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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1902.06285 | Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank | For many applications the collection of labeled data is expensive laborious. Exploitation of unlabeled data during training is thus a long pursued objective of machine learning. Self-supervised learning addresses this by positing an auxiliary task (different, but related to the supervised task) for which data is abundantly available. In this paper, we show how ranking can be used as a proxy task for some regression problems. As another contribution, we propose an efficient backpropagation technique for Siamese networks which prevents the redundant computation introduced by the multi-branch network architecture. We apply our framework to two regression problems: Image Quality Assessment (IQA) and Crowd Counting. For both we show how to automatically generate ranked image sets from unlabeled data. Our results show that networks trained to regress to the ground truth targets for labeled data and to simultaneously learn to rank unlabeled data obtain significantly better, state-of-the-art results for both IQA and crowd counting. In addition, we show that measuring network uncertainty on the self-supervised proxy task is a good measure of informativeness of unlabeled data. This can be used to drive an algorithm for active learning and we show that this reduces labeling effort by up to 50%. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 121,729 |
1606.01352 | Implementation of real-time moving horizon estimation for robust air
data sensor fault diagnosis in the RECONFIGURE benchmark | This paper presents robust fault diagnosis and estimation for the calibrated airspeed and angle-of-attack sensor faults in the RECONFIGURE benchmark. We adopt a low-order longitudinal model augmented with wind dynamics. In order to enhance sensitivity to faults in the presence of winds, we propose a constrained residual generator by formulating a constrained moving horizon estimation problem and exploiting the bounds of winds. The moving horizon estimation problem requires solving a nonlinear program in real time, which is challenging for flight control computers. This challenge is addressed by adopting an efficient structure-exploiting algorithm within a real-time iteration scheme. Specific approximations and simplifications are performed to enable the implementation of the algorithm using the Airbus graphical symbol library for industrial validation and verification. The simulation tests on the RECONFIGURE benchmark over different flight points and maneuvers show the efficacy of the proposed approach. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 56,795 |
2007.16054 | Learning to Learn to Compress | In this paper we present an end-to-end meta-learned system for image compression. Traditional machine learning based approaches to image compression train one or more neural network for generalization performance. However, at inference time, the encoder or the latent tensor output by the encoder can be optimized for each test image. This optimization can be regarded as a form of adaptation or benevolent overfitting to the input content. In order to reduce the gap between training and inference conditions, we propose a new training paradigm for learned image compression, which is based on meta-learning. In a first phase, the neural networks are trained normally. In a second phase, the Model-Agnostic Meta-learning approach is adapted to the specific case of image compression, where the inner-loop performs latent tensor overfitting, and the outer loop updates both encoder and decoder neural networks based on the overfitting performance. Furthermore, after meta-learning, we propose to overfit and cluster the bias terms of the decoder on training image patches, so that at inference time the optimal content-specific bias terms can be selected at encoder-side. Finally, we propose a new probability model for lossless compression, which combines concepts from both multi-scale and super-resolution probability model approaches. We show the benefits of all our proposed ideas via carefully designed experiments. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | true | 189,836 |
2410.00332 | Vision Language Models Know Law of Conservation without Understanding
More-or-Less | Conservation is a critical milestone of cognitive development considered to be supported by both the understanding of quantitative concepts and the reversibility of operations. To assess whether this critical component of human intelligence has emerged in Vision Language Models, we have curated the ConserveBench, a battery of 365 cognitive experiments across four dimensions of physical quantities: volume, solid quantity, length, and number. The former two involve transformational tasks which require reversibility understanding. The latter two involve non-transformational tasks which assess quantity understanding. Surprisingly, we find that while Vision Language Models are generally good at transformational tasks, they tend to fail at non-transformational tasks. There is a dissociation between understanding the reversibility of operations and understanding of quantity, which both are believed to be the cornerstones of the understanding of law of conservation in humans. $\href{https://growing-ai-like-a-child.github.io/pages/Conservation/}{Website}$ | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 493,322 |
1901.02620 | Fast CNN-Based Object Tracking Using Localization Layers and Deep
Features Interpolation | Object trackers based on Convolution Neural Network (CNN) have achieved state-of-the-art performance on recent tracking benchmarks, while they suffer from slow computational speed. The high computational load arises from the extraction of the feature maps of the candidate and training patches in every video frame. The candidate and training patches are typically placed randomly around the previous target location and the estimated target location respectively. In this paper, we propose novel schemes to speed-up the processing of the CNN-based trackers. We input the whole region-of-interest once to the CNN to eliminate the redundant computations of the random candidate patches. In addition to classifying each candidate patch as an object or background, we adapt the CNN to classify the target location inside the object patches as a coarse localization step, and we employ bilinear interpolation for the CNN feature maps as a fine localization step. Moreover, bilinear interpolation is exploited to generate CNN feature maps of the training patches without actually forwarding the training patches through the network which achieves a significant reduction of the required computations. Our tracker does not rely on offline video training. It achieves competitive performance results on the OTB benchmark with 8x speed improvements compared to the equivalent tracker. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 118,251 |
2401.08688 | Automated Answer Validation using Text Similarity | Automated answer validation can help improve learning outcomes by providing appropriate feedback to learners, and by making question answering systems and online learning solutions more widely available. There have been some works in science question answering which show that information retrieval methods outperform neural methods, especially in the multiple choice version of this problem. We implement Siamese neural network models and produce a generalised solution to this problem. We compare our supervised model with other text similarity based solutions. | false | false | false | false | false | true | false | false | true | false | false | false | false | false | false | false | false | false | 421,990 |
2107.10731 | Neural Variational Gradient Descent | Particle-based approximate Bayesian inference approaches such as Stein Variational Gradient Descent (SVGD) combine the flexibility and convergence guarantees of sampling methods with the computational benefits of variational inference. In practice, SVGD relies on the choice of an appropriate kernel function, which impacts its ability to model the target distribution -- a challenging problem with only heuristic solutions. We propose Neural Variational Gradient Descent (NVGD), which is based on parameterizing the witness function of the Stein discrepancy by a deep neural network whose parameters are learned in parallel to the inference, mitigating the necessity to make any kernel choices whatsoever. We empirically evaluate our method on popular synthetic inference problems, real-world Bayesian linear regression, and Bayesian neural network inference. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 247,387 |
2011.11191 | Socially Aware Crowd Navigation with Multimodal Pedestrian Trajectory
Prediction for Autonomous Vehicles | Seamlessly operating an autonomous vehicle in a crowded pedestrian environment is a very challenging task. This is because human movement and interactions are very hard to predict in such environments. Recent work has demonstrated that reinforcement learning-based methods have the ability to learn to drive in crowds. However, these methods can have very poor performance due to inaccurate predictions of the pedestrians' future state as human motion prediction has a large variance. To overcome this problem, we propose a new method, SARL-SGAN-KCE, that combines a deep socially aware attentive value network with a human multimodal trajectory prediction model to help identify the optimal driving policy. We also introduce a novel technique to extend the discrete action space with minimal additional computational requirements. The kinematic constraints of the vehicle are also considered to ensure smooth and safe trajectories. We evaluate our method against the state of art methods for crowd navigation and provide an ablation study to show that our method is safer and closer to human behaviour. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 207,748 |
2404.15005 | Scandium Aluminum Nitride Overmoded Bulk Acoustic Resonators for Future
Wireless Communication | This work reports on the modeling, fabrication, and experimental characterization of a 13 GHz 30% Scandium-doped Aluminum Nitride (ScAlN) Overmoded Bulk Acoustic Resonator (OBAR) for high-frequency Radio Frequency (RF) applications, notably in 5G technology and beyond. The Finite Element Analysis (FEA) optimization process targets the top and bottom metal electrode thicknesses, balancing the electromechanical coupling coefficient and acoustic energy distribution to enhance device Figure of Merit (FOM). Experimental results on fabricated devices employing platinum and aluminum as bottom and top electrode, respectively, demonstrate a quality factor at resonance (Qs) of 210 and a coupling coefficient (kt2) of 5.2% at 13.3 GHz for the second bulk thickness overtone, effectively validating the simulation framework and hinting at the possible implementation of OBARs for advanced RF filters in 5G networks. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 448,912 |
2108.03576 | BeatNet: CRNN and Particle Filtering for Online Joint Beat Downbeat and
Meter Tracking | The online estimation of rhythmic information, such as beat positions, downbeat positions, and meter, is critical for many real-time music applications. Musical rhythm comprises complex hierarchical relationships across time, rendering its analysis intrinsically challenging and at times subjective. Furthermore, systems which attempt to estimate rhythmic information in real-time must be causal and must produce estimates quickly and efficiently. In this work, we introduce an online system for joint beat, downbeat, and meter tracking, which utilizes causal convolutional and recurrent layers, followed by a pair of sequential Monte Carlo particle filters applied during inference. The proposed system does not need to be primed with a time signature in order to perform downbeat tracking, and is instead able to estimate meter and adjust the predictions over time. Additionally, we propose an information gate strategy to significantly decrease the computational cost of particle filtering during the inference step, making the system much faster than previous sampling-based methods. Experiments on the GTZAN dataset, which is unseen during training, show that the system outperforms various online beat and downbeat tracking systems and achieves comparable performance to a baseline offline joint method. | false | false | true | false | true | true | true | false | false | false | false | false | false | false | false | false | false | false | 249,708 |
2405.16148 | Accelerating Transformers with Spectrum-Preserving Token Merging | Increasing the throughput of the Transformer architecture, a foundational component used in numerous state-of-the-art models for vision and language tasks (e.g., GPT, LLaVa), is an important problem in machine learning. One recent and effective strategy is to merge token representations within Transformer models, aiming to reduce computational and memory requirements while maintaining accuracy. Prior works have proposed algorithms based on Bipartite Soft Matching (BSM), which divides tokens into distinct sets and merges the top k similar tokens. However, these methods have significant drawbacks, such as sensitivity to token-splitting strategies and damage to informative tokens in later layers. This paper presents a novel paradigm called PiToMe, which prioritizes the preservation of informative tokens using an additional metric termed the energy score. This score identifies large clusters of similar tokens as high-energy, indicating potential candidates for merging, while smaller (unique and isolated) clusters are considered as low-energy and preserved. Experimental findings demonstrate that PiToMe saved from 40-60\% FLOPs of the base models while exhibiting superior off-the-shelf performance on image classification (0.5\% average performance drop of ViT-MAE-H compared to 2.6\% as baselines), image-text retrieval (0.3\% average performance drop of CLIP on Flickr30k compared to 4.5\% as others), and analogously in visual questions answering with LLaVa-7B. Furthermore, PiToMe is theoretically shown to preserve intrinsic spectral properties of the original token space under mild conditions | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 457,283 |
2209.11024 | Google Coral-based edge computing person reidentification using human
parsing combined with analytical method | Person reidentification (re-ID) is becoming one of the most significant application areas of computer vision due to its importance for science and social security. Due to enormous size and scale of camera systems it is beneficial to develop edge computing re-ID applications where at least part of the analysis could be performed by the cameras. However, conventional re-ID relies heavily on deep learning (DL) computationally demanding models which are not readily applicable for edge computing. In this paper we adapt a recently proposed re-ID method that combines DL human parsing with analytical feature extraction and ranking schemes to be more suitable for edge computing re-ID. First, we compare parsers that use ResNet101, ResNet18, MobileNetV2, and OSNet backbones and show that parsing can be performed using compact backbones with sufficient accuracy. Second, we transfer parsers to tensor processing unit (TPU) of Google Coral Dev Board and show that it can act as a portable edge computing re-ID station. We also implement the analytical part of re-ID method on Coral CPU to ensure that it can perform a complete re-ID cycle. For quantitative analysis we compare inference speed, parsing masks, and re-ID accuracy on GPU and Coral TPU depending on parser backbone. We also discuss possible application scenarios of edge computing in re-ID taking into account known limitations mainly related to memory and storage space of portable devices. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 319,052 |
2012.02076 | SSGD: A safe and efficient method of gradient descent | With the vigorous development of artificial intelligence technology, various engineering technology applications have been implemented one after another. The gradient descent method plays an important role in solving various optimization problems, due to its simple structure, good stability and easy implementation. In multi-node machine learning system, the gradients usually need to be shared. Shared gradients are generally unsafe. Attackers can obtain training data simply by knowing the gradient information. In this paper, to prevent gradient leakage while keeping the accuracy of model, we propose the super stochastic gradient descent approach to update parameters by concealing the modulus length of gradient vectors and converting it or them into a unit vector. Furthermore, we analyze the security of super stochastic gradient descent approach. Our algorithm can defend against attacks on the gradient. Experiment results show that our approach is obviously superior to prevalent gradient descent approaches in terms of accuracy, robustness, and adaptability to large-scale batches. | false | false | false | false | true | false | true | false | false | false | false | true | true | false | false | false | false | true | 209,637 |
