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2107.04422 | Policy Gradient Methods for Distortion Risk Measures | We propose policy gradient algorithms which learn risk-sensitive policies in a reinforcement learning (RL) framework. Our proposed algorithms maximize the distortion risk measure (DRM) of the cumulative reward in an episodic Markov decision process in on-policy and off-policy RL settings, respectively. We derive a variant of the policy gradient theorem that caters to the DRM objective, and integrate it with a likelihood ratio-based gradient estimation scheme. We derive non-asymptotic bounds that establish the convergence of our proposed algorithms to an approximate stationary point of the DRM objective. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 245,456 |
2405.16191 | Rocket Landing Control with Grid Fins and Path-following using MPC | In this project, we attempt to optimize a landing trajectory of a rocket. The goal is to minimize the total fuel consumption during the landing process using different techniques. Once the optimal and feasible trajectory is generated using batch approach, we attempt to follow the path using a Model Predictive Control (MPC) based algorithm, called Trajectory Optimizing Path following Estimation from Demonstration (TOPED), in order to generalize to similar initial states and models, where we introduce a novel cost function for the MPC to solve. We further show that TOPED can follow a demonstration trajectory well in practice under model mismatch and different initial states. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 457,301 |
1204.4347 | Change-Of-Bases Abstractions for Non-Linear Systems | We present abstraction techniques that transform a given non-linear dynamical system into a linear system or an algebraic system described by polynomials of bounded degree, such that, invariant properties of the resulting abstraction can be used to infer invariants for the original system. The abstraction techniques rely on a change-of-basis transformation that associates each state variable of the abstract system with a function involving the state variables of the original system. We present conditions under which a given change of basis transformation for a non-linear system can define an abstraction. Furthermore, the techniques developed here apply to continuous systems defined by Ordinary Differential Equations (ODEs), discrete systems defined by transition systems and hybrid systems that combine continuous as well as discrete subsystems. The techniques presented here allow us to discover, given a non-linear system, if a change of bases transformation involving degree-bounded polynomials yielding an algebraic abstraction exists. If so, our technique yields the resulting abstract system, as well. This approach is further extended to search for a change of bases transformation that abstracts a given non-linear system into a system of linear differential inclusions. Our techniques enable the use of analysis techniques for linear systems to infer invariants for non-linear systems. We present preliminary evidence of the practical feasibility of our ideas using a prototype implementation. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | true | 15,588 |
2110.12403 | Learning to Estimate Without Bias | The Gauss Markov theorem states that the weighted least squares estimator is a linear minimum variance unbiased estimation (MVUE) in linear models. In this paper, we take a first step towards extending this result to non linear settings via deep learning with bias constraints. The classical approach to designing non-linear MVUEs is through maximum likelihood estimation (MLE) which often involves computationally challenging optimizations. On the other hand, deep learning methods allow for non-linear estimators with fixed computational complexity. Learning based estimators perform optimally on average with respect to their training set but may suffer from significant bias in other parameters. To avoid this, we propose to add a simple bias constraint to the loss function, resulting in an estimator we refer to as Bias Constrained Estimator (BCE). We prove that this yields asymptotic MVUEs that behave similarly to the classical MLEs and asymptotically attain the Cramer Rao bound. We demonstrate the advantages of our approach in the context of signal to noise ratio estimation as well as covariance estimation. A second motivation to BCE is in applications where multiple estimates of the same unknown are averaged for improved performance. Examples include distributed sensor networks and data augmentation in test-time. In such applications, we show that BCE leads to asymptotically consistent estimators. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 262,838 |
2203.03872 | Visual anomaly detection in video by variational autoencoder | Video anomalies detection is the intersection of anomaly detection and visual intelligence. It has commercial applications in surveillance, security, self-driving cars and crop monitoring. Videos can capture a variety of anomalies. Due to efforts needed to label training data, unsupervised approaches to train anomaly detection models for videos is more practical An autoencoder is a neural network that is trained to recreate its input using latent representation of input also called a bottleneck layer. Variational autoencoder uses distribution (mean and variance) as compared to latent vector as bottleneck layer and can have better regularization effect. In this paper we have demonstrated comparison between performance of convolutional LSTM versus a variation convolutional LSTM autoencoder | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | false | false | 284,257 |
2412.00176 | Art-Free Generative Models: Art Creation Without Graphic Art Knowledge | We explore the question: "How much prior art knowledge is needed to create art?" To investigate this, we propose a text-to-image generation model trained without access to art-related content. We then introduce a simple yet effective method to learn an art adapter using only a few examples of selected artistic styles. Our experiments show that art generated using our method is perceived by users as comparable to art produced by models trained on large, art-rich datasets. Finally, through data attribution techniques, we illustrate how examples from both artistic and non-artistic datasets contributed to the creation of new artistic styles. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 512,552 |
2208.09734 | A Multi-Head Model for Continual Learning via Out-of-Distribution Replay | This paper studies class incremental learning (CIL) of continual learning (CL). Many approaches have been proposed to deal with catastrophic forgetting (CF) in CIL. Most methods incrementally construct a single classifier for all classes of all tasks in a single head network. To prevent CF, a popular approach is to memorize a small number of samples from previous tasks and replay them during training of the new task. However, this approach still suffers from serious CF as the parameters learned for previous tasks are updated or adjusted with only the limited number of saved samples in the memory. This paper proposes an entirely different approach that builds a separate classifier (head) for each task (called a multi-head model) using a transformer network, called MORE. Instead of using the saved samples in memory to update the network for previous tasks/classes in the existing approach, MORE leverages the saved samples to build a task specific classifier (adding a new classification head) without updating the network learned for previous tasks/classes. The model for the new task in MORE is trained to learn the classes of the task and also to detect samples that are not from the same data distribution (i.e., out-of-distribution (OOD)) of the task. This enables the classifier for the task to which the test instance belongs to produce a high score for the correct class and the classifiers of other tasks to produce low scores because the test instance is not from the data distributions of these classifiers. Experimental results show that MORE outperforms state-of-the-art baselines and is also naturally capable of performing OOD detection in the continual learning setting. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 313,816 |
2501.06923 | Optimal Online Bookmaking for Binary Games | In online betting, the bookmaker can update the payoffs it offers on a particular event many times before the event takes place, and the updated payoffs may depend on the bets accumulated thus far. We study the problem of bookmaking with the goal of maximizing the return in the worst-case, with respect to the gamblers' behavior and the event's outcome. We formalize this problem as the \emph{Optimal Online Bookmaking game}, and provide the exact solution for the binary case. To this end, we develop the optimal bookmaking strategy, which relies on a new technique called bi-balancing trees, that assures that the house loss is the same for all \emph{decisive} betting sequences, where the gambler bets all its money on a single outcome in each round. | false | false | false | false | false | false | true | false | false | true | false | false | false | false | false | false | false | true | 524,186 |
2305.11381 | Online Learning in a Creator Economy | The creator economy has revolutionized the way individuals can profit through online platforms. In this paper, we initiate the study of online learning in the creator economy by modeling the creator economy as a three-party game between the users, platform, and content creators, with the platform interacting with the content creator under a principal-agent model through contracts to encourage better content. Additionally, the platform interacts with the users to recommend new content, receive an evaluation, and ultimately profit from the content, which can be modeled as a recommender system. Our study aims to explore how the platform can jointly optimize the contract and recommender system to maximize the utility in an online learning fashion. We primarily analyze and compare two families of contracts: return-based contracts and feature-based contracts. Return-based contracts pay the content creator a fraction of the reward the platform gains. In contrast, feature-based contracts pay the content creator based on the quality or features of the content, regardless of the reward the platform receives. We show that under smoothness assumptions, the joint optimization of return-based contracts and recommendation policy provides a regret $\Theta(T^{2/3})$. For the feature-based contract, we introduce a definition of intrinsic dimension $d$ to characterize the hardness of learning the contract and provide an upper bound on the regret $\mathcal{O}(T^{(d+1)/(d+2)})$. The upper bound is tight for the linear family. | false | false | false | false | false | true | true | false | false | false | false | false | false | true | false | false | false | true | 365,500 |
1806.05938 | Query K-means Clustering and the Double Dixie Cup Problem | We consider the problem of approximate $K$-means clustering with outliers and side information provided by same-cluster queries and possibly noisy answers. Our solution shows that, under some mild assumptions on the smallest cluster size, one can obtain an $(1+\epsilon)$-approximation for the optimal potential with probability at least $1-\delta$, where $\epsilon>0$ and $\delta\in(0,1)$, using an expected number of $O(\frac{K^3}{\epsilon \delta})$ noiseless same-cluster queries and comparison-based clustering of complexity $O(ndK + \frac{K^3}{\epsilon \delta})$, here, $n$ denotes the number of points and $d$ the dimension of space. Compared to a handful of other known approaches that perform importance sampling to account for small cluster sizes, the proposed query technique reduces the number of queries by a factor of roughly $O(\frac{K^6}{\epsilon^3})$, at the cost of possibly missing very small clusters. We extend this settings to the case where some queries to the oracle produce erroneous information, and where certain points, termed outliers, do not belong to any clusters. Our proof techniques differ from previous methods used for $K$-means clustering analysis, as they rely on estimating the sizes of the clusters and the number of points needed for accurate centroid estimation and subsequent nontrivial generalizations of the double Dixie cup problem. We illustrate the performance of the proposed algorithm both on synthetic and real datasets, including MNIST and CIFAR $10$. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 100,588 |
1911.05649 | Air-Writing Translater: A Novel Unsupervised Domain Adaptation Method
for Inertia-Trajectory Translation of In-air Handwriting | As a new way of human-computer interaction, inertial sensor based in-air handwriting can provide a natural and unconstrained interaction to express more complex and richer information in 3D space. However, most of the existing in-air handwriting work is mainly focused on handwritten character recognition, which makes these work suffer from poor readability of inertial signal and lack of labeled samples. To address these two problems, we use unsupervised domain adaptation method to reconstruct the trajectory of inertial signal and generate inertial samples using online handwritten trajectories. In this paper, we propose an AirWriting Translater model to learn the bi-directional translation between trajectory domain and inertial domain in the absence of paired inertial and trajectory samples. Through semantic-level adversarial training and latent classification loss, the proposed model learns to extract domain-invariant content between inertial signal and trajectory, while preserving semantic consistency during the translation across the two domains. We carefully design the architecture, so that the proposed framework can accept inputs of arbitrary length and translate between different sampling rates. We also conduct experiments on two public datasets: 6DMG (in-air handwriting dataset) and CT (handwritten trajectory dataset), the results on the two datasets demonstrate that the proposed network successes in both Inertia-to Trajectory and Trajectory-to-Inertia translation tasks. | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | false | false | 153,329 |
2410.00030 | AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression
with Minimal Impact on Traffic Classification | Network monitoring generates massive volumes of IP flow records, posing significant challenges for storage and analysis. This paper presents a novel deep learning-based approach to compressing these records using autoencoders, enabling direct analysis of compressed data without requiring decompression. Unlike traditional compression methods, our approach reduces data volume while retaining the utility of compressed data for downstream analysis tasks, including distinguishing modern application protocols and encrypted traffic from popular services. Through extensive experiments on a real-world network traffic dataset, we demonstrate that our autoencoder-based compression achieves a 1.313x reduction in data size while maintaining 99.27% accuracy in a multi-class traffic classification task, compared to 99.77% accuracy with uncompressed data. This marginal decrease in performance is offset by substantial gains in storage and processing efficiency. The implications of this work extend to more efficient network monitoring and scalable, real-time network management solutions. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 493,197 |
2301.09783 | Several families of ternary negacyclic codes and their duals | Constacyclic codes contain cyclic codes as a subclass and have nice algebraic structures. Constacyclic codes have theoretical importance, as they are connected to a number of areas of mathematics and outperform cyclic codes in several aspects. Negacyclic codes are a subclass of constacyclic codes and are distance-optimal in many cases. However, compared with the extensive study of cyclic codes, negacyclic codes are much less studied. In this paper, several families of ternary negacyclic codes and their duals are constructed and analysed. These families of negacyclic codes and their duals contain distance-optimal codes and have very good parameters in general. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 341,603 |
2105.04181 | KDExplainer: A Task-oriented Attention Model for Explaining Knowledge
