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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0809.3273 | Direct and Reverse Secret-Key Capacities of a Quantum Channel | We define the direct and reverse secret-key capacities of a memoryless quantum channel as the optimal rates that entanglement-based quantum key distribution protocols can reach by using a single forward classical communication (direct reconciliation) or a single feedback classical communication (reverse reconciliation). In particular, the reverse secret-key capacity can be positive for antidegradable channels, where no forward strategy is known to be secure. This property is explicitly shown in the continuous variable framework by considering arbitrary one-mode Gaussian channels. | false | false | false | false | false | false | false | false | false | true | false | false | true | false | false | false | false | false | 2,379 |
2302.13709 | Cyber-Secure Teleoperation With Encrypted Four-Channel Bilateral Control | This study developed an encrypted four-channel bilateral control system that enables posture synchronization and force feedback for leader and follower robot arms. The encrypted bilateral control system communicates encrypted signals and operates with encrypted control parameters using homomorphic encryption. We created two-axis robot arms and identified them to obtain a nonlinear model consisting of a linear system and a nonlinear disturbance. Disturbance and reaction-force observers and a proportional-derivative controller were designed to construct a networked robot-manipulation system. The controllers in the constructed bilateral control system were securely implemented on dedicated computers using a controller encryption technique. The experimental results demonstrate that the developed bilateral control system can facilitate encrypted communication and controller, synchronize the posture, and feed the reaction force back. Through experimental validation, the encrypted four-channel bilateral control system enables the inheritance of control performance from the original (unencrypted) bilateral control system. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 348,037 |
2412.05590 | Active Sequential Posterior Estimation for Sample-Efficient
Simulation-Based Inference | Computer simulations have long presented the exciting possibility of scientific insight into complex real-world processes. Despite the power of modern computing, however, it remains challenging to systematically perform inference under simulation models. This has led to the rise of simulation-based inference (SBI), a class of machine learning-enabled techniques for approaching inverse problems with stochastic simulators. Many such methods, however, require large numbers of simulation samples and face difficulty scaling to high-dimensional settings, often making inference prohibitive under resource-intensive simulators. To mitigate these drawbacks, we introduce active sequential neural posterior estimation (ASNPE). ASNPE brings an active learning scheme into the inference loop to estimate the utility of simulation parameter candidates to the underlying probabilistic model. The proposed acquisition scheme is easily integrated into existing posterior estimation pipelines, allowing for improved sample efficiency with low computational overhead. We further demonstrate the effectiveness of the proposed method in the travel demand calibration setting, a high-dimensional inverse problem commonly requiring computationally expensive traffic simulators. Our method outperforms well-tuned benchmarks and state-of-the-art posterior estimation methods on a large-scale real-world traffic network, as well as demonstrates a performance advantage over non-active counterparts on a suite of SBI benchmark environments. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 514,895 |
2502.14293 | Graph Anomaly Detection via Adaptive Test-time Representation Learning
across Out-of-Distribution Domains | Graph Anomaly Detection (GAD) has demonstrated great effectiveness in identifying unusual patterns within graph-structured data. However, while labeled anomalies are often scarce in emerging applications, existing supervised GAD approaches are either ineffective or not applicable when moved across graph domains due to distribution shifts and heterogeneous feature spaces. To address these challenges, we present AdaGraph-T3, a novel test-time training framework for cross-domain GAD. AdaGraph-T3 combines supervised and self-supervised learning during training while adapting to a new domain during test time using only self-supervised learning by leveraging a homophily-based affinity score that captures domain-invariant properties of anomalies. Our framework introduces four key innovations to cross-domain GAD: an effective self-supervision scheme, an attention-based mechanism that dynamically learns edge importance weights during message passing, domain-specific encoders for handling heterogeneous features, and class-aware regularization to address imbalance. Experiments across multiple cross-domain settings demonstrate that AdaGraph-T3 significantly outperforms existing approaches, achieving average improvements of over 6.6% in AUROC and 7.9% in AUPRC compared to the best competing model. | false | false | false | true | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 535,760 |
2303.12096 | Reply to: Inability of a graph neural network heuristic to outperform
greedy algorithms in solving combinatorial optimization problems | We provide a comprehensive reply to the comment written by Stefan Boettcher [arXiv:2210.00623] and argue that the comment singles out one particular non-representative example problem, entirely focusing on the maximum cut problem (MaxCut) on sparse graphs, for which greedy algorithms are expected to perform well. Conversely, we highlight the broader algorithmic development underlying our original work, and (within our original framework) provide additional numerical results showing sizable improvements over our original data, thereby refuting the comment's original performance statements. Furthermore, it has already been shown that physics-inspired graph neural networks (PI-GNNs) can outperform greedy algorithms, in particular on hard, dense instances. We also argue that the internal (parallel) anatomy of graph neural networks is very different from the (sequential) nature of greedy algorithms, and (based on their usage at the scale of real-world social networks) point out that graph neural networks have demonstrated their potential for superior scalability compared to existing heuristics such as extremal optimization. Finally, we conclude highlighting the conceptual novelty of our work and outline some potential extensions. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 353,137 |
2004.07692 | A Hybrid Objective Function for Robustness of Artificial Neural Networks
-- Estimation of Parameters in a Mechanical System | In several studies, hybrid neural networks have proven to be more robust against noisy input data compared to plain data driven neural networks. We consider the task of estimating parameters of a mechanical vehicle model based on acceleration profiles. We introduce a convolutional neural network architecture that is capable to predict the parameters for a family of vehicle models that differ in the unknown parameters. We introduce a convolutional neural network architecture that given sequential data predicts the parameters of the underlying data's dynamics. This network is trained with two objective functions. The first one constitutes a more naive approach that assumes that the true parameters are known. The second objective incorporates the knowledge of the underlying dynamics and is therefore considered as hybrid approach. We show that in terms of robustness, the latter outperforms the first objective on noisy input data. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 172,851 |
2010.02888 | Testing Tail Weight of a Distribution Via Hazard Rate | Understanding the shape of a distribution of data is of interest to people in a great variety of fields, as it may affect the types of algorithms used for that data. We study one such problem in the framework of distribution property testing, characterizing the number of samples required to to distinguish whether a distribution has a certain property or is far from having that property. In particular, given samples from a distribution, we seek to characterize the tail of the distribution, that is, understand how many elements appear infrequently. We develop an algorithm based on a careful bucketing scheme that distinguishes light-tailed distributions from non-light-tailed ones with respect to a definition based on the hazard rate, under natural smoothness and ordering assumptions. We bound the number of samples required for this test to succeed with high probability in terms of the parameters of the problem, showing that it is polynomial in these parameters. Further, we prove a hardness result that implies that this problem cannot be solved without any assumptions. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 199,202 |
2403.15351 | Multi-Review Fusion-in-Context | Grounded text generation, encompassing tasks such as long-form question-answering and summarization, necessitates both content selection and content consolidation. Current end-to-end methods are difficult to control and interpret due to their opaqueness. Accordingly, recent works have proposed a modular approach, with separate components for each step. Specifically, we focus on the second subtask, of generating coherent text given pre-selected content in a multi-document setting. Concretely, we formalize Fusion-in-Context (FiC) as a standalone task, whose input consists of source texts with highlighted spans of targeted content. A model then needs to generate a coherent passage that includes all and only the target information. Our work includes the development of a curated dataset of 1000 instances in the reviews domain, alongside a novel evaluation framework for assessing the faithfulness and coverage of highlights, which strongly correlate to human judgment. Several baseline models exhibit promising outcomes and provide insightful analyses. This study lays the groundwork for further exploration of modular text generation in the multi-document setting, offering potential improvements in the quality and reliability of generated content. Our benchmark, FuseReviews, including the dataset, evaluation framework, and designated leaderboard, can be found at https://fusereviews.github.io/. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 440,504 |
2012.06757 | Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial
Attacks on Graphs | Adversarial attacks on graphs have attracted considerable research interests. Existing works assume the attacker is either (partly) aware of the victim model, or able to send queries to it. These assumptions are, however, unrealistic. To bridge the gap between theoretical graph attacks and real-world scenarios, in this work, we propose a novel and more realistic setting: strict black-box graph attack, in which the attacker has no knowledge about the victim model at all and is not allowed to send any queries. To design such an attack strategy, we first propose a generic graph filter to unify different families of graph-based models. The strength of attacks can then be quantified by the change in the graph filter before and after attack. By maximizing this change, we are able to find an effective attack strategy, regardless of the underlying model. To solve this optimization problem, we also propose a relaxation technique and approximation theories to reduce the difficulty as well as the computational expense. Experiments demonstrate that, even with no exposure to the model, the Macro-F1 drops 6.4% in node classification and 29.5% in graph classification, which is a significant result compared with existent works. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 211,215 |
2010.01773 | MetaPhys: Few-Shot Adaptation for Non-Contact Physiological Measurement | There are large individual differences in physiological processes, making designing personalized health sensing algorithms challenging. Existing machine learning systems struggle to generalize well to unseen subjects or contexts and can often contain problematic biases. Video-based physiological measurement is not an exception. Therefore, learning personalized or customized models from a small number of unlabeled samples is very attractive as it would allow fast calibrations to improve generalization and help correct biases. In this paper, we present a novel meta-learning approach called MetaPhys for personalized video-based cardiac measurement for contactless pulse and heart rate monitoring. Our method uses only 18-seconds of video for customization and works effectively in both supervised and unsupervised manners. We evaluate our proposed approach on two benchmark datasets and demonstrate superior performance in cross-dataset evaluation with substantial reductions (42% to 44%) in errors compared with state-of-the-art approaches. We have also demonstrated our proposed method significantly helps reduce the bias in skin type. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 198,786 |
1711.01867 | Analysis of group evolution prediction in complex networks | In the world, in which acceptance and the identification with social communities are highly desired, the ability to predict evolution of groups over time appears to be a vital but very complex research problem. Therefore, we propose a new, adaptable, generic and mutli-stage method for Group Evolution Prediction (GEP) in complex networks, that facilitates reasoning about the future states of the recently discovered groups. The precise GEP modularity enabled us to carry out extensive and versatile empirical studies on many real-world complex / social networks to analyze the impact of numerous setups and parameters like time window type and size, group detection method, evolution chain length, prediction models, etc. Additionally, many new predictive features reflecting the group state at a given time have been identified and tested. Some other research problems like enriching learning evolution chains with external data have been analyzed as well. | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 83,968 |
2401.03115 | Transferable Learned Image Compression-Resistant Adversarial
Perturbations | Adversarial attacks can readily disrupt the image classification system, revealing the vulnerability of DNN-based recognition tasks. While existing adversarial perturbations are primarily applied to uncompressed images or compressed images by the traditional image compression method, i.e., JPEG, limited studies have investigated the robustness of models for image classification in the context of DNN-based image compression. With the rapid evolution of advanced image compression, DNN-based learned image compression has emerged as the promising approach for transmitting images in many security-critical applications, such as cloud-based face recognition and autonomous driving, due to its superior performance over traditional compression. Therefore, there is a pressing need to fully investigate the robustness of a classification system post-processed by learned image compression. To bridge this research gap, we explore the adversarial attack on a new pipeline that targets image classification models that utilize learned image compressors as pre-processing modules. Furthermore, to enhance the transferability of perturbations across various quality levels and architectures of learned image compression models, we introduce a saliency score-based sampling method to enable the fast generation of transferable perturbation. Extensive experiments with popular attack methods demonstrate the enhanced transferability of our proposed method when attacking images that have been post-processed with different learned image compression models. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | true | 419,967 |
2109.09304 | Deformed semicircle law and concentration of nonlinear random matrices
for ultra-wide neural networks | In this paper, we investigate a two-layer fully connected neural network of the form $f(X)=\frac{1}{\sqrt{d_1}}\boldsymbol{a}^\top \sigma\left(WX\right)$, where $X\in\mathbb{R}^{d_0\times n}$ is a deterministic data matrix, $W\in\mathbb{R}^{d_1\times d_0}$ and $\boldsymbol{a}\in\mathbb{R}^{d_1}$ are random Gaussian weights, and $\sigma$ is a nonlinear activation function. We study the limiting spectral distributions of two empirical kernel matrices associated with $f(X)$: the empirical conjugate kernel (CK) and neural tangent kernel (NTK), beyond the linear-width regime ($d_1\asymp n$). We focus on the $\textit{ultra-wide regime}$, where the width $d_1$ of the first layer is much larger than the sample size $n$. Under appropriate assumptions on $X$ and $\sigma$, a deformed semicircle law emerges as $d_1/n\to\infty$ and $n\to\infty$. We first prove this limiting law for generalized sample covariance matrices with some dependency. To specify it for our neural network model, we provide a nonlinear Hanson-Wright inequality that is suitable for neural networks with random weights and Lipschitz activation functions. We also demonstrate non-asymptotic concentrations of the empirical CK and NTK around their limiting kernels in the spectral norm, along with lower bounds on their smallest eigenvalues. As an application, we show that random feature regression induced by the empirical kernel achieves the same asymptotic performance as its limiting kernel regression under the ultra-wide regime. This allows us to calculate the asymptotic training and test errors for random feature regression using the corresponding kernel regression. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 256,228 |
