designated knowledge from a pre-trained network and injecting it back when necessary. During the knowledge deposit process, the specified knowledge is extracted from the target network and stored in a deposit module, while the insensitive or general knowledge of the target network is preserved and further augmented. During knowledge withdrawal, the taken-off knowledge is added back to the target network. The deposit and withdraw processes only demand for a few epochs of finetuning on the removal data, ensuring both data and time efficiency. We conduct experiments on several datasets, and demonstrate that the proposed LIRF strategy yields encouraging results with gratifying generalization capability. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 308,509 |
2301.09937 | Explainable Deep Reinforcement Learning: State of the Art and Challenges | Interpretability, explainability and transparency are key issues to introducing Artificial Intelligence methods in many critical domains: This is important due to ethical concerns and trust issues strongly connected to reliability, robustness, auditability and fairness, and has important consequences towards keeping the human in the loop in high levels of automation, especially in critical cases for decision making, where both (human and the machine) play important roles. While the research community has given much attention to explainability of closed (or black) prediction boxes, there are tremendous needs for explainability of closed-box methods that support agents to act autonomously in the real world. Reinforcement learning methods, and especially their deep versions, are such closed-box methods. In this article we aim to provide a review of state of the art methods for explainable deep reinforcement learning methods, taking also into account the needs of human operators - i.e., of those that take the actual and critical decisions in solving real-world problems. We provide a formal specification of the deep reinforcement learning explainability problems, and we identify the necessary components of a general explainable reinforcement learning framework. Based on these, we provide a comprehensive review of state of the art methods, categorizing them in classes according to the paradigm they follow, the interpretable models they use, and the surface representation of explanations provided. The article concludes identifying open questions and important challenges. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 341,653 |
1806.01650 | A Possibility Distribution Based Multi-Criteria Decision Algorithm for
Resilient Supplier Selection Problems | Thus far, limited research has been performed on resilient supplier selection - a problem that requires simultaneous consideration of a set of numerical and linguistic evaluation criteria, which are substantially different from traditional supplier selection problem. Essentially, resilient supplier selection entails key sourcing decision for an organization to gain competitive advantage. In the presence of multiple conflicting evaluation criteria, contradicting decision makers, and imprecise decision relevant information (DRI), this problem becomes even more difficult to solve with the classical optimization approaches. However, prior research focusing on MCDA based supplier selection problem has been lacking in the ability to provide a seamless integration of numerical and linguistic evaluation criteria along with the consideration of multiple decision makers. To address these challenges, we present a comprehensive decision-making framework for ranking a set of suppliers from resiliency perspective. The proposed algorithm is capable of leveraging imprecise and aggregated DRI obtained from crisp numerical assessments and reliability adjusted linguistic appraisals from a group of decision makers. We adapt two popular tools - Single Valued Neutrosophic Sets (SVNS) and Interval-valued fuzzy sets (IVFS), and for the first time extend them to incorporate both crisp and linguistic evaluations in a group decision making platform to obtain aggregated SVNS and IVFS decision matrix. This information is then used to rank the resilient suppliers by using TOPSIS method. We present a case study to illustrate the mechanism of the proposed algorithm. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 99,599 |
2410.15158 | Automated Segmentation and Analysis of Cone Photoreceptors in Multimodal
Adaptive Optics Imaging | Accurate detection and segmentation of cone cells in the retina are essential for diagnosing and managing retinal diseases. In this study, we used advanced imaging techniques, including confocal and non-confocal split detector images from adaptive optics scanning light ophthalmoscopy (AOSLO), to analyze photoreceptors for improved accuracy. Precise segmentation is crucial for understanding each cone cell's shape, area, and distribution. It helps to estimate the surrounding areas occupied by rods, which allows the calculation of the density of cone photoreceptors in the area of interest. In turn, density is critical for evaluating overall retinal health and functionality. We explored two U-Net-based segmentation models: StarDist for confocal and Cellpose for calculated modalities. Analyzing cone cells in images from two modalities and achieving consistent results demonstrates the study's reliability and potential for clinical application. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 500,395 |
2202.01470 | Towards 3D Scene Reconstruction from Locally Scale-Aligned Monocular
Video Depth | Existing monocular depth estimation methods have achieved excellent robustness in diverse scenes, but they can only retrieve affine-invariant depth, up to an unknown scale and shift. However, in some video-based scenarios such as video depth estimation and 3D scene reconstruction from a video, the unknown scale and shift residing in per-frame prediction may cause the depth inconsistency. To solve this problem, we propose a locally weighted linear regression method to recover the scale and shift with very sparse anchor points, which ensures the scale consistency along consecutive frames. Extensive experiments show that our method can boost the performance of existing state-of-the-art approaches by 50% at most over several zero-shot benchmarks. Besides, we merge over 6.3 million RGBD images to train strong and robust depth models. Our produced ResNet50-backbone model even outperforms the state-of-the-art DPT ViT-Large model. Combining with geometry-based reconstruction methods, we formulate a new dense 3D scene reconstruction pipeline, which benefits from both the scale consistency of sparse points and the robustness of monocular methods. By performing the simple per-frame prediction over a video, the accurate 3D scene shape can be recovered. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 278,494 |
2411.16447 | Model-based reinforcement corrosion prediction: Continuous calibration
with Bayesian optimization and corrosion wire sensor data | Chloride-induced corrosion significantly contributes to the degradation of reinforced concrete structures, making accurate predictions of chloride migration and its effects on material durability critical. This paper explores two modeling approaches to estimate the effective diffusion coefficient for chloride transport. The first approach follows Gehlen's interpretable diffusion model, which is based on established physical principles and incorporates time and temperature dependencies in predicting chloride migration. The second approach is a neural network-based method, where the neural network approximates the effective diffusion coefficient. In a subsequent step, the calibrated models are used to predict the penetration depth of the critical chloride content, taking into account the uncertainty in the critical chloride content. The models are calibrated using experimental data measured by a wire sensor installed in a concrete test bridge. The calibration results are compared to effective diffusion coefficients derived from drilling dust samples. A comparison of both approaches reveals the advantages of the physics-based model in terms of transparency and interpretability, while the neural network model demonstrates flexibility and adaptability in data-driven predictions. This study emphasizes the importance of combining traditional and machine learning-based methods to improve the accuracy of chloride migration predictions in reinforced concrete. | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 511,030 |
cs/0104018 | Several new domain-type and boundary-type numerical discretization
schemes with radial basis function | This paper is concerned with a few novel RBF-based numerical schemes discretizing partial differential equations. For boundary-type methods, we derive the indirect and direct symmetric boundary knot methods (BKM). The resulting interpolation matrix of both is always symmetric irrespective of boundary geometry and conditions. In particular, the direct BKM applies the practical physical variables rather than expansion coefficients and becomes very competitive to the boundary element method. On the other hand, based on the multiple reciprocity principle, we invent the RBF-based boundary particle method (BPM) for general inhomogeneous problems without a need using inner nodes. The direct and symmetric BPM schemes are also developed. For domain-type RBF discretization schemes, by using the Green integral we develop a new Hermite RBF scheme called as the modified Kansa method (MKM), which differs from the symmetric Hermite RBF scheme in that the MKM discretizes both governing equation and boundary conditions on the same boundary nodes. The local spline version of the MKM is named as the finite knot method (FKM). Both MKM and FKM significantly reduce calculation errors at nodes adjacent to boundary. In addition, the nonsingular high-order fundamental or general solution is strongly recommended as the RBF in the domain-type methods and dual reciprocity method approximation of particular solution relating to the BKM. It is stressed that all the above discretization methods of boundary-type and domain-type are symmetric, meshless, and integration-free. The spline-based schemes will produce desirable symmetric sparse banded interpolation matrix. In appendix, we present a Hermite scheme to eliminate edge effect on the RBF geometric modeling and imaging. | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | 537,319 |
2106.15649 | Multi-Scale Spectrogram Modelling for Neural Text-to-Speech | We propose a novel Multi-Scale Spectrogram (MSS) modelling approach to synthesise speech with an improved coarse and fine-grained prosody. We present a generic multi-scale spectrogram prediction mechanism where the system first predicts coarser scale mel-spectrograms that capture the suprasegmental information in speech, and later uses these coarser scale mel-spectrograms to predict finer scale mel-spectrograms capturing fine-grained prosody. We present details for two specific versions of MSS called Word-level MSS and Sentence-level MSS where the scales in our system are motivated by the linguistic units. The Word-level MSS models word, phoneme, and frame-level spectrograms while Sentence-level MSS models sentence-level spectrogram in addition. Subjective evaluations show that Word-level MSS performs statistically significantly better compared to the baseline on two voices. | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 243,825 |
2312.02607 | Projective Space Stern Decoding and Application to SDitH | We show that here standard decoding algorithms for generic linear codes over a finite field can speeded up by a factor which is essentially the size of the finite field by reducing it to a low weight codeword problem and working in the relevant projective space. We apply this technique to SDitH and show that the parameters of both the original submission and the updated version fall short of meeting the security requirements asked by the NIST. | false | false | false | false | false | false | false | false | false | true | false | false | true | false | false | false | false | false | 412,942 |
