The first application of GANs to super-resolution was in SRGAN, which outperformed prior methods and achieved state-of-the-art results #REFR .

The authors attributed this, in part, to their use of a perceptual loss function that accounted for perceptual similarity instead of just similarity in pixel space.

At that time, a common problem with superresolution was the presence of artifacts when upsampling.

ESRGAN addressed this issue by identifying that batch normalization layers tended to create unwanted artifacts #OTHEREFR .

They also improved the perceptual loss function and used Residual-in-Residual Dense Blocks to generate more realistic images consistently.

Additionally, they later proposed REAL-ESRGAN, which incorporated a u-net discriminator and spectral normalization #OTHEREFR .

Toward Super-Resolution For Appearance-Based Gaze Estimation

Abstract: Gaze tracking is a valuable tool with a broad range of applications in various fields, including medicine, psychology, virtual reality, marketing, and safety. Therefore, it is essential to have gaze tracking software that is cost-efficient and high-performing. Accurately predicting gaze remains a difficult task, particularly in real-world situations where images are affected by motion blur, video compression, and noise. Super-resolution has been shown to improve image quality from a visual perspective. This work examines the usefulness of super-resolution for improving appearancebased gaze tracking. We show that not all SR models preserve the gaze direction. We propose a two-step framework based on SwinIR super-resolution model. The proposed method consistently outperforms the state-of-the-art, particularly in scenarios involving low-resolution or degraded images. Furthermore, we examine the use of superresolution through the lens of self-supervised learning for gaze prediction. Self-supervised learning aims to learn from unlabelled data to reduce the amount of required labeled data for downstream tasks. We propose a novel architecture called "SuperVision" by fusing an SR backbone network to a ResNet18 (with some skip connections). The proposed SuperVision method uses 5x less labeled data and yet outperforms, by 15%, the state-of-the-art method of GazeTR which uses 100% of training data. We will make our code publicly available upon publication.

Photo-Realistic Single Image Super-Resolution Using A Generative Adversarial Network

Abstract: Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large upscaling factors? The behavior of optimization-based super-resolution methods is principally driven by the choice of the objective function. Recent work has largely focused on minimizing the mean squared reconstruction error. The resulting estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details and are perceptually unsatisfying in the sense that they fail to match the fidelity expected at the higher resolution. In this paper, we present SRGAN, a generative adversarial network (GAN) for image super-resolution (SR). To our knowledge, it is the first framework capable of inferring photo-realistic natural images for 4x upscaling factors. To achieve this, we propose a perceptual loss function which consists of an adversarial loss and a content loss. The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images. In addition, we use a content loss motivated by perceptual similarity instead of similarity in pixel space. Our deep residual network is able to recover photo-realistic textures from heavily downsampled images on public benchmarks. An extensive mean-opinion-score (MOS) test shows hugely significant gains in perceptual quality using SRGAN. The MOS scores obtained with SRGAN are closer to those of the original high-resolution images than to those obtained with any state-of-the-art method.


Training details: We use the base version of the pre-trained language model BERT #REFR and RoBERTa as our backbone models.

For CLAIF, we use unpaired sentences from the training set of STS-B as original sentences to construct sentence pairs from scratch and randomly sample two other sentences for each original sentence to construct two sentence pairs with a similarity score of 0.

For CLHAIF, following previous studies #OTHEREFR , we use the SNLI and MNLI datasets to construct sentence pairs and add a AI feedback similarity score for each sentence pair.

We only use the AI feedback scores for positive pairs in our experiments of CLHAIF.

Besides, to demonstrate the scalability of CLAIF, we use sentence pairs constructed from STS-B and from NLI datasets for the training of CLAIF, which we called CLAIF scaled .

We list statistics of some datasets used for different methods in Table 2 .

We use the development set of STS-B as our validation set.

In CLAIF, we use the mean pooling strategy to get sentence embeddings for BERT and RoBERTa.

For CLHAIF, we take the same pooling strategy as the corresponding baseline. Other implementation details are in Appendix A.

