The following lemma #REFR will be useful to measure the difference between two value functions under two MDPs and reward functions.

, π n−1 ), we have the following corollary which will be intensively used in the analysis. Corollary 2.

Given δ ∈ (0, 1), the following inequality holds for any n, h ≥ 2 with probability at least 1 − δ/2:

E s h−1 ∼(P ,Πn) a h−1 ,a h ∼U (A) s h ∼P (•|s h−1 ,a h−1 ) f n h (s h , a h ) 2 ≤ ζ n .

We define P h V h+1 (s h , a h ) = E s∼P h (•|s h ,a h ) [V (s)] for shorthand. Lemma 9. (Simulation lemma).

Suppose P 1 and P 2 are two MDPs and r 1 , r 2 are the corresponding reward functions. Given a policy π, we have,

V π h,P1,r1 (s h ) − V π h,P2,r2 (s h ) = H h =h E s h ∼(P 2 ,π) a h ∼π r 1 (s h , a h ) − r 2 (s h , a h ) + (P 1,h − P 2,h )V π h +1,P1,r (s h , a h )|s h = H h =h E s h ∼(P 1 ,π) a h ∼π r 1 (s h , a h ) − r 2 (s h , a h ) + (P 1,h − P 2,h )V π h +1,P2,r (s h , a h )|s h .

The following lemma is a standard inequality in regret analysis for linear models in reinforcement learning (see Lemma G.2 in #OTHEREFR and Lemma 10 in #OTHEREFR ). Lemma 10. (Elliptical potential lemma).

Consider a sequence of d×d positive semidefinite matrices X 1 , . . .

Improved Sample Complexity For Reward-Free Reinforcement Learning Under Low-Rank Mdps

Abstract: In reward-free reinforcement learning (RL), an agent explores the environment first without any reward information, in order to achieve certain learning goals afterwards for any given reward. In this paper we focus on reward-free RL under low-rank MDP models, in which both the representation and linear weight vectors are unknown. Although various algorithms have been proposed for reward-free low-rank MDPs, the corresponding sample complexity is still far from being satisfactory. In this work, we first provide the first known sample complexity lower bound that holds for any algorithm under low-rank MDPs. This lower bound implies it is strictly harder to find a near-optimal policy under low-rank MDPs than under linear MDPs. We then propose a novel model-based algorithm, coined RAFFLE, and show it can both find an-optimal policy and achieve an-accurate system identification via reward-free exploration, with a sample complexity significantly improving the previous results. Such a sample complexity matches our lower bound in the dependence on , as well as on K in the large d regime, where d and K respectively denote the representation dimension and action space cardinality. Finally, we provide a planning algorithm (without further interaction with true environment) for RAFFLE to learn a near-accurate representation, which is the first known representation learning guarantee under the same setting.

Unifying Pac And Regret: Uniform Pac Bounds For Episodic Reinforcement Learning

Abstract: Statistical performance bounds for reinforcement learning (RL) algorithms can be critical for high-stakes applications like healthcare. This paper introduces a new framework for theoretically measuring the performance of such algorithms called Uniform-PAC, which is a strengthening of the classical Probably Approximately Correct (PAC) framework. In contrast to the PAC framework, the uniform version may be used to derive high probability regret guarantees and so forms a bridge between the two setups that has been missing in the literature. We demonstrate the benefits of the new framework for finite-state episodic MDPs with a new algorithm that is Uniform-PAC and simultaneously achieves optimal regret and PAC guarantees except for a factor of the horizon.


The σ sub-problems (4.3) and (4.16) are solved by NLCG method #REFR .

In this example, the background conductivity σ 0 = 1 in Ω C and the object Ω 2 = [−0.5, 0.5] × [−0.5, 0.5] × [−1.0, −0.5] and the exact abnormal conductivity is given by σ = 5 in Ω 2 and σ = 0 in Ω C \Ω 2 .

So the exact conductivity is 6 in Ω 2 and 1 in Ω C \Ω 2 .

The mesh M h we used in this example has 798138 edges.

In the Algorithm 1, 3 NLCG iterations is used for Algorithm 1 and 5 NLCG iterations is used for Algorithm 2.

We choose the truncation parameter m = 0.02 × max{iter, 50} and M = 15 in the iter-th iteration.

Figure 2 shows isosurfaces of the recovered σ with isovalue 2.3 in 20th, 40th, 60th and 80th iterations by Algorithm 1.

The results tell us that the reconstructions approaches to the exact conductivity with iterations.

As we cans see, the isosurface becomes stable after 40-th iteration.

Iterative Methods For An Inverse Eddy Current Problem With Total Variation Regularization

Abstract: Conductivity reconstruction in an inverse eddy current problem is considered in the present paper. With the electric field measurement on part of domain boundary, we formulate the reconstruction problem to a constrained optimization problem with total variation regularization. Existence and stability are proved for the solution to the optimization problem. The finite element method is employed to discretize the optimization problem. The gradient Lipschitz properties of the objective functional are established for the the discrete optimization problems. We propose the alternating direction method of multipliers to solve the discrete problem. Based on the the gradient Lipschitz property, we prove the convergence by extending the admissible set to the whole finite element space. Finally, we show some numerical experiments to illustrate the efficiency of the proposed methods.

Mathematical And Numerical Study Of A Three-Dimensional Inverse Eddy Current Problem

Abstract: We study an inverse problem associated with an eddy current model. We first address the ill-posedness of the inverse problem by proving the compactness of the forward map with respect to the conductivity and the non-uniqueness of the recovery process. Then by virtue of non-radiating source conceptions, we establish a regularity result for the tangential trace of the true solution on the boundary, which is necessary to justify our subsequent mathematical formulation. After that, we formulate the inverse problem as a constrained optimization problem with an appropriate regularization and prove the existence and stability of the regularized minimizers. To facilitate the numerical solution of the nonlinear non-convex constrained optimization, we introduce a feasible Lagrangian and its discrete variant. Then the gradient of the objective functional is derived using the adjoint technique. By means of the gradient, a nonlinear conjugate gradient method is formulated for solving the optimization system, and a Sobolev gradient is incorporated to accelerate the iterative process. Numerical examples are provided to demonstrate the feasibility of the proposed algorithm.


The Transformer layer #REFR consists of two modules: a multi-head attention and a feed-forward network (FFN).

Specifically, each head is represented by four main matrices: the query matrix

W q i ∈ R d m ×d q /h , the key matrix W k i ∈ R d m × d k h , the value matrix W v i ∈ R d m × d v

, and takes the hidden states H ∈ R l×d m of the previous layer as input, where d denotes the dimension of the model, h represents the number of head, and i denotes the index of layer number. The output of attention is given by:

Multimodal Graph Transformer For Multimodal Question Answering

Abstract: Despite the success of Transformer models in vision and language tasks, they often learn knowledge from enormous data implicitly and cannot utilize structured input data directly. On the other hand, structured learning approaches such as graph neural networks (GNNs) that integrate prior information can barely compete with Transformer models. In this work, we aim to benefit from both worlds and propose a novel Multimodal Graph Transformer for question answering tasks that requires performing reasoning across multiple modalities. We introduce a graph-involved plug-and-play quasi-attention mechanism to incorporate multimodal graph information, acquired from text and visual data, to the vanilla self-attention as effective prior. In particular, we construct the text graph, dense region graph, and semantic graph to generate adjacency matrices, and then compose them with input vision and language features to perform downstream reasoning. Such a way of regularizing self-attention with graph information significantly improves the inferring ability and helps align features from different modalities. We validate the effectiveness of Multimodal Graph Transformer over its Transformer baselines on GQA, VQAv2, and MultiModalQA datasets.

Attention Is All You Need

Abstract: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.


We do not use pretrained word embeddings as #REFR do; in our case token embeddings are learned from scratch during training.

This model consists of an NER-based tokenizer layer and a pointer-generator network.

The NERbased tokenizer layer converts the words in the input document to a sequence of tokens, thus preserving an entity name as a single token.

In particular, it uses the named entity recognizer in spaCy 1 However, we do not use entity types.

Here, the main role of NER is that instead of directly feeding the normal tokens of the input document into the encoder, we are passing the NER-based tokens.

Named Entity Recognition Based Automatic Generation Of Research Highlights

Abstract: A scientific paper is traditionally prefaced by an abstract that summarizes the paper. Recently, research highlights that focus on the main findings of the paper have emerged as a complementary summary in addition to an abstract. However, highlights are not yet as common as abstracts, and are absent in many papers. In this paper, we aim to automatically generate research highlights using different sections of a research paper as input. We investigate whether the use of named entity recognition on the input improves the quality of the generated highlights. In particular, we have used two deep learning-based models: the first is a pointer-generator network, and the second augments the first model with coverage mechanism. We then augment each of the above models with named entity recognition features. The proposed method can be used to produce highlights for papers with missing highlights. Our experiments show that adding named entity information improves the performance of the deep learning-based summarizers in terms of ROUGE, METEOR and BERTScore measures.

