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2110.09860 | Bilateral-ViT for Robust Fovea Localization | The fovea is an important anatomical landmark of the retina. Detecting the location of the fovea is essential for the analysis of many retinal diseases. However, robust fovea localization remains a challenging problem, as the fovea region often appears fuzzy, and retina diseases may further obscure its appearance. This paper proposes a novel Vision Transformer (ViT) approach that integrates information both inside and outside the fovea region to achieve robust fovea localization. Our proposed network, named Bilateral-Vision-Transformer (Bilateral-ViT), consists of two network branches: a transformer-based main network branch for integrating global context across the entire fundus image and a vessel branch for explicitly incorporating the structure of blood vessels. The encoded features from both network branches are subsequently merged with a customized Multi-scale Feature Fusion (MFF) module. Our comprehensive experiments demonstrate that the proposed approach is significantly more robust for diseased images and establishes the new state of the arts using the Messidor and PALM datasets. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 261,952 |
2202.03392 | Large-scale Personalized Video Game Recommendation via Social-aware
Contextualized Graph Neural Network | Because of the large number of online games available nowadays, online game recommender systems are necessary for users and online game platforms. The former can discover more potential online games of their interests, and the latter can attract users to dwell longer in the platform. This paper investigates the characteristics of user behaviors with respect to the online games on the Steam platform. Based on the observations, we argue that a satisfying recommender system for online games is able to characterize: personalization, game contextualization and social connection. However, simultaneously solving all is rather challenging for game recommendation. Firstly, personalization for game recommendation requires the incorporation of the dwelling time of engaged games, which are ignored in existing methods. Secondly, game contextualization should reflect the complex and high-order properties of those relations. Last but not least, it is problematic to use social connections directly for game recommendations due to the massive noise within social connections. To this end, we propose a Social-aware Contextualized Graph Neural Recommender System (SCGRec), which harnesses three perspectives to improve game recommendation. We conduct a comprehensive analysis of users' online game behaviors, which motivates the necessity of handling those three characteristics in the online game recommendation. | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | 279,187 |
2006.02425 | Equivariant Flows: Exact Likelihood Generative Learning for Symmetric
Densities | Normalizing flows are exact-likelihood generative neural networks which approximately transform samples from a simple prior distribution to samples of the probability distribution of interest. Recent work showed that such generative models can be utilized in statistical mechanics to sample equilibrium states of many-body systems in physics and chemistry. To scale and generalize these results, it is essential that the natural symmetries in the probability density -- in physics defined by the invariances of the target potential -- are built into the flow. We provide a theoretical sufficient criterion showing that the distribution generated by \textit{equivariant} normalizing flows is invariant with respect to these symmetries by design. Furthermore, we propose building blocks for flows which preserve symmetries which are usually found in physical/chemical many-body particle systems. Using benchmark systems motivated from molecular physics, we demonstrate that those symmetry preserving flows can provide better generalization capabilities and sampling efficiency. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 180,037 |
2009.04131 | SoK: Certified Robustness for Deep Neural Networks | Great advances in deep neural networks (DNNs) have led to state-of-the-art performance on a wide range of tasks. However, recent studies have shown that DNNs are vulnerable to adversarial attacks, which have brought great concerns when deploying these models to safety-critical applications such as autonomous driving. Different defense approaches have been proposed against adversarial attacks, including: a) empirical defenses, which can usually be adaptively attacked again without providing robustness certification; and b) certifiably robust approaches, which consist of robustness verification providing the lower bound of robust accuracy against any attacks under certain conditions and corresponding robust training approaches. In this paper, we systematize certifiably robust approaches and related practical and theoretical implications and findings. We also provide the first comprehensive benchmark on existing robustness verification and training approaches on different datasets. In particular, we 1) provide a taxonomy for the robustness verification and training approaches, as well as summarize the methodologies for representative algorithms, 2) reveal the characteristics, strengths, limitations, and fundamental connections among these approaches, 3) discuss current research progresses, theoretical barriers, main challenges, and future directions for certifiably robust approaches for DNNs, and 4) provide an open-sourced unified platform to evaluate 20+ representative certifiably robust approaches. | false | false | false | false | false | false | true | false | false | false | false | false | true | false | false | false | false | false | 194,973 |
1709.05665 | Automatic Tool Landmark Detection for Stereo Vision in Robot-Assisted
Retinal Surgery | Computer vision and robotics are being increasingly applied in medical interventions. Especially in interventions where extreme precision is required they could make a difference. One such application is robot-assisted retinal microsurgery. In recent works, such interventions are conducted under a stereo-microscope, and with a robot-controlled surgical tool. The complementarity of computer vision and robotics has however not yet been fully exploited. In order to improve the robot control we are interested in 3D reconstruction of the anatomy and in automatic tool localization using a stereo microscope. In this paper, we solve this problem for the first time using a single pipeline, starting from uncalibrated cameras to reach metric 3D reconstruction and registration, in retinal microsurgery. The key ingredients of our method are: (a) surgical tool landmark detection, and (b) 3D reconstruction with the stereo microscope, using the detected landmarks. To address the former, we propose a novel deep learning method that detects and recognizes keypoints in high definition images at higher than real-time speed. We use the detected 2D keypoints along with their corresponding 3D coordinates obtained from the robot sensors to calibrate the stereo microscope using an affine projection model. We design an online 3D reconstruction pipeline that makes use of smoothness constraints and performs robot-to-camera registration. The entire pipeline is extensively validated on open-sky porcine eye sequences. Quantitative and qualitative results are presented for all steps. | false | false | false | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | 80,926 |
2111.10866 | CpT: Convolutional Point Transformer for 3D Point Cloud Processing | We present CpT: Convolutional point Transformer - a novel deep learning architecture for dealing with the unstructured nature of 3D point cloud data. CpT is an improvement over existing attention-based Convolutions Neural Networks as well as previous 3D point cloud processing transformers. It achieves this feat due to its effectiveness in creating a novel and robust attention-based point set embedding through a convolutional projection layer crafted for processing dynamically local point set neighbourhoods. The resultant point set embedding is robust to the permutations of the input points. Our novel CpT block builds over local neighbourhoods of points obtained via a dynamic graph computation at each layer of the networks' structure. It is fully differentiable and can be stacked just like convolutional layers to learn global properties of the points. We evaluate our model on standard benchmark datasets such as ModelNet40, ShapeNet Part Segmentation, and the S3DIS 3D indoor scene semantic segmentation dataset to show that our model can serve as an effective backbone for various point cloud processing tasks when compared to the existing state-of-the-art approaches. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 267,473 |
2111.07707 | Simultaneously Achieving Sublinear Regret and Constraint Violations for
Online Convex Optimization with Time-varying Constraints | In this paper, we develop a novel virtual-queue-based online algorithm for online convex optimization (OCO) problems with long-term and time-varying constraints and conduct a performance analysis with respect to the dynamic regret and constraint violations. We design a new update rule of dual variables and a new way of incorporating time-varying constraint functions into the dual variables. To the best of our knowledge, our algorithm is the first parameter-free algorithm to simultaneously achieve sublinear dynamic regret and constraint violations. Our proposed algorithm also outperforms the state-of-the-art results in many aspects, e.g., our algorithm does not require the Slater condition. Meanwhile, for a group of practical and widely-studied constrained OCO problems in which the variation of consecutive constraints is smooth enough across time, our algorithm achieves $O(1)$ constraint violations. Furthermore, we extend our algorithm and analysis to the case when the time horizon $T$ is unknown. Finally, numerical experiments are conducted to validate the theoretical guarantees of our algorithm, and some applications of our proposed framework will be outlined. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 266,460 |
1912.00622 | Patchy Image Structure Classification Using Multi-Orientation Region
Transform | Exterior contour and interior structure are both vital features for classifying objects. However, most of the existing methods consider exterior contour feature and internal structure feature separately, and thus fail to function when classifying patchy image structures that have similar contours and flexible structures. To address above limitations, this paper proposes a novel Multi-Orientation Region Transform (MORT), which can effectively characterize both contour and structure features simultaneously, for patchy image structure classification. MORT is performed over multiple orientation regions at multiple scales to effectively integrate patchy features, and thus enables a better description of the shape in a coarse-to-fine manner. Moreover, the proposed MORT can be extended to combine with the deep convolutional neural network techniques, for further enhancement of classification accuracy. Very encouraging experimental results on the challenging ultra-fine-grained cultivar recognition task, insect wing recognition task, and large variation butterfly recognition task are obtained, which demonstrate the effectiveness and superiority of the proposed MORT over the state-of-the-art methods in classifying patchy image structures. Our code and three patchy image structure datasets are available at: https://github.com/XiaohanYu-GU/MReT2019. | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | false | false | 155,836 |
2103.13921 | The Resh Programming Language for Multirobot Orchestration | This paper describes Resh, a new, statically typed, interpreted programming language and associated runtime for orchestrating multirobot systems. The main features of Resh are: (1) It offloads much of the tedious work of programming such systems away from the programmer and into the language runtime; (2) It is based on a small set of temporal and locational operators; and (3) It is not restricted to specific robot types or tasks. The Resh runtime consists of three engines that collaborate to run a Resh program using the available robots in their current environment. This paper describes both Resh and its runtime and gives examples of its use. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | true | 226,664 |
1308.5585 | Rewriting XPath Queries using View Intersections: Tractability versus
Completeness | The standard approach for optimization of XPath queries by rewriting using views techniques consists in navigating inside a view's output, thus allowing the usage of only one view in the rewritten query. Algorithms for richer classes of XPath rewritings, using intersection or joins on node identifiers, have been proposed, but they either lack completeness guarantees, or require additional information about the data. We identify the tightest restrictions under which an XPath can be rewritten in polynomial time using an intersection of views and propose an algorithm that works for any documents or type of identifiers. As a side-effect, we analyze the complexity of the related problem of deciding if an XPath with intersection can be equivalently rewritten as one without intersection or union. We extend our formal study of the view-based rewriting problem for XPath by describing also (i) algorithms for more complex rewrite plans, with no limitations on the number of intersection and navigation steps inside view outputs they employ, and (ii) adaptations of our techniques to deal with XML documents without persistent node Ids, in the presence of XML keys. Complementing our computational complexity study, we describe a proof-of-concept implementation of our techniques and possible choices that may speed up execution in practice, regarding how rewrite plans are built, tested and executed. We also give a thorough experimental evaluation of these techniques, focusing on scalability and the running time improvements achieved by the execution of view-based plans. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | 26,650 |
2304.00524 | A Survey on Federated Learning for the Healthcare Metaverse: Concepts,
Applications, Challenges, and Future Directions | Recent technological advancements have considerately improved healthcare systems to provide various intelligent healthcare services and improve the quality of life. Federated learning (FL), a new branch of artificial intelligence (AI), opens opportunities to deal with privacy issues in healthcare systems and exploit data and computing resources available at distributed devices. Additionally, the Metaverse, through integrating emerging technologies, such as AI, cloud edge computing, Internet of Things (IoT), blockchain, and semantic communications, has transformed many vertical domains in general and the healthcare sector in particular. Obviously, FL shows many benefits and provides new opportunities for conventional and Metaverse healthcare, motivating us to provide a survey on the usage of FL for Metaverse healthcare systems. First, we present preliminaries to IoT-based healthcare systems, FL in conventional healthcare, and Metaverse healthcare. The benefits of FL in Metaverse healthcare are then discussed, from improved privacy and scalability, better interoperability, better data management, and extra security to automation and low-latency healthcare services. Subsequently, we discuss several applications pertaining to FL-enabled Metaverse healthcare, including medical diagnosis, patient monitoring, medical education, infectious disease, and drug discovery. Finally, we highlight significant challenges and potential solutions toward the realization of FL in Metaverse healthcare. | false | false | false | false | true | false | false | false | false | false | false | false | false | true | false | false | false | false | 355,722 |
2401.13527 | SpeechGPT-Gen: Scaling Chain-of-Information Speech Generation | Benefiting from effective speech modeling, current Speech Large Language Models (SLLMs) have demonstrated exceptional capabilities in in-context speech generation and efficient generalization to unseen speakers. However, the prevailing information modeling process is encumbered by certain redundancies, leading to inefficiencies in speech generation. We propose Chain-of-Information Generation (CoIG), a method for decoupling semantic and perceptual information in large-scale speech generation. Building on this, we develop SpeechGPT-Gen, an 8-billion-parameter SLLM efficient in semantic and perceptual information modeling. It comprises an autoregressive model based on LLM for semantic information modeling and a non-autoregressive model employing flow matching for perceptual information modeling. Additionally, we introduce the novel approach of infusing semantic information into the prior distribution to enhance the efficiency of flow matching. Extensive experimental results demonstrate that SpeechGPT-Gen markedly excels in zero-shot text-to-speech, zero-shot voice conversion, and speech-to-speech dialogue, underscoring CoIG's remarkable proficiency in capturing and modeling speech's semantic and perceptual dimensions. Code and models are available at https://github.com/0nutation/SpeechGPT. | false | false | true | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 423,755 |
