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2209.08010 | Causes of Catastrophic Forgetting in Class-Incremental Semantic
Segmentation | Class-incremental learning for semantic segmentation (CiSS) is presently a highly researched field which aims at updating a semantic segmentation model by sequentially learning new semantic classes. A major challenge in CiSS is overcoming the effects of catastrophic forgetting, which describes the sudden drop of accuracy on previously learned classes after the model is trained on a new set of classes. Despite latest advances in mitigating catastrophic forgetting, the underlying causes of forgetting specifically in CiSS are not well understood. Therefore, in a set of experiments and representational analyses, we demonstrate that the semantic shift of the background class and a bias towards new classes are the major causes of forgetting in CiSS. Furthermore, we show that both causes mostly manifest themselves in deeper classification layers of the network, while the early layers of the model are not affected. Finally, we demonstrate how both causes are effectively mitigated utilizing the information contained in the background, with the help of knowledge distillation and an unbiased cross-entropy loss. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 317,972 |
2405.14007 | A Practice in Enrollment Prediction with Markov Chain Models | Enrollment projection is a critical aspect of university management, guiding decisions related to resource allocation and revenue forecasting. However, despite its importance, there remains a lack of transparency regarding the methodologies utilized by many institutions. This paper presents an innovative approach to enrollment projection using Markov Chain modeling, drawing upon a case study conducted at Eastern Michigan University (EMU). Markov Chain modeling emerges as a promising approach for enrollment projection, offering precise predictions based on historical trends. This paper outlines the implementation of Enhanced Markov Chain modeling at EMU, detailing the methodology used to compute transition probabilities and evaluate model performance. Despite challenges posed by external uncertainties such as the COVID-19 pandemic, Markov Chain modeling has demonstrated impressive accuracy, with an average difference of less than 1 percent between predicted and actual enrollments. The paper concludes with a discussion of future directions and opportunities for collaboration among institutions. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 456,195 |
2407.10614 | Investigating shocking events in the Ethereum stablecoin ecosystem
through temporal multilayer graph structure | In the dynamic landscape of the Web, we are witnessing the emergence of the Web3 paradigm, which dictates that platforms should rely on blockchain technology and cryptocurrencies to sustain themselves and their profitability. Cryptocurrencies are characterised by high market volatility and susceptibility to substantial crashes, issues that require temporal analysis methodologies able to tackle the high temporal resolution, heterogeneity and scale of blockchain data. While existing research attempts to analyse crash events, fundamental questions persist regarding the optimal time scale for analysis, differentiation between long-term and short-term trends, and the identification and characterisation of shock events within these decentralised systems. This paper addresses these issues by examining cryptocurrencies traded on the Ethereum blockchain, with a spotlight on the crash of the stablecoin TerraUSD and the currency LUNA designed to stabilise it. Utilising complex network analysis and a multi-layer temporal graph allows the study of the correlations between the layers representing the currencies and system evolution across diverse time scales. The investigation sheds light on the strong interconnections among stablecoins pre-crash and the significant post-crash transformations. We identify anomalous signals before, during, and after the collapse, emphasising their impact on graph structure metrics and user movement across layers. This paper pioneers temporal, cross-chain graph analysis to explore a cryptocurrency collapse. It emphasises the importance of temporal analysis for studies on web-derived data and how graph-based analysis can enhance traditional econometric results. Overall, this research carries implications beyond its field, for example for regulatory agencies aiming to safeguard users from shocks and monitor investment risks for citizens and clients. | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 473,050 |
1802.08331 | Diverse Exploration for Fast and Safe Policy Improvement | We study an important yet under-addressed problem of quickly and safely improving policies in online reinforcement learning domains. As its solution, we propose a novel exploration strategy - diverse exploration (DE), which learns and deploys a diverse set of safe policies to explore the environment. We provide DE theory explaining why diversity in behavior policies enables effective exploration without sacrificing exploitation. Our empirical study shows that an online policy improvement algorithm framework implementing the DE strategy can achieve both fast policy improvement and safe online performance. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 91,080 |
2007.04448 | Emergence of Hierarchy in Networked Endorsement Dynamics | Many social and biological systems are characterized by enduring hierarchies, including those organized around prestige in academia, dominance in animal groups, and desirability in online dating. Despite their ubiquity, the general mechanisms that explain the creation and endurance of such hierarchies are not well understood. We introduce a generative model for the dynamics of hierarchies using time-varying networks in which new links are formed based on the preferences of nodes in the current network and old links are forgotten over time. The model produces a range of hierarchical structures, ranging from egalitarianism to bistable hierarchies, and we derive critical points that separate these regimes in the limit of long system memory. Importantly, our model supports statistical inference, allowing for a principled comparison of generative mechanisms using data. We apply the model to study hierarchical structures in empirical data on hiring patterns among mathematicians, dominance relations among parakeets, and friendships among members of a fraternity, observing several persistent patterns as well as interpretable differences in the generative mechanisms favored by each. Our work contributes to the growing literature on statistically grounded models of time-varying networks. | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 186,351 |
1908.01479 | Imaging with highly incomplete and corrupted data | We consider the problem of imaging sparse scenes from a few noisy data using an $l_1$-minimization approach. This problem can be cast as a linear system of the form $A \, \rho =b$, where $A$ is an $N\times K$ measurement matrix. We assume that the dimension of the unknown sparse vector $\rho \in {\mathbb{C}}^K$ is much larger than the dimension of the data vector $b \in {\mathbb{C}}^N$, i.e, $K \gg N$. We provide a theoretical framework that allows us to examine under what conditions the $\ell_1$-minimization problem admits a solution that is close to the exact one in the presence of noise. Our analysis shows that $l_1$-minimization is not robust for imaging with noisy data when high resolution is required. To improve the performance of $l_1$-minimization we propose to solve instead the augmented linear system $ [A \, | \, C] \rho =b$, where the $N \times \Sigma$ matrix $C$ is a noise collector. It is constructed so as its column vectors provide a frame on which the noise of the data, a vector of dimension $N$, can be well approximated. Theoretically, the dimension $\Sigma$ of the noise collector should be $e^N$ which would make its use not practical. However, our numerical results illustrate that robust results in the presence of noise can be obtained with a large enough number of columns $\Sigma \approx 10 K$. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 140,777 |
2112.10325 | Incremental Cross-view Mutual Distillation for Self-supervised Medical
CT Synthesis | Due to the constraints of the imaging device and high cost in operation time, computer tomography (CT) scans are usually acquired with low intra-slice resolution. Improving the intra-slice resolution is beneficial to the disease diagnosis for both human experts and computer-aided systems. To this end, this paper builds a novel medical slice synthesis to increase the between-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy to accomplish this task in the self-supervised learning manner. Specifically, we model this problem from three different views: slice-wise interpolation from axial view and pixel-wise interpolation from coronal and sagittal views. Under this circumstance, the models learned from different views can distill valuable knowledge to guide the learning processes of each other. We can repeat this process to make the models synthesize intermediate slice data with increasing inter-slice resolution. To demonstrate the effectiveness of the proposed approach, we conduct comprehensive experiments on a large-scale CT dataset. Quantitative and qualitative comparison results show that our method outperforms state-of-the-art algorithms by clear margins. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 272,409 |
1808.08402 | How do Convolutional Neural Networks Learn Design? | In this paper, we aim to understand the design principles in book cover images which are carefully crafted by experts. Book covers are designed in a unique way, specific to genres which convey important information to their readers. By using Convolutional Neural Networks (CNN) to predict book genres from cover images, visual cues which distinguish genres can be highlighted and analyzed. In order to understand these visual clues contributing towards the decision of a genre, we present the application of Layer-wise Relevance Propagation (LRP) on the book cover image classification results. We use LRP to explain the pixel-wise contributions of book cover design and highlight the design elements contributing towards particular genres. In addition, with the use of state-of-the-art object and text detection methods, insights about genre-specific book cover designs are discovered. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | true | 105,940 |
2208.03692 | Multi-Stage NMPC for a MAV based Collision Free Navigation under Varying
Communication Delays | Time delays in communication networks are one of the main concerns in deploying robots with computation boards on the edge. This article proposes a multi-stage Nonlinear Model Predictive Control (NMPC) that is capable of handling varying network-induced time delays for establishing a control framework being able to guarantee collision-free Micro Aerial Vehicles (MAVs) navigation. This study introduces a novel approach that considers different sampling times by a tree of discretization scenarios contrary to the existing typical multi-stage NMPC where system uncertainties are modeled by a tree of scenarios. Additionally, the proposed method considers adaptive weights for the multi-stage NMPC scenarios based on the probability of time delays in the communication link. As a result of the multi-stage NMPC, the obtained optimal control action is valid for multiple sampling times. Finally, the overall effectiveness of the proposed novel control framework is demonstrated in various tests and different simulation environments. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 311,869 |
2412.10450 | Regional Weather Variable Predictions by Machine Learning with
Near-Surface Observational and Atmospheric Numerical Data | Accurate and timely regional weather prediction is vital for sectors dependent on weather-related decisions. Traditional prediction methods, based on atmospheric equations, often struggle with coarse temporal resolutions and inaccuracies. This paper presents a novel machine learning (ML) model, called MiMa (short for Micro-Macro), that integrates both near-surface observational data from Kentucky Mesonet stations (collected every five minutes, known as Micro data) and hourly atmospheric numerical outputs (termed as Macro data) for fine-resolution weather forecasting. The MiMa model employs an encoder-decoder transformer structure, with two encoders for processing multivariate data from both datasets and a decoder for forecasting weather variables over short time horizons. Each instance of the MiMa model, called a modelet, predicts the values of a specific weather parameter at an individual Mesonet station. The approach is extended with Re-MiMa modelets, which are designed to predict weather variables at ungauged locations by training on multivariate data from a few representative stations in a region, tagged with their elevations. Re-MiMa (short for Regional-MiMa) can provide highly accurate predictions across an entire region, even in areas without observational stations. Experimental results show that MiMa significantly outperforms current models, with Re-MiMa offering precise short-term forecasts for ungauged locations, marking a significant advancement in weather forecasting accuracy and applicability. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 516,939 |
1105.5789 | Clustering and Classification in Text Collections Using Graph Modularity | A new fast algorithm for clustering and classification of large collections of text documents is introduced. The new algorithm employs the bipartite graph that realizes the word-document matrix of the collection. Namely, the modularity of the bipartite graph is used as the optimization functional. Experiments performed with the new algorithm on a number of text collections had shown a competitive quality of the clustering (classification), and a record-breaking speed. | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | true | 10,564 |
1906.04309 | Associative Convolutional Layers | Motivated by the necessity for parameter efficiency in distributed machine learning and AI-enabled edge devices, we provide a general and easy to implement method for significantly reducing the number of parameters of Convolutional Neural Networks (CNNs), during both the training and inference phases. We introduce a simple auxiliary neural network which can generate the convolutional filters of any CNN architecture from a low dimensional latent space. This auxiliary neural network, which we call "Convolutional Slice Generator" (CSG), is unique to the network and provides the association between its convolutional layers. During the training of the CNN, instead of training the filters of the convolutional layers, only the parameters of the CSG and their corresponding "code vectors" are trained. This results in a significant reduction of the number of parameters due to the fact that the CNN can be fully represented using only the parameters of the CSG, the code vectors, the fully connected layers, and the architecture of the CNN. We evaluate our approach by applying it to ResNet and DenseNet models when trained on CIFAR-10 and ImageNet datasets. While reducing the number of parameters by $\approx 2 \times$ on average, the accuracies of these networks remain within 1$\%$ of their original counterparts and in some cases there is an increase in the accuracy. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 134,670 |
2005.06602 | CIRCE at SemEval-2020 Task 1: Ensembling Context-Free and
Context-Dependent Word Representations | This paper describes the winning contribution to SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection (Subtask 2) handed in by team UG Student Intern. We present an ensemble model that makes predictions based on context-free and context-dependent word representations. The key findings are that (1) context-free word representations are a powerful and robust baseline, (2) a sentence classification objective can be used to obtain useful context-dependent word representations, and (3) combining those representations increases performance on some datasets while decreasing performance on others. | false | false | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | 177,043 |
2301.08078 | Stable Contact Guaranteeing Motion/Force Control for an Aerial
Manipulator on an Arbitrarily Tilted Surface | This study aims to design a motion/force controller for an aerial manipulator which guarantees the tracking of time-varying motion/force trajectories as well as the stability during the transition between free and contact motions. To this end, we model the force exerted on the end-effector as the Kelvin-Voigt linear model and estimate its parameters by recursive least-squares estimator. Then, the gains of the disturbance-observer (DOB)-based motion/force controller are calculated based on the stability conditions considering both the model uncertainties in the dynamic equation and switching between the free and contact motions. To validate the proposed controller, we conducted the time-varying motion/force tracking experiments with different approach speeds and orientations of the surface. The results show that our controller enables the aerial manipulator to track the time-varying motion/force trajectories. | false | false | false | false | false | false | false | true | false | false | true | false | false | false | false | false | false | false | 341,090 |