2403.00880 | CIDGMed: Causal Inference-Driven Medication Recommendation with Enhanced
Dual-Granularity Learning | Medication recommendation aims to integrate patients' long-term health records to provide accurate and safe medication combinations for specific health states. Existing methods often fail to deeply explore the true causal relationships between diseases/procedures and medications, resulting in biased recommendations. Additionally, in medication representation learning, the relationships between information at different granularities of medications, coarse-grained (medication itself) and fine-grained (molecular level), are not effectively integrated, leading to biases in representation learning. To address these limitations, we propose the Causal Inference-driven Dual-Granularity Medication Recommendation method (CIDGMed). Our approach leverages causal inference to uncover the relationships between diseases/procedures and medications, thereby enhancing the rationality and interpretability of recommendations. By integrating coarse-grained medication effects with fine-grained molecular structure information, CIDGMed provides a comprehensive representation of medications. Additionally, we employ a bias correction model during the prediction phase to further refine recommendations, ensuring both accuracy and safety. Through extensive experiments, CIDGMed significantly outperforms current state-of-the-art models across multiple metrics, achieving a 2.54% increase in accuracy, a 3.65% reduction in side effects, and a 39.42% improvement in time efficiency. Additionally, we demonstrate the rationale of CIDGMed through a case study. | false | false | false | false | true | true | false | false | false | false | false | false | false | false | false | false | false | false | 434,171 |
2409.13624 | Safe stabilization using generalized Lyapunov barrier function | This paper addresses the safe stabilization problem, focusing on controlling the system state to the origin while avoiding entry into unsafe state sets. The current methods for solving this issue rely on smooth Lyapunov and barrier functions, which do not always ensure the existence of an effective controller even when such smooth functions are created. To tackle this challenge, we introduce the concept of a generalized (nonsmooth) Lyapunov barrier function (GenLBF), which guarantees the existence of a safe and stable controller. We outline a systematic approach for constructing a GenLBF, including a technique for efficiently calculating the upper generalized derivative of the GenLBF. Using the constructed GenLBF, we propose a method for certifying safe stabilization of autonomous systems and design a piecewise continuous feedback control to achieve safe stabilization of non-autonomous systems. A general controller refinement strategy is further proposed to help the state trajectory escape from undesired local points occurring in systems with special physical structure. A thorough theoretical analysis demonstrates the effectiveness of our method in addressing the safe stabilization problem for systems with single or multiple bounded unsafe state sets. Extensive simulations of linear and nonlinear systems further illustrate the efficacy of the proposed method and its superiority over the smooth control Lyapunov barrier function method. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 490,073 |
2103.06168 | Towards automated brain aneurysm detection in TOF-MRA: open data, weak
labels, and anatomical knowledge | Brain aneurysm detection in Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) has undergone drastic improvements with the advent of Deep Learning (DL). However, performances of supervised DL models heavily rely on the quantity of labeled samples, which are extremely costly to obtain. Here, we present a DL model for aneurysm detection that overcomes the issue with ''weak'' labels: oversized annotations which are considerably faster to create. Our weak labels resulted to be four times faster to generate than their voxel-wise counterparts. In addition, our model leverages prior anatomical knowledge by focusing only on plausible locations for aneurysm occurrence. We frst train and evaluate our model through cross-validation on an in-house TOF-MRA dataset comprising 284 subjects (170 females / 127 healthy controls / 157 patients with 198 aneurysms). On this dataset, our best model achieved a sensitivity of 83%, with False Positive (FP) rate of 0.8 per patient. To assess model generalizability, we then participated in a challenge for aneurysm detection with TOF-MRA data (93 patients, 20 controls, 125 aneurysms). On the public challenge, sensitivity was 68% (FP rate=2.5), ranking 4th/18 on the open leaderboard. We found no signifcant diference in sensitivity between aneurysm risk-of-rupture groups (p=0.75), locations (p=0.72), or sizes (p=0.15). Data, code and model weights are released under permissive licenses. We demonstrate that weak labels and anatomical knowledge can alleviate the necessity for prohibitively expensive voxel-wise annotations. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 224,215 |
2306.04422 | A Gamified Interaction with a Humanoid Robot to explain Therapeutic
Procedures in Pediatric Asthma | In chronic diseases, obtaining a correct diagnosis and providing the most appropriate treatments often is not enough to guarantee an improvement of the clinical condition of a patient. Poor adherence to medical prescriptions constitutes one of the main causes preventing achievement of therapeutic goals. This is generally true especially for certain diseases and specific target patients, such as children. An engaging and entertaining technology can be exploited in support of clinical practices to achieve better health outcomes. Our assumption is that a gamified session with a humanoid robot, compared to the usual methodologies for therapeutic education, can be more incisive in learning the correct inhalation procedure in children affected by asthma. In this perspective, we describe an interactive module implemented on the Pepper robotic platform and the setting of a study that was planned in 2020 to be held at the Pneumoallergology Pediatric clinic of CNR in Palermo. The study was canceled due to the COVID-19 pandemic. Our long-term goal is to assess, by means of a qualitative-quantitative survey plan, the impact of such an educational action, evaluating possible improvement in the adherence to the treatment. | true | false | false | false | true | false | false | true | false | false | false | false | false | false | false | false | false | false | 371,735 |
2102.08183 | Comparison of semi-supervised deep learning algorithms for audio
classification | In this article, we adapted five recent SSL methods to the task of audio classification. The first two methods, namely Deep Co-Training (DCT) and Mean Teacher (MT), involve two collaborative neural networks. The three other algorithms, called MixMatch (MM), ReMixMatch (RMM), and FixMatch (FM), are single-model methods that rely primarily on data augmentation strategies. Using the Wide-ResNet-28-2 architecture in all our experiments, 10% of labeled data and the remaining 90% as unlabeled data for training, we first compare the error rates of the five methods on three standard benchmark audio datasets: Environmental Sound Classification (ESC-10), UrbanSound8K (UBS8K), and Google Speech Commands (GSC). In all but one cases, MM, RMM, and FM outperformed MT and DCT significantly, MM and RMM being the best methods in most experiments. On UBS8K and GSC, MM achieved 18.02% and 3.25% error rate (ER), respectively, outperforming models trained with 100% of the available labeled data, which reached 23.29% and 4.94%, respectively. RMM achieved the best results on ESC-10 (12.00% ER), followed by FM which reached 13.33%. Second, we explored adding the mixup augmentation, used in MM and RMM, to DCT, MT, and FM. In almost all cases, mixup brought consistent gains. For instance, on GSC, FM reached 4.44% and 3.31% ER without and with mixup. Our PyTorch code will be made available upon paper acceptance at https:// github. com/ Labbe ti/ SSLH. | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 220,370 |
1308.3956 | Target Assignment in Robotic Networks: Distance Optimality Guarantees
and Hierarchical Strategies | We study the problem of multi-robot target assignment to minimize the total distance traveled by the robots until they all reach an equal number of static targets. In the first half of the paper, we present a necessary and sufficient condition under which true distance optimality can be achieved for robots with limited communication and target-sensing ranges. Moreover, we provide an explicit, non-asymptotic formula for computing the number of robots needed to achieve distance optimality in terms of the robots' communication and target-sensing ranges with arbitrary guaranteed probabilities. The same bounds are also shown to be asymptotically tight. In the second half of the paper, we present suboptimal strategies for use when the number of robots cannot be chosen freely. Assuming first that all targets are known to all robots, we employ a hierarchical communication model in which robots communicate only with other robots in the same partitioned region. This hierarchical communication model leads to constant approximations of true distance-optimal solutions under mild assumptions. We then revisit the limited communication and sensing models. By combining simple rendezvous-based strategies with a hierarchical communication model, we obtain decentralized hierarchical strategies that achieve constant approximation ratios with respect to true distance optimality. Results of simulation show that the approximation ratio is as low as 1.4. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 26,515 |
2211.04698 | Unsupervised Extractive Summarization with Heterogeneous Graph
Embeddings for Chinese Document | In the scenario of unsupervised extractive summarization, learning high-quality sentence representations is essential to select salient sentences from the input document. Previous studies focus more on employing statistical approaches or pre-trained language models (PLMs) to extract sentence embeddings, while ignoring the rich information inherent in the heterogeneous types of interaction between words and sentences. In this paper, we are the first to propose an unsupervised extractive summarizaiton method with heterogeneous graph embeddings (HGEs) for Chinese document. A heterogeneous text graph is constructed to capture different granularities of interactions by incorporating graph structural information. Moreover, our proposed graph is general and flexible where additional nodes such as keywords can be easily integrated. Experimental results demonstrate that our method consistently outperforms the strong baseline in three summarization datasets. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 329,326 |
2104.09116 | TransCrowd: weakly-supervised crowd counting with transformers | The mainstream crowd counting methods usually utilize the convolution neural network (CNN) to regress a density map, requiring point-level annotations. However, annotating each person with a point is an expensive and laborious process. During the testing phase, the point-level annotations are not considered to evaluate the counting accuracy, which means the point-level annotations are redundant. Hence, it is desirable to develop weakly-supervised counting methods that just rely on count-level annotations, a more economical way of labeling. Current weakly-supervised counting methods adopt the CNN to regress a total count of the crowd by an image-to-count paradigm. However, having limited receptive fields for context modeling is an intrinsic limitation of these weakly-supervised CNN-based methods. These methods thus cannot achieve satisfactory performance, with limited applications in the real world. The transformer is a popular sequence-to-sequence prediction model in natural language processing (NLP), which contains a global receptive field. In this paper, we propose TransCrowd, which reformulates the weakly-supervised crowd counting problem from the perspective of sequence-to-count based on transformers. We observe that the proposed TransCrowd can effectively extract the semantic crowd information by using the self-attention mechanism of transformer. To the best of our knowledge, this is the first work to adopt a pure transformer for crowd counting research. Experiments on five benchmark datasets demonstrate that the proposed TransCrowd achieves superior performance compared with all the weakly-supervised CNN-based counting methods and gains highly competitive counting performance compared with some popular fully-supervised counting methods. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 231,120 |
2311.04710 | The Quest for Content: A Survey of Search-Based Procedural Content
Generation for Video Games | Video games demand is constantly increasing, which requires the costly production of large amounts of content. Towards this challenge, researchers have developed Search-Based Procedural Content Generation (SBPCG), that is, the (semi-)automated creation of content through search algorithms. We survey the current state of SBPCG, reporting work appeared in the field between 2011-2022 and identifying open research challenges. The results lead to recommendations for practitioners and to the identification of several potential future research avenues for SBPCG. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | true | 406,321 |
2408.01839 | Complexity of Minimizing Projected-Gradient-Dominated Functions with
Stochastic First-order Oracles | This work investigates the performance limits of projected stochastic first-order methods for minimizing functions under the $(\alpha,\tau,\mathcal{X})$-projected-gradient-dominance property, that asserts the sub-optimality gap $F(\mathbf{x})-\min_{\mathbf{x}'\in \mathcal{X}}F(\mathbf{x}')$ is upper-bounded by $\tau\cdot\|\mathcal{G}_{\eta,\mathcal{X}}(\mathbf{x})\|^{\alpha}$ for some $\alpha\in[1,2)$ and $\tau>0$ and $\mathcal{G}_{\eta,\mathcal{X}}(\mathbf{x})$ is the projected-gradient mapping with $\eta>0$ as a parameter. For non-convex functions, we show that the complexity lower bound of querying a batch smooth first-order stochastic oracle to obtain an $\epsilon$-global-optimum point is $\Omega(\epsilon^{-{2}/{\alpha}})$. Furthermore, we show that a projected variance-reduced first-order algorithm can obtain the upper complexity bound of $\mathcal{O}(\epsilon^{-{2}/{\alpha}})$, matching the lower bound. For convex functions, we establish a complexity lower bound of $\Omega(\log(1/\epsilon)\cdot\epsilon^{-{2}/{\alpha}})$ for minimizing functions under a local version of gradient-dominance property, which also matches the upper complexity bound of accelerated stochastic subgradient methods. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 478,388 |
1601.04595 | Multi-Processor Approximate Message Passing Using Lossy Compression | In this paper, a communication-efficient multi-processor compressed sensing framework based on the approximate message passing algorithm is proposed. We perform lossy compression on the data being communicated between processors, resulting in a reduction in communication costs with a minor degradation in recovery quality. In the proposed framework, a new state evolution formulation takes the quantization error into account, and analytically determines the coding rate required in each iteration. Two approaches for allocating the coding rate, an online back-tracking heuristic and an optimal allocation scheme based on dynamic programming, provide significant reductions in communication costs. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | true | 51,039 |