1703.01168 | Sum-set Inequalities from Aligned Image Sets: Instruments for Robust
GDoF Bounds | We present sum-set inequalities specialized to the generalized degrees of freedom (GDoF) framework. These are information theoretic lower bounds on the entropy of bounded density linear combinations of discrete, power-limited dependent random variables in terms of the joint entropies of arbitrary linear combinations of new random variables that are obtained by power level partitioning of the original random variables. These bounds generalize the aligned image sets approach, and are useful instruments to obtain GDoF characterizations for wireless networks, especially with multiple antenna nodes, subject to arbitrary channel strength and channel uncertainty levels. To demonstrate the utility of these bounds, we consider a non-trivial instance of wireless networks - a two user interference channel with different number of antennas at each node, and different levels of partial channel knowledge available to the transmitters. We obtain tight GDoF characterization for specific instance of this channel with the aid of sum-set inequalities. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 69,306 |
2305.15842 | Text-to-Motion Retrieval: Towards Joint Understanding of Human Motion
Data and Natural Language | Due to recent advances in pose-estimation methods, human motion can be extracted from a common video in the form of 3D skeleton sequences. Despite wonderful application opportunities, effective and efficient content-based access to large volumes of such spatio-temporal skeleton data still remains a challenging problem. In this paper, we propose a novel content-based text-to-motion retrieval task, which aims at retrieving relevant motions based on a specified natural-language textual description. To define baselines for this uncharted task, we employ the BERT and CLIP language representations to encode the text modality and successful spatio-temporal models to encode the motion modality. We additionally introduce our transformer-based approach, called Motion Transformer (MoT), which employs divided space-time attention to effectively aggregate the different skeleton joints in space and time. Inspired by the recent progress in text-to-image/video matching, we experiment with two widely-adopted metric-learning loss functions. Finally, we set up a common evaluation protocol by defining qualitative metrics for assessing the quality of the retrieved motions, targeting the two recently-introduced KIT Motion-Language and HumanML3D datasets. The code for reproducing our results is available at https://github.com/mesnico/text-to-motion-retrieval. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 367,808 |
2303.11634 | Deep Q-Network Based Decision Making for Autonomous Driving | Currently decision making is one of the biggest challenges in autonomous driving. This paper introduces a method for safely navigating an autonomous vehicle in highway scenarios by combining deep Q-Networks and insight from control theory. A Deep Q-Network is trained in simulation to serve as a central decision-making unit by proposing targets for a trajectory planner. The generated trajectories in combination with a controller for longitudinal movement are used to execute lane change maneuvers. In order to prove the functionality of this approach it is evaluated on two different highway traffic scenarios. Furthermore, the impact of different state representations on the performance and training process is analyzed. The results show that the proposed system can produce efficient and safe driving behavior. | false | false | false | false | false | false | true | true | false | false | false | false | false | false | false | false | false | false | 352,935 |
0803.0146 | Polynomial time algorithms for bi-criteria, multi-objective and ratio
problems in clustering and imaging. Part I: Normalized cut and ratio regions | Partitioning and grouping of similar objects plays a fundamental role in image segmentation and in clustering problems. In such problems a typical goal is to group together similar objects, or pixels in the case of image processing. At the same time another goal is to have each group distinctly dissimilar from the rest and possibly to have the group size fairly large. These goals are often combined as a ratio optimization problem. One example of such problem is the normalized cut problem, another is the ratio regions problem. We devise here the first polynomial time algorithms solving these problems optimally. The algorithms are efficient and combinatorial. This contrasts with the heuristic approaches used in the image segmentation literature that formulate those problems as nonlinear optimization problems, which are then relaxed and solved with spectral techniques in real numbers. These approaches not only fail to deliver an optimal solution, but they are also computationally expensive. The algorithms presented here use as a subroutine a minimum $s,t-cut procedure on a related graph which is of polynomial size. The output consists of the optimal solution to the respective ratio problem, as well as a sequence of nested solution with respect to any relative weighting of the objectives of the numerator and denominator. An extension of the results here to bi-criteria and multi-criteria objective functions is presented in part II. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | true | 1,379 |
1005.4292 | Application Of Fuzzy System In Segmentation Of MRI Brain Tumor | Segmentation of images holds an important position in the area of image processing. It becomes more important whi le typically dealing with medical images where presurgery and post surgery decisions are required for the purpose of initiating and speeding up the recovery process. Segmentation of 3-D tumor structures from magnetic resonance images (MRI) is a very challenging problem due to the variability of tumor geometry and intensity patterns. Level set evolution combining global smoothness with the flexibility of topology changes offers significant advantages over the conventional statistical classification followed by mathematical morphology. Level set evolution with constant propagation needs to be initialized either completely inside or outside the tumor and can leak through weak or missing boundary parts. Replacing the constant propagation term by a statistical force overcomes these limitations and results in a convergence to a stable solution. Using MR images presenting tumors, probabilities for background and tumor regions are calculated from a pre- and post-contrast difference image and mixture modeling fit of the histogram. The whole image is used for initialization of the level set evolution to segment the tumor boundaries. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 6,553 |
1507.02449 | Finding trends and statistical patterns in name mentions in news | We extract the individual names of persons mentioned in news reports from a Philippine-based daily in the English language from 2010-2012. Names are extracted using a learning algorithm that filters adjacent capitalized words and runs it through a database of non-names grown through training. The number of mentions of individual names shows strong temporal fluctuations, indicative of the nature of "hot" trends and issues in society. Despite these strong variations, however, we observe stable rank-frequency distributions across different years in the form of power-laws with scaling exponents \alpha = 0.7, reminiscent of the Zipf's law observed in lexical (i.e. non-name) words. Additionally, we observe that the adjusted frequency for each rank, or the frequency divided by the number of unique names having the same rank, shows a distribution with dual scaling behavior, with the higher-ranked names preserving the \alpha exponent and the lower-ranked ones showing a power-law exponent \alpha' = 2.9. We reproduced the results using a model wherein the names are taken from a Barabasi-Albert network representing the social structure of the system. These results suggest that names, which represent individuals in the society, are archived differently from regular words. | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 44,989 |
2402.15039 | Descripci\'on autom\'atica de secciones delgadas de rocas: una
aplicaci\'on Web | The identification and characterization of various rock types is one of the fundamental activities for geology and related areas such as mining, petroleum, environment, industry and construction. Traditionally, a human specialist is responsible for analyzing and explaining details about the type, composition, texture, shape and other properties using rock samples collected in-situ or prepared in a laboratory. The results become subjective based on experience, in addition to consuming a large investment of time and effort. The present proposal uses artificial intelligence techniques combining computer vision and natural language processing to generate a textual and verbal description from a thin section image of rock. We build a dataset of images and their respective textual descriptions for the training of a model that associates the relevant features of the image extracted by EfficientNetB7 with the textual description generated by a Transformer network, reaching an accuracy value of 0.892 and a BLEU value of 0.71. This model can be a useful resource for research, professional and academic work, so it has been deployed through a Web application for public use. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 431,965 |
2310.00310 | An easy zero-shot learning combination: Texture Sensitive Semantic
Segmentation IceHrNet and Advanced Style Transfer Learning Strategy | We proposed an easy method of Zero-Shot semantic segmentation by using style transfer. In this case, we successfully used a medical imaging dataset (Blood Cell Imagery) to train a model for river ice semantic segmentation. First, we built a river ice semantic segmentation dataset IPC_RI_SEG using a fixed camera and covering the entire ice melting process of the river. Second, a high-resolution texture fusion semantic segmentation network named IceHrNet is proposed. The network used HRNet as the backbone and added ASPP and Decoder segmentation heads to retain low-level texture features for fine semantic segmentation. Finally, a simple and effective advanced style transfer learning strategy was proposed, which can perform zero-shot transfer learning based on cross-domain semantic segmentation datasets, achieving a practical effect of 87% mIoU for semantic segmentation of river ice without target training dataset (25% mIoU for None Stylized, 65% mIoU for Conventional Stylized, our strategy improved by 22%). Experiments showed that the IceHrNet outperformed the state-of-the-art methods on the texture-focused dataset IPC_RI_SEG, and achieved an excellent result on the shape-focused river ice datasets. In zero-shot transfer learning, IceHrNet achieved an increase of 2 percentage points compared to other methods. Our code and model are published on https://github.com/PL23K/IceHrNet. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 395,924 |
1912.13002 | Opytimizer: A Nature-Inspired Python Optimizer | Optimization aims at selecting a feasible set of parameters in an attempt to solve a particular problem, being applied in a wide range of applications, such as operations research, machine learning fine-tuning, and control engineering, among others. Nevertheless, traditional iterative optimization methods use the evaluation of gradients and Hessians to find their solutions, not being practical due to their computational burden and when working with non-convex functions. Recent biological-inspired methods, known as meta-heuristics, have arisen in an attempt to fulfill these problems. Even though they do not guarantee to find optimal solutions, they usually find a suitable solution. In this paper, we proposed a Python-based meta-heuristic optimization framework denoted as Opytimizer. Several methods and classes are implemented to provide a user-friendly workspace among diverse meta-heuristics, ranging from evolutionary- to swarm-based techniques. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | 158,987 |