2410.12222 | On A Scale From 1 to 5: Quantifying Hallucination in Faithfulness
Evaluation | Hallucination has been a popular topic in natural language generation (NLG). In real-world applications, unfaithful content can result in poor data quality or loss of trust from end users. Thus, it is crucial to fact-check before adopting NLG for production usage, which can be expensive if done manually. In this paper, we investigate automated faithfulness evaluation in guided NLG. We developed a rubric template and used large language models (LLMs) to score the generation on quantifiable scales. We compared popular LLMs as well as widely adopted natural language inference (NLI) models in scoring quality and sensitivity. In addition, we developed methods for the generation of synthetic unfaithful data, as well as heuristics to quantify the percentage of hallucination. Our results on 4 travel-domain industry dataset show that GPT-4 can provide accurate judgement and explanation of whether a source and a generation are factually consistent. Furthermore, we found that tuning NLI models on synthetic data can improve performance. Lastly, we present insights on the latency and cost of deploying such a system. | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 498,913 |
2310.13932 | Trajectory and Power Design for Aerial Multi-User Covert Communications | Unmanned aerial vehicles (UAVs) can provide wireless access to terrestrial users, regardless of geographical constraints, and will be an important part of future communication systems. In this paper, a multi-user downlink dual-UAVs enabled covert communication system was investigated, in which a UAV transmits secure information to ground users in the presence of multiple wardens as well as a friendly jammer UAV transmits artificial jamming signals to fight with the wardens. The scenario of wardens being outfitted with a single antenna is considered, and the detection error probability (DEP) of wardens with finite observations is researched. Then, considering the uncertainty of wardens' location, a robust optimization problem with worst-case covertness constraint is formulated to maximize the average covert rate by jointly optimizing power allocation and trajectory. To cope with the optimization problem, an algorithm based on successive convex approximation methods is proposed. Thereafter, the results are extended to the case where all the wardens are equipped with multiple antennas. After analyzing the DEP in this scenario, a tractable lower bound of the DEP is obtained by utilizing Pinsker's inequality. Subsequently, the non-convex optimization problem was established and efficiently coped by utilizing a similar algorithm as in the single-antenna scenario. Numerical results indicate the effectiveness of our proposed algorithm. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 401,644 |
2212.06628 | Target Defense against Sequentially Arriving Intruders | We consider a variant of the target defense problem where a single defender is tasked to capture a sequence of incoming intruders. The intruders' objective is to breach the target boundary without being captured by the defender. As soon as the current intruder breaches the target or gets captured by the defender, the next intruder appears at a random location on a fixed circle surrounding the target. Therefore, the defender's final location at the end of the current game becomes its initial location for the next game. Thus, the players pick strategies that are advantageous for the current as well as for the future games. Depending on the information available to the players, each game is divided into two phases: partial information and full information phase. Under some assumptions on the sensing and speed capabilities, we analyze the agents' strategies in both phases. We derive equilibrium strategies for both the players to optimize the capture percentage using the notions of engagement surface and capture circle. We quantify the percentage of capture for both finite and infinite sequences of incoming intruders. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | 336,169 |
2203.04820 | Participation Factor-Based Adaptive Model Reduction for Fast Power
System Simulation | This paper describes an adaptive method to reduce a nonlinear power system model for fast and accurate transient stability simulation. It presents an approach to analyze and rank participation factors of each system state variable into dominant system modes excited by a disturbance so as to determine which regions or generators can be reduced without impacting the accuracy of simulation for a study area. In this approach, the generator models located in an external area with large participation factors are nonlinearly reduced and the rest of the generators will be linearized. The simulation results confirm that the assessment of the level of interaction between generators and system modes by participation factors is effective in enhancing the accuracy and speed of power system models. The proposed method is applied to the Northeastern Power Coordinating Council region system with 48-machine, 140-bus power system model and the results are compared with two cases including fully linearized model reduction and model reduction using the rotor angle deviation criteria. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | true | 284,616 |
2106.12702 | A Mixed-Integer Conic Programming Formulation for Computing the
Flexibility Index under Multivariate Gaussian Uncertainty | We present a methodology for computing the flexibility index when uncertainty is characterized using multivariate Gaussian random variables. Our approach computes the flexibility index by solving a mixed-integer conic program (MICP). This methodology directly characterizes ellipsoidal sets to capture correlations in contrast to previous methodologies that employ approximations. We also show that, under a Gaussian representation, the flexibility index can be used to obtain a lower bound for the so-called stochastic flexibility index (i.e., the probability of having feasible operation). Our results also show that the methodology can be generalized to capture different types of uncertainty sets. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 242,808 |
2111.03265 | EpilNet: A Novel Approach to IoT based Epileptic Seizure Prediction and
Diagnosis System using Artificial Intelligence | Epilepsy is one of the most occurring neurological diseases. The main characteristic of this disease is a frequent seizure, which is an electrical imbalance in the brain. It is generally accompanied by shaking of body parts and even leads (fainting). In the past few years, many treatments have come up. These mainly involve the use of anti-seizure drugs for controlling seizures. But in 70% of cases, these drugs are not effective, and surgery is the only solution when the condition worsens. So patients need to take care of themselves while having a seizure and be safe. Wearable electroencephalogram (EEG) devices have come up with the development in medical science and technology. These devices help in the analysis of brain electrical activities. EEG helps in locating the affected cortical region. The most important is that it can predict any seizure in advance on-site. This has resulted in a sudden increase in demand for effective and efficient seizure prediction and diagnosis systems. A novel approach to epileptic seizure prediction and diagnosis system EpilNet is proposed in the present paper. It is a one-dimensional (1D) convolution neural network. EpilNet gives the testing accuracy of 79.13% for five classes, leading to a significant increase of about 6-7% compared to related works. The developed Web API helps in bringing EpilNet into practical use. Thus, it is an integrated system for both patients and doctors. The system will help patients prevent injury or accidents and increase the efficiency of the treatment process by doctors in the hospitals. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 265,108 |
2205.10553 | Robot Person Following in Uniform Crowd Environment | Person-tracking robots have many applications, such as in security, elderly care, and socializing robots. Such a task is particularly challenging when the person is moving in a Uniform crowd. Also, despite significant progress of trackers reported in the literature, state-of-the-art trackers have hardly addressed person following in such scenarios. In this work, we focus on improving the perceptivity of a robot for a person following task by developing a robust and real-time applicable object tracker. We present a new robot person tracking system with a new RGB-D tracker, Deep Tracking with RGB-D (DTRD) that is resilient to tricky challenges introduced by the uniform crowd environment. Our tracker utilizes transformer encoder-decoder architecture with RGB and depth information to discriminate the target person from similar distractors. A substantial amount of comprehensive experiments and results demonstrate that our tracker has higher performance in two quantitative evaluation metrics and confirms its superiority over other SOTA trackers. | false | false | false | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | 297,756 |
2411.05540 | CRepair: CVAE-based Automatic Vulnerability Repair Technology | Software vulnerabilities are flaws in computer software systems that pose significant threats to the integrity, security, and reliability of modern software and its application data. These vulnerabilities can lead to substantial economic losses across various industries. Manual vulnerability repair is not only time-consuming but also prone to errors. To address the challenges of vulnerability repair, researchers have proposed various solutions, with learning-based automatic vulnerability repair techniques gaining widespread attention. However, existing methods often focus on learning more vulnerability data to improve repair outcomes, while neglecting the diverse characteristics of vulnerable code, and suffer from imprecise vulnerability localization.To address these shortcomings, this paper proposes CRepair, a CVAE-based automatic vulnerability repair technology aimed at fixing security vulnerabilities in system code. We first preprocess the vulnerability data using a prompt-based method to serve as input to the model. Then, we apply causal inference techniques to map the vulnerability feature data to probability distributions. By employing multi-sample feature fusion, we capture diverse vulnerability feature information. Finally, conditional control is used to guide the model in repairing the vulnerabilities.Experimental results demonstrate that the proposed method significantly outperforms other benchmark models, achieving a perfect repair rate of 52%. The effectiveness of the approach is validated from multiple perspectives, advancing AI-driven code vulnerability repair and showing promising applications. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | true | 506,702 |
2309.08622 | Representation Learning in Low-rank Slate-based Recommender Systems | Reinforcement learning (RL) in recommendation systems offers the potential to optimize recommendations for long-term user engagement. However, the environment often involves large state and action spaces, which makes it hard to efficiently learn and explore. In this work, we propose a sample-efficient representation learning algorithm, using the standard slate recommendation setup, to treat this as an online RL problem with low-rank Markov decision processes (MDPs). We also construct the recommender simulation environment with the proposed setup and sampling method. | false | false | false | false | true | true | false | false | false | false | false | false | false | false | false | false | false | false | 392,255 |
2202.09346 | Improving Molecular Contrastive Learning via Faulty Negative Mitigation
and Decomposed Fragment Contrast | Deep learning has been a prevalence in computational chemistry and widely implemented in molecule property predictions. Recently, self-supervised learning (SSL), especially contrastive learning (CL), gathers growing attention for the potential to learn molecular representations that generalize to the gigantic chemical space. Unlike supervised learning, SSL can directly leverage large unlabeled data, which greatly reduces the effort to acquire molecular property labels through costly and time-consuming simulations or experiments. However, most molecular SSL methods borrow the insights from the machine learning community but neglect the unique cheminformatics (e.g., molecular fingerprints) and multi-level graphical structures (e.g., functional groups) of molecules. In this work, we propose iMolCLR: improvement of Molecular Contrastive Learning of Representations with graph neural networks (GNNs) in two aspects, (1) mitigating faulty negative contrastive instances via considering cheminformatics similarities between molecule pairs; (2) fragment-level contrasting between intra- and inter-molecule substructures decomposed from molecules. Experiments have shown that the proposed strategies significantly improve the performance of GNN models on various challenging molecular property predictions. In comparison to the previous CL framework, iMolCLR demonstrates an averaged 1.3% improvement of ROC-AUC on 7 classification benchmarks and an averaged 4.8% decrease of the error on 5 regression benchmarks. On most benchmarks, the generic GNN pre-trained by iMolCLR rivals or even surpasses supervised learning models with sophisticated architecture designs and engineered features. Further investigations demonstrate that representations learned through iMolCLR intrinsically embed scaffolds and functional groups that can reason molecule similarities. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 281,161 |
1910.01601 | SensorDrop: A Reinforcement Learning Framework for Communication
Overhead Reduction on the Edge | In IoT solutions, it is usually desirable to collect data from a large number of distributed IoT sensors at a central node in the cloud for further processing. One of the main design challenges of such solutions is the high communication overhead between the sensors and the central node (especially for multimedia data). In this paper, we aim to reduce the communication overhead and propose a method that is able to determine which sensors should send their data to the central node and which to drop data. The idea is that some sensors may have data which are correlated with others and some may have data that are not essential for the operation to be performed at the central node. As such decisions are application dependent and may change over time, they should be learned during the operation of the system, for that we propose a method based on Advantage Actor-Critic (A2C) reinforcement learning which gradually learns which sensor's data is cost-effective to be sent to the central node. The proposed approach has been evaluated on a multi-view multi-camera dataset, and we observe a significant reduction in communication overhead with marginal degradation in object classification accuracy. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 147,981 |
2301.13816 | Execution-based Code Generation using Deep Reinforcement Learning | The utilization of programming language (PL) models, pre-trained on large-scale code corpora, as a means of automating software engineering processes has demonstrated considerable potential in streamlining various code generation tasks such as code completion, code translation, and program synthesis. However, current approaches mainly rely on supervised fine-tuning objectives borrowed from text generation, neglecting unique sequence-level characteristics of code, including but not limited to compilability as well as syntactic and functional correctness. To address this limitation, we propose PPOCoder, a new framework for code generation that synergistically combines pre-trained PL models with Proximal Policy Optimization (PPO) which is a widely used deep reinforcement learning technique. By utilizing non-differentiable feedback from code execution and structure alignment, PPOCoder seamlessly integrates external code-specific knowledge into the model optimization process. It's important to note that PPOCoder is a task-agnostic and model-agnostic framework that can be used across different code generation tasks and PLs. Extensive experiments on three code generation tasks demonstrate the effectiveness of our proposed approach compared to SOTA methods, achieving significant improvements in compilation success rates and functional correctness across different PLs. | false | false | false | false | true | false | true | false | true | false | false | false | false | false | false | false | false | true | 343,041 |