2412.12621 | Jailbreaking? One Step Is Enough! | Large language models (LLMs) excel in various tasks but remain vulnerable to jailbreak attacks, where adversaries manipulate prompts to generate harmful outputs. Examining jailbreak prompts helps uncover the shortcomings of LLMs. However, current jailbreak methods and the target model's defenses are engaged in an independent and adversarial process, resulting in the need for frequent attack iterations and redesigning attacks for different models. To address these gaps, we propose a Reverse Embedded Defense Attack (REDA) mechanism that disguises the attack intention as the "defense". intention against harmful content. Specifically, REDA starts from the target response, guiding the model to embed harmful content within its defensive measures, thereby relegating harmful content to a secondary role and making the model believe it is performing a defensive task. The attacking model considers that it is guiding the target model to deal with harmful content, while the target model thinks it is performing a defensive task, creating an illusion of cooperation between the two. Additionally, to enhance the model's confidence and guidance in "defensive" intentions, we adopt in-context learning (ICL) with a small number of attack examples and construct a corresponding dataset of attack examples. Extensive evaluations demonstrate that the REDA method enables cross-model attacks without the need to redesign attack strategies for different models, enables successful jailbreak in one iteration, and outperforms existing methods on both open-source and closed-source models. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 517,951 |
2307.15588 | OAFuser: Towards Omni-Aperture Fusion for Light Field Semantic
Segmentation | Light field cameras are capable of capturing intricate angular and spatial details. This allows for acquiring complex light patterns and details from multiple angles, significantly enhancing the precision of image semantic segmentation. However, two significant issues arise: (1) The extensive angular information of light field cameras contains a large amount of redundant data, which is overwhelming for the limited hardware resources of intelligent agents. (2) A relative displacement difference exists in the data collected by different micro-lenses. To address these issues, we propose an Omni-Aperture Fusion model (OAFuser) that leverages dense context from the central view and extracts the angular information from sub-aperture images to generate semantically consistent results. To simultaneously streamline the redundant information from the light field cameras and avoid feature loss during network propagation, we present a simple yet very effective Sub-Aperture Fusion Module (SAFM). This module efficiently embeds sub-aperture images in angular features, allowing the network to process each sub-aperture image with a minimal computational demand of only (around 1GFlops). Furthermore, to address the mismatched spatial information across viewpoints, we present a Center Angular Rectification Module (CARM) to realize feature resorting and prevent feature occlusion caused by misalignment. The proposed OAFuser achieves state-of-the-art performance on four UrbanLF datasets in terms of all evaluation metrics and sets a new record of 84.93% in mIoU on the UrbanLF-Real Extended dataset, with a gain of +3.69%. The source code for OAFuser is available at https://github.com/FeiBryantkit/OAFuser. | false | false | false | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | 382,312 |
1910.01847 | Dual Learning Algorithm for Delayed Conversions | In display advertising, predicting the conversion rate (CVR), meaning the probability that a user takes a predefined action on an advertiser's website, is a fundamental task for estimating the value of displaying an advertisement to a user. There are two main challenges in CVR prediction due to delayed feedback. First, some positive labels are not correctly observed in training data because some conversions do not occur immediately after a click. Second, delay mechanisms are not uniform among instances, meaning some positive feedback are much more frequently observed than others. It is widely acknowledged that these problems lead to severe bias in CVR prediction. To overcome these challenges, we propose two unbiased estimators: one for CVR prediction and the other for bias estimation. Subsequently, we propose a dual learning algorithm in which a CVR predictor and a bias estimator are trained in alternating fashion using only observable conversions. The proposed algorithm is the first of its kind to address the two major challenges in a theoretically sophisticated manner. Empirical evaluations using synthetic datasets demonstrate the practical value of the proposed approach. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 148,055 |
2406.12412 | A Novel Algorithm for Community Detection in Networks using Rough Sets
and Consensus Clustering | Complex networks, such as those in social, biological, and technological systems, often present challenges to the task of community detection. Our research introduces a novel rough clustering based consensus community framework (RC-CCD) for effective structure identification of network communities. The RC-CCD method employs rough set theory to handle uncertainties within data and utilizes a consensus clustering approach to aggregate multiple clustering results, enhancing the reliability and accuracy of community detection. This integration allows the RC-CCD to effectively manage overlapping communities, which are often present in complex networks. This approach excels at detecting overlapping communities, offering a detailed and accurate representation of network structures. Comprehensive testing on benchmark networks generated by the Lancichinetti-Fortunato-Radicchi method showcased the strength and adaptability of the new proposal to varying node degrees and community sizes. Cross-comparisons of RC-CCD versus other well known detection algorithms outcomes highlighted its stability and adaptability. | false | false | false | true | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 465,393 |
2312.11043 | TDeLTA: A Light-weight and Robust Table Detection Method based on
Learning Text Arrangement | The diversity of tables makes table detection a great challenge, leading to existing models becoming more tedious and complex. Despite achieving high performance, they often overfit to the table style in training set, and suffer from significant performance degradation when encountering out-of-distribution tables in other domains. To tackle this problem, we start from the essence of the table, which is a set of text arranged in rows and columns. Based on this, we propose a novel, light-weighted and robust Table Detection method based on Learning Text Arrangement, namely TDeLTA. TDeLTA takes the text blocks as input, and then models the arrangement of them with a sequential encoder and an attention module. To locate the tables precisely, we design a text-classification task, classifying the text blocks into 4 categories according to their semantic roles in the tables. Experiments are conducted on both the text blocks parsed from PDF and extracted by open-source OCR tools, respectively. Compared to several state-of-the-art methods, TDeLTA achieves competitive results with only 3.1M model parameters on the large-scale public datasets. Moreover, when faced with the cross-domain data under the 0-shot setting, TDeLTA outperforms baselines by a large margin of nearly 7%, which shows the strong robustness and transferability of the proposed model. | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 416,428 |
cs/0008004 | Comparing two trainable grammatical relations finders | Grammatical relationships (GRs) form an important level of natural language processing, but different sets of GRs are useful for different purposes. Therefore, one may often only have time to obtain a small training corpus with the desired GR annotations. On such a small training corpus, we compare two systems. They use different learning techniques, but we find that this difference by itself only has a minor effect. A larger factor is that in English, a different GR length measure appears better suited for finding simple argument GRs than for finding modifier GRs. We also find that partitioning the data may help memory-based learning. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 537,176 |
0810.0154 | Optimization of sequences in CDMA systems: a statistical-mechanics
approach | Statistical mechanics approach is useful not only in analyzing macroscopic system performance of wireless communication systems, but also in discussing design problems of wireless communication systems. In this paper, we discuss a design problem of spreading sequences in code-division multiple-access (CDMA) systems, as an example demonstrating the usefulness of statistical mechanics approach. We analyze, via replica method, the average mutual information between inputs and outputs of a randomly-spread CDMA channel, and discuss the optimization problem with the average mutual information as a measure of optimization. It has been shown that the average mutual information is maximized by orthogonally-invariant random Welch bound equality (WBE) spreading sequences. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 2,438 |
2108.06027 | PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense
Passage Retrieval | Recently, dense passage retrieval has become a mainstream approach to finding relevant information in various natural language processing tasks. A number of studies have been devoted to improving the widely adopted dual-encoder architecture. However, most of the previous studies only consider query-centric similarity relation when learning the dual-encoder retriever. In order to capture more comprehensive similarity relations, we propose a novel approach that leverages both query-centric and PAssage-centric sImilarity Relations (called PAIR) for dense passage retrieval. To implement our approach, we make three major technical contributions by introducing formal formulations of the two kinds of similarity relations, generating high-quality pseudo labeled data via knowledge distillation, and designing an effective two-stage training procedure that incorporates passage-centric similarity relation constraint. Extensive experiments show that our approach significantly outperforms previous state-of-the-art models on both MSMARCO and Natural Questions datasets. | false | false | false | false | true | true | false | false | true | false | false | false | false | false | false | false | false | false | 250,483 |
2108.05524 | Silhouette based View embeddings for Gait Recognition under Multiple
Views | Gait recognition under multiple views is an important computer vision and pattern recognition task. In the emerging convolutional neural network based approaches, the information of view angle is ignored to some extent. Instead of direct view estimation and training view-specific recognition models, we propose a compatible framework that can embed view information into existing architectures of gait recognition. The embedding is simply achieved by a selective projection layer. Experimental results on two large public datasets show that the proposed framework is very effective. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 250,322 |
2404.15608 | Understanding and Improving CNNs with Complex Structure Tensor: A
Biometrics Study | Our study provides evidence that CNNs struggle to effectively extract orientation features. We show that the use of Complex Structure Tensor, which contains compact orientation features with certainties, as input to CNNs consistently improves identification accuracy compared to using grayscale inputs alone. Experiments also demonstrated that our inputs, which were provided by mini complex conv-nets, combined with reduced CNN sizes, outperformed full-fledged, prevailing CNN architectures. This suggests that the upfront use of orientation features in CNNs, a strategy seen in mammalian vision, not only mitigates their limitations but also enhances their explainability and relevance to thin-clients. Experiments were done on publicly available data sets comprising periocular images for biometric identification and verification (Close and Open World) using 6 State of the Art CNN architectures. We reduced SOA Equal Error Rate (EER) on the PolyU dataset by 5-26% depending on data and scenario. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 449,158 |
2203.06823 | SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image
Labels for Quantitative Clinical Evaluation | Magnetic resonance imaging (MRI) is a cornerstone of modern medical imaging. However, long image acquisition times, the need for qualitative expert analysis, and the lack of (and difficulty extracting) quantitative indicators that are sensitive to tissue health have curtailed widespread clinical and research studies. While recent machine learning methods for MRI reconstruction and analysis have shown promise for reducing this burden, these techniques are primarily validated with imperfect image quality metrics, which are discordant with clinically-relevant measures that ultimately hamper clinical deployment and clinician trust. To mitigate this challenge, we present the Stanford Knee MRI with Multi-Task Evaluation (SKM-TEA) dataset, a collection of quantitative knee MRI (qMRI) scans that enables end-to-end, clinically-relevant evaluation of MRI reconstruction and analysis tools. This 1.6TB dataset consists of raw-data measurements of ~25,000 slices (155 patients) of anonymized patient MRI scans, the corresponding scanner-generated DICOM images, manual segmentations of four tissues, and bounding box annotations for sixteen clinically relevant pathologies. We provide a framework for using qMRI parameter maps, along with image reconstructions and dense image labels, for measuring the quality of qMRI biomarker estimates extracted from MRI reconstruction, segmentation, and detection techniques. Finally, we use this framework to benchmark state-of-the-art baselines on this dataset. We hope our SKM-TEA dataset and code can enable a broad spectrum of research for modular image reconstruction and image analysis in a clinically informed manner. Dataset access, code, and benchmarks are available at https://github.com/StanfordMIMI/skm-tea. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 285,233 |
1311.6810 | Identification of geometrical and elastostatic parameters of heavy
industrial robots | The paper focuses on the stiffness modeling of heavy industrial robots with gravity compensators. The main attention is paid to the identification of geometrical and elastostatic parameters and calibration accuracy. To reduce impact of the measurement errors, the set of manipulator configurations for calibration experiments is optimized with respect to the proposed performance measure related to the end-effector position accuracy. Experimental results are presented that illustrate the advantages of the developed technique. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 28,688 |
1811.03157 | Forensic Discrimination between Traditional and Compressive Imaging
Systems | Compressive sensing is a new technology for modern computational imaging systems. In comparison to widespread conventional image sensing, the compressive imaging paradigm requires specific forensic analysis techniques and tools. In this regards, one of basic scenarios in image forensics is to distinguish traditionally sensed images from sophisticated compressively sensed ones. To do this, we first mathematically and systematically model the imaging system based on compressive sensing technology. Afterwards, a simplified version of the whole model is presented, which is appropriate for forensic investigation applications. We estimate the nonlinear system of compressive sensing with a linear model. Then, we model the imaging pipeline as an inverse problem and demonstrate that different imagers have discriminative degradation kernels. Hence, blur kernels of various imaging systems have utilized as footprints for discriminating image acquisition sources. In order to accomplish the identification cycle, we have utilized the state-of-the-art Convolutional Neural Network (CNN) and Support Vector Machine (SVM) approaches to learn a classification system from estimated blur kernels. Numerical experiments show promising identification results. Simulation codes are available for research and development purposes. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 112,765 |
2005.10881 | Revisiting Membership Inference Under Realistic Assumptions | We study membership inference in settings where some of the assumptions typically used in previous research are relaxed. First, we consider skewed priors, to cover cases such as when only a small fraction of the candidate pool targeted by the adversary are actually members and develop a PPV-based metric suitable for this setting. This setting is more realistic than the balanced prior setting typically considered by researchers. Second, we consider adversaries that select inference thresholds according to their attack goals and develop a threshold selection procedure that improves inference attacks. Since previous inference attacks fail in imbalanced prior setting, we develop a new inference attack based on the intuition that inputs corresponding to training set members will be near a local minimum in the loss function, and show that an attack that combines this with thresholds on the per-instance loss can achieve high PPV even in settings where other attacks appear to be ineffective. Code for our experiments can be found here: https://github.com/bargavj/EvaluatingDPML. | false | false | false | false | false | false | true | false | false | false | false | false | true | false | false | false | false | false | 178,308 |