Improving Contrastive Learning Of Sentence Embeddings From Ai Feedback

Abstract: Contrastive learning has become a popular approach in natural language processing, particularly for the learning of sentence embeddings. However, the discrete nature of natural language makes it difficult to ensure the quality of positive and negative sample pairs generated through data augmentation methods. Although supervised contrastive learning can produce more accurate sample pairs with human feedback labels, it still lacks fine-grained training signals. In this paper, we propose to improve Contrastive Learning of sentence embeddings from AI Feedback (CLAIF). Our method utilizes AI feedback from large pretrained language models (LLMs) to construct sample pairs with fine-grained sample similarity scores to improve contrastive learning. Besides, we combine human feedback and AI feedback to provide better supervision signals for supervised contrastive learning of sentence embeddings. Experimental results show that our method achieves state-of-the-art performance on several semantic textual similarity (STS) and transfer learning tasks compared to other unsupervised and supervised contrastive learning methods. 1

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).


Using notations in #REFR , a search problem P is a binary relation, and we say that S is a solution to instance x iff (x, S) ∈ P .

Following notations from [AKT20a], a Min-Cut data structure for a graph family F is a data structure that given a graph G ∈ F, after a preprocessing phase which makes t pq (m) Max-Flow calls on G and spends t po (m) time outside of these calls, can answer Min-Cut queries for any two nodes s, t ∈ V in amortized query time (or output sensitive time) t mc (k st ), where k st denotes the output size (number of edges or nodes in the output cut or separator).

In particular, it means that the preprocessing time is at most t pq (m) •Ô(m) + t po (m) using [CKL + 22], and the query time is O(k st • t mc (k st )).

We may also be interested in Min-Cut data structures that support queries of Max-Flow values instead of cuts, and we denote their query time by t mf (m).

Let SOL(x) = {S : (x, S) ∈ P } denote the set of solutions for instance x.

We say that P is a total function if every instance x has at least one solution, i.e. SOL(x) = ∅.

Let ⊥ be the "don't know" symbol and assume ⊥ / ∈ SOL(x) for all x.

In particular, in our context of Min-Cut data structures, x is a graph, and SOL(x) is the set of all Min-Cut data structures with t mc (m) =Ô(1) for x where m is the number of edges in x.

Next, we define the nondeterministic complexity of a total function.

(Almost) Ruling Out Seth Lower Bounds For All-Pairs Max-Flow

Abstract: The All-Pairs Max-Flow problem has gained significant popularity in the last two decades, and many results are known regarding its fine-grained complexity. Despite this, wide gaps remain in our understanding of the time complexity for several basic variants of the problem. In this paper, we aim to bridge this gap by providing algorithms, conditional lower bounds, and non-reducibility results. Notably, we show that for most problem settings, deterministic reductions based on the Strong Exponential Time Hypothesis (SETH) cannot rule out n 4−o(1) time algorithms under a hypothesis called NSETH. We present results for the following two cases, where some of the bounds assume that the matrix multiplication exponent ω = 2. Node-Capacities. For directed graphs with general node-capacities, we show that for any fixed ε > 0, a deterministic SETH-based reduction cannot rule out O(n 3+ε) time algorithms under NSETH, which is tight with the known conditional lower bound of n 3−o(1) devised for sparse graphs under SETH [Abboud et al., ToC 2021]. The same non-reducibility bound immediately applies to undirected graphs with unit node-capacities, which can be compared to an m 3/2−o(1) + n 2 SETH-based lower bound following from a combination of previous works. Edge-Capacities. For directed graphs with unit edge-capacities, we similarly rule out an √ m • n 5/2+ε−o(1) SETH-based lower bound under NSETH, which can be contrasted with the known mn 1−o(1) SETH-based lower bound [Krauthgamer and Trabelsi, TALG 2018]. While it remains open whether deterministic SETH-based reductions can rule out an n 4−o(1) time algorithm for general edge-capacities, a consequence of our results is that a subquartic fine-grained reduction from the directed edge-capacitated setting to the directed node-capacitated setting would rule out such deterministic SETH-based reductions under NSETH. As a step towards ruling out even mn 1+ε−o(1) SETH lower bounds for undirected graphs with unit node-capacities, we design a new randomized O m 2+o(1) time combinatorial algorithm. This is an improvement over the recent O m 11/5+o(1) time algorithm [Huang et al., STOC 2023] and matching their m 2−o(1) lower bound (up to subpolynomial factors), thus essentially settling the time complexity for this setting of the problem. More generally, our main technical contribution is the insight that st-cuts can be verified quickly, and that in most settings, st-flows can be shipped succinctly (i.e., with respect to the flow support). This is a key idea in our non-reducibility results, and it may be of independent interest.