1804.07701 | Practical Issues in the Synthesis of Ternary Sequences | Several issues related to the practical synthesis of ternary sequences with specified spectra are addressed in this paper. Specifically, sequences with harmonic multiples of two and three suppressed are studied, given their relevance when testing and characterizing nonlinear systems. In particular, the effect of non-uniform Digital to Analog Converter (DAC) levels on the spectral properties of the generated signal is analyzed. It is analytically shown that the DAC non-uniform levels result in degraded harmonic suppression performance. Moreover, a new approach is proposed for designing ternary sequences, which is flexible and can be adapted to suit different requirements. The resulting sequences, denoted as randomized constrained sequences, are characterized theoretically by deriving an analytical expression of the power spectral density. Furthermore, they are extensively compared with three synthesis approaches proposed in the literature. The approach is validated by numerical simulations and experimental results, showing the potential to achieve harmonic suppression performance of approximately 100 dB. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | true | 95,576 |
2103.11759 | On how generalised entropies without parameters impact information
optimisation processes | As an application of generalised statistical mechanics, it is studied a possible route toward a consistent generalised information theory in terms of a family of non-extensive, non-parametric entropies $H^\pm_D(P)$. Unlike other proposals based on non-extensive entropies with a parameter dependence, our scheme is asymptotically equivalent to the one formulated by Shannon, while it differs in regions where the density of states is reasonably small, which leads to information distributions constrained to their background. Two basic concepts are discussed to this aim. First, we prove two effective coding theorems for the entropies $H^\pm_D(P)$. Then we calculate the channel capacity of a binary symmetric channel (BSC) and a binary erasure channel (BEC) in terms of these entropies. We found that processes such as data compression and channel capacity maximisation can be improved in regions where there is a low density of states, whereas for high densities our results coincide with Shannon's formulation. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 225,920 |
2202.13202 | TaSPM: Targeted Sequential Pattern Mining | Sequential pattern mining (SPM) is an important technique of pattern mining, which has many applications in reality. Although many efficient sequential pattern mining algorithms have been proposed, there are few studies can focus on target sequences. Targeted querying sequential patterns can not only reduce the number of sequences generated by SPM, but also improve the efficiency of users in performing pattern analysis. The current algorithms available on targeted sequence querying are based on specific scenarios and cannot be generalized to other applications. In this paper, we formulate the problem of targeted sequential pattern mining and propose a generic framework namely TaSPM, based on the fast CM-SPAM algorithm. What's more, to improve the efficiency of TaSPM on large-scale datasets and multiple-items-based sequence datasets, we propose several pruning strategies to reduce meaningless operations in mining processes. Totally four pruning strategies are designed in TaSPM, and hence it can terminate unnecessary pattern extensions quickly and achieve better performance. Finally, we conduct extensive experiments on different datasets to compare the existing SPM algorithms with TaSPM. Experiments show that the novel targeted mining algorithm TaSPM can achieve faster running time and less memory consumption. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | true | false | 282,525 |
1206.6853 | A theoretical study of Y structures for causal discovery | There are several existing algorithms that under appropriate assumptions can reliably identify a subset of the underlying causal relationships from observational data. This paper introduces the first computationally feasible score-based algorithm that can reliably identify causal relationships in the large sample limit for discrete models, while allowing for the possibility that there are unobserved common causes. In doing so, the algorithm does not ever need to assign scores to causal structures with unobserved common causes. The algorithm is based on the identification of so called Y substructures within Bayesian network structures that can be learned from observational data. An example of a Y substructure is A -> C, B -> C, C -> D. After providing background on causal discovery, the paper proves the conditions under which the algorithm is reliable in the large sample limit. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 17,078 |
2210.01910 | Learning Signal Temporal Logic through Neural Network for Interpretable
Classification | Machine learning techniques using neural networks have achieved promising success for time-series data classification. However, the models that they produce are challenging to verify and interpret. In this paper, we propose an explainable neural-symbolic framework for the classification of time-series behaviors. In particular, we use an expressive formal language, namely Signal Temporal Logic (STL), to constrain the search of the computation graph for a neural network. We design a novel time function and sparse softmax function to improve the soundness and precision of the neural-STL framework. As a result, we can efficiently learn a compact STL formula for the classification of time-series data through off-the-shelf gradient-based tools. We demonstrate the computational efficiency, compactness, and interpretability of the proposed method through driving scenarios and naval surveillance case studies, compared with state-of-the-art baselines. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 321,448 |
2502.01397 | Can message-passing GNN approximate triangular factorizations of sparse
matrices? | We study fundamental limitations of Graph Neural Networks (GNNs) for learning sparse matrix preconditioners. While recent works have shown promising results using GNNs to predict incomplete factorizations, we demonstrate that the local nature of message passing creates inherent barriers for capturing non-local dependencies required for optimal preconditioning. We introduce a new benchmark dataset of matrices where good sparse preconditioners exist but require non-local computations, constructed using both synthetic examples and real-world matrices. Our experimental results show that current GNN architectures struggle to approximate these preconditioners, suggesting the need for new architectural approaches beyond traditional message passing networks. We provide theoretical analysis and empirical evidence to explain these limitations, with implications for the broader use of GNNs in numerical linear algebra. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | true | 529,839 |
2502.01882 | Latent Lexical Projection in Large Language Models: A Novel Approach to
Implicit Representation Refinement | Generating semantically coherent text requires a robust internal representation of linguistic structures, which traditional embedding techniques often fail to capture adequately. A novel approach, Latent Lexical Projection (LLP), is introduced to refine lexical representations through a structured transformation into a latent space, thereby enhancing the alignment between input embeddings and their contextual meanings. The method integrates an optimized projection mechanism within an existing language model architecture, enabling more accurate token selection while maintaining syntactic integrity. Evaluations across multiple benchmarks indicate a reduction in perplexity and an increase in BLEU scores, suggesting improvements in predictive accuracy and fluency. The analysis of lexical diversity reveals a more varied vocabulary in generated text, addressing common issues of redundancy and repetitive phrase structures. Further assessments of entropy distributions demonstrate a decline in uncertainty during decoding, reflecting enhanced confidence in word selection. Additionally, long-range dependency retention exhibits measurable gains, with increased classification accuracy at extended token distances. Computational efficiency remains within manageable constraints, despite the added projection mechanism, highlighting the practicality of LLP for integration into existing architectures. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 530,073 |
1905.05964 | Deep Kinship Verification via Appearance-shape Joint Prediction and
Adaptation-based Approach | Kinship verification aims to identify the kin relation between two given face images. It is a very challenging problem due to the lack of training data and facial similarity variations between kinship pairs. In this work, we build a novel appearance and shape based deep learning pipeline. First we adopt the knowledge learned from general face recognition network to learn general facial features. Afterwards, we learn kinship oriented appearance and shape features from kinship pairs and combine them for the final prediction. We have evaluated the model performance on a widely used popular benchmark and demonstrated the superiority over the state-of-the-art. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 130,871 |
2006.09011 | Improved Techniques for Training Score-Based Generative Models | Score-based generative models can produce high quality image samples comparable to GANs, without requiring adversarial optimization. However, existing training procedures are limited to images of low resolution (typically below 32x32), and can be unstable under some settings. We provide a new theoretical analysis of learning and sampling from score models in high dimensional spaces, explaining existing failure modes and motivating new solutions that generalize across datasets. To enhance stability, we also propose to maintain an exponential moving average of model weights. With these improvements, we can effortlessly scale score-based generative models to images with unprecedented resolutions ranging from 64x64 to 256x256. Our score-based models can generate high-fidelity samples that rival best-in-class GANs on various image datasets, including CelebA, FFHQ, and multiple LSUN categories. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 182,393 |
1711.07992 | Generating Analytic Insights on Human Behaviour using Image Processing | This paper proposes a method to track human figures in physical spaces and then utilizes this data to generate several data points such as footfall distribution, demographic analysis,heat maps as well as gender distribution. The proposed framework aims to establish this while utilizing minimum computational resources while remaining real time. It is often useful to have information such as what kind of people visit a certain place or what hour of the day experiences maximum activity, Such analysis can be used improve sales, manage huge number of people as well as predict future behaviour. The proposed framework is designed in a way such that it can take input streams from IP cameras and use that to generate relevant data points using open source tools such as OpenCV and raspberryPi. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 85,111 |
1706.08198 | English-Japanese Neural Machine Translation with
Encoder-Decoder-Reconstructor | Neural machine translation (NMT) has recently become popular in the field of machine translation. However, NMT suffers from the problem of repeating or missing words in the translation. To address this problem, Tu et al. (2017) proposed an encoder-decoder-reconstructor framework for NMT using back-translation. In this method, they selected the best forward translation model in the same manner as Bahdanau et al. (2015), and then trained a bi-directional translation model as fine-tuning. Their experiments show that it offers significant improvement in BLEU scores in Chinese-English translation task. We confirm that our re-implementation also shows the same tendency and alleviates the problem of repeating and missing words in the translation on a English-Japanese task too. In addition, we evaluate the effectiveness of pre-training by comparing it with a jointly-trained model of forward translation and back-translation. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 75,959 |
2407.20642 | Effectively Leveraging CLIP for Generating Situational Summaries of
Images and Videos | Situation recognition refers to the ability of an agent to identify and understand various situations or contexts based on available information and sensory inputs. It involves the cognitive process of interpreting data from the environment to determine what is happening, what factors are involved, and what actions caused those situations. This interpretation of situations is formulated as a semantic role labeling problem in computer vision-based situation recognition. Situations depicted in images and videos hold pivotal information, essential for various applications like image and video captioning, multimedia retrieval, autonomous systems and event monitoring. However, existing methods often struggle with ambiguity and lack of context in generating meaningful and accurate predictions. Leveraging multimodal models such as CLIP, we propose ClipSitu, which sidesteps the need for full fine-tuning and achieves state-of-the-art results in situation recognition and localization tasks. ClipSitu harnesses CLIP-based image, verb, and role embeddings to predict nouns fulfilling all the roles associated with a verb, providing a comprehensive understanding of depicted scenarios. Through a cross-attention Transformer, ClipSitu XTF enhances the connection between semantic role queries and visual token representations, leading to superior performance in situation recognition. We also propose a verb-wise role prediction model with near-perfect accuracy to create an end-to-end framework for producing situational summaries for out-of-domain images. We show that situational summaries empower our ClipSitu models to produce structured descriptions with reduced ambiguity compared to generic captions. Finally, we extend ClipSitu to video situation recognition to showcase its versatility and produce comparable performance to state-of-the-art methods. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 477,227 |
2107.11022 | AD-GAN: End-to-end Unsupervised Nuclei Segmentation with Aligned
Disentangling Training | We consider unsupervised cell nuclei segmentation in this paper. Exploiting the recently-proposed unpaired image-to-image translation between cell nuclei images and randomly synthetic masks, existing approaches, e.g., CycleGAN, have achieved encouraging results. However, these methods usually take a two-stage pipeline and fail to learn end-to-end in cell nuclei images. More seriously, they could lead to the lossy transformation problem, i.e., the content inconsistency between the original images and the corresponding segmentation output. To address these limitations, we propose a novel end-to-end unsupervised framework called Aligned Disentangling Generative Adversarial Network (AD-GAN). Distinctively, AD-GAN introduces representation disentanglement to separate content representation (the underling spatial structure) from style representation (the rendering of the structure). With this framework, spatial structure can be preserved explicitly, enabling a significant reduction of macro-level lossy transformation. We also propose a novel training algorithm able to align the disentangled content in the latent space to reduce micro-level lossy transformation. Evaluations on real-world 2D and 3D datasets show that AD-GAN substantially outperforms the other comparison methods and the professional software both quantitatively and qualitatively. Specifically, the proposed AD-GAN leads to significant improvement over the current best unsupervised methods by an average 17.8% relatively (w.r.t. the metric DICE) on four cell nuclei datasets. As an unsupervised method, AD-GAN even performs competitive with the best supervised models, taking a further leap towards end-to-end unsupervised nuclei segmentation. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 247,475 |