Abstractive Text Summarization Using Sequence-To-Sequence Rnns And Beyond

Abstract: In this work, we model abstractive text summarization using Attentional Encoder-Decoder Recurrent Neural Networks, and show that they achieve state-of-the-art performance on two different corpora. We propose several novel models that address critical problems in summarization that are not adequately modeled by the basic architecture, such as modeling key-words, capturing the hierarchy of sentence-to-word structure, and emitting words that are rare or unseen at training time. Our work shows that many of our proposed models contribute to further improvement in performance. We also propose a new dataset consisting of multi-sentence summaries, and establish performance benchmarks for further research.

token embeddings
pretrained word embeddings


Using the system level parameterization #REFR , they provide equivalent reformulations of the resulting minimax regret optimal control problems as semidefinite programs (SDPs).

Using operator-theoretic techniques from robust control, controllers (in state-space representation) are synthesized to minimize the worst-case regret over all possible disturbances with bounded energy.

Building on this framework, #OTHEREFR and #OTHEREFR synthesize regret optimal controllers subject to state and This work was supported in part by The Nature Conservancy, in part by the Cornell Atkinson Center for Sustainability, and in part by the Natural Sciences and Engineering Research Council of Canada.

The authors are with the School of Electrical and Computer Engineering, Cornell Univ., Ithaca, NY, 14853, USA.

Emails: Shuhao Yan (sy499@cornell.edu), Feras Al Taha (foa6@cornell.edu), Eilyan Bitar (eyb5@cornell.edu).

While the resultant controllers are robust with respect to the worst-case disturbance realization, they may be conservative if the underlying disturbance is actually stochastic in nature.

Taking a probabilistic view, we propose an alternative approach to regret optimal control, where controllers are designed to minimize the worst-case expected regret over all probability distributions in a given ambiguity set specified in terms of the type-2 Wasserstein distance.

In this setting, the expected regret is defined as the difference between an expected cost incurred by a causal controller and the expected cost incurred by the optimal noncausal controller with perfect knowledge of the disturbance trajectory at the outset.

The proposed approach can also be thought of as an alternative to distributionally robust control, see, i.e., #OTHEREFR , #OTHEREFR and references therein.

While controllers minimizing the worst-case expected cost are robust with respect to the worst-case distribution, they may incur large expected regret under other distributions in the ambiguity set.

A Distributionally Robust Approach To Regret Optimal Control Using The Wasserstein Distance

Abstract: This paper proposes a distributionally robust approach to regret optimal control of discrete-time linear dynamical systems with quadratic costs subject to stochastic additive disturbance on the state process. The underlying probability distribution of the disturbance process is unknown, but assumed to lie in a given ball of distributions defined in terms of the type-2 Wasserstein distance. In this framework, strictly causal linear disturbance feedback controllers are designed to minimize the worst-case expected regret. The regret incurred by a controller is defined as the difference between the cost it incurs in response to a realization of the disturbance process and the cost incurred by the optimal noncausal controller which has perfect knowledge of the disturbance process realization at the outset. Building on a well-established duality theory for optimal transport problems, we show how to equivalently reformulate this minimax regret optimal control problem as a tractable semidefinite program. The equivalent dual reformulation also allows us to characterize a worst-case distribution achieving the worst-case expected regret in relation to the distribution at the center of the Wasserstein ball.

System Level Synthesis

Abstract: This article surveys the System Level Synthesis framework, which presents a novel perspective on constrained robust and optimal controller synthesis for linear systems. We show how SLS shifts the controller synthesis task from the design of a controller to the design of the entire closed loop system, and highlight the benefits of this approach in terms of scalability and transparency. We emphasize two particular applications of SLS, namely large-scale distributed optimal control and robust control. In the case of distributed control, we show how SLS allows for localized controllers to be computed, extending robust and optimal control methods to large-scale systems under practical and realistic assumptions. In the case of robust control, we show how SLS allows for novel design methodologies that, for the first time, quantify the degradation in performance of a robust controller due to model uncertainty -- such transparency is key in allowing robust control methods to interact, in a principled way, with modern techniques from machine learning and statistical inference. Throughout, we emphasize practical and efficient computational solutions, and demonstrate our methods on easy to understand case studies.


SENet #REFR differs from RAM and STN in the way that SENet innovatively introduces attention mechanisms to the detector to refine the channels of features.

Attention mechanisms #OTHEREFR enable the selection of information that is required to obtain a target, with outputs dynamically weighted according to input characteristics.

Demonstrates efficient, attention mechanism is added to the object detectors.

RAM #OTHEREFR is a pioneer in introducing attention mechanisms into deep neural networks.

RAM showed that the attention mechanisms are effective in working with neural networks, then related works have been presented sequentially. STN #OTHEREFR introduces a sub-network to select important regions.

Related work has been launched with CBAM #OTHEREFR , ECANet #OTHEREFR , SRM #OTHEREFR and GSop #OTHEREFR , all of which achieved successful records.

Recently, transformer #OTHEREFR has become more and more popular.

For example, VIT #OTHEREFR is pioneering the concept of utilizing the transformer encoder for feature extraction and prediction.

A Novel Multi To Single Module For Small Object Detection

Abstract: Small object detection presents a significant challenge in computer vision and object detection. The performance of small object detectors is often compromised by a lack of pixels and less significant features. This issue stems from information misalignment caused by variations in feature scale and information loss during feature processing. In response to this challenge, this paper proposes a novel the Multi to Single Module (M2S), which enhances a specific layer through improving feature extraction and refining features. Specifically, M2S includes the proposed Cross-scale Aggregation Module (CAM) and explored Dual Relationship Module (DRM) to improve information extraction capabilities and feature refinement effects. Moreover, this paper enhances the accuracy of small object detection by utilizing M2S to generate an additional detection head. The effectiveness of the proposed method is evaluated on two datasets, VisDrone2021-DET and SeaDronesSeeV2. The experimental results demonstrate its improved performance compared with existing methods. Compared to the baseline model (YOLOv5s), M2S improves the accuracy by about 1.1% on the VisDrone2021-DET testing dataset and 15.68% on the SeaDronesSeeV2 validation set. 1 Introduction Small object detection ([1],[2],[3],[4],[5]) has long been a challenge in object detection, which aims to accurately detect small objects (those under 32 pixels by 32 pixels) with very few visual features in the image. A detection model consisting of a backbone network, an encoder, and a decoder is proposed in You Only Look One-level Feature (YOLOF) [6]. It is proposed in YOLOF that by choosing the right scale features for output to a particular level with multiple-input and single-output, comparable performance to multiple-input and multiple-output can be obtained. In the small object detection task, this paper proposes a multiple-input to the single-output module that outputs with a low-level feature with high resolution. It provides the detector with more accurate and richer information, thereby improving its performance. Feature Pyramid network (FPN) [7] success is due to the idea of divide and conquer. Divide and conquer ensures that image features are available in different layers, whereas if only a unidirectional flow of information is delivered, then FPN ends up with a limitation at each layer. The result is that each layer of character can only be thinking within its own perspective. Thus, previous work has been devoted to increasing the interaction of different levels of feature and allowing more global thinking at each level. FPN appears as the backbone network of excellent detectors SSD [8], YOLO [9], and RCNN [10]. EffientDet [11]uses a cross-edge connecting BiFPN and repeats it to obtain better accuracy and efficiency. Although the previous works have shown gratifying performance, object detection still has a problem: the poor detection of small-scale instance objects. Based on this problem, this paper proposes a module to enhance communication at different network levels and improve feature extraction efficiency. The Cross-scale Fusion Module

Squeeze-And-Excitation Networks

Abstract: Convolutional neural networks are built upon the convolution operation, which extracts informative features by fusing spatial and channel-wise information together within local receptive fields. In order to boost the representational power of a network, much existing work has shown the benefits of enhancing spatial encoding. In this work, we focus on channels and propose a novel architectural unit, which we term the"Squeeze-and-Excitation"(SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. We demonstrate that by stacking these blocks together, we can construct SENet architectures that generalise extremely well across challenging datasets. Crucially, we find that SE blocks produce significant performance improvements for existing state-of-the-art deep architectures at slight computational cost. SENets formed the foundation of our ILSVRC 2017 classification submission which won first place and significantly reduced the top-5 error to 2.251%, achieving a 25% relative improvement over the winning entry of 2016.


Semi-supervised methods employed additional information, like segmentation maps, to guide registration without requiring them during inference #REFR .

Recent learning-based registration methods can be categorized into supervised #OTHEREFR and unsupervised registration #OTHEREFR .

Supervised registration predicts deformation fields minimizing the difference with a "ground truth" deformation.

"Ground truth" maps are often generated using iterative methods or via simulation, which fundamentally limits the performance of supervised approaches #OTHEREFR .

Unsupervised registration methods employ a classical loss evaluating image similarity and deformation regularity, thus forming an end-to-end training pipeline #OTHEREFR .

Recent methods extend this strategy to the spherical domain by parameterizing the brain surfaces in a 2D grid that accounts for distortions #OTHEREFR or directly on the sphere using spherical kernels #OTHEREFR .

Learning-based methods improved registration run time substantially during inference, while achieving superior or similar accuracy relative to iterative methods.

However, these methods do not account for variations between geometry and function within a subject.