New Algorithms And Lower Bounds For All-Pairs Max-Flow In Undirected Graphs

Abstract: We investigate the time-complexity of the All-Pairs Max-Flow problem: Given a graph with $n$ nodes and $m$ edges, compute for all pairs of nodes the maximum-flow value between them. If Max-Flow (the version with a given source-sink pair $s,t$) can be solved in time $T(m)$, then an $O(n^2) \cdot T(m)$ is a trivial upper bound. But can we do better? ::: For directed graphs, recent results in fine-grained complexity suggest that this time bound is essentially optimal. In contrast, for undirected graphs with edge capacities, a seminal algorithm of Gomory and Hu (1961) runs in much faster time $O(n)\cdot T(m)$. Under the plausible assumption that Max-Flow can be solved in near-linear time $m^{1+o(1)}$, this half-century old algorithm yields an $nm^{1+o(1)}$ bound. Several other algorithms have been designed through the years, including $\tilde{O}(mn)$ time for unit-capacity edges (unconditionally), but none of them break the $O(mn)$ barrier. Meanwhile, no super-linear lower bound was shown for undirected graphs. ::: We design the first hardness reductions for All-Pairs Max-Flow in undirected graphs, giving an essentially optimal lower bound for the node-capacities setting. For edge capacities, our efforts to prove similar lower bounds have failed, but we have discovered a surprising new algorithm that breaks the $O(mn)$ barrier for graphs with unit-capacity edges! Assuming $T(m)=m^{1+o(1)}$, our algorithm runs in time $m^{3/2 +o(1)}$ and outputs a cut-equivalent tree (similarly to the Gomory-Hu algorithm). Even with current Max-Flow algorithms we improve state-of-the-art in many density regimes.


This rate is at par with the convergence rate of the continuous-time model of Nesterov's method #REFR .

The Schrödinger equation describing QHD is often too complicated to solve analytically.

Fortunately, we can find a closed-form solution of QHD when f is a quadratic function.

In the following calculation, we consider the one-dimensional case f (x) = 1 2 x 2 for simplicity.

It is worth noting that the same method also applies to general finite-dimensional quadratic forms f (x) = 1 2 x T Ax with a positive semidefinite matrix A #OTHEREFR .

It turns out that, if we choose the same time-dependent parameters as in Nesterov's accelerated gradient descent, the convergence rate is E[f ] ∼Ψt = O(t −3 ).

Our result does not mean QHD can not achieve faster convergence for quadratic objective functions.

In fact, one can use a linear function for β t , and the convergence rate can be exponentially fast.

Here, our goal is to compare QHD to the classical ODE model of Nesterov's method.

We choose the following time-dependent parameters in the QHD Hamiltonian (A.23):

Quantum Hamiltonian Descent *

Abstract: Gradient descent is a fundamental algorithm in both theory and practice for continuous optimization. Identifying its quantum counterpart would be appealing to both theoretical and practical quantum applications. A conventional approach to quantum speedups in optimization relies on the quantum acceleration of intermediate steps of classical algorithms, while keeping the overall algorithmic trajectory and solution quality unchanged. We propose Quantum Hamiltonian Descent (QHD), which is derived from the path integral of dynamical systems referring to the continuous-time limit of classical gradient descent algorithms, as a truly quantum counterpart of classical gradient methods where the contribution from classically-prohibited trajectories can significantly boost QHD's performance for non-convex optimization. Moreover, QHD is described as a Hamiltonian evolution efficiently simulatable on both digital and analog quantum computers. By embedding the dynamics of QHD into the evolution of the socalled Quantum Ising Machine (including D-Wave and others), we empirically observe that the D-Wave-implemented QHD outperforms a selection of state-of-the-art gradient-based classical solvers and the standard quantum adiabatic algorithm, based on the time-to-solution metric, on non-convex constrained quadratic programming instances up to 75 dimensions. Finally, we propose a "three-phase picture" to explain the behavior of QHD, especially its difference from the quantum adiabatic algorithm.