1204.2677 | The Geographic Flow of Music | The social media website last.fm provides a detailed snapshot of what its users in hundreds of cities listen to each week. After suitably normalizing this data, we use it to test three hypotheses related to the geographic flow of music. The first is that although many of the most popular artists are listened to around the world, music preferences are closely related to nationality, language, and geographic location. We find support for this hypothesis, with a couple of minor, yet interesting, exceptions. Our second hypothesis is that some cities are consistently early adopters of new music (and early to snub stale music). To test this hypothesis, we adapt a method previously used to detect the leadership networks present in flocks of birds. We find empirical support for the claim that a similar leadership network exists among cities, and this finding is the main contribution of the paper. Finally, we test the hypothesis that large cities tend to be ahead of smaller cities-we find only weak support for this hypothesis. | false | false | false | true | false | false | false | false | false | false | false | false | false | true | false | false | false | false | 15,432 |
2307.02060 | Traversability Analysis for Autonomous Driving in Complex Environment: A
LiDAR-based Terrain Modeling Approach | For autonomous driving, traversability analysis is one of the most basic and essential tasks. In this paper, we propose a novel LiDAR-based terrain modeling approach, which could output stable, complete and accurate terrain models and traversability analysis results. As terrain is an inherent property of the environment that does not change with different view angles, our approach adopts a multi-frame information fusion strategy for terrain modeling. Specifically, a normal distributions transform mapping approach is adopted to accurately model the terrain by fusing information from consecutive LiDAR frames. Then the spatial-temporal Bayesian generalized kernel inference and bilateral filtering are utilized to promote the stability and completeness of the results while simultaneously retaining the sharp terrain edges. Based on the terrain modeling results, the traversability of each region is obtained by performing geometric connectivity analysis between neighboring terrain regions. Experimental results show that the proposed method could run in real-time and outperforms state-of-the-art approaches. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 377,574 |
2407.07406 | Weakly-supervised Medical Image Segmentation with Gaze Annotations | Eye gaze that reveals human observational patterns has increasingly been incorporated into solutions for vision tasks. Despite recent explorations on leveraging gaze to aid deep networks, few studies exploit gaze as an efficient annotation approach for medical image segmentation which typically entails heavy annotating costs. In this paper, we propose to collect dense weak supervision for medical image segmentation with a gaze annotation scheme. To train with gaze, we propose a multi-level framework that trains multiple networks from discriminative human attention, simulated with a set of pseudo-masks derived by applying hierarchical thresholds on gaze heatmaps. Furthermore, to mitigate gaze noise, a cross-level consistency is exploited to regularize overfitting noisy labels, steering models toward clean patterns learned by peer networks. The proposed method is validated on two public medical datasets of polyp and prostate segmentation tasks. We contribute a high-quality gaze dataset entitled GazeMedSeg as an extension to the popular medical segmentation datasets. To the best of our knowledge, this is the first gaze dataset for medical image segmentation. Our experiments demonstrate that gaze annotation outperforms previous label-efficient annotation schemes in terms of both performance and annotation time. Our collected gaze data and code are available at: https://github.com/med-air/GazeMedSeg. | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | false | false | 471,752 |
2210.02442 | Making Your First Choice: To Address Cold Start Problem in Vision Active
Learning | Active learning promises to improve annotation efficiency by iteratively selecting the most important data to be annotated first. However, we uncover a striking contradiction to this promise: active learning fails to select data as efficiently as random selection at the first few choices. We identify this as the cold start problem in vision active learning, caused by a biased and outlier initial query. This paper seeks to address the cold start problem by exploiting the three advantages of contrastive learning: (1) no annotation is required; (2) label diversity is ensured by pseudo-labels to mitigate bias; (3) typical data is determined by contrastive features to reduce outliers. Experiments are conducted on CIFAR-10-LT and three medical imaging datasets (i.e. Colon Pathology, Abdominal CT, and Blood Cell Microscope). Our initial query not only significantly outperforms existing active querying strategies but also surpasses random selection by a large margin. We foresee our solution to the cold start problem as a simple yet strong baseline to choose the initial query for vision active learning. Code is available: https://github.com/c-liangyu/CSVAL | false | false | false | false | true | false | true | false | false | false | false | true | false | false | false | false | false | false | 321,648 |
2403.06059 | Test-time Distribution Learning Adapter for Cross-modal Visual Reasoning | Vision-Language Pre-Trained (VLP) models, such as CLIP, have demonstrated remarkable effectiveness in learning generic visual representations. Several approaches aim to efficiently adapt VLP models to downstream tasks with limited supervision, aiming to leverage the acquired knowledge from VLP models. However, these methods suffer from either introducing biased representations or requiring high computational complexity, which hinders their effectiveness in fine-tuning the CLIP model. Moreover, when a model is trained on data specific to a particular domain, its ability to generalize to uncharted domains diminishes. In this work, we propose Test-Time Distribution LearNing Adapter (TT-DNA) which directly works during the testing period. Specifically, we estimate Gaussian distributions to model visual features of the few-shot support images to capture the knowledge from the support set. The cosine similarity between query image and the feature distribution of support images is used as the prediction of visual adapter. Subsequently, the visual adapter's prediction merges with the original CLIP prediction via a residual connection, resulting in the final prediction. Our extensive experimental results on visual reasoning for human object interaction demonstrate that our proposed TT-DNA outperforms existing state-of-the-art methods by large margins. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 436,276 |
2205.02414 | Subverting Fair Image Search with Generative Adversarial Perturbations | In this work we explore the intersection fairness and robustness in the context of ranking: when a ranking model has been calibrated to achieve some definition of fairness, is it possible for an external adversary to make the ranking model behave unfairly without having access to the model or training data? To investigate this question, we present a case study in which we develop and then attack a state-of-the-art, fairness-aware image search engine using images that have been maliciously modified using a Generative Adversarial Perturbation (GAP) model. These perturbations attempt to cause the fair re-ranking algorithm to unfairly boost the rank of images containing people from an adversary-selected subpopulation. We present results from extensive experiments demonstrating that our attacks can successfully confer significant unfair advantage to people from the majority class relative to fairly-ranked baseline search results. We demonstrate that our attacks are robust across a number of variables, that they have close to zero impact on the relevance of search results, and that they succeed under a strict threat model. Our findings highlight the danger of deploying fair machine learning algorithms in-the-wild when (1) the data necessary to achieve fairness may be adversarially manipulated, and (2) the models themselves are not robust against attacks. | false | false | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | 294,929 |
2003.11619 | Deep Networks as Logical Circuits: Generalization and Interpretation | Not only are Deep Neural Networks (DNNs) black box models, but also we frequently conceptualize them as such. We lack good interpretations of the mechanisms linking inputs to outputs. Therefore, we find it difficult to analyze in human-meaningful terms (1) what the network learned and (2) whether the network learned. We present a hierarchical decomposition of the DNN discrete classification map into logical (AND/OR) combinations of intermediate (True/False) classifiers of the input. Those classifiers that can not be further decomposed, called atoms, are (interpretable) linear classifiers. Taken together, we obtain a logical circuit with linear classifier inputs that computes the same label as the DNN. This circuit does not structurally resemble the network architecture, and it may require many fewer parameters, depending on the configuration of weights. In these cases, we obtain simultaneously an interpretation and generalization bound (for the original DNN), connecting two fronts which have historically been investigated separately. Unlike compression techniques, our representation is. We motivate the utility of this perspective by studying DNNs in simple, controlled settings, where we obtain superior generalization bounds despite using only combinatorial information (e.g. no margin information). We demonstrate how to "open the black box" on the MNIST dataset. We show that the learned, internal, logical computations correspond to semantically meaningful (unlabeled) categories that allow DNN descriptions in plain English. We improve the generalization of an already trained network by interpreting, diagnosing, and replacing components the logical circuit that is the DNN. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 169,659 |
1906.07633 | From Clustering to Cluster Explanations via Neural Networks | A recent trend in machine learning has been to enrich learned models with the ability to explain their own predictions. The emerging field of Explainable AI (XAI) has so far mainly focused on supervised learning, in particular, deep neural network classifiers. In many practical problems however, label information is not given and the goal is instead to discover the underlying structure of the data, for example, its clusters. While powerful methods exist for extracting the cluster structure in data, they typically do not answer the question why a certain data point has been assigned to a given cluster. We propose a new framework that can, for the first time, explain cluster assignments in terms of input features in an efficient and reliable manner. It is based on the novel insight that clustering models can be rewritten as neural networks - or 'neuralized'. Cluster predictions of the obtained networks can then be quickly and accurately attributed to the input features. Several showcases demonstrate the ability of our method to assess the quality of learned clusters and to extract novel insights from the analyzed data and representations. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 135,647 |
2109.11615 | Keypoints-Based Deep Feature Fusion for Cooperative Vehicle Detection of
Autonomous Driving | Sharing collective perception messages (CPM) between vehicles is investigated to decrease occlusions so as to improve the perception accuracy and safety of autonomous driving. However, highly accurate data sharing and low communication overhead is a big challenge for collective perception, especially when real-time communication is required among connected and automated vehicles. In this paper, we propose an efficient and effective keypoints-based deep feature fusion framework built on the 3D object detector PV-RCNN, called Fusion PV-RCNN (FPV-RCNN for short), for collective perception. We introduce a high-performance bounding box proposal matching module and a keypoints selection strategy to compress the CPM size and solve the multi-vehicle data fusion problem. Besides, we also propose an effective localization error correction module based on the maximum consensus principle to increase the robustness of the data fusion. Compared to a bird's-eye view (BEV) keypoints feature fusion, FPV-RCNN achieves improved detection accuracy by about 9% at a high evaluation criterion (IoU 0.7) on the synthetic dataset COMAP dedicated to collective perception. In addition, its performance is comparable to two raw data fusion baselines that have no data loss in sharing. Moreover, our method also significantly decreases the CPM size to less than 0.3 KB, and is thus about 50 times smaller than the BEV feature map sharing used in previous works. Even with further decreased CPM feature channels, i.e., from 128 to 32, the detection performance does not show apparent drops. The code of our method is available at https://github.com/YuanYunshuang/FPV_RCNN. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 256,996 |
2408.06354 | Automated Romberg Test: Leveraging a CNN and Centre of Mass Analysis for
Sensory Ataxia Diagnosis | This paper proposes a novel method to diagnose sensory ataxia via an automated Romberg Test - the current de facto medical procedure used to diagnose this condition. It utilizes a convolutional neural network to predict joint locations, used for the calculation of various bio-mechanical markers such as the center of mass of the subject and various joint angles. This information is used in combination with data filtering techniques such as Kalman Filters, and center of mass analysis which helped make accurate inferences about the relative weight distribution in the lateral and anterior-posterior axes, and provide an objective, mathematically based diagnosis of this condition. In order to evaluate the performance of this method, testing was performed using dual weight scales and pre-annotated diagnosis videos taken from medical settings. These two methods both quantified the veritable weight distribution upon the ground surface with a ground truth and provided a real-world estimate of accuracy for the proposed method. A mean absolute error of 0.2912 percent was found for the calculated relative weight distribution difference, and an accuracy of 83.33 percent was achieved on diagnoses. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 480,175 |
2401.13556 | Extension of the Injected-Absorbed-Current Method applied to DC-DC
Converters with Input Filter, Output Post-filter and Feedforward
Compensations | In railway applications, it is common to use an LC filter connected between the catenary and the input port of the main converter of the auxiliary and traction systems. In addition, in the auxiliary systems, there is a converter operating as a battery charger, which requires a very low ripple in the output current and output voltage, so a postfilter may be placed at the output port of the converter. This article proposes a step-by-step methodology to extend the injected-absorbed-current (IAC) method in order to obtain transfer functions that consider the effects of the input filter, output postfilter, and some feedforward compensations. The proposed methodology allows reusing the characteristic coefficients of the DC-DC converter model derived from the existing IAC method. One of the advantages of the proposed methodology is that the transfer functions obtained in this article are valid for cases where both, one or none of the filters, are implemented. Finally, for the experimental validation of the proposed methodology, the phase-shifted full-bridge converter was selected as a convenient example. Furthermore, the experimental measurements have been performed on two prototypes. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 423,768 |
2312.04799 | An Overview of MLCommons Cloud Mask Benchmark: Related Research and Data | Cloud masking is a crucial task that is well-motivated for meteorology and its applications in environmental and atmospheric sciences. Its goal is, given satellite images, to accurately generate cloud masks that identify each pixel in image to contain either cloud or clear sky. In this paper, we summarize some of the ongoing research activities in cloud masking, with a focus on the research and benchmark currently conducted in MLCommons Science Working Group. This overview is produced with the hope that others will have an easier time getting started and collaborate on the activities related to MLCommons Cloud Mask Benchmark. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | true | 413,829 |
1907.13246 | A Temporal Clustering Algorithm for Achieving the trade-off between the
User Experience and the Equipment Economy in the Context of IoT | We present here the Temporal Clustering Algorithm (TCA), an incremental learning algorithm applicable to problems of anticipatory computing in the context of the Internet of Things. This algorithm was tested in a specific prediction scenario of consumption of an electric water dispenser typically used in tropical countries, in which the ambient temperature is around 30-degree Celsius. In this context, the user typically wants to drinking iced water therefore uses the cooler function of the dispenser. Real and synthetic water consumption data was used to test a forecasting capacity on how much energy can be saved by predicting the pattern of use of the equipment. In addition to using a small constant amount of memory, which allows the algorithm to be implemented at the lowest cost, while using microcontrollers with a small amount of memory (less than 1Kbyte) available on the market. The algorithm can also be configured according to user preference, prioritizing comfort, keeping the water at the desired temperature longer, or prioritizing energy savings. The main result is that the TCA achieved energy savings of up to 40% compared to the conventional mode of operation of the dispenser with an average success rate higher than 90% in its times of use. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 140,321 |