Joint Cortical Registration Of Geometry And Function Using Semi-Supervised Learning

Abstract: Brain surface-based image registration, an important component of brain image analysis, establishes spatial correspondence between cortical surfaces. Existing iterative and learning-based approaches focus on accurate registration of folding patterns of the cerebral cortex, and assume that geometry predicts function and thus functional areas will also be well aligned. However, structure/functional variability of anatomically corresponding areas across subjects has been widely reported. In this work, we introduce a learning-based cortical registration framework, JOSA, which jointly aligns folding patterns and functional maps while simultaneously learning an optimal atlas. We demonstrate that JOSA can substantially improve registration performance in both anatomical and functional domains over existing methods. By employing a semi-supervised training strategy, the proposed framework obviates the need for functional data during inference, enabling its use in broad neuroscientific domains where functional data may not be observed.

Voxelmorph: A Learning Framework For Deformable Medical Image Registration

Abstract: We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical image registration. Traditional registration methods optimize an objective function for each pair of images, which can be time-consuming for large datasets or rich deformation models. In contrast to this approach and building on recent learning-based methods, we formulate registration as a function that maps an input image pair to a deformation field that aligns these images. We parameterize the function via a convolutional neural network and optimize the parameters of the neural network on a set of images. Given a new pair of scans, VoxelMorph rapidly computes a deformation field by directly evaluating the function. In this paper, we explore two different training strategies. In the first (unsupervised) setting, we train the model to maximize standard image matching objective functions that are based on the image intensities. In the second setting, we leverage auxiliary segmentations available in the training data. We demonstrate that the unsupervised model’s accuracy is comparable to the state-of-the-art methods while operating orders of magnitude faster. We also show that VoxelMorph trained with auxiliary data improves registration accuracy at test time and evaluate the effect of training set size on registration. Our method promises to speed up medical image analysis and processing pipelines while facilitating novel directions in learning-based registration and its applications. Our code is freely available at https://github.com/voxelmorph/voxelmorph .


In this study, we adopt the original MAE pre-trained on ImageNet #REFR for fair comparisons with other models.

MAE #OTHEREFR is pre-trained by the masked image modeling (MIM) task #OTHEREFR inspired by the success of masked language modeling in NLP #OTHEREFR . MAE adopts an asymmetric encoder-decoder architecture.

The encoder is only applied to the visible subset of patches (without mask tokens).

The lightweight decoder reconstructs the image from the encoded visible patches and mask tokens.

The mask ratio is as high as 75% to reduce the heavy spatial redundancy of images.

MVP #OTHEREFR ) uses an MAE model pre-trained on images in the wild, e.g., from YouTube #OTHEREFR or Egocentric videos #OTHEREFR to provide effective representations for motor control.

iBOT #OTHEREFR can be viewed as the combination of self-distillation (DINO) and masked image modeling.

Self-distillation is used to train a teacher network/online tokenizer that captures high-level visual semantics.

Masked image modeling is used to train a student network that can recover each masked patch token to its corresponding teacher network/online tokenizer output.

CLIP #OTHEREFR ) is a simple and scalable method to learn visual representations with language supervision.

CLIP forces the image representations to be aligned with paired captions through contrastive learning.

For Pre-Trained Vision Models In Motor Control, Not All Policy Learning Methods Are Created Equal

Abstract: In recent years, increasing attention has been directed to leveraging pre-trained vision models for motor control. While existing works mainly emphasize the importance of this pre-training phase, the arguably equally important role played by downstream policy learning during controlspecific fine-tuning is often neglected. It thus remains unclear if pre-trained vision models are consistent in their effectiveness under different control policies. To bridge this gap in understanding, we conduct a comprehensive study on 14 pretrained vision models using 3 distinct classes of policy learning methods, including reinforcement learning (RL), imitation learning through behavior cloning (BC), and imitation learning with a visual reward function (VRF). Our study yields a series of intriguing results, including the discovery that the effectiveness of pre-training is highly dependent on the choice of the downstream policy learning algorithm. We show that conventionally accepted evaluation based on RL methods is highly variable and therefore unreliable, and further advocate for using more robust methods like VRF and BC. To facilitate more universal evaluations of pre-trained models and their policy learning methods in the future, we also release a benchmark of 21 tasks across 3 different environments alongside our work.

Imagenet Large Scale Visual Recognition Challenge

Abstract: The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements.


To test our approach, we use PoseNet #REFR with a ResNet34-based feature extractor for conformalization.

We employ conformalized quantile regression (CQR) #OTHEREFR to extract univariate uncertainty bands for each output coordinate in VO.

However, the presented techniques are generalizable to other feature-based and direct predictors for VO.

The PoseNet model outputs translational elements x, y, and z, as well as rotational elements p, q, and r of angle w in radians.

To reduce the number of response variables from seven to six, we convert the orientation quaternion (w, p, q, r) to Euler angles (roll φ, pitch θ, yaw ψ).

After pre-training, we apply CQR to obtain prediction intervals along each one-dimensional variable, which reveals the predictive uncertainty. Fig. 2 depicts the process for this method.

The main goal of CQR is to produce prediction intervals where the probability of true realizations falling within these intervals is guaranteed to be equivalent to or greater than some chosen coverage rate p, i.e., P{Y ∈ C(X) | X = x} ≥ p.

Lightweight, Uncertainty-Aware Conformalized Visual Odometry

Abstract: Data-driven visual odometry (VO) is a critical subroutine for autonomous edge robotics, and recent progress in the field has produced highly accurate point predictions in complex environments. However, emerging autonomous edge robotics devices like insect-scale drones and surgical robots lack a computationally efficient framework to estimate VO's predictive uncertainties. Meanwhile, as edge robotics continue to proliferate into mission-critical application spaces, awareness of model's the predictive uncertainties has become crucial for risk-aware decision-making. This paper addresses this challenge by presenting a novel, lightweight, and statistically robust framework that leverages conformal inference (CI) to extract VO's uncertainty bands. Our approach represents the uncertainties using flexible, adaptable, and adjustable prediction intervals that, on average, guarantee the inclusion of the ground truth across all degrees of freedom (DOF) of pose estimation. We discuss the architectures of generative deep neural networks for estimating multivariate uncertainty bands along with point (mean) prediction. We also present techniques to improve the uncertainty estimation accuracy, such as leveraging Monte Carlo dropout (MC-dropout) for data augmentation. Finally, we propose a novel training loss function that combines interval scoring and calibration loss with traditional training metrics-mean-squared error and KLdivergence-to improve uncertainty-aware learning. Our simulation results demonstrate that the presented framework consistently captures true uncertainty in pose estimations across different datasets, estimation models, and applied noise types, indicating its wide applicability.

Posenet: A Convolutional Network For Real-Time 6-Dof Camera Relocalization

Abstract: We present a robust and real-time monocular six degree of freedom relocalization system. Our system trains a convolutional neural network to regress the 6-DOF camera pose from a single RGB image in an end-to-end manner with no need of additional engineering or graph optimisation. The algorithm can operate indoors and outdoors in real time, taking 5ms per frame to compute. It obtains approximately 2m and 3 degrees accuracy for large scale outdoor scenes and 0.5m and 5 degrees accuracy indoors. This is achieved using an efficient 23 layer deep convnet, demonstrating that convnets can be used to solve complicated out of image plane regression problems. This was made possible by leveraging transfer learning from large scale classification data. We show that the PoseNet localizes from high level features and is robust to difficult lighting, motion blur and different camera intrinsics where point based SIFT registration fails. Furthermore we show how the pose feature that is produced generalizes to other scenes allowing us to regress pose with only a few dozen training examples.


To have a unitless performance measure that can be applied across models, Geifman et al., #REFR introduce a normalized AURC, named Excess-AURC (E-AURC).

Finally, we propose to enlarge the confidence gap by finding flat minima, which yields strong performance on extensive experiments. AURC & E-AURC.

AURC measures the area under the curve drawn by plotting the risk according to coverage.

The coverage indicates the ratio of samples whose confidence estimates are higher than some confidence threshold, and the risk, also known as the selective risk #OTHEREFR , is an error rate computed by using those samples.

A low value of AURC implies that correct and incorrect predictions can be well-separable by confidence estimates associated with samples.

Inherently, AURC is affected by the predictive performance of a model.

Specifically, E-AURC can be computed by subtracting the optimal AURC, the lowest possible value for a given model, from the empirical AURC.

Rethinking Confidence Calibration For Failure Prediction

Abstract: Reliable confidence estimation for the predictions is important in many safety-critical applications. However, modern deep neural networks are often overconfident for their incorrect predictions. Recently, many calibration methods have been proposed to alleviate the overconfidence problem. With calibrated confidence, a primary and practical purpose is to detect misclassification errors by filtering out low-confidence predictions (known as failure prediction). In this paper, we find a general, widely-existed but actually-neglected phenomenon that most confidence calibration methods are useless or harmful for failure prediction. We investigate this problem and reveal that popular confidence calibration methods often lead to worse confidence separation between correct and incorrect samples, making it more difficult to decide whether to trust a prediction or not. Finally, inspired by the natural connection between flat minima and confidence separation, we propose a simple hypothesis: flat minima is beneficial for failure prediction. We verify this hypothesis via extensive experiments and further boost the performance by combining two different flat minima techniques. Our code is available at https://github.com/Impression2805/FMFP.