2010.07892 | Robotic Pick-and-Place With Uncertain Object Instance Segmentation and
Shape Completion | We consider robotic pick-and-place of partially visible, novel objects, where goal placements are non-trivial, e.g., tightly packed into a bin. One approach is (a) use object instance segmentation and shape completion to model the objects and (b) use a regrasp planner to decide grasps and places displacing the models to their goals. However, it is critical for the planner to account for uncertainty in the perceived models, as object geometries in unobserved areas are just guesses. We account for perceptual uncertainty by incorporating it into the regrasp planner's cost function. We compare seven different costs. One of these, which uses neural networks to estimate probability of grasp and place stability, consistently outperforms uncertainty-unaware costs and evaluates faster than Monte Carlo sampling. On a real robot, the proposed cost results in successfully packing objects tightly into a bin 7.8% more often versus the commonly used minimum-number-of-grasps cost. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 200,981 |
2407.10077 | Transferable 3D Adversarial Shape Completion using Diffusion Models | Recent studies that incorporate geometric features and transformers into 3D point cloud feature learning have significantly improved the performance of 3D deep-learning models. However, their robustness against adversarial attacks has not been thoroughly explored. Existing attack methods primarily focus on white-box scenarios and struggle to transfer to recently proposed 3D deep-learning models. Even worse, these attacks introduce perturbations to 3D coordinates, generating unrealistic adversarial examples and resulting in poor performance against 3D adversarial defenses. In this paper, we generate high-quality adversarial point clouds using diffusion models. By using partial points as prior knowledge, we generate realistic adversarial examples through shape completion with adversarial guidance. The proposed adversarial shape completion allows for a more reliable generation of adversarial point clouds. To enhance attack transferability, we delve into the characteristics of 3D point clouds and employ model uncertainty for better inference of model classification through random down-sampling of point clouds. We adopt ensemble adversarial guidance for improved transferability across different network architectures. To maintain the generation quality, we limit our adversarial guidance solely to the critical points of the point clouds by calculating saliency scores. Extensive experiments demonstrate that our proposed attacks outperform state-of-the-art adversarial attack methods against both black-box models and defenses. Our black-box attack establishes a new baseline for evaluating the robustness of various 3D point cloud classification models. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 472,830 |
2205.02450 | Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline
Reinforcement Learning | Dynamic mechanism design has garnered significant attention from both computer scientists and economists in recent years. By allowing agents to interact with the seller over multiple rounds, where agents' reward functions may change with time and are state-dependent, the framework is able to model a rich class of real-world problems. In these works, the interaction between agents and sellers is often assumed to follow a Markov Decision Process (MDP). We focus on the setting where the reward and transition functions of such an MDP are not known a priori, and we are attempting to recover the optimal mechanism using an a priori collected data set. In the setting where the function approximation is employed to handle large state spaces, with only mild assumptions on the expressiveness of the function class, we are able to design a dynamic mechanism using offline reinforcement learning algorithms. Moreover, learned mechanisms approximately have three key desiderata: efficiency, individual rationality, and truthfulness. Our algorithm is based on the pessimism principle and only requires a mild assumption on the coverage of the offline data set. To the best of our knowledge, our work provides the first offline RL algorithm for dynamic mechanism design without assuming uniform coverage. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 294,944 |
2310.07361 | Domain Generalization Guided by Gradient Signal to Noise Ratio of
Parameters | Overfitting to the source domain is a common issue in gradient-based training of deep neural networks. To compensate for the over-parameterized models, numerous regularization techniques have been introduced such as those based on dropout. While these methods achieve significant improvements on classical benchmarks such as ImageNet, their performance diminishes with the introduction of domain shift in the test set i.e. when the unseen data comes from a significantly different distribution. In this paper, we move away from the classical approach of Bernoulli sampled dropout mask construction and propose to base the selection on gradient-signal-to-noise ratio (GSNR) of network's parameters. Specifically, at each training step, parameters with high GSNR will be discarded. Furthermore, we alleviate the burden of manually searching for the optimal dropout ratio by leveraging a meta-learning approach. We evaluate our method on standard domain generalization benchmarks and achieve competitive results on classification and face anti-spoofing problems. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 398,944 |
2005.07960 | Data Driven Aircraft Trajectory Prediction with Deep Imitation Learning | The current Air Traffic Management (ATM) system worldwide has reached its limits in terms of predictability, efficiency and cost effectiveness. Different initiatives worldwide propose trajectory-oriented transformations that require high fidelity aircraft trajectory planning and prediction capabilities, supporting the trajectory life cycle at all stages efficiently. Recently proposed data-driven trajectory prediction approaches provide promising results. In this paper we approach the data-driven trajectory prediction problem as an imitation learning task, where we aim to imitate experts "shaping" the trajectory. Towards this goal we present a comprehensive framework comprising the Generative Adversarial Imitation Learning state of the art method, in a pipeline with trajectory clustering and classification methods. This approach, compared to other approaches, can provide accurate predictions for the whole trajectory (i.e. with a prediction horizon until reaching the destination) both at the pre-tactical (i.e. starting at the departure airport at a specific time instant) and at the tactical (i.e. from any state while flying) stages, compared to state of the art approaches. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 177,452 |
1805.08079 | Faster Neural Network Training with Approximate Tensor Operations | We propose a novel technique for faster deep neural network training which systematically applies sample-based approximation to the constituent tensor operations, i.e., matrix multiplications and convolutions. We introduce new sampling techniques, study their theoretical properties, and prove that they provide the same convergence guarantees when applied to SGD training. We apply approximate tensor operations to single and multi-node training of MLP and CNN networks on MNIST, CIFAR-10 and ImageNet datasets. We demonstrate up to 66% reduction in the amount of computations and communication, and up to 1.37x faster training time while maintaining negligible or no impact on the final test accuracy. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | true | false | false | 98,039 |
2408.04382 | Judgment2vec: Apply Graph Analytics to Searching and Recommendation of
Similar Judgments | In court practice, legal professionals rely on their training to provide opinions that resolve cases, one of the most crucial aspects being the ability to identify similar judgments from previous courts efficiently. However, finding a similar case is challenging and often depends on experience, legal domain knowledge, and extensive labor hours, making veteran lawyers or judges indispensable. This research aims to automate the analysis of judgment text similarity. We utilized a judgment dataset labeled as the "golden standard" by experts, which includes human-verified features that can be converted into an "expert similarity score." We then constructed a knowledge graph based on "case-article" relationships, ranking each case using natural language processing to derive a "Node2vec similarity score." By evaluating these two similarity scores, we identified their discrepancies and relationships. The results can significantly reduce the labor hours required for legal searches and recommendations, with potential applications extending to various fields of information retrieval. | false | false | false | false | true | true | false | false | false | false | false | false | false | false | false | false | false | false | 479,367 |
1602.01003 | Using Node Centrality and Optimal Control to Maximize Information
Diffusion in Social Networks | We model information dissemination as a susceptible-infected epidemic process and formulate a problem to jointly optimize seeds for the epidemic and time varying resource allocation over the period of a fixed duration campaign running on a social network with a given adjacency matrix. Individuals in the network are grouped according to their centrality measure and each group is influenced by an external control function---implemented through advertisements---during the campaign duration. The aim is to maximize an objective function which is a linear combination of the reward due to the fraction of informed individuals at the deadline, and the aggregated cost of applying controls (advertising) over the campaign duration. We also study a problem variant with a fixed budget constraint. We set up the optimality system using Pontryagin's Maximum Principle from optimal control theory and solve it numerically using the forward-backward sweep technique. Our formulation allows us to compare the performance of various centrality measures (pagerank, degree, closeness and betweenness) in maximizing the spread of a message in the optimal control framework. We find that degree---a simple and local measure---performs well on the three social networks used to demonstrate results: scientific collaboration, Slashdot and Facebook. The optimal strategy targets central nodes when the resource is scarce, but non-central nodes are targeted when the resource is in abundance. Our framework is general and can be used in similar studies for other disease or information spread models---that can be modeled using a system of ordinary differential equations---for a network with a known adjacency matrix. | false | false | false | true | false | false | false | false | false | false | true | false | false | false | true | false | false | false | 51,645 |
2006.16189 | DOME: Recommendations for supervised machine learning validation in
biology | Modern biology frequently relies on machine learning to provide predictions and improve decision processes. There have been recent calls for more scrutiny on machine learning performance and possible limitations. Here we present a set of community-wide recommendations aiming to help establish standards of supervised machine learning validation in biology. Adopting a structured methods description for machine learning based on data, optimization, model, evaluation (DOME) will aim to help both reviewers and readers to better understand and assess the performance and limitations of a method or outcome. The recommendations are formulated as questions to anyone wishing to pursue implementation of a machine learning algorithm. Answers to these questions can be easily included in the supplementary material of published papers. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 184,740 |
2012.04726 | Edited Media Understanding: Reasoning About Implications of Manipulated
Images | Multimodal disinformation, from `deepfakes' to simple edits that deceive, is an important societal problem. Yet at the same time, the vast majority of media edits are harmless -- such as a filtered vacation photo. The difference between this example, and harmful edits that spread disinformation, is one of intent. Recognizing and describing this intent is a major challenge for today's AI systems. We present the task of Edited Media Understanding, requiring models to answer open-ended questions that capture the intent and implications of an image edit. We introduce a dataset for our task, EMU, with 48k question-answer pairs written in rich natural language. We evaluate a wide variety of vision-and-language models for our task, and introduce a new model PELICAN, which builds upon recent progress in pretrained multimodal representations. Our model obtains promising results on our dataset, with humans rating its answers as accurate 40.35% of the time. At the same time, there is still much work to be done -- humans prefer human-annotated captions 93.56% of the time -- and we provide analysis that highlights areas for further progress. | false | false | false | false | false | false | false | false | true | false | false | true | false | false | false | false | false | false | 210,544 |
2012.02978 | Design and Implementation of Path Trackers for Ackermann Drive based
Vehicles | This article is an overview of the various literature on path tracking methods and their implementation in simulation and realistic operating environments.The scope of this study includes analysis, implementation,tuning, and comparison of some selected path tracking methods commonly used in practice for trajectory tracking in autonomous vehicles. Many of these methods are applicable at low speed due to the linear assumption for the system model, and hence, some methods are also included that consider nonlinearities present in lateral vehicle dynamics during high-speed navigation. The performance evaluation and comparison of tracking methods are carried out on realistic simulations and a dedicated instrumented passenger car, Mahindra e2o, to get a performance idea of all the methods in realistic operating conditions and develop tuning methodologies for each of the methods. It has been observed that our model predictive control-based approach is able to perform better compared to the others in medium velocity ranges. | false | false | false | false | false | false | false | true | false | false | true | false | false | false | false | false | false | false | 209,939 |
2112.02498 | Consistent Training and Decoding For End-to-end Speech Recognition Using
Lattice-free MMI | Recently, End-to-End (E2E) frameworks have achieved remarkable results on various Automatic Speech Recognition (ASR) tasks. However, Lattice-Free Maximum Mutual Information (LF-MMI), as one of the discriminative training criteria that show superior performance in hybrid ASR systems, is rarely adopted in E2E ASR frameworks. In this work, we propose a novel approach to integrate LF-MMI criterion into E2E ASR frameworks in both training and decoding stages. The proposed approach shows its effectiveness on two of the most widely used E2E frameworks including Attention-Based Encoder-Decoders (AEDs) and Neural Transducers (NTs). Experiments suggest that the introduction of the LF-MMI criterion consistently leads to significant performance improvements on various datasets and different E2E ASR frameworks. The best of our models achieves competitive CER of 4.1\% / 4.4\% on Aishell-1 dev/test set; we also achieve significant error reduction on Aishell-2 and Librispeech datasets over strong baselines. | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 269,861 |
2409.16953 | Path-adaptive Spatio-Temporal State Space Model for Event-based