2406.11577 | Mathematical Entities: Corpora and Benchmarks | Mathematics is a highly specialized domain with its own unique set of challenges. Despite this, there has been relatively little research on natural language processing for mathematical texts, and there are few mathematical language resources aimed at NLP. In this paper, we aim to provide annotated corpora that can be used to study the language of mathematics in different contexts, ranging from fundamental concepts found in textbooks to advanced research mathematics. We preprocess the corpora with a neural parsing model and some manual intervention to provide part-of-speech tags, lemmas, and dependency trees. In total, we provide 182397 sentences across three corpora. We then aim to test and evaluate several noteworthy natural language processing models using these corpora, to show how well they can adapt to the domain of mathematics and provide useful tools for exploring mathematical language. We evaluate several neural and symbolic models against benchmarks that we extract from the corpus metadata to show that terminology extraction and definition extraction do not easily generalize to mathematics, and that additional work is needed to achieve good performance on these metrics. Finally, we provide a learning assistant that grants access to the content of these corpora in a context-sensitive manner, utilizing text search and entity linking. Though our corpora and benchmarks provide useful metrics for evaluating mathematical language processing, further work is necessary to adapt models to mathematics in order to provide more effective learning assistants and apply NLP methods to different mathematical domains. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 464,952 |
1302.3597 | A Framework for Decision-Theoretic Planning I: Combining the Situation
Calculus, Conditional Plans, Probability and Utility | This paper shows how we can combine logical representations of actions and decision theory in such a manner that seems natural for both. In particular we assume an axiomatization of the domain in terms of situation calculus, using what is essentially Reiter's solution to the frame problem, in terms of the completion of the axioms defining the state change. Uncertainty is handled in terms of the independent choice logic, which allows for independent choices and a logic program that gives the consequences of the choices. As part of the consequences are a specification of the utility of (final) states. The robot adopts robot plans, similar to the GOLOG programming language. Within this logic, we can define the expected utility of a conditional plan, based on the axiomatization of the actions, the uncertainty and the utility. The ?planning' problem is to find the plan with the highest expected utility. This is related to recent structured representations for POMDPs; here we use stochastic situation calculus rules to specify the state transition function and the reward/value function. Finally we show that with stochastic frame axioms, actions representations in probabilistic STRIPS are exponentially larger than using the representation proposed here. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 22,063 |
2302.00606 | The RW3D: A multi-modal panel dataset to understand the psychological
impact of the pandemic | Besides far-reaching public health consequences, the COVID-19 pandemic had a significant psychological impact on people around the world. To gain further insight into this matter, we introduce the Real World Worry Waves Dataset (RW3D). The dataset combines rich open-ended free-text responses with survey data on emotions, significant life events, and psychological stressors in a repeated-measures design in the UK over three years (2020: n=2441, 2021: n=1716 and 2022: n=1152). This paper provides background information on the data collection procedure, the recorded variables, participants' demographics, and higher-order psychological and text-based derived variables that emerged from the data. The RW3D is a unique primary data resource that could inspire new research questions on the psychological impact of the pandemic, especially those that connect modalities (here: text data, psychological survey variables and demographics) over time. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 343,275 |
2106.06908 | Domain Generalization on Medical Imaging Classification using Episodic
Training with Task Augmentation | Medical imaging datasets usually exhibit domain shift due to the variations of scanner vendors, imaging protocols, etc. This raises the concern about the generalization capacity of machine learning models. Domain generalization (DG), which aims to learn a model from multiple source domains such that it can be directly generalized to unseen test domains, seems particularly promising to medical imaging community. To address DG, recent model-agnostic meta-learning (MAML) has been introduced, which transfers the knowledge from previous training tasks to facilitate the learning of novel testing tasks. However, in clinical practice, there are usually only a few annotated source domains available, which decreases the capacity of training task generation and thus increases the risk of overfitting to training tasks in the paradigm. In this paper, we propose a novel DG scheme of episodic training with task augmentation on medical imaging classification. Based on meta-learning, we develop the paradigm of episodic training to construct the knowledge transfer from episodic training-task simulation to the real testing task of DG. Motivated by the limited number of source domains in real-world medical deployment, we consider the unique task-level overfitting and we propose task augmentation to enhance the variety during training task generation to alleviate it. With the established learning framework, we further exploit a novel meta-objective to regularize the deep embedding of training domains. To validate the effectiveness of the proposed method, we perform experiments on histopathological images and abdominal CT images. | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | false | false | 240,678 |
2006.06404 | A model for the spread of an epidemic from local to global: A case study
of COVID-19 in India | In this paper we propose an epidemiological model for the spread of COVID-19. The dynamics of the spread is based on four fundamental categories of people in a population: Tested and infected, Non-Tested but infected, Tested but not infected, and non-Tested and not infected. The model is based on two levels of dynamics of spread in the population: at local level and at the global level. The local level growth is described with data and parameters which include testing statistics for COVID-19, preventive measures such as nationwide lockdown, and the migration of people across neighboring locations. In the context of India, the local locations are considered as districts and migration or traffic flow across districts are defined by normalized edge weight of the metapopulation network of districts which are infected with COVID-19. Based on this local growth, state level predictions for number of people tested with COVID-19 positive are made. Further, considering the local locations as states, prediction is made for the country level. The values of the model parameters are determined using grid search and minimizing an error function while training the model with real data. The predictions are made based on the present statistics of testing, and certain linear and log-linear growth of testing at state and country level. Finally, it is shown that the spread can be contained if number of testing can be increased linearly or log-linearly by certain factors along with the preventive measures in near future. This is also necessary to prevent the sharp growth in the count of infected and to get rid of the second wave of pandemic. | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 181,417 |
1612.03239 | When multiplicative noise stymies control | We consider the stabilization of an unstable discrete-time linear system that is observed over a channel corrupted by continuous multiplicative noise. Our main result shows that if the system growth is large enough, then the system cannot be stabilized in a second-moment sense. This is done by showing that the probability that the state magnitude remains bounded must go to zero with time. Our proof technique recursively bounds the conditional density of the system state (instead of focusing on the second moment) to bound the progress the controller can make. This sidesteps the difficulty encountered in using the standard data-rate theorem style approach; that approach does not work because the mutual information per round between the system state and the observation is potentially unbounded. It was known that a system with multiplicative observation noise can be stabilized using a simple memoryless linear strategy if the system growth is suitably bounded. In this paper, we show that while memory cannot improve the performance of a linear scheme, a simple non-linear scheme that uses one-step memory can do better than the best linear scheme. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 65,350 |
2204.14133 | Network Topology Optimization via Deep Reinforcement Learning | Topology impacts important network performance metrics, including link utilization, throughput and latency, and is of central importance to network operators. However, due to the combinatorial nature of network topology, it is extremely difficult to obtain an optimal solution, especially since topology planning in networks also often comes with management-specific constraints. As a result, local optimization with hand-tuned heuristic methods from human experts are often adopted in practice. Yet, heuristic methods cannot cover the global topology design space while taking into account constraints, and cannot guarantee to find good solutions. In this paper, we propose a novel deep reinforcement learning (DRL) algorithm, called Advantage Actor Critic-Graph Searching (A2C-GS), for network topology optimization. A2C-GS consists of three novel components, including a verifier to validate the correctness of a generated network topology, a graph neural network (GNN) to efficiently approximate topology rating, and a DRL actor layer to conduct a topology search. A2C-GS can efficiently search over large topology space and output topology with satisfying performance. We conduct a case study based on a real network scenario, and our experimental results demonstrate the superior performance of A2C-GS in terms of both efficiency and performance. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | true | 294,065 |
2006.04700 | Multimodal Future Localization and Emergence Prediction for Objects in
Egocentric View with a Reachability Prior | In this paper, we investigate the problem of anticipating future dynamics, particularly the future location of other vehicles and pedestrians, in the view of a moving vehicle. We approach two fundamental challenges: (1) the partial visibility due to the egocentric view with a single RGB camera and considerable field-of-view change due to the egomotion of the vehicle; (2) the multimodality of the distribution of future states. In contrast to many previous works, we do not assume structural knowledge from maps. We rather estimate a reachability prior for certain classes of objects from the semantic map of the present image and propagate it into the future using the planned egomotion. Experiments show that the reachability prior combined with multi-hypotheses learning improves multimodal prediction of the future location of tracked objects and, for the first time, the emergence of new objects. We also demonstrate promising zero-shot transfer to unseen datasets. Source code is available at $\href{https://github.com/lmb-freiburg/FLN-EPN-RPN}{\text{this https URL.}}$ | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 180,786 |
2212.09849 | Dataless Knowledge Fusion by Merging Weights of Language Models | Fine-tuning pre-trained language models has become the prevalent paradigm for building downstream NLP models. Oftentimes fine-tuned models are readily available but their training data is not, due to data privacy or intellectual property concerns. This creates a barrier to fusing knowledge across individual models to yield a better single model. In this paper, we study the problem of merging individual models built on different training data sets to obtain a single model that performs well both across all data set domains and can generalize on out-of-domain data. We propose a dataless knowledge fusion method that merges models in their parameter space, guided by weights that minimize prediction differences between the merged model and the individual models. Over a battery of evaluation settings, we show that the proposed method significantly outperforms baselines such as Fisher-weighted averaging or model ensembling. Further, we find that our method is a promising alternative to multi-task learning that can preserve or sometimes improve over the individual models without access to the training data. Finally, model merging is more efficient than training a multi-task model, thus making it applicable to a wider set of scenarios. | false | false | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | 337,232 |
2310.01415 | GPT-Driver: Learning to Drive with GPT | We present a simple yet effective approach that can transform the OpenAI GPT-3.5 model into a reliable motion planner for autonomous vehicles. Motion planning is a core challenge in autonomous driving, aiming to plan a driving trajectory that is safe and comfortable. Existing motion planners predominantly leverage heuristic methods to forecast driving trajectories, yet these approaches demonstrate insufficient generalization capabilities in the face of novel and unseen driving scenarios. In this paper, we propose a novel approach to motion planning that capitalizes on the strong reasoning capabilities and generalization potential inherent to Large Language Models (LLMs). The fundamental insight of our approach is the reformulation of motion planning as a language modeling problem, a perspective not previously explored. Specifically, we represent the planner inputs and outputs as language tokens, and leverage the LLM to generate driving trajectories through a language description of coordinate positions. Furthermore, we propose a novel prompting-reasoning-finetuning strategy to stimulate the numerical reasoning potential of the LLM. With this strategy, the LLM can describe highly precise trajectory coordinates and also its internal decision-making process in natural language. We evaluate our approach on the large-scale nuScenes dataset, and extensive experiments substantiate the effectiveness, generalization ability, and interpretability of our GPT-based motion planner. Code is now available at https://github.com/PointsCoder/GPT-Driver. | false | false | false | false | true | false | false | true | true | false | false | true | false | false | false | false | false | false | 396,412 |
2105.06251 | Learning Weakly Convex Sets in Metric Spaces | One of the central problems studied in the theory of machine learning is the question of whether, for a given class of hypotheses, it is possible to efficiently find a {consistent} hypothesis, i.e., which has zero training error. While problems involving {\em convex} hypotheses have been extensively studied, the question of whether efficient learning is possible for non-convex hypotheses composed of possibly several disconnected regions is still less understood. Although it has been shown quite a while ago that efficient learning of weakly convex hypotheses, a parameterized relaxation of convex hypotheses, is possible for the special case of Boolean functions, the question of whether this idea can be developed into a generic paradigm has not been studied yet. In this paper, we provide a positive answer and show that the consistent hypothesis finding problem can indeed be solved in polynomial time for a broad class of weakly convex hypotheses over metric spaces. To this end, we propose a general domain-independent algorithm for finding consistent weakly convex hypotheses and prove sufficient conditions for its efficiency that characterize the corresponding hypothesis classes. To illustrate our general algorithm and its properties, we discuss several non-trivial learning examples to demonstrate how it can be used to efficiently solve the corresponding consistent hypothesis finding problem. Without the weak convexity constraint, these problems are known to be computationally intractable. We then proceed to show that the general idea of our algorithm can even be extended to the case of extensional weakly convex hypotheses, as it naturally arise, e.g., when performing vertex classification in graphs. We prove that using our extended algorithm, the problem can be solved in polynomial time provided the distances in the domain can be computed efficiently. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 235,067 |
2404.03208 | HiMAL: A Multimodal Hierarchical Multi-task Auxiliary Learning framework