2103.08833 | Skeleton Aware Multi-modal Sign Language Recognition | Sign language is commonly used by deaf or speech impaired people to communicate but requires significant effort to master. Sign Language Recognition (SLR) aims to bridge the gap between sign language users and others by recognizing signs from given videos. It is an essential yet challenging task since sign language is performed with the fast and complex movement of hand gestures, body posture, and even facial expressions. Recently, skeleton-based action recognition attracts increasing attention due to the independence between the subject and background variation. However, skeleton-based SLR is still under exploration due to the lack of annotations on hand keypoints. Some efforts have been made to use hand detectors with pose estimators to extract hand key points and learn to recognize sign language via Neural Networks, but none of them outperforms RGB-based methods. To this end, we propose a novel Skeleton Aware Multi-modal SLR framework (SAM-SLR) to take advantage of multi-modal information towards a higher recognition rate. Specifically, we propose a Sign Language Graph Convolution Network (SL-GCN) to model the embedded dynamics and a novel Separable Spatial-Temporal Convolution Network (SSTCN) to exploit skeleton features. RGB and depth modalities are also incorporated and assembled into our framework to provide global information that is complementary to the skeleton-based methods SL-GCN and SSTCN. As a result, SAM-SLR achieves the highest performance in both RGB (98.42\%) and RGB-D (98.53\%) tracks in 2021 Looking at People Large Scale Signer Independent Isolated SLR Challenge. Our code is available at https://github.com/jackyjsy/CVPR21Chal-SLR | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 224,999 |
2307.01782 | GHOST: A Graph Neural Network Accelerator using Silicon Photonics | Graph neural networks (GNNs) have emerged as a powerful approach for modelling and learning from graph-structured data. Multiple fields have since benefitted enormously from the capabilities of GNNs, such as recommendation systems, social network analysis, drug discovery, and robotics. However, accelerating and efficiently processing GNNs require a unique approach that goes beyond conventional artificial neural network accelerators, due to the substantial computational and memory requirements of GNNs. The slowdown of scaling in CMOS platforms also motivates a search for alternative implementation substrates. In this paper, we present GHOST, the first silicon-photonic hardware accelerator for GNNs. GHOST efficiently alleviates the costs associated with both vertex-centric and edge-centric operations. It implements separately the three main stages involved in running GNNs in the optical domain, allowing it to be used for the inference of various widely used GNN models and architectures, such as graph convolution networks and graph attention networks. Our simulation studies indicate that GHOST exhibits at least 10.2x better throughput and 3.8x better energy efficiency when compared to GPU, TPU, CPU and multiple state-of-the-art GNN hardware accelerators. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | true | 377,473 |
2304.07957 | A Question-Answering Approach to Key Value Pair Extraction from
Form-like Document Images | In this paper, we present a new question-answering (QA) based key-value pair extraction approach, called KVPFormer, to robustly extracting key-value relationships between entities from form-like document images. Specifically, KVPFormer first identifies key entities from all entities in an image with a Transformer encoder, then takes these key entities as \textbf{questions} and feeds them into a Transformer decoder to predict their corresponding \textbf{answers} (i.e., value entities) in parallel. To achieve higher answer prediction accuracy, we propose a coarse-to-fine answer prediction approach further, which first extracts multiple answer candidates for each identified question in the coarse stage and then selects the most likely one among these candidates in the fine stage. In this way, the learning difficulty of answer prediction can be effectively reduced so that the prediction accuracy can be improved. Moreover, we introduce a spatial compatibility attention bias into the self-attention/cross-attention mechanism for \Ours{} to better model the spatial interactions between entities. With these new techniques, our proposed \Ours{} achieves state-of-the-art results on FUNSD and XFUND datasets, outperforming the previous best-performing method by 7.2\% and 13.2\% in F1 score, respectively. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 358,538 |
2411.08906 | Assessing the Auditability of AI-integrating Systems: A Framework and
Learning Analytics Case Study | Audits contribute to the trustworthiness of Learning Analytics (LA) systems that integrate Artificial Intelligence (AI) and may be legally required in the future. We argue that the efficacy of an audit depends on the auditability of the audited system. Therefore, systems need to be designed with auditability in mind. We present a framework for assessing the auditability of AI-integrating systems that consists of three parts: (1) Verifiable claims about the validity, utility and ethics of the system, (2) Evidence on subjects (data, models or the system) in different types (documentation, raw sources and logs) to back or refute claims, (3) Evidence must be accessible to auditors via technical means (APIs, monitoring tools, explainable AI, etc.). We apply the framework to assess the auditability of Moodle's dropout prediction system and a prototype AI-based LA. We find that Moodle's auditability is limited by incomplete documentation, insufficient monitoring capabilities and a lack of available test data. The framework supports assessing the auditability of AI-based LA systems in use and improves the design of auditable systems and thus of audits. | false | false | false | false | true | false | false | false | false | false | false | false | false | true | false | false | false | false | 508,065 |
1101.2182 | The Degrees of Freedom of Compute-and-Forward | We analyze the asymptotic behavior of compute-and-forward relay networks in the regime of high signal-to-noise ratios. We consider a section of such a network consisting of K transmitters and K relays. The aim of the relays is to reliably decode an invertible function of the messages sent by the transmitters. An upper bound on the capacity of this system can be obtained by allowing full cooperation among the transmitters and among the relays, transforming the network into a K times K multiple-input multiple-output (MIMO) channel. The number of degrees of freedom of compute-and-forward is hence at most K. In this paper, we analyze the degrees of freedom achieved by the lattice coding implementation of compute-and-forward proposed recently by Nazer and Gastpar. We show that this lattice implementation achieves at most 2/(1+1/K)\leq 2 degrees of freedom, thus exhibiting a very different asymptotic behavior than the MIMO upper bound. This raises the question if this gap of the lattice implementation to the MIMO upper bound is inherent to compute-and-forward in general. We answer this question in the negative by proposing a novel compute-and-forward implementation achieving K degrees of freedom. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 8,776 |
1407.1538 | Large-Scale Multi-Label Learning with Incomplete Label Assignments | Multi-label learning deals with the classification problems where each instance can be assigned with multiple labels simultaneously. Conventional multi-label learning approaches mainly focus on exploiting label correlations. It is usually assumed, explicitly or implicitly, that the label sets for training instances are fully labeled without any missing labels. However, in many real-world multi-label datasets, the label assignments for training instances can be incomplete. Some ground-truth labels can be missed by the labeler from the label set. This problem is especially typical when the number instances is very large, and the labeling cost is very high, which makes it almost impossible to get a fully labeled training set. In this paper, we study the problem of large-scale multi-label learning with incomplete label assignments. We propose an approach, called MPU, based upon positive and unlabeled stochastic gradient descent and stacked models. Unlike prior works, our method can effectively and efficiently consider missing labels and label correlations simultaneously, and is very scalable, that has linear time complexities over the size of the data. Extensive experiments on two real-world multi-label datasets show that our MPU model consistently outperform other commonly-used baselines. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 34,449 |
2407.18547 | Mechanism Design for Locating Facilities with Capacities with
Insufficient Resources | This paper explores the Mechanism Design aspects of the $m$-Capacitated Facility Location Problem where the total facility capacity is less than the number of agents. Following the framework outlined by Aziz et al., the Social Welfare of the facility location is determined through a First-Come-First-Served (FCFS) game, in which agents compete once the facility positions are established. When the number of facilities is $m > 1$, the Nash Equilibrium (NE) of the FCFS game is not unique, making the utility of the agents and the concept of truthfulness unclear. To tackle these issues, we consider absolutely truthful mechanisms, i.e. mechanisms that prevent agents from misreporting regardless of the strategies used during the FCFS game. We combine this stricter truthfulness requirement with the notion of Equilibrium Stable (ES) mechanisms, which are mechanisms whose Social Welfare does not depend on the NE of the FCFS game. We demonstrate that the class of percentile mechanisms is absolutely truthful and identify the conditions under which they are ES. We also show that the approximation ratio of each ES percentile mechanism is bounded and determine its value. Notably, when all the facilities have the same capacity and the number of agents is sufficiently large, it is possible to achieve an approximation ratio smaller than $1+\frac{1}{2m-1}$. Finally, we extend our study to encompass higher-dimensional problems. Within this framework, we demonstrate that the class of ES percentile mechanisms is even more restricted and characterize the mechanisms that are both ES and absolutely truthful. We further support our findings by empirically evaluating the performance of the mechanisms when the agents are the samples of a distribution. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | true | 476,422 |
2407.12410 | Proximity-based Self-Federated Learning | In recent advancements in machine learning, federated learning allows a network of distributed clients to collaboratively develop a global model without needing to share their local data. This technique aims to safeguard privacy, countering the vulnerabilities of conventional centralized learning methods. Traditional federated learning approaches often rely on a central server to coordinate model training across clients, aiming to replicate the same model uniformly across all nodes. However, these methods overlook the significance of geographical and local data variances in vast networks, potentially affecting model effectiveness and applicability. Moreover, relying on a central server might become a bottleneck in large networks, such as the ones promoted by edge computing. Our paper introduces a novel, fully-distributed federated learning strategy called proximity-based self-federated learning that enables the self-organised creation of multiple federations of clients based on their geographic proximity and data distribution without exchanging raw data. Indeed, unlike traditional algorithms, our approach encourages clients to share and adjust their models with neighbouring nodes based on geographic proximity and model accuracy. This method not only addresses the limitations posed by diverse data distributions but also enhances the model's adaptability to different regional characteristics creating specialized models for each federation. We demonstrate the efficacy of our approach through simulations on well-known datasets, showcasing its effectiveness over the conventional centralized federated learning framework. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 473,917 |
2501.10526 | Solving Sparse Finite Element Problems on Neuromorphic Hardware | We demonstrate that scalable neuromorphic hardware can implement the finite element method, which is a critical numerical method for engineering and scientific discovery. Our approach maps the sparse interactions between neighboring finite elements to small populations of neurons that dynamically update according to the governing physics of a desired problem description. We show that for the Poisson equation, which describes many physical systems such as gravitational and electrostatic fields, this cortical-inspired neural circuit can achieve comparable levels of numerical accuracy and scaling while enabling the use of inherently parallel and energy-efficient neuromorphic hardware. We demonstrate that this approach can be used on the Intel Loihi 2 platform and illustrate how this approach can be extended to nontrivial mesh geometries and dynamics. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | true | false | true | 525,557 |
2412.02065 | Leveraging Large Language Models to Democratize Access to Costly
Financial Datasets for Academic Research | Unequal access to costly datasets essential for empirical research has long hindered researchers from disadvantaged institutions, limiting their ability to contribute to their fields and advance their careers. Recent breakthroughs in Large Language Models (LLMs) have the potential to democratize data access by automating data collection from unstructured sources. We develop and evaluate a novel methodology using GPT-4o-mini within a Retrieval-Augmented Generation (RAG) framework to collect data from corporate disclosures. Our approach achieves human-level accuracy in collecting CEO pay ratios from approximately 10,000 proxy statements and Critical Audit Matters (CAMs) from more than 12,000 10-K filings, with LLM processing times of 9 and 40 minutes respectively, each at a cost under $10. This stands in stark contrast to the hundreds of hours needed for manual collection or the thousands of dollars required for commercial database subscriptions. To foster a more inclusive research community by empowering researchers with limited resources to explore new avenues of inquiry, we share our methodology and the resulting datasets. | false | true | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 513,365 |
2008.02839 | Learned convex regularizers for inverse problems | We consider the variational reconstruction framework for inverse problems and propose to learn a data-adaptive input-convex neural network (ICNN) as the regularization functional. The ICNN-based convex regularizer is trained adversarially to discern ground-truth images from unregularized reconstructions. Convexity of the regularizer is desirable since (i) one can establish analytical convergence guarantees for the corresponding variational reconstruction problem and (ii) devise efficient and provable algorithms for reconstruction. In particular, we show that the optimal solution to the variational problem converges to the ground-truth if the penalty parameter decays sub-linearly with respect to the norm of the noise. Further, we prove the existence of a sub-gradient-based algorithm that leads to a monotonically decreasing error in the parameter space with iterations. To demonstrate the performance of our approach for solving inverse problems, we consider the tasks of deblurring natural images and reconstructing images in computed tomography (CT), and show that the proposed convex regularizer is at least competitive with and sometimes superior to state-of-the-art data-driven techniques for inverse problems. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 190,722 |
2410.00151 | Scheherazade: Evaluating Chain-of-Thought Math Reasoning in LLMs with