Bias-Reduced Uncertainty Estimation For Deep Neural Classifiers

Abstract: We consider the problem of uncertainty estimation in the context of (non-Bayesian) deep neural classification. In this context, all known methods are based on extracting uncertainty signals from a trained network optimized to solve the classification problem at hand. We demonstrate that such techniques tend to introduce biased estimates for instances whose predictions are supposed to be highly confident. We argue that this deficiency is an artifact of the dynamics of training with SGD-like optimizers, and it has some properties similar to overfitting. Based on this observation, we develop an uncertainty estimation algorithm that selectively estimates the uncertainty of highly confident points, using earlier snapshots of the trained model, before their estimates are jittered (and way before they are ready for actual classification). We present extensive experiments indicating that the proposed algorithm provides uncertainty estimates that are consistently better than all known methods.


Numerical Optimization For Max-Loss Form

Square Attack does not have as effective attack performance as APGDs (see Table 2 in later sections) but is included as #REFR states that the diversity in the evaluation algorithms is important to achieve reliable numerical RE.

For p = 2, sequential projection-first onto the box and then the norm ball, at least finds a feasible solution (see our clarification of these projections in Appendix A.1). Therefore, PGD is viable for these cases.

For other general p metrics and nonp metrics, analytical projectors mostly cannot be derived and existing PGD methods do not apply.

Previous work has shown that the solution quality of PGD is sensitive to hyperparameter tuning, e.g., step-size schedule and iteration budget #OTHEREFR .

PGD variants, such as AGPD methods where 'A' stands for Auto(matic) #OTHEREFR , try to make the tuning process automatic by combining a heuristic adaptive step-size schedule and momentum acceleration under a fixed iteration budget and are built into the popular AutoAttack package #OTHEREFR .

AutoAttack also includes non-PGD methods, e.g., Square Attack #OTHEREFR , which is based on gradientfree random search.

Optimization And Optimizers For Adversarial Robustness

Abstract: Empirical robustness evaluation (RE) of deep learning models against adversarial perturbations entails solving nontrivial constrained optimization problems. Existing numerical algorithms that are commonly used to solve them in practice predominantly rely on projected gradient, and mostly handle perturbations modeled by the 1, 2 and ∞ distances. In this paper, we introduce a novel algorithmic framework that blends a general-purpose constrained-optimization solver PyGRANSO with Constraint-Folding (PWCF), which can add more reliability and generality to the state-of-the-art RE packages, e.g., AutoAttack. Regarding reliability, PWCF provides solutions with stationarity measures and feasibility tests to assess the solution quality. For generality, PWCF can handle perturbation models that are typically inaccessible to the existing projected gradient methods; the main requirement is the distance metric to be almost everywhere differentiable. Taking advantage of PWCF and other existing numerical algorithms, we further explore the distinct patterns in the solutions found for solving these optimization problems using various combinations of losses, perturbation models, and optimization algorithms. We then discuss the implications of these patterns on the current robustness evaluation and adversarial training.

Reliable Evaluation Of Adversarial Robustness With An Ensemble Of Diverse Parameter-Free Attacks

Abstract: The field of defense strategies against adversarial attacks has significantly grown over the last years, but progress is hampered as the evaluation of adversarial defenses is often insufficient and thus gives a wrong impression of robustness. Many promising defenses could be broken later on, making it difficult to identify the state-of-the-art. Frequent pitfalls in the evaluation are improper tuning of hyperparameters of the attacks, gradient obfuscation or masking. In this paper we first propose two extensions of the PGD-attack overcoming failures due to suboptimal step size and problems of the objective function. We then combine our novel attacks with two complementary existing ones to form a parameter-free, computationally affordable and user-independent ensemble of attacks to test adversarial robustness. We apply our ensemble to over 40 models from papers published at recent top machine learning and computer vision venues. In all except one of the cases we achieve lower robust test accuracy than reported in these papers, often by more than $10\%$, identifying several broken defenses.


For lower values of α, as client distributions become more skewed, there can be significant changes in accuracy between training runs #REFR ).

We use SGD as our optimizer, with weight decay of 1 × 10 −4 following Yao et al. 2021; #OTHEREFR .

In these experiments, we observe that FedAvg often (though not always) performs better with group normalization as indicated by #OTHEREFR

(2020) while FedAvg+WSM is able to perform well with both group and batch normalization, very frequently achieving the best results with batchnormalization.

We therefore treat the normalization method as a hyperparameter and provide the best results for both batch and group normalization for both methods at each learning rate.

Validation Throughout the training process the global model is periodically evaluated on the aggregation of the client validation sets to gauge overall training progress, the test set on the other hand is only used at the end of the training process.

Since we focus our analysis on the highly heterogeneous case in which the Dirichlet distributions at each client are parameterized by α = 0.1, we observe higher variance in our results.

This is especially true for smaller datasets such as CIFAR-10 and CIFAR-100.

To mitigate these effects on the validation statistics, we follow the lead of and report our final accuracy as the average of the test accuracies taken over the last 100 rounds of training.

The right column show the accuracy of client i's model after training when evaluated on client k's data.

Re-Weighted Softmax Cross-Entropy To Control Forgetting In Federated Learning

Abstract: In Federated Learning, a global model is learned by aggregating model updates computed at a set of independent client nodes, to reduce communication costs multiple gradient steps are performed at each node prior to aggregation. A key challenge in this setting is data heterogeneity across clients resulting in differing local objectives which can lead clients to overly minimize their own local objective, diverging from the global solution. We demonstrate that individual client models experience a catastrophic forgetting with respect to data from other clients and propose an efficient approach that modifies the cross-entropy objective on a per-client basis by re-weighting the softmax logits prior to computing the loss. This approach shields classes outside a client's label set from abrupt representation change and we empirically demonstrate it can alleviate client forgetting and provide consistent improvements to standard federated learning algorithms. Our method is particularly beneficial under the most challenging federated learning settings where data heterogeneity is high and client participation in each round is low.

Measuring The Effects Of Non-Identical Data Distribution For Federated Visual Classification

Abstract: Federated Learning enables visual models to be trained in a privacy-preserving way using real-world data from mobile devices. Given their distributed nature, the statistics of the data across these devices is likely to differ significantly. In this work, we look at the effect such non-identical data distributions has on visual classification via Federated Learning. We propose a way to synthesize datasets with a continuous range of identicalness and provide performance measures for the Federated Averaging algorithm. We show that performance degrades as distributions differ more, and propose a mitigation strategy via server momentum. Experiments on CIFAR-10 demonstrate improved classification performance over a range of non-identicalness, with classification accuracy improved from 30.1% to 76.9% in the most skewed settings.


The K min values obtained from the numerical evaluations are compared with the theoretical result in #REFR , which substantiates the theoretical derivation for K min . In addition, the results in Fig.

It should also be noted that the minimum DEP becomes larger with the more number of cooperative users, i.e., K = 25, which results in a higher interference power and ends up making Willie more confused.

Now, we numerically evaluate the minimum detection error probabilities, ζ min for different numbers of cooperative users, K between 1 and 100 setting P a to either 0.67 or 0.83.

The numerical evaluations are obtained by searching for the minimums of the empirical DEP for K values. The evaluations of minimum DEPs are depicted in Fig.

3 where the minimum number of cooperative users, K min to meet the covert constraint, ζ ≥ 1 − , for P a = 0.67 and 0.83 are given by 28 and 43, respectively, when = 0.05.

Note that K min is the value on abscissa corresponding to ζ min = 1 − = 0.95 on the ordinate.

3 tell that the minimum DEP, ζ min is in proportion to the number of cooperative users, K.

The minimum number of cooperative users, K min also grows with the increasing transmit power, P a which is predicted by the closed-form expression of K min in #OTHEREFR .

4 , the throughput, η = RP c versus the covert rate, R is depicted for = 0.030 and 0.025 values when M = 500, and P a = 0.83. The numerical results in Fig.

4 are simply given by the product of the empirical connection probability and the covert rate.

Multi-User Cooperation For Covert Communication Under Quasi-Static Fading

Abstract: This work studies a covert communication scheme for an uplink multiuser scenario in which some users are opportunistically selected to help a covert user. In particular, the selected users emit interfering signals via an orthogonal resource dedicated to the covert user together with signals for their own communications using orthogonal resources allocated to the selected users, which helps the covert user hide the presence of the covert communication. For the covert communication scheme, we carry out extensive analysis and find system parameters in closed forms. The analytic derivation for the system parameters allow one to find the optimal combination of system parameters by performing a simple one-dimensional search. In addition, the analytic results elucidate relations among the system parameters. In particular, it will be proved that the optimal strategy for the non-covert users is an on-off scheme with equal transmit power. The theoretical results derived in this work are confirmed by comparing them with numerical results obtained with exhaustive searches. Finally, we demonstrate that the results of work can be utilized in versatile ways by demonstrating a design of covert communication with energy efficiency into account.

Fundamental Limits Of Communication With Low Probability Of Detection

Abstract: This paper considers the problem of communication over a discrete memoryless channel (DMC) or an additive white Gaussian noise (AWGN) channel subject to the constraint that the probability that an adversary who observes the channel outputs can detect the communication is low. Specifically, the relative entropy between the output distributions when a codeword is transmitted and when no input is provided to the channel must be sufficiently small. For a DMC whose output distribution induced by the"off"input symbol is not a mixture of the output distributions induced by other input symbols, it is shown that the maximum amount of information that can be transmitted under this criterion scales like the square root of the blocklength. The same is true for the AWGN channel. Exact expressions for the scaling constant are also derived.