2306.00048 | A Hamming-Like Bound for Degenerate Stabilizer Codes | The quantum Hamming bound was originally put forward as an upper bound on the parameters of nondegenerate quantum codes, but over the past few decades much work has been done to show that many degenerate quantum codes must also obey this bound. In this paper, we show that there is a Hamming-like bound stricter than the quantum Hamming bound that applies to degenerate $t$-error-correcting stabilizer codes of length greater than some positive integer $N(t)$. We show that this bound holds for all single-error-correcting degenerate stabilizer codes, forcing all but a handful of optimal distance-3 stabilizer codes to be nondegenerate. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 369,856 |
2104.00683 | SimPoE: Simulated Character Control for 3D Human Pose Estimation | Accurate estimation of 3D human motion from monocular video requires modeling both kinematics (body motion without physical forces) and dynamics (motion with physical forces). To demonstrate this, we present SimPoE, a Simulation-based approach for 3D human Pose Estimation, which integrates image-based kinematic inference and physics-based dynamics modeling. SimPoE learns a policy that takes as input the current-frame pose estimate and the next image frame to control a physically-simulated character to output the next-frame pose estimate. The policy contains a learnable kinematic pose refinement unit that uses 2D keypoints to iteratively refine its kinematic pose estimate of the next frame. Based on this refined kinematic pose, the policy learns to compute dynamics-based control (e.g., joint torques) of the character to advance the current-frame pose estimate to the pose estimate of the next frame. This design couples the kinematic pose refinement unit with the dynamics-based control generation unit, which are learned jointly with reinforcement learning to achieve accurate and physically-plausible pose estimation. Furthermore, we propose a meta-control mechanism that dynamically adjusts the character's dynamics parameters based on the character state to attain more accurate pose estimates. Experiments on large-scale motion datasets demonstrate that our approach establishes the new state of the art in pose accuracy while ensuring physical plausibility. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 228,085 |
2110.13465 | CS-Rep: Making Speaker Verification Networks Embracing
Re-parameterization | Automatic speaker verification (ASV) systems, which determine whether two speeches are from the same speaker, mainly focus on verification accuracy while ignoring inference speed. However, in real applications, both inference speed and verification accuracy are essential. This study proposes cross-sequential re-parameterization (CS-Rep), a novel topology re-parameterization strategy for multi-type networks, to increase the inference speed and verification accuracy of models. CS-Rep solves the problem that existing re-parameterization methods are unsuitable for typical ASV backbones. When a model applies CS-Rep, the training-period network utilizes a multi-branch topology to capture speaker information, whereas the inference-period model converts to a time-delay neural network (TDNN)-like plain backbone with stacked TDNN layers to achieve the fast inference speed. Based on CS-Rep, an improved TDNN with friendly test and deployment called Rep-TDNN is proposed. Compared with the state-of-the-art model ECAPA-TDNN, which is highly recognized in the industry, Rep-TDNN increases the actual inference speed by about 50% and reduces the EER by 10%. The code will be released. | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 263,191 |
2302.12833 | An Efficient Instance Segmentation Approach for Extracting Fission Gas
Bubbles on U-10Zr Annular Fuel | U-10Zr-based nuclear fuel is pursued as a primary candidate for next-generation sodium-cooled fast reactors. However, more advanced characterization and analysis are needed to form a fundamental understating of the fuel performance, and make U-10Zr fuel qualify for commercial use. The movement of lanthanides across the fuel section from the hot fuel center to the cool cladding surface is one of the key factors to affect fuel performance. In the advanced annular U-10Zr fuel, the lanthanides present as fission gas bubbles. Due to a lack of annotated data, existing literature utilized a multiple-threshold method to separate the bubbles and calculate bubble statistics on an annular fuel. However, the multiple-threshold method cannot achieve robust performance on images with different qualities and contrasts, and cannot distinguish different bubbles. This paper proposes a hybrid framework for efficient bubble segmentation. We develop a bubble annotation tool and generate the first fission gas bubble dataset with more than 3000 bubbles from 24 images. A multi-task deep learning network integrating U-Net and ResNet is designed to accomplish instance-level bubble segmentation. Combining the segmentation results and image processing step achieves the best recall ratio of more than 90% with very limited annotated data. Our model shows outstanding improvement by comparing the previously proposed thresholding method. The proposed method has promising to generate a more accurate quantitative analysis of fission gas bubbles on U-10Zr annular fuels. The results will contribute to identifying the bubbles with lanthanides and finally build the relationship between the thermal gradation and lanthanides movements of U-10Zr annular fuels. Mover, the deep learning model is applicable to other similar material micro-structure segmentation tasks. | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | false | false | 347,710 |
2006.03222 | Adaptive Multi-Feature Budgeted Profit Maximization in Social Networks | Online social network has been one of the most important platforms for viral marketing. Most of existing researches about diffusion of adoptions of new products on networks are about one diffusion. That is, only one piece of information about the product is spread on the network. However, in fact, one product may have multiple features and the information about different features may spread independently in social network. When a user would like to purchase the product, he would consider all of the features of the product comprehensively not just consider one. Based on this, we propose a novel problem, multi-feature budgeted profit maximization (MBPM) problem, which first considers budgeted profit maximization under multiple features propagation of one product. Given a social network with each node having an activation cost and a profit, MBPM problem seeks for a seed set with expected cost no more than the budget to make the total expected profit as large as possible. We consider MBPM problem under the adaptive setting, where seeds are chosen iteratively and next seed is selected according to current diffusion results. We study adaptive MBPM problem under two models, oracle model and noise model. The oracle model assumes conditional expected marginal profit of any node could be obtained in O(1) time and a (1-1/e) expected approximation policy is proposed. Under the noise model, we estimate conditional expected marginal profit of a node by modifying the EPIC algorithm and propose an efficient policy, which could return a (1-exp({\epsilon}-1)) expected approximation ratio. Several experiments are conducted on six realistic datasets to compare our proposed policies with their corresponding non-adaptive algorithms and some heuristic adaptive policies. Experimental results show efficiencies and superiorities of our policies. | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 180,253 |
2305.18258 | Maximize to Explore: One Objective Function Fusing Estimation, Planning,
and Exploration | In online reinforcement learning (online RL), balancing exploration and exploitation is crucial for finding an optimal policy in a sample-efficient way. To achieve this, existing sample-efficient online RL algorithms typically consist of three components: estimation, planning, and exploration. However, in order to cope with general function approximators, most of them involve impractical algorithmic components to incentivize exploration, such as optimization within data-dependent level-sets or complicated sampling procedures. To address this challenge, we propose an easy-to-implement RL framework called \textit{Maximize to Explore} (\texttt{MEX}), which only needs to optimize \emph{unconstrainedly} a single objective that integrates the estimation and planning components while balancing exploration and exploitation automatically. Theoretically, we prove that \texttt{MEX} achieves a sublinear regret with general function approximations for Markov decision processes (MDP) and is further extendable to two-player zero-sum Markov games (MG). Meanwhile, we adapt deep RL baselines to design practical versions of \texttt{MEX}, in both model-free and model-based manners, which can outperform baselines by a stable margin in various MuJoCo environments with sparse rewards. Compared with existing sample-efficient online RL algorithms with general function approximations, \texttt{MEX} achieves similar sample efficiency while enjoying a lower computational cost and is more compatible with modern deep RL methods. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | true | 368,920 |
2105.11636 | FILTRA: Rethinking Steerable CNN by Filter Transform | Steerable CNN imposes the prior knowledge of transformation invariance or equivariance in the network architecture to enhance the the network robustness on geometry transformation of data and reduce overfitting. It has been an intuitive and widely used technique to construct a steerable filter by augmenting a filter with its transformed copies in the past decades, which is named as filter transform in this paper. Recently, the problem of steerable CNN has been studied from aspect of group representation theory, which reveals the function space structure of a steerable kernel function. However, it is not yet clear on how this theory is related to the filter transform technique. In this paper, we show that kernel constructed by filter transform can also be interpreted in the group representation theory. This interpretation help complete the puzzle of steerable CNN theory and provides a novel and simple approach to implement steerable convolution operators. Experiments are executed on multiple datasets to verify the feasibility of the proposed approach. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 236,769 |
2107.01779 | Depth Quality-Inspired Feature Manipulation for Efficient RGB-D Salient
Object Detection | RGB-D salient object detection (SOD) recently has attracted increasing research interest by benefiting conventional RGB SOD with extra depth information. However, existing RGB-D SOD models often fail to perform well in terms of both efficiency and accuracy, which hinders their potential applications on mobile devices and real-world problems. An underlying challenge is that the model accuracy usually degrades when the model is simplified to have few parameters. To tackle this dilemma and also inspired by the fact that depth quality is a key factor influencing the accuracy, we propose a novel depth quality-inspired feature manipulation (DQFM) process, which is efficient itself and can serve as a gating mechanism for filtering depth features to greatly boost the accuracy. DQFM resorts to the alignment of low-level RGB and depth features, as well as holistic attention of the depth stream to explicitly control and enhance cross-modal fusion. We embed DQFM to obtain an efficient light-weight model called DFM-Net, where we also design a tailored depth backbone and a two-stage decoder for further efficiency consideration. Extensive experimental results demonstrate that our DFM-Net achieves state-of-the-art accuracy when comparing to existing non-efficient models, and meanwhile runs at 140ms on CPU (2.2$\times$ faster than the prior fastest efficient model) with only $\sim$8.5Mb model size (14.9% of the prior lightest). Our code will be available at https://github.com/zwbx/DFM-Net. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 244,589 |
2307.02071 | A Comparison of Machine Learning Methods for Data with High-Cardinality
Categorical Variables | High-cardinality categorical variables are variables for which the number of different levels is large relative to the sample size of a data set, or in other words, there are few data points per level. Machine learning methods can have difficulties with high-cardinality variables. In this article, we empirically compare several versions of two of the most successful machine learning methods, tree-boosting and deep neural networks, and linear mixed effects models using multiple tabular data sets with high-cardinality categorical variables. We find that, first, machine learning models with random effects have higher prediction accuracy than their classical counterparts without random effects, and, second, tree-boosting with random effects outperforms deep neural networks with random effects. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 377,579 |
1806.08230 | Throughput Maximization in Cloud-Radio Access Networks using Rate-Aware
Network Coding | One of the most promising techniques for network-wide interference management necessitates a redesign of the network architecture known as cloud radio access network (CRAN). The cloud is responsible for coordinating multiple Remote Radio Heads (RRHs) and scheduling users to their radio resources blocks (RRBs). The transmit frame of each RRH consists of several orthogonal RRBs each maintained at a certain power level (PL). While previous works considered a vanilla version in which each RRB can serve a single user, this paper proposes mixing the flows of multiple users using instantly decodable network coding (IDNC). As such, the total throughput is maximized. The joint user scheduling and power adaptation problem is solved by designing, for each RRB, a subgraph in which each vertex represents potential user-RRH associations, encoded files, transmission rates, and PLs for one specific RRB. It is shown that the original problem is equivalent to a maximum-weight clique problem over the union of all subgraphs, called herein the CRAN-IDNCgraph. Extensive simulation results are provided to attest the effectiveness of the proposed solution against state of the art algorithms. In particular, the presented simulation results reveal that the method achieves substantial performance gains for all system configurations which collaborates the theoretical findings. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 101,117 |
2303.16344 | The State of Diversity and Inclusion in Apache: A Pulse Check | Diversity and inclusion in open source software (OSS) is a multifaceted concept that arises from differences in contributors' gender, seniority, language, region, and other characteristics. D&I has received growing attention in OSS ecosystems and projects, and various programs have been implemented to foster contributor diversity. However, we do not yet know how the state of D&I is evolving. By understanding the state of D&I in OSS projects, the community can develop new and adjust current strategies to foster diversity among contributors and gain insights into the mechanisms and processes that facilitate the development of inclusive communities. In this paper, we report and compare the results of two surveys of Apache Software Foundation (ASF) contributors conducted over two years (n=624 & n=432), considering a variety of D&I aspects. We see improvements in engagement among those traditionally underrepresented in OSS, particularly those who are in gender minority or not confident in English. Yet, the gender gap in the number of contributors remains. We expect this study to help communities tailor their efforts in promoting D&I in OSS. | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 354,826 |
1812.02948 | A Survey on Artificial Intelligence Trends in Spacecraft Guidance
Dynamics and Control | The rapid developments of Artificial Intelligence in the last decade are influencing Aerospace Engineering to a great extent and research in this context is proliferating. We share our observations on the recent developments in the area of Spacecraft Guidance Dynamics and Control, giving selected examples on success stories that have been motivated by mission designs. Our focus is on evolutionary optimisation, tree searches and machine learning, including deep learning and reinforcement learning as the key technologies and drivers for current and future research in the field. From a high-level perspective, we survey various scenarios for which these approaches have been successfully applied or are under strong scientific investigation. Whenever possible, we highlight the relations and synergies that can be obtained by combining different techniques and projects towards future domains for which newly emerging artificial intelligence techniques are expected to become game changers. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | 115,899 |