Distillation | Knowledge distillation (KD) has recently emerged as an efficacious scheme for learning compact deep neural networks (DNNs). Despite the promising results achieved, the rationale that interprets the behavior of KD has yet remained largely understudied. In this paper, we introduce a novel task-oriented attention model, termed as KDExplainer, to shed light on the working mechanism underlying the vanilla KD. At the heart of KDExplainer is a Hierarchical Mixture of Experts (HME), in which a multi-class classification is reformulated as a multi-task binary one. Through distilling knowledge from a free-form pre-trained DNN to KDExplainer, we observe that KD implicitly modulates the knowledge conflicts between different subtasks, and in reality has much more to offer than label smoothing. Based on such findings, we further introduce a portable tool, dubbed as virtual attention module (VAM), that can be seamlessly integrated with various DNNs to enhance their performance under KD. Experimental results demonstrate that with a negligible additional cost, student models equipped with VAM consistently outperform their non-VAM counterparts across different benchmarks. Furthermore, when combined with other KD methods, VAM remains competent in promoting results, even though it is only motivated by vanilla KD. The code is available at https://github.com/zju-vipa/KDExplainer. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 234,417 |
2404.10641 | A Cloud Resources Portfolio Optimization Business Model -- From Theory
to Practice | Cloud resources have become increasingly important, with many businesses using cloud solutions to supplement or outright replace their existing IT infrastructure. However, as there is a plethora of providers with varying products, services, and markets, it has become increasingly more challenging to keep track of the best solutions for each application. Cloud service intermediaries aim to alleviate this problem by offering services that help users meet their requirements. This paper aims to lay the groundwork for developing a cloud portfolio management platform and its business model, defined via a business model canvas. Furthermore, a prototype of a platform is developed offering a cloud portfolio optimization service, using two algorithms developed in previous research to create suitable and well-utilized allocations for a customer's applications. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | true | 447,185 |
1301.2293 | Aggregating Learned Probabilistic Beliefs | We consider the task of aggregating beliefs of severalexperts. We assume that these beliefs are represented as probabilitydistributions. We argue that the evaluation of any aggregationtechnique depends on the semantic context of this task. We propose aframework, in which we assume that nature generates samples from a`true' distribution and different experts form their beliefs based onthe subsets of the data they have a chance to observe. Naturally, theideal aggregate distribution would be the one learned from thecombined sample sets. Such a formulation leads to a natural way tomeasure the accuracy of the aggregation mechanism.We show that the well-known aggregation operator LinOP is ideallysuited for that task. We propose a LinOP-based learning algorithm,inspired by the techniques developed for Bayesian learning, whichaggregates the experts' distributions represented as Bayesiannetworks. Our preliminary experiments show that this algorithmperforms well in practice. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 20,968 |
2109.09791 | Prediction of severe thunderstorm events with ensemble deep learning and
radar data | The problem of nowcasting extreme weather events can be addressed by applying either numerical methods for the solution of dynamic model equations or data-driven artificial intelligence algorithms. Within this latter framework, the present paper illustrates how a deep learning method, exploiting videos of radar reflectivity frames as input, can be used to realize a warning machine able to sound timely alarms of possible severe thunderstorm events. From a technical viewpoint, the computational core of this approach is the use of a value-weighted skill score for both transforming the probabilistic outcomes of the deep neural network into binary classification and assessing the forecasting performances. The warning machine has been validated against weather radar data recorded in the Liguria region, in Italy, | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 256,392 |
2003.03913 | FarSee-Net: Real-Time Semantic Segmentation by Efficient Multi-scale
Context Aggregation and Feature Space Super-resolution | Real-time semantic segmentation is desirable in many robotic applications with limited computation resources. One challenge of semantic segmentation is to deal with the object scale variations and leverage the context. How to perform multi-scale context aggregation within limited computation budget is important. In this paper, firstly, we introduce a novel and efficient module called Cascaded Factorized Atrous Spatial Pyramid Pooling (CF-ASPP). It is a lightweight cascaded structure for Convolutional Neural Networks (CNNs) to efficiently leverage context information. On the other hand, for runtime efficiency, state-of-the-art methods will quickly decrease the spatial size of the inputs or feature maps in the early network stages. The final high-resolution result is usually obtained by non-parametric up-sampling operation (e.g. bilinear interpolation). Differently, we rethink this pipeline and treat it as a super-resolution process. We use optimized super-resolution operation in the up-sampling step and improve the accuracy, especially in sub-sampled input image scenario for real-time applications. By fusing the above two improvements, our methods provide better latency-accuracy trade-off than the other state-of-the-art methods. In particular, we achieve 68.4% mIoU at 84 fps on the Cityscapes test set with a single Nivida Titan X (Maxwell) GPU card. The proposed module can be plugged into any feature extraction CNN and benefits from the CNN structure development. | false | false | false | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | 167,403 |
2209.04696 | Cooperation and Competition: Flocking with Evolutionary Multi-Agent
Reinforcement Learning | Flocking is a very challenging problem in a multi-agent system; traditional flocking methods also require complete knowledge of the environment and a precise model for control. In this paper, we propose Evolutionary Multi-Agent Reinforcement Learning (EMARL) in flocking tasks, a hybrid algorithm that combines cooperation and competition with little prior knowledge. As for cooperation, we design the agents' reward for flocking tasks according to the boids model. While for competition, agents with high fitness are designed as senior agents, and those with low fitness are designed as junior, letting junior agents inherit the parameters of senior agents stochastically. To intensify competition, we also design an evolutionary selection mechanism that shows effectiveness on credit assignment in flocking tasks. Experimental results in a range of challenging and self-contrast benchmarks demonstrate that EMARL significantly outperforms the full competition or cooperation methods. | false | false | false | false | true | false | false | true | false | false | false | false | false | false | true | false | false | false | 316,855 |
1312.3543 | Optimal Distributed Control for Networked Control Systems with Delays | In networked control systems (NCS), sensing and control signals between the plant and controllers are typically transmitted wirelessly. Thus, the time delay plays an important role for the stability of NCS, especially with distributed controllers. In this paper, the optimal control strategy is derived for distributed control networks with time delays. In particular, we form the optimal control problem as a non-cooperative linear quadratic game (LQG). Then, the optimal control strategy of each controller is obtained that is based on the current state and the last control strategies. The proposed optimal distributed controller reduces to some known controllers under certain conditions. Moreover, we illustrate the application of the proposed distributed controller to load frequency control in power grid systems. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | true | 29,048 |
2412.03735 | VidHalluc: Evaluating Temporal Hallucinations in Multimodal Large
Language Models for Video Understanding | Multimodal large language models (MLLMs) have recently shown significant advancements in video understanding, excelling in content reasoning and instruction-following tasks. However, the problem of hallucination, where models generate inaccurate or misleading content, remains underexplored in the video domain. Building on the observation that the visual encoder of MLLMs often struggles to differentiate between video pairs that are visually distinct but semantically similar, we introduce VidHalluc, the largest benchmark designed to examine hallucinations in MLLMs for video understanding tasks. VidHalluc assesses hallucinations across three critical dimensions: (1) action, (2) temporal sequence, and (3) scene transition. VidHalluc consists of 5,002 videos, paired based on semantic similarity and visual differences, focusing on cases where hallucinations are most likely to occur. Through comprehensive testing, our experiments show that most MLLMs are vulnerable to hallucinations across these dimensions. Furthermore, we propose DINO-HEAL, a training-free method that reduces hallucinations by incorporating spatial saliency information from DINOv2 to reweight visual features during inference. Our results demonstrate that DINO-HEAL consistently improves performance on VidHalluc, achieving an average improvement of 3.02% in mitigating hallucinations among all tasks. Both the VidHalluc benchmark and DINO-HEAL code can be accessed via $\href{https://vid-halluc.github.io/}{\text{this link}}$. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 514,080 |
1808.09658 | APRIL: Interactively Learning to Summarise by Combining Active
Preference Learning and Reinforcement Learning | We propose a method to perform automatic document summarisation without using reference summaries. Instead, our method interactively learns from users' preferences. The merit of preference-based interactive summarisation is that preferences are easier for users to provide than reference summaries. Existing preference-based interactive learning methods suffer from high sample complexity, i.e. they need to interact with the oracle for many rounds in order to converge. In this work, we propose a new objective function, which enables us to leverage active learning, preference learning and reinforcement learning techniques in order to reduce the sample complexity. Both simulation and real-user experiments suggest that our method significantly advances the state of the art. Our source code is freely available at https://github.com/UKPLab/emnlp2018-april. | false | false | false | false | true | false | true | false | true | false | false | false | false | false | false | false | false | false | 106,242 |
1501.01372 | Weighted Schatten $p$-Norm Minimization for Image Denoising with Local
and Nonlocal Regularization | This paper presents a patch-wise low-rank based image denoising method with constrained variational model involving local and nonlocal regularization. On one hand, recent patch-wise methods can be represented as a low-rank matrix approximation problem whose convex relaxation usually depends on nuclear norm minimization (NNM). Here, we extend the NNM to the nonconvex schatten p-norm minimization with additional weights assigned to different singular values, which is referred to as the Weighted Schatten p-Norm Minimization (WSNM). An efficient algorithm is also proposed to solve the WSNM problem. The proposed WSNM not only gives better approximation to the original low-rank assumption, but also considers physical meanings of different data components. On the other hand, due to the naive aggregation schema which integrates all the denoised patches into a whole image, current patch-wise denoising methods always produce various degree of artifacts in denoised results. Therefore, to further reduce artifacts, a data-driven regularizer called Steering Total Variation (STV) combined with nonlocal TV is derived for a variational model, which imposes local and nonlocal consistency constraints on the patch-wise denoised image. A highly simple but efficient algorithm is proposed to solve this variational model with convergence guarantee. Both WSNM and local \& nonlocal consistent regularization are integrated into an iterative restoration framework to produce final results. Extensive experimental testing shows, both qualitatively and quantitatively, that the proposed method can effectively remove noise, as well as reduce artifacts compared with state-of-the-art methods. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 39,080 |
2412.05232 | LIAR: Leveraging Inference Time Alignment (Best-of-N) to Jailbreak LLMs
in Seconds | Traditional jailbreaks have successfully exposed vulnerabilities in LLMs, primarily relying on discrete combinatorial optimization, while more recent methods focus on training LLMs to generate adversarial prompts. However, both approaches are computationally expensive and slow, often requiring significant resources to generate a single successful attack. We hypothesize that the inefficiency of these methods arises from an inadequate characterization of the jailbreak problem itself. To address this gap, we approach the jailbreak problem as an alignment problem, leading us to propose LIAR (Leveraging Inference time Alignment to jailbReak), a fast and efficient best-of-N approach tailored for jailbreak attacks. LIAR offers several key advantages: it eliminates the need for additional training, operates in a fully black-box setting, significantly reduces computational overhead, and produces more human-readable adversarial prompts while maintaining competitive attack success rates. Our results demonstrate that a best-of-N approach is a simple yet highly effective strategy for evaluating the robustness of aligned LLMs, achieving attack success rates (ASR) comparable to state-of-the-art methods while offering a 10x improvement in perplexity and a significant speedup in Time-to-Attack, reducing execution time from tens of hours to seconds. Additionally, We also provide sub-optimality guarantees for the proposed LIAR. Our work highlights the potential of efficient, alignment-based jailbreak strategies for assessing and stress-testing AI safety measures. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 514,743 |
1912.13007 | World Programs for Model-Based Learning and Planning in Compositional
State and Action Spaces | Some of the most important tasks take place in environments which lack cheap and perfect simulators, thus hampering the application of model-free reinforcement learning (RL). While model-based RL aims to learn a dynamics model, in a more general case the learner does not know a priori what the action space is. Here we propose a formalism where the learner induces a world program by learning a dynamics model and the actions in graph-based compositional environments by observing state-state transition examples. Then, the learner can perform RL with the world program as the simulator for complex planning tasks. We highlight a recent application, and propose a challenge for the community to assess world program-based planning. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 158,988 |
2009.03836 | Graph neural networks-based Scheduler for Production planning problems