Recognition with Arbitrary Duration | Event cameras are bio-inspired sensors that capture the intensity changes asynchronously and output event streams with distinct advantages, such as high temporal resolution. To exploit event cameras for object/action recognition, existing methods predominantly sample and aggregate events in a second-level duration at every fixed temporal interval (or frequency). However, they often face difficulties in capturing the spatiotemporal relationships for longer, e.g., minute-level, events and generalizing across varying temporal frequencies. To fill the gap, we present a novel framework, dubbed PAST-SSM, exhibiting superior capacity in recognizing events with arbitrary duration (e.g., 0.1s to 4.5s) and generalizing to varying inference frequencies. Our key insight is to learn the spatiotemporal relationships from the encoded event features via the state space model (SSM) -- whose linear complexity makes it ideal for modeling high temporal resolution events with longer sequences. To achieve this goal, we first propose a Path-Adaptive Event Aggregation and Scan (PEAS) module to encode events of varying duration into features with fixed dimensions by adaptively scanning and selecting aggregated event frames. On top of PEAS, we introduce a novel Multi-faceted Selection Guiding (MSG) loss to minimize the randomness and redundancy of the encoded features. This subtly enhances the model generalization across different inference frequencies. Lastly, the SSM is employed to better learn the spatiotemporal properties from the encoded features. Moreover, we build a minute-level event-based recognition dataset, named ArDVS100, with arbitrary duration for the benefit of the community. Extensive experiments prove that our method outperforms prior arts by +3.45%, +0.38% and +8.31% on the DVS Action, SeAct and HARDVS datasets, respectively. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 491,592 |
2404.02930 | What Blocks My Blockchain's Throughput? Developing a Generalizable
Approach for Identifying Bottlenecks in Permissioned Blockchains | Permissioned blockchains have been proposed for a variety of use cases that require decentralization yet address enterprise requirements that permissionless blockchains to date cannot satisfy -- particularly in terms of performance. However, popular permissioned blockchains still exhibit a relatively low maximum throughput in comparison to established centralized systems. Consequently, researchers have conducted several benchmarking studies on different permissioned blockchains to identify their limitations and -- in some cases -- their bottlenecks in an attempt to find avenues for improvement. Yet, these approaches are highly heterogeneous, difficult to compare, and require a high level of expertise in the implementation of the underlying specific blockchain. In this paper, we develop a more unified and graphical approach for identifying bottlenecks in permissioned blockchains based on a systematic review of related work, experiments with the Distributed Ledger Performance Scan (DLPS), and an extension of its graphical evaluation functionalities. We conduct in-depth case studies on Hyperledger Fabric and Quorum, two widely used permissioned blockchains with distinct architectural designs, demonstrating the adaptability of our framework across different blockchains. We provide researchers and practitioners working on evaluating or improving permissioned blockchains with a toolkit, guidelines on what data to document, and insights on how to proceed in the search process for bottlenecks. | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | true | false | 444,056 |
1708.04423 | Distributed Weighted Sum-Rate Maximization in Multicell MU-MIMO OFDMA
Downlink | This paper considers distributed linear beamforming in downlink multicell multiuser orthogonal frequency-division multiple access networks. A fast convergent solution maximizing the weighted sum- rate with per base station (BS) transmiting power constraint is formulated. We approximate the non- convex weighted sum-rate maximization (WSRM) problem with a semidefinite relaxed solvable convex form by means of a series of approximation based on interference alignment (IA) analysis. The WSRM optimization is a two-stage optimization process. In the first stage, the IA conditions are satisfied. In the second stage, the convex approximation of the non-convex WSRM is obtained based on the consequences of IA, and high signal-to-interference-plus-noise ratio assumption. Compared to the conventional iterative distributed algorithms where the BSs exchange additional information at each iteration, the BSs of our proposed solution optimize their beamformers locally without reporting additional information during the iterative procedure. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 78,944 |
2407.00979 | Cross-Modal Attention Alignment Network with Auxiliary Text Description
for zero-shot sketch-based image retrieval | In this paper, we study the problem of zero-shot sketch-based image retrieval (ZS-SBIR). The prior methods tackle the problem in a two-modality setting with only category labels or even no textual information involved. However, the growing prevalence of Large-scale pre-trained Language Models (LLMs), which have demonstrated great knowledge learned from web-scale data, can provide us with an opportunity to conclude collective textual information. Our key innovation lies in the usage of text data as auxiliary information for images, thus leveraging the inherent zero-shot generalization ability that language offers. To this end, we propose an approach called Cross-Modal Attention Alignment Network with Auxiliary Text Description for zero-shot sketch-based image retrieval. The network consists of three components: (i) a Description Generation Module that generates textual descriptions for each training category by prompting an LLM with several interrogative sentences, (ii) a Feature Extraction Module that includes two ViTs for sketch and image data, a transformer for extracting tokens of sentences of each training category, finally (iii) a Cross-modal Alignment Module that exchanges the token features of both text-sketch and text-image using cross-attention mechanism, and align the tokens locally and globally. Extensive experiments on three benchmark datasets show our superior performances over the state-of-the-art ZS-SBIR methods. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 469,092 |
2011.11880 | Effective Parallelism for Equation and Jacobian Evaluation in Power Flow
Calculation | This letter investigates parallelism approaches for equation and Jacobian evaluations in large-scale power flow calculation. Two levels of parallelism are proposed and analyzed: inter-model parallelism, which evaluates models in parallel, and intra-model parallelism, which evaluates calculations within each model in parallel. Parallelism techniques such as multi-threading and single instruction multiple data (SIMD) vectorization are discussed, implemented, and benchmarked as six calculation workflows. Case studies on the 70,000-bus synthetic grid show that equation evaluations can be accelerated by ten times, and the overall Newton power flow advances the state of the art by 20%. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 207,977 |
2207.12647 | Cross-Modal Causal Relational Reasoning for Event-Level Visual Question
Answering | Existing visual question answering methods often suffer from cross-modal spurious correlations and oversimplified event-level reasoning processes that fail to capture event temporality, causality, and dynamics spanning over the video. In this work, to address the task of event-level visual question answering, we propose a framework for cross-modal causal relational reasoning. In particular, a set of causal intervention operations is introduced to discover the underlying causal structures across visual and linguistic modalities. Our framework, named Cross-Modal Causal RelatIonal Reasoning (CMCIR), involves three modules: i) Causality-aware Visual-Linguistic Reasoning (CVLR) module for collaboratively disentangling the visual and linguistic spurious correlations via front-door and back-door causal interventions; ii) Spatial-Temporal Transformer (STT) module for capturing the fine-grained interactions between visual and linguistic semantics; iii) Visual-Linguistic Feature Fusion (VLFF) module for learning the global semantic-aware visual-linguistic representations adaptively. Extensive experiments on four event-level datasets demonstrate the superiority of our CMCIR in discovering visual-linguistic causal structures and achieving robust event-level visual question answering. The datasets, code, and models are available at https://github.com/HCPLab-SYSU/CMCIR. | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | false | false | 310,074 |
2201.01415 | Problem-dependent attention and effort in neural networks with
applications to image resolution and model selection | This paper introduces two new ensemble-based methods to reduce the data and computation costs of image classification. They can be used with any set of classifiers and do not require additional training. In the first approach, data usage is reduced by only analyzing a full-sized image if the model has low confidence in classifying a low-resolution pixelated version. When applied on the best performing classifiers considered here, data usage is reduced by 61.2% on MNIST, 69.6% on KMNIST, 56.3% on FashionMNIST, 84.6% on SVHN, 40.6% on ImageNet, and 27.6% on ImageNet-V2, all with a less than 5% reduction in accuracy. However, for CIFAR-10, the pixelated data are not particularly informative, and the ensemble approach increases data usage while reducing accuracy. In the second approach, compute costs are reduced by only using a complex model if a simpler model has low confidence in its classification. Computation cost is reduced by 82.1% on MNIST, 47.6% on KMNIST, 72.3% on FashionMNIST, 86.9% on SVHN, 89.2% on ImageNet, and 81.5% on ImageNet-V2, all with a less than 5% reduction in accuracy; for CIFAR-10 the corresponding improvements are smaller at 13.5%. When cost is not an object, choosing the projection from the most confident model for each observation increases validation accuracy to 81.0% from 79.3% for ImageNet and to 69.4% from 67.5% for ImageNet-V2. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 274,244 |
1510.00783 | Trilateral Large-Scale OSN Account Linkability Study | In the last decade, Online Social Networks (OSNs) have taken the world by storm. They range from superficial to professional, from focused to general-purpose, and, from free-form to highly structured. Numerous people have multiple accounts within the same OSN and even more people have an account on more than one OSN. Since all OSNs involve some amount of user input, often in written form, it is natural to consider whether multiple incarnations of the same person in various OSNs can be effectively correlated or linked. One intuitive means of linking accounts is by using stylometric analysis. This paper reports on (what we believe to be) the first trilateral large-scale stylometric OSN linkability study. Its outcome has important implications for OSN privacy. The study is trilateral since it involves three OSNs with very different missions: (1) Yelp, known primarily for its user-contributed reviews of various venues, e.g, dining and entertainment, (2) Twitter, popular for its pithy general-purpose micro-blogging style, and (3) Flickr, used exclusively for posting and labeling (describing) photographs. As our somewhat surprising results indicate, stylometric linkability of accounts across these heterogeneous OSNs is both viable and quite effective. The main take-away of this work is that, despite OSN heterogeneity, it is very challenging for one person to maintain privacy across multiple active accounts on different OSNs. | false | false | false | true | false | false | false | false | false | false | false | false | true | true | false | false | false | false | 47,547 |
2105.02470 | Generalized Multimodal ELBO | Multiple data types naturally co-occur when describing real-world phenomena and learning from them is a long-standing goal in machine learning research. However, existing self-supervised generative models approximating an ELBO are not able to fulfill all desired requirements of multimodal models: their posterior approximation functions lead to a trade-off between the semantic coherence and the ability to learn the joint data distribution. We propose a new, generalized ELBO formulation for multimodal data that overcomes these limitations. The new objective encompasses two previous methods as special cases and combines their benefits without compromises. In extensive experiments, we demonstrate the advantage of the proposed method compared to state-of-the-art models in self-supervised, generative learning tasks. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 233,829 |
2402.12608 | Patient-Centric Knowledge Graphs: A Survey of Current Methods,
Challenges, and Applications | Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in healthcare that focuses on individualized patient care by mapping the patient's health information in a holistic and multi-dimensional way. PCKGs integrate various types of health data to provide healthcare professionals with a comprehensive understanding of a patient's health, enabling more personalized and effective care. This literature review explores the methodologies, challenges, and opportunities associated with PCKGs, focusing on their role in integrating disparate healthcare data and enhancing patient care through a unified health perspective. In addition, this review also discusses the complexities of PCKG development, including ontology design, data integration techniques, knowledge extraction, and structured representation of knowledge. It highlights advanced techniques such as reasoning, semantic search, and inference mechanisms essential in constructing and evaluating PCKGs for actionable healthcare insights. We further explore the practical applications of PCKGs in personalized medicine, emphasizing their significance in improving disease prediction and formulating effective treatment plans. Overall, this review provides a foundational perspective on the current state-of-the-art and best practices of PCKGs, guiding future research and applications in this dynamic field. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 430,909 |
2401.16430 | An Information Retrieval and Extraction Tool for Covid-19 Related Papers | Background: The COVID-19 pandemic has caused severe impacts on health systems worldwide. Its critical nature and the increased interest of individuals and organizations to develop countermeasures to the problem has led to a surge of new studies in scientific journals. Objetive: We sought to develop a tool that incorporates, in a novel way, aspects of Information Retrieval (IR) and Extraction (IE) applied to the COVID-19 Open Research Dataset (CORD-19). The main focus of this paper is to provide researchers with a better search tool for COVID-19 related papers, helping them find reference papers and hightlight relevant entities in text. Method: We applied Latent Dirichlet Allocation (LDA) to model, based on research aspects, the topics of all English abstracts in CORD-19. Relevant named entities of each abstract were extracted and linked to the corresponding UMLS concept. Regular expressions and the K-Nearest Neighbors algorithm were used to rank relevant papers. Results: Our tool has shown the potential to assist researchers by automating a topic-based search of CORD-19 papers. Nonetheless, we identified that more fine-tuned topic modeling parameters and increased accuracy of the research aspect classifier model could lead to a more accurate and reliable tool. Conclusion: We emphasize the need of new automated tools to help researchers find relevant COVID-19 documents, in addition to automatically extracting useful information contained in them. Our work suggests that combining different algorithms and models could lead to new ways of browsing COVID-19 paper data. | false | false | false | false | false | true | false | false | true | false | false | false | false | false | false | false | false | false | 424,824 |
2011.11188 | Integrating Deep Learning in Domain Sciences at Exascale | This paper presents some of the current challenges in designing deep learning artificial intelligence (AI) and integrating it with traditional high-performance computing (HPC) simulations. We evaluate existing packages for their ability to run deep learning models and applications on large-scale HPC systems efficiently, identify challenges, and propose new asynchronous parallelization and optimization techniques for current large-scale heterogeneous systems and upcoming exascale systems. These developments, along with existing HPC AI software capabilities, have been integrated into MagmaDNN, an open-source HPC deep learning framework. Many deep learning frameworks are targeted at data scientists and fall short in providing quality integration into existing HPC workflows. This paper discusses the necessities of an HPC deep learning framework and how those needs can be provided (e.g., as in MagmaDNN) through a deep integration with existing HPC libraries, such as MAGMA and its modular memory management, MPI, CuBLAS, CuDNN, MKL, and HIP. Advancements are also illustrated through the use of algorithmic enhancements in reduced- and mixed-precision, as well as asynchronous optimization methods. Finally, we present illustrations and potential solutions for enhancing traditional compute- and data-intensive applications at ORNL and UTK with AI. The approaches and future challenges are illustrated in materials science, imaging, and climate applications. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 207,746 |