1812.07868 | Crack Detection Using Enhanced Thresholding on UAV based Collected
Images | This paper proposes a thresholding approach for crack detection in an unmanned aerial vehicle (UAV) based infrastructure inspection system. The proposed algorithm performs recursively on the intensity histogram of UAV-taken images to exploit their crack-pixels appearing at the low intensity interval. A quantified criterion of interclass contrast is proposed and employed as an object cost and stop condition for the recursive process. Experiments on different datasets show that our algorithm outperforms different segmentation approaches to accurately extract crack features of some commercial buildings. | false | false | false | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | 116,894 |
1307.7562 | On the convergence of weighted-average consensus | In this note we give sufficient conditions for the convergence of the iterative algorithm called weighted-average consensus in directed graphs. We study the discrete-time form of this algorithm. We use standard techniques from matrix theory to prove the main result. As a particular case one can obtain well-known results for non-weighted average consensus. We also give a corollary for undirected graphs. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 26,119 |
2209.09898 | Text2Light: Zero-Shot Text-Driven HDR Panorama Generation | High-quality HDRIs(High Dynamic Range Images), typically HDR panoramas, are one of the most popular ways to create photorealistic lighting and 360-degree reflections of 3D scenes in graphics. Given the difficulty of capturing HDRIs, a versatile and controllable generative model is highly desired, where layman users can intuitively control the generation process. However, existing state-of-the-art methods still struggle to synthesize high-quality panoramas for complex scenes. In this work, we propose a zero-shot text-driven framework, Text2Light, to generate 4K+ resolution HDRIs without paired training data. Given a free-form text as the description of the scene, we synthesize the corresponding HDRI with two dedicated steps: 1) text-driven panorama generation in low dynamic range(LDR) and low resolution, and 2) super-resolution inverse tone mapping to scale up the LDR panorama both in resolution and dynamic range. Specifically, to achieve zero-shot text-driven panorama generation, we first build dual codebooks as the discrete representation for diverse environmental textures. Then, driven by the pre-trained CLIP model, a text-conditioned global sampler learns to sample holistic semantics from the global codebook according to the input text. Furthermore, a structure-aware local sampler learns to synthesize LDR panoramas patch-by-patch, guided by holistic semantics. To achieve super-resolution inverse tone mapping, we derive a continuous representation of 360-degree imaging from the LDR panorama as a set of structured latent codes anchored to the sphere. This continuous representation enables a versatile module to upscale the resolution and dynamic range simultaneously. Extensive experiments demonstrate the superior capability of Text2Light in generating high-quality HDR panoramas. In addition, we show the feasibility of our work in realistic rendering and immersive VR. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | true | 318,675 |
1612.08872 | Context-Free Path Querying with Structural Representation of Result | Graph data model and graph databases are very popular in various areas such as bioinformatics, semantic web, and social networks. One specific problem in the area is a path querying with constraints formulated in terms of formal grammars. The query in this approach is written as grammar, and paths querying is graph parsing with respect to given grammar. There are several solutions to it, but how to provide structural representation of query result which is practical for answer processing and debugging is still an open problem. In this paper we propose a graph parsing technique which allows one to build such representation with respect to given grammar in polynomial time and space for arbitrary context-free grammar and graph. Proposed algorithm is based on generalized LL parsing algorithm, while previous solutions are based mostly on CYK or Earley algorithms, which reduces time complexity in some cases. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | 66,126 |
2205.13921 | Federated Semi-Supervised Learning with Prototypical Networks | With the increasing computing power of edge devices, Federated Learning (FL) emerges to enable model training without privacy concerns. The majority of existing studies assume the data are fully labeled on the client side. In practice, however, the amount of labeled data is often limited. Recently, federated semi-supervised learning (FSSL) is explored as a way to effectively utilize unlabeled data during training. In this work, we propose ProtoFSSL, a novel FSSL approach based on prototypical networks. In ProtoFSSL, clients share knowledge with each other via lightweight prototypes, which prevents the local models from diverging. For computing loss on unlabeled data, each client creates accurate pseudo-labels based on shared prototypes. Jointly with labeled data, the pseudo-labels provide training signals for local prototypes. Compared to a FSSL approach based on weight sharing, the prototype-based inter-client knowledge sharing significantly reduces both communication and computation costs, enabling more frequent knowledge sharing between more clients for better accuracy. In multiple datasets, ProtoFSSL results in higher accuracy compared to the recent FSSL methods with and without knowledge sharing, such as FixMatch, FedRGD, and FedMatch. On SVHN dataset, ProtoFSSL performs comparably to fully supervised FL methods. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 299,140 |
2201.00649 | SAE: Sequential Anchored Ensembles | Computing the Bayesian posterior of a neural network is a challenging task due to the high-dimensionality of the parameter space. Anchored ensembles approximate the posterior by training an ensemble of neural networks on anchored losses designed for the optima to follow the Bayesian posterior. Training an ensemble, however, becomes computationally expensive as its number of members grows since the full training procedure is repeated for each member. In this note, we present Sequential Anchored Ensembles (SAE), a lightweight alternative to anchored ensembles. Instead of training each member of the ensemble from scratch, the members are trained sequentially on losses sampled with high auto-correlation, hence enabling fast convergence of the neural networks and efficient approximation of the Bayesian posterior. SAE outperform anchored ensembles, for a given computational budget, on some benchmarks while showing comparable performance on the others and achieved 2nd and 3rd place in the light and extended tracks of the NeurIPS 2021 Approximate Inference in Bayesian Deep Learning competition. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 274,025 |
2410.07658 | SeMv-3D: Towards Semantic and Mutil-view Consistency simultaneously for
General Text-to-3D Generation with Triplane Priors | Recent advancements in generic 3D content generation from text prompts have been remarkable by fine-tuning text-to-image diffusion (T2I) models or employing these T2I models as priors to learn a general text-to-3D model. While fine-tuning-based methods ensure great alignment between text and generated views, i.e., semantic consistency, their ability to achieve multi-view consistency is hampered by the absence of 3D constraints, even in limited view. In contrast, prior-based methods focus on regressing 3D shapes with any view that maintains uniformity and coherence across views, i.e., multi-view consistency, but such approaches inevitably compromise visual-textual alignment, leading to a loss of semantic details in the generated objects. To achieve semantic and multi-view consistency simultaneously, we propose SeMv-3D, a novel framework for general text-to-3d generation. Specifically, we propose a Triplane Prior Learner (TPL) that learns triplane priors with 3D spatial features to maintain consistency among different views at the 3D level, e.g., geometry and texture. Moreover, we design a Semantic-aligned View Synthesizer (SVS) that preserves the alignment between 3D spatial features and textual semantics in latent space. In SVS, we devise a simple yet effective batch sampling and rendering strategy that can generate arbitrary views in a single feed-forward inference. Extensive experiments present our SeMv-3D's superiority over state-of-the-art performances with semantic and multi-view consistency in any view. Our code and more visual results are available at https://anonymous.4open.science/r/SeMv-3D-6425. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 496,733 |
2204.02281 | Design Guidelines for Inclusive Speaker Verification Evaluation Datasets | Speaker verification (SV) provides billions of voice-enabled devices with access control, and ensures the security of voice-driven technologies. As a type of biometrics, it is necessary that SV is unbiased, with consistent and reliable performance across speakers irrespective of their demographic, social and economic attributes. Current SV evaluation practices are insufficient for evaluating bias: they are over-simplified and aggregate users, not representative of real-life usage scenarios, and consequences of errors are not accounted for. This paper proposes design guidelines for constructing SV evaluation datasets that address these short-comings. We propose a schema for grading the difficulty of utterance pairs, and present an algorithm for generating inclusive SV datasets. We empirically validate our proposed method in a set of experiments on the VoxCeleb1 dataset. Our results confirm that the count of utterance pairs/speaker, and the difficulty grading of utterance pairs have a significant effect on evaluation performance and variability. Our work contributes to the development of SV evaluation practices that are inclusive and fair. | false | false | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | 289,884 |
1609.00461 | Network clustering and community detection using modulus of families of
loops | We study the structure of loops in networks using the notion of modulus of loop families. We introduce a new measure of network clustering by quantifying the richness of families of (simple) loops. Modulus tries to minimize the expected overlap among loops by spreading the expected link-usage optimally. We propose weighting networks using these expected link-usages to improve classical community detection algorithms. We show that the proposed method enhances the performance of certain algorithms, such as spectral partitioning and modularity maximization heuristics, on standard benchmarks. | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 60,481 |
2011.01417 | Non-Equilibrium Skewness, Market Crises, and Option Pricing: Non-Linear
Langevin Model of Markets with Supersymmetry | This paper presents a tractable model of non-linear dynamics of market returns using a Langevin approach. Due to non-linearity of an interaction potential, the model admits regimes of both small and large return fluctuations. Langevin dynamics are mapped onto an equivalent quantum mechanical (QM) system. Borrowing ideas from supersymmetric quantum mechanics (SUSY QM), a parameterized ground state wave function (WF) of this QM system is used as a direct input to the model, which also fixes a non-linear Langevin potential. Using a two-component Gaussian mixture as a ground state WF with an asymmetric double well potential produces a tractable low-parametric model with interpretable parameters, referred to as the NES (Non-Equilibrium Skew) model. Supersymmetry (SUSY) is then used to find time-dependent solutions of the model in an analytically tractable way. Additional approximations give rise to a final practical version of the NES model, where real-measure and risk-neutral return distributions are given by three component Gaussian mixtures. This produces a closed-form approximation for option pricing in the NES model by a mixture of three Black-Scholes prices, providing accurate calibration to option prices for either benign or distressed market environments, while using only a single volatility parameter. These results stand in stark contrast to the most of other option pricing models such as local, stochastic, or rough volatility models that need more complex specifications of noise to fit the market data. | false | true | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 204,573 |