using Reinforcement Learning | Reinforcement learning (RL) is increasingly adopted in job shop scheduling problems (JSSP). But RL for JSSP is usually done using a vectorized representation of machine features as the state space. It has three major problems: (1) the relationship between the machine units and the job sequence is not fully captured, (2) exponential increase in the size of the state space with increasing machines/jobs, and (3) the generalization of the agent to unseen scenarios. We present a novel framework - GraSP-RL, GRAph neural network-based Scheduler for Production planning problems using Reinforcement Learning. It represents JSSP as a graph and trains the RL agent using features extracted using a graph neural network (GNN). While the graph is itself in the non-euclidean space, the features extracted using the GNNs provide a rich encoding of the current production state in the euclidean space, which is then used by the RL agent to select the next job. Further, we cast the scheduling problem as a decentralized optimization problem in which the learning agent is assigned to all the production units and the agent learns asynchronously from the data collected on all the production units. The GraSP-RL is then applied to a complex injection molding production environment with 30 jobs and 4 machines. The task is to minimize the makespan of the production plan. The schedule planned by GraSP-RL is then compared and analyzed with a priority dispatch rule algorithm like first-in-first-out (FIFO) and metaheuristics like tabu search (TS) and genetic algorithm (GA). The proposed GraSP-RL outperforms the FIFO, TS, and GA for the trained task of planning 30 jobs in JSSP. We further test the generalization capability of the trained agent on two different problem classes: Open shop system (OSS) and Reactive JSSP (RJSSP) where our method produces results better than FIFO and comparable results to TS and GA. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 194,904 |
1902.09525 | Fast Decoder for Overloaded Uniquely Decodable Synchronous Optical CDMA | In this paper, we propose a fast decoder algorithm for uniquely decodable (errorless) code sets for overloaded synchronous optical code-division multiple-access (O-CDMA) systems. The proposed decoder is designed in a such a way that the users can uniquely recover the information bits with a very simple decoder, which uses only a few comparisons. Compared to maximum-likelihood (ML) decoder, which has a high computational complexity for even moderate code lengths, the proposed decoder has much lower computational complexity. Simulation results in terms of bit error rate (BER) demonstrate that the performance of the proposed decoder for a given BER requires only 1-2 dB higher signal-to-noise ratio (SNR) than the ML decoder. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 122,431 |
1507.00567 | Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge
Evolution | Cloud controllers aim at responding to application demands by automatically scaling the compute resources at runtime to meet performance guarantees and minimize resource costs. Existing cloud controllers often resort to scaling strategies that are codified as a set of adaptation rules. However, for a cloud provider, applications running on top of the cloud infrastructure are more or less black-boxes, making it difficult at design time to define optimal or pre-emptive adaptation rules. Thus, the burden of taking adaptation decisions often is delegated to the cloud application. Yet, in most cases, application developers in turn have limited knowledge of the cloud infrastructure. In this paper, we propose learning adaptation rules during runtime. To this end, we introduce FQL4KE, a self-learning fuzzy cloud controller. In particular, FQL4KE learns and modifies fuzzy rules at runtime. The benefit is that for designing cloud controllers, we do not have to rely solely on precise design-time knowledge, which may be difficult to acquire. FQL4KE empowers users to specify cloud controllers by simply adjusting weights representing priorities in system goals instead of specifying complex adaptation rules. The applicability of FQL4KE has been experimentally assessed as part of the cloud application framework ElasticBench. The experimental results indicate that FQL4KE outperforms our previously developed fuzzy controller without learning mechanisms and the native Azure auto-scaling. | false | false | false | false | true | false | true | false | false | false | true | false | false | false | false | false | false | true | 44,772 |
1910.02035 | Manufacturing Dispatching using Reinforcement and Transfer Learning | Efficient dispatching rule in manufacturing industry is key to ensure product on-time delivery and minimum past-due and inventory cost. Manufacturing, especially in the developed world, is moving towards on-demand manufacturing meaning a high mix, low volume product mix. This requires efficient dispatching that can work in dynamic and stochastic environments, meaning it allows for quick response to new orders received and can work over a disparate set of shop floor settings. In this paper we address this problem of dispatching in manufacturing. Using reinforcement learning (RL), we propose a new design to formulate the shop floor state as a 2-D matrix, incorporate job slack time into state representation, and design lateness and tardiness rewards function for dispatching purpose. However, maintaining a separate RL model for each production line on a manufacturing shop floor is costly and often infeasible. To address this, we enhance our deep RL model with an approach for dispatching policy transfer. This increases policy generalization and saves time and cost for model training and data collection. Experiments show that: (1) our approach performs the best in terms of total discounted reward and average lateness, tardiness, (2) the proposed policy transfer approach reduces training time and increases policy generalization. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 148,112 |
2109.10385 | Learning to Guide Human Attention on Mobile Telepresence Robots with 360
Vision | Mobile telepresence robots (MTRs) allow people to navigate and interact with a remote environment that is in a place other than the person's true location. Thanks to the recent advances in 360 degree vision, many MTRs are now equipped with an all-degree visual perception capability. However, people's visual field horizontally spans only about 120 degree of the visual field captured by the robot. To bridge this observability gap toward human-MTR shared autonomy, we have developed a framework, called GHAL360, to enable the MTR to learn a goal-oriented policy from reinforcements for guiding human attention using visual indicators. Three telepresence environments were constructed using datasets that are extracted from Matterport3D and collected from a real robot respectively. Experimental results show that GHAL360 outperformed the baselines from the literature in the efficiency of a human-MTR team completing target search tasks. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 256,587 |
2410.10735 | Embedding Self-Correction as an Inherent Ability in Large Language
Models for Enhanced Mathematical Reasoning | Accurate mathematical reasoning with Large Language Models (LLMs) is crucial in revolutionizing domains that heavily rely on such reasoning. However, LLMs often encounter difficulties in certain aspects of mathematical reasoning, leading to flawed reasoning and erroneous results. To mitigate these issues, we introduce a novel mechanism, the Chain of Self-Correction (CoSC), specifically designed to embed self-correction as an inherent ability in LLMs, enabling them to validate and rectify their own results. The CoSC mechanism operates through a sequence of self-correction stages. In each stage, the LLMs generate a program to address a given problem, execute this program using program-based tools to obtain an output, subsequently verify this output. Based on the verification, the LLMs either proceed to the next correction stage or finalize the answer. This iterative self-correction process allows the LLMs to refine its reasoning steps and improve the accuracy of its mathematical reasoning. We implement CoSC using a two-phase fine-tuning approach. First, LLMs are trained with a relatively small volume of seeding data generated from GPT-4. Then, we enhance CoSC by training with a larger volume of self-generated data, without relying on GPT-4. Experiments show that CoSC significantly boosts performance on standard mathematical datasets compared to existing open-source LLMs. Notably, our CoSC-Code-34B model achieved a 53.5% score on the challenging MATH dataset, outperforming models like ChatGPT, GPT-4, and multi-modal LLMs such as GPT-4V and Gemini-1.0. Importantly, CoSC operates in a zero-shot manner without requiring demonstrations. | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 498,204 |
cs/0511028 | MIMO Diversity in the Presence of Double Scattering | The potential benefits of multiple-antenna systems may be limited by two types of channel degradations rank deficiency and spatial fading correlation of the channel. In this paper, we assess the effects of these degradations on the diversity performance of multiple-input multiple-output (MIMO) systems, with an emphasis on orthogonal space-time block codes, in terms of the symbol error probability, the effective fading figure (EFF), and the capacity at low signal-to-noise ratio (SNR). In particular, we consider a general family of MIMO channels known as double-scattering channels, which encompasses a variety of propagation environments from independent and identically distributed Rayleigh to degenerate keyhole or pinhole cases by embracing both rank-deficient and spatial correlation effects. It is shown that a MIMO system with $n_T$ transmit and $n_R$ receive antennas achieves the diversity of order $\frac{\n_T n_S n_R}{\max(n_T,n_S,n_R)}$ in a double-scattering channel with $n_S$ effective scatterers. We also quantify the combined effect of the spatial correlation and the lack of scattering richness on the EFF and the low-SNR capacity in terms of the correlation figures of transmit, receive, and scatterer correlation matrices. We further show the monotonicity properties of these performance measures with respect to the strength of spatial correlation, characterized by the eigenvalue majorization relations of the correlation matrices. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 539,066 |
1901.01091 | Adaptive Density Estimation for Generative Models | Unsupervised learning of generative models has seen tremendous progress over recent years, in particular due to generative adversarial networks (GANs), variational autoencoders, and flow-based models. GANs have dramatically improved sample quality, but suffer from two drawbacks: (i) they mode-drop, i.e., do not cover the full support of the train data, and (ii) they do not allow for likelihood evaluations on held-out data. In contrast, likelihood-based training encourages models to cover the full support of the train data, but yields poorer samples. These mutual shortcomings can in principle be addressed by training generative latent variable models in a hybrid adversarial-likelihood manner. However, we show that commonly made parametric assumptions create a conflict between them, making successful hybrid models non trivial. As a solution, we propose to use deep invertible transformations in the latent variable decoder. This approach allows for likelihood computations in image space, is more efficient than fully invertible models, and can take full advantage of adversarial training. We show that our model significantly improves over existing hybrid models: offering GAN-like samples, IS and FID scores that are competitive with fully adversarial models, and improved likelihood scores. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 117,919 |
2110.05882 | Concepts of Self-maintaining Robots and Their Design | This paper proposes an initial theory for robotic systems that can be fully self-maintaining. The new design principles focus on functional survival of the robots over long periods of time without human maintenance. Self-maintaining semi-autonomous mobile robots are in great demand in nuclear disposal sites from where their removal for maintenance is undesirable due to their radioactive contamination. Similar are requirements for robots in various defence tasks or space missions. For optimal design, modular solutions are balanced against capabilities to replace smaller components in a robot by itself or by help from another robot. Modules are proposed for the basic platform, which enable self-maintenance within a team of robots helping each other. The primary method of self-maintenance is replacement of malfunctioning modules or components by the robots themselves. Replacement necessitates a robot team's ability to diagnose and replace malfunctioning modules as needed. Due to their design, these robots still remain manually re-configurable if opportunity arises for human intervention. Apart from the basic principles, an evolutionary design approach is presented and a first mathematical theory of the reliability of a team of self-maintaining robots is introduced. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 260,440 |
1810.03988 | GPU based Parallel Optimization for Real Time Panoramic Video Stitching | Panoramic video is a sort of video recorded at the same point of view to record the full scene. With the development of video surveillance and the requirement for 3D converged video surveillance in smart cities, CPU and GPU are required to possess strong processing abilities to make panoramic video. The traditional panoramic products depend on post processing, which results in high power consumption, low stability and unsatisfying performance in real time. In order to solve these problems,we propose a real-time panoramic video stitching framework.The framework we propose mainly consists of three algorithms, LORB image feature extraction algorithm, feature point matching algorithm based on LSH and GPU parallel video stitching algorithm based on CUDA.The experiment results show that the algorithm mentioned can improve the performance in the stages of feature extraction of images stitching and matching, the running speed of which is 11 times than that of the traditional ORB algorithm and 639 times than that of the traditional SIFT algorithm. Based on analyzing the GPU resources occupancy rate of each resolution image stitching, we further propose a stream parallel strategy to maximize the utilization of GPU resources. Compared with the L-ORB algorithm, the efficiency of this strategy is improved by 1.6-2.5 times, and it can make full use of GPU resources. The performance of the system accomplished in the paper is 29.2 times than that of the former embedded one, while the power dissipation is reduced to 10W. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 109,935 |
1811.09747 | Amortized Bayesian inference for clustering models | We develop methods for efficient amortized approximate Bayesian inference over posterior distributions of probabilistic clustering models, such as Dirichlet process mixture models. The approach is based on mapping distributed, symmetry-invariant representations of cluster arrangements into conditional probabilities. The method parallelizes easily, yields iid samples from the approximate posterior of cluster assignments with the same computational cost of a single Gibbs sampler sweep, and can easily be applied to both conjugate and non-conjugate models, as training only requires samples from the generative model. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 114,310 |
2304.00615 | An Intrinsic Framework of Information Retrieval Evaluation Measures | Information retrieval (IR) evaluation measures are cornerstones for determining the suitability and task performance efficiency of retrieval systems. Their metric and scale properties enable to compare one system against another to establish differences or similarities. Based on the representational theory of measurement, this paper determines these properties by exploiting the information contained in a retrieval measure itself. It establishes the intrinsic framework of a retrieval measure, which is the common scenario when the domain set is not explicitly specified. A method to determine the metric and scale properties of any retrieval measure is provided, requiring knowledge of only some of its attained values. The method establishes three main categories of retrieval measures according to their intrinsic properties. Some common user-oriented and system-oriented evaluation measures are classified according to the presented taxonomy. | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | 355,752 |
1808.06306 | Reed-Solomon codes over small fields with constrained generator matrices | We give constructions of some special cases of $[n,k]$ Reed-Solomon codes over finite fields of size at least $n$ and $n+1$ whose generator matrices have constrained support. Furthermore, we consider a generalisation of the GM-MDS conjecture proposed by Lovett in 2018. We show that Lovett's conjecture is false in general and we specify when the conjecture is true. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 105,509 |