for predicting and explaining Alzheimer disease progression | Objective: We aimed to develop and validate a novel multimodal framework HiMAL (Hierarchical, Multi-task Auxiliary Learning) framework, for predicting cognitive composite functions as auxiliary tasks that estimate the longitudinal risk of transition from Mild Cognitive Impairment (MCI) to Alzheimer Disease (AD). Methods: HiMAL utilized multimodal longitudinal visit data including imaging features, cognitive assessment scores, and clinical variables from MCI patients in the Alzheimer Disease Neuroimaging Initiative (ADNI) dataset, to predict at each visit if an MCI patient will progress to AD within the next 6 months. Performance of HiMAL was compared with state-of-the-art single-task and multi-task baselines using area under the receiver operator curve (AUROC) and precision recall curve (AUPRC) metrics. An ablation study was performed to assess the impact of each input modality on model performance. Additionally, longitudinal explanations regarding risk of disease progression were provided to interpret the predicted cognitive decline. Results: Out of 634 MCI patients (mean [IQR] age : 72.8 [67-78], 60% men), 209 (32%) progressed to AD. HiMAL showed better prediction performance compared to all single-modality singe-task baselines (AUROC = 0.923 [0.915-0.937]; AUPRC= 0.623 [0.605-0.644]; all p<0.05). Ablation analysis highlighted that imaging and cognition scores with maximum contribution towards prediction of disease progression. Discussion: Clinically informative model explanations anticipate cognitive decline 6 months in advance, aiding clinicians in future disease progression assessment. HiMAL relies on routinely collected EHR variables for proximal (6 months) prediction of AD onset, indicating its translational potential for point-of-care monitoring and managing of high-risk patients. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 444,165 |
2008.04357 | Directional Laplacian Centrality for Cyber Situational Awareness | Cyber operations is drowning in diverse, high-volume, multi-source data. In order to get a full picture of current operations and identify malicious events and actors analysts must see through data generated by a mix of human activity and benign automated processes. Although many monitoring and alert systems exist, they typically use signature-based detection methods. We introduce a general method rooted in spectral graph theory to discover patterns and anomalies without a priori knowledge of signatures. We derive and propose a new graph-theoretic centrality measure based on the derivative of the graph Laplacian matrix in the direction of a vertex. To build intuition about our measure we show how it identifies the most central vertices in standard network data sets and compare to other graph centrality measures. Finally, we focus our attention on studying its effectiveness in identifying important IP addresses in network flow data. Using both real and synthetic network flow data, we conduct several experiments to test our measure's sensitivity to two types of injected attack profiles, and show that vertices participating in injected attack profiles exhibit noticeable changes in our centrality measures, even when the injected anomalies are relatively small, and in the presence of simulated network dynamics. | false | false | false | true | false | false | false | false | false | false | false | false | true | false | false | false | false | true | 191,196 |
2302.14705 | AccelTran: A Sparsity-Aware Accelerator for Dynamic Inference with
Transformers | Self-attention-based transformer models have achieved tremendous success in the domain of natural language processing. Despite their efficacy, accelerating the transformer is challenging due to its quadratic computational complexity and large activation sizes. Existing transformer accelerators attempt to prune its tokens to reduce memory access, albeit with high compute overheads. Moreover, previous works directly operate on large matrices involved in the attention operation, which limits hardware utilization. In order to address these challenges, this work proposes a novel dynamic inference scheme, DynaTran, which prunes activations at runtime with low overhead, substantially reducing the number of ineffectual operations. This improves the throughput of transformer inference. We further propose tiling the matrices in transformer operations along with diverse dataflows to improve data reuse, thus enabling higher energy efficiency. To effectively implement these methods, we propose AccelTran, a novel accelerator architecture for transformers. Extensive experiments with different models and benchmarks demonstrate that DynaTran achieves higher accuracy than the state-of-the-art top-k hardware-aware pruning strategy while attaining up to 1.2$\times$ higher sparsity. One of our proposed accelerators, AccelTran-Edge, achieves 330K$\times$ higher throughput with 93K$\times$ lower energy requirement when compared to a Raspberry Pi device. On the other hand, AccelTran-Server achieves 5.73$\times$ higher throughput and 3.69$\times$ lower energy consumption compared to the state-of-the-art transformer co-processor, Energon. The simulation source code is available at https://github.com/jha-lab/acceltran. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 348,405 |
2111.03418 | Meta-Forecasting by combining Global Deep Representations with Local
Adaptation | While classical time series forecasting considers individual time series in isolation, recent advances based on deep learning showed that jointly learning from a large pool of related time series can boost the forecasting accuracy. However, the accuracy of these methods suffers greatly when modeling out-of-sample time series, significantly limiting their applicability compared to classical forecasting methods. To bridge this gap, we adopt a meta-learning view of the time series forecasting problem. We introduce a novel forecasting method, called Meta Global-Local Auto-Regression (Meta-GLAR), that adapts to each time series by learning in closed-form the mapping from the representations produced by a recurrent neural network (RNN) to one-step-ahead forecasts. Crucially, the parameters ofthe RNN are learned across multiple time series by backpropagating through the closed-form adaptation mechanism. In our extensive empirical evaluation we show that our method is competitive with the state-of-the-art in out-of-sample forecasting accuracy reported in earlier work. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 265,161 |
2411.05809 | Is it me, or is A larger than B: Uncovering the determinants of
relational cognitive dissonance resolution | This study explores the computational mechanisms underlying the resolution of cognitive dissonances. We focus on scenarios in which an observation violates the expected relationship between objects. For instance, an agent expects object A to be smaller than B in some feature space but observes the opposite. One solution is to adjust the expected relationship according to the new observation and change the expectation to A being larger than B. An alternative solution would be to adapt the representation of A and B in the feature space such that in the new representation, the relationship that A is smaller than B is maintained. While both pathways resolve the dissonance, they generalize differently to different tasks. Using Artificial Neural Networks (ANNs) capable of relational learning, we demonstrate the existence of these two pathways and show that the chosen pathway depends on the dissonance magnitude. Large dissonances alter the representation of the objects, while small dissonances lead to adjustments in the expected relationships. We show that this effect arises from the inherently different learning dynamics of relationships and representations and study the implications. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 506,805 |
2302.05826 | Asymptotically Optimal Coded Distributed Computing via Combinatorial
Designs | Coded distributed computing (CDC) introduced by Li \emph{et al.} can greatly reduce the communication load for MapReduce computing systems. In the general cascaded CDC with $K$ workers, $N$ input files and $Q$ Reduce functions, each input file will be mapped by $r$ workers and each Reduce function will be computed by $s$ workers such that coding techniques can be applied to achieve the maximum multicast gain. The main drawback of most existing CDC schemes is that they require the original data to be split into a large number of input files that grows exponentially with $K$, which can significantly increase the coding complexity and degrade system performance. In this paper, we first use a classic combinatorial structure $t$-design, for any integer $t\geq 2$, to develop a low-complexity and asymptotically optimal CDC with $r=s$. The main advantages of our scheme via $t$-design are two-fold: 1) having much smaller $N$ and $Q$ than the existing schemes under the same parameters $K$, $r$ and $s$; and 2) achieving smaller communication loads compared with the state-of-the-art schemes. Remarkably, unlike the previous schemes that realize on large operation fields, our scheme operates on the minimum binary field $\mathbb{F}_2$. Furthermore, we show that our construction method can incorporate the other combinatorial structures that have a similar property to $t$-design. For instance, we use $t$-GDD to obtain another asymptotically optimal CDC scheme over $\mathbb{F}_2$ that has different parameters from $t$-design. Finally, we show that our construction method can also be used to construct CDC schemes with $r\neq s$ that have small file number and Reduce function number. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 345,182 |
2106.02811 | Full-Dimensional Rate Enhancement for UAV-Enabled Communications via
Intelligent Omni-Surface | This paper investigates the achievable rate maximization problem of a downlink unmanned aerial vehicle (UAV)-enabled communication system aided by an intelligent omni-surface (IOS). Different from the state-of-the-art reconfigurable intelligent surface (RIS) that only reflects incident signals, the IOS can simultaneously reflect and transmit the signals, thereby providing full-dimensional rate enhancement. To tackle such a problem, we formulate it by jointly optimizing the IOS's phase shift and the UAV trajectory. Although it is difficult to solve it optimally due to its non-convexity, we propose an efficient iterative algorithm to obtain a high-quality suboptimal solution. Simulation results show that the IOS-assisted UAV communications can achieve more significant improvement in achievable rates than other benchmark schemes. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 239,040 |
1907.02211 | Optimal Decision Trees for the Algorithm Selection Problem: Integer
Programming Based Approaches | Even though it is well known that for most relevant computational problems different algorithms may perform better on different classes of problem instances, most researchers still focus on determining a single best algorithmic configuration based on aggregate results such as the average. In this paper, we propose Integer Programming based approaches to build decision trees for the Algorithm Selection Problem. These techniques allow automate three crucial decisions: (i) discerning the most important problem features to determine problem classes; (ii) grouping the problems into classes and (iii) select the best algorithm configuration for each class. To evaluate this new approach, extensive computational experiments were executed using the linear programming algorithms implemented in the COIN-OR Branch & Cut solver across a comprehensive set of instances, including all MIPLIB benchmark instances. The results exceeded our expectations. While selecting the single best parameter setting across all instances decreased the total running time by 22%, our approach decreased the total running time by 40% on average across 10-fold cross validation experiments. These results indicate that our method generalizes quite well and does not overfit. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 137,559 |
2301.06428 | Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic
Optimization | We consider the optimization problem of the form $\min_{x \in \mathbb{R}^d} f(x) \triangleq \mathbb{E}_{\xi} [F(x; \xi)]$, where the component $F(x;\xi)$ is $L$-mean-squared Lipschitz but possibly nonconvex and nonsmooth. The recently proposed gradient-free method requires at most $\mathcal{O}( L^4 d^{3/2} \epsilon^{-4} + \Delta L^3 d^{3/2} \delta^{-1} \epsilon^{-4})$ stochastic zeroth-order oracle complexity to find a $(\delta,\epsilon)$-Goldstein stationary point of objective function, where $\Delta = f(x_0) - \inf_{x \in \mathbb{R}^d} f(x)$ and $x_0$ is the initial point of the algorithm. This paper proposes a more efficient algorithm using stochastic recursive gradient estimators, which improves the complexity to $\mathcal{O}(L^3 d^{3/2} \epsilon^{-3}+ \Delta L^2 d^{3/2} \delta^{-1} \epsilon^{-3})$. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 340,641 |
2005.03857 | Efficient Computation Reduction in Bayesian Neural Networks Through
Feature Decomposition and Memorization | Bayesian method is capable of capturing real world uncertainties/incompleteness and properly addressing the over-fitting issue faced by deep neural networks. In recent years, Bayesian Neural Networks (BNNs) have drawn tremendous attentions of AI researchers and proved to be successful in many applications. However, the required high computation complexity makes BNNs difficult to be deployed in computing systems with limited power budget. In this paper, an efficient BNN inference flow is proposed to reduce the computation cost then is evaluated by means of both software and hardware implementations. A feature decomposition and memorization (\texttt{DM}) strategy is utilized to reform the BNN inference flow in a reduced manner. About half of the computations could be eliminated compared to the traditional approach that has been proved by theoretical analysis and software validations. Subsequently, in order to resolve the hardware resource limitations, a memory-friendly computing framework is further deployed to reduce the memory overhead introduced by \texttt{DM} strategy. Finally, we implement our approach in Verilog and synthesise it with 45 $nm$ FreePDK technology. Hardware simulation results on multi-layer BNNs demonstrate that, when compared with the traditional BNN inference method, it provides an energy consumption reduction of 73\% and a 4$\times$ speedup at the expense of 14\% area overhead. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 176,282 |
2009.04007 | Revisiting LSTM Networks for Semi-Supervised Text Classification via
Mixed Objective Function | In this paper, we study bidirectional LSTM network for the task of text classification using both supervised and semi-supervised approaches. Several prior works have suggested that either complex pretraining schemes using unsupervised methods such as language modeling (Dai and Le 2015; Miyato, Dai, and Goodfellow 2016) or complicated models (Johnson and Zhang 2017) are necessary to achieve a high classification accuracy. However, we develop a training strategy that allows even a simple BiLSTM model, when trained with cross-entropy loss, to achieve competitive results compared with more complex approaches. Furthermore, in addition to cross-entropy loss, by using a combination of entropy minimization, adversarial, and virtual adversarial losses for both labeled and unlabeled data, we report state-of-the-art results for text classification task on several benchmark datasets. In particular, on the ACL-IMDB sentiment analysis and AG-News topic classification datasets, our method outperforms current approaches by a substantial margin. We also show the generality of the mixed objective function by improving the performance on relation extraction task. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 194,941 |
2403.07003 | Evacuation Management Framework towards Smart City-wide Intelligent
Emergency Interactive Response System | A smart city solution toward future 6G network deployment allows small and medium sized enterprises (SMEs), industry, and government entities to connect with the infrastructures and play a crucial role in enhancing emergency preparedness with advanced sensors. The objective of this work is to propose a set of coordinated technological solutions to transform an existing emergency response system into an intelligent interactive system, thereby improving the public services and the quality of life for residents at home, on road, in hospitals, transport hubs, etc. In this context, we consider a city wide view from three different application scenes that are closely related to peoples daily life, to optimize the actions taken at relevant departments. Therefore, using artificial intelligence (AI) and machine learning (ML) techniques to enable the next generation connected vehicle experiences, we specifically focus on accidents happening in indoor households, urban roads, and at large public facilities. This smart interactive response system will benefit from advanced sensor fusion and AI by formulating a real time dynamic model. | false | false | false | false | true | false | true | false | false | false | false | false | false | true | false | false | false | true | 436,700 |
1902.06740 | Leveraging Communication Topologies Between Learning Agents in Deep