2105.09179 | On Interpretation and Measurement of Soft Attributes for Recommendation | We address how to robustly interpret natural language refinements (or critiques) in recommender systems. In particular, in human-human recommendation settings people frequently use soft attributes to express preferences about items, including concepts like the originality of a movie plot, the noisiness of a venue, or the complexity of a recipe. While binary tagging is extensively studied in the context of recommender systems, soft attributes often involve subjective and contextual aspects, which cannot be captured reliably in this way, nor be represented as objective binary truth in a knowledge base. This also adds important considerations when measuring soft attribute ranking. We propose a more natural representation as personalized relative statements, rather than as absolute item properties. We present novel data collection techniques and evaluation approaches, and a new public dataset. We also propose a set of scoring approaches, from unsupervised to weakly supervised to fully supervised, as a step towards interpreting and acting upon soft attribute based critiques. | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | 235,994 |
2305.06563 | Spatiotemporal Regularized Tucker Decomposition Approach for Traffic
Data Imputation | In intelligent transportation systems, traffic data imputation, estimating the missing value from partially observed data is an inevitable and challenging task. Previous studies have not fully considered traffic data's multidimensionality and spatiotemporal correlations, but they are vital to traffic data recovery, especially for high-level missing scenarios. To address this problem, we propose a novel spatiotemporal regularized Tucker decomposition method. First, the traffic matrix is converted into a third-order tensor. Then, based on Tucker decomposition, the tensor is approximated by multiplying non-negative factor matrices with a sparse core tensor. Notably, we do not need to set the tensor rank or determine it through matrix nuclear-norm minimization or tensor rank minimization. The low rankness is characterized by the $l_1$-norm of the core tensor, while the manifold regularization and temporal constraint are employed to capture spatiotemporal correlations and further improve imputation performance. We use an alternating proximal gradient method with guaranteed convergence to address the proposed model. Numerical experiments show that our proposal outperforms matrix-based and tensor-based baselines on real-world spatiotemporal traffic datasets in various missing scenarios. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 363,584 |
2405.00819 | ICU Bloodstream Infection Prediction: A Transformer-Based Approach for
EHR Analysis | We introduce RatchetEHR, a novel transformer-based framework designed for the predictive analysis of electronic health records (EHR) data in intensive care unit (ICU) settings, with a specific focus on bloodstream infection (BSI) prediction. Leveraging the MIMIC-IV dataset, RatchetEHR demonstrates superior predictive performance compared to other methods, including RNN, LSTM, and XGBoost, particularly due to its advanced handling of sequential and temporal EHR data. A key innovation in RatchetEHR is the integration of the Graph Convolutional Transformer (GCT) component, which significantly enhances the ability to identify hidden structural relationships within EHR data, resulting in more accurate clinical predictions. Through SHAP value analysis, we provide insights into influential features for BSI prediction. RatchetEHR integrates multiple advancements in deep learning which together provide accurate predictions even with a relatively small sample size and highly imbalanced dataset. This study contributes to medical informatics by showcasing the application of advanced AI techniques in healthcare and sets a foundation for further research to optimize these capabilities in EHR data analysis. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 451,090 |
2403.10100 | Efficient Multiplayer Battle Game Optimizer for Adversarial Robust
Neural Architecture Search | This paper introduces a novel metaheuristic algorithm, known as the efficient multiplayer battle game optimizer (EMBGO), specifically designed for addressing complex numerical optimization tasks. The motivation behind this research stems from the need to rectify identified shortcomings in the original MBGO, particularly in search operators during the movement phase, as revealed through ablation experiments. EMBGO mitigates these limitations by integrating the movement and battle phases to simplify the original optimization framework and improve search efficiency. Besides, two efficient search operators: differential mutation and L\'evy flight are introduced to increase the diversity of the population. To evaluate the performance of EMBGO comprehensively and fairly, numerical experiments are conducted on benchmark functions such as CEC2017, CEC2020, and CEC2022, as well as engineering problems. Twelve well-established MA approaches serve as competitor algorithms for comparison. Furthermore, we apply the proposed EMBGO to the complex adversarial robust neural architecture search (ARNAS) tasks and explore its robustness and scalability. The experimental results and statistical analyses confirm the efficiency and effectiveness of EMBGO across various optimization tasks. As a potential optimization technique, EMBGO holds promise for diverse applications in real-world problems and deep learning scenarios. The source code of EMBGO is made available in \url{https://github.com/RuiZhong961230/EMBGO}. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | 438,055 |
1909.12196 | Deep Video Deblurring: The Devil is in the Details | Video deblurring for hand-held cameras is a challenging task, since the underlying blur is caused by both camera shake and object motion. State-of-the-art deep networks exploit temporal information from neighboring frames, either by means of spatio-temporal transformers or by recurrent architectures. In contrast to these involved models, we found that a simple baseline CNN can perform astonishingly well when particular care is taken w.r.t. the details of model and training procedure. To that end, we conduct a comprehensive study regarding these crucial details, uncovering extreme differences in quantitative and qualitative performance. Exploiting these details allows us to boost the architecture and training procedure of a simple baseline CNN by a staggering 3.15dB, such that it becomes highly competitive w.r.t. cutting-edge networks. This raises the question whether the reported accuracy difference between models is always due to technical contributions or also subject to such orthogonal, but crucial details. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 147,049 |
2412.16160 | Online High-Frequency Trading Stock Forecasting with Automated Feature
Clustering and Radial Basis Function Neural Networks | This study presents an autonomous experimental machine learning protocol for high-frequency trading (HFT) stock price forecasting that involves a dual competitive feature importance mechanism and clustering via shallow neural network topology for fast training. By incorporating the k-means algorithm into the radial basis function neural network (RBFNN), the proposed method addresses the challenges of manual clustering and the reliance on potentially uninformative features. More specifically, our approach involves a dual competitive mechanism for feature importance, combining the mean-decrease impurity (MDI) method and a gradient descent (GD) based feature importance mechanism. This approach, tested on HFT Level 1 order book data for 20 S&P 500 stocks, enhances the forecasting ability of the RBFNN regressor. Our findings suggest that an autonomous approach to feature selection and clustering is crucial, as each stock requires a different input feature space. Overall, by automating the feature selection and clustering processes, we remove the need for manual topological grid search and provide a more efficient way to predict LOB's mid-price. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 519,382 |
2203.04044 | Single-trajectory map equation | Community detection, the process of identifying module structures in complex systems represented on networks, is an effective tool in various fields of science. The map equation, which is an information-theoretic framework based on the random walk on a network, is a particularly popular community detection method. Despite its outstanding performance in many applications, the inner workings of the map equation have not been thoroughly studied. Herein, we revisit the original formulation of the map equation and address the existence of its ``raw form,'' which we refer to as the single-trajectory map equation. This raw form sheds light on many details behind the principle of the map equation that are hidden in the steady-state limit of the random walk. Most importantly, the single-trajectory map equation provides a more balanced community structure, naturally reducing the tendency of the overfitting phenomenon in the map equation. | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 284,331 |
2204.11707 | Optimal security hardening over a probabilistic attack graph: a case
study of an industrial control system using the CySecTool tool | CySecTool is a tool that finds a cost-optimal security controls portfolio in a given budget for a probabilistic attack graph. A portfolio is a set of counter-measures, or controls, against vulnerabilities adopted for a computer system, while an attack graph is a type of a threat scenario model. In an attack graph, nodes are privilege states of the attacker, edges are vulnerabilities escalating privileges, and controls reduce the probabilities of some vulnerabilities being exploited. The tool builds on an optimisation algorithm published by Khouzani et al. (2019), enabling a user to quickly create, edit, and incrementally improve models, analyse results for given portfolios and display the best solutions for all possible budgets in the form of a Pareto frontier. A case study was performed utilising a system graph and suspected attack paths prepared by industrial security engineers based on an industrial source with which they work. The goal of the case study is to model a supervisory control and data acquisition (SCADA) industrial system which, due to having the potential to harm people, necessitates strong protection while not allowing the use of typical penetration tools like vulnerability scanners. Results are analysed to show how a cyber-security analyst would use CySecTool to store cyber-security intelligence and draw further conclusions. | false | false | false | false | false | false | false | false | false | false | true | false | true | false | false | false | false | false | 293,238 |
1511.02093 | Evaluation of the Hamming weights of a class of linear codes based on
Gauss sums | Linear codes with a few weights have been widely investigated in recent years. In this paper, we mainly use Gauss sums to represent the Hamming weights of a class of $q$-ary linear codes under some certain conditions, where $q$ is a power of a prime. The lower bound of its minimum Hamming distance is obtained. In some special cases, we evaluate the weight distributions of the linear codes by semi-primitive Gauss sums and obtain some one-weight, two-weight linear codes. It is quite interesting that we find new optimal codes achieving some bounds on linear codes. The linear codes in this paper can be used in secret sharing schemes, authentication codes and data storage systems. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 48,582 |