1804.03821 | ExFuse: Enhancing Feature Fusion for Semantic Segmentation | Modern semantic segmentation frameworks usually combine low-level and high-level features from pre-trained backbone convolutional models to boost performance. In this paper, we first point out that a simple fusion of low-level and high-level features could be less effective because of the gap in semantic levels and spatial resolution. We find that introducing semantic information into low-level features and high-resolution details into high-level features is more effective for the later fusion. Based on this observation, we propose a new framework, named ExFuse, to bridge the gap between low-level and high-level features thus significantly improve the segmentation quality by 4.0\% in total. Furthermore, we evaluate our approach on the challenging PASCAL VOC 2012 segmentation benchmark and achieve 87.9\% mean IoU, which outperforms the previous state-of-the-art results. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 94,702 |
2003.12088 | An Efficient Machine Learning Approach for Accurate Short Term Solar
Power Prediction | Solar based electricity generations have experienced strong and impactful growth in recent years. The regulation, scheduling, dispatching, and unit commitment of intermittent solar power is dependent on the accuracy of the forecasting methods. In this paper, a robust Expanded Extreme Learning Machine (EELM) is proposed to accurately predict solar power for different time horizons and weather conditions. The proposed EELM technique has no randomness due to the absence of random input layer weights and takes very less time to predict the solar power efficiently. The performance of the proposed EELM is validated through historical data collected from the National Renewable Energy Laboratory (NREL) through various performance metrics. The efficacy of the proposed EELM method is evaluated against basic ELM and Functional Link Neural Network (FLNN) for 5 minutes and 1 hour ahead time horizon. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 169,807 |
2305.13628 | Improving Self-training for Cross-lingual Named Entity Recognition with
Contrastive and Prototype Learning | In cross-lingual named entity recognition (NER), self-training is commonly used to bridge the linguistic gap by training on pseudo-labeled target-language data. However, due to sub-optimal performance on target languages, the pseudo labels are often noisy and limit the overall performance. In this work, we aim to improve self-training for cross-lingual NER by combining representation learning and pseudo label refinement in one coherent framework. Our proposed method, namely ContProto mainly comprises two components: (1) contrastive self-training and (2) prototype-based pseudo-labeling. Our contrastive self-training facilitates span classification by separating clusters of different classes, and enhances cross-lingual transferability by producing closely-aligned representations between the source and target language. Meanwhile, prototype-based pseudo-labeling effectively improves the accuracy of pseudo labels during training. We evaluate ContProto on multiple transfer pairs, and experimental results show our method brings in substantial improvements over current state-of-the-art methods. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 366,590 |
2307.02720 | On-Device Constrained Self-Supervised Speech Representation Learning for
Keyword Spotting via Knowledge Distillation | Large self-supervised models are effective feature extractors, but their application is challenging under on-device budget constraints and biased dataset collection, especially in keyword spotting. To address this, we proposed a knowledge distillation-based self-supervised speech representation learning (S3RL) architecture for on-device keyword spotting. Our approach used a teacher-student framework to transfer knowledge from a larger, more complex model to a smaller, light-weight model using dual-view cross-correlation distillation and the teacher's codebook as learning objectives. We evaluated our model's performance on an Alexa keyword spotting detection task using a 16.6k-hour in-house dataset. Our technique showed exceptional performance in normal and noisy conditions, demonstrating the efficacy of knowledge distillation methods in constructing self-supervised models for keyword spotting tasks while working within on-device resource constraints. | false | false | true | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 377,784 |
2106.11918 | From SIR to SEAIRD: a novel data-driven modeling approach based on the
Grey-box System Theory to predict the dynamics of COVID-19 | Common compartmental modeling for COVID-19 is based on a priori knowledge and numerous assumptions. Additionally, they do not systematically incorporate asymptomatic cases. Our study aimed at providing a framework for data-driven approaches, by leveraging the strengths of the grey-box system theory or grey-box identification, known for its robustness in problem solving under partial, incomplete, or uncertain data. Empirical data on confirmed cases and deaths, extracted from an open source repository were used to develop the SEAIRD compartment model. Adjustments were made to fit current knowledge on the COVID-19 behavior. The model was implemented and solved using an Ordinary Differential Equation solver and an optimization tool. A cross-validation technique was applied, and the coefficient of determination $R^2$ was computed in order to evaluate the goodness-of-fit of the model. %to the data. Key epidemiological parameters were finally estimated and we provided the rationale for the construction of SEAIRD model. When applied to Brazil's cases, SEAIRD produced an excellent agreement to the data, with an %coefficient of determination $R^2$ $\geq 90\%$. The probability of COVID-19 transmission was generally high ($\geq 95\%$). On the basis of a 20-day modeling data, the incidence rate of COVID-19 was as low as 3 infected cases per 100,000 exposed persons in Brazil and France. Within the same time frame, the fatality rate of COVID-19 was the highest in France (16.4\%) followed by Brazil (6.9\%), and the lowest in Russia ($\leq 1\%$). SEAIRD represents an asset for modeling infectious diseases in their dynamical stable phase, especially for new viruses when pathophysiology knowledge is very limited. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 242,556 |
2408.16907 | Ig3D: Integrating 3D Face Representations in Facial Expression Inference | Reconstructing 3D faces with facial geometry from single images has allowed for major advances in animation, generative models, and virtual reality. However, this ability to represent faces with their 3D features is not as fully explored by the facial expression inference (FEI) community. This study therefore aims to investigate the impacts of integrating such 3D representations into the FEI task, specifically for facial expression classification and face-based valence-arousal (VA) estimation. To accomplish this, we first assess the performance of two 3D face representations (both based on the 3D morphable model, FLAME) for the FEI tasks. We further explore two fusion architectures, intermediate fusion and late fusion, for integrating the 3D face representations with existing 2D inference frameworks. To evaluate our proposed architecture, we extract the corresponding 3D representations and perform extensive tests on the AffectNet and RAF-DB datasets. Our experimental results demonstrate that our proposed method outperforms the state-of-the-art AffectNet VA estimation and RAF-DB classification tasks. Moreover, our method can act as a complement to other existing methods to boost performance in many emotion inference tasks. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 484,497 |
2007.08001 | Computation Offloading in Beyond 5G Networks: A Distributed Learning
Framework and Applications | Facing the trend of merging wireless communications and multi-access edge computing (MEC), this article studies computation offloading in the beyond fifth-generation networks. To address the technical challenges originating from the uncertainties and the sharing of limited resource in an MEC system, we formulate the computation offloading problem as a multi-agent Markov decision process, for which a distributed learning framework is proposed. We present a case study on resource orchestration in computation offloading to showcase the potentials of an online distributed reinforcement learning algorithm developed under the proposed framework. Experimental results demonstrate that our learning algorithm outperforms the benchmark resource orchestration algorithms. Furthermore, we outline the research directions worth in-depth investigation to minimize the time cost, which is one of the main practical issues that prevent the implementation of the proposed distributed learning framework. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 187,486 |
1609.07114 | A Stable FDTD Method with Embedded Reduced-Order Models | The computational efficiency of the Finite-Difference Time-Domain (FDTD) method can be significantly reduced by the presence of complex objects with fine features. Small geometrical details impose a fine mesh and a reduced time step, significantly increasing computational cost. Model order reduction has been proposed as a systematic way to generate compact models for complex objects, that one can then instantiate into a main FDTD mesh. However, the stability of FDTD with embedded reduced models remains an open problem. We propose a systematic method to generate reduced models for FDTD domains, and embed them into a main FDTD mesh with guaranteed stability up to the Courant-Friedrichs-Lewy (CFL) limit of the fine mesh. With a simple perturbation technique, the CFL of the whole scheme can be further extended beyond the fine grid's CFL limit. Reduced models can be created for arbitrary domains containing inhomogeneous and lossy materials. Numerical tests confirm the stability of the proposed method, and its potential to accelerate multiscale FDTD simulations. | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 61,394 |
2102.00397 | Learning Interpretable Deep State Space Model for Probabilistic Time
Series Forecasting | Probabilistic time series forecasting involves estimating the distribution of future based on its history, which is essential for risk management in downstream decision-making. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks and the dependency is modeled by recurrent neural nets. We take the automatic relevance determination (ARD) view and devise a network to exploit the exogenous variables in addition to time series. In particular, our ARD network can incorporate the uncertainty of the exogenous variables and eventually helps identify useful exogenous variables and suppress those irrelevant for forecasting. The distribution of multi-step ahead forecasts are approximated by Monte Carlo simulation. We show in experiments that our model produces accurate and sharp probabilistic forecasts. The estimated uncertainty of our forecasting also realistically increases over time, in a spontaneous manner. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 217,767 |
2204.08651 | Evolving Programmable Computational Metamaterials | Granular metamaterials are a promising choice for the realization of mechanical computing devices. As preliminary evidence of this, we demonstrate here how to embed Boolean logic gates (AND and XOR) into a granular metamaterial by evolving where particular grains are placed in the material. Our results confirm the existence of gradients of increasing "AND-ness" and "XOR-ness" within the space of possible materials that can be followed by evolutionary search. We measure the computational functionality of a material by probing how it transforms bits encoded as vibrations with zero or non-zero amplitude. We compared the evolution of materials built from mass-contrasting particles and materials built from stiffness-contrasting particles, and found that the latter were more evolvable. We believe this work may pave the way toward evolutionary design of increasingly sophisticated, programmable, and computationally dense metamaterials with certain advantages over more traditional computational substrates. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | true | 292,164 |
2406.13663 | Model Internals-based Answer Attribution for Trustworthy
Retrieval-Augmented Generation | Ensuring the verifiability of model answers is a fundamental challenge for retrieval-augmented generation (RAG) in the question answering (QA) domain. Recently, self-citation prompting was proposed to make large language models (LLMs) generate citations to supporting documents along with their answers. However, self-citing LLMs often struggle to match the required format, refer to non-existent sources, and fail to faithfully reflect LLMs' context usage throughout the generation. In this work, we present MIRAGE --Model Internals-based RAG Explanations -- a plug-and-play approach using model internals for faithful answer attribution in RAG applications. MIRAGE detects context-sensitive answer tokens and pairs them with retrieved documents contributing to their prediction via saliency methods. We evaluate our proposed approach on a multilingual extractive QA dataset, finding high agreement with human answer attribution. On open-ended QA, MIRAGE achieves citation quality and efficiency comparable to self-citation while also allowing for a finer-grained control of attribution parameters. Our qualitative evaluation highlights the faithfulness of MIRAGE's attributions and underscores the promising application of model internals for RAG answer attribution. | false | false | false | false | true | false | true | false | true | false | false | false | false | false | false | false | false | false | 465,944 |
2409.01534 | Think Twice Before Recognizing: Large Multimodal Models for General
Fine-grained Traffic Sign Recognition | We propose a new strategy called think twice before recognizing to improve fine-grained traffic sign recognition (TSR). Fine-grained TSR in the wild is difficult due to the complex road conditions, and existing approaches particularly struggle with cross-country TSR when data is lacking. Our strategy achieves effective fine-grained TSR by stimulating the multiple-thinking capability of large multimodal models (LMM). We introduce context, characteristic, and differential descriptions to design multiple thinking processes for the LMM. The context descriptions with center coordinate prompt optimization help the LMM to locate the target traffic sign in the original road images containing multiple traffic signs and filter irrelevant answers through the proposed prior traffic sign hypothesis. The characteristic description is based on few-shot in-context learning of template traffic signs, which decreases the cross-domain difference and enhances the fine-grained recognition capability of the LMM. The differential descriptions of similar traffic signs optimize the multimodal thinking capability of the LMM. The proposed method is independent of training data and requires only simple and uniform instructions. We conducted extensive experiments on three benchmark datasets and two real-world datasets from different countries, and the proposed method achieves state-of-the-art TSR results on all five datasets. | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | false | true | 485,369 |
2110.11572 | Reinforcement Learning for Process Control with Application in
Semiconductor Manufacturing | Process control is widely discussed in the manufacturing process, especially for semiconductor manufacturing. Due to unavoidable disturbances in manufacturing, different process controllers are proposed to realize variation reduction. Since reinforcement learning (RL) has shown great advantages in learning actions from interactions with a dynamic system, we introduce RL methods for process control and propose a new controller called RL-based controller. Considering the fact that most existing process controllers mainly rely on a linear model assumption for the process input-output relationship, we first propose theoretical properties of RL-based controllers according to the linear model assumption. Then the performance of RL-based controllers and traditional process controllers (e.g., exponentially weighted moving average (EWMA), general harmonic rule (GHR) controllers) are compared for linear processes. Furthermore, we find that the RL-based controllers have potential advantages to deal with other complicated nonlinear processes that are with and without assumed explicit model formulations. The intensive numerical studies validate the advantages of the proposed RL-based controllers. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 262,530 |