Reinforcement Learning | A common technique to improve learning performance in deep reinforcement learning (DRL) and many other machine learning algorithms is to run multiple learning agents in parallel. A neglected component in the development of these algorithms has been how best to arrange the learning agents involved to improve distributed search. Here we draw upon results from the networked optimization literatures suggesting that arranging learning agents in communication networks other than fully connected topologies (the implicit way agents are commonly arranged in) can improve learning. We explore the relative performance of four popular families of graphs and observe that one such family (Erdos-Renyi random graphs) empirically outperforms the de facto fully-connected communication topology across several DRL benchmark tasks. Additionally, we observe that 1000 learning agents arranged in an Erdos-Renyi graph can perform as well as 3000 agents arranged in the standard fully-connected topology, showing the large learning improvement possible when carefully designing the topology over which agents communicate. We complement these empirical results with a theoretical investigation of why our alternate topologies perform better. Overall, our work suggests that distributed machine learning algorithms could be made more effective if the communication topology between learning agents was optimized. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 121,831 |
0809.4834 | Relevance Feedback in Conceptual Image Retrieval: A User Evaluation | The Visual Object Information Retrieval (VOIR) system described in this paper implements an image retrieval approach that combines two layers, the conceptual and the visual layer. It uses terms from a textual thesaurus to represent the conceptual information and also works with image regions, the visual information. The terms are related with the image regions through a weighted association enabling the execution of concept-level queries. VOIR uses region-based relevance feedback to improve the quality of the results in each query session and to discover new associations between text and image. This paper describes a user-centred and task-oriented comparative evaluation of VOIR which was undertaken considering three distinct versions of VOIR: a full-fledge version; one supporting relevance feedback only at image level; and a third version not supporting relevance feedback at all. The evaluation performed showed the usefulness of region based relevance feedback in the context of VOIR prototype. | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | 2,419 |
2202.12316 | AutoIP: A United Framework to Integrate Physics into Gaussian Processes | Physical modeling is critical for many modern science and engineering applications. From a data science or machine learning perspective, where more domain-agnostic, data-driven models are pervasive, physical knowledge -- often expressed as differential equations -- is valuable in that it is complementary to data, and it can potentially help overcome issues such as data sparsity, noise, and inaccuracy. In this work, we propose a simple, yet powerful and general framework -- AutoIP, for Automatically Incorporating Physics -- that can integrate all kinds of differential equations into Gaussian Processes (GPs) to enhance prediction accuracy and uncertainty quantification. These equations can be linear or nonlinear, spatial, temporal, or spatio-temporal, complete or incomplete with unknown source terms, and so on. Based on kernel differentiation, we construct a GP prior to sample the values of the target function, equation-related derivatives, and latent source functions, which are all jointly from a multivariate Gaussian distribution. The sampled values are fed to two likelihoods: one to fit the observations, and the other to conform to the equation. We use the whitening method to evade the strong dependency between the sampled function values and kernel parameters, and we develop a stochastic variational learning algorithm. AutoIP shows improvement upon vanilla GPs in both simulation and several real-world applications, even using rough, incomplete equations. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 282,187 |
2412.14771 | ALKAFI-LLAMA3: Fine-Tuning LLMs for Precise Legal Understanding in
Palestine | Large Language Models (LLMs) have demonstrated remarkable potential in diverse domains, yet their application in the legal sector, particularly in low-resource contexts, remains limited. This study addresses the challenges of adapting LLMs to the Palestinian legal domain, where political instability, fragmented legal frameworks, and limited AI resources hinder effective machine-learning applications. We present a fine-tuned model based on a quantized version of Llama-3.2-1B-Instruct, trained on a synthetic data set derived from Palestinian legal texts. Using smaller-scale models and strategically generated question-answer pairs, we achieve a cost-effective, locally sustainable solution that provides accurate and contextually relevant legal guidance. Our experiments demonstrate promising performance on various query types, ranging from yes/no questions and narrative explanations to complex legal differentiations, while highlighting areas for improvement, such as handling calculation-based inquiries and structured list formatting. This work provides a pathway for the deployment of AI-driven legal assistance tools tailored to the needs of resource-constrained environments. | false | false | false | false | true | false | true | false | true | false | false | false | false | false | false | false | false | false | 518,855 |
1704.02853 | SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations
from Scientific Publications | We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials. Although this was a new task, we had a total of 26 submissions across 3 evaluation scenarios. We expect the task and the findings reported in this paper to be relevant for researchers working on understanding scientific content, as well as the broader knowledge base population and information extraction communities. | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 71,524 |
2311.14838 | OpusCleaner and OpusTrainer, open source toolkits for training Machine
Translation and Large language models | Developing high quality machine translation systems is a labour intensive, challenging and confusing process for newcomers to the field. We present a pair of tools OpusCleaner and OpusTrainer that aim to simplify the process, reduce the amount of work and lower the entry barrier for newcomers. OpusCleaner is a data downloading, cleaning, and proprocessing toolkit. It is designed to allow researchers to quickly download, visualise and preprocess bilingual (or monolingual) data that comes from many different sources, each of them with different quality, issues, and unique filtering/preprocessing requirements. OpusTrainer is a data scheduling and data augmenting tool aimed at building large scale, robust machine translation systems and large language models. It features deterministic data mixing from many different sources, on-the-fly data augmentation and more. Using these tools, we showcase how we can use it to create high quality machine translation model robust to noisy user input; multilingual models and terminology aware models. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 410,275 |
1301.6725 | Loopy Belief Propagation for Approximate Inference: An Empirical Study | Recently, researchers have demonstrated that loopy belief propagation - the use of Pearls polytree algorithm IN a Bayesian network WITH loops OF error- correcting codes.The most dramatic instance OF this IS the near Shannon - limit performance OF Turbo Codes codes whose decoding algorithm IS equivalent TO loopy belief propagation IN a chain - structured Bayesian network. IN this paper we ask : IS there something special about the error - correcting code context, OR does loopy propagation WORK AS an approximate inference schemeIN a more general setting? We compare the marginals computed using loopy propagation TO the exact ones IN four Bayesian network architectures, including two real - world networks : ALARM AND QMR.We find that the loopy beliefs often converge AND WHEN they do, they give a good approximation TO the correct marginals.However,ON the QMR network, the loopy beliefs oscillated AND had no obvious relationship TO the correct posteriors. We present SOME initial investigations INTO the cause OF these oscillations, AND show that SOME simple methods OF preventing them lead TO the wrong results. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 21,518 |
2302.02676 | Chain of Hindsight Aligns Language Models with Feedback | Learning from human preferences is important for language models to match human needs and to align with human and social values. Prior works have achieved remarkable successes by learning from human feedback to understand and follow instructions. Nonetheless, these methods are either founded on hand-picked model generations that are favored by human annotators, rendering them inefficient in terms of data utilization and challenging to apply in general, or they depend on reinforcement learning, which often suffers from imperfect reward functions and relies on extremely challenging optimizations. In this work, we propose a novel technique, Chain of Hindsight, that is easy to optimize and can learn from any form of feedback, regardless of its polarity. Our idea is inspired by how humans learn from extensive feedback presented in the form of languages. We convert all types of feedback into sequences of sentences, which are then used to fine-tune the model, allowing us to take advantage of the language comprehension capabilities of language models. We condition the model on a sequence of model generations paired with feedback. By doing so, the model is trained to generate outputs based on feedback, while learning to identify and correct negative attributes or errors. Applying our method to large language models, we observed that Chain of Hindsight significantly surpasses previous methods in aligning language models with human preferences. We report significant improvements on summarization and dialogue benchmarks, with our approach markedly preferred in human evaluations. | false | false | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | 344,087 |
2107.03774 | Optimizing Data Processing in Space for Object Detection in Satellite
Imagery | There is a proliferation in the number of satellites launched each year, resulting in downlinking of terabytes of data each day. The data received by ground stations is often unprocessed, making this an expensive process considering the large data sizes and that not all of the data is useful. This, coupled with the increasing demand for real-time data processing, has led to a growing need for on-orbit processing solutions. In this work, we investigate the performance of CNN-based object detectors on constrained devices by applying different image compression techniques to satellite data. We examine the capabilities of the NVIDIA Jetson Nano and NVIDIA Jetson AGX Xavier; low-power, high-performance computers, with integrated GPUs, small enough to fit on-board a nanosatellite. We take a closer look at object detection networks, including the Single Shot MultiBox Detector (SSD) and Region-based Fully Convolutional Network (R-FCN) models that are pre-trained on DOTA - a Large Scale Dataset for Object Detection in Aerial Images. The performance is measured in terms of execution time, memory consumption, and accuracy, and are compared against a baseline containing a server with two powerful GPUs. The results show that by applying image compression techniques, we are able to improve the execution time and memory consumption, achieving a fully runnable dataset. A lossless compression technique achieves roughly a 10% reduction in execution time and about a 3% reduction in memory consumption, with no impact on the accuracy. While a lossy compression technique improves the execution time by up to 144% and the memory consumption is reduced by as much as 97%. However, it has a significant impact on accuracy, varying depending on the compression ratio. Thus the application and ratio of these compression techniques may differ depending on the required level of accuracy for a particular task. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | true | 245,256 |
2108.00768 | Cross-cultural Mood Perception in Pop Songs and its Alignment with Mood
Detection Algorithms | Do people from different cultural backgrounds perceive the mood in music the same way? How closely do human ratings across different cultures approximate automatic mood detection algorithms that are often trained on corpora of predominantly Western popular music? Analyzing 166 participants responses from Brazil, South Korea, and the US, we examined the similarity between the ratings of nine categories of perceived moods in music and estimated their alignment with four popular mood detection algorithms. We created a dataset of 360 recent pop songs drawn from major music charts of the countries and constructed semantically identical mood descriptors across English, Korean, and Portuguese languages. Multiple participants from the three countries rated their familiarity, preference, and perceived moods for a given song. Ratings were highly similar within and across cultures for basic mood attributes such as sad, cheerful, and energetic. However, we found significant cross-cultural differences for more complex characteristics such as dreamy and love. To our surprise, the results of mood detection algorithms were uniformly correlated across human ratings from all three countries and did not show a detectable bias towards any particular culture. Our study thus suggests that the mood detection algorithms can be considered as an objective measure at least within the popular music context. | false | false | true | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | 248,827 |
1608.07202 | Capacity-achieving and Flicker-free FEC coding scheme for Dimmable
Visible Light Communication Based on Polar Codes | Visible light communication (VLC) could provide short-range optical wireless communication together with illumination using LED lighting. However, conventional forward error correction (FEC) codes for reliable communication do not have the features for dimming support and flicker mitigation which are required in VLC for the main functionality of lighting. Therefore, auxiliary coding techniques are usually needed, which eventually reduce the coding efficiency and increase the complexity. In this paper, a polar codes-based FEC coding scheme for dimmable VLC is proposed to increase the coding efficiency and simplify the coding structure. Experimental results show that the proposed scheme has the following advantages: 1) equal probability of 1's and 0's in codewords, which is inherently supporting 50% dimming balance; 2) short run length property (about 90% bits have runs shorter than 5) which can avoid flickers and additional run-length limited line coding; 3) higher coding efficiency about twofold than that of other coding schemes; 4) capacity achieving error correction performance with low-complexity encoding and decoding, which is about 3 dB higher coding gain than that of RS(64,32) in IEEE standard for dimming ratio 50% and about 1 dB higher coding gain than that of LDPC codes for dimming ratio 25% (or 75%). | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 60,204 |
2309.07064 | A Comprehensive Analysis of the Role of Artificial Intelligence and
Machine Learning in Modern Digital Forensics and Incident Response | In the dynamic landscape of digital forensics, the integration of Artificial Intelligence (AI) and Machine Learning (ML) stands as a transformative technology, poised to amplify the efficiency and precision of digital forensics investigations. However, the use of ML and AI in digital forensics is still in its nascent stages. As a result, this paper gives a thorough and in-depth analysis that goes beyond a simple survey and review. The goal is to look closely at how AI and ML techniques are used in digital forensics and incident response. This research explores cutting-edge research initiatives that cross domains such as data collection and recovery, the intricate reconstruction of cybercrime timelines, robust big data analysis, pattern recognition, safeguarding the chain of custody, and orchestrating responsive strategies to hacking incidents. This endeavour digs far beneath the surface to unearth the intricate ways AI-driven methodologies are shaping these crucial facets of digital forensics practice. While the promise of AI in digital forensics is evident, the challenges arising from increasing database sizes and evolving criminal tactics necessitate ongoing collaborative research and refinement within the digital forensics profession. This study examines the contributions, limitations, and gaps in the existing research, shedding light on the potential and limitations of AI and ML techniques. By exploring these different research areas, we highlight the critical need for strategic planning, continual research, and development to unlock AI's full potential in digital forensics and incident response. Ultimately, this paper underscores the significance of AI and ML integration in digital forensics, offering insights into their benefits, drawbacks, and broader implications for tackling modern cyber threats. | false | false | false | false | true | false | false | false | false | false | false | false | true | false | false | false | false | true | 391,637 |