To address this issue, we combine DDAQ and TD3 #REFR to form TD3AQ. Three key modifications to improve the performance are introduced.

In previous work (P-DQN), the objective function was defined as the sum of all Q-values, which posed a problem when back propagating gradients: updating the weights in the Qnetwork during back propagation affected all weights in the network, including those not related to the action with the highest Q-value, potentially causing the algorithm to update in an incorrect direction.

DDAQ addresses this issue by using the maximum Q-value as the objective function, ensuring that only the weights corresponding to the action a d with the highest Q-value are updated.

The back propagation process of DDAQ is illustrated in Fig. 2 .

2) Twin Delayed Deep Deterministic Actor-Q (TD3AQ): DDPG has shown potential in achieving good performance, but its robustness is limited due to its sensitivity to hyperparameters.

This is mainly because DDPG tends to overestimate Q-values, which results in poor policy updates.

a) Target policy smoothing: a noise sampled from a normal distribution is added to the action generated by the target actor network.

This technique acts as a regularizer that prevents the actor network from converging to a suboptimal solution.

Specifically, the action was smoothed by adding a clipped noise, defined as follows:

where a c min and a c max are the lower and upper bounds of the continuous action, respectively. The noise is defined as:

Mixed-Integer Optimal Control Via Reinforcement Learning: A Case Study On Hybrid Vehicle Energy Management

Abstract: Many optimal control problems require the simultaneous output of continuous and discrete control variables. Such problems are usually formulated as mixed-integer optimal control (MIOC) problems, which are challenging to solve due to the complexity of the solution space. Numerical methods such as branchand-bound are computationally expensive and unsuitable for realtime control. This paper proposes a novel continuous-discrete reinforcement learning (CDRL) algorithm, twin delayed deep deterministic actor-Q (TD3AQ), for MIOC problems. TD3AQ combines the advantages of both actor-critic and Q-learning methods, and can handle the continuous and discrete action spaces simultaneously. The proposed algorithm is evaluated on a hybrid electric vehicle (HEV) energy management problem, where real-time control of the continuous variable engine torque and discrete variable gear ratio is essential to maximize fuel economy while satisfying driving constraints. Simulation results on different drive cycles show that TD3AQ can achieve nearoptimal solutions compared to dynamic programming (DP) and outperforms the state-of-the-art discrete RL algorithm Rainbow, which is adopted for MIOC by discretizing continuous actions into a finite set of discrete values.

Addressing Function Approximation Error In Actor-Critic Methods

Abstract: In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies. We show that this problem persists in an actor-critic setting and propose novel mechanisms to minimize its effects on both the actor and critic. Our algorithm takes the minimum value between a pair of critics to restrict overestimation and delays policy updates to reduce per-update error. We evaluate our method on the suite of OpenAI gym tasks, outperforming the state of the art in every environment tested.


For a detailed tutorial on linear mixed models in the Bayesian setting, see chapter 5 of Nicenboim et al. (2023), or #REFR .

2023results, we report only regression path duration and first-pass regressions out from the pre-critical adverb and the critical verb; for full details of all experimental results, please see the original paper.

The effects of animacy and subjecthood (coded as sum contrasts) were analyzed using Bayesian mixed-effects models.

Subject and item were specified as random effects in the models, with a full variance-covariance matrix for subject and item random effects.

The models were implemented with brms #OTHEREFR , an interface to Stan #OTHEREFR .

Priors were mildly informative Gaussian distributions for the linear model coefficients (intercept and slopes) and mildly informative regularizing Lewandowski-Kurowicka-Joe (LKJ) priors #OTHEREFR for random effects correlation matrices; setting the LKJ prior's parameter ν to 2 downweights extreme correlations like ±1.

(2023) showed reading time patterns consistent with effects of subjecthood (syntactic interference) and effects of animacy (semantic interference).

Figure 3 shows that on the pre-critical adverb, the effect of subjecthood shows longer regression path duration (RPD) and more first-pass regressions out for conditions that have a +subject distractor (95% credible intervals (CrIs): RPD [17, 63] ms, FPR [3, 11]%).

Similarly, the effect of animacy shows longer regression-path duration and an increase in first-pass regressions out for conditions with animate distractors compared to conditions with inanimate distractors (95% CrIs: RPD [8, 57] ms, FPR [2, 8]%).

The subjecthood × animacy interaction in regression-path duration is centered on zero; for first-pass regressions, the interaction has a negative sign ([−7, 0]%).

Seam: An Integrated Activation-Coupled Model Of Sentence Processing And Eye Movements In Reading

Abstract: Models of eye-movement control during reading, developed largely within psychology, usually focus on visual, attentional, and motor processes but neglect postlexical language processing; by contrast, models of sentence comprehension processes, developed largely within psycholinguistics, generally focus only on postlexical language processes. We present a model that combines these two research threads, by integrating eye-movement control and sentence processing. Developing such an integrated model is extremely challenging and computationally demanding, but such an integration is an important step toward complete mathematical models of natural language comprehension in reading. We combine the SWIFT model of eye-movement control (Engbert et al.,

Bayesian Linear Mixed Models Using Stan: A Tutorial For Psychologists, Linguists, And Cognitive Scientists

Abstract: With the arrival of the R packages nlme and lme4, linear mixed models (LMMs) have come to be widely used in experimentally-driven areas like psychology, linguistics, and cognitive science. This tutorial provides a practical introduction to fitting LMMs in a Bayesian framework using the probabilistic programming language Stan. We choose Stan (rather than WinBUGS or JAGS) because it provides an elegant and scalable framework for fitting models in most of the standard applications of LMMs. We ease the reader into fitting increasingly complex LMMs, first using a two-condition repeated measures self-paced reading study, followed by a more complex $2\times 2$ repeated measures factorial design that can be generalized to much more complex designs.


We have collected 200 random navigation trajectories on 86 (training 72 + validation 14) scenes from the Gibson #REFR dataset.

We first trained the pair of encoder and decoder, and then trained the F loc and F stop with the frozen parameters of the encoder.

The neural networks used in RNR-Map are all trained with the same dataset.

The data sample for each neural network is also created in a similar manner.

We sample a trajectory from the dataset and select a subset of frames from the trajectory.

The pose information of the subset is normalized with the first frame of the sampled subset, considering the first frame as the origin.

Renderable Neural Radiance Map For Visual Navigation

Abstract: We propose a novel type of map for visual navigation, a renderable neural radiance map (RNR-Map), which is designed to contain the overall visual information of a 3D environment. The RNR-Map has a grid form and consists of latent codes at each pixel. These latent codes are embedded from image observations, and can be converted to the neural radiance field which enables image rendering given a camera pose. The recorded latent codes implicitly contain visual information about the environment, which makes the RNR-Map visually descriptive. This visual information in RNR-Map can be a useful guideline for visual localization and navigation. We develop localization and navigation frameworks that can effectively utilize the RNR-Map. We evaluate the proposed frameworks on camera tracking, visual localization, and image-goal navigation. Experimental results show that the RNR-Map-based localization framework can find the target location based on a single query image with fast speed and competitive accuracy compared to other baselines. Also, this localization framework is robust to environmental changes, and even finds the most visually similar places when a query image from a different environment is given. The proposed navigation framework outperforms the existing image-goal navigation methods in difficult scenarios, under odometry and actuation noises. The navigation framework shows 65.7% success rate in curved scenarios of the NRNS [23] dataset, which is an improvement of 18.6% over the current state-of-the-art. Project page: https: //rllab-snu.github.io/projects/RNR-Map/

Gibson Env: Real-World Perception For Embodied Agents

Abstract: Developing visual perception models for active agents and sensorimotor control are cumbersome to be done in the physical world, as existing algorithms are too slow to efficiently learn in real-time and robots are fragile and costly. This has given rise to learning-in-simulation which consequently casts a question on whether the results transfer to real-world. In this paper, we are concerned with the problem of developing real-world perception for active agents, propose Gibson Virtual Environment for this purpose, and showcase sample perceptual tasks learned therein. Gibson is based on virtualizing real spaces, rather than using artificially designed ones, and currently includes over 1400 floor spaces from 572 full buildings. The main characteristics of Gibson are: I. being from the real-world and reflecting its semantic complexity, II. having an internal synthesis mechanism,"Goggles", enabling deploying the trained models in real-world without needing further domain adaptation, III. embodiment of agents and making them subject to constraints of physics and space.


First, we propose an evolutionary algorithm-focused adaptation of the adversarial generation framework recently made popular by #REFR .

Gradient-based optimization of Deep Neural Networks (LeCun, Bengio & Hinton, 2015; Schmidhuber, 2015), by virtue of its ability to leverage modern computational resources, has established itself as this decade's single most influential line of work in Artificial Intelligence.

However, given the exponentially falling cost of computation, it seems rather likely that other techniques with similarly strong scaling properties will eventually become of interest.

An increasingly strong case is being made for evolutionary computation to be one of these.

Indeed, when provided sufficient computation, simple evolutionary algorithms paired with deep networks have begun to rival popular modern methods #OTHEREFR .

In this work, we attempt to further this effort through three contributions.