1103.4012 | On the accuracy of language trees | Historical linguistics aims at inferring the most likely language phylogenetic tree starting from information concerning the evolutionary relatedness of languages. The available information are typically lists of homologous (lexical, phonological, syntactic) features or characters for many different languages. From this perspective the reconstruction of language trees is an example of inverse problems: starting from present, incomplete and often noisy, information, one aims at inferring the most likely past evolutionary history. A fundamental issue in inverse problems is the evaluation of the inference made. A standard way of dealing with this question is to generate data with artificial models in order to have full access to the evolutionary process one is going to infer. This procedure presents an intrinsic limitation: when dealing with real data sets, one typically does not know which model of evolution is the most suitable for them. A possible way out is to compare algorithmic inference with expert classifications. This is the point of view we take here by conducting a thorough survey of the accuracy of reconstruction methods as compared with the Ethnologue expert classifications. We focus in particular on state-of-the-art distance-based methods for phylogeny reconstruction using worldwide linguistic databases. In order to assess the accuracy of the inferred trees we introduce and characterize two generalizations of standard definitions of distances between trees. Based on these scores we quantify the relative performances of the distance-based algorithms considered. Further we quantify how the completeness and the coverage of the available databases affect the accuracy of the reconstruction. Finally we draw some conclusions about where the accuracy of the reconstructions in historical linguistics stands and about the leading directions to improve it. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 9,695 |
1909.07587 | A Hybrid Deep Learning Approach for Diagnosis of the Erythemato-Squamous
Disease | The diagnosis of the Erythemato-squamous disease (ESD) is accepted as a difficult problem in dermatology. ESD is a form of skin disease. It generally causes redness of the skin and also may cause loss of skin. They are generally due to genetic or environmental factors. ESD comprises six classes of skin conditions namely, pityriasis rubra pilaris, lichen planus, chronic dermatitis, psoriasis, seboreic dermatitis and pityriasis rosea. The automated diagnosis of ESD can help doctors and dermatologists in reducing the efforts from their end and in taking faster decisions for treatment. The literature is replete with works that used conventional machine learning methods for the diagnosis of ESD. However, there isn't much instances of application of Deep learning for the diagnosis of ESD. In this paper, we propose a novel hybrid deep learning approach i.e. Derm2Vec for the diagnosis of the ESD. Derm2Vec is a hybrid deep learning model that consists of both Autoencoders and Deep Neural Networks. We also apply a conventional Deep Neural Network (DNN) for the classification of ESD. We apply both Derm2Vec and DNN along with other traditional machine learning methods on a real world dermatology dataset. The Derm2Vec method is found to be the best performer (when taking the prediction accuracy into account) followed by DNN and Extreme Gradient Boosting.The mean CV score of Derm2Vec, DNN and Extreme Gradient Boosting are 96.92 percent, 96.65 percent and 95.80 percent respectively. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 145,711 |
2401.02702 | VoxelNextFusion: A Simple, Unified and Effective Voxel Fusion Framework
for Multi-Modal 3D Object Detection | LiDAR-camera fusion can enhance the performance of 3D object detection by utilizing complementary information between depth-aware LiDAR points and semantically rich images. Existing voxel-based methods face significant challenges when fusing sparse voxel features with dense image features in a one-to-one manner, resulting in the loss of the advantages of images, including semantic and continuity information, leading to sub-optimal detection performance, especially at long distances. In this paper, we present VoxelNextFusion, a multi-modal 3D object detection framework specifically designed for voxel-based methods, which effectively bridges the gap between sparse point clouds and dense images. In particular, we propose a voxel-based image pipeline that involves projecting point clouds onto images to obtain both pixel- and patch-level features. These features are then fused using a self-attention to obtain a combined representation. Moreover, to address the issue of background features present in patches, we propose a feature importance module that effectively distinguishes between foreground and background features, thus minimizing the impact of the background features. Extensive experiments were conducted on the widely used KITTI and nuScenes 3D object detection benchmarks. Notably, our VoxelNextFusion achieved around +3.20% in AP@0.7 improvement for car detection in hard level compared to the Voxel R-CNN baseline on the KITTI test dataset | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 419,814 |
2112.09366 | Scenario-Based Safety Assessment Framework for Automated Vehicles | Automated vehicles (AVs) are expected to increase traffic safety and traffic efficiency, among others by enabling flexible mobility-on-demand systems. This is particularly important in Singapore, being one of the world's most densely populated countries, which is why the Singaporean authorities are currently actively facilitating the deployment of AVs. As a consequence, however, the need arises for a formal AV road approval procedure. To this end, a safety assessment framework is proposed, which combines aspects of the standardized functional safety design methodology with a traffic scenario-based approach. The latter involves using driving data to extract AV-relevant traffic scenarios. The underlying approach is based on decomposition of scenarios into elementary events, subsequent scenario parametrization, and sampling of the estimated probability density functions of the scenario parameters to create test scenarios. The resulting test scenarios are subsequently employed for virtual testing in a simulation environment and physical testing on a proving ground and in real life. As a result, the proposed assessment pipeline thus provides statistically relevant and quantitative measures for the AV performance in a relatively short time frame due to the simulation-based approach. Ultimately, the proposed methodology provides authorities with a formal road approval procedure for AVs. In particular, the proposed methodology will support the Singaporean Land Transport Authority for road approval of AVs. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 272,126 |
2012.01665 | Dual-Branch Network with Dual-Sampling Modulated Dice Loss for Hard
Exudate Segmentation from Colour Fundus Images | Automated segmentation of hard exudates in colour fundus images is a challenge task due to issues of extreme class imbalance and enormous size variation. This paper aims to tackle these issues and proposes a dual-branch network with dual-sampling modulated Dice loss. It consists of two branches: large hard exudate biased learning branch and small hard exudate biased learning branch. Both of them are responsible for their own duty separately. Furthermore, we propose a dual-sampling modulated Dice loss for the training such that our proposed dual-branch network is able to segment hard exudates in different sizes. In detail, for the first branch, we use a uniform sampler to sample pixels from predicted segmentation mask for Dice loss calculation, which leads to this branch naturally be biased in favour of large hard exudates as Dice loss generates larger cost on misidentification of large hard exudates than small hard exudates. For the second branch, we use a re-balanced sampler to oversample hard exudate pixels and undersample background pixels for loss calculation. In this way, cost on misidentification of small hard exudates is enlarged, which enforces the parameters in the second branch fit small hard exudates well. Considering that large hard exudates are much easier to be correctly identified than small hard exudates, we propose an easy-to-difficult learning strategy by adaptively modulating the losses of two branches. We evaluate our proposed method on two public datasets and results demonstrate that ours achieves state-of-the-art performances. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 209,485 |
2408.05502 | GEM: Context-Aware Gaze EstiMation with Visual Search Behavior Matching
for Chest Radiograph | Gaze estimation is pivotal in human scene comprehension tasks, particularly in medical diagnostic analysis. Eye-tracking technology facilitates the recording of physicians' ocular movements during image interpretation, thereby elucidating their visual attention patterns and information-processing strategies. In this paper, we initially define the context-aware gaze estimation problem in medical radiology report settings. To understand the attention allocation and cognitive behavior of radiologists during the medical image interpretation process, we propose a context-aware Gaze EstiMation (GEM) network that utilizes eye gaze data collected from radiologists to simulate their visual search behavior patterns throughout the image interpretation process. It consists of a context-awareness module, visual behavior graph construction, and visual behavior matching. Within the context-awareness module, we achieve intricate multimodal registration by establishing connections between medical reports and images. Subsequently, for a more accurate simulation of genuine visual search behavior patterns, we introduce a visual behavior graph structure, capturing such behavior through high-order relationships (edges) between gaze points (nodes). To maintain the authenticity of visual behavior, we devise a visual behavior-matching approach, adjusting the high-order relationships between them by matching the graph constructed from real and estimated gaze points. Extensive experiments on four publicly available datasets demonstrate the superiority of GEM over existing methods and its strong generalizability, which also provides a new direction for the effective utilization of diverse modalities in medical image interpretation and enhances the interpretability of models in the field of medical imaging. https://github.com/Tiger-SN/GEM | false | false | false | false | false | false | false | false | true | false | false | true | false | false | false | false | false | false | 479,818 |
2406.16151 | Monte Carlo Planning for Stochastic Control on Constrained Markov
Decision Processes | In the world of stochastic control, especially in economics and engineering, Markov Decision Processes (MDPs) can effectively model various stochastic decision processes, from asset management to transportation optimization. These underlying MDPs, upon closer examination, often reveal a specifically constrained causal structure concerning the transition and reward dynamics. By exploiting this structure, we can obtain a reduction in the causal representation of the problem setting, allowing us to solve of the optimal value function more efficiently. This work defines an MDP framework, the \texttt{SD-MDP}, where we disentangle the causal structure of MDPs' transition and reward dynamics, providing distinct partitions on the temporal causal graph. With this stochastic reduction, the \texttt{SD-MDP} reflects a general class of resource allocation problems. This disentanglement further enables us to derive theoretical guarantees on the estimation error of the value function under an optimal policy by allowing independent value estimation from Monte Carlo sampling. Subsequently, by integrating this estimator into well-known Monte Carlo planning algorithms, such as Monte Carlo Tree Search (MCTS), we derive bounds on the simple regret of the algorithm. Finally, we quantify the policy improvement of MCTS under the \texttt{SD-MDP} framework by demonstrating that the MCTS planning algorithm achieves higher expected reward (lower costs) under a constant simulation budget, on a tangible economic example based on maritime refuelling. | false | false | false | false | true | false | true | false | false | false | true | false | false | false | false | false | false | false | 467,018 |