2311.14530 | Machine Translation for Ge'ez Language | Machine translation (MT) for low-resource languages such as Ge'ez, an ancient language that is no longer the native language of any community, faces challenges such as out-of-vocabulary words, domain mismatches, and lack of sufficient labeled training data. In this work, we explore various methods to improve Ge'ez MT, including transfer-learning from related languages, optimizing shared vocabulary and token segmentation approaches, finetuning large pre-trained models, and using large language models (LLMs) for few-shot translation with fuzzy matches. We develop a multilingual neural machine translation (MNMT) model based on languages relatedness, which brings an average performance improvement of about 4 BLEU compared to standard bilingual models. We also attempt to finetune the NLLB-200 model, one of the most advanced translation models available today, but find that it performs poorly with only 4k training samples for Ge'ez. Furthermore, we experiment with using GPT-3.5, a state-of-the-art LLM, for few-shot translation with fuzzy matches, which leverages embedding similarity-based retrieval to find context examples from a parallel corpus. We observe that GPT-3.5 achieves a remarkable BLEU score of 9.2 with no initial knowledge of Ge'ez, but still lower than the MNMT baseline of 15.2. Our work provides insights into the potential and limitations of different approaches for low-resource and ancient language MT. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 410,140 |
2309.16179 | BEVHeight++: Toward Robust Visual Centric 3D Object Detection | While most recent autonomous driving system focuses on developing perception methods on ego-vehicle sensors, people tend to overlook an alternative approach to leverage intelligent roadside cameras to extend the perception ability beyond the visual range. We discover that the state-of-the-art vision-centric bird's eye view detection methods have inferior performances on roadside cameras. This is because these methods mainly focus on recovering the depth regarding the camera center, where the depth difference between the car and the ground quickly shrinks while the distance increases. In this paper, we propose a simple yet effective approach, dubbed BEVHeight++, to address this issue. In essence, we regress the height to the ground to achieve a distance-agnostic formulation to ease the optimization process of camera-only perception methods. By incorporating both height and depth encoding techniques, we achieve a more accurate and robust projection from 2D to BEV spaces. On popular 3D detection benchmarks of roadside cameras, our method surpasses all previous vision-centric methods by a significant margin. In terms of the ego-vehicle scenario, our BEVHeight++ possesses superior over depth-only methods. Specifically, it yields a notable improvement of +1.9% NDS and +1.1% mAP over BEVDepth when evaluated on the nuScenes validation set. Moreover, on the nuScenes test set, our method achieves substantial advancements, with an increase of +2.8% NDS and +1.7% mAP, respectively. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 395,252 |
2408.07297 | Estimate collective cooperativeness of driving agents in mixed traffic
flow | Cooperation is a ubiquitous phenomenon in many natural, social, and engineered systems that contain multiple agents. Characterizing and quantifying cooperativeness of driving agents is of interest and significance for two reasons. Theoretically, it will enhance the understanding of micro-macro connections and emergence of cooperation in mixed traffic. Pragmatically, this understanding will benefit the design and operations of automated and mixed-autonomy transportation systems. However, it remains unclear how the cooperativeness can be accurately defined and quantified from empirical data, and it remains open when and to what extent collective cooperativeness exists. This paper is intended to fill the gap. We propose a unified conceptual framework to estimate collective cooperativeness of driving agents leveraging a recent behavioral equilibrium model of mixed autonomy traffic (Li et al. 2022a). This framework is interpretable, theoretically consistent, and enables quantifying collective cooperativeness of traffic agents from trajectory data. We apply the framework to multilane freeway traffic employing NGSIM I-80 trajectory data set and careful data selection. Our case study indicates the existence of collective cooperativeness between human-driven passenger cars and trucks in real-world traffic and reveals its other properties that are otherwise unknown. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | true | false | false | false | 480,533 |
2404.13868 | TeamTrack: A Dataset for Multi-Sport Multi-Object Tracking in Full-pitch
Videos | Multi-object tracking (MOT) is a critical and challenging task in computer vision, particularly in situations involving objects with similar appearances but diverse movements, as seen in team sports. Current methods, largely reliant on object detection and appearance, often fail to track targets in such complex scenarios accurately. This limitation is further exacerbated by the lack of comprehensive and diverse datasets covering the full view of sports pitches. Addressing these issues, we introduce TeamTrack, a pioneering benchmark dataset specifically designed for MOT in sports. TeamTrack is an extensive collection of full-pitch video data from various sports, including soccer, basketball, and handball. Furthermore, we perform a comprehensive analysis and benchmarking effort to underscore TeamTrack's utility and potential impact. Our work signifies a crucial step forward, promising to elevate the precision and effectiveness of MOT in complex, dynamic settings such as team sports. The dataset, project code and competition is released at: https://atomscott.github.io/TeamTrack/. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 448,477 |
cs/0212018 | Real numbers having ultimately periodic representations in abstract
numeration systems | Using a genealogically ordered infinite regular language, we know how to represent an interval of R. Numbers having an ultimately periodic representation play a special role in classical numeration systems. The aim of this paper is to characterize the numbers having an ultimately periodic representation in generalized systems built on a regular language. The syntactical properties of these words are also investigated. Finally, we show the equivalence of the classical "theta"-expansions with our generalized representations in some special case related to a Pisot number "theta". | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | true | 537,748 |
2203.07978 | Control Barrier Functions for Systems with Multiple Control Inputs | Control Barrier Functions (CBFs) are becoming popular tools in guaranteeing safety for nonlinear systems and constraints, and they can reduce a constrained optimal control problem into a sequence of Quadratic Programs (QPs) for affine control systems. The recently proposed High Order Control Barrier Functions (HOCBFs) work for arbitrary relative degree constraints. One of the challenges in a HOCBF is to address the relative degree problem when a system has multiple control inputs, i.e., the relative degree could be defined with respect to different components of the control vector. This paper proposes two methods for HOCBFs to deal with systems with multiple control inputs: a general integral control method and a method which is simpler but limited to specific classes of physical systems. When control bounds are involved, the feasibility of the above mentioned QPs can also be significantly improved with the proposed methods. We illustrate our approaches on a unicyle model with two control inputs, and compare the two proposed methods to demonstrate their effectiveness and performance. | false | false | false | false | false | false | false | true | false | false | true | false | false | false | false | false | false | false | 285,626 |
2403.00103 | On Robustness and Generalization of ML-Based Congestion Predictors to
Valid and Imperceptible Perturbations | There is substantial interest in the use of machine learning (ML)-based techniques throughout the electronic computer-aided design (CAD) flow, particularly methods based on deep learning. However, while deep learning methods have achieved state-of-the-art performance in several applications, recent work has demonstrated that neural networks are generally vulnerable to small, carefully chosen perturbations of their input (e.g. a single pixel change in an image). In this work, we investigate robustness in the context of ML-based EDA tools -- particularly for congestion prediction. As far as we are aware, we are the first to explore this concept in the context of ML-based EDA. We first describe a novel notion of imperceptibility designed specifically for VLSI layout problems defined on netlists and cell placements. Our definition of imperceptibility is characterized by a guarantee that a perturbation to a layout will not alter its global routing. We then demonstrate that state-of-the-art CNN and GNN-based congestion models exhibit brittleness to imperceptible perturbations. Namely, we show that when a small number of cells (e.g. 1%-5% of cells) have their positions shifted such that a measure of global congestion is guaranteed to remain unaffected (e.g. 1% of the design adversarially shifted by 0.001% of the layout space results in a predicted decrease in congestion of up to 90%, while no change in congestion is implied by the perturbation). In other words, the quality of a predictor can be made arbitrarily poor (i.e. can be made to predict that a design is "congestion-free") for an arbitrary input layout. Next, we describe a simple technique to train predictors that improves robustness to these perturbations. Our work indicates that CAD engineers should be cautious when integrating neural network-based mechanisms in EDA flows to ensure robust and high-quality results. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 433,855 |
2305.14987 | Investigating Table-to-Text Generation Capabilities of LLMs in
Real-World Information Seeking Scenarios | Tabular data is prevalent across various industries, necessitating significant time and effort for users to understand and manipulate for their information-seeking purposes. The advancements in large language models (LLMs) have shown enormous potential to improve user efficiency. However, the adoption of LLMs in real-world applications for table information seeking remains underexplored. In this paper, we investigate the table-to-text capabilities of different LLMs using four datasets within two real-world information seeking scenarios. These include the LogicNLG and our newly-constructed LoTNLG datasets for data insight generation, along with the FeTaQA and our newly-constructed F2WTQ datasets for query-based generation. We structure our investigation around three research questions, evaluating the performance of LLMs in table-to-text generation, automated evaluation, and feedback generation, respectively. Experimental results indicate that the current high-performing LLM, specifically GPT-4, can effectively serve as a table-to-text generator, evaluator, and feedback generator, facilitating users' information seeking purposes in real-world scenarios. However, a significant performance gap still exists between other open-sourced LLMs (e.g., Tulu and LLaMA-2) and GPT-4 models. Our data and code are publicly available at https://github.com/yale-nlp/LLM-T2T. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 367,375 |
2307.06797 | Fast and Functional Structured Data Generators Rooted in
Out-of-Equilibrium Physics | In this study, we address the challenge of using energy-based models to produce high-quality, label-specific data in complex structured datasets, such as population genetics, RNA or protein sequences data. Traditional training methods encounter difficulties due to inefficient Markov chain Monte Carlo mixing, which affects the diversity of synthetic data and increases generation times. To address these issues, we use a novel training algorithm that exploits non-equilibrium effects. This approach, applied on the Restricted Boltzmann Machine, improves the model's ability to correctly classify samples and generate high-quality synthetic data in only a few sampling steps. The effectiveness of this method is demonstrated by its successful application to four different types of data: handwritten digits, mutations of human genomes classified by continental origin, functionally characterized sequences of an enzyme protein family, and homologous RNA sequences from specific taxonomies. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 379,179 |
2410.05804 | CASA: Class-Agnostic Shared Attributes in Vision-Language Models for
Efficient Incremental Object Detection | Incremental object detection (IOD) is challenged by background shift, where background categories in sequential data may include previously learned or future classes. Inspired by the vision-language foundation models such as CLIP, these models capture shared attributes from extensive image-text paired data during pre-training. We propose a novel method utilizing attributes in vision-language foundation models for incremental object detection. Our method constructs a Class-Agnostic Shared Attribute base (CASA) to capture common semantic information among incremental classes. Specifically, we utilize large language models to generate candidate textual attributes and select the most relevant ones based on current training data, recording their significance in an attribute assignment matrix. For subsequent tasks, we freeze the retained attributes and continue selecting from the remaining candidates while updating the attribute assignment matrix accordingly. Furthermore, we employ OWL-ViT as our baseline, preserving the original parameters of the pre-trained foundation model. Our method adds only 0.7% to parameter storage through parameter-efficient fine-tuning to significantly enhance the scalability and adaptability of IOD. Extensive two-phase and multi-phase experiments on the COCO dataset demonstrate the state-of-the-art performance of our proposed method. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 495,926 |
1502.00068 | TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries | The proliferation of massive datasets combined with the development of sophisticated analytical techniques have enabled a wide variety of novel applications such as improved product recommendations, automatic image tagging, and improved speech-driven interfaces. These and many other applications can be supported by Predictive Analytic Queries (PAQs). A major obstacle to supporting PAQs is the challenging and expensive process of identifying and training an appropriate predictive model. Recent efforts aiming to automate this process have focused on single node implementations and have assumed that model training itself is a black box, thus limiting the effectiveness of such approaches on large-scale problems. In this work, we build upon these recent efforts and propose an integrated PAQ planning architecture that combines advanced model search techniques, bandit resource allocation via runtime algorithm introspection, and physical optimization via batching. The result is TuPAQ, a component of the MLbase system, which solves the PAQ planning problem with comparable quality to exhaustive strategies but an order of magnitude more efficiently than the standard baseline approach, and can scale to models trained on terabytes of data across hundreds of machines. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | true | true | 39,759 |
1903.04982 | A Capsule-unified Framework of Deep Neural Networks for Graphical
Programming | Recently, the growth of deep learning has produced a large number of deep neural networks. How to describe these networks unifiedly is becoming an important issue. We first formalize neural networks in a mathematical definition, give their directed graph representations, and prove a generation theorem about the induced networks of connected directed acyclic graphs. Then, using the concept of capsule to extend neural networks, we set up a capsule-unified framework for deep learning, including a mathematical definition of capsules, an induced model for capsule networks and a universal backpropagation algorithm for training them. Finally, we discuss potential applications of the framework to graphical programming with standard graphical symbols of capsules, neurons, and connections. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 124,078 |
1502.04933 | Block-Level Unitary Query: Incorporating Orthogonal-like Space-time Code
with Query Diversity for MIMO Backscatter RFID | Because of the emerging field of Internet of Things (IoT), future backscatter RFID is required to be more reliable and data intensive. Motivated by this, orthogonal space-time block code (OSTBC), which is very successful in mobile communications for its low complexity and high performance, has already been investigated for backscatter RFID. On the other hand, a recently proposed scheme called unitary query was shown to be able to considerably improve the reliability of backscatter radio by exploiting query diversity. Therefore incorporating the classical OSTBC (at the tag end) with the recently proposed unitary query (at the query end) seems to be promising. However, in this paper, we show that simple, direct employment of OSTBC together with unitary query incurs a linear decoding problem and eventually leads to a severe performance degradation. As a re-design of the recently proposed unitary query and the classical OSTBC specifically for MIMO backscatter RFID, we present a BUTQ-mOSTBC design pair idea by proposing the block-level unitary query (BUTQ) at the query end and the corresponding modified OSTBC (mOSTBC) at the tag end. The proposed BUTQ-mOSTBC can resolve the linear decoding problem, keep the simplicity and high performance properties of the classical OSTBC, and achieve the query diversity for the $M \times L \times N$ MIMO backscatter RFID channel. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 40,318 |