Chain-of-Problems | Benchmarks are critical for measuring progress of math reasoning abilities of Large Language Models (LLMs). However, existing widely-used benchmarks such as GSM8K have been rendered less useful as multiple cutting-edge LLMs achieve over 94% accuracy. While harder benchmarks have been proposed, their creation is often manual and expensive. We present Scheherazade, an automated approach for producing challenging mathematical reasoning benchmarks by logically chaining mathematical reasoning problems. We propose two different chaining methods, forward chaining and backward chaining, which require reasoning forward and backward through the chain respectively. We apply Scheherazade on GSM8K to create GSM8K-Scheherazade and evaluate 3 frontier LLMs and OpenAI's o1-preview on it. We show that while frontier models' performance declines precipitously at only a few questions chained, a preliminary evaluation suggests o1-preview performance persists up to 5 questions chained backwards. In addition, while all other models perform worse when problems are chained backwards, o1-preview performs better on backward-chained benchmarks. We will release the dataset and code publicly. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 493,247 |
2401.00055 | Online Algorithmic Recourse by Collective Action | Research on algorithmic recourse typically considers how an individual can reasonably change an unfavorable automated decision when interacting with a fixed decision-making system. This paper focuses instead on the online setting, where system parameters are updated dynamically according to interactions with data subjects. Beyond the typical individual-level recourse, the online setting opens up new ways for groups to shape system decisions by leveraging the parameter update rule. We show empirically that recourse can be improved when users coordinate by jointly computing their feature perturbations, underscoring the importance of collective action in mitigating adverse automated decisions. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 418,864 |
2304.08247 | MedAlpaca -- An Open-Source Collection of Medical Conversational AI
Models and Training Data | As large language models (LLMs) like OpenAI's GPT series continue to make strides, we witness the emergence of artificial intelligence applications in an ever-expanding range of fields. In medicine, these LLMs hold considerable promise for improving medical workflows, diagnostics, patient care, and education. Yet, there is an urgent need for open-source models that can be deployed on-premises to safeguard patient privacy. In our work, we present an innovative dataset consisting of over 160,000 entries, specifically crafted to fine-tune LLMs for effective medical applications. We investigate the impact of fine-tuning these datasets on publicly accessible pre-trained LLMs, and subsequently, we juxtapose the performance of pre-trained-only models against the fine-tuned models concerning the examinations that future medical doctors must pass to achieve certification. | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 358,642 |
2211.14490 | Resampling community detection to maximize propagation in complex
network | Identifying important nodes in complex networks is essential in theoretical and applied fields. A small number of such nodes have deterministic power to decide information spreading, so it is of importance to find a set of nodes that maximize the propagation in networks. Based on baseline ranking methods, various improved methods were proposed, but there does not exist one enhanced method that covers all the base methods. In this paper, we propose a penalized method called RCD-Map, which is short for resampling community detection to maximize propagation, on five baseline ranking methods(Degree centrality, Closeness centrality, Betweennees centrality, K-shell and PageRank) with nodes' local community information. We perturbed the original graph by resampling to decrease the biases and randomness brought by community detection methods-both overlapping and non-overlapping methods. To assess the performance of our identifying method, SIR(susceptible-infected-recovered) model is applied to simulate the information propagation process. The result shows that methods with penalties perform better with a vaster propagation range in general. | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 332,859 |
2410.06912 | Compositional Entailment Learning for Hyperbolic Vision-Language Models | Image-text representation learning forms a cornerstone in vision-language models, where pairs of images and textual descriptions are contrastively aligned in a shared embedding space. Since visual and textual concepts are naturally hierarchical, recent work has shown that hyperbolic space can serve as a high-potential manifold to learn vision-language representation with strong downstream performance. In this work, for the first time we show how to fully leverage the innate hierarchical nature of hyperbolic embeddings by looking beyond individual image-text pairs. We propose Compositional Entailment Learning for hyperbolic vision-language models. The idea is that an image is not only described by a sentence but is itself a composition of multiple object boxes, each with their own textual description. Such information can be obtained freely by extracting nouns from sentences and using openly available localized grounding models. We show how to hierarchically organize images, image boxes, and their textual descriptions through contrastive and entailment-based objectives. Empirical evaluation on a hyperbolic vision-language model trained with millions of image-text pairs shows that the proposed compositional learning approach outperforms conventional Euclidean CLIP learning, as well as recent hyperbolic alternatives, with better zero-shot and retrieval generalization and clearly stronger hierarchical performance. | false | false | false | false | true | false | true | false | false | false | false | true | false | false | false | false | false | false | 496,391 |
1809.04693 | An Online Plug-and-Play Algorithm for Regularized Image Reconstruction | Plug-and-play priors (PnP) is a powerful framework for regularizing imaging inverse problems by using advanced denoisers within an iterative algorithm. Recent experimental evidence suggests that PnP algorithms achieve state-of-the-art performance in a range of imaging applications. In this paper, we introduce a new online PnP algorithm based on the iterative shrinkage/thresholding algorithm (ISTA). The proposed algorithm uses only a subset of measurements at every iteration, which makes it scalable to very large datasets. We present a new theoretical convergence analysis, for both batch and online variants of PnP-ISTA, for denoisers that do not necessarily correspond to proximal operators. We also present simulations illustrating the applicability of the algorithm to image reconstruction in diffraction tomography. The results in this paper have the potential to expand the applicability of the PnP framework to very large and redundant datasets. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 107,629 |
1806.09768 | Optimal Streaming Erasure Codes over the Three-Node Relay Network | This paper investigates low-latency streaming codes for a three-node relay network. The source transmits a sequence of messages (streaming messages) to the destination through the relay between them, where the first-hop channel from the source to the relay and the second-hop channel from the relay to the destination are subject to packet erasures. Every source message must be recovered perfectly at the destination subject to a fixed decoding delay of $T$ time slots. In any sliding window of $T+1$ time slots, we assume no more than $N_1$ and $N_2$ erasures are introduced by the first-hop channel and second-hop channel respectively. Under this channel loss assumption, we fully characterize the maximum achievable rate in terms of $T$, $N_1$ and $N_2$. The achievability is proved by using a symbol-wise decode-forward strategy where the source symbols within the same message are decoded by the relay with different delays. The converse is proved by analyzing the maximum achievable rate for each channel when the erasures in the other channel are consecutive (bursty). In addition, we show that traditional message-wise decode-forward strategies, which require the source symbols within the same message to be decoded by the relay with the same delay, are sub-optimal in general. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 101,413 |
2201.07784 | On Distributed Lossy Coding of Symmetrically Correlated Gaussian Sources | A distributed lossy compression network with $L$ encoders and a decoder is considered. Each encoder observes a source and sends a compressed version to the decoder. The decoder produces a joint reconstruction of target signals with the mean squared error distortion below a given threshold. It is assumed that the observed sources can be expressed as the sum of target signals and corruptive noises which are independently generated from two symmetric multivariate Gaussian distributions. The minimum compression rate of this network versus the distortion threshold is referred to as the rate-distortion function, for which an explicit lower bound is established by solving a minimization problem. Our lower bound matches the well-known Berger-Tung upper bound for some values of the distortion threshold. The asymptotic gap between the upper and lower bounds is characterized in the large $L$ limit. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 276,131 |
2305.12394 | Pruning Pre-trained Language Models with Principled Importance and
Self-regularization | Iterative pruning is one of the most effective compression methods for pre-trained language models. We discovered that finding the optimal pruning decision is an equality-constrained 0-1 Integer Linear Programming problem. The solution to this optimization problem leads to a principled importance criterion which we use to rank parameters during iterative model pruning. To mitigate the poor generalization at high sparsity levels, we propose a self-regularization scheme where model prediction is regularized by the latest checkpoint with increasing sparsity throughout pruning. Our experiments on natural language understanding, question-answering, named entity recognition, and data-to-text generation with various Transformer-based PLMs show the effectiveness of the approach at various sparsity levels. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 365,976 |
cs/9809107 | Computing Declarative Prosodic Morphology | This paper describes a computational, declarative approach to prosodic morphology that uses inviolable constraints to denote small finite candidate sets which are filtered by a restrictive incremental optimization mechanism. The new approach is illustrated with an implemented fragment of Modern Hebrew verbs couched in MicroCUF, an expressive constraint logic formalism. For generation and parsing of word forms, I propose a novel off-line technique to eliminate run-time optimization. It produces a finite-state oracle that efficiently restricts the constraint interpreter's search space. As a byproduct, unknown words can be analyzed without special mechanisms. Unlike pure finite-state transducer approaches, this hybrid setup allows for more expressivity in constraints to specify e.g. token identity for reduplication or arithmetic constraints for phonetics. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 540,410 |
2402.01712 | Socially Aware Synthetic Data Generation for Suicidal Ideation Detection
Using Large Language Models | Suicidal ideation detection is a vital research area that holds great potential for improving mental health support systems. However, the sensitivity surrounding suicide-related data poses challenges in accessing large-scale, annotated datasets necessary for training effective machine learning models. To address this limitation, we introduce an innovative strategy that leverages the capabilities of generative AI models, such as ChatGPT, Flan-T5, and Llama, to create synthetic data for suicidal ideation detection. Our data generation approach is grounded in social factors extracted from psychology literature and aims to ensure coverage of essential information related to suicidal ideation. In our study, we benchmarked against state-of-the-art NLP classification models, specifically, those centered around the BERT family structures. When trained on the real-world dataset, UMD, these conventional models tend to yield F1-scores ranging from 0.75 to 0.87. Our synthetic data-driven method, informed by social factors, offers consistent F1-scores of 0.82 for both models, suggesting that the richness of topics in synthetic data can bridge the performance gap across different model complexities. Most impressively, when we combined a mere 30% of the UMD dataset with our synthetic data, we witnessed a substantial increase in performance, achieving an F1-score of 0.88 on the UMD test set. Such results underscore the cost-effectiveness and potential of our approach in confronting major challenges in the field, such as data scarcity and the quest for diversity in data representation. | false | false | false | false | true | false | true | false | true | false | false | false | false | false | false | false | false | false | 426,161 |
2312.09043 | Topic Bias in Emotion Classification | Emotion corpora are typically sampled based on keyword/hashtag search or by asking study participants to generate textual instances. In any case, these corpora are not uniform samples representing the entirety of a domain. We hypothesize that this practice of data acquisition leads to unrealistic correlations between overrepresented topics in these corpora that harm the generalizability of models. Such topic bias could lead to wrong predictions for instances like "I organized the service for my aunt's funeral." when funeral events are over-represented for instances labeled with sadness, despite the emotion of pride being more appropriate here. In this paper, we study this topic bias both from the data and the modeling perspective. We first label a set of emotion corpora automatically via topic modeling and show that emotions in fact correlate with specific topics. Further, we see that emotion classifiers are confounded by such topics. Finally, we show that the established debiasing method of adversarial correction via gradient reversal mitigates the issue. Our work points out issues with existing emotion corpora and that more representative resources are required for fair evaluation of models predicting affective concepts from text. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 415,580 |
2111.11276 | Branching Time Active Inference: empirical study and complexity class
analysis | Active inference is a state-of-the-art framework for modelling the brain that explains a wide range of mechanisms such as habit formation, dopaminergic discharge and curiosity. However, recent implementations suffer from an exponential complexity class when computing the prior over all the possible policies up to the time horizon. Fountas et al (2020) used Monte Carlo tree search to address this problem, leading to very good results in two different tasks. Additionally, Champion et al (2021a) proposed a tree search approach based on (temporal) structure learning. This was enabled by the development of a variational message passing approach to active inference, which enables compositional construction of Bayesian networks for active inference. However, this message passing tree search approach, which we call branching-time active inference (BTAI), has never been tested empirically. In this paper, we present an experimental study of BTAI in the context of a maze solving agent. In this context, we show that both improved prior preferences and deeper search help mitigate the vulnerability to local minima. Then, we compare BTAI to standard active inference (AcI) on a graph navigation task. We show that for small graphs, both BTAI and AcI successfully solve the task. For larger graphs, AcI exhibits an exponential (space) complexity class, making the approach intractable. However, BTAI explores the space of policies more efficiently, successfully scaling to larger graphs. Then, BTAI was compared to the POMCP algorithm on the frozen lake environment. The experiments suggest that BTAI and the POMCP algorithm accumulate a similar amount of reward. Also, we describe when BTAI receives more rewards than the POMCP agent, and when the opposite is true. Finally, we compared BTAI to the approach of Fountas et al (2020) on the dSprites dataset, and we discussed the pros and cons of each approach. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 267,608 |
2408.16322 | BEVal: A Cross-dataset Evaluation Study of BEV Segmentation Models for