Next, we revisit the dynamic complexification of neural networks, component key to the field's prior success #OTHEREFR .

Finally, we present a highly scalable inter-process communication protocol, designed for evolutionary algorithms to better utilize modern computing systems.

We find that these techniques, once provided adequate resources, are able on short time-frames to produce artificial behaviour almost indistinguishable from human subjects in a video game environment.

Evolving Artificial Neural Networks To Imitate Human Behaviour In Shinobi Iii : Return Of The Ninja Master Présenté Par

Abstract: Our society is increasingly fond of computational tools. This phenomenon has greatly increased over the past decade following, among other factors, the emergence of a new Artificial Intelligence paradigm. Specifically, the coupling of two algorithmic techniques, Deep Neural Networks and Stochastic Gradient Descent, thrusted by an exponentially increasing computing capacity, has and is continuing to become a major asset in many modern technologies. However, as progress takes its course, some still wonder whether other methods could similarly or even more greatly benefit from these various hardware advances. In order to further this study, we delve in this thesis into Evolutionary Algorithms and their application to Dynamic Neural Networks, two techniques which despite enjoying many advantageous properties have yet to find their niche in contemporary Artificial Intelligence. We find that by elaborating new methods while exploiting strong computational resources, it becomes possible to develop strongly performing agents on a variety of benchmarks but also some other agents behaving very similarly to human subjects on the video game Shinobi III : Return of The Ninja Master, typical complex tasks previously out of reach for non-gradient-based optimization.

Generative Adversarial Nets

Abstract: We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to ½ everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.


We follow #REFR to predict the Cholesky decomposition of the covariance matrix in case of the illegal covariance matrix from direct regression. Details are further demonstrated in the Appendix.

However, as aforementioned, isotropic Gaussian functions may be too simple to accurately represent the real distributions of landmarks.

To better model the probability distributions, we use an anisotropic Gaussian function to estimate the uncertainty:

2, we use a full two-dimensional covariance matrix Σ i rather than a single value σ i to represent the heatmap, thus introducing anisotropy into the distribution.

According to Cholesky Factorization Theorem #OTHEREFR , the target covariance matrix can be decomposed as:

In our work, we calculate a i , b i , and c i by utilizing the multi-scale features extracted by the encoder of U-Net architecture.

Uncertainty-Aware U-Net For Medical Landmark Detection

Abstract: Heatmap-based methods play an important role in anatomical landmark detection. However, most current heatmap-based methods assume that the distributions of all landmarks are the same and the distribution of each landmark is isotropic, which may not be in line with reality. For example, the landmark on the jaw is more likely to be located along the edge and less likely to be located inside or outside the jaw. Manually annotating tends to follow similar rules, resulting in an anisotropic distribution for annotated landmarks, which represents the uncertainty in the annotation. To estimate the uncertainty, we propose a module named Pyramid Covariance Predictor to predict the covariance matrices of the target Gaussian distributions, which determine the distributions of landmarks and represent the uncertainty of landmark annotation. Specifically, the Pyramid Covariance Predictor utilizes the pyramid features extracted by the encoder of the backbone U-Net and predicts the Cholesky decomposition of the covariance matrix of the landmark location distribution. Experimental results show that the proposed Pyramid Covariance Predictor can accurately predict the distributions and improve the performance of anatomical landmark detection.

Luvli Face Alignment: Estimating Landmarks' Location, Uncertainty, And Visibility Likelihood

Abstract: Modern face alignment methods have become quite accurate at predicting the locations of facial landmarks, but they do not typically estimate the uncertainty of their predicted locations nor predict whether landmarks are visible. In this paper, we present a novel framework for jointly predicting landmark locations, associated uncertainties of these predicted locations, and landmark visibilities. We model these as mixed random variables and estimate them using a deep network trained with our proposed Location, Uncertainty, and Visibility Likelihood (LUVLi) loss. In addition, we release an entirely new labeling of a large face alignment dataset with over 19,000 face images in a full range of head poses. Each face is manually labeled with the ground-truth locations of 68 landmarks, with the additional information of whether each landmark is unoccluded, self-occluded (due to extreme head poses), or externally occluded. Not only does our joint estimation yield accurate estimates of the uncertainty of predicted landmark locations, but it also yields state-of-the-art estimates for the landmark locations themselves on multiple standard face alignment datasets. Our method's estimates of the uncertainty of predicted landmark locations could be used to automatically identify input images on which face alignment fails, which can be critical for downstream tasks.


CAER (Context Aware Emotion Recognition) #REFR is a deep Convolutional Network which consists of two stream encoding networks to separately extract the facial and context features which are fused using an adaptive fusion network.

It uses a joint embedding training approach which uses emotional keywords associated with a given image and aligns its learned text embedding (pretrained on massive text data) with image embedding extracted from a standard feature backbone (ResNet50).

To adapt EmotionNet for our task, we used word2vec #OTHEREFR for extracting text embeddings and ResNet50 for frames.

Since we use a video as input, the frame features are max-pooled to generate a single representation.

We use the proposed embedding loss and provide the emotion labels as the keywords for joint embedding training.

This learned ResNet50 is finetuned for multilabel emotion recognition where the individual frame features are max-pooled before passing to the logits layer.

Detections from our extended face tracks are used as inputs for the face encoding stream and the full video frame with masked faces was used as input to context encoding stream.

Since CAER is designed to extract emotions from images we adapt it to videos by applying max-pooling over the fused features from both the streams to generate a single representation for a video.

This adapted model is trained to predict multiple scene-level emotions.

M2FNet #OTHEREFR is a transformer based model originally developed for Emotion Recognition in Conversations (ERC) and features a fusion-attention mechanism to modulate the attention given to each utterance considering the audio and visual features.

As this model is designed for utterance emotion recognition we apply a max-pooling operation over the final outputs of fusion attention module to generate a feature representation for all the utterances in a video.

How You Feelin'? Learning Emotions And Mental States In Movie Scenes

Abstract: Movie story analysis requires understanding characters' emotions and mental states. Towards this goal, we formulate emotion understanding as predicting a diverse and multi-label set of emotions at the level of a movie scene and for each character. We propose EmoTx, a multimodal Transformer-based architecture that ingests videos, multiple characters, and dialog utterances to make joint predictions. By leveraging annotations from the MovieGraphs dataset [74], we aim to predict classic emotions (e.g. happy, angry) and other mental states (e.g. honest, helpful). We conduct experiments on the most frequently occurring 10 and 25 labels, and a mapping that clusters 181 labels to 26. Ablation studies and comparison against adapted stateof-the-art emotion recognition approaches shows the effectiveness of EmoTx. Analyzing EmoTx's self-attention scores reveals that expressive emotions often look at character tokens while other mental states rely on video and dialog cues.

Context-Aware Emotion Recognition Networks

Abstract: Traditional techniques for emotion recognition have focused on the facial expression analysis only, thus providing limited ability to encode context that comprehensively represents the emotional responses. We present deep networks for context-aware emotion recognition, called CAER-Net, that exploit not only human facial expression but also context information in a joint and boosting manner. The key idea is to hide human faces in a visual scene and seek other contexts based on an attention mechanism. Our networks consist of two sub-networks, including two-stream encoding networks to seperately extract the features of face and context regions, and adaptive fusion networks to fuse such features in an adaptive fashion. We also introduce a novel benchmark for context-aware emotion recognition, called CAER, that is more appropriate than existing benchmarks both qualitatively and quantitatively. On several benchmarks, CAER-Net proves the effect of context for emotion recognition. Our dataset is available at this http URL.


Another popular type of unstructured evidence dataset often considered is metadata which offers information complementary to textual sources #REFR .

In general, fact verification is the task of assessing the authenticity of a claim supported by a validated corpus of documents.

Fact-checking introduces objective references and external knowledge to fake news detection, which is more reliable than relying simply on news text and visual features to make judgments.

Fact verification can also increase users' awareness of precaution, which helps them try to find fact-checking information when exposed to fake news.

There are many types of fact-verified datasets, and many of them are uni-modal datasets. For example, Thorne et al.

#OTHEREFR considered Wikipedia as the source of textual evidence and annotated the sentences that support or refute each claim.

Although most uni-modal datasets focus on unstructured evidence, structured knowledge has also been used.

Some datasets consist of semi-structured data tables with the ability to convey information concisely and flexibly. Wang et al.

#OTHEREFR extracted tables from scientific articles and required evidence selection in the form of cells selected from tables.

Recently, fact verification has also begun to consider building multi-modal datasets to improve the accuracy of fake news detection.

A dataset named Mocheg #OTHEREFR consisted of textual and visual evidence and used three labels: support, refute, and NEI.

Ino At Factify 2: Structure Coherence Based Multi-Modal Fact Verification

Abstract: This paper describes our approach to the multi-modal fact verification (FACTIFY) challenge at AAAI2023. In recent years, with the widespread use of social media, fake news can spread rapidly and negatively impact social security. Automatic claim verification becomes more and more crucial to combat fake news. In fact verification involving multiple modal data, there should be a structural coherence between claim and document. Therefore, we proposed a structure coherence-based multi-modal fact verification scheme to classify fake news. Our structure coherence includes the following four aspects: sentence length, vocabulary similarity, semantic similarity, and image similarity. Specifically, CLIP and Sentence BERT are combined to extract text features, and ResNet50 is used to extract image features. In addition, we also extract the length of the text as well as the lexical similarity. Then the features were concatenated and passed through the random forest classifier. Finally, our weighted average F1 score has reached 0.8079, achieving 2nd place in FACTIFY2.