2309.04418 | Realistic pedestrian behaviour in the CARLA simulator using VR and mocap | Simulations are gaining increasingly significance in the field of autonomous driving due to the demand for rapid prototyping and extensive testing. Employing physics-based simulation brings several benefits at an affordable cost, while mitigating potential risks to prototypes, drivers, and vulnerable road users. However, there exit two primary limitations. Firstly, the reality gap which refers to the disparity between reality and simulation and prevents the simulated autonomous driving systems from having the same performance in the real world. Secondly, the lack of empirical understanding regarding the behavior of real agents, such as backup drivers or passengers, as well as other road users such as vehicles, pedestrians, or cyclists. Agent simulation is commonly implemented through deterministic or randomized probabilistic pre-programmed models, or generated from real-world data; but it fails to accurately represent the behaviors adopted by real agents while interacting within a specific simulated scenario. This paper extends the description of our proposed framework to enable real-time interaction between real agents and simulated environments, by means immersive virtual reality and human motion capture systems within the CARLA simulator for autonomous driving. We have designed a set of usability examples that allow the analysis of the interactions between real pedestrians and simulated autonomous vehicles and we provide a first measure of the user's sensation of presence in the virtual environment. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 390,716 |
2202.05799 | Rate-matching the regret lower-bound in the linear quadratic regulator
with unknown dynamics | The theory of reinforcement learning currently suffers from a mismatch between its empirical performance and the theoretical characterization of its performance, with consequences for, e.g., the understanding of sample efficiency, safety, and robustness. The linear quadratic regulator with unknown dynamics is a fundamental reinforcement learning setting with significant structure in its dynamics and cost function, yet even in this setting there is a gap between the best known regret lower-bound of $\Omega_p(\sqrt{T})$ and the best known upper-bound of $O_p(\sqrt{T}\,\text{polylog}(T))$. The contribution of this paper is to close that gap by establishing a novel regret upper-bound of $O_p(\sqrt{T})$. Our proof is constructive in that it analyzes the regret of a concrete algorithm, and simultaneously establishes an estimation error bound on the dynamics of $O_p(T^{-1/4})$ which is also the first to match the rate of a known lower-bound. The two keys to our improved proof technique are (1) a more precise upper- and lower-bound on the system Gram matrix and (2) a self-bounding argument for the expected estimation error of the optimal controller. | false | false | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | 279,991 |
2110.04292 | Toward a Visual Concept Vocabulary for GAN Latent Space | A large body of recent work has identified transformations in the latent spaces of generative adversarial networks (GANs) that consistently and interpretably transform generated images. But existing techniques for identifying these transformations rely on either a fixed vocabulary of pre-specified visual concepts, or on unsupervised disentanglement techniques whose alignment with human judgments about perceptual salience is unknown. This paper introduces a new method for building open-ended vocabularies of primitive visual concepts represented in a GAN's latent space. Our approach is built from three components: (1) automatic identification of perceptually salient directions based on their layer selectivity; (2) human annotation of these directions with free-form, compositional natural language descriptions; and (3) decomposition of these annotations into a visual concept vocabulary, consisting of distilled directions labeled with single words. Experiments show that concepts learned with our approach are reliable and composable -- generalizing across classes, contexts, and observers, and enabling fine-grained manipulation of image style and content. | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | false | false | 259,830 |
2408.09918 | Expressive Power of Temporal Message Passing | Graph neural networks (GNNs) have recently been adapted to temporal settings, often employing temporal versions of the message-passing mechanism known from GNNs. We divide temporal message passing mechanisms from literature into two main types: global and local, and establish Weisfeiler-Leman characterisations for both. This allows us to formally analyse expressive power of temporal message-passing models. We show that global and local temporal message-passing mechanisms have incomparable expressive power when applied to arbitrary temporal graphs. However, the local mechanism is strictly more expressive than the global mechanism when applied to colour-persistent temporal graphs, whose node colours are initially the same in all time points. Our theoretical findings are supported by experimental evidence, underlining practical implications of our analysis. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 481,645 |
2310.02543 | Provable Tensor Completion with Graph Information | Graphs, depicting the interrelations between variables, has been widely used as effective side information for accurate data recovery in various matrix/tensor recovery related applications. In this paper, we study the tensor completion problem with graph information. Current research on graph-regularized tensor completion tends to be task-specific, lacking generality and systematic approaches. Furthermore, a recovery theory to ensure performance remains absent. Moreover, these approaches overlook the dynamic aspects of graphs, treating them as static akin to matrices, even though graphs could exhibit dynamism in tensor-related scenarios. To confront these challenges, we introduce a pioneering framework in this paper that systematically formulates a novel model, theory, and algorithm for solving the dynamic graph regularized tensor completion problem. For the model, we establish a rigorous mathematical representation of the dynamic graph, based on which we derive a new tensor-oriented graph smoothness regularization. By integrating this regularization into a tensor decomposition model based on transformed t-SVD, we develop a comprehensive model simultaneously capturing the low-rank and similarity structure of the tensor. In terms of theory, we showcase the alignment between the proposed graph smoothness regularization and a weighted tensor nuclear norm. Subsequently, we establish assurances of statistical consistency for our model, effectively bridging a gap in the theoretical examination of the problem involving tensor recovery with graph information. In terms of the algorithm, we develop a solution of high effectiveness, accompanied by a guaranteed convergence, to address the resulting model. To showcase the prowess of our proposed model in contrast to established ones, we provide in-depth numerical experiments encompassing synthetic data as well as real-world datasets. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 396,892 |
2312.05708 | Context Tuning for Retrieval Augmented Generation | Large language models (LLMs) have the remarkable ability to solve new tasks with just a few examples, but they need access to the right tools. Retrieval Augmented Generation (RAG) addresses this problem by retrieving a list of relevant tools for a given task. However, RAG's tool retrieval step requires all the required information to be explicitly present in the query. This is a limitation, as semantic search, the widely adopted tool retrieval method, can fail when the query is incomplete or lacks context. To address this limitation, we propose Context Tuning for RAG, which employs a smart context retrieval system to fetch relevant information that improves both tool retrieval and plan generation. Our lightweight context retrieval model uses numerical, categorical, and habitual usage signals to retrieve and rank context items. Our empirical results demonstrate that context tuning significantly enhances semantic search, achieving a 3.5-fold and 1.5-fold improvement in Recall@K for context retrieval and tool retrieval tasks respectively, and resulting in an 11.6% increase in LLM-based planner accuracy. Additionally, we show that our proposed lightweight model using Reciprocal Rank Fusion (RRF) with LambdaMART outperforms GPT-4 based retrieval. Moreover, we observe context augmentation at plan generation, even after tool retrieval, reduces hallucination. | false | false | false | false | true | true | true | false | false | false | false | false | false | false | false | false | false | false | 414,195 |
1401.5869 | An Enhanced Branch-and-bound Algorithm for the Talent Scheduling Problem | The talent scheduling problem is a simplified version of the real-world film shooting problem, which aims to determine a shooting sequence so as to minimize the total cost of the actors involved. In this article, we first formulate the problem as an integer linear programming model. Next, we devise a branch-and-bound algorithm to solve the problem. The branch-and-bound algorithm is enhanced by several accelerating techniques, including preprocessing, dominance rules and caching search states. Extensive experiments over two sets of benchmark instances suggest that our algorithm is superior to the current best exact algorithm. Finally, the impacts of different parameter settings are disclosed by some additional experiments. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 30,263 |
1911.05289 | The Deep Learning Revolution and Its Implications for Computer
Architecture and Chip Design | The past decade has seen a remarkable series of advances in machine learning, and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas, including computer vision, speech recognition, language translation, and natural language understanding tasks. This paper is a companion paper to a keynote talk at the 2020 International Solid-State Circuits Conference (ISSCC) discussing some of the advances in machine learning, and their implications on the kinds of computational devices we need to build, especially in the post-Moore's Law-era. It also discusses some of the ways that machine learning may also be able to help with some aspects of the circuit design process. Finally, it provides a sketch of at least one interesting direction towards much larger-scale multi-task models that are sparsely activated and employ much more dynamic, example- and task-based routing than the machine learning models of today. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 153,223 |
2308.01905 | Revisiting Deformable Convolution for Depth Completion | Depth completion, which aims to generate high-quality dense depth maps from sparse depth maps, has attracted increasing attention in recent years. Previous work usually employs RGB images as guidance, and introduces iterative spatial propagation to refine estimated coarse depth maps. However, most of the propagation refinement methods require several iterations and suffer from a fixed receptive field, which may contain irrelevant and useless information with very sparse input. In this paper, we address these two challenges simultaneously by revisiting the idea of deformable convolution. We propose an effective architecture that leverages deformable kernel convolution as a single-pass refinement module, and empirically demonstrate its superiority. To better understand the function of deformable convolution and exploit it for depth completion, we further systematically investigate a variety of representative strategies. Our study reveals that, different from prior work, deformable convolution needs to be applied on an estimated depth map with a relatively high density for better performance. We evaluate our model on the large-scale KITTI dataset and achieve state-of-the-art level performance in both accuracy and inference speed. Our code is available at https://github.com/AlexSunNik/ReDC. | false | false | false | false | true | false | true | false | false | false | false | true | false | false | false | false | false | false | 383,406 |
1307.0036 | Increasing Compression Ratio in PNG Images by k-Modulus Method for Image
Transformation | Image compression is an important filed in image processing. The science welcomes any tinny contribution that may increase the compression ratio by whichever insignificant percentage. Therefore, the essential contribution in this paper is to increase the compression ratio for the well known Portable Network Graphics (PNG) image file format. The contribution starts with converting the original PNG image into k-Modulus Method (k-MM). Practically, taking k equals to ten, and then the pixels in the constructed image will be integers divisible by ten. Since PNG uses Lempel-Ziv compression algorithm, then the ability to reduce file size will increase according to the repetition in pixels in each k-by-k window according to the transformation done by k-MM. Experimental results show that the proposed technique (k-PNG) produces high compression ratio with smaller file size in comparison to the original PNG file. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | true | 25,518 |
2410.20050 | AutoMIR: Effective Zero-Shot Medical Information Retrieval without
Relevance Labels | Medical information retrieval (MIR) is essential for retrieving relevant medical knowledge from diverse sources, including electronic health records, scientific literature, and medical databases. However, achieving effective zero-shot dense retrieval in the medical domain poses substantial challenges due to the lack of relevance-labeled data. In this paper, we introduce a novel approach called Self-Learning Hypothetical Document Embeddings (SL-HyDE) to tackle this issue. SL-HyDE leverages large language models (LLMs) as generators to generate hypothetical documents based on a given query. These generated documents encapsulate key medical context, guiding a dense retriever in identifying the most relevant documents. The self-learning framework progressively refines both pseudo-document generation and retrieval, utilizing unlabeled medical corpora without requiring any relevance-labeled data. Additionally, we present the Chinese Medical Information Retrieval Benchmark (CMIRB), a comprehensive evaluation framework grounded in real-world medical scenarios, encompassing five tasks and ten datasets. By benchmarking ten models on CMIRB, we establish a rigorous standard for evaluating medical information retrieval systems. Experimental results demonstrate that SL-HyDE significantly surpasses existing methods in retrieval accuracy while showcasing strong generalization and scalability across various LLM and retriever configurations. CMIRB data and evaluation code are publicly available at: https://github.com/CMIRB-benchmark/CMIRB. | false | false | false | false | true | true | false | false | false | false | false | false | false | false | false | false | false | false | 502,626 |
2207.02811 | Multi-View Object Pose Refinement With Differentiable Renderer | This paper introduces a novel multi-view 6 DoF object pose refinement approach focusing on improving methods trained on synthetic data. It is based on the DPOD detector, which produces dense 2D-3D correspondences between the model vertices and the image pixels in each frame. We have opted for the use of multiple frames with known relative camera transformations, as it allows introduction of geometrical constraints via an interpretable ICP-like loss function. The loss function is implemented with a differentiable renderer and is optimized iteratively. We also demonstrate that a full detection and refinement pipeline, which is trained solely on synthetic data, can be used for auto-labeling real data. We perform quantitative evaluation on LineMOD, Occlusion, Homebrewed and YCB-V datasets and report excellent performance in comparison to the state-of-the-art methods trained on the synthetic and real data. We demonstrate empirically that our approach requires only a few frames and is robust to close camera locations and noise in extrinsic camera calibration, making its practical usage easier and more ubiquitous. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 306,627 |
2403.19579 | The Bad Batches: Enhancing Self-Supervised Learning in Image
Classification Through Representative Batch Curation | The pursuit of learning robust representations without human supervision is a longstanding challenge. The recent advancements in self-supervised contrastive learning approaches have demonstrated high performance across various representation learning challenges. However, current methods depend on the random transformation of training examples, resulting in some cases of unrepresentative positive pairs that can have a large impact on learning. This limitation not only impedes the convergence of the learning process but the robustness of the learnt representation as well as requiring larger batch sizes to improve robustness to such bad batches. This paper attempts to alleviate the influence of false positive and false negative pairs by employing pairwise similarity calculations through the Fr\'echet ResNet Distance (FRD), thereby obtaining robust representations from unlabelled data. The effectiveness of the proposed method is substantiated by empirical results, where a linear classifier trained on self-supervised contrastive representations achieved an impressive 87.74\% top-1 accuracy on STL10 and 99.31\% on the Flower102 dataset. These results emphasize the potential of the proposed approach in pushing the boundaries of the state-of-the-art in self-supervised contrastive learning, particularly for image classification tasks. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 442,400 |