2002.09182 | Quantum secret sharing using GHZ state qubit positioning and selective
qubits strategy for secret reconstruction | The work presents a novel quantum secret sharing strategy based on GHZ product state sharing between three parties. The dealer, based on the classical information to be shared, toggles his qubit and shares the product state. The other parties make their Bell measurements and collude to reconstruct the secret. Unlike the other protocols, this protocol does not involve the entire initial state reconstruction, rather uses selective qubits to discard the redundant qubits at the time of reconstruction to decrypt the secret. The protocol also allows for security against malicious attacks by an adversary without affecting the integrity of the secret. The security of the protocol lies in the fact that each party's correct announcement of their measurement is required for reconstruction, failing which the reconstruction process is jeopardized, thereby ascertaining the $(3,3)$ scheme which can further be extended for a $(n,n)$ scheme. | false | false | false | false | false | false | false | false | false | true | false | false | true | false | false | false | false | false | 164,999 |
1807.06233 | Robust Deep Multi-modal Learning Based on Gated Information Fusion
Network | The goal of multi-modal learning is to use complimentary information on the relevant task provided by the multiple modalities to achieve reliable and robust performance. Recently, deep learning has led significant improvement in multi-modal learning by allowing for the information fusion in the intermediate feature levels. This paper addresses a problem of designing robust deep multi-modal learning architecture in the presence of imperfect modalities. We introduce deep fusion architecture for object detection which processes each modality using the separate convolutional neural network (CNN) and constructs the joint feature map by combining the intermediate features from the CNNs. In order to facilitate the robustness to the degraded modalities, we employ the gated information fusion (GIF) network which weights the contribution from each modality according to the input feature maps to be fused. The weights are determined through the convolutional layers followed by a sigmoid function and trained along with the information fusion network in an end-to-end fashion. Our experiments show that the proposed GIF network offers the additional architectural flexibility to achieve robust performance in handling some degraded modalities, and show a significant performance improvement based on Single Shot Detector (SSD) for KITTI dataset using the proposed fusion network and data augmentation schemes. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 103,088 |
2209.11034 | SEER: Safe Efficient Exploration for Aerial Robots using Learning to
Predict Information Gain | We address the problem of efficient 3-D exploration in indoor environments for micro aerial vehicles with limited sensing capabilities and payload/power constraints. We develop an indoor exploration framework that uses learning to predict the occupancy of unseen areas, extracts semantic features, samples viewpoints to predict information gains for different exploration goals, and plans informative trajectories to enable safe and smart exploration. Extensive experimentation in simulated and real-world environments shows the proposed approach outperforms the state-of-the-art exploration framework by 24% in terms of the total path length in a structured indoor environment and with a higher success rate during exploration. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 319,055 |
2109.04405 | An Accelerated Proximal Gradient-based Model Predictive Control
Algorithm | In this letter, an accelerated quadratic programming (QP) algorithm is proposed based on the proximal gradient method. The algorithm can achieve convergence rate $O(1/p^{\alpha})$, where $p$ is the iteration number and $\alpha$ is the given positive integer. The proposed algorithm improves the convergence rate of existing algorithms that achieve $O(1/p^{2})$. The key idea is that iterative parameters are selected from a group of specific high order polynomial equations. The performance of the proposed algorithm is assessed on the randomly generated model predictive control (MPC) optimization problems. The experimental results show that our algorithm can outperform the state-of-the-art optimization software MOSEK and ECOS for the small size MPC problems. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 254,394 |
2501.03085 | Personalized Fashion Recommendation with Image Attributes and Aesthetics
Assessment | Personalized fashion recommendation is a difficult task because 1) the decisions are highly correlated with users' aesthetic appetite, which previous work frequently overlooks, and 2) many new items are constantly rolling out that cause strict cold-start problems in the popular identity (ID)-based recommendation methods. These new items are critical to recommend because of trend-driven consumerism. In this work, we aim to provide more accurate personalized fashion recommendations and solve the cold-start problem by converting available information, especially images, into two attribute graphs focusing on optimized image utilization and noise-reducing user modeling. Compared with previous methods that separate image and text as two components, the proposed method combines image and text information to create a richer attributes graph. Capitalizing on the advancement of large language and vision models, we experiment with extracting fine-grained attributes efficiently and as desired using two different prompts. Preliminary experiments on the IQON3000 dataset have shown that the proposed method achieves competitive accuracy compared with baselines. | false | false | false | false | true | true | false | false | false | false | false | false | false | false | false | false | false | false | 522,757 |
1703.04096 | Improving Interpretability of Deep Neural Networks with Semantic
Information | Interpretability of deep neural networks (DNNs) is essential since it enables users to understand the overall strengths and weaknesses of the models, conveys an understanding of how the models will behave in the future, and how to diagnose and correct potential problems. However, it is challenging to reason about what a DNN actually does due to its opaque or black-box nature. To address this issue, we propose a novel technique to improve the interpretability of DNNs by leveraging the rich semantic information embedded in human descriptions. By concentrating on the video captioning task, we first extract a set of semantically meaningful topics from the human descriptions that cover a wide range of visual concepts, and integrate them into the model with an interpretive loss. We then propose a prediction difference maximization algorithm to interpret the learned features of each neuron. Experimental results demonstrate its effectiveness in video captioning using the interpretable features, which can also be transferred to video action recognition. By clearly understanding the learned features, users can easily revise false predictions via a human-in-the-loop procedure. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 69,835 |
2302.14063 | How optimal transport can tackle gender biases in multi-class
neural-network classifiers for job recommendations? | Automatic recommendation systems based on deep neural networks have become extremely popular during the last decade. Some of these systems can however be used for applications which are ranked as High Risk by the European Commission in the A.I. act, as for instance for online job candidate recommendation. When used in the European Union, commercial AI systems for this purpose will then be required to have to proper statistical properties with regard to potential discrimination they could engender. This motivated our contribution, where we present a novel optimal transport strategy to mitigate undesirable algorithmic biases in multi-class neural-network classification. Our stratey is model agnostic and can be used on any multi-class classification neural-network model. To anticipate the certification of recommendation systems using textual data, we then used it on the Bios dataset, for which the learning task consists in predicting the occupation of female and male individuals, based on their LinkedIn biography. Results show that it can reduce undesired algorithmic biases in this context to lower levels than a standard strategy. | false | false | false | false | true | false | true | false | false | false | false | false | false | true | false | false | false | false | 348,152 |
2310.07807 | FedSym: Unleashing the Power of Entropy for Benchmarking the Algorithms
for Federated Learning | Federated learning (FL) is a decentralized machine learning approach where independent learners process data privately. Its goal is to create a robust and accurate model by aggregating and retraining local models over multiple rounds. However, FL faces challenges regarding data heterogeneity and model aggregation effectiveness. In order to simulate real-world data, researchers use methods for data partitioning that transform a dataset designated for centralized learning into a group of sub-datasets suitable for distributed machine learning with different data heterogeneity. In this paper, we study the currently popular data partitioning techniques and visualize their main disadvantages: the lack of precision in the data diversity, which leads to unreliable heterogeneity indexes, and the inability to incrementally challenge the FL algorithms. To resolve this problem, we propose a method that leverages entropy and symmetry to construct 'the most challenging' and controllable data distributions with gradual difficulty. We introduce a metric to measure data heterogeneity among the learning agents and a transformation technique that divides any dataset into splits with precise data diversity. Through a comparative study, we demonstrate the superiority of our method over existing FL data partitioning approaches, showcasing its potential to challenge model aggregation algorithms. Experimental results indicate that our approach gradually challenges the FL strategies, and the models trained on FedSym distributions are more distinct. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 399,130 |
2006.04391 | AutoMat -- Automatic Differentiation for Generalized Standard Materials
on GPUs | We propose a universal method for the evaluation of generalized standard materials that greatly simplifies the material law implementation process. By means of automatic differentiation and a numerical integration scheme, AutoMat reduces the implementation effort to two potential functions. By moving AutoMat to the GPU, we close the performance gap to conventional evaluation routines and demonstrate in detail that the expression level reverse mode of automatic differentiation as well as its extension to second order derivatives can be applied inside CUDA kernels. We underline the effectiveness and the applicability of AutoMat by integrating it into the FFT-based homogenization scheme of Moulinec and Suquet and discuss the benefits of using AutoMat with respect to runtime and solution accuracy for an elasto-viscoplastic example. | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | 180,680 |
2112.15011 | Radiology Report Generation with a Learned Knowledge Base and
Multi-modal Alignment | In clinics, a radiology report is crucial for guiding a patient's treatment. However, writing radiology reports is a heavy burden for radiologists. To this end, we present an automatic, multi-modal approach for report generation from a chest x-ray. Our approach, motivated by the observation that the descriptions in radiology reports are highly correlated with specific information of the x-ray images, features two distinct modules: (i) Learned knowledge base: To absorb the knowledge embedded in the radiology reports, we build a knowledge base that can automatically distil and restore medical knowledge from textual embedding without manual labour; (ii) Multi-modal alignment: to promote the semantic alignment among reports, disease labels, and images, we explicitly utilize textual embedding to guide the learning of the visual feature space. We evaluate the performance of the proposed model using metrics from both natural language generation and clinic efficacy on the public IU-Xray and MIMIC-CXR datasets. Our ablation study shows that each module contributes to improving the quality of generated reports. Furthermore, with the assistance of both modules, our approach outperforms state-of-the-art methods over almost all the metrics. | false | false | false | false | false | false | false | false | true | false | false | true | false | false | false | false | false | false | 273,672 |
2104.12005 | Wireless Federated Learning (WFL) for 6G Networks -- Part II: The
Compute-then-Transmit NOMA Paradigm | As it has been discussed in the first part of this work, the utilization of advanced multiple access protocols and the joint optimization of the communication and computing resources can facilitate the reduction of delay for wireless federated learning (WFL), which is of paramount importance for the efficient integration of WFL in the sixth generation of wireless networks (6G). To this end, in this second part we introduce and optimize a novel communication protocol for WFL networks, that is based on non-orthogonal multiple access (NOMA). More specifically, the Compute-then-Transmit NOMA (CT-NOMA) protocol is introduced, where users terminate concurrently the local model training and then simultaneously transmit the trained parameters to the central server. Moreover, two different detection schemes for the mitigation of inter-user interference in NOMA are considered and evaluated, which correspond to fixed and variable decoding order during the successive interference cancellation process. Furthermore, the computation and communication resources are jointly optimized for both considered schemes, with the aim to minimize the total delay during a WFL communication round. Finally, the simulation results verify the effectiveness of CT-NOMA in terms of delay reduction, compared to the considered benchmark that is based on time-division multiple access. | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | false | false | 232,084 |
1603.02416 | Correlation Measure Equivalence in Dynamic Causal Structures of Quantum
Gravity | We prove an equivalence transformation between the correlation measure functions of the causally-unbiased quantum gravity space and the causally-biased standard space. The theory of quantum gravity fuses the dynamic (nonfixed) causal structure of general relativity and the quantum uncertainty of quantum mechanics. In a quantum gravity space, the events are causally nonseparable and all time bias vanishes, which makes it no possible to use the standard causally-biased entropy and the correlation measure functions. Since a corrected causally-unbiased entropy function leads to an undefined, obscure mathematical structure, in our approach the correction is made in the data representation of the causally-unbiased space. Here we prove that the standard causally-biased entropy function with a data correction can be used to identify correlations in dynamic causal structures. As a corollary, all mathematical properties of the causally-biased correlation measure functions are preserved in the causally-unbiased space. The equivalence transformation allows us to measure correlations in a quantum gravity space with the stable, well-defined mathematical background and apparatus of the causally-biased functions of quantum Shannon theory. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 53,010 |
2211.08900 | Convergence analysis of unsupervised Legendre-Galerkin neural networks