2103.00704 | FedPower: Privacy-Preserving Distributed Eigenspace Estimation | Eigenspace estimation is fundamental in machine learning and statistics, which has found applications in PCA, dimension reduction, and clustering, among others. The modern machine learning community usually assumes that data come from and belong to different organizations. The low communication power and the possible privacy breaches of data make the computation of eigenspace challenging. To address these challenges, we propose a class of algorithms called \textsf{FedPower} within the federated learning (FL) framework. \textsf{FedPower} leverages the well-known power method by alternating multiple local power iterations and a global aggregation step, thus improving communication efficiency. In the aggregation, we propose to weight each local eigenvector matrix with {\it Orthogonal Procrustes Transformation} (OPT) for better alignment. To ensure strong privacy protection, we add Gaussian noise in each iteration by adopting the notion of \emph{differential privacy} (DP). We provide convergence bounds for \textsf{FedPower} that are composed of different interpretable terms corresponding to the effects of Gaussian noise, parallelization, and random sampling of local machines. Additionally, we conduct experiments to demonstrate the effectiveness of our proposed algorithms. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 222,368 |
2401.15508 | Proto-MPC: An Encoder-Prototype-Decoder Approach for Quadrotor Control
in Challenging Winds | Quadrotors are increasingly used in the evolving field of aerial robotics for their agility and mechanical simplicity. However, inherent uncertainties, such as aerodynamic effects coupled with quadrotors' operation in dynamically changing environments, pose significant challenges for traditional, nominal model-based control designs. We propose a multi-task meta-learning method called Encoder-Prototype-Decoder (EPD), which has the advantage of effectively balancing shared and distinctive representations across diverse training tasks. Subsequently, we integrate the EPD model into a model predictive control problem (Proto-MPC) to enhance the quadrotor's ability to adapt and operate across a spectrum of dynamically changing tasks with an efficient online implementation. We validate the proposed method in simulations, which demonstrates Proto-MPC's robust performance in trajectory tracking of a quadrotor being subject to static and spatially varying side winds. | false | false | false | false | false | false | true | true | false | false | true | false | false | false | false | false | false | false | 424,477 |
1804.08875 | Data-driven Summarization of Scientific Articles | Data-driven approaches to sequence-to-sequence modelling have been successfully applied to short text summarization of news articles. Such models are typically trained on input-summary pairs consisting of only a single or a few sentences, partially due to limited availability of multi-sentence training data. Here, we propose to use scientific articles as a new milestone for text summarization: large-scale training data come almost for free with two types of high-quality summaries at different levels - the title and the abstract. We generate two novel multi-sentence summarization datasets from scientific articles and test the suitability of a wide range of existing extractive and abstractive neural network-based summarization approaches. Our analysis demonstrates that scientific papers are suitable for data-driven text summarization. Our results could serve as valuable benchmarks for scaling sequence-to-sequence models to very long sequences. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 95,856 |
1503.01655 | Studying the Wikipedia Hyperlink Graph for Relatedness and
Disambiguation | Hyperlinks and other relations in Wikipedia are a extraordinary resource which is still not fully understood. In this paper we study the different types of links in Wikipedia, and contrast the use of the full graph with respect to just direct links. We apply a well-known random walk algorithm on two tasks, word relatedness and named-entity disambiguation. We show that using the full graph is more effective than just direct links by a large margin, that non-reciprocal links harm performance, and that there is no benefit from categories and infoboxes, with coherent results on both tasks. We set new state-of-the-art figures for systems based on Wikipedia links, comparable to systems exploiting several information sources and/or supervised machine learning. Our approach is open source, with instruction to reproduce results, and amenable to be integrated with complementary text-based methods. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 40,857 |
2202.01288 | Imitation Learning by Estimating Expertise of Demonstrators | Many existing imitation learning datasets are collected from multiple demonstrators, each with different expertise at different parts of the environment. Yet, standard imitation learning algorithms typically treat all demonstrators as homogeneous, regardless of their expertise, absorbing the weaknesses of any suboptimal demonstrators. In this work, we show that unsupervised learning over demonstrator expertise can lead to a consistent boost in the performance of imitation learning algorithms. We develop and optimize a joint model over a learned policy and expertise levels of the demonstrators. This enables our model to learn from the optimal behavior and filter out the suboptimal behavior of each demonstrator. Our model learns a single policy that can outperform even the best demonstrator, and can be used to estimate the expertise of any demonstrator at any state. We illustrate our findings on real-robotic continuous control tasks from Robomimic and discrete environments such as MiniGrid and chess, out-performing competing methods in $21$ out of $23$ settings, with an average of $7\%$ and up to $60\%$ improvement in terms of the final reward. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 278,427 |
2211.15992 | MoDA: Map style transfer for self-supervised Domain Adaptation of
embodied agents | We propose a domain adaptation method, MoDA, which adapts a pretrained embodied agent to a new, noisy environment without ground-truth supervision. Map-based memory provides important contextual information for visual navigation, and exhibits unique spatial structure mainly composed of flat walls and rectangular obstacles. Our adaptation approach encourages the inherent regularities on the estimated maps to guide the agent to overcome the prevalent domain discrepancy in a novel environment. Specifically, we propose an efficient learning curriculum to handle the visual and dynamics corruptions in an online manner, self-supervised with pseudo clean maps generated by style transfer networks. Because the map-based representation provides spatial knowledge for the agent's policy, our formulation can deploy the pretrained policy networks from simulators in a new setting. We evaluate MoDA in various practical scenarios and show that our proposed method quickly enhances the agent's performance in downstream tasks including localization, mapping, exploration, and point-goal navigation. | false | false | false | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | 333,477 |
1806.03645 | Deep Curiosity Loops in Social Environments | Inspired by infants' intrinsic motivation to learn, which values informative sensory channels contingent on their immediate social environment, we developed a deep curiosity loop (DCL) architecture. The DCL is composed of a learner, which attempts to learn a forward model of the agent's state-action transition, and a novel reinforcement-learning (RL) component, namely, an Action-Convolution Deep Q-Network, which uses the learner's prediction error as reward. The environment for our agent is composed of visual social scenes, composed of sitcom video streams, thereby both the learner and the RL are constructed as deep convolutional neural networks. The agent's learner learns to predict the zero-th order of the dynamics of visual scenes, resulting in intrinsic rewards proportional to changes within its social environment. The sources of these socially informative changes within the sitcom are predominantly motions of faces and hands, leading to the unsupervised curiosity-based learning of social interaction features. The face and hand detection is represented by the value function and the social interaction optical-flow is represented by the policy. Our results suggest that face and hand detection are emergent properties of curiosity-based learning embedded in social environments. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | 100,050 |
2410.14158 | A Mirror Descent Perspective of Smoothed Sign Descent | Recent work by Woodworth et al. (2020) shows that the optimization dynamics of gradient descent for overparameterized problems can be viewed as low-dimensional dual dynamics induced by a mirror map, explaining the implicit regularization phenomenon from the mirror descent perspective. However, the methodology does not apply to algorithms where update directions deviate from true gradients, such as ADAM. We use the mirror descent framework to study the dynamics of smoothed sign descent with a stability constant $\varepsilon$ for regression problems. We propose a mirror map that establishes equivalence to dual dynamics under some assumptions. By studying dual dynamics, we characterize the convergent solution as an approximate KKT point of minimizing a Bregman divergence style function, and show the benefit of tuning the stability constant $\varepsilon$ to reduce the KKT error. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 499,900 |
1105.2902 | A Multi-Purpose Scenario-based Simulator for Smart House Environments | Developing smart house systems has been a great challenge for researchers and engineers in this area because of the high cost of implementation and evaluation process of these systems, while being very time consuming. Testing a designed smart house before actually building it is considered as an obstacle towards an efficient smart house project. This is because of the variety of sensors, home appliances and devices available for a real smart environment. In this paper, we present the design and implementation of a multi-purpose smart house simulation system for designing and simulating all aspects of a smart house environment. This simulator provides the ability to design the house plan and different virtual sensors and appliances in a two dimensional model of the virtual house environment. This simulator can connect to any external smart house remote controlling system, providing evaluation capabilities to their system much easier than before. It also supports detailed adding of new emerging sensors and devices to help maintain its compatibility with future simulation needs. Scenarios can also be defined for testing various possible combinations of device states; so different criteria and variables can be simply evaluated without the need of experimenting on a real environment. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 10,372 |
2007.07876 | Upper Counterfactual Confidence Bounds: a New Optimism Principle for
Contextual Bandits | The principle of optimism in the face of uncertainty is one of the most widely used and successful ideas in multi-armed bandits and reinforcement learning. However, existing optimistic algorithms (primarily UCB and its variants) often struggle to deal with general function classes and large context spaces. In this paper, we study general contextual bandits with an offline regression oracle and propose a simple, generic principle to design optimistic algorithms, dubbed "Upper Counterfactual Confidence Bounds" (UCCB). The key innovation of UCCB is building confidence bounds in policy space, rather than in action space as is done in UCB. We demonstrate that these algorithms are provably optimal and computationally efficient in handling general function classes and large context spaces. Furthermore, we illustrate that the UCCB principle can be seamlessly extended to infinite-action general contextual bandits, provide the first solutions to these settings when employing an offline regression oracle. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 187,460 |
1710.11272 | Empirical analysis of non-linear activation functions for Deep Neural
Networks in classification tasks | We provide an overview of several non-linear activation functions in a neural network architecture that have proven successful in many machine learning applications. We conduct an empirical analysis on the effectiveness of using these function on the MNIST classification task, with the aim of clarifying which functions produce the best results overall. Based on this first set of results, we examine the effects of building deeper architectures with an increasing number of hidden layers. We also survey the impact of using, on the same task, different initialisation schemes for the weights of our neural network. Using these sets of experiments as a base, we conclude by providing a optimal neural network architecture that yields impressive results in accuracy on the MNIST classification task. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 83,557 |
2403.11447 | Motion-aware 3D Gaussian Splatting for Efficient Dynamic Scene
Reconstruction | 3D Gaussian Splatting (3DGS) has become an emerging tool for dynamic scene reconstruction. However, existing methods focus mainly on extending static 3DGS into a time-variant representation, while overlooking the rich motion information carried by 2D observations, thus suffering from performance degradation and model redundancy. To address the above problem, we propose a novel motion-aware enhancement framework for dynamic scene reconstruction, which mines useful motion cues from optical flow to improve different paradigms of dynamic 3DGS. Specifically, we first establish a correspondence between 3D Gaussian movements and pixel-level flow. Then a novel flow augmentation method is introduced with additional insights into uncertainty and loss collaboration. Moreover, for the prevalent deformation-based paradigm that presents a harder optimization problem, a transient-aware deformation auxiliary module is proposed. We conduct extensive experiments on both multi-view and monocular scenes to verify the merits of our work. Compared with the baselines, our method shows significant superiority in both rendering quality and efficiency. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 438,701 |
2309.15604 | Entropic Matching for Expectation Propagation of Markov Jump Processes | This paper addresses the problem of statistical inference for latent continuous-time stochastic processes, which is often intractable, particularly for discrete state space processes described by Markov jump processes. To overcome this issue, we propose a new tractable inference scheme based on an entropic matching framework that can be embedded into the well-known expectation propagation algorithm. We demonstrate the effectiveness of our method by providing closed-form results for a simple family of approximate distributions and apply it to the general class of chemical reaction networks, which are a crucial tool for modeling in systems biology. Moreover, we derive closed form expressions for point estimation of the underlying parameters using an approximate expectation maximization procedure. We evaluate the performance of our method on various chemical reaction network instantiations, including a stochastic Lotka-Voltera example, and discuss its limitations and potential for future improvements. Our proposed approach provides a promising direction for addressing complex continuous-time Bayesian inference problems. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 395,037 |
2106.14446 | Approximately Envy-Free Budget-Feasible Allocation | In the budget-feasible allocation problem, a set of items with varied sizes and values are to be allocated to a group of agents. Each agent has a budget constraint on the total size of items she can receive. The goal is to compute a feasible allocation that is envy-free (EF), in which the agents do not envy each other for the items they receive, nor do they envy a charity, who is endowed with all the unallocated items. Since EF allocations barely exist even without budget constraints, we are interested in the relaxed notion of envy-freeness up to one item (EF1). The computation of both exact and approximate EF1 allocations remains largely open, despite a recent effort by Wu et al. (IJCAI 2021) in showing that any budget-feasible allocation that maximizes the Nash Social Welfare (NSW) is 1/4-approximate EF1. In this paper, we move one step forward by showing that for agents with identical additive valuations, a 1/2-approximate EF1 allocation can be computed in polynomial time. For the uniform-budget and two-agent cases, we propose efficient algorithms for computing an exact EF1 allocation. We also consider the large budget setting, i.e., when the item sizes are infinitesimal compared with the agents' budgets, and show that both the NSW maximizing allocation and the allocation our polynomial-time algorithm computes have an approximation close to 1 regarding EF1. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | true | 243,415 |