2104.04004 | ACERAC: Efficient reinforcement learning in fine time discretization | One of the main goals of reinforcement learning (RL) is to provide a~way for physical machines to learn optimal behavior instead of being programmed. However, effective control of the machines usually requires fine time discretization. The most common RL methods apply independent random elements to each action, which is not suitable in that setting. It is not feasible because it causes the controlled system to jerk, and does not ensure sufficient exploration since a~single action is not long enough to create a~significant experience that could be translated into policy improvement. In our view these are the main obstacles that prevent application of RL in contemporary control systems. To address these pitfalls, in this paper we introduce an RL framework and adequate analytical tools for actions that may be stochastically dependent in subsequent time instances. We also introduce an RL algorithm that approximately optimizes a~policy that produces such actions. It applies experience replay to adjust likelihood of sequences of previous actions to optimize expected $n$-step returns the policy yields. The efficiency of this algorithm is verified against four other RL methods (CDAU, PPO, SAC, ACER) in four simulated learning control problems (Ant, HalfCheetah, Hopper, and Walker2D) in diverse time discretization. The algorithm introduced here outperforms the competitors in most cases considered. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 229,249 |
2501.19159 | GDO: Gradual Domain Osmosis | In this paper, we propose a new method called Gradual Domain Osmosis, which aims to solve the problem of smooth knowledge migration from source domain to target domain in Gradual Domain Adaptation (GDA). Traditional Gradual Domain Adaptation methods mitigate domain bias by introducing intermediate domains and self-training strategies, but often face the challenges of inefficient knowledge migration or missing data in intermediate domains. In this paper, we design an optimisation framework based on the hyperparameter $\lambda$ by dynamically balancing the loss weights of the source and target domains, which enables the model to progressively adjust the strength of knowledge migration ($\lambda$ incrementing from 0 to 1) during the training process, thus achieving cross-domain generalisation more efficiently. Specifically, the method incorporates self-training to generate pseudo-labels and iteratively updates the model by minimising a weighted loss function to ensure stability and robustness during progressive adaptation in the intermediate domain. The experimental part validates the effectiveness of the method on rotated MNIST, colour-shifted MNIST, portrait dataset and forest cover type dataset, and the results show that it outperforms existing baseline methods. The paper further analyses the impact of the dynamic tuning strategy of the hyperparameter $\lambda$ on the performance through ablation experiments, confirming the advantages of progressive domain penetration in mitigating the domain bias and enhancing the model generalisation capability. The study provides a theoretical support and practical framework for asymptotic domain adaptation and expands its application potential in dynamic environments. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 529,037 |
2405.13066 | Practical Performance of a Distributed Processing Framework for
Machine-Learning-based NIDS | Network Intrusion Detection Systems (NIDSs) detect intrusion attacks in network traffic. In particular, machine-learning-based NIDSs have attracted attention because of their high detection rates of unknown attacks. A distributed processing framework for machine-learning-based NIDSs employing a scalable distributed stream processing system has been proposed in the literature. However, its performance, when machine-learning-based classifiers are implemented has not been comprehensively evaluated. In this study, we implement five representative classifiers (Decision Tree, Random Forest, Naive Bayes, SVM, and kNN) based on this framework and evaluate their throughput and latency. By conducting the experimental measurements, we investigate the difference in the processing performance among these classifiers and the bottlenecks in the processing performance of the framework. | false | false | false | false | false | false | true | false | false | false | false | false | true | false | false | false | false | true | 455,790 |
2407.14498 | Enhancing Layout Hotspot Detection Efficiency with YOLOv8 and PCA-Guided
Augmentation | In this paper, we present a YOLO-based framework for layout hotspot detection, aiming to enhance the efficiency and performance of the design rule checking (DRC) process. Our approach leverages the YOLOv8 vision model to detect multiple hotspots within each layout image, even when dealing with large layout image sizes. Additionally, to enhance pattern-matching effectiveness, we introduce a novel approach to augment the layout image using information extracted through Principal Component Analysis (PCA). The core of our proposed method is an algorithm that utilizes PCA to extract valuable auxiliary information from the layout image. This extracted information is then incorporated into the layout image as an additional color channel. This augmentation significantly improves the accuracy of multi-hotspot detection while reducing the false alarm rate of the object detection algorithm. We evaluate the effectiveness of our framework using four datasets generated from layouts found in the ICCAD-2019 benchmark dataset. The results demonstrate that our framework achieves a precision (recall) of approximately 83% (86%) while maintaining a false alarm rate of less than 7.4\%. Also, the studies show that the proposed augmentation approach could improve the detection ability of never-seen-before (NSB) hotspots by about 10%. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 474,800 |
2011.09973 | List-Decodable Mean Estimation in Nearly-PCA Time | Traditionally, robust statistics has focused on designing estimators tolerant to a minority of contaminated data. Robust list-decodable learning focuses on the more challenging regime where only a minority $\frac 1 k$ fraction of the dataset is drawn from the distribution of interest, and no assumptions are made on the remaining data. We study the fundamental task of list-decodable mean estimation in high dimensions. Our main result is a new list-decodable mean estimation algorithm for bounded covariance distributions with optimal sample complexity and error rate, running in nearly-PCA time. Assuming the ground truth distribution on $\mathbb{R}^d$ has bounded covariance, our algorithm outputs a list of $O(k)$ candidate means, one of which is within distance $O(\sqrt{k})$ from the truth. Our algorithm runs in time $\widetilde{O}(ndk)$ for all $k = O(\sqrt{d}) \cup \Omega(d)$, where $n$ is the size of the dataset. We also show that a variant of our algorithm has runtime $\widetilde{O}(ndk)$ for all $k$, at the expense of an $O(\sqrt{\log k})$ factor in the recovery guarantee. This runtime matches up to logarithmic factors the cost of performing a single $k$-PCA on the data, which is a natural bottleneck of known algorithms for (very) special cases of our problem, such as clustering well-separated mixtures. Prior to our work, the fastest list-decodable mean estimation algorithms had runtimes $\widetilde{O}(n^2 d k^2)$ and $\widetilde{O}(nd k^{\ge 6})$. Our approach builds on a novel soft downweighting method, $\mathsf{SIFT}$, which is arguably the simplest known polynomial-time mean estimation technique in the list-decodable learning setting. To develop our fast algorithms, we boost the computational cost of $\mathsf{SIFT}$ via a careful "win-win-win" analysis of an approximate Ky Fan matrix multiplicative weights procedure we develop, which we believe may be of independent interest. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 207,380 |
2303.17919 | Grounding Object Relations in Language-Conditioned Robotic Manipulation
with Semantic-Spatial Reasoning | Grounded understanding of natural language in physical scenes can greatly benefit robots that follow human instructions. In object manipulation scenarios, existing end-to-end models are proficient at understanding semantic concepts, but typically cannot handle complex instructions involving spatial relations among multiple objects. which require both reasoning object-level spatial relations and learning precise pixel-level manipulation affordances. We take an initial step to this challenge with a decoupled two-stage solution. In the first stage, we propose an object-centric semantic-spatial reasoner to select which objects are relevant for the language instructed task. The segmentation of selected objects are then fused as additional input to the affordance learning stage. Simply incorporating the inductive bias of relevant objects to a vision-language affordance learning agent can effectively boost its performance in a custom testbed designed for object manipulation with spatial-related language instructions. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 355,393 |
2406.02237 | Self-Modifying State Modeling for Simultaneous Machine Translation | Simultaneous Machine Translation (SiMT) generates target outputs while receiving stream source inputs and requires a read/write policy to decide whether to wait for the next source token or generate a new target token, whose decisions form a \textit{decision path}. Existing SiMT methods, which learn the policy by exploring various decision paths in training, face inherent limitations. These methods not only fail to precisely optimize the policy due to the inability to accurately assess the individual impact of each decision on SiMT performance, but also cannot sufficiently explore all potential paths because of their vast number. Besides, building decision paths requires unidirectional encoders to simulate streaming source inputs, which impairs the translation quality of SiMT models. To solve these issues, we propose \textbf{S}elf-\textbf{M}odifying \textbf{S}tate \textbf{M}odeling (SM$^2$), a novel training paradigm for SiMT task. Without building decision paths, SM$^2$ individually optimizes decisions at each state during training. To precisely optimize the policy, SM$^2$ introduces Self-Modifying process to independently assess and adjust decisions at each state. For sufficient exploration, SM$^2$ proposes Prefix Sampling to efficiently traverse all potential states. Moreover, SM$^2$ ensures compatibility with bidirectional encoders, thus achieving higher translation quality. Experiments show that SM$^2$ outperforms strong baselines. Furthermore, SM$^2$ allows offline machine translation models to acquire SiMT ability with fine-tuning. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 460,680 |
2412.01824 | X-Prompt: Towards Universal In-Context Image Generation in
Auto-Regressive Vision Language Foundation Models | In-context generation is a key component of large language models' (LLMs) open-task generalization capability. By leveraging a few examples as context, LLMs can perform both in-domain and out-of-domain tasks. Recent advancements in auto-regressive vision-language models (VLMs) built upon LLMs have showcased impressive performance in text-to-image generation. However, the potential of in-context learning for general image generation tasks remains largely unexplored. To address this, we introduce X-Prompt, a purely auto-regressive large-vision language model designed to deliver competitive performance across a wide range of both seen and unseen image generation tasks, all within a unified in-context learning framework. X-Prompt incorporates a specialized design that efficiently compresses valuable features from in-context examples, supporting longer in-context token sequences and improving its ability to generalize to unseen tasks. A unified training task for both text and image prediction enables X-Prompt to handle general image generation with enhanced task awareness from in-context examples. Extensive experiments validate the model's performance across diverse seen image generation tasks and its capacity to generalize to previously unseen tasks. | false | false | false | false | true | false | true | false | false | false | false | true | false | false | false | false | false | true | 513,281 |
1506.03936 | A Novel Hybrid Approach for Cephalometric Landmark Detection | Cephalometric analysis has an important role in dentistry and especially in orthodontics as a treatment planning tool to gauge the size and special relationships of the teeth, jaws and cranium. The first step of using such analyses is localizing some important landmarks known as cephalometric landmarks on craniofacial in x-ray image. The past decade has seen a growing interest in automating this process. In this paper, a novel hybrid approach is proposed for automatic detection of cephalometric landmarks. Here, the landmarks are categorized into three main sets according to their anatomical characteristics and usage in well-known cephalometric analyses. Consequently, to have a reliable and accurate detection system, three methods named edge tracing, weighted template matching, and analysis based estimation are designed, each of which is consistent and well-suited for one category. Edge tracing method is suggested to predict those landmarks which are located on edges. Weighted template matching method is well-suited for landmarks located in an obvious and specific structure which can be extracted or searchable in a given x-ray image. The last but not the least method is named analysis based estimation. This method is based on the fact that in cephalometric analyses the relations between landmarks are used and the locations of some landmarks are never used individually. Therefore the third suggested method has a novelty in estimating the desired relations directly. The effectiveness of the proposed approach is compared with the state of the art methods and the results were promising especially in real world applications. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 44,111 |
2003.03741 | PUMiner: Mining Security Posts from Developer Question and Answer
Websites with PU Learning | Security is an increasing concern in software development. Developer Question and Answer (Q&A) websites provide a large amount of security discussion. Existing studies have used human-defined rules to mine security discussions, but these works still miss many posts, which may lead to an incomplete analysis of the security practices reported on Q&A websites. Traditional supervised Machine Learning methods can automate the mining process; however, the required negative (non-security) class is too expensive to obtain. We propose a novel learning framework, PUMiner, to automatically mine security posts from Q&A websites. PUMiner builds a context-aware embedding model to extract features of the posts, and then develops a two-stage PU model to identify security content using the labelled Positive and Unlabelled posts. We evaluate PUMiner on more than 17.2 million posts on Stack Overflow and 52,611 posts on Security StackExchange. We show that PUMiner is effective with the validation performance of at least 0.85 across all model configurations. Moreover, Matthews Correlation Coefficient (MCC) of PUMiner is 0.906, 0.534 and 0.084 points higher than one-class SVM, positive-similarity filtering, and one-stage PU models on unseen testing posts, respectively. PUMiner also performs well with an MCC of 0.745 for scenarios where string matching totally fails. Even when the ratio of the labelled positive posts to the unlabelled ones is only 1:100, PUMiner still achieves a strong MCC of 0.65, which is 160% better than fully-supervised learning. Using PUMiner, we provide the largest and up-to-date security content on Q&A websites for practitioners and researchers. | false | false | false | false | false | true | true | false | false | false | false | false | false | false | false | false | false | true | 167,347 |
1805.12595 | The Arbitrarily Varying Gaussian Relay Channel with Sender Frequency
Division | We consider the arbitrarily varying Gaussian relay channel with sender frequency division. We determine the random code capacity, and establish lower and upper bounds on the deterministic code capacity. It is observed that when the channel input is subject to a low power limit, the deterministic code capacity may be strictly lower than the random code capacity, and the gap vanishes as the input becomes less constrained. A second model addressed in this paper is the general case of primitive arbitrarily varying relay channels. We develop lower and upper bounds on the random code capacity, and give conditions under which the deterministic code capacity coincides with the random code capacity, and conditions under which it is lower. Then, we establish the capacity of the primitive counterpart of the arbitrarily varying Gaussian relay channel with sender frequency division. In this case, the deterministic and random code capacities are the same. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 99,210 |
2011.10274 | Learning Synthetic to Real Transfer for Localization and Navigational
Tasks | Autonomous navigation consists in an agent being able to navigate without human intervention or supervision, it affects both high level planning and low level control. Navigation is at the crossroad of multiple disciplines, it combines notions of computer vision, robotics and control. This work aimed at creating, in a simulation, a navigation pipeline whose transfer to the real world could be done with as few efforts as possible. Given the limited time and the wide range of problematic to be tackled, absolute navigation performances while important was not the main objective. The emphasis was rather put on studying the sim2real gap which is one the major bottlenecks of modern robotics and autonomous navigation. To design the navigation pipeline four main challenges arise; environment, localization, navigation and planning. The iGibson simulator is picked for its photo-realistic textures and physics engine. A topological approach to tackle space representation was picked over metric approaches because they generalize better to new environments and are less sensitive to change of conditions. The navigation pipeline is decomposed as a localization module, a planning module and a local navigation module. These modules utilize three different networks, an image representation extractor, a passage detector and a local policy. The laters are trained on specifically tailored tasks with some associated datasets created for those specific tasks. Localization is the ability for the agent to localize itself against a specific space representation. It must be reliable, repeatable and robust to a wide variety of transformations. Localization is tackled as an image retrieval task using a deep neural network trained on an auxiliary task as a feature descriptor extractor. The local policy is trained with behavioral cloning from expert trajectories gathered with ROS navigation stack. | false | false | false | false | true | false | false | true | false | false | false | true | false | false | false | false | false | false | 207,462 |