for linear second-order elliptic PDEs | In this paper, we perform the convergence analysis of unsupervised Legendre--Galerkin neural networks (ULGNet), a deep-learning-based numerical method for solving partial differential equations (PDEs). Unlike existing deep learning-based numerical methods for PDEs, the ULGNet expresses the solution as a spectral expansion with respect to the Legendre basis and predicts the coefficients with deep neural networks by solving a variational residual minimization problem. Since the corresponding loss function is equivalent to the residual induced by the linear algebraic system depending on the choice of basis functions, we prove that the minimizer of the discrete loss function converges to the weak solution of the PDEs. Numerical evidence will also be provided to support the theoretical result. Key technical tools include the variant of the universal approximation theorem for bounded neural networks, the analysis of the stiffness and mass matrices, and the uniform law of large numbers in terms of the Rademacher complexity. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 330,810 |
2204.00266 | Multifaceted Improvements for Conversational Open-Domain Question
Answering | Open-domain question answering (OpenQA) is an important branch of textual QA which discovers answers for the given questions based on a large number of unstructured documents. Effectively mining correct answers from the open-domain sources still has a fair way to go. Existing OpenQA systems might suffer from the issues of question complexity and ambiguity, as well as insufficient background knowledge. Recently, conversational OpenQA is proposed to address these issues with the abundant contextual information in the conversation. Promising as it might be, there exist several fundamental limitations including the inaccurate question understanding, the coarse ranking for passage selection, and the inconsistent usage of golden passage in the training and inference phases. To alleviate these limitations, in this paper, we propose a framework with Multifaceted Improvements for Conversational open-domain Question Answering (MICQA). Specifically, MICQA has three significant advantages. First, the proposed KL-divergence based regularization is able to lead to a better question understanding for retrieval and answer reading. Second, the added post-ranker module can push more relevant passages to the top placements and be selected for reader with a two-aspect constrains. Third, the well designed curriculum learning strategy effectively narrows the gap between the golden passage settings of training and inference, and encourages the reader to find true answer without the golden passage assistance. Extensive experiments conducted on the publicly available dataset OR-QuAC demonstrate the superiority of MICQA over the state-of-the-art model in conversational OpenQA task. | false | false | false | false | false | true | false | false | true | false | false | false | false | false | false | false | false | false | 289,194 |
1907.03395 | Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and
Graph Attention Networks | Predicting the future trajectories of multiple interacting agents in a scene has become an increasingly important problem for many different applications ranging from control of autonomous vehicles and social robots to security and surveillance. This problem is compounded by the presence of social interactions between humans and their physical interactions with the scene. While the existing literature has explored some of these cues, they mainly ignored the multimodal nature of each human's future trajectory. In this paper, we present Social-BiGAT, a graph-based generative adversarial network that generates realistic, multimodal trajectory predictions by better modelling the social interactions of pedestrians in a scene. Our method is based on a graph attention network (GAT) that learns reliable feature representations that encode the social interactions between humans in the scene, and a recurrent encoder-decoder architecture that is trained adversarially to predict, based on the features, the humans' paths. We explicitly account for the multimodal nature of the prediction problem by forming a reversible transformation between each scene and its latent noise vector, as in Bicycle-GAN. We show that our framework achieves state-of-the-art performance comparing it to several baselines on existing trajectory forecasting benchmarks. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 137,852 |
2104.10535 | Exploiting Learned Policies in Focal Search | Recent machine-learning approaches to deterministic search and domain-independent planning employ policy learning to speed up search. Unfortunately, when attempting to solve a search problem by successively applying a policy, no guarantees can be given on solution quality. The problem of how to effectively use a learned policy within a bounded-suboptimal search algorithm remains largely as an open question. In this paper, we propose various ways in which such policies can be integrated into Focal Search, assuming that the policy is a neural network classifier. Furthermore, we provide mathematical foundations for some of the resulting algorithms. To evaluate the resulting algorithms over a number of policies with varying accuracy, we use synthetic policies which can be generated for a target accuracy for problems where the search space can be held in memory. We evaluate our focal search variants over three benchmark domains using our synthetic approach, and on the 15-puzzle using a neural network learned using 1.5 million examples. We observe that Discrepancy Focal Search, which we show expands the node which maximizes an approximation of the probability that its corresponding path is a prefix of an optimal path, obtains, in general, the best results in terms of runtime and solution quality. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 231,615 |
1102.5549 | Instant Replay: Investigating statistical Analysis in Sports | Technology has had an unquestionable impact on the way people watch sports. Along with this technological evolution has come a higher standard to ensure a good viewing experience for the casual sports fan. It can be argued that the pervasion of statistical analysis in sports serves to satiate the fan's desire for detailed sports statistics. The goal of statistical analysis in sports is a simple one: to eliminate subjective analysis. In this paper, we review previous work that attempts to analyze various aspects in sports by using ideas from Markov Chains, Bayesian Inference and Markov Chain Monte Carlo (MCMC) methods. The unifying goal of these works is to achieve an accurate representation of the player's ability, the sport, or the environmental effects on the player's performance. With the prevalence of cheap computation, it is possible that using techniques in Artificial Intelligence could improve the result of statistical analysis in sport. This is best illustrated when evaluating football using Neuro Dynamic Programming, a Control Theory paradigm heavily based on theory in Stochastic processes. The results from this method suggest that statistical analysis in sports may benefit from using ideas from the area of Control Theory or Machine Learning | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 9,399 |
2112.13809 | Improving Deep Image Matting via Local Smoothness Assumption | Natural image matting is a fundamental and challenging computer vision task. Conventionally, the problem is formulated as an underconstrained problem. Since the problem is ill-posed, further assumptions on the data distribution are required to make the problem well-posed. For classical matting methods, a commonly adopted assumption is the local smoothness assumption on foreground and background colors. However, the use of such assumptions was not systematically considered for deep learning based matting methods. In this work, we consider two local smoothness assumptions which can help improving deep image matting models. Based on the local smoothness assumptions, we propose three techniques, i.e., training set refinement, color augmentation and backpropagating refinement, which can improve the performance of the deep image matting model significantly. We conduct experiments to examine the effectiveness of the proposed algorithm. The experimental results show that the proposed method has favorable performance compared with existing matting methods. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 273,363 |
2202.12003 | IBIA: Bayesian Inference via Incremental Build-Infer-Approximate
operations on Clique Trees | Exact inference in Bayesian networks is intractable and has an exponential dependence on the size of the largest clique in the corresponding clique tree (CT), necessitating approximations. Factor based methods to bound clique sizes are more accurate than structure based methods, but expensive since they involve inference of beliefs in a large number of candidate structure or region graphs. We propose an alternative approach for approximate inference based on an incremental build-infer-approximate (IBIA) paradigm, which converts the Bayesian network into a data structure containing a sequence of linked clique tree forests (SLCTF), with clique sizes bounded by a user-specified value. In the incremental build stage of this approach, CTFs are constructed incrementally by adding variables to the CTFs as long as clique sizes are within the specified bound. Once the clique size constraint is reached, the CTs in the CTF are calibrated in the infer stage of IBIA. The resulting clique beliefs are used in the approximate phase to get an approximate CTF with reduced clique sizes. The approximate CTF forms the starting point for the next CTF in the sequence. These steps are repeated until all variables are added to a CTF in the sequence. We prove that our algorithm for incremental construction of clique trees always generates a valid CT and our approximation technique preserves the joint beliefs of the variables within a clique. Based on this, we show that the SLCTF data structure can be used for efficient approximate inference of partition function and prior and posterior marginals. More than 500 benchmarks were used to test the method and the results show a significant reduction in error when compared to other approximate methods, with competitive runtimes. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 282,081 |
2402.06640 | Modeling and Optimization of Epidemiological Control Policies Through
Reinforcement Learning | Pandemics involve the high transmission of a disease that impacts global and local health and economic patterns. The impact of a pandemic can be minimized by enforcing certain restrictions on a community. However, while minimizing infection and death rates, these restrictions can also lead to economic crises. Epidemiological models help propose pandemic control strategies based on non-pharmaceutical interventions such as social distancing, curfews, and lockdowns, reducing the economic impact of these restrictions. However, designing manual control strategies while considering disease spread and economic status is non-trivial. Optimal strategies can be designed through multi-objective reinforcement learning (MORL) models, which demonstrate how restrictions can be used to optimize the outcome of a pandemic. In this research, we utilized an epidemiological Susceptible, Exposed, Infected, Recovered, Deceased (SEIRD) model: a compartmental model for virtually simulating a pandemic day by day. We combined the SEIRD model with a deep double recurrent Q-network to train a reinforcement learning agent to enforce the optimal restriction on the SEIRD simulation based on a reward function. We tested two agents with unique reward functions and pandemic goals to obtain two strategies. The first agent placed long lockdowns to reduce the initial spread of the disease, followed by cyclical and shorter lockdowns to mitigate the resurgence of the disease. The second agent provided similar infection rates but an improved economy by implementing a 10-day lockdown and 20-day no-restriction cycle. This use of reinforcement learning and epidemiological modeling allowed for both economic and infection mitigation in multiple pandemic scenarios. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 428,373 |
2305.09625 | Conditional variational autoencoder with Gaussian process regression
recognition for parametric models | In this article, we present a data-driven method for parametric models with noisy observation data. Gaussian process regression based reduced order modeling (GPR-based ROM) can realize fast online predictions without using equations in the offline stage. However, GPR-based ROM does not perform well for complex systems since POD projection are naturally linear. Conditional variational autoencoder (CVAE) can address this issue via nonlinear neural networks but it has more model complexity, which poses challenges for training and tuning hyperparameters. To this end, we propose a framework of CVAE with Gaussian process regression recognition (CVAE-GPRR). The proposed method consists of a recognition model and a likelihood model. In the recognition model, we first extract low-dimensional features from data by POD to filter the redundant information with high frequency. And then a non-parametric model GPR is used to learn the map from parameters to POD latent variables, which can also alleviate the impact of noise. CVAE-GPRR can achieve the similar accuracy to CVAE but with fewer parameters. In the likelihood model, neural networks are used to reconstruct data. Besides the samples of POD latent variables and input parameters, physical variables are also added as the inputs to make predictions in the whole physical space. This can not be achieved by either GPR-based ROM or CVAE. Moreover, the numerical results show that CVAE-GPRR may alleviate the overfitting issue in CVAE. | false | true | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 364,709 |
2106.13081 | Artifact Detection and Correction in EEG data: A Review | Electroencephalography (EEG) has countless applications across many of fields. However, EEG applications are limited by low signal-to-noise ratios. Multiple types of artifacts contribute to the noisiness of EEG, and many techniques have been proposed to detect and correct these artifacts. These techniques range from simply detecting and rejecting artifact ridden segments, to extracting the noise component from the EEG signal. In this paper we review a variety of recent and classical techniques for EEG data artifact detection and correction with a focus on the last half-decade. We compare the strengths and weaknesses of the approaches and conclude with proposed future directions for the field. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 242,955 |
1503.06391 | Predictive model of the human muscle fatigue: application to repetitive
push-pull tasks with light external load | Repetitive tasks in industrial works may contribute to health problems among operators, such as musculo-skeletal disorders, in part due to insufficient control of muscle fatigue. In this paper, a predictive model of fatigue is proposed for repetitive push/pull operations. Assumptions generally accepted in the literature are first explicitly set in this framework. Then, an earlier static fatigue model is recalled and extended to quasi-static situations. Specifically, the maximal torque that can be generated at a joint is not considered as constant, but instead varies over time accordingly to the operator's changing posture. The fatigue model is implemented with this new consideration and evaluated in a simulation of push/pull operation. Reference to this paper should be made as follows: Sakka, S., Chablat, D., Ma, R. and Bennis, F. (2015) 'Predictive model of the human muscle fatigue: application to repetitive push-pull tasks with light external load', Int. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 41,340 |
2110.10220 | Patch Based Transformation for Minimum Variance Beamformer Image
Approximation Using Delay and Sum Pipeline | In the recent past, there have been several efforts in accelerating computationally heavy beamforming algorithms such as minimum variance distortionless response (MVDR) beamforming to achieve real-time performance comparable to the popular delay and sum (DAS) beamforming. This has been achieved using a variety of neural network architectures ranging from fully connected neural networks (FCNNs), convolutional neural networks (CNNs) and general adversarial networks (GANs). However most of these approaches are working with optimizations considering image level losses and hence require a significant amount of dataset to ensure that the process of beamforming is learned. In this work, a patch level U-Net based neural network is proposed, where the delay compensated radio frequency (RF) patch for a fixed region in space (e.g. 32x32) is transformed through a U-Net architecture and multiplied with DAS apodization weights and optimized for similarity with MVDR image of the patch. Instead of framing the beamforming problem as a regression problem to estimate the apodization weights, the proposed approach treats the non-linear transformation of the RF data space that can account for the data driven weight adaptation done by the MVDR approach in the parameters of the network. In this way, it is also observed that by restricting the input to a patch the model will learn the beamforming pipeline as an image non-linear transformation problem. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 262,070 |