2305.10744 | Online Resource Allocation in Episodic Markov Decision Processes | This paper studies a long-term resource allocation problem over multiple periods where each period requires a multi-stage decision-making process. We formulate the problem as an online allocation problem in an episodic finite-horizon constrained Markov decision process with an unknown non-stationary transition function and stochastic non-stationary reward and resource consumption functions. We propose the observe-then-decide regime and improve the existing decide-then-observe regime, while the two settings differ in how the observations and feedback about the reward and resource consumption functions are given to the decision-maker. We develop an online dual mirror descent algorithm that achieves near-optimal regret bounds for both settings. For the observe-then-decide regime, we prove that the expected regret against the dynamic clairvoyant optimal policy is bounded by $\tilde O(\rho^{-1}{H^{3/2}}S\sqrt{AT})$ where $\rho\in(0,1)$ is the budget parameter, $H$ is the length of the horizon, $S$ and $A$ are the numbers of states and actions, and $T$ is the number of episodes. For the decide-then-observe regime, we show that the regret against the static optimal policy that has access to the mean reward and mean resource consumption functions is bounded by $\tilde O(\rho^{-1}{H^{3/2}}S\sqrt{AT})$ with high probability. We test the numerical efficiency of our method for a variant of the resource-constrained inventory management problem. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 365,212 |
2311.14390 | Directly Attention Loss Adjusted Prioritized Experience Replay | Prioritized Experience Replay (PER) enables the model to learn more about relatively important samples by artificially changing their accessed frequencies. However, this non-uniform sampling method shifts the state-action distribution that is originally used to estimate Q-value functions, which brings about the estimation deviation. In this article, an novel off policy reinforcement learning training framework called Directly Attention Loss Adjusted Prioritized Experience Replay (DALAP) is proposed, which can directly quantify the changed extent of the shifted distribution through Parallel Self-Attention network, so as to accurately compensate the error. In addition, a Priority-Encouragement mechanism is designed simultaneously to optimize the sample screening criterion, and further improve the training efficiency. In order to verify the effectiveness and generality of DALAP, we integrate it with the value-function based, the policy-gradient based and multi-agent reinforcement learning algorithm, respectively. The multiple groups of comparative experiments show that DALAP has the significant advantages of both improving the convergence rate and reducing the training variance. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 410,092 |
2112.14949 | Decentralized Optimization Over the Stiefel Manifold by an Approximate
Augmented Lagrangian Function | In this paper, we focus on the decentralized optimization problem over the Stiefel manifold, which is defined on a connected network of $d$ agents. The objective is an average of $d$ local functions, and each function is privately held by an agent and encodes its data. The agents can only communicate with their neighbors in a collaborative effort to solve this problem. In existing methods, multiple rounds of communications are required to guarantee the convergence, giving rise to high communication costs. In contrast, this paper proposes a decentralized algorithm, called DESTINY, which only invokes a single round of communications per iteration. DESTINY combines gradient tracking techniques with a novel approximate augmented Lagrangian function. The global convergence to stationary points is rigorously established. Comprehensive numerical experiments demonstrate that DESTINY has a strong potential to deliver a cutting-edge performance in solving a variety of testing problems. | false | false | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | 273,661 |
2211.02940 | Effective Audio Classification Network Based on Paired Inverse Pyramid
Structure and Dense MLP Block | Recently, massive architectures based on Convolutional Neural Network (CNN) and self-attention mechanisms have become necessary for audio classification. While these techniques are state-of-the-art, these works' effectiveness can only be guaranteed with huge computational costs and parameters, large amounts of data augmentation, transfer from large datasets and some other tricks. By utilizing the lightweight nature of audio, we propose an efficient network structure called Paired Inverse Pyramid Structure (PIP) and a network called Paired Inverse Pyramid Structure MLP Network (PIPMN). The PIPMN reaches 96\% of Environmental Sound Classification (ESC) accuracy on the UrbanSound8K dataset and 93.2\% of Music Genre Classification (MGC) on the GTAZN dataset, with only 1 million parameters. Both of the results are achieved without data augmentation or model transfer. Public code is available at: https://github.com/JNAIC/PIPMN | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 328,761 |
2311.06834 | Osteoporosis Prediction from Hand and Wrist X-rays using Image
Segmentation and Self-Supervised Learning | Osteoporosis is a widespread and chronic metabolic bone disease that often remains undiagnosed and untreated due to limited access to bone mineral density (BMD) tests like Dual-energy X-ray absorptiometry (DXA). In response to this challenge, current advancements are pivoting towards detecting osteoporosis by examining alternative indicators from peripheral bone areas, with the goal of increasing screening rates without added expenses or time. In this paper, we present a method to predict osteoporosis using hand and wrist X-ray images, which are both widely accessible and affordable, though their link to DXA-based data is not thoroughly explored. Initially, our method segments the ulnar, radius, and metacarpal bones using a foundational model for image segmentation. Then, we use a self-supervised learning approach to extract meaningful representations without the need for explicit labels, and move on to classify osteoporosis in a supervised manner. Our method is evaluated on a dataset with 192 individuals, cross-referencing their verified osteoporosis conditions against the standard DXA test. With a notable classification score (AUC=0.83), our model represents a pioneering effort in leveraging vision-based techniques for osteoporosis identification from the peripheral skeleton sites. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 407,096 |
2110.12786 | Dictionary Learning Using Rank-One Atomic Decomposition (ROAD) | Dictionary learning aims at seeking a dictionary under which the training data can be sparsely represented. Methods in the literature typically formulate the dictionary learning problem as an optimization w.r.t. two variables, i.e., dictionary and sparse coefficients, and solve it by alternating between two stages: sparse coding and dictionary update. The key contribution of this work is a Rank-One Atomic Decomposition (ROAD) formulation where dictionary learning is cast as an optimization w.r.t. a single variable which is a set of rank one matrices. The resulting algorithm is hence single-stage. Compared with two-stage algorithms, ROAD minimizes the sparsity of the coefficients whilst keeping the data consistency constraint throughout the whole learning process. An alternating direction method of multipliers (ADMM) is derived to solve the optimization problem and the lower bound of the penalty parameter is computed to guarantees a global convergence despite non-convexity of the optimization formulation. From practical point of view, ROAD reduces the number of tuning parameters required in other benchmark algorithms. Numerical tests demonstrate that ROAD outperforms other benchmark algorithms for both synthetic data and real data, especially when the number of training samples is small. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 262,968 |
2405.13686 | Embedding Generalized Semantic Knowledge into Few-Shot Remote Sensing
Segmentation | Few-shot segmentation (FSS) for remote sensing (RS) imagery leverages supporting information from limited annotated samples to achieve query segmentation of novel classes. Previous efforts are dedicated to mining segmentation-guiding visual cues from a constrained set of support samples. However, they still struggle to address the pronounced intra-class differences in RS images, as sparse visual cues make it challenging to establish robust class-specific representations. In this paper, we propose a holistic semantic embedding (HSE) approach that effectively harnesses general semantic knowledge, i.e., class description (CD) embeddings.Instead of the naive combination of CD embeddings and visual features for segmentation decoding, we investigate embedding the general semantic knowledge during the feature extraction stage.Specifically, in HSE, a spatial dense interaction module allows the interaction of visual support features with CD embeddings along the spatial dimension via self-attention.Furthermore, a global content modulation module efficiently augments the global information of the target category in both support and query features, thanks to the transformative fusion of visual features and CD embeddings.These two components holistically synergize general CD embeddings and visual cues, constructing a robust class-specific representation.Through extensive experiments on the standard FSS benchmark, the proposed HSE approach demonstrates superior performance compared to peer work, setting a new state-of-the-art. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 456,036 |
1812.07069 | An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep
Reinforcement Learning Agents | Much human and computational effort has aimed to improve how deep reinforcement learning algorithms perform on benchmarks such as the Atari Learning Environment. Comparatively less effort has focused on understanding what has been learned by such methods, and investigating and comparing the representations learned by different families of reinforcement learning (RL) algorithms. Sources of friction include the onerous computational requirements, and general logistical and architectural complications for running Deep RL algorithms at scale. We lessen this friction, by (1) training several algorithms at scale and releasing trained models, (2) integrating with a previous Deep RL model release, and (3) releasing code that makes it easy for anyone to load, visualize, and analyze such models. This paper introduces the Atari Zoo framework, which contains models trained across benchmark Atari games, in an easy-to-use format, as well as code that implements common modes of analysis and connects such models to a popular neural network visualization library. Further, to demonstrate the potential of this dataset and software package, we show initial quantitative and qualitative comparisons between the performance and representations of several deep RL algorithms, highlighting interesting and previously unknown distinctions between them. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | 116,737 |
2309.15442 | Template Model Inspired Task Space Learning for Robust Bipedal