Autonomous Driving | Current research in semantic bird's-eye view segmentation for autonomous driving focuses solely on optimizing neural network models using a single dataset, typically nuScenes. This practice leads to the development of highly specialized models that may fail when faced with different environments or sensor setups, a problem known as domain shift. In this paper, we conduct a comprehensive cross-dataset evaluation of state-of-the-art BEV segmentation models to assess their performance across different training and testing datasets and setups, as well as different semantic categories. We investigate the influence of different sensors, such as cameras and LiDAR, on the models' ability to generalize to diverse conditions and scenarios. Additionally, we conduct multi-dataset training experiments that improve models' BEV segmentation performance compared to single-dataset training. Our work addresses the gap in evaluating BEV segmentation models under cross-dataset validation. And our findings underscore the importance of enhancing model generalizability and adaptability to ensure more robust and reliable BEV segmentation approaches for autonomous driving applications. The code for this paper available at https://github.com/manueldiaz96/beval . | false | false | false | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | 484,291 |
2209.12074 | Self-supervised Learning for Unintentional Action Prediction | Distinguishing if an action is performed as intended or if an intended action fails is an important skill that not only humans have, but that is also important for intelligent systems that operate in human environments. Recognizing if an action is unintentional or anticipating if an action will fail, however, is not straightforward due to lack of annotated data. While videos of unintentional or failed actions can be found in the Internet in abundance, high annotation costs are a major bottleneck for learning networks for these tasks. In this work, we thus study the problem of self-supervised representation learning for unintentional action prediction. While previous works learn the representation based on a local temporal neighborhood, we show that the global context of a video is needed to learn a good representation for the three downstream tasks: unintentional action classification, localization and anticipation. In the supplementary material, we show that the learned representation can be used for detecting anomalies in videos as well. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 319,406 |
2406.17473 | TSynD: Targeted Synthetic Data Generation for Enhanced Medical Image
Classification | The usage of medical image data for the training of large-scale machine learning approaches is particularly challenging due to its scarce availability and the costly generation of data annotations, typically requiring the engagement of medical professionals. The rapid development of generative models allows towards tackling this problem by leveraging large amounts of realistic synthetically generated data for the training process. However, randomly choosing synthetic samples, might not be an optimal strategy. In this work, we investigate the targeted generation of synthetic training data, in order to improve the accuracy and robustness of image classification. Therefore, our approach aims to guide the generative model to synthesize data with high epistemic uncertainty, since large measures of epistemic uncertainty indicate underrepresented data points in the training set. During the image generation we feed images reconstructed by an auto encoder into the classifier and compute the mutual information over the class-probability distribution as a measure for uncertainty.We alter the feature space of the autoencoder through an optimization process with the objective of maximizing the classifier uncertainty on the decoded image. By training on such data we improve the performance and robustness against test time data augmentations and adversarial attacks on several classifications tasks. | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | false | false | 467,587 |
1703.08383 | Smart Augmentation - Learning an Optimal Data Augmentation Strategy | A recurring problem faced when training neural networks is that there is typically not enough data to maximize the generalization capability of deep neural networks(DNN). There are many techniques to address this, including data augmentation, dropout, and transfer learning. In this paper, we introduce an additional method which we call Smart Augmentation and we show how to use it to increase the accuracy and reduce overfitting on a target network. Smart Augmentation works by creating a network that learns how to generate augmented data during the training process of a target network in a way that reduces that networks loss. This allows us to learn augmentations that minimize the error of that network. Smart Augmentation has shown the potential to increase accuracy by demonstrably significant measures on all datasets tested. In addition, it has shown potential to achieve similar or improved performance levels with significantly smaller network sizes in a number of tested cases. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 70,573 |
1702.03176 | A clustering approach to heterogeneous change detection | Change detection in heterogeneous multitemporal satellite images is a challenging and still not much studied topic in remote sensing and earth observation. This paper focuses on comparison of image pairs covering the same geographical area and acquired by two different sensors, one optical radiometer and one synthetic aperture radar, at two different times. We propose a clustering-based technique to detect changes, identified as clusters that split or merge in the different images. To evaluate potentials and limitations of our method, we perform experiments on real data. Preliminary results confirm the relationship between splits and merges of clusters and the occurrence of changes. However, it becomes evident that it is necessary to incorporate prior, ancillary, or application-specific information to improve the interpretation of clustering results and to identify unambiguously the areas of change. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 68,086 |
2403.06625 | AC/DC optimal power flow and techno-economic assessment for hybrid
microgrids: TIGON CEDER demonstrator | In the recent years, the interest in electric direct current (DC) technologies (such as converters, batteries, electric vehicles, etc.) is increasing due to its potential on energy efficiency and sustainability. However, the vast majority of electric systems and networks are based on alternating current (AC), as they also have certain advantages regarding cost-effective transport and robustness. In this paper, an AC/DC optimal power flow method for hybrid microgrids and several key performance indicators (KPIs) for its techno-economic assessment are presented. The combination of both calculations allows users to clearly determine the viability of their hybrid microgrids. AC/DC networks have been modelled considering their most common elements. For the power flow method, a polynomial optimisation is formulated considering four different objective functions: the minimisation of energy losses, voltage deviation and operational costs, and also the maximisation of the microgrid generation. The power flow method and the techno-economic analysis have been implemented in Python and validated in the Centro de Desarrollo de Energ\'ias Renovables (CEDER) demonstrator for TIGON. The results show that the calculated power flow variables and the ones measured at CEDER are practically the same. In addition, the KPIs have been obtained and compared for four operating scenarios: baseline, no battery, battery flexibility and virtual battery (VB) flexibility. The last one result in the most profitable option. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 436,536 |
1608.07443 | Using an epidemiological approach to maximize data survival in the
internet of things | The internet of things (IoT) has gained worldwide attention in recent years. It transforms the everyday objects that surround us into proactive actors of the Internet, generating and consuming information. An important issue related to the appearance of such large-scale self-coordinating IoT is the reliability and the collaboration between the objects in the presence of environmental hazards. High failure rates lead to significant loss of data. Therefore, data survivability is a main challenge of the IoT. In this paper, we have developed a compartmental e-Epidemic SIR (Susceptible-Infectious-Recovered) model to save the data in the network and let it survive after attacks. Furthermore, our model takes into account the dynamic topology of the network where natural death (crashing nodes) and birth are defined and analyzed. Theoretical methods and simulations are employed to solve and simulate the system of equations developed and to analyze the model. | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | true | 60,231 |
1803.08022 | Twitter for Sparking a Movement, Reddit for Sharing the Moment: #metoo
through the Lens of Social Media | Social media platforms are revolutionizing the way users communicate by increasing the exposure to highly stigmatized issues in the society. Sexual abuse is one such issue that recently took over social media via attaching the hashtag #metoo to the shared posts. Individuals with different backgrounds and ethnicities began sharing their unfortunate personal experiences of being assaulted. Through comparative analysis of the tweets via #meToo on Twitter versus the posts shared on the #meToo subreddit, this paper makes an initial attempt to assess public reactions and emotions. Though nearly equal ratios of negative and positive posts are shared on both platforms, Reddit posts are focused on the sexual assaults within families and workplaces while Twitter posts are on showing empathy and encouraging others to continue the #metoo movement. The data collected in this research and preliminary analysis demonstrate that users use various ways to share their experience, exchange ideas and encourage each other, and social media is suitable for groundswells such as #metoo movement. | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 93,182 |
2102.06392 | Complete Power Reallocation for MU-MIMO under Per-Antenna Power
Constraint | This paper proposes a beamforming method under a per-antenna power constraint (PAPC). Although many beamformer designs with the PAPC need to solve complex optimization problems, the proposed complete power reallocation (CPR) method can generate beamformers with excellent performance only with linear operations. CPR is designed to have a simple structure, making it highly flexible and practical. In this paper, three CPR variations considering algorithm convergence speed, sum-rate maximization, and robustness to channel uncertainty are developed. Simulation results verify that CPR and its variations satisfy their design criteria, and, hence, CPR can be readily utilized for various purposes. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 219,737 |
1906.00426 | New non-linearity parameters of Boolean functions | The study of non-linearity (linearity) of Boolean function was initiated by Rothaus in 1976. The classical non-linearity of a Boolean function is the minimum Hamming distance of its truth table to that of affine functions. In this note we introduce new "multidimensional" non-linearity parameters $(N_f,H_f)$ for conventional and vectorial Boolean functions $f$ with $m$ coordinates in $n$ variables. The classical non-linearity may be treated as a 1-dimensional parameter in the new definition. $r$-dimensional parameters for $r\geq 2$ are relevant to possible multidimensional extensions of the Fast Correlation Attack in stream ciphers and Linear Cryptanalysis in block ciphers. Besides we introduce a notion of optimal vectorial Boolean functions relevant to the new parameters. For $r=1$ and even $n\geq 2m$ optimal Boolean functions are exactly perfect nonlinear functions (generalizations of Rothaus' bent functions) defined by Nyberg in 1991. By a computer search we find that this property holds for $r=2, m=1, n=4$ too. That is an open problem for larger $n,m$ and $r\geq 2$. The definitions may be easily extended to $q$-ary functions. | false | false | false | false | false | false | false | false | false | true | false | false | true | false | false | false | false | false | 133,393 |
2405.11070 | Jill Watson: A Virtual Teaching Assistant powered by ChatGPT | Conversational AI agents often require extensive datasets for training that are not publicly released, are limited to social chit-chat or handling a specific domain, and may not be easily extended to accommodate the latest advances in AI technologies. This paper introduces Jill Watson, a conversational Virtual Teaching Assistant (VTA) leveraging the capabilities of ChatGPT. Jill Watson based on ChatGPT requires no prior training and uses a modular design to allow the integration of new APIs using a skill-based architecture inspired by XiaoIce. Jill Watson is also well-suited for intelligent textbooks as it can process and converse using multiple large documents. We exclusively utilize publicly available resources for reproducibility and extensibility. Comparative analysis shows that our system outperforms the legacy knowledge-based Jill Watson as well as the OpenAI Assistants service. We employ many safety measures that reduce instances of hallucinations and toxicity. The paper also includes real-world examples from a classroom setting that demonstrate different features of Jill Watson and its effectiveness. | false | false | false | false | true | false | true | false | true | false | false | false | false | false | false | false | false | false | 454,992 |
2312.03207 | Satellite Imagery and AI: A New Era in Ocean Conservation, from Research
to Deployment and Impact | Illegal, unreported, and unregulated (IUU) fishing poses a global threat to ocean habitats. Publicly available satellite data offered by NASA and the European Space Agency (ESA) provide an opportunity to actively monitor this activity. Effectively leveraging satellite data for maritime conservation requires highly reliable machine learning models operating globally with minimal latency. This paper introduces three specialized computer vision models designed for synthetic aperture radar (Sentinel-1), optical imagery (Sentinel-2), and nighttime lights (Suomi-NPP/NOAA-20). It also presents best practices for developing and delivering real-time computer vision services for conservation. These models have been deployed in Skylight, a real time maritime monitoring platform, which is provided at no cost to users worldwide. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 413,167 |
2401.08598 | NutritionVerse-Real: An Open Access Manually Collected 2D Food Scene
Dataset for Dietary Intake Estimation | Dietary intake estimation plays a crucial role in understanding the nutritional habits of individuals and populations, aiding in the prevention and management of diet-related health issues. Accurate estimation requires comprehensive datasets of food scenes, including images, segmentation masks, and accompanying dietary intake metadata. In this paper, we introduce NutritionVerse-Real, an open access manually collected 2D food scene dataset for dietary intake estimation with 889 images of 251 distinct dishes and 45 unique food types. The NutritionVerse-Real dataset was created by manually collecting images of food scenes in real life, measuring the weight of every ingredient and computing the associated dietary content of each dish using the ingredient weights and nutritional information from the food packaging or the Canada Nutrient File. Segmentation masks were then generated through human labelling of the images. We provide further analysis on the data diversity to highlight potential biases when using this data to develop models for dietary intake estimation. NutritionVerse-Real is publicly available at https://www.kaggle.com/datasets/nutritionverse/nutritionverse-real as part of an open initiative to accelerate machine learning for dietary sensing. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 421,952 |
2403.12093 | Learning Macroeconomic Policies based on Microfoundations: A Stackelberg
Mean Field Game Approach | The Lucas critique emphasizes the importance of considering microfoundations, how micro-agents (i.e., households) respond to policy changes, in macroeconomic policymaking. However, due to the vast scale and complex dynamics among micro-agents, predicting microfoundations is challenging. Consequently, this paper introduces a Stackelberg Mean Field Game (SMFG) approach that models macroeconomic policymaking based on microfoundations, with the government as the leader and micro-agents as dynamic followers. This approach treats large-scale micro-agents as a population, to optimize macroeconomic policies by learning the dynamic response of this micro-population. Our experimental results indicate that the SMFG approach outperforms real-world macroeconomic policies, existing AI-based and economic methods, enabling the learned macroeconomic policy to achieve the highest performance while guiding large-scale micro-agents toward maximal social welfare. Additionally, when extended to real-world scenarios, households that do not adopt the SMFG policy experience lower utility and wealth than adopters, thereby increasing the attractiveness of our policy. In summary, this paper contributes to the field of AI for economics by offering an effective tool for modeling and solving macroeconomic policymaking issues. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 439,033 |