2405.10027 | The Real Price of Bandit Information in Multiclass Classification | We revisit the classical problem of multiclass classification with bandit feedback (Kakade, Shalev-Shwartz and Tewari, 2008), where each input classifies to one of $K$ possible labels and feedback is restricted to whether the predicted label is correct or not. Our primary inquiry is with regard to the dependency on the number of labels $K$, and whether $T$-step regret bounds in this setting can be improved beyond the $\smash{\sqrt{KT}}$ dependence exhibited by existing algorithms. Our main contribution is in showing that the minimax regret of bandit multiclass is in fact more nuanced, and is of the form $\smash{\widetilde{\Theta}\left(\min \left\{|H| + \sqrt{T}, \sqrt{KT \log |H|} \right\} \right) }$, where $H$ is the underlying (finite) hypothesis class. In particular, we present a new bandit classification algorithm that guarantees regret $\smash{\widetilde{O}(|H|+\sqrt{T})}$, improving over classical algorithms for moderately-sized hypothesis classes, and give a matching lower bound establishing tightness of the upper bounds (up to log-factors) in all parameter regimes. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 454,623 |
1310.3595 | Stabilizing discrete-time switched linear systems | This article deals with stabilizing discrete-time switched linear systems. Our contributions are threefold: Firstly, given a family of linear systems possibly containing unstable dynamics, we propose a large class of switching signals that stabilize a switched system generated by the switching signal and the given family of systems. Secondly, given a switched system, a sufficient condition for the existence of the proposed switching signal is derived by expressing the switching signal as an infinite walk on a directed graph representing the switched system. Thirdly, given a family of linear systems, we propose an algorithmic technique to design a switching signal for stabilizing the corresponding switched system. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 27,757 |
cs/0408064 | Proportional Conflict Redistribution Rules for Information Fusion | In this paper we propose five versions of a Proportional Conflict Redistribution rule (PCR) for information fusion together with several examples. From PCR1 to PCR2, PCR3, PCR4, PCR5 one increases the complexity of the rules and also the exactitude of the redistribution of conflicting masses. PCR1 restricted from the hyper-power set to the power set and without degenerate cases gives the same result as the Weighted Average Operator (WAO) proposed recently by J{\o}sang, Daniel and Vannoorenberghe but does not satisfy the neutrality property of vacuous belief assignment. That's why improved PCR rules are proposed in this paper. PCR4 is an improvement of minC and Dempster's rules. The PCR rules redistribute the conflicting mass, after the conjunctive rule has been applied, proportionally with some functions depending on the masses assigned to their corresponding columns in the mass matrix. There are infinitely many ways these functions (weighting factors) can be chosen depending on the complexity one wants to deal with in specific applications and fusion systems. Any fusion combination rule is at some degree ad-hoc. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 538,323 |
2107.02086 | One-Cycle Pruning: Pruning ConvNets Under a Tight Training Budget | Introducing sparsity in a neural network has been an efficient way to reduce its complexity while keeping its performance almost intact. Most of the time, sparsity is introduced using a three-stage pipeline: 1) train the model to convergence, 2) prune the model according to some criterion, 3) fine-tune the pruned model to recover performance. The last two steps are often performed iteratively, leading to reasonable results but also to a time-consuming and complex process. In our work, we propose to get rid of the first step of the pipeline and to combine the two other steps in a single pruning-training cycle, allowing the model to jointly learn for the optimal weights while being pruned. We do this by introducing a novel pruning schedule, named One-Cycle Pruning, which starts pruning from the beginning of the training, and until its very end. Adopting such a schedule not only leads to better performing pruned models but also drastically reduces the training budget required to prune a model. Experiments are conducted on a variety of architectures (VGG-16 and ResNet-18) and datasets (CIFAR-10, CIFAR-100 and Caltech-101), and for relatively high sparsity values (80%, 90%, 95% of weights removed). Our results show that One-Cycle Pruning consistently outperforms commonly used pruning schedules such as One-Shot Pruning, Iterative Pruning and Automated Gradual Pruning, on a fixed training budget. | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | false | false | 244,697 |
1711.06663 | Multiresolution and Hierarchical Analysis of Astronomical Spectroscopic
Cubes using 3D Discrete Wavelet Transform | The intrinsically hierarchical and blended structure of interstellar molecular clouds, plus the always increasing resolution of astronomical instruments, demand advanced and automated pattern recognition techniques for identifying and connecting source components in spectroscopic cubes. We extend the work done in multiresolution analysis using Wavelets for astronomical 2D images to 3D spectroscopic cubes, combining the results with the Dendrograms approach to offer a hierarchical representation of connections between sources at different scale levels. We test our approach in real data from the ALMA observatory, exploring different Wavelet families and assessing the main parameter for source identification (i.e., RMS) at each level. Our approach shows that is feasible to perform multiresolution analysis for the spatial and frequency domains simultaneously rather than analyzing each spectral channel independently. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 84,821 |
2305.09645 | StructGPT: A General Framework for Large Language Model to Reason over
Structured Data | In this paper, we study how to improve the zero-shot reasoning ability of large language models~(LLMs) over structured data in a unified way. Inspired by the study on tool augmentation for LLMs, we develop an \emph{Iterative Reading-then-Reasoning~(IRR)} approach for solving question answering tasks based on structured data, called \textbf{StructGPT}. In our approach, we construct the specialized function to collect relevant evidence from structured data (\ie \emph{reading}), and let LLMs concentrate the reasoning task based on the collected information (\ie \emph{reasoning}). Specially, we propose an \emph{invoking-linearization-generation} procedure to support LLMs in reasoning on the structured data with the help of the external interfaces. By iterating this procedures with provided interfaces, our approach can gradually approach the target answer to a given query. Extensive experiments conducted on three types of structured data demonstrate the effectiveness of our approach, which can significantly boost the performance of ChatGPT and achieve comparable performance against the full-data supervised-tuning baselines. Our codes and data are publicly available at~\url{https://github.com/RUCAIBox/StructGPT}. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 364,716 |
2311.09027 | Assessing the Robustness of Intelligence-Driven Reinforcement Learning | Robustness to noise is of utmost importance in reinforcement learning systems, particularly in military contexts where high stakes and uncertain environments prevail. Noise and uncertainty are inherent features of military operations, arising from factors such as incomplete information, adversarial actions, or unpredictable battlefield conditions. In RL, noise can critically impact decision-making, mission success, and the safety of personnel. Reward machines offer a powerful tool to express complex reward structures in RL tasks, enabling the design of tailored reinforcement signals that align with mission objectives. This paper considers the problem of the robustness of intelligence-driven reinforcement learning based on reward machines. The preliminary results presented suggest the need for further research in evidential reasoning and learning to harden current state-of-the-art reinforcement learning approaches before being mission-critical-ready. | false | false | false | false | true | false | true | false | false | false | false | false | true | false | false | false | false | false | 407,962 |
2405.20513 | Deep Modeling of Non-Gaussian Aleatoric Uncertainty | Deep learning offers promising new ways to accurately model aleatoric uncertainty in robotic estimation systems, particularly when the uncertainty distributions do not conform to traditional assumptions of being fixed and Gaussian. In this study, we formulate and evaluate three fundamental deep learning approaches for conditional probability density modeling to quantify non-Gaussian aleatoric uncertainty: parametric, discretized, and generative modeling. We systematically compare the respective strengths and weaknesses of these three methods on simulated non-Gaussian densities as well as on real-world terrain-relative navigation data. Our results show that these deep learning methods can accurately capture complex uncertainty patterns, highlighting their potential for improving the reliability and robustness of estimation systems. | false | false | false | false | true | false | true | true | false | false | false | true | false | false | false | false | false | false | 459,384 |
2405.05722 | TraceGrad: a Framework Learning Expressive SO(3)-equivariant Non-linear
Representations for Electronic-Structure Hamiltonian Prediction | We propose a framework to combine strong non-linear expressiveness with strict SO(3)-equivariance in prediction of the electronic-structure Hamiltonian, by exploring the mathematical relationships between SO(3)-invariant and SO(3)-equivariant quantities and their representations. The proposed framework, called TraceGrad, first constructs theoretical SO(3)-invariant trace quantities derived from the Hamiltonian targets, and use these invariant quantities as supervisory labels to guide the learning of high-quality SO(3)-invariant features. Given that SO(3)-invariance is preserved under non-linear operations, the learning of invariant features can extensively utilize non-linear mappings, thereby fully capturing the non-linear patterns inherent in physical systems. Building on this, we propose a gradient-based mechanism to induce SO(3)-equivariant encodings of various degrees from the learned SO(3)-invariant features. This mechanism can incorporate powerful non-linear expressive capabilities into SO(3)-equivariant features with consistency of physical dimensions to the regression targets, while theoretically preserving equivariant properties, establishing a strong foundation for predicting Hamiltonian. Our method achieves state-of-the-art performance in prediction accuracy across eight challenging benchmark databases on Hamiltonian prediction. Experimental results demonstrate that this approach not only improves the accuracy of Hamiltonian prediction but also significantly enhances the prediction for downstream physical quantities, and also markedly improves the acceleration performance for the traditional Density Functional Theory algorithms. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 453,028 |
1509.08392 | Properties of Eventually Positive Linear Input-Output Systems | In this paper, we consider the systems with trajectories originating in the nonnegative orthant becoming nonnegative after some finite time transient. First we consider dynamical systems (i.e., fully observable systems with no inputs), which we call eventually positive. We compute forward-invariant cones and Lyapunov functions for these systems. We then extend the notion of eventually positive systems to the input-output system case. Our extension is performed in such a manner, that some valuable properties of classical internally positive input-output systems are preserved. For example, their induced norms can be computed using linear programming and the energy functions have nonnegative derivatives. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 47,367 |
2412.08291 | Code LLMs: A Taxonomy-based Survey | Large language models (LLMs) have demonstrated remarkable capabilities across various NLP tasks and have recently expanded their impact to coding tasks, bridging the gap between natural languages (NL) and programming languages (PL). This taxonomy-based survey provides a comprehensive analysis of LLMs in the NL-PL domain, investigating how these models are utilized in coding tasks and examining their methodologies, architectures, and training processes. We propose a taxonomy-based framework that categorizes relevant concepts, providing a unified classification system to facilitate a deeper understanding of this rapidly evolving field. This survey offers insights into the current state and future directions of LLMs in coding tasks, including their applications and limitations. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 516,031 |
2404.15814 | Fast Ensembling with Diffusion Schr\"odinger Bridge | Deep Ensemble (DE) approach is a straightforward technique used to enhance the performance of deep neural networks by training them from different initial points, converging towards various local optima. However, a limitation of this methodology lies in its high computational overhead for inference, arising from the necessity to store numerous learned parameters and execute individual forward passes for each parameter during the inference stage. We propose a novel approach called Diffusion Bridge Network (DBN) to address this challenge. Based on the theory of the Schr\"odinger bridge, this method directly learns to simulate an Stochastic Differential Equation (SDE) that connects the output distribution of a single ensemble member to the output distribution of the ensembled model, allowing us to obtain ensemble prediction without having to invoke forward pass through all the ensemble models. By substituting the heavy ensembles with this lightweight neural network constructing DBN, we achieved inference with reduced computational cost while maintaining accuracy and uncertainty scores on benchmark datasets such as CIFAR-10, CIFAR-100, and TinyImageNet. Our implementation is available at https://github.com/kim-hyunsu/dbn. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 449,251 |
2310.04664 | Learning to Rank Onset-Occurring-Offset Representations for
Micro-Expression Recognition | This paper focuses on the research of micro-expression recognition (MER) and proposes a flexible and reliable deep learning method called learning to rank onset-occurring-offset representations (LTR3O). The LTR3O method introduces a dynamic and reduced-size sequence structure known as 3O, which consists of onset, occurring, and offset frames, for representing micro-expressions (MEs). This structure facilitates the subsequent learning of ME-discriminative features. A noteworthy advantage of the 3O structure is its flexibility, as the occurring frame is randomly extracted from the original ME sequence without the need for accurate frame spotting methods. Based on the 3O structures, LTR3O generates multiple 3O representation candidates for each ME sample and incorporates well-designed modules to measure and calibrate their emotional expressiveness. This calibration process ensures that the distribution of these candidates aligns with that of macro-expressions (MaMs) over time. Consequently, the visibility of MEs can be implicitly enhanced, facilitating the reliable learning of more discriminative features for MER. Extensive experiments were conducted to evaluate the performance of LTR3O using three widely-used ME databases: CASME II, SMIC, and SAMM. The experimental results demonstrate the effectiveness and superior performance of LTR3O, particularly in terms of its flexibility and reliability, when compared to recent state-of-the-art MER methods. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 397,751 |