2307.13470 | Combinatorial Auctions and Graph Neural Networks for Local Energy
Flexibility Markets | This paper proposes a new combinatorial auction framework for local energy flexibility markets, which addresses the issue of prosumers' inability to bundle multiple flexibility time intervals. To solve the underlying NP-complete winner determination problems, we present a simple yet powerful heterogeneous tri-partite graph representation and design graph neural network-based models. Our models achieve an average optimal value deviation of less than 5\% from an off-the-shelf optimization tool and show linear inference time complexity compared to the exponential complexity of the commercial solver. Contributions and results demonstrate the potential of using machine learning to efficiently allocate energy flexibility resources in local markets and solving optimization problems in general. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 381,598 |
1705.09966 | Attribute-Guided Face Generation Using Conditional CycleGAN | We are interested in attribute-guided face generation: given a low-res face input image, an attribute vector that can be extracted from a high-res image (attribute image), our new method generates a high-res face image for the low-res input that satisfies the given attributes. To address this problem, we condition the CycleGAN and propose conditional CycleGAN, which is designed to 1) handle unpaired training data because the training low/high-res and high-res attribute images may not necessarily align with each other, and to 2) allow easy control of the appearance of the generated face via the input attributes. We demonstrate impressive results on the attribute-guided conditional CycleGAN, which can synthesize realistic face images with appearance easily controlled by user-supplied attributes (e.g., gender, makeup, hair color, eyeglasses). Using the attribute image as identity to produce the corresponding conditional vector and by incorporating a face verification network, the attribute-guided network becomes the identity-guided conditional CycleGAN which produces impressive and interesting results on identity transfer. We demonstrate three applications on identity-guided conditional CycleGAN: identity-preserving face superresolution, face swapping, and frontal face generation, which consistently show the advantage of our new method. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 74,310 |
2303.03378 | PaLM-E: An Embodied Multimodal Language Model | Large language models excel at a wide range of complex tasks. However, enabling general inference in the real world, e.g., for robotics problems, raises the challenge of grounding. We propose embodied language models to directly incorporate real-world continuous sensor modalities into language models and thereby establish the link between words and percepts. Input to our embodied language model are multi-modal sentences that interleave visual, continuous state estimation, and textual input encodings. We train these encodings end-to-end, in conjunction with a pre-trained large language model, for multiple embodied tasks including sequential robotic manipulation planning, visual question answering, and captioning. Our evaluations show that PaLM-E, a single large embodied multimodal model, can address a variety of embodied reasoning tasks, from a variety of observation modalities, on multiple embodiments, and further, exhibits positive transfer: the model benefits from diverse joint training across internet-scale language, vision, and visual-language domains. Our largest model, PaLM-E-562B with 562B parameters, in addition to being trained on robotics tasks, is a visual-language generalist with state-of-the-art performance on OK-VQA, and retains generalist language capabilities with increasing scale. | false | false | false | false | true | false | true | true | false | false | false | false | false | false | false | false | false | false | 349,706 |
1104.0651 | Meaningful Clustered Forest: an Automatic and Robust Clustering
Algorithm | We propose a new clustering technique that can be regarded as a numerical method to compute the proximity gestalt. The method analyzes edge length statistics in the MST of the dataset and provides an a contrario cluster detection criterion. The approach is fully parametric on the chosen distance and can detect arbitrarily shaped clusters. The method is also automatic, in the sense that only a single parameter is left to the user. This parameter has an intuitive interpretation as it controls the expected number of false detections. We show that the iterative application of our method can (1) provide robustness to noise and (2) solve a masking phenomenon in which a highly populated and salient cluster dominates the scene and inhibits the detection of less-populated, but still salient, clusters. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 9,864 |
1606.04930 | Deep Learning for Music | Our goal is to be able to build a generative model from a deep neural network architecture to try to create music that has both harmony and melody and is passable as music composed by humans. Previous work in music generation has mainly been focused on creating a single melody. More recent work on polyphonic music modeling, centered around time series probability density estimation, has met some partial success. In particular, there has been a lot of work based off of Recurrent Neural Networks combined with Restricted Boltzmann Machines (RNN-RBM) and other similar recurrent energy based models. Our approach, however, is to perform end-to-end learning and generation with deep neural nets alone. | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 57,326 |
2310.02113 | FLEDGE: Ledger-based Federated Learning Resilient to Inference and
Backdoor Attacks | Federated learning (FL) is a distributed learning process that uses a trusted aggregation server to allow multiple parties (or clients) to collaboratively train a machine learning model without having them share their private data. Recent research, however, has demonstrated the effectiveness of inference and poisoning attacks on FL. Mitigating both attacks simultaneously is very challenging. State-of-the-art solutions have proposed the use of poisoning defenses with Secure Multi-Party Computation (SMPC) and/or Differential Privacy (DP). However, these techniques are not efficient and fail to address the malicious intent behind the attacks, i.e., adversaries (curious servers and/or compromised clients) seek to exploit a system for monetization purposes. To overcome these limitations, we present a ledger-based FL framework known as FLEDGE that allows making parties accountable for their behavior and achieve reasonable efficiency for mitigating inference and poisoning attacks. Our solution leverages crypto-currency to increase party accountability by penalizing malicious behavior and rewarding benign conduct. We conduct an extensive evaluation on four public datasets: Reddit, MNIST, Fashion-MNIST, and CIFAR-10. Our experimental results demonstrate that (1) FLEDGE provides strong privacy guarantees for model updates without sacrificing model utility; (2) FLEDGE can successfully mitigate different poisoning attacks without degrading the performance of the global model; and (3) FLEDGE offers unique reward mechanisms to promote benign behavior during model training and/or model aggregation. | false | false | false | false | false | false | true | false | false | false | false | false | true | false | false | false | false | true | 396,713 |
2202.09597 | STAR-RIS-NOMA Networks: An Error Performance Perspective | This letter investigates the bit error rate (BER) performance of simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) in non-orthogonal multiple access (NOMA) networks. In the investigated network, a STAR-RIS serves multiple non-orthogonal users located on either side of the surface by utilizing the mode switching protocol. We derive the closed-form BER expressions in perfect and imperfect successive interference cancellation cases. Furthermore, asymptotic analyses are also conducted to provide further insights into the BER behavior in the high signal-to-noise ratio region. Finally, the accuracy of our theoretical analysis is validated through Monte Carlo simulations. The obtained results reveal that the BER performance of STAR-RIS-NOMA outperforms that of the classical NOMA system, and STAR-RIS might be a promising NOMA 2.0 solution. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 281,253 |
2312.09439 | Smart Roads: Roadside Perception, Vehicle-Road Cooperation and Business
Model | Smart roads have become an essential component of intelligent transportation systems (ITS). The roadside perception technology, a critical aspect of smart roads, utilizes various sensors, roadside units (RSUs), and edge computing devices to gather real-time traffic data for vehicle-road cooperation. However, the full potential of smart roads in improving the safety and efficiency of autonomous vehicles only can be realized through the mass deployment of roadside perception and communication devices. On the one hand, roadside devices require significant investment but can only achieve monitoring function currently, resulting in no profitability for investors. On the other hand, drivers lack trust in the safety of autonomous driving technology, making it difficult to promote large-scale commercial applications. To deal with the dilemma of mass deployment, we propose a novel smart-road vehicle-guiding architecture for vehicle-road cooperative autonomous driving, based on which we then propose the corresponding business model and analyze its benefits from both operator and driver perspectives. The numerical simulations validate that our proposed smart road solution can enhance driving safety and traffic efficiency. Moreover, we utilize the cost-benefit analysis (CBA) model to assess the economic advantages of the proposed business model which indicates that the smart highway that can provide vehicle-guided-driving services for autonomous vehicles yields more profit than the regular highway. | false | false | false | false | false | false | false | true | false | false | true | false | false | false | false | false | false | false | 415,721 |
2410.16027 | ComPO: Community Preferences for Language Model Personalization | Conventional algorithms for training language models (LMs) with human feedback rely on preferences that are assumed to account for an "average" user, disregarding subjectivity and finer-grained variations. Recent studies have raised concerns that aggregating such diverse and often contradictory human feedback to finetune models results in generic models that generate outputs not preferred by many user groups, as they tend to average out styles and norms. To address this issue, we draw inspiration from recommendation systems and propose ComPO, a method to personalize preference optimization in LMs by contextualizing the probability distribution of model outputs with the preference provider. Focusing on group-level preferences rather than individuals, we collect and release ComPRed, a question answering dataset with community-level preferences from Reddit. This dataset facilitates studying diversity in preferences without incurring privacy concerns associated with individual feedback. Our experiments reveal that conditioning language models on a community identifier (i.e., subreddit name) during preference tuning substantially enhances model performance. Conversely, replacing this context with random subreddit identifiers significantly diminishes performance, highlighting the effectiveness of our approach in tailoring responses to communities' preferences. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 500,846 |
1904.06807 | Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance
for Cross-View Image Translation | Cross-view image translation is challenging because it involves images with drastically different views and severe deformation. In this paper, we propose a novel approach named Multi-Channel Attention SelectionGAN (SelectionGAN) that makes it possible to generate images of natural scenes in arbitrary viewpoints, based on an image of the scene and a novel semantic map. The proposed SelectionGAN explicitly utilizes the semantic information and consists of two stages. In the first stage, the condition image and the target semantic map are fed into a cycled semantic-guided generation network to produce initial coarse results. In the second stage, we refine the initial results by using a multi-channel attention selection mechanism. Moreover, uncertainty maps automatically learned from attentions are used to guide the pixel loss for better network optimization. Extensive experiments on Dayton, CVUSA and Ego2Top datasets show that our model is able to generate significantly better results than the state-of-the-art methods. The source code, data and trained models are available at https://github.com/Ha0Tang/SelectionGAN. | false | false | false | false | true | false | true | false | false | false | false | true | false | false | false | false | false | true | 127,636 |
1004.3549 | Signature Region of Interest using Auto cropping | A new approach for signature region of interest pre-processing was presented. It used new auto cropping preparation on the basis of the image content, where the intensity value of pixel is the source of cropping. This approach provides both the possibility of improving the performance of security systems based on signature images, and also the ability to use only the region of interest of the used image to suit layout design of biometric systems. Underlying the approach is a novel segmentation method which identifies the exact region of foreground of signature for feature extraction usage. Evaluation results of this approach shows encouraging prospects by eliminating the need for false region isolating, reduces the time cost associated with signature false points detection, and addresses enhancement issues. A further contribution of this paper is an automated cropping stage in bio-secure based systems. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 6,220 |
2409.01768 | Mapping Safe Zones for Co-located Human-UAV Interaction | Recent advances in robotics bring us closer to the reality of living, co-habiting, and sharing personal spaces with robots. However, it is not clear how close a co-located robot can be to a human in a shared environment without making the human uncomfortable or anxious. This research aims to map safe and comfortable zones for co-located aerial robots. The objective is to identify the distances at which a drone causes discomfort to a co-located human and to create a map showing no-fly, moderate-fly, and safe-fly zones. We recruited a total of 18 participants and conducted two indoor laboratory experiments, one with a single drone and the other set with two drones. Our results show that multiple drones cause more discomfort when close to a co-located human than a single drone. We observed that distances below 200 cm caused discomfort, the moderate fly zone was 200 - 300 cm, and the safe-fly zone was any distance greater than 300 cm in single drone experiments. The safe zones were pushed further away by 100 cm for the multiple drone experiments. In this paper, we present the preliminary findings on safe-fly zones for multiple drones. Further work would investigate the impact of a higher number of aerial robots, the speed of approach, direction of travel, and noise level on co-located humans, and autonomously develop 3D models of trust zones and safe zones for co-located aerial swarms. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 485,462 |
1510.01006 | Monitoring Potential Drug Interactions and Reactions via Network