A Differential Equation For Modeling Nesterov'S Accelerated Gradient Method: Theory And Insights

Abstract: We derive a second-order ordinary differential equation (ODE) which is the limit of Nesterov's accelerated gradient method. This ODE exhibits approximate equivalence to Nesterov's scheme and thus can serve as a tool for analysis. We show that the continuous time ODE allows for a better understanding of Nesterov's scheme. As a byproduct, we obtain a family of schemes with similar convergence rates. The ODE interpretation also suggests restarting Nesterov's scheme leading to an algorithm, which can be rigorously proven to converge at a linear rate whenever the objective is strongly convex.


One such approach to solving this maximization would be the straight-forward application of projected gradient descent (PGD) #REFR .

In order to achieve robustness to this class of perturbations we use a min-max optimization approach common in the adversarial robustness literature, but modified for our problem setting:

We use × to denote element-wise product throughout.

The above formulation uses a weighting α to trade off between the standard task loss, the first term, and the adversarial loss incurred by the neural network being non-robust in the human specified shortcut directions, the second term.

We can leverage the many advances in adversarial robustness in order to approximately solve the maximization problem posed in the second half of our loss term.

Given an input, x, a mask m, and a positive value κ, PGD uses a first-order optimization approach to arrive at an input x * that approximately solves the maximization in the second term of our loss.

We refer to the approach of using PGD in our loss formulation as PGD-Ex.

Given the non-convexity of this maximization, however, no guarantees can be made about the quality of the approximate solution x * .

Reliance on a local attack method in our optimization has the potential to bias our approaches to the specific attack chosen, as observed in the adversarial robustness literature #OTHEREFR .

Both local explanations and local adversarial attacks share this weakness.

Robust Learning From Explanations

Abstract: Machine learning from explanations (MLX) is an approach to learning that uses human-provided annotations of relevant features for each input to ensure that model predictions are right for the right reasons. Existing MLX approaches rely heavily on a specific model interpretation approach and require strong parameter regularization to align model and human explanations, leading to suboptimal performance. We recast MLX as an adversarial robustness problem, where human explanations specify a lower dimensional manifold from which perturbations can be drawn, and show both theoretically and empirically how this approach alleviates the need for strong parameter regularization. We consider various approaches to achieving robustness, leading to improved performance over prior MLX methods. Finally, we combine robustness with an earlier MLX method, yielding state-of-the-art results on both synthetic and real-world benchmarks.

Towards Deep Learning Models Resistant To Adversarial Attacks

Abstract: Recent work has demonstrated that neural networks are vulnerable to adversarial examples, i.e., inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of deep learning models. To address this problem, we study the adversarial robustness of neural networks through the lens of robust optimization. This approach provides us with a broad and unifying view on much of the prior work on this topic. Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal. In particular, they specify a concrete security guarantee that would protect against any adversary. These methods let us train networks with significantly improved resistance to a wide range of adversarial attacks. They also suggest the notion of security against a first-order adversary as a natural and broad security guarantee. We believe that robustness against such well-defined classes of adversaries is an important stepping stone towards fully resistant deep learning models.


mR@100 on the SGDet setting for head, body, and tail classes.

† denotes that the bi-level sampling is applied on the model to achieve these results. Bold numbers indicate the best performances.

Devil'S On The Edges: Selective Quad Attention For Scene Graph Generation

Abstract: Scene graph generation aims to construct a semantic graph structure from an image such that its nodes and edges respectively represent objects and their relationships. One of the major challenges for the task lies in the presence of distracting objects and relationships in images; contextual reasoning is strongly distracted by irrelevant objects or backgrounds and, more importantly, a vast number of irrelevant candidate relations. To tackle the issue, we propose the Selective Quad Attention Network (SQUAT) that learns to select relevant object pairs and disambiguate them via diverse contextual interactions. SQUAT consists of two main components: edge selection and quad attention. The edge selection module selects relevant object pairs, i.e., edges in the scene graph, which helps contextual reasoning, and the quad attention module then updates the edge features using both edge-to-node and edge-to-edge cross-attentions to capture contextual information between objects and object pairs. Experiments demonstrate the strong performance and robustness of SQUAT, achieving the state of the art on the Visual Genome and Open Images v6 benchmarks.