2103.03935 | An automated approach to mitigate transcription errors in braille texts
for the Portuguese language | The quota system in Brazil made it possible to include blind students in higher education. Teachers' lack of knowledge about the braille system can represent a barrier between them and students who use it for writing and reading. Computer-vision-based transcription solutions represent mechanisms for reducing understanding restrictions on this system. However, such tools face nuisances inherent to image processing systems, e.g., illumination, noise, and scale, harming the result. This paper presents an automated approach to mitigate transcription errors in braille texts for the Portuguese language. We propose a selection function, combined with dictionaries, that provides the best correspondence of words based on their braille representation. We validated our proposal on a dataset of synthetic images by submitting them to different noise levels and testing the proposal's robustness. Experimental results confirm the effectiveness of the solution compared to a standard approach. As a contribution of this paper, we expect to provide a method to support robust and adaptable solutions to real use conditions. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 223,459 |
2304.05238 | Diagnosing and Augmenting Feature Representations in Correctional
Inverse Reinforcement Learning | Robots have been increasingly better at doing tasks for humans by learning from their feedback, but still often suffer from model misalignment due to missing or incorrectly learned features. When the features the robot needs to learn to perform its task are missing or do not generalize well to new settings, the robot will not be able to learn the task the human wants and, even worse, may learn a completely different and undesired behavior. Prior work shows how the robot can detect when its representation is missing some feature and can, thus, ask the human to be taught about the new feature; however, these works do not differentiate between features that are completely missing and those that exist but do not generalize to new environments. In the latter case, the robot would detect misalignment and simply learn a new feature, leading to an arbitrarily growing feature representation that can, in turn, lead to spurious correlations and incorrect learning down the line. In this work, we propose separating the two sources of misalignment: we propose a framework for determining whether a feature the robot needs is incorrectly learned and does not generalize to new environment setups vs. is entirely missing from the robot's representation. Once we detect the source of error, we show how the human can initiate the realignment process for the model: if the feature is missing, we follow prior work for learning new features; however, if the feature exists but does not generalize, we use data augmentation to expand its training and, thus, complete the correction. We demonstrate the proposed approach in experiments with a simulated 7DoF robot manipulator and physical human corrections. | false | false | false | false | false | false | false | true | false | false | true | false | false | false | false | false | false | false | 357,547 |
2404.15245 | Mining Invariance from Nonlinear Multi-Environment Data: Binary
Classification | Making predictions in an unseen environment given data from multiple training environments is a challenging task. We approach this problem from an invariance perspective, focusing on binary classification to shed light on general nonlinear data generation mechanisms. We identify a unique form of invariance that exists solely in a binary setting that allows us to train models invariant over environments. We provide sufficient conditions for such invariance and show it is robust even when environmental conditions vary greatly. Our formulation admits a causal interpretation, allowing us to compare it with various frameworks. Finally, we propose a heuristic prediction method and conduct experiments using real and synthetic datasets. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 449,004 |
1510.04747 | Tensor vs Matrix Methods: Robust Tensor Decomposition under Block Sparse
Perturbations | Robust tensor CP decomposition involves decomposing a tensor into low rank and sparse components. We propose a novel non-convex iterative algorithm with guaranteed recovery. It alternates between low-rank CP decomposition through gradient ascent (a variant of the tensor power method), and hard thresholding of the residual. We prove convergence to the globally optimal solution under natural incoherence conditions on the low rank component, and bounded level of sparse perturbations. We compare our method with natural baselines which apply robust matrix PCA either to the {\em flattened} tensor, or to the matrix slices of the tensor. Our method can provably handle a far greater level of perturbation when the sparse tensor is block-structured. This naturally occurs in many applications such as the activity detection task in videos. Our experiments validate these findings. Thus, we establish that tensor methods can tolerate a higher level of gross corruptions compared to matrix methods. | false | false | false | false | false | false | true | false | false | true | false | false | false | false | false | false | false | false | 47,945 |
2011.07355 | Towards transformation-resilient provenance detection of digital media | Advancements in deep generative models have made it possible to synthesize images, videos and audio signals that are difficult to distinguish from natural signals, creating opportunities for potential abuse of these capabilities. This motivates the problem of tracking the provenance of signals, i.e., being able to determine the original source of a signal. Watermarking the signal at the time of signal creation is a potential solution, but current techniques are brittle and watermark detection mechanisms can easily be bypassed by applying post-processing transformations (cropping images, shifting pitch in the audio etc.). In this paper, we introduce ReSWAT (Resilient Signal Watermarking via Adversarial Training), a framework for learning transformation-resilient watermark detectors that are able to detect a watermark even after a signal has been through several post-processing transformations. Our detection method can be applied to domains with continuous data representations such as images, videos or sound signals. Experiments on watermarking image and audio signals show that our method can reliably detect the provenance of a signal, even if it has been through several post-processing transformations, and improve upon related work in this setting. Furthermore, we show that for specific kinds of transformations (perturbations bounded in the L2 norm), we can even get formal guarantees on the ability of our model to detect the watermark. We provide qualitative examples of watermarked image and audio samples in https://drive.google.com/open?id=1-yZ0WIGNu2Iez7UpXBjtjVgZu3jJjFga. | false | false | false | false | false | false | true | false | false | false | false | false | true | false | false | false | false | false | 206,520 |
1805.04982 | Index Set Fourier Series Features for Approximating Multi-dimensional
Periodic Kernels | Periodicity is often studied in timeseries modelling with autoregressive methods but is less popular in the kernel literature, particularly for higher dimensional problems such as in textures, crystallography, and quantum mechanics. Large datasets often make modelling periodicity untenable for otherwise powerful non-parametric methods like Gaussian Processes (GPs) which typically incur an $\mathcal{O}(N^3)$ computational burden and, consequently, are unable to scale to larger datasets. To this end we introduce a method termed \emph{Index Set Fourier Series Features} to tractably exploit multivariate Fourier series and efficiently decompose periodic kernels on higher-dimensional data into a series of basis functions. We show that our approximation produces significantly less predictive error than alternative approaches such as those based on random Fourier features and achieves better generalisation on regression problems with periodic data. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 97,352 |
2411.05475 | 3D-Printed Dual-Polarized Magneto-Electric Dipole Antenna with Wideband
High Isolation for Full-Duplex Applications | The paper introduces a novel dual-port dual-polarized magneto-electric dipole (MED) antenna with orthogonal Gamma and inverted-Gamma shape probes, which was fabricated by means of an additive 3D metal printing process. Electromagnetic wave simulation and RF measurement report a resonance bandwidth from 3 GHz to 4 GHz at both MED's ports with respect to a standing wave ratio of less than 2. The cross-polarization isolation (XPI) between the MED's ports was also measured to be greater than 50 dB across its entire resonance bandwidth. The paper also thoroughly examines the impact of misalignments in the polarization of the MED probes on the XPI level. The broadband resonance and excellent isolation between the MED ports make it a strong candidate for a full-duplex wireless transceiver in network infrastructure. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 506,680 |
1806.03636 | Transformationally Identical and Invariant Convolutional Neural Networks
through Symmetric Element Operators | Mathematically speaking, a transformationally invariant operator, such as a transformationally identical (TI) matrix kernel (i.e., K= T{K}), commutes with the transformation (T{.}) itself when they operate on the first operand matrix. We found that by consistently applying the same type of TI kernels in a convolutional neural networks (CNN) system, the commutative property holds throughout all layers of convolution processes with and without involving an activation function and/or a 1D convolution across channels within a layer. We further found that any CNN possessing the same TI kernel property for all convolution layers followed by a flatten layer with weight sharing among their transformation corresponding elements would output the same result for all transformation versions of the original input vector. In short, CNN[ Vi ] = CNN[ T{Vi} ] providing every K = T{K} in CNN, where Vi denotes input vector and CNN[.] represents the whole CNN process as a function of input vector that produces an output vector. With such a transformationally identical CNN (TI-CNN) system, each transformation, that is not associated with a predefined TI used in data augmentation, would inherently include all of its corresponding transformation versions of the input vector for the training. Hence the use of same TI property for every kernel in the CNN would serve as an orientation or a translation independent training guide in conjunction with the error-backpropagation during the training. This TI kernel property is desirable for applications requiring a highly consistent output result from corresponding transformation versions of an input. Several C programming routines are provided to facilitate interested parties of using the TI-CNN technique which is expected to produce a better generalization performance than its ordinary CNN counterpart. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 100,048 |
2111.12940 | Towards Fewer Annotations: Active Learning via Region Impurity and
Prediction Uncertainty for Domain Adaptive Semantic Segmentation | Self-training has greatly facilitated domain adaptive semantic segmentation, which iteratively generates pseudo labels on unlabeled target data and retrains the network. However, realistic segmentation datasets are highly imbalanced, pseudo labels are typically biased to the majority classes and basically noisy, leading to an error-prone and suboptimal model. In this paper, we propose a simple region-based active learning approach for semantic segmentation under a domain shift, aiming to automatically query a small partition of image regions to be labeled while maximizing segmentation performance. Our algorithm, Region Impurity and Prediction Uncertainty (RIPU), introduces a new acquisition strategy characterizing the spatial adjacency of image regions along with the prediction confidence. We show that the proposed region-based selection strategy makes more efficient use of a limited budget than image-based or point-based counterparts. Further, we enforce local prediction consistency between a pixel and its nearest neighbors on a source image. Alongside, we develop a negative learning loss to make the features more discriminative. Extensive experiments demonstrate that our method only requires very few annotations to almost reach the supervised performance and substantially outperforms state-of-the-art methods. The code is available at https://github.com/BIT-DA/RIPU. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 268,133 |
2211.09454 | DeepPrivacy2: Towards Realistic Full-Body Anonymization | Generative Adversarial Networks (GANs) are widely adapted for anonymization of human figures. However, current state-of-the-art limit anonymization to the task of face anonymization. In this paper, we propose a novel anonymization framework (DeepPrivacy2) for realistic anonymization of human figures and faces. We introduce a new large and diverse dataset for human figure synthesis, which significantly improves image quality and diversity of generated images. Furthermore, we propose a style-based GAN that produces high quality, diverse and editable anonymizations. We demonstrate that our full-body anonymization framework provides stronger privacy guarantees than previously proposed methods. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 330,981 |
2407.09557 | Deep Reinforcement Learning Strategies in Finance: Insights into Asset
Holding, Trading Behavior, and Purchase Diversity | Recent deep reinforcement learning (DRL) methods in finance show promising outcomes. However, there is limited research examining the behavior of these DRL algorithms. This paper aims to investigate their tendencies towards holding or trading financial assets as well as purchase diversity. By analyzing their trading behaviors, we provide insights into the decision-making processes of DRL models in finance applications. Our findings reveal that each DRL algorithm exhibits unique trading patterns and strategies, with A2C emerging as the top performer in terms of cumulative rewards. While PPO and SAC engage in significant trades with a limited number of stocks, DDPG and TD3 adopt a more balanced approach. Furthermore, SAC and PPO tend to hold positions for shorter durations, whereas DDPG, A2C, and TD3 display a propensity to remain stationary for extended periods. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 472,644 |
2407.11927 | Bayesian Causal Forests for Longitudinal Data: Assessing the Impact of
Part-Time Work on Growth in High School Mathematics Achievement | Modelling growth in student achievement is a significant challenge in the field of education. Understanding how interventions or experiences such as part-time work can influence this growth is also important. Traditional methods like difference-in-differences are effective for estimating causal effects from longitudinal data. Meanwhile, Bayesian non-parametric methods have recently become popular for estimating causal effects from single time point observational studies. However, there remains a scarcity of methods capable of combining the strengths of these two approaches to flexibly estimate heterogeneous causal effects from longitudinal data. Motivated by two waves of data from the High School Longitudinal Study, the NCES' most recent longitudinal study which tracks a representative sample of over 20,000 students in the US, our study introduces a longitudinal extension of Bayesian Causal Forests. This model allows for the flexible identification of both individual growth in mathematical ability and the effects of participation in part-time work. Simulation studies demonstrate the predictive performance and reliable uncertainty quantification of the proposed model. Results reveal the negative impact of part time work for most students, but hint at potential benefits for those students with an initially low sense of school belonging. Clear signs of a widening achievement gap between students with high and low academic achievement are also identified. Potential policy implications are discussed, along with promising areas for future research. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 473,677 |