Locomotion | This work presents a hierarchical framework for bipedal locomotion that combines a Reinforcement Learning (RL)-based high-level (HL) planner policy for the online generation of task space commands with a model-based low-level (LL) controller to track the desired task space trajectories. Different from traditional end-to-end learning approaches, our HL policy takes insights from the angular momentum-based linear inverted pendulum (ALIP) to carefully design the observation and action spaces of the Markov Decision Process (MDP). This simple yet effective design creates an insightful mapping between a low-dimensional state that effectively captures the complex dynamics of bipedal locomotion and a set of task space outputs that shape the walking gait of the robot. The HL policy is agnostic to the task space LL controller, which increases the flexibility of the design and generalization of the framework to other bipedal robots. This hierarchical design results in a learning-based framework with improved performance, data efficiency, and robustness compared with the ALIP model-based approach and state-of-the-art learning-based frameworks for bipedal locomotion. The proposed hierarchical controller is tested in three different robots, Rabbit, a five-link underactuated planar biped; Walker2D, a seven-link fully-actuated planar biped; and Digit, a 3D humanoid robot with 20 actuated joints. The trained policy naturally learns human-like locomotion behaviors and is able to effectively track a wide range of walking speeds while preserving the robustness and stability of the walking gait even under adversarial conditions. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 394,969 |
2003.02542 | Efficient and Effective Similar Subtrajectory Search with Deep
Reinforcement Learning | Similar trajectory search is a fundamental problem and has been well studied over the past two decades. However, the similar subtrajectory search (SimSub) problem, aiming to return a portion of a trajectory (i.e., a subtrajectory) which is the most similar to a query trajectory, has been mostly disregarded despite that it could capture trajectory similarity in a finer-grained way and many applications take subtrajectories as basic units for analysis. In this paper, we study the SimSub problem and develop a suite of algorithms including both exact and approximate ones. Among those approximate algorithms, two that are based on deep reinforcement learning stand out and outperform those non-learning based algorithms in terms of effectiveness and efficiency. We conduct experiments on real-world trajectory datasets, which verify the effectiveness and efficiency of the proposed algorithms. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | true | false | 166,964 |
2409.12390 | A Novel Perspective for Multi-modal Multi-label Skin Lesion
Classification | The efficacy of deep learning-based Computer-Aided Diagnosis (CAD) methods for skin diseases relies on analyzing multiple data modalities (i.e., clinical+dermoscopic images, and patient metadata) and addressing the challenges of multi-label classification. Current approaches tend to rely on limited multi-modal techniques and treat the multi-label problem as a multiple multi-class problem, overlooking issues related to imbalanced learning and multi-label correlation. This paper introduces the innovative Skin Lesion Classifier, utilizing a Multi-modal Multi-label TransFormer-based model (SkinM2Former). For multi-modal analysis, we introduce the Tri-Modal Cross-attention Transformer (TMCT) that fuses the three image and metadata modalities at various feature levels of a transformer encoder. For multi-label classification, we introduce a multi-head attention (MHA) module to learn multi-label correlations, complemented by an optimisation that handles multi-label and imbalanced learning problems. SkinM2Former achieves a mean average accuracy of 77.27% and a mean diagnostic accuracy of 77.85% on the public Derm7pt dataset, outperforming state-of-the-art (SOTA) methods. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 489,562 |
1911.01486 | Probabilistic Super-Resolution of Solar Magnetograms: Generating Many
Explanations and Measuring Uncertainties | Machine learning techniques have been successfully applied to super-resolution tasks on natural images where visually pleasing results are sufficient. However in many scientific domains this is not adequate and estimations of errors and uncertainties are crucial. To address this issue we propose a Bayesian framework that decomposes uncertainties into epistemic and aleatoric uncertainties. We test the validity of our approach by super-resolving images of the Sun's magnetic field and by generating maps measuring the range of possible high resolution explanations compatible with a given low resolution magnetogram. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 152,110 |
2006.07063 | Privacy Against Adversarial Classification in Cyber-Physical Systems | For a class of Cyber-Physical Systems (CPSs), we address the problem of performing computations over the cloud without revealing private information about the structure and operation of the system. We model CPSs as a collection of input-output dynamical systems (the system operation modes). Depending on the mode the system is operating on, the output trajectory is generated by one of these systems in response to driving inputs. Output measurements and driving inputs are sent to the cloud for processing purposes. We capture this "processing" through some function (of the input-output trajectory) that we require the cloud to compute accurately - referred here as the trajectory utility. However, for privacy reasons, we would like to keep the mode private, i.e., we do not want the cloud to correctly identify what mode of the CPS produced a given trajectory. To this end, we distort trajectories before transmission and send the corrupted data to the cloud. We provide mathematical tools (based on output-regulation techniques) to properly design distorting mechanisms so that: 1) the original and distorted trajectories lead to the same utility; and the distorted data leads the cloud to misclassify the mode. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 181,675 |
2208.06734 | An Answer Verbalization Dataset for Conversational Question Answerings
over Knowledge Graphs | We introduce a new dataset for conversational question answering over Knowledge Graphs (KGs) with verbalized answers. Question answering over KGs is currently focused on answer generation for single-turn questions (KGQA) or multiple-tun conversational question answering (ConvQA). However, in a real-world scenario (e.g., voice assistants such as Siri, Alexa, and Google Assistant), users prefer verbalized answers. This paper contributes to the state-of-the-art by extending an existing ConvQA dataset with multiple paraphrased verbalized answers. We perform experiments with five sequence-to-sequence models on generating answer responses while maintaining grammatical correctness. We additionally perform an error analysis that details the rates of models' mispredictions in specified categories. Our proposed dataset extended with answer verbalization is publicly available with detailed documentation on its usage for wider utility. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 312,809 |
1812.10422 | Machine Learning in Official Statistics | In the first half of 2018, the Federal Statistical Office of Germany (Destatis) carried out a "Proof of Concept Machine Learning" as part of its Digital Agenda. A major component of this was surveys on the use of machine learning methods in official statistics, which were conducted at selected national and international statistical institutions and among the divisions of Destatis. It was of particular interest to find out in which statistical areas and for which tasks machine learning is used and which methods are applied. This paper is intended to make the results of the surveys publicly accessible. | false | false | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | 117,363 |
2406.09982 | Constrained Motion Planning for a Robotic Endoscope Holder based on
Hierarchical Quadratic Programming | Minimally Invasive Surgeries (MIS) are challenging for surgeons due to the limited field of view and constrained range of motion imposed by narrow access ports. These challenges can be addressed by robot-assisted endoscope systems which provide precise and stabilized positioning, as well as constrained and smooth motion control of the endoscope. In this work, we propose an online hierarchical optimization framework for visual servoing control of the endoscope in MIS. The framework prioritizes maintaining a remote-center-of-motion (RCM) constraint to prevent tissue damage, while a visual tracking task is defined as a secondary task to enable autonomous tracking of visual features of interest. We validated our approach using a 6-DOF Denso VS050 manipulator and achieved optimization solving times under 0.4 ms and maximum RCM deviation of approximately 0.4 mm. Our results demonstrate the effectiveness of the proposed approach in addressing the constrained motion planning challenges of MIS, enabling precise and autonomous endoscope positioning and visual tracking. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 464,175 |
2011.13417 | Generative Layout Modeling using Constraint Graphs | We propose a new generative model for layout generation. We generate layouts in three steps. First, we generate the layout elements as nodes in a layout graph. Second, we compute constraints between layout elements as edges in the layout graph. Third, we solve for the final layout using constrained optimization. For the first two steps, we build on recent transformer architectures. The layout optimization implements the constraints efficiently. We show three practical contributions compared to the state of the art: our work requires no user input, produces higher quality layouts, and enables many novel capabilities for conditional layout generation. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | true | 208,467 |
2103.09666 | Multimodal End-to-End Sparse Model for Emotion Recognition | Existing works on multimodal affective computing tasks, such as emotion recognition, generally adopt a two-phase pipeline, first extracting feature representations for each single modality with hand-crafted algorithms and then performing end-to-end learning with the extracted features. However, the extracted features are fixed and cannot be further fine-tuned on different target tasks, and manually finding feature extraction algorithms does not generalize or scale well to different tasks, which can lead to sub-optimal performance. In this paper, we develop a fully end-to-end model that connects the two phases and optimizes them jointly. In addition, we restructure the current datasets to enable the fully end-to-end training. Furthermore, to reduce the computational overhead brought by the end-to-end model, we introduce a sparse cross-modal attention mechanism for the feature extraction. Experimental results show that our fully end-to-end model significantly surpasses the current state-of-the-art models based on the two-phase pipeline. Moreover, by adding the sparse cross-modal attention, our model can maintain performance with around half the computation in the feature extraction part. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 225,226 |
1407.2232 | Toward Using Surrogates to Accelerate Solution of Stochastic Electricity
Grid Operations Problems | Stochastic unit commitment models typically handle uncertainties in forecast demand by considering a finite number of realizations from a stochastic process model for loads. Accurate evaluations of expectations or higher moments for the quantities of interest require a prohibitively large number of model evaluations. In this paper we propose an alternative approach based on using surrogate models valid over the range of the forecast uncertainty. We consider surrogate models based on Polynomial Chaos expansions, constructed using sparse quadrature methods. Considering expected generation cost, we demonstrate the approach can lead to several orders of magnitude reduction in computational cost relative to using Monte Carlo sampling on the original model, for a given target error threshold. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 34,512 |