1503.04885 | Optimal control of the state statistics for a linear stochastic system | We consider a variant of the classical linear quadratic Gaussian regulator (LQG) in which penalties on the endpoint state are replaced by the specification of the terminal state distribution. The resulting theory considerably differs from LQG as well as from formulations that bound the probability of violating state constraints. We develop results for optimal state-feedback control in the two cases where i) steering of the state distribution is to take place over a finite window of time with minimum energy, and ii) the goal is to maintain the state at a stationary distribution over an infinite horizon with minimum power. For both problems the distribution of noise and state are Gaussian. In the first case, we show that provided the system is controllable, the state can be steered to any terminal Gaussian distribution over any specified finite time-interval. In the second case, we characterize explicitly the covariance of admissible stationary state distributions that can be maintained with constant state-feedback control. The conditions for optimality are expressed in terms of a system of dynamically coupled Riccati equations in the finite horizon case and in terms of algebraic conditions for the stationary case. In the case where the noise and control share identical input channels, the Riccati equations for finite-horizon steering become homogeneous and can be solved in closed form. The present paper is largely based on our recent work in arxiv.org/abs/1408.2222, arxiv.org/abs/1410.3447 and presents an overview of certain key results. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 41,190 |
1609.09588 | One-Lee weight and two-Lee weight $\mathbb{Z}_2\mathbb{Z}_2[u]$-additive
codes | In this paper, we study one-Lee weight and two-Lee weight codes over $\mathbb{Z}_{2}\mathbb{Z}_{2}[u]$, where $u^{2}=0$. Some properties of one-Lee weight $\mathbb{Z}_{2}\mathbb{Z}_{2}[u]$-additive codes are given, and a complete classification of one-Lee weight $\mathbb{Z}_2\mathbb{Z}_2[u]$-additive formally self-dual codes is obtained. The structure of two-Lee weight projective $\mathbb{Z}_2\mathbb{Z}_2[u]$ codes is determined. Some optimal binary linear codes are obtained directly from one-Lee weight and two-Lee weight $\mathbb{Z}_{2}\mathbb{Z}_{2}[u]$-additive codes via the extended Gray map. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 61,748 |
2105.00459 | Fast Power Control Adaptation via Meta-Learning for Random Edge Graph
Neural Networks | Power control in decentralized wireless networks poses a complex stochastic optimization problem when formulated as the maximization of the average sum rate for arbitrary interference graphs. Recent work has introduced data-driven design methods that leverage graph neural network (GNN) to efficiently parametrize the power control policy mapping channel state information (CSI) to the power vector. The specific GNN architecture, known as random edge GNN (REGNN), defines a non-linear graph convolutional architecture whose spatial weights are tied to the channel coefficients, enabling a direct adaption to channel conditions. This paper studies the higher-level problem of enabling fast adaption of the power control policy to time-varying topologies. To this end, we apply first-order meta-learning on data from multiple topologies with the aim of optimizing for a few-shot adaptation to new network configurations. | false | false | false | false | false | false | true | false | false | true | false | false | false | false | false | false | false | false | 233,229 |
2210.03441 | Decentralized Vision-Based Byzantine Agent Detection in Multi-Robot
Systems with IOTA Smart Contracts | Multiple opportunities lie at the intersection of multi-robot systems and distributed ledger technologies (DLTs). In this work, we investigate the potential of new DLT solutions such as IOTA, for detecting anomalies and byzantine agents in multi-robot systems in a decentralized manner. Traditional blockchain approaches are not applicable to real-world networked and decentralized robotic systems where connectivity conditions are not ideal. To address this, we leverage recent advances in partition-tolerant and byzantine-tolerant collaborative decision-making processes with IOTA smart contracts. We show how our work in vision-based anomaly and change detection can be applied to detecting byzantine agents within multiple robots operating in the same environment. We show that IOTA smart contracts add a low computational overhead while allowing to build trust within the multi-robot system. The proposed approach effectively enables byzantine robot detection based on the comparison of images submitted by the different robots and detection of anomalies and changes between them. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 322,038 |
2412.02376 | Flexible-Antenna Systems: A Pinching-Antenna Perspective | Flexible-antenna systems have recently received significant research interest due to their capability to reconfigure wireless channels intelligently. This paper focuses on a new type of flexible-antenna technology, termed pinching antennas, which can be realized by applying small dielectric particles on a waveguide. Analytical results are first developed for the simple case with a single pinching antenna and a single waveguide, where the unique feature of the pinching-antenna system to create strong line-of-sight links and mitigate large-scale path loss is demonstrated. An advantageous feature of pinching-antenna systems is that multiple pinching antennas can be activated on a single waveguide at no extra cost; however, they must be fed with the same signal. This feature motivates the application of non-orthogonal multiple access (NOMA), and analytical results are provided to demonstrate the superior performance of NOMA-assisted pinching-antenna systems. Finally, the case with multiple pinching antennas and multiple waveguides is studied, which resembles a classical multiple-input single-input (MISO) interference channel. By exploiting the capability of pinching antennas to reconfigure the wireless channel, it is revealed that a performance upper bound on the interference channel becomes achievable, where the achievability conditions are also identified. Computer simulation results are presented to verify the developed analytical results and demonstrate the superior performance of pinching-antenna systems. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 513,520 |
2402.07152 | Explainable Global Wildfire Prediction Models using Graph Neural
Networks | Wildfire prediction has become increasingly crucial due to the escalating impacts of climate change. Traditional CNN-based wildfire prediction models struggle with handling missing oceanic data and addressing the long-range dependencies across distant regions in meteorological data. In this paper, we introduce an innovative Graph Neural Network (GNN)-based model for global wildfire prediction. We propose a hybrid model that combines the spatial prowess of Graph Convolutional Networks (GCNs) with the temporal depth of Long Short-Term Memory (LSTM) networks. Our approach uniquely transforms global climate and wildfire data into a graph representation, addressing challenges such as null oceanic data locations and long-range dependencies inherent in traditional models. Benchmarking against established architectures using an unseen ensemble of JULES-INFERNO simulations, our model demonstrates superior predictive accuracy. Furthermore, we emphasise the model's explainability, unveiling potential wildfire correlation clusters through community detection and elucidating feature importance via Integrated Gradient analysis. Our findings not only advance the methodological domain of wildfire prediction but also underscore the importance of model transparency, offering valuable insights for stakeholders in wildfire management. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 428,583 |
2411.13056 | Efficient Masked AutoEncoder for Video Object Counting and A Large-Scale
Benchmark | The dynamic imbalance of the fore-background is a major challenge in video object counting, which is usually caused by the sparsity of foreground objects. This often leads to severe under- and over-prediction problems and has been less studied in existing works. To tackle this issue in video object counting, we propose a density-embedded Efficient Masked Autoencoder Counting (E-MAC) framework in this paper. To effectively capture the dynamic variations across frames, we utilize an optical flow-based temporal collaborative fusion that aligns features to derive multi-frame density residuals. The counting accuracy of the current frame is boosted by harnessing the information from adjacent frames. More importantly, to empower the representation ability of dynamic foreground objects for intra-frame, we first take the density map as an auxiliary modality to perform $\mathtt{D}$ensity-$\mathtt{E}$mbedded $\mathtt{M}$asked m$\mathtt{O}$deling ($\mathtt{DEMO}$) for multimodal self-representation learning to regress density map. However, as $\mathtt{DEMO}$ contributes effective cross-modal regression guidance, it also brings in redundant background information and hard to focus on foreground regions. To handle this dilemma, we further propose an efficient spatial adaptive masking derived from density maps to boost efficiency. In addition, considering most existing datasets are limited to human-centric scenarios, we first propose a large video bird counting dataset $\textit{DroneBird}$, in natural scenarios for migratory bird protection. Extensive experiments on three crowd datasets and our $\textit{DroneBird}$ validate our superiority against the counterparts. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 509,662 |
2106.08531 | Latent Representation in Human-Robot Interaction with Explicit
Consideration of Periodic Dynamics | This paper presents a new data-driven framework for analyzing periodic physical human-robot interaction (pHRI) in latent state space. To elaborate human understanding and/or robot control during pHRI, the model representing pHRI is critical. Recent developments of deep learning technologies would enable us to learn such a model from a dataset collected from the actual pHRI. Our framework is developed based on variational recurrent neural network (VRNN), which can inherently handle time-series data like one pHRI generates. This paper modifies VRNN in order to include the latent dynamics from robot to human explicitly. In addition, to analyze periodic motions like walking, we integrate a new recurrent network based on reservoir computing (RC), which has random and fixed connections between numerous neurons, with VRNN. By augmenting RC into complex domain, periodic behavior can be represented as the phase rotation in complex domain without decaying the amplitude. For verification of the proposed framework, a rope-rotation/swinging experiment was analyzed. The proposed framework, trained on the dataset collected from the experiment, achieved the latent state space where the differences in periodic motions can be distinguished. Such a well-distinguished space yielded the best prediction accuracy of the human observations and the robot actions. The attached video can be seen in youtube: https://youtu.be/umn0MVcIpsY | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 241,324 |
1901.06442 | The Conditional Information Leakage Given Eavesdropper's Received
Signals in Wiretap Channels | Information leakage in Wyner's wiretap channel model is usually defined as the mutual information between the secret message and the eavesdropper's received signal. We define a new quantity called "conditional information leakage given the eavesdropper's received signals," which expresses the amount of information that eavesdropper gains from his/her received signal. A benefit of introducing this quantity is that we can develop a fast algorithm for computing the conditional information leakage, which has linear complexity in the code length $n$, while the complexity for computing the usual information leakage is exponential in $n$. Validity of such a conditional information leakage as a security criterion is confirmed by studying the cases of binary symmetric channels and binary erasure channels. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 118,998 |
1901.03254 | Quantum-inspired sublinear algorithm for solving low-rank semidefinite
programming | Semidefinite programming (SDP) is a central topic in mathematical optimization with extensive studies on its efficient solvers. In this paper, we present a proof-of-principle sublinear-time algorithm for solving SDPs with low-rank constraints; specifically, given an SDP with $m$ constraint matrices, each of dimension $n$ and rank $r$, our algorithm can compute any entry and efficient descriptions of the spectral decomposition of the solution matrix. The algorithm runs in time $O(m\cdot\mathrm{poly}(\log n,r,1/\varepsilon))$ given access to a sampling-based low-overhead data structure for the constraint matrices, where $\varepsilon$ is the precision of the solution. In addition, we apply our algorithm to a quantum state learning task as an application. Technically, our approach aligns with 1) SDP solvers based on the matrix multiplicative weight (MMW) framework by Arora and Kale [TOC '12]; 2) sampling-based dequantizing framework pioneered by Tang [STOC '19]. In order to compute the matrix exponential required in the MMW framework, we introduce two new techniques that may be of independent interest: $\bullet$ Weighted sampling: assuming sampling access to each individual constraint matrix $A_{1},\ldots,A_{\tau}$, we propose a procedure that gives a good approximation of $A=A_{1}+\cdots+A_{\tau}$. $\bullet$ Symmetric approximation: we propose a sampling procedure that gives the \emph{spectral decomposition} of a low-rank Hermitian matrix $A$. To the best of our knowledge, this is the first sampling-based algorithm for spectral decomposition, as previous works only give singular values and vectors. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 118,368 |
2408.04262 | CoBooM: Codebook Guided Bootstrapping for Medical Image Representation
Learning | Self-supervised learning (SSL) has emerged as a promising paradigm for medical image analysis by harnessing unannotated data. Despite their potential, the existing SSL approaches overlook the high anatomical similarity inherent in medical images. This makes it challenging for SSL methods to capture diverse semantic content in medical images consistently. This work introduces a novel and generalized solution that implicitly exploits anatomical similarities by integrating codebooks in SSL. The codebook serves as a concise and informative dictionary of visual patterns, which not only aids in capturing nuanced anatomical details but also facilitates the creation of robust and generalized feature representations. In this context, we propose CoBooM, a novel framework for self-supervised medical image learning by integrating continuous and discrete representations. The continuous component ensures the preservation of fine-grained details, while the discrete aspect facilitates coarse-grained feature extraction through the structured embedding space. To understand the effectiveness of CoBooM, we conduct a comprehensive evaluation of various medical datasets encompassing chest X-rays and fundus images. The experimental results reveal a significant performance gain in classification and segmentation tasks. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 479,319 |
2206.07839 | Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness | Certifiable robustness is a highly desirable property for adopting deep neural networks (DNNs) in safety-critical scenarios, but often demands tedious computations to establish. The main hurdle lies in the massive amount of non-linearity in large DNNs. To trade off the DNN expressiveness (which calls for more non-linearity) and robustness certification scalability (which prefers more linearity), we propose a novel solution to strategically manipulate neurons, by "grafting" appropriate levels of linearity. The core of our proposal is to first linearize insignificant ReLU neurons, to eliminate the non-linear components that are both redundant for DNN performance and harmful to its certification. We then optimize the associated slopes and intercepts of the replaced linear activations for restoring model performance while maintaining certifiability. Hence, typical neuron pruning could be viewed as a special case of grafting a linear function of the fixed zero slopes and intercept, that might overly restrict the network flexibility and sacrifice its performance. Extensive experiments on multiple datasets and network backbones show that our linearity grafting can (1) effectively tighten certified bounds; (2) achieve competitive certifiable robustness without certified robust training (i.e., over 30% improvements on CIFAR-10 models); and (3) scale up complete verification to large adversarially trained models with 17M parameters. Codes are available at https://github.com/VITA-Group/Linearity-Grafting. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 302,905 |