2005.13865 | Towards Decision Support in Dynamic Bi-Objective Vehicle Routing | We consider a dynamic bi-objective vehicle routing problem, where a subset of customers ask for service over time. Therein, the distance traveled by a single vehicle and the number of unserved dynamic requests is minimized by a dynamic evolutionary multi-objective algorithm (DEMOA), which operates on discrete time windows (eras). A decision is made at each era by a decision-maker, thus any decision depends on irreversible decisions made in foregoing eras. To understand effects of sequences of decision-making and interactions/dependencies between decisions made, we conduct a series of experiments. More precisely, we fix a set of decision-maker preferences $D$ and the number of eras $n_t$ and analyze all $|D|^{n_t}$ combinations of decision-maker options. We find that for random uniform instances (a) the final selected solutions mainly depend on the final decision and not on the decision history, (b) solutions are quite robust with respect to the number of unvisited dynamic customers, and (c) solutions of the dynamic approach can even dominate solutions obtained by a clairvoyant EMOA. In contrast, for instances with clustered customers, we observe a strong dependency on decision-making history as well as more variance in solution diversity. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | 179,123 |
1908.11687 | Torus Breakdown in a Uni Junction Memristor | Experimental study of a uni junction transistor (UJT) has enabled to show that this electronic component has the same features as the so-called "memristor". So, we have used the memristor's direct current (DC) vM--iM characteristic for modeling the UJT's DC current--voltage characteristic. This has led us to confirm on the one hand, that the UJT is a memristor and, on the other hand, to propose a new four-dimensional autonomous dynamical system allowing to describe experimentally observed phenomena such as the transition from a limit cycle to torus breakdown. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 143,444 |
2409.11512 | Good Grasps Only: A data engine for self-supervised fine-tuning of pose
estimation using grasp poses for verification | In this paper, we present a novel method for self-supervised fine-tuning of pose estimation. Leveraging zero-shot pose estimation, our approach enables the robot to automatically obtain training data without manual labeling. After pose estimation the object is grasped, and in-hand pose estimation is used for data validation. Our pipeline allows the system to fine-tune while the process is running, removing the need for a learning phase. The motivation behind our work lies in the need for rapid setup of pose estimation solutions. Specifically, we address the challenging task of bin picking, which plays a pivotal role in flexible robotic setups. Our method is implemented on a robotics work-cell, and tested with four different objects. For all objects, our method increases the performance and outperforms a state-of-the-art method trained on the CAD model of the objects. Project page available at gogoengine.github.io | false | false | false | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | 489,187 |
2202.13116 | Mobile Device Association and Resource Allocation in Small-Cell IoT
Networks with Mobile Edge Computing and Caching | To meet the need of computation-sensitive (CS) and high-rate (HR) communications, the framework of mobile edge computing and caching has been widely regarded as a promising solution. When such a framework is implemented in small-cell IoT (Internet of Tings) networks, it is a key and open topic how to assign mobile edge computing and caching servers to mobile devices (MDs) with CS and HR communications. Since these servers are integrated into small base stations (BSs), the assignment of them refers to not only the BS selection (i.e., MD association), but also the selection of computing and caching modes. To mitigate the network interference and thus enhance the system performance, some highly-effective resource partitioning mechanisms are introduced for access and backhaul links firstly. After that a problem with minimizing the sum of MDs' weighted delays is formulated to attain a goal of joint MD association and resource allocation under limited resources. Considering that the MD association and resource allocation parameters are coupling in such a formulated problem, we develop an alternating optimization algorithm according to the coalitional game and convex optimization theorems. To ensure that the designed algorithm begins from a feasible initial solution, we develop an initiation algorithm according to the conventional best channel association, which is used for comparison and the input of coalition game in the simulation. Simulation results show that the algorithm designed for minimizing the sum of MDs' weighted delays may achieve a better performance than the initiation (best channel association) algorithm in general. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 282,490 |
2103.00573 | RuSentEval: Linguistic Source, Encoder Force! | The success of pre-trained transformer language models has brought a great deal of interest on how these models work, and what they learn about language. However, prior research in the field is mainly devoted to English, and little is known regarding other languages. To this end, we introduce RuSentEval, an enhanced set of 14 probing tasks for Russian, including ones that have not been explored yet. We apply a combination of complementary probing methods to explore the distribution of various linguistic properties in five multilingual transformers for two typologically contrasting languages -- Russian and English. Our results provide intriguing findings that contradict the common understanding of how linguistic knowledge is represented, and demonstrate that some properties are learned in a similar manner despite the language differences. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 222,327 |
2101.11056 | A Model of Densifying Collaboration Networks | Research collaborations provide the foundation for scientific advances, but we have only recently begun to understand how they form and grow on a global scale. Here we analyze a model of the growth of research collaboration networks to explain the empirical observations that the number of collaborations scales superlinearly with institution size, though at different rates (heterogeneous densification), the number of institutions grows as a power of the number of researchers (Heaps' law) and institution sizes approximate Zipf's law. This model has three mechanisms: (i) researchers are preferentially hired by large institutions, (ii) new institutions trigger more potential institutions, and (iii) researchers collaborate with friends-of-friends. We show agreement between these assumptions and empirical data, through analysis of co-authorship networks spanning two centuries. We then develop a theoretical understanding of this model, which reveals emergent heterogeneous scaling such that the number of collaborations between institutions scale with an institution's size. | false | false | false | true | false | false | false | false | false | false | false | false | false | true | false | false | false | false | 217,139 |
2012.08312 | QUARC: Quaternion Multi-Modal Fusion Architecture For Hate Speech
Classification | Hate speech, quite common in the age of social media, at times harmless but can also cause mental trauma to someone or even riots in communities. Image of a religious symbol with derogatory comment or video of a man abusing a particular community, all become hate speech with its every modality (such as text, image, and audio) contributing towards it. Models based on a particular modality of hate speech post on social media are not useful, rather, we need models like multi-modal fusion models that consider both image and text while classifying hate speech. Text-image fusion models are heavily parameterized, hence we propose a quaternion neural network-based model having additional fusion components for each pair of modalities. The model is tested on the MMHS150K twitter dataset for hate speech classification. The model shows an almost 75% reduction in parameters and also benefits us in terms of storage space and training time while being at par in terms of performance as compared to its real counterpart. | false | false | true | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | 211,737 |
2209.07463 | Omnipredictors for Constrained Optimization | The notion of omnipredictors (Gopalan, Kalai, Reingold, Sharan and Wieder ITCS 2021), suggested a new paradigm for loss minimization. Rather than learning a predictor based on a known loss function, omnipredictors can easily be post-processed to minimize any one of a rich family of loss functions compared with the loss of hypotheses in a class $\mathcal C$. It has been shown that such omnipredictors exist and are implied (for all convex and Lipschitz loss functions) by the notion of multicalibration from the algorithmic fairness literature. In this paper, we introduce omnipredictors for constrained optimization and study their complexity and implications. The notion that we introduce allows the learner to be unaware of the loss function that will be later assigned as well as the constraints that will be later imposed, as long as the subpopulations that are used to define these constraints are known. We show how to obtain omnipredictors for constrained optimization problems, relying on appropriate variants of multicalibration. We also investigate the implications of this notion when the constraints used are so-called group fairness notions. | false | false | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | true | 317,756 |
1510.00889 | Background Image Generation Using Boolean Operations | Tracking moving objects from a video sequence requires segmentation of these objects from the background image. However, getting the actual background image automatically without object detection and using only the video is difficult. In this paper, we describe a novel algorithm that generates background from real world images without foreground detection. The algorithm assumes that the background image is shown in the majority of the video. Given this simple assumption, the method described in this paper is able to accurately generate, with high probability, the background image from a video using only a small number of binary operations. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 47,555 |
2211.16525 | Proactive Moderation of Online Discussions: Existing Practices and the
Potential for Algorithmic Support | To address the widespread problem of uncivil behavior, many online discussion platforms employ human moderators to take action against objectionable content, such as removing it or placing sanctions on its authors. This reactive paradigm of taking action against already-posted antisocial content is currently the most common form of moderation, and has accordingly underpinned many recent efforts at introducing automation into the moderation process. Comparatively less work has been done to understand other moderation paradigms -- such as proactively discouraging the emergence of antisocial behavior rather than reacting to it -- and the role algorithmic support can play in these paradigms. In this work, we investigate such a proactive framework for moderation in a case study of a collaborative setting: Wikipedia Talk Pages. We employ a mixed methods approach, combining qualitative and design components for a holistic analysis. Through interviews with moderators, we find that despite a lack of technical and social support, moderators already engage in a number of proactive moderation behaviors, such as preemptively intervening in conversations to keep them on track. Further, we explore how automation could assist with this existing proactive moderation workflow by building a prototype tool, presenting it to moderators, and examining how the assistance it provides might fit into their workflow. The resulting feedback uncovers both strengths and drawbacks of the prototype tool and suggests concrete steps towards further developing such assisting technology so it can most effectively support moderators in their existing proactive moderation workflow. | true | false | false | false | true | false | false | false | true | false | false | false | false | true | false | false | false | false | 333,659 |
2312.14439 | PUMA: Efficient Continual Graph Learning for Node Classification with
Graph Condensation | When handling streaming graphs, existing graph representation learning models encounter a catastrophic forgetting problem, where previously learned knowledge of these models is easily overwritten when learning with newly incoming graphs. In response, Continual Graph Learning (CGL) emerges as a novel paradigm enabling graph representation learning from streaming graphs. Our prior work, Condense and Train (CaT) is a replay-based CGL framework with a balanced continual learning procedure, which designs a small yet effective memory bankn for replaying. Although the CaT alleviates the catastrophic forgetting problem, there exist three issues: (1) The graph condensation only focuses on labelled nodes while neglecting abundant information carried by unlabelled nodes; (2) The continual training scheme of the CaT overemphasises on the previously learned knowledge, limiting the model capacity to learn from newly added memories; (3) Both the condensation process and replaying process of the CaT are time-consuming. In this paper, we propose a PsUdo-label guided Memory bAnk (PUMA) CGL framework, extending from the CaT to enhance its efficiency and effectiveness by overcoming the above-mentioned weaknesses and limits. To fully exploit the information in a graph, PUMA expands the coverage of nodes during graph condensation with both labelled and unlabelled nodes. Furthermore, a training-from-scratch strategy is proposed to upgrade the previous continual learning scheme for a balanced training between the historical and the new graphs. Besides, PUMA uses a one-time prorogation and wide graph encoders to accelerate the graph condensation and the graph encoding process in the training stage to improve the efficiency of the whole framework. Extensive experiments on six datasets for the node classification task demonstrate the state-of-the-art performance and efficiency over existing methods. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 417,635 |
2004.05333 | Bit-Parallel Vector Composability for Neural Acceleration | Conventional neural accelerators rely on isolated self-sufficient functional units that perform an atomic operation while communicating the results through an operand delivery-aggregation logic. Each single unit processes all the bits of their operands atomically and produce all the bits of the results in isolation. This paper explores a different design style, where each unit is only responsible for a slice of the bit-level operations to interleave and combine the benefits of bit-level parallelism with the abundant data-level parallelism in deep neural networks. A dynamic collection of these units cooperate at runtime to generate bits of the results, collectively. Such cooperation requires extracting new grouping between the bits, which is only possible if the operands and operations are vectorizable. The abundance of Data Level Parallelism and mostly repeated execution patterns, provides a unique opportunity to define and leverage this new dimension of Bit-Parallel Vector Composability. This design intersperses bit parallelism within data-level parallelism and dynamically interweaves the two together. As such, the building block of our neural accelerator is a Composable Vector Unit that is a collection of Narrower-Bitwidth Vector Engines, which are dynamically composed or decomposed at the bit granularity. Using six diverse CNN and LSTM deep networks, we evaluate this design style across four design points: with and without algorithmic bitwidth heterogeneity and with and without availability of a high-bandwidth off-chip memory. Across these four design points, Bit-Parallel Vector Composability brings (1.4x to 3.5x) speedup and (1.1x to 2.7x) energy reduction. We also comprehensively compare our design style to the Nvidia RTX 2080 TI GPU, which also supports INT-4 execution. The benefits range between 28.0x and 33.7x improvement in Performance-per-Watt. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 172,162 |
2310.04306 | Towards A Robust Group-level Emotion Recognition via Uncertainty-Aware
Learning | Group-level emotion recognition (GER) is an inseparable part of human behavior analysis, aiming to recognize an overall emotion in a multi-person scene. However, the existing methods are devoted to combing diverse emotion cues while ignoring the inherent uncertainties under unconstrained environments, such as congestion and occlusion occurring within a group. Additionally, since only group-level labels are available, inconsistent emotion predictions among individuals in one group can confuse the network. In this paper, we propose an uncertainty-aware learning (UAL) method to extract more robust representations for GER. By explicitly modeling the uncertainty of each individual, we utilize stochastic embedding drawn from a Gaussian distribution instead of deterministic point embedding. This representation captures the probabilities of different emotions and generates diverse predictions through this stochasticity during the inference stage. Furthermore, uncertainty-sensitive scores are adaptively assigned as the fusion weights of individuals' face within each group. Moreover, we develop an image enhancement module to enhance the model's robustness against severe noise. The overall three-branch model, encompassing face, object, and scene component, is guided by a proportional-weighted fusion strategy and integrates the proposed uncertainty-aware method to produce the final group-level output. Experimental results demonstrate the effectiveness and generalization ability of our method across three widely used databases. | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | false | false | 397,600 |