1910.14377 | Image-Guided Depth Upsampling via Hessian and TV Priors | We propose a method that combines sparse depth (LiDAR) measurements with an intensity image and to produce a dense high-resolution depth image. As there are few, but accurate, depth measurements from the scene, our method infers the remaining depth values by incorporating information from the intensity image, namely the magnitudes and directions of the identified edges, and by assuming that the scene is composed mostly of flat surfaces. Such inference is achieved by solving a convex optimisation problem with properly weighted regularisers that are based on the `1-norm (specifically, on total variation). We solve the resulting problem with a computationally efficient ADMM-based algorithm. Using the SYNTHIA and KITTI datasets, our experiments show that the proposed method achieves a depth reconstruction performance comparable to or better than other model-based methods. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 151,633 |
2306.14901 | Phonon dynamic behaviors induced by amorphous interlayer at
heterointerfaces | Interface impedes heat flow in heterostructures and the interfacial thermal resistance (ITR) has become a critical issue for thermal dissipation in electronic devices. To explore the mechanism leading to the ITR, in this work, the dynamic behaviors of phonons passing through the GaN/AlN interface with an amorphous interlayer is investigated by using phonon wave packet simulation. It is found the amorphous interlayer significantly impedes phonon transport across the interface, and leads to remarkable phonon mode conversions, such as LA$\rightarrow$TA, TA$\rightarrow$LA, and LA$\rightarrow$TO conversion. However, due to mode conversion and inelastic scattering, we found a portion of high-frequency TA phonons, which are higher than the cut-off frequency and cannot transmit across the ideal sharp interface, can partially transmit across the amorphous interlayer, which introduces additional thermal transport channels through the interface and has positive effect on interfacial thermal conductance. According to phonon transmission coefficient, it is found the ITR increases with increasing of amorphous interlayer thickness L. The phonon transmission coefficient exhibits an obvious oscillation behavior, which is attributed to the multiple phonon scattering in the amorphous interlayer, and the oscillation period is further revealed to be consistent with the theoretical prediction by the two-beam interference equation. In addition, obvious phonon frequency shifts and phonon energy localization phenomena were observed in the amorphous interlayer. Finally, to improve phonon transmission, the interface morphology was further optimized via the annealing reconstruction technique, which results in re-crystallization of the amorphous interlayer and the decrease of ITR by ~21% as L=2 nm. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 375,855 |
2304.10663 | Meta Semantics: Towards better natural language understanding and
reasoning | Natural language understanding is one of the most challenging topics in artificial intelligence. Deep neural network methods, particularly large language module (LLM) methods such as ChatGPT and GPT-3, have powerful flexibility to adopt informal text but are weak on logical deduction and suffer from the out-of-vocabulary (OOV) problem. On the other hand, rule-based methods such as Mathematica, Semantic web, and Lean, are excellent in reasoning but cannot handle the complex and changeable informal text. Inspired by pragmatics and structuralism, we propose two strategies to solve the OOV problem and a semantic model for better natural language understanding and reasoning. | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 359,500 |
2012.11486 | Leaf Segmentation and Counting with Deep Learning: on Model Certainty,
Test-Time Augmentation, Trade-Offs | Plant phenotyping tasks such as leaf segmentation and counting are fundamental to the study of phenotypic traits. Since it is well-suited for these tasks, deep supervised learning has been prevalent in recent works proposing better performing models at segmenting and counting leaves. Despite good efforts from research groups, one of the main challenges for proposing better methods is still the limitation of labelled data availability. The main efforts of the field seem to be augmenting existing limited data sets, and some aspects of the modelling process have been under-discussed. This paper explores such topics and present experiments that led to the development of the best-performing method in the Leaf Segmentation Challenge and in another external data set of Komatsuna plants. The model has competitive performance while been arguably simpler than other recently proposed ones. The experiments also brought insights such as the fact that model cardinality and test-time augmentation may have strong applications in object segmentation of single class and high occlusion, and regarding the data distribution of recently proposed data sets for benchmarking. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 212,650 |
2401.02135 | PosCUDA: Position based Convolution for Unlearnable Audio Datasets | Deep learning models require large amounts of clean data to acheive good performance. To avoid the cost of expensive data acquisition, researchers use the abundant data available on the internet. This raises significant privacy concerns on the potential misuse of personal data for model training without authorisation. Recent works such as CUDA propose solutions to this problem by adding class-wise blurs to make datasets unlearnable, i.e a model can never use the acquired dataset for learning. However these methods often reduce the quality of the data making it useless for practical applications. We introduce PosCUDA, a position based convolution for creating unlearnable audio datasets. PosCUDA uses class-wise convolutions on small patches of audio. The location of the patches are based on a private key for each class, hence the model learns the relations between positional blurs and labels, while failing to generalize. We empirically show that PosCUDA can achieve unlearnability while maintaining the quality of the original audio datasets. Our proposed method is also robust to different audio feature representations such as MFCC, raw audio and different architectures such as transformers, convolutional networks etc. | false | false | true | false | false | false | true | false | false | false | false | false | true | false | false | false | false | false | 419,614 |
2411.10614 | To Shuffle or not to Shuffle: Auditing DP-SGD with Shuffling | Differentially Private Stochastic Gradient Descent (DP-SGD) is a popular method for training machine learning models with formal Differential Privacy (DP) guarantees. As DP-SGD processes the training data in batches, it uses Poisson sub-sampling to select batches at each step. However, due to computational and compatibility benefits, replacing sub-sampling with shuffling has become common practice. Yet, since tight theoretical guarantees for shuffling are currently unknown, prior work using shuffling reports DP guarantees as though Poisson sub-sampling was used. This prompts the need to verify whether this discrepancy is reflected in a gap between the theoretical guarantees from state-of-the-art models and the actual privacy leakage. To do so, we introduce a novel DP auditing procedure to analyze DP-SGD with shuffling. We show that state-of-the-art DP models trained with shuffling appreciably overestimated privacy guarantees (up to 4x). In the process, we assess the impact of several parameters, such as batch size, privacy budget, and threat model, on privacy leakage. Finally, we study two variations of the shuffling procedure found in the wild, which result in further privacy leakage. Overall, our work empirically attests to the risk of using shuffling instead of Poisson sub-sampling vis-\`a-vis the actual privacy leakage of DP-SGD. | false | false | false | false | false | false | true | false | false | false | false | false | true | false | false | false | false | false | 508,715 |
2407.10543 | Understanding the Dependence of Perception Model Competency on Regions
in an Image | While deep neural network (DNN)-based perception models are useful for many applications, these models are black boxes and their outputs are not yet well understood. To confidently enable a real-world, decision-making system to utilize such a perception model without human intervention, we must enable the system to reason about the perception model's level of competency and respond appropriately when the model is incompetent. In order for the system to make an intelligent decision about the appropriate action when the model is incompetent, it would be useful for the system to understand why the model is incompetent. We explore five novel methods for identifying regions in the input image contributing to low model competency, which we refer to as image cropping, segment masking, pixel perturbation, competency gradients, and reconstruction loss. We assess the ability of these five methods to identify unfamiliar objects, recognize regions associated with unseen classes, and identify unexplored areas in an environment. We find that the competency gradients and reconstruction loss methods show great promise in identifying regions associated with low model competency, particularly when aspects of the image that are unfamiliar to the perception model are causing this reduction in competency. Both of these methods boast low computation times and high levels of accuracy in detecting image regions that are unfamiliar to the model, allowing them to provide potential utility in decision-making pipelines. The code for reproducing our methods and results is available on GitHub: https://github.com/sarapohland/explainable-competency. | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | false | false | 473,026 |
2403.18393 | Tensor-based Graph Learning with Consistency and Specificity for
Multi-view Clustering | In the context of multi-view clustering, graph learning is recognized as a crucial technique, which generally involves constructing an adaptive neighbor graph based on probabilistic neighbors, and then learning a consensus graph to for clustering. However, they are confronted with two limitations. Firstly, they often rely on Euclidean distance to measure similarity when constructing the adaptive neighbor graph, which proves inadequate in capturing the intrinsic structure among data points in practice. Secondly, most of these methods focus solely on consensus graph, ignoring unique information from each view. Although a few graph-based studies have considered using specific information as well, the modelling approach employed does not exclude the noise impact from the specific component. To this end, we propose a novel tensor-based multi-view graph learning framework that simultaneously considers consistency and specificity, while effectively eliminating the influence of noise. Specifically, we calculate similarity distance on the Stiefel manifold to preserve the intrinsic properties of data. By making an assumption that the learned neighbor graph of each view comprises a consistent part, a specific part, and a noise part, we formulate a new tensor-based target graph learning paradigm for noise-free graph fusion. Owing to the benefits of tensor singular value decomposition (t-SVD) in uncovering high-order correlations, this model is capable of achieving a complete understanding of the target graph. Furthermore, we derive an algorithm to address the optimization problem. Experiments on six datasets have demonstrated the superiority of our method. We have released the source code on https://github.com/lshi91/CSTGL-Code. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 441,920 |
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