1510.03035 | Reliability Analysis of Processes with Moving Cracked Material | The reliability of processes with moving elastic and isotropic material containing initial cracks is considered in terms of fracture. The material is modelled as a moving plate which is simply supported from two of its sides and subjected to homogeneous tension acting in the travelling direction. For tension, two models are studied: i) tension is constant with respect to time, and ii) tension varies temporally according to an Ornstein-Uhlenbeck process. Cracks of random length are assumed to occur in the material according to a stochastic counting process. For a general counting process, a representation of the nonfracture probability of the system is obtained that exploits conditional Monte Carlo simulation. Explicit formulae are derived for special cases. To study the reliability of the system with temporally varying tension, a known explicit result for the first passage time of an Ornstein-Uhlenbeck process to a constant boundary is utilized. Numerical examples are provided for printing presses and paper material. | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 47,800 |
1504.03509 | Regret vs. Communication: Distributed Stochastic Multi-Armed Bandits and
Beyond | In this paper, we consider the distributed stochastic multi-armed bandit problem, where a global arm set can be accessed by multiple players independently. The players are allowed to exchange their history of observations with each other at specific points in time. We study the relationship between regret and communication. When the time horizon is known, we propose the Over-Exploration strategy, which only requires one-round communication and whose regret does not scale with the number of players. When the time horizon is unknown, we measure the frequency of communication through a new notion called the density of the communication set, and give an exact characterization of the interplay between regret and communication. Specifically, a lower bound is established and stable strategies that match the lower bound are developed. The results and analyses in this paper are specific but can be translated into more general settings. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 42,039 |
1402.6243 | Globally Optimal Cooperation in Dense Cognitive Radio Networks | The problem of calculating the local and global decision thresholds in hard decisions based cooperative spectrum sensing is well known for its mathematical intractability. Previous work relied on simple suboptimal counting rules for decision fusion in order to avoid the exhaustive numerical search required for obtaining the optimal thresholds. However, these simple rules are not globally optimal as they do not maximize the overall global detection probability by jointly selecting local and global thresholds. Instead, they maximize the detection probability for a specific global threshold. In this paper, a globally optimal decision fusion rule for Primary User signal detection based on the Neyman- Pearson (NP) criterion is derived. The algorithm is based on a novel representation for the global performance metrics in terms of the regularized incomplete beta function. Based on this mathematical representation, it is shown that the globally optimal NP hard decision fusion test can be put in the form of a conventional one dimensional convex optimization problem. A binary search for the global threshold can be applied yielding a complexity of O(log2(N)), where N represents the number of cooperating users. The logarithmic complexity is appreciated because we are concerned with dense networks, and thus N is expected to be large. The proposed optimal scheme outperforms conventional counting rules, such as the OR, AND, and MAJORITY rules. It is shown via simulations that, although the optimal rule tends to the simple OR rule when the number of cooperating secondary users is small, it offers significant SNR gain in dense cognitive radio networks with large number of cooperating users. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | true | 31,159 |
2005.14716 | Prosody leaks into the memories of words | The average predictability (aka informativity) of a word in context has been shown to condition word duration (Seyfarth, 2014). All else being equal, words that tend to occur in more predictable environments are shorter than words that tend to occur in less predictable environments. One account of the informativity effect on duration is that the acoustic details of probabilistic reduction are stored as part of a word's mental representation. Other research has argued that predictability effects are tied to prosodic structure in integral ways. With the aim of assessing a potential prosodic basis for informativity effects in speech production, this study extends past work in two directions; it investigated informativity effects in another large language, Mandarin Chinese, and broadened the study beyond word duration to additional acoustic dimensions, pitch and intensity, known to index prosodic prominence. The acoustic information of content words was extracted from a large telephone conversation speech corpus with over 400,000 tokens and 6,000 word types spoken by 1,655 individuals and analyzed for the effect of informativity using frequency statistics estimated from a 431 million word subtitle corpus. Results indicated that words with low informativity have shorter durations, replicating the effect found in English. In addition, informativity had significant effects on maximum pitch and intensity, two phonetic dimensions related to prosodic prominence. Extending this interpretation, these results suggest that predictability is closely linked to prosodic prominence, and that the lexical representation of a word includes phonetic details associated with its average prosodic prominence in discourse. In other words, the lexicon absorbs prosodic influences on speech production. | false | false | true | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 179,340 |
1801.08228 | Visual-Inertial Odometry-enhanced Geometrically Stable ICP for Mapping
Applications using Aerial Robots | This paper presents a visual-inertial odometry-enhanced geometrically stable Iterative Closest Point (ICP) algorithm for accurate mapping using aerial robots. The proposed method employs a visual-inertial odometry framework in order to provide robust priors to the ICP step and calculate the overlap among point clouds derived from an onboard time-of-flight depth sensor. Within the overlapping parts of the point clouds, the method samples points such that the distribution of normals among them is as large as possible. As different geometries and sensor trajectories will influence the performance of the alignment process, evaluation of the expected geometric stability of the ICP step is conducted. It is only when this test is successful that the matching, outlier rejection, and minimization of the error metric ICP steps are conducted and the new relative translation and rotational components are estimated, otherwise the system relies on the visual-inertial odometry transformation estimates. The proposed strategy was evaluated within handheld, automated and fully autonomous exploration and mapping missions using a small aerial robot and was shown to provide robust results of superior quality at an affordable increase of the computational load. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 88,919 |
1804.04267 | Energy Efficient Distributed Worst Case Robust Power Allocation in
Massive MIMO | This letter proposes an energy efficient distributed worst case robust power allocation in massive multiple input multiple output (MIMO) system. We assume a bounded channel state information (CSI) error and all channels lie in some bounded uncertainty region. The problem is formulated as max-min one with infinite constraint. At first, we solve the inner problem with triangle and Cauchy-Schwarz inequality, then by fractional programming and successive convex approximation (SCA) technique problem transfers to a convex optimization. Finally closed form transmit power is obtained with distribution way. Simulation results demonstrate proposed algorithm convergence and validate robust power allocation. Also, the appropriate number of transmit antenna to have maximum energy efficiency in simulation result is shown. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 94,799 |
1705.09339 | Rejection-Cascade of Gaussians: Real-time adaptive background
subtraction framework | Background-Foreground classification is a well-studied problem in computer vision. Due to the pixel-wise nature of modeling and processing in the algorithm, it is usually difficult to satisfy real-time constraints. There is a trade-off between the speed (because of model complexity) and accuracy. Inspired by the rejection cascade of Viola-Jones classifier, we decompose the Gaussian Mixture Model (GMM) into an adaptive cascade of Gaussians(CoG). We achieve a good improvement in speed without compromising the accuracy with respect to the baseline GMM model. We demonstrate a speed-up factor of 4-5x and 17 percent average improvement in accuracy over Wallflowers surveillance datasets. The CoG is then demonstrated to over the latent space representation of images of a convolutional variational autoencoder(VAE). We provide initial results over CDW-2014 dataset, which could speed up background subtraction for deep architectures. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 74,176 |
2008.00727 | Deep Bayesian Bandits: Exploring in Online Personalized Recommendations | Recommender systems trained in a continuous learning fashion are plagued by the feedback loop problem, also known as algorithmic bias. This causes a newly trained model to act greedily and favor items that have already been engaged by users. This behavior is particularly harmful in personalised ads recommendations, as it can also cause new campaigns to remain unexplored. Exploration aims to address this limitation by providing new information about the environment, which encompasses user preference, and can lead to higher long-term reward. In this work, we formulate a display advertising recommender as a contextual bandit and implement exploration techniques that require sampling from the posterior distribution of click-through-rates in a computationally tractable manner. Traditional large-scale deep learning models do not provide uncertainty estimates by default. We approximate these uncertainty measurements of the predictions by employing a bootstrapped model with multiple heads and dropout units. We benchmark a number of different models in an offline simulation environment using a publicly available dataset of user-ads engagements. We test our proposed deep Bayesian bandits algorithm in the offline simulation and online AB setting with large-scale production traffic, where we demonstrate a positive gain of our exploration model. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | true | false | false | 190,093 |
1904.03061 | A Literature Study of Embeddings on Source Code | Natural language processing has improved tremendously after the success of word embedding techniques such as word2vec. Recently, the same idea has been applied on source code with encouraging results. In this survey, we aim to collect and discuss the usage of word embedding techniques on programs and source code. The articles in this survey have been collected by asking authors of related work and with an extensive search on Google Scholar. Each article is categorized into five categories: 1. embedding of tokens 2. embedding of functions or methods 3. embedding of sequences or sets of method calls 4. embedding of binary code 5. other embeddings. We also provide links to experimental data and show some remarkable visualization of code embeddings. In summary, word embedding has been successfully applied on different granularities of source code. With access to countless open-source repositories, we see a great potential of applying other data-driven natural language processing techniques on source code in the future. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 126,599 |
2311.05707 | FMViT: A multiple-frequency mixing Vision Transformer | The transformer model has gained widespread adoption in computer vision tasks in recent times. However, due to the quadratic time and memory complexity of self-attention, which is proportional to the number of input tokens, most existing Vision Transformers (ViTs) encounter challenges in achieving efficient performance in practical industrial deployment scenarios, such as TensorRT and CoreML, where traditional CNNs excel. Although some recent attempts have been made to design CNN-Transformer hybrid architectures to tackle this problem, their overall performance has not met expectations. To tackle these challenges, we propose an efficient hybrid ViT architecture named FMViT. This approach enhances the model's expressive power by blending high-frequency features and low-frequency features with varying frequencies, enabling it to capture both local and global information effectively. Additionally, we introduce deploy-friendly mechanisms such as Convolutional Multigroup Reparameterization (gMLP), Lightweight Multi-head Self-Attention (RLMHSA), and Convolutional Fusion Block (CFB) to further improve the model's performance and reduce computational overhead. Our experiments demonstrate that FMViT surpasses existing CNNs, ViTs, and CNNTransformer hybrid architectures in terms of latency/accuracy trade-offs for various vision tasks. On the TensorRT platform, FMViT outperforms Resnet101 by 2.5% (83.3% vs. 80.8%) in top-1 accuracy on the ImageNet dataset while maintaining similar inference latency. Moreover, FMViT achieves comparable performance with EfficientNet-B5, but with a 43% improvement in inference speed. On CoreML, FMViT outperforms MobileOne by 2.6% in top-1 accuracy on the ImageNet dataset, with inference latency comparable to MobileOne (78.5% vs. 75.9%). Our code can be found at https://github.com/tany0699/FMViT. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 406,669 |
2401.04890 | Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse
Actions, Interventions and Sparse Temporal Dependencies | This work introduces a novel principle for disentanglement we call mechanism sparsity regularization, which applies when the latent factors of interest depend sparsely on observed auxiliary variables and/or past latent factors. We propose a representation learning method that induces disentanglement by simultaneously learning the latent factors and the sparse causal graphical model that explains them. We develop a nonparametric identifiability theory that formalizes this principle and shows that the latent factors can be recovered by regularizing the learned causal graph to be sparse. More precisely, we show identifiablity up to a novel equivalence relation we call "consistency", which allows some latent factors to remain entangled (hence the term partial disentanglement). To describe the structure of this entanglement, we introduce the notions of entanglement graphs and graph preserving functions. We further provide a graphical criterion which guarantees complete disentanglement, that is identifiability up to permutations and element-wise transformations. We demonstrate the scope of the mechanism sparsity principle as well as the assumptions it relies on with several worked out examples. For instance, the framework shows how one can leverage multi-node interventions with unknown targets on the latent factors to disentangle them. We further draw connections between our nonparametric results and the now popular exponential family assumption. Lastly, we propose an estimation procedure based on variational autoencoders and a sparsity constraint and demonstrate it on various synthetic datasets. This work is meant to be a significantly extended version of Lachapelle et al. (2022). | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 420,576 |
2007.07773 | Vision-Based Fall Event Detection in Complex Background Using Attention
Guided Bi-directional LSTM | Fall event detection, as one of the greatest risks to the elderly, has been a hot research issue in the solitary scene in recent years. Nevertheless, there are few researches on the fall event detection in complex background. Different from most conventional background subtraction methods which depend on background modeling, Mask R-CNN method based on deep learning technique can clearly extract the moving object in noise background. We further propose an attention guided Bi-directional LSTM model for the final fall event detection. To demonstrate the efficiency, the proposed method is verified in the public dataset and self-build dataset. Evaluation of the algorithm performances in comparison with other state-of-the-art methods indicates that the proposed design is accurate and robust, which means it is suitable for the task of fall event detection in complex situation. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 187,435 |
2103.16370 | Distribution Alignment: A Unified Framework for Long-tail Visual