2301.12331 | Time out of Mind: Generating Rate of Speech conditioned on emotion and
speaker | Voice synthesis has seen significant improvements in the past decade resulting in highly intelligible voices. Further investigations have resulted in models that can produce variable speech, including conditional emotional expression. The problem lies, however, in a focus on phrase-level modifications and prosodic vocal features. Using the CREMA-D dataset we have trained a GAN conditioned on emotion to generate worth lengths for a given input text. These word lengths are relative to neutral speech and can be provided, through speech synthesis markup language (SSML) to a text-to-speech (TTS) system to generate more expressive speech. Additionally, a generative model is also trained using implicit maximum likelihood estimation (IMLE) and a comparative analysis with GANs is included. We were able to achieve better performances on objective measures for neutral speech, and better time alignment for happy speech when compared to an out-of-box model. However, further investigation of subjective evaluation is required. | false | false | true | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 342,491 |
2308.01097 | Spatio-Temporal Branching for Motion Prediction using Motion Increments | Human motion prediction (HMP) has emerged as a popular research topic due to its diverse applications, but it remains a challenging task due to the stochastic and aperiodic nature of future poses. Traditional methods rely on hand-crafted features and machine learning techniques, which often struggle to model the complex dynamics of human motion. Recent deep learning-based methods have achieved success by learning spatio-temporal representations of motion, but these models often overlook the reliability of motion data. Additionally, the temporal and spatial dependencies of skeleton nodes are distinct. The temporal relationship captures motion information over time, while the spatial relationship describes body structure and the relationships between different nodes. In this paper, we propose a novel spatio-temporal branching network using incremental information for HMP, which decouples the learning of temporal-domain and spatial-domain features, extracts more motion information, and achieves complementary cross-domain knowledge learning through knowledge distillation. Our approach effectively reduces noise interference and provides more expressive information for characterizing motion by separately extracting temporal and spatial features. We evaluate our approach on standard HMP benchmarks and outperform state-of-the-art methods in terms of prediction accuracy. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 383,143 |
2410.06052 | Concurrent-Learning Based Relative Localization in Shape Formation of
Robot Swarms (Extended version) | In this paper, we address the shape formation problem for massive robot swarms in environments where external localization systems are unavailable. Achieving this task effectively with solely onboard measurements is still scarcely explored and faces some practical challenges. To solve this challenging problem, we propose the following novel results. Firstly, to estimate the relative positions among neighboring robots, a concurrent-learning based estimator is proposed. It relaxes the persistent excitation condition required in the classical ones such as least-square estimator. Secondly, we introduce a finite-time agreement protocol to determine the shape location. This is achieved by estimating the relative position between each robot and a randomly assigned seed robot. The initial position of the seed one marks the shape location. Thirdly, based on the theoretical results of the relative localization, a novel behavior-based control strategy is devised. This strategy not only enables adaptive shape formation of large group of robots but also enhances the observability of inter-robot relative localization. Numerical simulation results are provided to verify the performance of our proposed strategy compared to the state-of-the-art ones. Additionally, outdoor experiments on real robots further demonstrate the practical effectiveness and robustness of our methods. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | 496,032 |
2309.08454 | Mixture Encoder Supporting Continuous Speech Separation for Meeting
Recognition | Many real-life applications of automatic speech recognition (ASR) require processing of overlapped speech. A commonmethod involves first separating the speech into overlap-free streams and then performing ASR on the resulting signals. Recently, the inclusion of a mixture encoder in the ASR model has been proposed. This mixture encoder leverages the original overlapped speech to mitigate the effect of artifacts introduced by the speech separation. Previously, however, the method only addressed two-speaker scenarios. In this work, we extend this approach to more natural meeting contexts featuring an arbitrary number of speakers and dynamic overlaps. We evaluate the performance using different speech separators, including the powerful TF-GridNet model. Our experiments show state-of-the-art performance on the LibriCSS dataset and highlight the advantages of the mixture encoder. Furthermore, they demonstrate the strong separation of TF-GridNet which largely closes the gap between previous methods and oracle separation. | false | false | true | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | 392,176 |
0901.4761 | A Knowledge Discovery Framework for Learning Task Models from User
Interactions in Intelligent Tutoring Systems | Domain experts should provide relevant domain knowledge to an Intelligent Tutoring System (ITS) so that it can guide a learner during problemsolving learning activities. However, for many ill-defined domains, the domain knowledge is hard to define explicitly. In previous works, we showed how sequential pattern mining can be used to extract a partial problem space from logged user interactions, and how it can support tutoring services during problem-solving exercises. This article describes an extension of this approach to extract a problem space that is richer and more adapted for supporting tutoring services. We combined sequential pattern mining with (1) dimensional pattern mining (2) time intervals, (3) the automatic clustering of valued actions and (4) closed sequences mining. Some tutoring services have been implemented and an experiment has been conducted in a tutoring system. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 3,080 |
2006.09615 | De-Anonymizing Text by Fingerprinting Language Generation | Components of machine learning systems are not (yet) perceived as security hotspots. Secure coding practices, such as ensuring that no execution paths depend on confidential inputs, have not yet been adopted by ML developers. We initiate the study of code security of ML systems by investigating how nucleus sampling---a popular approach for generating text, used for applications such as auto-completion---unwittingly leaks texts typed by users. Our main result is that the series of nucleus sizes for many natural English word sequences is a unique fingerprint. We then show how an attacker can infer typed text by measuring these fingerprints via a suitable side channel (e.g., cache access times), explain how this attack could help de-anonymize anonymous texts, and discuss defenses. | false | false | false | false | false | false | true | false | true | false | false | false | true | false | false | false | false | false | 182,599 |
1812.02050 | Point-to-Pose Voting based Hand Pose Estimation using Residual
Permutation Equivariant Layer | Recently, 3D input data based hand pose estimation methods have shown state-of-the-art performance, because 3D data capture more spatial information than the depth image. Whereas 3D voxel-based methods need a large amount of memory, PointNet based methods need tedious preprocessing steps such as K-nearest neighbour search for each point. In this paper, we present a novel deep learning hand pose estimation method for an unordered point cloud. Our method takes 1024 3D points as input and does not require additional information. We use Permutation Equivariant Layer (PEL) as the basic element, where a residual network version of PEL is proposed for the hand pose estimation task. Furthermore, we propose a voting based scheme to merge information from individual points to the final pose output. In addition to the pose estimation task, the voting-based scheme can also provide point cloud segmentation result without ground-truth for segmentation. We evaluate our method on both NYU dataset and the Hands2017Challenge dataset. Our method outperforms recent state-of-the-art methods, where our pose accuracy is currently the best for the Hands2017Challenge dataset. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 115,669 |
2401.04791 | SOS-Match: Segmentation for Open-Set Robust Correspondence Search and
Robot Localization in Unstructured Environments | We present SOS-Match, a novel framework for detecting and matching objects in unstructured environments. Our system consists of 1) a front-end mapping pipeline using a zero-shot segmentation model to extract object masks from images and track them across frames and 2) a frame alignment pipeline that uses the geometric consistency of object relationships to efficiently localize across a variety of conditions. We evaluate SOS-Match on the Batvik seasonal dataset which includes drone flights collected over a coastal plot of southern Finland during different seasons and lighting conditions. Results show that our approach is more robust to changes in lighting and appearance than classical image feature-based approaches or global descriptor methods, and it provides more viewpoint invariance than learning-based feature detection and description approaches. SOS-Match localizes within a reference map up to 46x faster than other feature-based approaches and has a map size less than 0.5% the size of the most compact other maps. SOS-Match is a promising new approach for landmark detection and correspondence search in unstructured environments that is robust to changes in lighting and appearance and is more computationally efficient than other approaches, suggesting that the geometric arrangement of segments is a valuable localization cue in unstructured environments. We release our datasets at https://acl.mit.edu/SOS-Match/. | false | false | false | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | 420,540 |
2305.11365 | Enhancing Transformer Backbone for Egocentric Video Action Segmentation | Egocentric temporal action segmentation in videos is a crucial task in computer vision with applications in various fields such as mixed reality, human behavior analysis, and robotics. Although recent research has utilized advanced visual-language frameworks, transformers remain the backbone of action segmentation models. Therefore, it is necessary to improve transformers to enhance the robustness of action segmentation models. In this work, we propose two novel ideas to enhance the state-of-the-art transformer for action segmentation. First, we introduce a dual dilated attention mechanism to adaptively capture hierarchical representations in both local-to-global and global-to-local contexts. Second, we incorporate cross-connections between the encoder and decoder blocks to prevent the loss of local context by the decoder. We also utilize state-of-the-art visual-language representation learning techniques to extract richer and more compact features for our transformer. Our proposed approach outperforms other state-of-the-art methods on the Georgia Tech Egocentric Activities (GTEA) and HOI4D Office Tools datasets, and we validate our introduced components with ablation studies. The source code and supplementary materials are publicly available on https://www.sail-nu.com/dxformer. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 365,493 |
1609.06942 | Randomized Independent Component Analysis | Independent component analysis (ICA) is a method for recovering statistically independent signals from observations of unknown linear combinations of the sources. Some of the most accurate ICA decomposition methods require searching for the inverse transformation which minimizes different approximations of the Mutual Information, a measure of statistical independence of random vectors. Two such approximations are the Kernel Generalized Variance or the Kernel Canonical Correlation which has been shown to reach the highest performance of ICA methods. However, the computational effort necessary just for computing these measures is cubic in the sample size. Hence, optimizing them becomes even more computationally demanding, in terms of both space and time. Here, we propose a couple of alternative novel measures based on randomized features of the samples - the Randomized Generalized Variance and the Randomized Canonical Correlation. The computational complexity of calculating the proposed alternatives is linear in the sample size and provide a controllable approximation of their Kernel-based non-random versions. We also show that optimization of the proposed statistical properties yields a comparable separation error at an order of magnitude faster compared to Kernel-based measures. | false | false | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | 61,365 |
1607.01478 | Mixed Strategy for Constrained Stochastic Optimal Control | Choosing control inputs randomly can result in a reduced expected cost in optimal control problems with stochastic constraints, such as stochastic model predictive control (SMPC). We consider a controller with initial randomization, meaning that the controller randomly chooses from K+1 control sequences at the beginning (called K-randimization).It is known that, for a finite-state, finite-action Markov Decision Process (MDP) with K constraints, K-randimization is sufficient to achieve the minimum cost. We found that the same result holds for stochastic optimal control problems with continuous state and action spaces.Furthermore, we show the randomization of control input can result in reduced cost when the optimization problem is nonconvex, and the cost reduction is equal to the duality gap. We then provide the necessary and sufficient conditions for the optimality of a randomized solution, and develop an efficient solution method based on dual optimization. Furthermore, in a special case with K=1 such as a joint chance-constrained problem, the dual optimization can be solved even more efficiently by root finding. Finally, we test the theories and demonstrate the solution method on multiple practical problems ranging from path planning to the planning of entry, descent, and landing (EDL) for future Mars missions. | false | false | false | false | true | false | false | true | false | false | true | false | false | false | false | false | false | false | 58,231 |
2410.19974 | Evaluating Cost-Accuracy Trade-offs in Multimodal Search Relevance
Judgements | Large Language Models (LLMs) have demonstrated potential as effective search relevance evaluators. However, there is a lack of comprehensive guidance on which models consistently perform optimally across various contexts or within specific use cases. In this paper, we assess several LLMs and Multimodal Language Models (MLLMs) in terms of their alignment with human judgments across multiple multimodal search scenarios. Our analysis investigates the trade-offs between cost and accuracy, highlighting that model performance varies significantly depending on the context. Interestingly, in smaller models, the inclusion of a visual component may hinder performance rather than enhance it. These findings highlight the complexities involved in selecting the most appropriate model for practical applications. | false | false | false | false | false | true | true | false | true | false | false | false | false | false | false | false | false | false | 502,592 |
2007.07247 | Multiview Detection with Feature Perspective Transformation | Incorporating multiple camera views for detection alleviates the impact of occlusions in crowded scenes. In a multiview system, we need to answer two important questions when dealing with ambiguities that arise from occlusions. First, how should we aggregate cues from the multiple views? Second, how should we aggregate unreliable 2D and 3D spatial information that has been tainted by occlusions? To address these questions, we propose a novel multiview detection system, MVDet. For multiview aggregation, existing methods combine anchor box features from the image plane, which potentially limits performance due to inaccurate anchor box shapes and sizes. In contrast, we take an anchor-free approach to aggregate multiview information by projecting feature maps onto the ground plane (bird's eye view). To resolve any remaining spatial ambiguity, we apply large kernel convolutions on the ground plane feature map and infer locations from detection peaks. Our entire model is end-to-end learnable and achieves 88.2% MODA on the standard Wildtrack dataset, outperforming the state-of-the-art by 14.1%. We also provide detailed analysis of MVDet on a newly introduced synthetic dataset, MultiviewX, which allows us to control the level of occlusion. Code and MultiviewX dataset are available at https://github.com/hou-yz/MVDet. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 187,278 |