Scene Graph Generation From Objects, Phrases And Region Captions

Abstract: Object detection, scene graph generation and region captioning, which are three scene understanding tasks at different semantic levels, are tied together: scene graphs are generated on top of objects detected in an image with their pairwise relationship predicted, while region captioning gives a language description of the objects, their attributes, relations, and other context information. In this work, to leverage the mutual connections across semantic levels, we propose a novel neural network model, termed as Multi-level Scene Description Network (denoted as MSDN), to solve the three vision tasks jointly in an end-to-end manner. Objects, phrases, and caption regions are first aligned with a dynamic graph based on their spatial and semantic connections. Then a feature refining structure is used to pass messages across the three levels of semantic tasks through the graph. We benchmark the learned model on three tasks, and show the joint learning across three tasks with our proposed method can bring mutual improvements over previous models. Particularly, on the scene graph generation task, our proposed method outperforms the state-of-art method with more than 3% margin.


For the training of patch embedder g(x), we use an initial learning rate of 1e-5 with Adam #REFR optimizer for 10000 iterations with batch size being 100.

For Camelyon16, we tile the WSIs into 256×256 patches on 20× magnification using the official code of #OTHEREFR , while for the HCC dataset the patches are 384×384 on 40× magnification following the pathologists' advice.

For both datasets, we use an ImageNet pre-trained ResNet50 7 to initialize g(x) (except DS-MIL #OTHEREFR ).

The instance embedding process is the same of #OTHEREFR , which means for each patch, it will be firstly embedded into a 1024-dimension vector, and then be projected to a 512-dimension hidden space for further bag-level training.

For the training of bag classifier f (x), we use an initial learning rate of 2e-4 with Adam #OTHEREFR optimizer for 200 epochs with batch size being 1.

Camelyon16 results are reported on the official test split, while the HCC dataset uses a 7:1:2 split for training, validation and test.

Three metrics are used for evaluation, namely area under curve (AUC), F1 score, and slide-level accuracy (Acc).

Experiments are all conducted on a Nvidia Tesla M40 (12GB).

Iteratively Coupled Multiple Instance Learning From Instance To Bag Classifier For Whole Slide Image Classification

Abstract: Whole Slide Image (WSI) classification remains a challenge due to their extremely high resolution and the absence of fine-grained labels. Presently, WSIs are usually classified as a Multiple Instance Learning (MIL) problem when only slide-level labels are available. MIL methods involve a patch embedding process and a bag-level classification process, but they are prohibitively expensive to be trained end-to-end. Therefore, existing methods usually train them separately, or directly skip the training of the embedder. Such schemes hinder the patch embedder's access to slide-level labels, resulting in inconsistencies within the entire MIL pipeline. To overcome this issue, we propose a novel framework called Iteratively Coupled MIL (ICMIL), which bridges the loss back-propagation process from the bag-level classifier to the patch embedder. In ICMIL, we use category information in the bag-level classifier to guide the patch-level fine-tuning of the patch feature extractor. The refined embedder then generates better instance representations for achieving a more accurate bag-level classifier. By coupling the patch embedder and bag classifier at a low cost, our proposed framework enables information exchange between the two processes, benefiting the entire MIL classification model. We tested our framework on two datasets using three different backbones, and our experimental results demonstrate consistent performance improvements over state-of-the-art MIL methods. Code will be made available upon acceptance.

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.


Second, applying NLP methods to learn a question answering model usually requires a large-scale training set #REFR .

Question answering is an important topic in the field of natural language processing, and many works have been proposed recently #OTHEREFR .

However, there are some challenges in using NLP algorithms for log question answering.

First, there exists a domain shift between general natural language and log data.

Log data includes domain-specific symbols, such as IP addresses and modular identifiers #OTHEREFR .

General NLP techniques consider these symbols to be out-of-domain terms and replace them with a special token, however they are crucial for the log domain and cannot be ignored.

However, to the best of our knowledge, there is no public question answering dataset.

It is difficult to implement an effective question answering system with limited data.

In this work, we propose a question answering system for unstructured logs, namely LogQA, which aims to answer questions in the form of natural language based on large-scale unstructured log corpus.

LogQA has two key components: Log Retriever and Log Reader. Log Retriever retrieves some relevant and helpful raw logs.

The goal of Log Reader is to predict exact answers based on retrieved logs.