Analysis of Instagram User Timelines | Much recent research aims to identify evidence for Drug-Drug Interactions (DDI) and Adverse Drug reactions (ADR) from the biomedical scientific literature. In addition to this "Bibliome", the universe of social media provides a very promising source of large-scale data that can help identify DDI and ADR in ways that have not been hitherto possible. Given the large number of users, analysis of social media data may be useful to identify under-reported, population-level pathology associated with DDI, thus further contributing to improvements in population health. Moreover, tapping into this data allows us to infer drug interactions with natural products--including cannabis--which constitute an array of DDI very poorly explored by biomedical research thus far. Our goal is to determine the potential of Instagram for public health monitoring and surveillance for DDI, ADR, and behavioral pathology at large. Using drug, symptom, and natural product dictionaries for identification of the various types of DDI and ADR evidence, we have collected ~7000 timelines. We report on 1) the development of a monitoring tool to easily observe user-level timelines associated with drug and symptom terms of interest, and 2) population-level behavior via the analysis of co-occurrence networks computed from user timelines at three different scales: monthly, weekly, and daily occurrences. Analysis of these networks further reveals 3) drug and symptom direct and indirect associations with greater support in user timelines, as well as 4) clusters of symptoms and drugs revealed by the collective behavior of the observed population. This demonstrates that Instagram contains much drug- and pathology specific data for public health monitoring of DDI and ADR, and that complex network analysis provides an important toolbox to extract health-related associations and their support from large-scale social media data. | false | false | false | true | false | true | false | false | false | false | false | false | false | true | false | false | false | false | 47,565 |
1511.00573 | From random walks to distances on unweighted graphs | Large unweighted directed graphs are commonly used to capture relations between entities. A fundamental problem in the analysis of such networks is to properly define the similarity or dissimilarity between any two vertices. Despite the significance of this problem, statistical characterization of the proposed metrics has been limited. We introduce and develop a class of techniques for analyzing random walks on graphs using stochastic calculus. Using these techniques we generalize results on the degeneracy of hitting times and analyze a metric based on the Laplace transformed hitting time (LTHT). The metric serves as a natural, provably well-behaved alternative to the expected hitting time. We establish a general correspondence between hitting times of the Brownian motion and analogous hitting times on the graph. We show that the LTHT is consistent with respect to the underlying metric of a geometric graph, preserves clustering tendency, and remains robust against random addition of non-geometric edges. Tests on simulated and real-world data show that the LTHT matches theoretical predictions and outperforms alternatives. | false | false | false | true | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 48,423 |
2208.09978 | Bayesian Complementary Kernelized Learning for Multidimensional
Spatiotemporal Data | Probabilistic modeling of multidimensional spatiotemporal data is critical to many real-world applications. As real-world spatiotemporal data often exhibits complex dependencies that are nonstationary and nonseparable, developing effective and computationally efficient statistical models to accommodate nonstationary/nonseparable processes containing both long-range and short-scale variations becomes a challenging task, in particular for large-scale datasets with various corruption/missing structures. In this paper, we propose a new statistical framework -- Bayesian Complementary Kernelized Learning (BCKL) -- to achieve scalable probabilistic modeling for multidimensional spatiotemporal data. To effectively characterize complex dependencies, BCKL integrates two complementary approaches -- kernelized low-rank tensor factorization and short-range spatiotemporal Gaussian Processes. Specifically, we use a multi-linear low-rank factorization component to capture the global/long-range correlations in the data and introduce an additive short-scale GP based on compactly supported kernel functions to characterize the remaining local variabilities. We develop an efficient Markov chain Monte Carlo (MCMC) algorithm for model inference and evaluate the proposed BCKL framework on both synthetic and real-world spatiotemporal datasets. Our experiment results show that BCKL offers superior performance in providing accurate posterior mean and high-quality uncertainty estimates, confirming the importance of both global and local components in modeling spatiotemporal data. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 313,905 |
2411.08375 | Developing an Effective Training Dataset to Enhance the Performance of
AI-based Speaker Separation Systems | This paper addresses the challenge of speaker separation, which remains an active research topic despite the promising results achieved in recent years. These results, however, often degrade in real recording conditions due to the presence of noise, echo, and other interferences. This is because neural models are typically trained on synthetic datasets consisting of mixed audio signals and their corresponding ground truths, which are generated using computer software and do not fully represent the complexities of real-world recording scenarios. The lack of realistic training sets for speaker separation remains a major hurdle, as obtaining individual sounds from mixed audio signals is a nontrivial task. To address this issue, we propose a novel method for constructing a realistic training set that includes mixture signals and corresponding ground truths for each speaker. We evaluate this dataset on a deep learning model and compare it to a synthetic dataset. We got a 1.65 dB improvement in Scale Invariant Signal to Distortion Ratio (SI-SDR) for speaker separation accuracy in realistic mixing. Our findings highlight the potential of realistic training sets for enhancing the performance of speaker separation models in real-world scenarios. | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 507,866 |
2207.00975 | Understanding Tieq Viet with Deep Learning Models | Deep learning is a powerful approach in recovering lost information as well as harder inverse function computation problems. When applied in natural language processing, this approach is essentially making use of context as a mean to recover information through likelihood maximization. Not long ago, a linguistic study called Tieq Viet was controversial among both researchers and society. We find this a great example to demonstrate the ability of deep learning models to recover lost information. In the proposal of Tieq Viet, some consonants in the standard Vietnamese are replaced. A sentence written in this proposal can be interpreted into multiple sentences in the standard version, with different meanings. The hypothesis that we want to test is whether a deep learning model can recover the lost information if we translate the text from Vietnamese to Tieq Viet. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 305,983 |
2210.05558 | Causal and Counterfactual Views of Missing Data Models | It is often said that the fundamental problem of causal inference is a missing data problem -- the comparison of responses to two hypothetical treatment assignments is made difficult because for every experimental unit only one potential response is observed. In this paper, we consider the implications of the converse view: that missing data problems are a form of causal inference. We make explicit how the missing data problem of recovering the complete data law from the observed law can be viewed as identification of a joint distribution over counterfactual variables corresponding to values had we (possibly contrary to fact) been able to observe them. Drawing analogies with causal inference, we show how identification assumptions in missing data can be encoded in terms of graphical models defined over counterfactual and observed variables. We review recent results in missing data identification from this viewpoint. In doing so, we note interesting similarities and differences between missing data and causal identification theories. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 322,904 |
2403.18402 | On Spectrogram Analysis in a Multiple Classifier Fusion Framework for
Power Grid Classification Using Electric Network Frequency | The Electric Network Frequency (ENF) serves as a unique signature inherent to power distribution systems. Here, a novel approach for power grid classification is developed, leveraging ENF. Spectrograms are generated from audio and power recordings across different grids, revealing distinctive ENF patterns that aid in grid classification through a fusion of classifiers. Four traditional machine learning classifiers plus a Convolutional Neural Network (CNN), optimized using Neural Architecture Search, are developed for One-vs-All classification. This process generates numerous predictions per sample, which are then compiled and used to train a shallow multi-label neural network specifically designed to model the fusion process, ultimately leading to the conclusive class prediction for each sample. Experimental findings reveal that both validation and testing accuracy outperform those of current state-of-the-art classifiers, underlining the effectiveness and robustness of the proposed methodology. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 441,923 |
1803.08970 | State measurement error-to-state stability results based on approximate
discrete-time models | Digital controller design for nonlinear systems may be complicated by the fact that an exact discrete-time plant model is not known. One existing approach employs approximate discrete-time models for stability analysis and control design, and ensures different types of closedloop stability properties based on the approximate model and on specific bounds on the mismatch between the exact and approximate models. Although existing conditions for practical stability exist, some of which consider the presence of process disturbances, input-to-state stability with respect to state-measurement errors and based on approximate discretetime models has not been addressed. In this paper, we thus extend existing results in two main directions: (a) we provide input-to-state stability (ISS)-related results where the input is the state measurement error and (b) our results allow for some specific varying-sampling-rate scenarios. We provide conditions to ensure semiglobal practical ISS, even under some specific forms of varying sampling rate. These conditions employ Lyapunov-like functions. We illustrate the application of our results on numerical examples, where we show that a bounded state-measurement error can cause a semiglobal practically stable system to diverge. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 93,375 |
2010.05545 | Local Search for Policy Iteration in Continuous Control | We present an algorithm for local, regularized, policy improvement in reinforcement learning (RL) that allows us to formulate model-based and model-free variants in a single framework. Our algorithm can be interpreted as a natural extension of work on KL-regularized RL and introduces a form of tree search for continuous action spaces. We demonstrate that additional computation spent on model-based policy improvement during learning can improve data efficiency, and confirm that model-based policy improvement during action selection can also be beneficial. Quantitatively, our algorithm improves data efficiency on several continuous control benchmarks (when a model is learned in parallel), and it provides significant improvements in wall-clock time in high-dimensional domains (when a ground truth model is available). The unified framework also helps us to better understand the space of model-based and model-free algorithms. In particular, we demonstrate that some benefits attributed to model-based RL can be obtained without a model, simply by utilizing more computation. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 200,180 |
1401.0892 | Optimum Trade-offs Between the Error Exponent and the Excess-Rate
Exponent of Variable-Rate Slepian-Wolf Coding | We analyze the optimal trade-off between the error exponent and the excess-rate exponent for variable-rate Slepian-Wolf codes. In particular, we first derive upper (converse) bounds on the optimal error and excess-rate exponents, and then lower (achievable) bounds, via a simple class of variable-rate codes which assign the same rate to all source blocks of the same type class. Then, using the exponent bounds, we derive bounds on the optimal rate functions, namely, the minimal rate assigned to each type class, needed in order to achieve a given target error exponent. The resulting excess-rate exponent is then evaluated. Iterative algorithms are provided for the computation of both bounds on the optimal rate functions and their excess-rate exponents. The resulting Slepian-Wolf codes bridge between the two extremes of fixed-rate coding, which has minimal error exponent and maximal excess-rate exponent, and average-rate coding, which has maximal error exponent and minimal excess-rate exponent. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 29,605 |
2311.06009 | Polar-Net: A Clinical-Friendly Model for Alzheimer's Disease Detection
in OCTA Images | Optical Coherence Tomography Angiography (OCTA) is a promising tool for detecting Alzheimer's disease (AD) by imaging the retinal microvasculature. Ophthalmologists commonly use region-based analysis, such as the ETDRS grid, to study OCTA image biomarkers and understand the correlation with AD. However, existing studies have used general deep computer vision methods, which present challenges in providing interpretable results and leveraging clinical prior knowledge. To address these challenges, we propose a novel deep-learning framework called Polar-Net. Our approach involves mapping OCTA images from Cartesian coordinates to polar coordinates, which allows for the use of approximate sector convolution and enables the implementation of the ETDRS grid-based regional analysis method commonly used in clinical practice. Furthermore, Polar-Net incorporates clinical prior information of each sector region into the training process, which further enhances its performance. Additionally, our framework adapts to acquire the importance of the corresponding retinal region, which helps researchers and clinicians understand the model's decision-making process in detecting AD and assess its conformity to clinical observations. Through evaluations on private and public datasets, we have demonstrated that Polar-Net outperforms existing state-of-the-art methods and provides more valuable pathological evidence for the association between retinal vascular changes and AD. In addition, we also show that the two innovative modules introduced in our framework have a significant impact on improving overall performance. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 406,786 |
1809.10491 | On the Regret Minimization of Nonconvex Online Gradient Ascent for
Online PCA | In this paper we focus on the problem of Online Principal Component Analysis in the regret minimization framework. For this problem, all existing regret minimization algorithms for the fully-adversarial setting are based on a positive semidefinite convex relaxation, and hence require quadratic memory and SVD computation (either thin of full) on each iteration, which amounts to at least quadratic runtime per iteration. This is in stark contrast to a corresponding stochastic i.i.d. variant of the problem, which was studied extensively lately, and admits very efficient gradient ascent algorithms that work directly on the natural non-convex formulation of the problem, and hence require only linear memory and linear runtime per iteration. This raises the question: can non-convex online gradient ascent algorithms be shown to minimize regret in online adversarial settings? In this paper we take a step forward towards answering this question. We introduce an \textit{adversarially-perturbed spiked-covariance model} in which, each data point is assumed to follow a fixed stochastic distribution with a non-zero spectral gap in the covariance matrix, but is then perturbed with some adversarial vector. This model is a natural extension of a well studied standard stochastic setting that allows for non-stationary (adversarial) patterns to arise in the data and hence, might serve as a significantly better approximation for real-world data-streams. We show that in an interesting regime of parameters, when the non-convex online gradient ascent algorithm is initialized with a "warm-start" vector, it provably minimizes the regret with high probability. We further discuss the possibility of computing such a "warm-start" vector, and also the use of regularization to obtain fast regret rates. Our theoretical findings are supported by empirical experiments on both synthetic and real-world data. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 108,924 |
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