2410.18875 | Exploring the Universe with SNAD: Anomaly Detection in Astronomy | SNAD is an international project with a primary focus on detecting astronomical anomalies within large-scale surveys, using active learning and other machine learning algorithms. The work carried out by SNAD not only contributes to the discovery and classification of various astronomical phenomena but also enhances our understanding and implementation of machine learning techniques within the field of astrophysics. This paper provides a review of the SNAD project and summarizes the advancements and achievements made by the team over several years. | true | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 502,066 |
2004.12207 | Internet-human infrastructures: Lessons from Havana's StreetNet | We propose a mixed-methods approach to understanding the human infrastructure underlying StreetNet (SNET), a distributed, community-run intranet that serves as the primary 'Internet' in Havana, Cuba. We bridge ethnographic studies and the study of social networks and organizations to understand the way that power is embedded in the structure of Havana's SNET. By quantitatively and qualitatively unpacking the human infrastructure of SNET, this work reveals how distributed infrastructure necessarily embeds the structural aspects of inequality distributed within that infrastructure. While traditional technical measurements of networks reflect the social, organizational, spatial, and technical constraints that shape the resulting network, ethnographies can help uncover the texture and role of these hidden supporting relationships. By merging these perspectives, this work contributes to our understanding of network roles in growing and maintaining distributed infrastructures, revealing new approaches to understanding larger, more complex Internet-human infrastructures---including the Internet and the WWW. | false | false | false | true | false | false | false | false | false | false | false | false | false | true | false | false | false | false | 174,166 |
2301.05466 | A Nearly-Linear Time Algorithm for Minimizing Risk of Conflict in Social
Networks | Concomitant with the tremendous prevalence of online social media platforms, the interactions among individuals are unprecedentedly enhanced. People are free to interact with acquaintances, express and exchange their own opinions through commenting, liking, retweeting on online social media, leading to resistance, controversy and other important phenomena over controversial social issues, which have been the subject of many recent works. In this paper, we study the problem of minimizing risk of conflict in social networks by modifying the initial opinions of a small number of nodes. We show that the objective function of the combinatorial optimization problem is monotone and supermodular. We then propose a na\"{\i}ve greedy algorithm with a $(1-1/e)$ approximation ratio that solves the problem in cubic time. To overcome the computation challenge for large networks, we further integrate several effective approximation strategies to provide a nearly linear time algorithm with a $(1-1/e-\epsilon)$ approximation ratio for any error parameter $\epsilon>0$. Extensive experiments on various real-world datasets demonstrate both the efficiency and effectiveness of our algorithms. In particular, the fast one scales to large networks with more than two million nodes, and achieves up to $20\times$ speed-up over the state-of-the-art algorithm. | false | false | false | true | false | false | false | false | false | false | false | false | false | true | false | false | false | false | 340,354 |
2104.10970 | Time series analysis with dynamic law exploration | In this paper we examine, how the dynamic laws governing the time evolution of a time series can be identified. We give a finite difference equation as well as a differential equation representation for that. We also study, how the required symmetries, like time reversal can be imposed on the laws. We study the compression performance of linear laws on sound data. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 231,773 |
2104.10879 | Efficient LiDAR Odometry for Autonomous Driving | LiDAR odometry plays an important role in self-localization and mapping for autonomous navigation, which is usually treated as a scan registration problem. Although having achieved promising performance on KITTI odometry benchmark, the conventional searching tree-based approach still has the difficulty in dealing with the large scale point cloud efficiently. The recent spherical range image-based method enjoys the merits of fast nearest neighbor search by spherical mapping. However, it is not very effective to deal with the ground points nearly parallel to LiDAR beams. To address these issues, we propose a novel efficient LiDAR odometry approach by taking advantage of both non-ground spherical range image and bird's-eye-view map for ground points. Moreover, a range adaptive method is introduced to robustly estimate the local surface normal. Additionally, a very fast and memory-efficient model update scheme is proposed to fuse the points and their corresponding normals at different time-stamps. We have conducted extensive experiments on KITTI odometry benchmark, whose promising results demonstrate that our proposed approach is effective. | false | false | false | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | 231,749 |
2303.06114 | Optimal Design of Validation Experiments for the Prediction of
Quantities of Interest | Numerical predictions of quantities of interest measured within physical systems rely on the use of mathematical models that should be validated, or at best, not invalidated. Model validation usually involves the comparison of experimental data (outputs from the system of interest) and model predictions, both obtained at a specific validation scenario. The design of this validation experiment should be directly relevant to the objective of the model, that of predicting a quantity of interest at a prediction scenario. In this paper, we address two specific issues arising when designing validation experiments. The first issue consists in determining an appropriate validation scenario in cases where the prediction scenario cannot be carried out in a controlled environment. The second issue concerns the selection of observations when the quantity of interest cannot be readily observed. The proposed methodology involves the computation of influence matrices that characterize the response surface of given model functionals. Minimization of the distance between influence matrices allow one for selecting a validation experiment most representative of the prediction scenario. We illustrate our approach on two numerical examples. The first example considers the validation of a simple model based on an ordinary differential equation governing an object in free fall to put in evidence the importance of the choice of the validation experiment. The second numerical experiment focuses on the transport of a pollutant and demonstrates the impact that the choice of the quantity of interest has on the validation experiment to be performed. | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 350,697 |
2410.04542 | Generative Flows on Synthetic Pathway for Drug Design | Generative models in drug discovery have recently gained attention as efficient alternatives to brute-force virtual screening. However, most existing models do not account for synthesizability, limiting their practical use in real-world scenarios. In this paper, we propose RxnFlow, which sequentially assembles molecules using predefined molecular building blocks and chemical reaction templates to constrain the synthetic chemical pathway. We then train on this sequential generating process with the objective of generative flow networks (GFlowNets) to generate both highly rewarded and diverse molecules. To mitigate the large action space of synthetic pathways in GFlowNets, we implement a novel action space subsampling method. This enables RxnFlow to learn generative flows over extensive action spaces comprising combinations of 1.2 million building blocks and 71 reaction templates without significant computational overhead. Additionally, RxnFlow can employ modified or expanded action spaces for generation without retraining, allowing for the introduction of additional objectives or the incorporation of newly discovered building blocks. We experimentally demonstrate that RxnFlow outperforms existing reaction-based and fragment-based models in pocket-specific optimization across various target pockets. Furthermore, RxnFlow achieves state-of-the-art performance on CrossDocked2020 for pocket-conditional generation, with an average Vina score of -8.85kcal/mol and 34.8% synthesizability. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 495,336 |
2311.17655 | Vulnerability of Automatic Identity Recognition to Audio-Visual
Deepfakes | The task of deepfakes detection is far from being solved by speech or vision researchers. Several publicly available databases of fake synthetic video and speech were built to aid the development of detection methods. However, existing databases typically focus on visual or voice modalities and provide no proof that their deepfakes can in fact impersonate any real person. In this paper, we present the first realistic audio-visual database of deepfakes SWAN-DF, where lips and speech are well synchronized and video have high visual and audio qualities. We took the publicly available SWAN dataset of real videos with different identities to create audio-visual deepfakes using several models from DeepFaceLab and blending techniques for face swapping and HiFiVC, DiffVC, YourTTS, and FreeVC models for voice conversion. From the publicly available speech dataset LibriTTS, we also created a separate database of only audio deepfakes LibriTTS-DF using several latest text to speech methods: YourTTS, Adaspeech, and TorToiSe. We demonstrate the vulnerability of a state of the art speaker recognition system, such as ECAPA-TDNN-based model from SpeechBrain, to the synthetic voices. Similarly, we tested face recognition system based on the MobileFaceNet architecture to several variants of our visual deepfakes. The vulnerability assessment show that by tuning the existing pretrained deepfake models to specific identities, one can successfully spoof the face and speaker recognition systems in more than 90% of the time and achieve a very realistic looking and sounding fake video of a given person. | false | false | true | false | true | false | false | false | false | false | false | true | false | false | false | false | false | true | 411,373 |
2105.02184 | PolarMask++: Enhanced Polar Representation for Single-Shot Instance
Segmentation and Beyond | Reducing the complexity of the pipeline of instance segmentation is crucial for real-world applications. This work addresses this issue by introducing an anchor-box free and single-shot instance segmentation framework, termed PolarMask, which reformulates the instance segmentation problem as predicting the contours of objects in the polar coordinate, with several appealing benefits. (1) The polar representation unifies instance segmentation (masks) and object detection (bounding boxes) into a single framework, reducing the design and computational complexity. (2) Two modules are carefully designed (i.e. soft polar centerness and polar IoU loss) to sample high-quality center examples and optimize polar contour regression, making the performance of PolarMask does not depend on the bounding box prediction results and thus becomes more efficient in training. (3) PolarMask is fully convolutional and can be easily embedded into most off-the-shelf detection methods. To further improve the accuracy of the framework, a Refined Feature Pyramid is introduced to further improve the feature representation at different scales, termed PolarMask++. Extensive experiments demonstrate the effectiveness of both PolarMask and PolarMask++, which achieve competitive results on instance segmentation in the challenging COCO dataset with single-model and single-scale training and testing, as well as new state-of-the-art results on rotate text detection and cell segmentation. We hope the proposed polar representation can provide a new perspective for designing algorithms to solve single-shot instance segmentation. The codes and models are available at: github.com/xieenze/PolarMask. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 233,746 |
2408.15954 | InstanSeg: an embedding-based instance segmentation algorithm optimized
for accurate, efficient and portable cell segmentation | Cell and nucleus segmentation are fundamental tasks for quantitative bioimage analysis. Despite progress in recent years, biologists and other domain experts still require novel algorithms to handle increasingly large and complex real-world datasets. These algorithms must not only achieve state-of-the-art accuracy, but also be optimized for efficiency, portability and user-friendliness. Here, we introduce InstanSeg: a novel embedding-based instance segmentation pipeline designed to identify cells and nuclei in microscopy images. Using six public cell segmentation datasets, we demonstrate that InstanSeg can significantly improve accuracy when compared to the most widely used alternative methods, while reducing the processing time by at least 60%. Furthermore, InstanSeg is designed to be fully serializable as TorchScript and supports GPU acceleration on a range of hardware. We provide an open-source implementation of InstanSeg in Python, in addition to a user-friendly, interactive QuPath extension for inference written in Java. Our code and pre-trained models are available at https://github.com/instanseg/instanseg . | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 484,137 |
2303.12711 | Geometry-Aware Latent Representation Learning for Modeling Disease
Progression of Barrett's Esophagus | Barrett's Esophagus (BE) is the only precursor known to Esophageal Adenocarcinoma (EAC), a type of esophageal cancer with poor prognosis upon diagnosis. Therefore, diagnosing BE is crucial in preventing and treating esophageal cancer. While supervised machine learning supports BE diagnosis, high interobserver variability in histopathological training data limits these methods. Unsupervised representation learning via Variational Autoencoders (VAEs) shows promise, as they map input data to a lower-dimensional manifold with only useful features, characterizing BE progression for improved downstream tasks and insights. However, the VAE's Euclidean latent space distorts point relationships, hindering disease progression modeling. Geometric VAEs provide additional geometric structure to the latent space, with RHVAE assuming a Riemannian manifold and $\mathcal{S}$-VAE a hyperspherical manifold. Our study shows that $\mathcal{S}$-VAE outperforms vanilla VAE with better reconstruction losses, representation classification accuracies, and higher-quality generated images and interpolations in lower-dimensional settings. By disentangling rotation information from the latent space, we improve results further using a group-based architecture. Additionally, we take initial steps towards $\mathcal{S}$-AE, a novel autoencoder model generating qualitative images without a variational framework, but retaining benefits of autoencoders such as stability and reconstruction quality. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 353,351 |
1410.5884 | Mean-Field Networks | The mean field algorithm is a widely used approximate inference algorithm for graphical models whose exact inference is intractable. In each iteration of mean field, the approximate marginals for each variable are updated by getting information from the neighbors. This process can be equivalently converted into a feedforward network, with each layer representing one iteration of mean field and with tied weights on all layers. This conversion enables a few natural extensions, e.g. untying the weights in the network. In this paper, we study these mean field networks (MFNs), and use them as inference tools as well as discriminative models. Preliminary experiment results show that MFNs can learn to do inference very efficiently and perform significantly better than mean field as discriminative models. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 36,948 |
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