Logqa: Question Answering In Unstructured Logs

Abstract: Modern systems produce a large volume of logs to record run-time status and events. System operators use these raw logs to track a system in order to obtain some useful information to diagnose system anomalies. One of the most important problems in this area is to help operators find the answers to log-based questions efficiently and user-friendly. In this work, we propose LogQA, which aims at answering logbased questions in the form of natural language based on largescale unstructured log corpora. Our system presents the answer to a question directly instead of returning a list of relevant snippets, thus offering better user-friendliness and efficiency. LogQA represents the first approach to solve question answering in lod domain. LogQA has two key components: Log Retriever and Log Reader. Log Retriever aims at retrieving relevant logs w.r.t. a given question, while Log Reader is responsible for inferring the final answer. Given the lack of a public dataset for log questing answering, we manually labelled a QA dataset of three open-source log corpus and will make them publicly available. We evaluated our proposed model on these datasets by comparing its performance with 6 other baseline methods. Our experimental results demonstrate that LogQA has outperformed other baseline methods.


Stable learning #REFR learns a set of global sample weights that could remove the confounding bias for all the potential treatments from data distribution.

Distribution shift in supervised learning.

Distribution shift problem has been studied in many literature #OTHEREFR .

Most works aim to learn a representation that performs well on different source domains simultaneously #OTHEREFR Mahajan et al., 2021; #OTHEREFR , following the idea of causal invariance #OTHEREFR Arjovsky et al., 2019) .

Structural equation models are often assumed for theoretical analysis #OTHEREFR Mahajan et al., 2021) .

Distributionally robust optimization optimizes a model's worst-case performance over some uncertainty set directly (Krueger et al., 2021; #OTHEREFR .

Disentangled representation learning #OTHEREFR Träuble et al., 2021; #OTHEREFR aims to learn representations where distinct and informative factors of variations in data are separated.

Theoretical understanding of contrastive learning.

A number of recent works also aim to theoretically explain the success of contrastive learning in IID settings.

One way to explain it is through the mutual information between positive samples #OTHEREFR . #OTHEREFR

(2019) directly analyze the generalization of InfoNCE loss based on the assumption that positive samples are drawn from the same latent classes. In the same setting, #OTHEREFR

Abstract: Self-Supervised Learning (SSL) is a paradigm that leverages unlabeled data for model training. Empirical studies show that SSL can achieve promising performance in distribution shift scenarios, where the downstream and training distributions differ. However, the theoretical understanding of its transferability remains limited. In this paper, we develop a theoretical framework to analyze the transferability of self-supervised contrastive learning, by investigating the impact of data augmentation on it. Our results reveal that the downstream performance of contrastive learning depends largely on the choice of data augmentation. Moreover, we show that contrastive learning fails to learn domain-invariant features, which limits its transferability. Based on these theoretical insights, we propose a novel method called Augmentation-robust Contrastive Learning (ArCL), which guarantees to learn domain-invariant features and can be easily integrated with existing contrastive learning algorithms. We conduct experiments on several datasets and show that ArCL significantly improves the transferability of contrastive learning. * Equal Contribution. This work was partially done when Xuyang was visiting Qing Yuan Research Institute.

Stable Learning Via Sample Reweighting

Abstract: We consider the problem of learning linear prediction models with model misspecification bias. In such case, the collinearity among input variables may inflate the error of parameter estimation, resulting in instability of prediction results when training and test distributions do not match. In this paper we theoretically analyze this fundamental problem and propose a sample reweighting method that reduces collinearity among input variables. Our method can be seen as a pretreatment of data to improve the condition of design matrix, and it can then be combined with any standard learning method for parameter estimation and variable selection. Empirical studies on both simulation and real datasets demonstrate the effectiveness of our method in terms of more stable performance across different distributed data.


In case that the solution of Problem 2 does not exist, we can derive an alternative trajectory for the CAVs by solving a two-level optimization problem numerically that includes piecing together the constrained and unconstrained arcs until the solution does not violate any constraints #REFR .

By solving Problem 2, the optimal exit time t f i along with the optimal trajectory #OTHEREFR and control law #OTHEREFR are obtained for CAV-i for t ∈ [t 0

If a feasible solution to Problem 2 exists, then the solution is a cubic polynomial that guarantees none of the constraints are activated.