Recognition | Despite the recent success of deep neural networks, it remains challenging to effectively model the long-tail class distribution in visual recognition tasks. To address this problem, we first investigate the performance bottleneck of the two-stage learning framework via ablative study. Motivated by our discovery, we propose a unified distribution alignment strategy for long-tail visual recognition. Specifically, we develop an adaptive calibration function that enables us to adjust the classification scores for each data point. We then introduce a generalized re-weight method in the two-stage learning to balance the class prior, which provides a flexible and unified solution to diverse scenarios in visual recognition tasks. We validate our method by extensive experiments on four tasks, including image classification, semantic segmentation, object detection, and instance segmentation. Our approach achieves the state-of-the-art results across all four recognition tasks with a simple and unified framework. The code and models will be made publicly available at: https://github.com/Megvii-BaseDetection/DisAlign | false | false | false | false | true | false | true | false | false | false | false | true | false | false | false | false | false | false | 227,561 |
2204.00619 | Maze Learning using a Hyperdimensional Predictive Processing Cognitive
Architecture | We present the COGnitive Neural GENerative system (CogNGen), a cognitive architecture that combines two neurobiologically-plausible, computational models: predictive processing and hyperdimensional/vector-symbolic models. We draw inspiration from architectures such as ACT-R and Spaun/Nengo. CogNGen is in broad agreement with these, providing a level of detail between ACT-R's high-level symbolic description of human cognition and Spaun's low-level neurobiological description, furthermore creating the groundwork for designing agents that learn continually from diverse tasks and model human performance at larger scales than what is possible with current systems. We test CogNGen on four maze-learning tasks, including those that test memory and planning, and find that CogNGen matches performance of deep reinforcement learning models and exceeds on a task designed to test memory. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | true | false | false | 289,324 |
2101.02916 | Accelerating Training of Batch Normalization: A Manifold Perspective | Batch normalization (BN) has become a critical component across diverse deep neural networks. The network with BN is invariant to positively linear re-scale transformation, which makes there exist infinite functionally equivalent networks with different scales of weights. However, optimizing these equivalent networks with the first-order method such as stochastic gradient descent will obtain a series of iterates converging to different local optima owing to their different gradients across training. To obviate this, we propose a quotient manifold \emph{PSI manifold}, in which all the equivalent weights of the network with BN are regarded as the same element. Next, we construct gradient descent and stochastic gradient descent on the proposed PSI manifold to train the network with BN. The two algorithms guarantee that every group of equivalent weights (caused by positively re-scaling) converge to the equivalent optima. Besides that, we give convergence rates of the proposed algorithms on the PSI manifold. The results show that our methods accelerate training compared with the algorithms on the Euclidean weight space. Finally, empirical results verify that our algorithms consistently improve the existing methods in both convergence rate and generalization ability under various experimental settings. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 214,772 |
1003.2724 | Particle Swarm Optimization Based Diophantine Equation Solver | The paper introduces particle swarm optimization as a viable strategy to find numerical solution of Diophantine equation, for which there exists no general method of finding solutions. The proposed methodology uses a population of integer particles. The candidate solutions in the feasible space are optimized to have better positions through particle best and global best positions. The methodology, which follows fully connected neighborhood topology, can offer many solutions of such equations. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | true | 5,914 |
2003.03473 | PoseNet3D: Learning Temporally Consistent 3D Human Pose via Knowledge
Distillation | Recovering 3D human pose from 2D joints is a highly unconstrained problem. We propose a novel neural network framework, PoseNet3D, that takes 2D joints as input and outputs 3D skeletons and SMPL body model parameters. By casting our learning approach in a student-teacher framework, we avoid using any 3D data such as paired/unpaired 3D data, motion capture sequences, depth images or multi-view images during training. We first train a teacher network that outputs 3D skeletons, using only 2D poses for training. The teacher network distills its knowledge to a student network that predicts 3D pose in SMPL representation. Finally, both the teacher and the student networks are jointly fine-tuned in an end-to-end manner using temporal, self-consistency and adversarial losses, improving the accuracy of each individual network. Results on Human3.6M dataset for 3D human pose estimation demonstrate that our approach reduces the 3D joint prediction error by 18% compared to previous unsupervised methods. Qualitative results on in-the-wild datasets show that the recovered 3D poses and meshes are natural, realistic, and flow smoothly over consecutive frames. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | true | 167,229 |
2001.03340 | Temporally Folded Convolutional Neural Networks for Sequence Forecasting | In this work we propose a novel approach to utilize convolutional neural networks for time series forecasting. The time direction of the sequential data with spatial dimensions $D=1,2$ is considered democratically as the input of a spatiotemporal $(D+1)$-dimensional convolutional neural network. Latter then reduces the data stream from $D +1 \to D$ dimensions followed by an incriminator cell which uses this information to forecast the subsequent time step. We empirically compare this strategy to convolutional LSTM's and LSTM's on their performance on the sequential MNIST and the JSB chorals dataset, respectively. We conclude that temporally folded convolutional neural networks (TFC's) may outperform the conventional recurrent strategies. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 159,950 |
2406.06435 | Language Models are Alignable Decision-Makers: Dataset and Application
to the Medical Triage Domain | In difficult decision-making scenarios, it is common to have conflicting opinions among expert human decision-makers as there may not be a single right answer. Such decisions may be guided by different attributes that can be used to characterize an individual's decision. We introduce a novel dataset for medical triage decision-making, labeled with a set of decision-maker attributes (DMAs). This dataset consists of 62 scenarios, covering six different DMAs, including ethical principles such as fairness and moral desert. We present a novel software framework for human-aligned decision-making by utilizing these DMAs, paving the way for trustworthy AI with better guardrails. Specifically, we demonstrate how large language models (LLMs) can serve as ethical decision-makers, and how their decisions can be aligned to different DMAs using zero-shot prompting. Our experiments focus on different open-source models with varying sizes and training techniques, such as Falcon, Mistral, and Llama 2. Finally, we also introduce a new form of weighted self-consistency that improves the overall quantified performance. Our results provide new research directions in the use of LLMs as alignable decision-makers. The dataset and open-source software are publicly available at: https://github.com/ITM-Kitware/llm-alignable-dm. | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 462,574 |
2204.04040 | Ontology Matching Through Absolute Orientation of Embedding Spaces | Ontology matching is a core task when creating interoperable and linked open datasets. In this paper, we explore a novel structure-based mapping approach which is based on knowledge graph embeddings: The ontologies to be matched are embedded, and an approach known as absolute orientation is used to align the two embedding spaces. Next to the approach, the paper presents a first, preliminary evaluation using synthetic and real-world datasets. We find in experiments with synthetic data, that the approach works very well on similarly structured graphs; it handles alignment noise better than size and structural differences in the ontologies. | false | false | false | false | true | true | true | false | false | false | false | false | false | false | false | false | true | false | 290,515 |
2305.12523 | Multi-Static Target Detection and Power Allocation for Integrated
Sensing and Communication in Cell-Free Massive MIMO | This paper studies an integrated sensing and communication (ISAC) system within a centralized cell-free massive MIMO (multiple-input multiple-output) network for target detection. ISAC transmit access points serve the user equipments in the downlink and optionally steer a beam toward the target in a multi-static sensing framework. A maximum a posteriori ratio test detector is developed for target detection in the presence of clutter, so-called target-free signals. Additionally, sensing spectral efficiency (SE) is introduced as a key metric, capturing the impact of resource utilization in ISAC. A power allocation algorithm is proposed to maximize the sensing signal-to-interference-plus-noise ratio while ensuring minimum communication requirements. Two ISAC configurations are studied: utilizing existing communication beams for sensing and using additional sensing beams. The proposed algorithm's efficiency is investigated in realistic and idealistic scenarios, corresponding to the presence and absence of the target-free channels, respectively. Despite performance degradation in the presence of target-free channels, the proposed algorithm outperforms the interference-unaware benchmark, leveraging clutter statistics. Comparisons with a fully communication-centric algorithm reveal superior performance in both cluttered and clutter-free environments. The incorporation of an extra sensing beam enhances detection performance for lower radar cross-section variances. Moreover, the results demonstrate the effectiveness of the integrated operation of sensing and communication compared to an orthogonal resource-sharing approach. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 366,042 |
2210.00120 | NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning | Neural Motion Planners (NMPs) have emerged as a promising tool for solving robot navigation tasks in complex environments. However, these methods often require expert data for learning, which limits their application to scenarios where data generation is time-consuming. Recent developments have also led to physics-informed deep neural models capable of representing complex dynamical Partial Differential Equations (PDEs). Inspired by these developments, we propose Neural Time Fields (NTFields) for robot motion planning in cluttered scenarios. Our framework represents a wave propagation model generating continuous arrival time to find path solutions informed by a nonlinear first-order PDE called Eikonal Equation. We evaluate our method in various cluttered 3D environments, including the Gibson dataset, and demonstrate its ability to solve motion planning problems for 4-DOF and 6-DOF robot manipulators where the traditional grid-based Eikonal planners often face the curse of dimensionality. Furthermore, the results show that our method exhibits high success rates and significantly lower computational times than the state-of-the-art methods, including NMPs that require training data from classical planners. | false | false | false | false | false | false | true | true | false | false | false | false | false | false | false | false | false | false | 320,739 |
1704.06765 | Subspace Tracking Algorithms for Millimeter Wave MIMO Channel Estimation
with Hybrid Beamforming | This paper proposes the use of subspace tracking algorithms for performing MIMO channel estimation at millimeter wave (mmWave) frequencies. Using a subspace approach, we develop a protocol enabling the estimation of the right (resp. left) singular vectors at the transmitter (resp. receiver) side; then, we adapt the projection approximation subspace tracking with deflation (PASTd) and the orthogonal Oja (OOJA) algorithms to our framework and obtain two channel estimation algorithms. The hybrid analog/digital nature of the beamformer is also explicitly taken into account at the algorithm design stage. Numerical results show that the proposed estimation algorithms are effective, and that they perform better than two relevant competing alternatives available in the open literature. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 72,219 |
2209.06423 | SCULPTOR: Skeleton-Consistent Face Creation Using a Learned Parametric
Generator | Recent years have seen growing interest in 3D human faces modelling due to its wide applications in digital human, character generation and animation. Existing approaches overwhelmingly emphasized on modeling the exterior shapes, textures and skin properties of faces, ignoring the inherent correlation between inner skeletal structures and appearance. In this paper, we present SCULPTOR, 3D face creations with Skeleton Consistency Using a Learned Parametric facial generaTOR, aiming to facilitate easy creation of both anatomically correct and visually convincing face models via a hybrid parametric-physical representation. At the core of SCULPTOR is LUCY, the first large-scale shape-skeleton face dataset in collaboration with plastic surgeons. Named after the fossils of one of the oldest known human ancestors, our LUCY dataset contains high-quality Computed Tomography (CT) scans of the complete human head before and after orthognathic surgeries, critical for evaluating surgery results. LUCY consists of 144 scans of 72 subjects (31 male and 41 female) where each subject has two CT scans taken pre- and post-orthognathic operations. Based on our LUCY dataset, we learn a novel skeleton consistent parametric facial generator, SCULPTOR, which can create the unique and nuanced facial features that help define a character and at the same time maintain physiological soundness. Our SCULPTOR jointly models the skull, face geometry and face appearance under a unified data-driven framework, by separating the depiction of a 3D face into shape blend shape, pose blend shape and facial expression blend shape. SCULPTOR preserves both anatomic correctness and visual realism in facial generation tasks compared with existing methods. Finally, we showcase the robustness and effectiveness of SCULPTOR in various fancy applications unseen before. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 317,399 |
2212.11966 | Removing Objects From Neural Radiance Fields | Neural Radiance Fields (NeRFs) are emerging as a ubiquitous scene representation that allows for novel view synthesis. Increasingly, NeRFs will be shareable with other people. Before sharing a NeRF, though, it might be desirable to remove personal information or unsightly objects. Such removal is not easily achieved with the current NeRF editing frameworks. We propose a framework to remove objects from a NeRF representation created from an RGB-D sequence. Our NeRF inpainting method leverages recent work in 2D image inpainting and is guided by a user-provided mask. Our algorithm is underpinned by a confidence based view selection procedure. It chooses which of the individual 2D inpainted images to use in the creation of the NeRF, so that the resulting inpainted NeRF is 3D consistent. We show that our method for NeRF editing is effective for synthesizing plausible inpaintings in a multi-view coherent manner. We validate our approach using a new and still-challenging dataset for the task of NeRF inpainting. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 337,927 |
2410.07272 | Boosting the Performance of Decentralized Federated Learning via
Catalyst Acceleration | Decentralized Federated Learning has emerged as an alternative to centralized architectures due to its faster training, privacy preservation, and reduced communication overhead. In decentralized communication, the server aggregation phase in Centralized Federated Learning shifts to the client side, which means that clients connect with each other in a peer-to-peer manner. However, compared to the centralized mode, data heterogeneity in Decentralized Federated Learning will cause larger variances between aggregated models, which leads to slow convergence in training and poor generalization performance in tests. To address these issues, we introduce Catalyst Acceleration and propose an acceleration Decentralized Federated Learning algorithm called DFedCata. It consists of two main components: the Moreau envelope function, which primarily addresses parameter inconsistencies among clients caused by data heterogeneity, and Nesterov's extrapolation step, which accelerates the aggregation phase. Theoretically, we prove the optimization error bound and generalization error bound of the algorithm, providing a further understanding of the nature of the algorithm and the theoretical perspectives on the hyperparameter choice. Empirically, we demonstrate the advantages of the proposed algorithm in both convergence speed and generalization performance on CIFAR10/100 with various non-iid data distributions. Furthermore, we also experimentally verify the theoretical properties of DFedCata. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 496,564 |
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