A Stylometric Inquiry Into Hyperpartisan And Fake News

Abstract: This paper reports on a writing style analysis of hyperpartisan (i.e., extremely one-sided) news in connection to fake news. It presents a large corpus of 1,627 articles that were manually fact-checked by professional journalists from BuzzFeed. The articles originated from 9 well-known political publishers, 3 each from the mainstream, the hyperpartisan left-wing, and the hyperpartisan right-wing. In sum, the corpus contains 299 fake news, 97% of which originated from hyperpartisan publishers. ::: We propose and demonstrate a new way of assessing style similarity between text categories via Unmasking---a meta-learning approach originally devised for authorship verification---, revealing that the style of left-wing and right-wing news have a lot more in common than any of the two have with the mainstream. Furthermore, we show that hyperpartisan news can be discriminated well by its style from the mainstream (F1=0.78), as can be satire from both (F1=0.81). Unsurprisingly, style-based fake news detection does not live up to scratch (F1=0.46). Nevertheless, the former results are important to implement pre-screening for fake news detectors.


Comparison With State-Of-The-Art 3D Gans

We also compute the identity similarity #REFR between the frontal view and side views of varying yaw angles under fixed lighting to evaluate the view consistency.

Except for EpiGRAF and EG3D, the real images are filtered with the matting network #OTHEREFR to remove the background. As shown in Tab.

1, LumiGAN can achieve state-of-the-art FID and KID only marginally worse than EG3D and EG3D-noBG yet much better than the relightable alternative VoluxGAN.

The difference between our lighting distribution and the unknown actual FFHQ lighting distribution is a probable cause of the slight regression from EG3D. Geometry Quality.

The geometry of the generated faces is evaluated by comparing the output normal maps with references predicted from a pretrained facial normal predictor #OTHEREFR .

We compute the normal maps' cosine similarity as well as the L1 distance of their Laplacians to measure the fidelity of the geometric details #OTHEREFR .

1, LumiGAN yields more accurate normals than non-relightable approaches thanks to the stronger geometric cues induced by the geometry-dependent physically based lighting module (also see Sec. 4.4).

An exception is VoluxGAN in the L1 Laplacian metric, which is likely biased for smooth inputs. Indeed, the qualitative comparison in Fig. 5 Figure 4 : Qualitative Comparison.

We compare our generated 3D faces lit in various conditions with 2 baselines: Shade-GAN #OTHEREFR and VoluxGAN #OTHEREFR .

ShadeGAN uses a Lambertian lighting model and is limited to simple directional lights with changing hues, while VoluxGAN and LumiGAN use the environment maps shown in the last row.

Lumigan: Unconditional Generation Of Relightable 3D Human Faces

Abstract: Unsupervised learning of 3D human faces from unstructured 2D image data is an active research area. While recent works have achieved an impressive level of photorealism, they commonly lack control of lighting, which prevents the generated assets from being deployed in novel environments. To this end, we introduce LumiGAN, an unconditional Generative Adversarial Network (GAN) for 3D human faces with a physically based lighting module that enables relighting under novel illumination at inference time. Unlike prior work, LumiGAN can create realistic shadow effects using an efficient visibility formulation that is learned in a selfsupervised manner. LumiGAN generates plausible physical properties for relightable faces, including surface normals, diffuse albedo, and specular tint without any ground truth data. In addition to relightability, we demonstrate significantly improved geometry generation compared to state-ofthe-art non-relightable 3D GANs and notably better photorealism than existing relightable GANs.

Arcface: Additive Angular Margin Loss For Deep Face Recognition

Abstract: One of the main challenges in feature learning using Deep Convolutional Neural Networks (DCNNs) for large-scale face recognition is the design of appropriate loss functions that can enhance the discriminative power. Centre loss penalises the distance between deep features and their corresponding class centres in the Euclidean space to achieve intra-class compactness. SphereFace assumes that the linear transformation matrix in the last fully connected layer can be used as a representation of the class centres in the angular space and therefore penalises the angles between deep features and their corresponding weights in a multiplicative way. Recently, a popular line of research is to incorporate margins in well-established loss functions in order to maximise face class separability. In this paper, we propose an Additive Angular Margin Loss (ArcFace) to obtain highly discriminative features for face recognition. The proposed ArcFace has a clear geometric interpretation due to its exact correspondence to geodesic distance on a hypersphere. We present arguably the most extensive experimental evaluation against all recent state-of-the-art face recognition methods on ten face recognition benchmarks which includes a new large-scale image database with trillions of pairs and a large-scale video dataset. We show that ArcFace consistently outperforms the state of the art and can be easily implemented with negligible computational overhead. To facilitate future research, the code has been made available.


The proposed neural network uses ADAM #REFR as the solver for weight optimization, along with a constant learning rate of 0.001.

The output is a one-hot-encoder vector that determines the probability of a word to be complex or simple, i.e, the vector [1 0] means the word is simple, while the vector [0 1] means the word is complex. The hidden layer employs ReLU as the activation function.

The ReLU activation function used is defined as the positive part of its argument (Equation 1).

We choose this activation function as it solves the vanishing gradient problem #OTHEREFR .

The final layer contains 2 units for classifying the words into complex or simple. These units employ the Sigmoid activation function (Equation 2).

The Sigmoid function is used to predict probabilities that exist in the range [0, 1], mapping the output of the model to the on-hot-encoder class representation.

The ADAM optimizer is a good choice for sparse gradients as it is invariant to their diagonal rescale #OTHEREFR .

Simplex: A Lexical Text Simplification Architecture

Abstract: Text simplification (TS) is the process of generating easy-to-understand sentences from a given sentence or piece of text. The aim of TS is to reduce both the lexical (which refers to vocabulary complexity and meaning) and syntactic (which refers to the sentence structure) complexity of a given text or sentence without the loss of meaning or nuance. In this paper, we present SIMPLEX, a novel simplification architecture for generating simplified English sentences. To generate a simplified sentence, the proposed architecture uses either word embeddings (i.e., Word2Vec) and perplexity, or sentence transformers (i.e., BERT, RoBERTa, and GPT2) and cosine similarity. The solution is incorporated into a user-friendly and simple-to-use software. We evaluate our system using two metrics, i.e., SARI, and Perplexity Decrease. Experimentally, we observe that the transformer models outperform the other models in terms of the SARI score. However, in terms of Perplexity, the Word-Embeddings-based models achieve the biggest decrease. Thus, the main contributions of this paper are: (1) We propose a new Word Embedding and Transformer based algorithm for text simplification; (2) We design SIMPLEX-a modular novel text simplification system-that can provide a baseline for further research; and (3) We perform an in-depth analysis of our solution and compare our results with two state-of-the-art models, i.e., LightLS [19] and NTS-w2v [44]. We also make the code publicly available online.

Adam: A Method For Stochastic Optimization

Abstract: We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.

proposed neural network
constant learning rate


Approaches using a single network for object detection and tracking bounding boxes, such as CenterTrack #REFR , either work offline or lack the ability to reconnect long-range tracks.

Therefore, it is critical to include such examples in the training of the tracking model.

In contrast, multitask methods train object detection and tracking networks jointly.

This is achieved by extracting REID features from a shared backbone #OTHEREFR and extending the bounding box prediction head with a single object tracking capability to regress the object location from the previous frame to the current frame #OTHEREFR , #OTHEREFR .

However, the best-performing methods on MOT benchmarks, and especially self-supervised learning (SSL) methods, treat object detection and REID feature estimation as two separate tasks, presumably since they can tailor the network architecture to each task and handle scale variations in the REID task by rescaling the image crops used for extracting tracking features #OTHEREFR .

Nevertheless, the self-supervised tracking setting requires a pre-trained object detector.

The latter scenarios require more advanced tracking logic accompanied by memory.

With the rise of query-based object detectors, such as DETR #OTHEREFR , #OTHEREFR , the tracking by query propagation paradigm #OTHEREFR , #OTHEREFR - #OTHEREFR has emerged.

These methods learn to track queries to recall the same instance across different frames in online or offline settings.

However, there are no identity-free approaches to train such models in the literature, which we could compare against our proposed method.

Self-supervised MOT: Self-Supervised Multi-Object Tracking (SSMOT) methods learn the association of object proposals without annotated track identities.

Self-Supervised Multi-Object Tracking From Consistency Across Timescales

Abstract: Self-supervised multi-object trackers have the potential to leverage the vast amounts of raw data recorded worldwide. However, they still fall short in re-identification accuracy compared to their supervised counterparts. We hypothesize that this deficiency results from restricting self-supervised objectives to single frames or frame pairs. Such designs lack sufficient visual appearance variations during training to learn consistent re-identification features. Therefore, we propose a training objective that learns re-identification features over a sequence of frames by enforcing consistent association scores across short and long timescales. Extensive evaluations on the BDD100K and MOT17 benchmarks demonstrate that our learned ReID features significantly reduce ID switches compared to other self-supervised methods, setting the new state of the art for selfsupervised multi-object tracking and even performing on par with supervised methods on the BDD100k benchmark.