2309.07087 | Developing a Novel Image Marker to Predict the Clinical Outcome of
Neoadjuvant Chemotherapy (NACT) for Ovarian Cancer Patients | Objective Neoadjuvant chemotherapy (NACT) is one kind of treatment for advanced stage ovarian cancer patients. However, due to the nature of tumor heterogeneity, the clinical outcomes to NACT vary significantly among different subgroups. Partial responses to NACT may lead to suboptimal debulking surgery, which will result in adverse prognosis. To address this clinical challenge, the purpose of this study is to develop a novel image marker to achieve high accuracy prognosis prediction of NACT at an early stage. Methods For this purpose, we first computed a total of 1373 radiomics features to quantify the tumor characteristics, which can be grouped into three categories: geometric, intensity, and texture features. Second, all these features were optimized by principal component analysis algorithm to generate a compact and informative feature cluster. This cluster was used as input for developing and optimizing support vector machine (SVM) based classifiers, which indicated the likelihood of receiving suboptimal cytoreduction after the NACT treatment. Two different kernels for SVM algorithm were explored and compared. A total of 42 ovarian cancer cases were retrospectively collected to validate the scheme. A nested leave-one-out cross-validation framework was adopted for model performance assessment. Results The results demonstrated that the model with a Gaussian radial basis function kernel SVM yielded an AUC (area under the ROC [receiver characteristic operation] curve) of 0.806. Meanwhile, this model achieved overall accuracy (ACC) of 83.3%, positive predictive value (PPV) of 81.8%, and negative predictive value (NPV) of 83.9%. Conclusion This study provides meaningful information for the development of radiomics based image markers in NACT treatment outcome prediction. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 391,647 |
2011.02379 | Asynchrony and Acceleration in Gossip Algorithms | This paper considers the minimization of a sum of smooth and strongly convex functions dispatched over the nodes of a communication network. Previous works on the subject either focus on synchronous algorithms, which can be heavily slowed down by a few slow nodes (the straggler problem), or consider a model of asynchronous operation (Boyd et al., 2006) in which adjacent nodes communicate at the instants of Poisson point processes. We have two main contributions. 1) We propose CACDM (a Continuously Accelerated Coordinate Dual Method), and for the Poisson model of asynchronous operation, we prove CACDM to converge to optimality at an accelerated convergence rate in the sense of Nesterov et Stich, 2017. In contrast, previously proposed asynchronous algorithms have not been proven to achieve such accelerated rate. While CACDM is based on discrete updates, the proof of its convergence crucially depends on a continuous time analysis. 2) We introduce a new communication scheme based on Loss-Networks, that is programmable in a fully asynchronous and decentralized way, unlike the Poisson model of asynchronous operation that does not capture essential aspects of asynchrony such as non-instantaneous communications and computations. Under this Loss-Network model of asynchrony, we establish for CDM (a Coordinate Dual Method) a rate of convergence in terms of the eigengap of the Laplacian of the graph weighted by local effective delays. We believe this eigengap to be a fundamental bottleneck for convergence rates of asynchronous optimization. Finally, we verify empirically that CACDM enjoys an accelerated convergence rate in the Loss-Network model of asynchrony. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | true | 204,911 |
2311.06258 | Post-COVID Highlights: Challenges and Solutions of AI Techniques for
Swift Identification of COVID-19 | Since the onset of the COVID-19 pandemic in 2019, there has been a concerted effort to develop cost-effective, non-invasive, and rapid AI-based tools. These tools were intended to alleviate the burden on healthcare systems, control the rapid spread of the virus, and enhance intervention outcomes, all in response to this unprecedented global crisis. As we transition into a post-COVID era, we retrospectively evaluate these proposed studies and offer a review of the techniques employed in AI diagnostic models, with a focus on the solutions proposed for different challenges. This review endeavors to provide insights into the diverse solutions designed to address the multifaceted challenges that arose during the pandemic. By doing so, we aim to prepare the AI community for the development of AI tools tailored to address public health emergencies effectively. | false | false | false | false | true | false | false | false | false | false | false | false | false | true | false | false | false | false | 406,870 |
1604.04722 | Where is the global corporate elite? A large-scale network study of
local and nonlocal interlocking directorates | Business elites reconfigure their locus of organization over time, from the city level, to the national level, and beyond. We ask what the current level of elite organization is and propose a novel theoretical and empirical approach to answer this question. Building on the universal distinction between local and nonlocal ties we use network analysis and community detection to dissect the global network of interlocking directorates among over five million firms. We find that elite orientation is indeed changing from the national to the transnational plane, but we register a considerable heterogeneity across different regions in the world. In some regions the business communities are organized along national borders, whereas in other areas the locus of organization is at the city level or international level. London dominates the global corporate elite network. Our findings underscore that the study of corporate elites requires an approach that is sensitive to levels of organization that go beyond the confines of nation states. | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 54,692 |
2005.10855 | Modeling and Optimization of Latency in Erasure-coded Storage Systems | As consumers are increasingly engaged in social networking and E-commerce activities, businesses grow to rely on Big Data analytics for intelligence, and traditional IT infrastructures continue to migrate to the cloud and edge, these trends cause distributed data storage demand to rise at an unprecedented speed. Erasure coding has seen itself quickly emerged as a promising technique to reduce storage cost while providing similar reliability as replicated systems, widely adopted by companies like Facebook, Microsoft and Google. However, it also brings new challenges in characterizing and optimizing the access latency when erasure codes are used in distributed storage. The aim of this monograph is to provide a review of recent progress (both theoretical and practical) on systems that employ erasure codes for distributed storage. In this monograph, we will first identify the key challenges and taxonomy of the research problems and then give an overview of different approaches that have been developed to quantify and model latency of erasure-coded storage. This includes recent work leveraging MDS-Reservation, Fork-Join, Probabilistic, and Delayed-Relaunch scheduling policies, as well as their applications to characterize access latency (e.g., mean, tail, asymptotic latency) of erasure-coded distributed storage systems. We will also extend the problem to the case when users are streaming videos from erasure-coded distributed storage systems. Next, we bridge the gap between theory and practice, and discuss lessons learned from prototype implementation. In particular, we will discuss exemplary implementations of erasure-coded storage, illuminate key design degrees of freedom and tradeoffs, and summarize remaining challenges in real-world storage systems such as in content delivery and caching. Open problems for future research are discussed at the end of each chapter. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | true | 178,299 |
2303.00135 | Deep learning for COVID-19 topic modelling via Twitter: Alpha, Delta and
Omicron | Topic modelling with innovative deep learning methods has gained interest for a wide range of applications that includes COVID-19. Topic modelling can provide, psychological, social and cultural insights for understanding human behaviour in extreme events such as the COVID-19 pandemic. In this paper, we use prominent deep learning-based language models for COVID-19 topic modelling taking into account data from emergence (Alpha) to the Omicron variant. We apply topic modeling to review the public behaviour across the first, second and third waves based on Twitter dataset from India. Our results show that the topics extracted for the subsequent waves had certain overlapping themes such as covers governance, vaccination, and pandemic management while novel issues aroused in political, social and economic situation during COVID-19 pandemic. We also found a strong correlation of the major topics qualitatively to news media prevalent at the respective time period. Hence, our framework has the potential to capture major issues arising during different phases of the COVID-19 pandemic which can be extended to other countries and regions. | false | false | false | false | false | true | true | false | true | false | false | false | false | false | false | false | false | false | 348,491 |
2502.13127 | Facilitating Long Context Understanding via Supervised Chain-of-Thought
Reasoning | Recent advances in Large Language Models (LLMs) have enabled them to process increasingly longer sequences, ranging from 2K to 2M tokens and even beyond. However, simply extending the input sequence length does not necessarily lead to effective long-context understanding. In this study, we integrate Chain-of-Thought (CoT) reasoning into LLMs in a supervised manner to facilitate effective long-context understanding. To achieve this, we introduce LongFinanceQA, a synthetic dataset in the financial domain designed to improve long-context reasoning. Unlike existing long-context synthetic data, LongFinanceQA includes intermediate CoT reasoning before the final conclusion, which encourages LLMs to perform explicit reasoning, improving accuracy and interpretability in long-context understanding. To generate synthetic CoT reasoning, we propose Property-driven Agentic Inference (PAI), an agentic framework that simulates human-like reasoning steps, including property extraction, retrieval, and summarization. We evaluate PAI's reasoning capabilities by assessing GPT-4o-mini w/ PAI on the Loong benchmark, outperforming standard GPT-4o-mini by 20.0%. Furthermore, we fine-tune LLaMA-3.1-8B-Instruct on LongFinanceQA, achieving a 24.6% gain on Loong's financial subset. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 535,214 |
2211.08542 | CXTrack: Improving 3D Point Cloud Tracking with Contextual Information | 3D single object tracking plays an essential role in many applications, such as autonomous driving. It remains a challenging problem due to the large appearance variation and the sparsity of points caused by occlusion and limited sensor capabilities. Therefore, contextual information across two consecutive frames is crucial for effective object tracking. However, points containing such useful information are often overlooked and cropped out in existing methods, leading to insufficient use of important contextual knowledge. To address this issue, we propose CXTrack, a novel transformer-based network for 3D object tracking, which exploits ConteXtual information to improve the tracking results. Specifically, we design a target-centric transformer network that directly takes point features from two consecutive frames and the previous bounding box as input to explore contextual information and implicitly propagate target cues. To achieve accurate localization for objects of all sizes, we propose a transformer-based localization head with a novel center embedding module to distinguish the target from distractors. Extensive experiments on three large-scale datasets, KITTI, nuScenes and Waymo Open Dataset, show that CXTrack achieves state-of-the-art tracking performance while running at 34 FPS. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 330,669 |
1702.01831 | Trimming the Independent Fat: Sufficient Statistics, Mutual Information,
and Predictability from Effective Channel States | One of the most fundamental questions one can ask about a pair of random variables X and Y is the value of their mutual information. Unfortunately, this task is often stymied by the extremely large dimension of the variables. We might hope to replace each variable by a lower-dimensional representation that preserves the relationship with the other variable. The theoretically ideal implementation is the use of minimal sufficient statistics, where it is well-known that either X or Y can be replaced by their minimal sufficient statistic about the other while preserving the mutual information. While intuitively reasonable, it is not obvious or straightforward that both variables can be replaced simultaneously. We demonstrate that this is in fact possible: the information X's minimal sufficient statistic preserves about Y is exactly the information that Y's minimal sufficient statistic preserves about X. As an important corollary, we consider the case where one variable is a stochastic process' past and the other its future and the present is viewed as a memoryful channel. In this case, the mutual information is the channel transmission rate between the channel's effective states. That is, the past-future mutual information (the excess entropy) is the amount of information about the future that can be predicted using the past. Translating our result about minimal sufficient statistics, this is equivalent to the mutual information between the forward- and reverse-time causal states of computational mechanics. We close by discussing multivariate extensions to this use of minimal sufficient statistics. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 67,878 |
1202.0460 | A Cooperative Bayesian Nonparametric Framework for Primary User Activity
Monitoring in Cognitive Radio Network | This paper introduces a novel approach that enables a number of cognitive radio devices that are observing the availability pattern of a number of primary users(PUs), to cooperate and use \emph{Bayesian nonparametric} techniques to estimate the distributions of the PUs' activity pattern, assumed to be completely unknown. In the proposed model, each cognitive node may have its own individual view on each PU's distribution, and, hence, seeks to find partners having a correlated perception. To address this problem, a coalitional game is formulated between the cognitive devices and an algorithm for cooperative coalition formation is proposed. It is shown that the proposed coalition formation algorithm allows the cognitive nodes that are experiencing a similar behavior from some PUs to self-organize into disjoint, independent coalitions. Inside each coalition, the cooperative cognitive nodes use a combination of Bayesian nonparametric models such as the Dirichlet process and statistical goodness of fit techniques in order to improve the accuracy of the estimated PUs' activity distributions. Simulation results show that the proposed algorithm significantly improves the estimates of the PUs' distributions and yields a performance advantage, in terms of reduction of the average achieved Kullback-Leibler distance between the real and the estimated distributions, reaching up to 36.5% relative the non-cooperative estimates. The results also show that the proposed algorithm enables the cognitive nodes to adapt their cooperative decisions when the actual PUs' distributions change due to, for example, PU mobility. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | true | 14,088 |
2107.00913 | Reinforcement Learning Provides a Flexible Approach for Realistic Supply
Chain Safety Stock Optimisation | Although safety stock optimisation has been studied for more than 60 years, most companies still use simplistic means to calculate necessary safety stock levels, partly due to the mismatch between existing analytical methods' emphases on deriving provably optimal solutions and companies' preferences to sacrifice optimal results in favour of more realistic problem settings. A newly emerging method from the field of Artificial Intelligence (AI), namely Reinforcement Learning (RL), offers promise in finding optimal solutions while accommodating more realistic problem features. Unlike analytical-based models, RL treats the problem as a black-box simulation environment mitigating against the problem of oversimplifying reality. As such, assumptions on stock keeping policy can be relaxed and a higher number of problem variables can be accommodated. While RL has been popular in other domains, its applications in safety stock optimisation remain scarce. In this paper, we investigate three RL methods, namely, Q-Learning, Temporal Difference Advantage Actor-Critic and Multi-agent Temporal Difference Advantage Actor-Critic for optimising safety stock in a linear chain of independent agents. We find that RL can simultaneously optimise both safety stock level and order quantity parameters of an inventory policy, unlike classical safety stock optimisation models where only safety stock level is optimised while order quantity is predetermined based on simple rules. This allows RL to model more complex supply chain procurement behaviour. However, RL takes longer time to arrive at solutions, necessitating future research on identifying and improving trade-offs between the use of AI and mathematical models are needed. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | 244,316 |
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