1805.04487 | Non-Stationary Texture Synthesis by Adversarial Expansion | The real world exhibits an abundance of non-stationary textures. Examples include textures with large-scale structures, as well as spatially variant and inhomogeneous textures. While existing example-based texture synthesis methods can cope well with stationary textures, non-stationary textures still pose a considerable challenge, which remains unresolved. In this paper, we propose a new approach for example-based non-stationary texture synthesis. Our approach uses a generative adversarial network (GAN), trained to double the spatial extent of texture blocks extracted from a specific texture exemplar. Once trained, the fully convolutional generator is able to expand the size of the entire exemplar, as well as of any of its sub-blocks. We demonstrate that this conceptually simple approach is highly effective for capturing large-scale structures, as well as other non-stationary attributes of the input exemplar. As a result, it can cope with challenging textures, which, to our knowledge, no other existing method can handle. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | true | 97,241 |
2202.11432 | Extension of Dynamic Mode Decomposition for dynamic systems with
incomplete information based on t-model of optimal prediction | The Dynamic Mode Decomposition has proved to be a very efficient technique to study dynamic data. This is entirely a data-driven approach that extracts all necessary information from data snapshots which are commonly supposed to be sampled from measurement. The application of this approach becomes problematic if the available data is incomplete because some dimensions of smaller scale either missing or unmeasured. Such setting occurs very often in modeling complex dynamical systems such as power grids, in particular with reduced-order modeling. To take into account the effect of unresolved variables the optimal prediction approach based on the Mori-Zwanzig formalism can be applied to obtain the most expected prediction under existing uncertainties. This effectively leads to the development of a time-predictive model accounting for the impact of missing data. In the present paper we provide a detailed derivation of the considered method from the Liouville equation and finalize it with the optimization problem that defines the optimal transition operator corresponding to the observed data. In contrast to the existing approach, we consider a first-order approximation of the Mori-Zwanzig decomposition, state the corresponding optimization problem and solve it with the gradient-based optimization method. The gradient of the obtained objective function is computed precisely through the automatic differentiation technique. The numerical experiments illustrate that the considered approach gives practically the same dynamics as the exact Mori-Zwanzig decomposition, but is less computationally intensive. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 281,887 |
2303.15530 | A New Index based on Power Splitting Indices for Predicting Proper Time
of Controlled Islanding | In the event of large disturbances, the practice of controlled islanding is used as a last resort to prevent cascading outages. The application of the strategy at the right time is crucial to maintaining system security. A controlled islanding strategy may be deployed efficiently at the right time by predicting the time of uncontrolled system splitting. The purpose of this study is to predict the appropriate islanding time to prevent catastrophic blackout and uncontrolled islanding based on existing relationships between coherent generator groups. A new instability index is derived from the proximity of inter-area oscillations to power splitting indices. Power splitting indices are derived using synchronization coefficients, which recognize the conditions in the system that warrant controlled islanding. The critical values of indices are calculated in offline mode using simulation data from IEEE 39-Buses, and their online performance is evaluated following a controlled islanding strategy. Through the introduction of these indices, system degradation can be effectively evaluated, and blackouts can be predicted early and prevented by controlled islanding at the right time. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 354,525 |
1907.07647 | Fly Safe: Aerial Swarm Robotics using Force Field Particle Swarm
Optimisation | Particle Swarm Optimisation (PSO) is a powerful optimisation algorithm that can be used to locate global maxima in a search space. Recent interest in swarms of Micro Aerial Vehicles (MAVs) begs the question as to whether PSO can be used as a method to enable real robotic swarms to locate a target goal point. However, the original PSO algorithm does not take into account collisions between particles during search. In this paper we propose a novel algorithm called Force Field Particle Swarm Optimisation (FFPSO) that designates repellent force fields to particles such that these fields provide an additional velocity component into the original PSO equations. We compare the performance of FFPSO with PSO and show that it has the ability to reduce the number of particle collisions during search to 0 whilst also being able to locate a target of interest in a similar amount of time. The scalability of the algorithm is also demonstrated via a set of experiments that considers how the number of crashes and the time taken to find the goal varies according to swarm size. Finally, we demonstrate the algorithms applicability on a swarm of real MAVs. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 138,930 |
2103.01513 | Computing foaming flows across scales: from breaking waves to
microfluidics | Crashing ocean waves, cappuccino froths and microfluidic bubble crystals are examples of foamy flows. Foamy flows are critical in numerous natural and industrial processes and remain notoriously difficult to compute as they involve coupled, multiscale physical processes. Computations need to resolve the interactions of the bubbles with the fluid and complex boundaries, while capturing the drainage and rupture of the microscopic liquid films at their interface. We present a novel multilayer simulation framework (Multi-VOF) that advances the state of the art in simulation capabilities of foamy flows. The framework introduces a novel scheme for the distinct handling of multiple neighboring bubbles and a new regularization method that produces sharp interfaces and removes spurious fragments. Multi-VOF is verified and validated with experimental results and complemented with open source, efficient scalable software. We demonstrate capturing of bubble crystalline structures in realistic microfluidics devices and foamy flows involving tens of thousands of bubbles in a waterfall. The present multilayer framework extends the classical volume-of-fluid methodology and allows for unprecedented large scale, predictive simulations of flows with multiple interfaces. | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | 222,648 |
1707.06039 | Quantum gate identification: error analysis, numerical results and
optical experiment | The identification of an unknown quantum gate is a significant issue in quantum technology. In this paper, we propose a quantum gate identification method within the framework of quantum process tomography. In this method, a series of pure states are inputted to the gate and then a fast state tomography on the output states is performed and the data are used to reconstruct the quantum gate. Our algorithm has computational complexity $O(d^3)$ with the system dimension $d$. The algorithm is compared with maximum likelihood estimation method for the running time, which shows the efficiency advantage of our method. An error upper bound is established for the identification algorithm and the robustness of the algorithm against the purity of input states is also tested. We perform quantum optical experiment on single-qubit Hadamard gate to verify the effectiveness of the identification algorithm. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 77,343 |
2404.10685 | Generating Human Interaction Motions in Scenes with Text Control | We present TeSMo, a method for text-controlled scene-aware motion generation based on denoising diffusion models. Previous text-to-motion methods focus on characters in isolation without considering scenes due to the limited availability of datasets that include motion, text descriptions, and interactive scenes. Our approach begins with pre-training a scene-agnostic text-to-motion diffusion model, emphasizing goal-reaching constraints on large-scale motion-capture datasets. We then enhance this model with a scene-aware component, fine-tuned using data augmented with detailed scene information, including ground plane and object shapes. To facilitate training, we embed annotated navigation and interaction motions within scenes. The proposed method produces realistic and diverse human-object interactions, such as navigation and sitting, in different scenes with various object shapes, orientations, initial body positions, and poses. Extensive experiments demonstrate that our approach surpasses prior techniques in terms of the plausibility of human-scene interactions, as well as the realism and variety of the generated motions. Code will be released upon publication of this work at https://research.nvidia.com/labs/toronto-ai/tesmo. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | true | 447,203 |
2408.05767 | Reference-free Hallucination Detection for Large Vision-Language Models | Large vision-language models (LVLMs) have made significant progress in recent years. While LVLMs exhibit excellent ability in language understanding, question answering, and conversations of visual inputs, they are prone to producing hallucinations. While several methods are proposed to evaluate the hallucinations in LVLMs, most are reference-based and depend on external tools, which complicates their practical application. To assess the viability of alternative methods, it is critical to understand whether the reference-free approaches, which do not rely on any external tools, can efficiently detect hallucinations. Therefore, we initiate an exploratory study to demonstrate the effectiveness of different reference-free solutions in detecting hallucinations in LVLMs. In particular, we conduct an extensive study on three kinds of techniques: uncertainty-based, consistency-based, and supervised uncertainty quantification methods on four representative LVLMs across two different tasks. The empirical results show that the reference-free approaches are capable of effectively detecting non-factual responses in LVLMs, with the supervised uncertainty quantification method outperforming the others, achieving the best performance across different settings. | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 479,928 |
2501.02313 | DiffGraph: Heterogeneous Graph Diffusion Model | Recent advances in Graph Neural Networks (GNNs) have revolutionized graph-structured data modeling, yet traditional GNNs struggle with complex heterogeneous structures prevalent in real-world scenarios. Despite progress in handling heterogeneous interactions, two fundamental challenges persist: noisy data significantly compromising embedding quality and learning performance, and existing methods' inability to capture intricate semantic transitions among heterogeneous relations, which impacts downstream predictions. To address these fundamental issues, we present the Heterogeneous Graph Diffusion Model (DiffGraph), a pioneering framework that introduces an innovative cross-view denoising strategy. This advanced approach transforms auxiliary heterogeneous data into target semantic spaces, enabling precise distillation of task-relevant information. At its core, DiffGraph features a sophisticated latent heterogeneous graph diffusion mechanism, implementing a novel forward and backward diffusion process for superior noise management. This methodology achieves simultaneous heterogeneous graph denoising and cross-type transition, while significantly simplifying graph generation through its latent-space diffusion capabilities. Through rigorous experimental validation on both public and industrial datasets, we demonstrate that DiffGraph consistently surpasses existing methods in link prediction and node classification tasks, establishing new benchmarks for robustness and efficiency in heterogeneous graph processing. The model implementation is publicly available at: https://github.com/HKUDS/DiffGraph. | false | false | false | false | true | true | true | false | false | false | false | false | false | false | false | false | false | false | 522,432 |
2405.17284 | An NLP Crosswalk Between the Common Core State Standards and NAEP Item
Specifications | Natural language processing (NLP) is rapidly developing for applications in educational assessment. In this paper, I describe an NLP-based procedure that can be used to support subject matter experts in establishing a crosswalk between item specifications and content standards. This paper extends recent work by proposing and demonstrating the use of multivariate similarity based on embedding vectors for sentences or texts. In particular, a hybrid regression procedure is demonstrated for establishing the match of each content standard to multiple item specifications. The procedure is used to evaluate the match of the Common Core State Standards (CCSS) for mathematics at grade 4 to the corresponding item specifications for the 2026 National Assessment of Educational Progress (NAEP). | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 457,845 |
1905.11132 | Small-time stabilization of nonholonomic or underactuated mechanical
systems: the unicycle and the slider examples | This paper concerns the small-time stabilization of some classes of mechanical systems which are not stabilizable by means of at least continuous state feedback laws. This is the case of nonholonomic mechanical systems, an example being the unicycle robot, or for underactuated mechanical systems, an example being the slider. Explicit time-varying feedback laws leading to small-time stabilization are constructed for these two control systems. The main tools are homogeneity, backstepping, and desingularization technics. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 132,338 |
2206.10775 | An Overview of Drone Energy Consumption Factors and Models | At present, there is a growing demand for drones with diverse capabilities that can be used in both civilian and military applications, and this topic is receiving increasing attention. When it comes to drone operations, the amount of energy they consume is a determining factor in their ability to achieve their full potential. According to this, it appears that it is necessary to identify the factors affecting the energy consumption of the unmanned air vehicle (UAV) during the mission process, as well as examine the general factors that influence the consumption of energy. This chapter aims to provide an overview of the current state of research in the area of UAV energy consumption and provide general categorizations of factors affecting UAV's energy consumption as well as an investigation of different energy models. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 304,027 |
2312.13252 | Zero-Shot Metric Depth with a Field-of-View Conditioned Diffusion Model | While methods for monocular depth estimation have made significant strides on standard benchmarks, zero-shot metric depth estimation remains unsolved. Challenges include the joint modeling of indoor and outdoor scenes, which often exhibit significantly different distributions of RGB and depth, and the depth-scale ambiguity due to unknown camera intrinsics. Recent work has proposed specialized multi-head architectures for jointly modeling indoor and outdoor scenes. In contrast, we advocate a generic, task-agnostic diffusion model, with several advancements such as log-scale depth parameterization to enable joint modeling of indoor and outdoor scenes, conditioning on the field-of-view (FOV) to handle scale ambiguity and synthetically augmenting FOV during training to generalize beyond the limited camera intrinsics in training datasets. Furthermore, by employing a more diverse training mixture than is common, and an efficient diffusion parameterization, our method, DMD (Diffusion for Metric Depth) achieves a 25\% reduction in relative error (REL) on zero-shot indoor and 33\% reduction on zero-shot outdoor datasets over the current SOTA using only a small number of denoising steps. For an overview see https://diffusion-vision.github.io/dmd | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 417,250 |
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