Tracking Objects As Points

Abstract: Tracking has traditionally been the art of following interest points through space and time. This changed with the rise of powerful deep networks. Nowadays, tracking is dominated by pipelines that perform object detection followed by temporal association, also known as tracking-by-detection. In this paper, we present a simultaneous detection and tracking algorithm that is simpler, faster, and more accurate than the state of the art. Our tracker, CenterTrack, applies a detection model to a pair of images and detections from the prior frame. Given this minimal input, CenterTrack localizes objects and predicts their associations with the previous frame. That's it. CenterTrack is simple, online (no peeking into the future), and real-time. It achieves 67.3% MOTA on the MOT17 challenge at 22 FPS and 89.4% MOTA on the KITTI tracking benchmark at 15 FPS, setting a new state of the art on both datasets. CenterTrack is easily extended to monocular 3D tracking by regressing additional 3D attributes. Using monocular video input, it achieves 28.3% AMOTA@0.2 on the newly released nuScenes 3D tracking benchmark, substantially outperforming the monocular baseline on this benchmark while running at 28 FPS.


Bidirectional Encoder Representations from Transformers (BERT) models #REFR is a stack of n encoders from the Transformer architecture.

• Q (query) is a matrix that represents the input in which the attention mechanism focuses on.

• K (key) is a matrix that represents the different elements in the input that the attention mechanism can attend to.

• V (value) is a matrix that represents the output of the attention mechanism.

• d k is the dimension of matrices Q and V and allows to stabilize the model during the training phase Hence, each head has its own set of parameters, allowing the model to learn different types of attention patterns.

The attentions resulting from each of the heads are then concatenated and projected on a dense layer.

Each BERT layer contains h attention heads with its own set of weights, which have been learned during training.

These weights determine how the model will attend to different parts of the input when making a prediction.

In this way, words are related to each other even in the case of long-term dependency.

BERT has been widely adopted and has achieved state-of-the-art performance on a variety of benchmarks such as GLUE #OTHEREFR for natural language understanding (NLU).

One of the key features of BERT is its bidirectional nature.

Abstract: Attention mechanisms have played a crucial role in the development of complex architectures such as Transformers in natural language processing. However, Transformers remain hard to interpret and are considered as black-boxes. This paper aims to assess how attention coefficients from Transformers can help in providing interpretability. A new attention-based interpretability method called CLaSsification-Attention (CLS-A) is proposed. CLS-A computes an interpretability score for each word based on the attention coefficient distribution related to the part specific to the classification task within the Transformer architecture. A human-grounded experiment is conducted to evaluate and compare CLS-A to other interpretability methods. The experimental protocol relies on the capacity of an interpretability method to provide explanation in line with human reasoning. Experiment design includes measuring reaction times and correct response rates by human subjects. CLS-A performs comparably to usual interpretability methods regarding average participant reaction time and accuracy. The lower computational cost of CLS-A compared to other interpretability methods and its availability by design within the classifier make it particularly interesting. Data analysis also highlights the link between the probability score of a classifier prediction and adequate explanations. Finally, our work confirms the relevancy of the use of CLS-A and shows to which extent self-attention contains rich information to explain Transformer classifiers.

Bert: Pre-Training Of Deep Bidirectional Transformers For Language Understanding

Abstract: We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. ::: BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).

Transformers (BERT) models
Bidirectional Encoder Representations


Sparse And Dense Point Cloud Comparison.

From the results, we can see, that for the Blender dataset #REFR case, the dense model is much better.

However, dense point clouds may not be required to achieve good reconstructions.

In this section, we compare models trained on dense reconstructions and sparse reconstructions.

Table 5 compares PSNR, SSIM metrics on two synethetic scenes from the Blender dataset #OTHEREFR and two real-world scenes from the mip-NeRF 360 dataset #OTHEREFR .

We also show the number of vertices of the tetrahedra which is the size of the input point cloud, including the random points added at the beginning.

Note, that in this experiment we only trained for 100, 000 iterations to save computation time.

This is to be expected since the number of points obtained from the sparse reconstruction on the Blender dataset is very small and our MLP does not have enough capacity to represent fine details.

However, even in regions with zero point coverage, we are still able to provide at least some low-frequency data (c.f . Fig. 7 ).

On the real-world scenes, the sparse point cloud model is almost on par with the dense one.

The sparse reconstructions on the real-world dataset provide many more points compared to the synthetic dataset case and the coverage is sufficient.

From these results, we conclude that on real-world data the sparse model can be sufficient to achieve a good performance.

Tetra-Nerf: Representing Neural Radiance Fields Using Tetrahedra

Abstract: 2) scene representation: dense tetrahedra 3) barycetric interpolation of features 4) shallow MLP predicts density and colour Figure 1. The input to Tetra-NeRF is a point cloud which is triangulated to get a set of tetrahedra used to represent the radiance field. Rays are sampled, and the field is queried. The barycentric interpolation is used to interpolate tetrahedra vertices, and the resulting features are passed to a shallow MLP to get the density and colours for volumetric rendering.

Nerf: Representing Scenes As Neural Radiance Fields For View Synthesis

Abstract: We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully-connected (non-convolutional) deep network, whose input is a single continuous 5D coordinate (spatial location $(x,y,z)$ and viewing direction $(\theta, \phi)$) and whose output is the volume density and view-dependent emitted radiance at that spatial location. We synthesize views by querying 5D coordinates along camera rays and use classic volume rendering techniques to project the output colors and densities into an image. Because volume rendering is naturally differentiable, the only input required to optimize our representation is a set of images with known camera poses. We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis. View synthesis results are best viewed as videos, so we urge readers to view our supplementary video for convincing comparisons.


Similarly, it is observed the proposed designs achieve better AoI performance than the equal time/bandwidth allocation schemes as in #REFR .

Moreover, it shows that the TDMA transmission scheme achieves lower AoI than the FDMA scheme.

This is as expected because in the former case the SNR of the currently scheduled device can be maximized via dynamically adjusted IRS passive beamforming, which thus achieves lower PER for each device.

It also should be noted that as the number of IRS elements increases, the performance gap between our proposed design and the benchmarks becomes smaller and then approaches zero.

This is because as M increases, the passive beamforming gain and thus the SNR of each packet is sufficiently large for approximately achieving error-free transmission, which renders the optimization of time/bandwidth allocation unnecessary.

Fig.3(b) shows the maximum average AoI versus the total bandwidth, B.

More interestingly, it shows that for the case when the total bandwidth is small, the proposed (c) The maximum average AoI versus the length of information bits.

FDMA transmission scheme even outperforms the proposed TDMA one, i.e., the latter is not always more favourable as shown in Fig. 3(a) .

This is because when frequency resource is extremely limited, optimizing the bandwidth allocation is more efficient than dynamically adjusting the passive beamforming for improving the performance of the worst user.

However, as the bandwidth increases, such performance gain becomes marginal, while the time-varying passive beamforming associated with each scheduled user is more useful, and thus the proposed TDMA transmission scheme outperforms the FDMA one.

Even more, as B increases up to sufficiently large (e.g., 1000 KHz as in the considered setup), both benchmark schemes achieve almost the same AoI performance as our proposed design.

Resource Allocation And Passive Beamforming For Irs-Assisted Urllc Systems

Abstract: In this correspondence, we investigate an intelligent reflective surface (IRS) assisted downlink ultra-reliable and lowlatency communication (URLLC) system, where an access point (AP) sends short packets to multiple devices with the help of an IRS. Specifically, a performance comparison between the frequency division multiple access (FDMA) and time division multiple access (TDMA) is conducted for the considered system, from the perspective of average age of information (AoI). Aiming to minimize the maximum average AoI among all devices by jointly optimizing the resource allocation and passive beamforming. However, the formulated problem is difficult to solve due to the non-convex objective function and coupled variables. Thus, we propose an alternating optimization based algorithm by dividing the original problem into two sub-problems which can be efficiently solved. Simulation results show that TDMA can achieve lower AoI by exploiting the time-selective passive beamforming of IRS for maximizing the signal to noise ratio (SNR) of each device consecutively. Moreover, it also shows that as the length of information bits becomes sufficiently large as compared to the available bandwidth, the proposed FDMA transmission scheme becomes more favorable instead, due to the more effective utilization of bandwidth.

Intelligent Reflecting Surface-Assisted Multiple Access With User Pairing: Noma Or Oma?

Abstract: The integration of intelligent reflecting surface (IRS) to multiple access networks is a cost-effective solution for boosting spectrum/energy efficiency and enlarging network coverage/ connections. However, due to the new capability of IRS in reconfiguring the wireless propagation channels, it is fundamentally unknown which multiple access scheme is superior in the IRSassisted wireless network. In this letter, we pursue a theoretical performance comparison between non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) in the IRSassisted downlink communication, for which the transmit power minimization problems are formulated under the discrete unitmodulus reflection constraint on each IRS element. We analyze the minimum transmit powers required by different multiple access schemes and compare them numerically, which turn out to not fully comply with the stereotyped superiority of NOMA over OMA in conventional systems without IRS. Moreover, to avoid the exponential complexity of the brute-force search for the optimal discrete IRS phase shifts, we propose a low-complexity solution to achieve near-optimal performance.