Coordination For Connected Automated Vehicles At Merging Roadways In Mixed Traffic Environment

Abstract: In this paper, we present a two-level optimal control framework to address motion coordination of connected automated vehicles (CAVs) in the presence of human-driven vehicles (HDVs) in merging scenarios. Our framework combines an unconstrained trajectory solution of a low-level energyoptimal control problem with an upper-level optimization problem that yields the minimum travel time for CAVs. We predict the future trajectories of the HDVs using Newell's car-following model. To handle potential deviations of HDVs' actual behavior from the one predicted, we provide a risktriggered re-planning mechanism for the CAVs based on timeto-conflict. The effectiveness of the proposed control framework is demonstrated via simulations with heterogeneous human driving behaviors and via experiments in a scaled environment.

A Decentralized Energy-Optimal Control Framework For Connected Automated Vehicles At Signal-Free Intersections

Abstract: We address the problem of optimally controlling connected and automated vehicles (CAVs) crossing an urban intersection without any explicit traffic signaling, so as to minimize energy consumption subject to a throughput maximization requirement. We show that the solution of the throughput maximization problem depends only on the hard safety constraints imposed on CAVs and its structure enables a decentralized optimal control problem formulation for energy minimization. We present a complete analytical solution of these decentralized problems and derive conditions under which feasible solutions satisfying all safety constraints always exist. The effectiveness of the proposed solution is illustrated through simulation which shows substantial dual benefits of the proposed decentralized framework by allowing CAVs to conserve momentum and fuel while also improving travel time.

alternative trajectory
two-level optimization problem


Kendall and Gal #REFR use the negative log-likelihood of the prediction model as the loss function to be minimized in pixel-wise tasks.

The objective of our work is to jointly estimate the disparity and its uncertainty.

An important benefit of this joint formulation is that the multi-task network learns to predict more accurate disparities than the standalone disparity estimator when the uncertainty subnetwork is added.

Given a stereo image pair X = {x l , x r }, with image dimensions H × W , and the corresponding ground truth disparity d, the predictiond of a stereo-matching network f θ can be represented asd = f θ (x l , x r ).

For each pixel i, the error (i) of the prediction is calculated using the L1 loss.

We take the formulation a step further by requiring that the network generate a distribution of uncertainties that matches the distribution of errors.

To this end, we propose to minimize the divergence D between the distributions of predicted uncertainty and actual disparity error.

In the following subsections, we present aleatoric uncertainty estimation (Section 3.1), the proposed KL divergence loss (Section 3.2), our network architecture (Section 3.3), and the combined loss function (Section 3.4).

Learning The Distribution Of Errors In Stereo Matching For Joint Disparity And Uncertainty Estimation

Abstract: We present a new loss function for joint disparity and uncertainty estimation in deep stereo matching. Our work is motivated by the need for precise uncertainty estimates and the observation that multi-task learning often leads to improved performance in all tasks. We show that this can be achieved by requiring the distribution of uncertainty to match the distribution of disparity errors via a KL divergence term in the network's loss function. A differentiable soft-histogramming technique is used to approximate the distributions so that they can be used in the loss. We experimentally assess the effectiveness of our approach and observe significant improvements in both disparity and uncertainty prediction on large datasets. Our code is available at https://github.com/lly00412/SEDNet.git.

What Uncertainties Do We Need In Bayesian Deep Learning For Computer Vision?

Abstract: There are two major types of uncertainty one can model. Aleatoric uncertainty captures noise inherent in the observations. On the other hand, epistemic uncertainty accounts for uncertainty in the model - uncertainty which can be explained away given enough data. Traditionally it has been difficult to model epistemic uncertainty in computer vision, but with new Bayesian deep learning tools this is now possible. We study the benefits of modeling epistemic vs. aleatoric uncertainty in Bayesian deep learning models for vision tasks. For this we present a Bayesian deep learning framework combining input-dependent aleatoric uncertainty together with epistemic uncertainty. We study models under the framework with per-pixel semantic segmentation and depth regression tasks. Further, our explicit uncertainty formulation leads to new loss functions for these tasks, which can be interpreted as learned attenuation. This makes the loss more robust to noisy data, also giving new state-of-the-art results on segmentation and depth regression benchmarks.


For example, Google offers a low-cost and lowpower version of the Google TPU called EdgeTPU #REFR , which can run dedicated neural networks with 8-bit precision.

In this section we discus neural accelerators that can support multiple models without hardware changes.

Efforts in accelerating the execution of TensorFlow Lite models using flexible hardware have focused on deploying systolic architectures for tensor operations to obtain very high throughput.

The systolic array size in the EdgeTPU has 64 x 64 multiply-add cells obtaining 4 TOPS at 480MHz, and it is much smaller than the TPU cloud configurations.

Layers with floatingpoint precision will run on a external CPU that would act as the host for the EdgeTPU device.

Xilinx has also focused on inference including support for TensorFlow, with the Xilinx DPU #OTHEREFR unit.

It is composed of a register configuration unit, the data controller and convolution computing modules optimized for the FPGA hardware resources.

The original hardware is specialized for convolutional neural networks, although alternative architectures and hardware configurations are made available for other model types.

Dynamically Reconfigurable Variable-Precision Sparse-Dense Matrix Acceleration In Tensorflow Lite

Abstract: In this paper, we present a dynamically reconfigurable hardware accelerator called FADES (Fused Architecture for DEnse and Sparse matrices). The FADES design offers multiple configuration options that trade off parallelism and complexity using a dataflow model to create four stages that read, compute, scale and write results. FADES is mapped to the programmable logic (PL) and integrated with the TensorFlow Lite inference engine running on the processing system (PS) of a heterogeneous SoC device. The accelerator is used to compute the tensor operations, while the dynamically reconfigurable approach can be used to switch precision between int8 and float modes. This dynamic reconfiguration enables better performance by allowing more cores to be mapped to the resource-constrained device and lower power consumption compared with supporting both arithmetic precisions simultaneously. We compare the proposed hardware with a high-performance systolic architecture for dense matrices obtaining 25% better performance in dense mode with half the DSP blocks in the same technology. In sparse mode, we show that the core can outperform dense mode even at low sparsity levels, and a single-core achieves up to 20x acceleration over the software-optimized NEON RUY library.

Machine Learning At The Network Edge: A Survey

Abstract: Devices comprising the Internet of Things, such as sensors and small cameras, usually have small memories and limited computational power. The proliferation of such resource-constrained devices in recent years has led to the generation of large quantities of data. These data-producing devices are appealing targets for machine learning applications but struggle to run machine learning algorithms due to their limited computing capability. They typically offload data to external computing systems (such as cloud servers) for further processing. The results of the machine learning computations are communicated back to the resource-scarce devices, but this worsens latency, leads to increased communication costs, and adds to privacy concerns. Therefore, efforts have been made to place additional computing devices at the edge of the network, i.e close to the IoT devices where the data is generated. Deploying machine learning systems on such edge devices alleviates the above issues by allowing computations to be performed close to the data sources. This survey describes major research efforts where machine learning has been deployed at the edge of computer networks.


The image features are then encoded at different spatial resolutions, resulting in tensors of shape #REFR .

In practical applications, autonomous software suppliers often want to upgrade their models using existing customer data without accessing their customers' data directly.

UC 3 represented a typical way of using public traffic data collected in vehicular networks for training, which often involves a large number of clients with small amounts of data and significant differences between datasets.

Lastly, UC 4 focused on federated learning for clients with different numbers of cameras, which can happen when data is collected from different vehicle models by different manufacturers. BEVT architecture.

We begin by feeding our input images X i,k ∈ R L k ×H×W ×3 through a 3-layer ResNet34 encoder.

To ensure consistency across inputs, we use the AMCM to resize all inputs to have L k = 4.

Each client is locally trained for one epoch with a batch size of 4 using AdamW [30] optimizer. Baselines.

Given the limited research on federated learning on BEVT, we conducted the first trial of training fedAvg on this platform.

However, to validate the effectiveness of FedCaP, we also incorporated recent research findings on federated transformer learning as a baseline on BEVT.

In addition to showcasing the results of local training on each client, we compared FedCaP with the following baselines:

Fedbevt: Federated Learning Bird'S Eye View Perception Transformer In Road Traffic Systems

Abstract: Bird's eye view (BEV) perception is becoming increasingly important in the field of autonomous driving. It uses multi-view camera data to learn a transformer model that directly projects the perception of the road environment onto the BEV perspective. However, training a transformer model often requires a large amount of data, and as camera data for road traffic is often private, it is typically not shared. Federated learning offers a solution that enables clients to collaborate and train models without exchanging data. In this paper, we propose FedBEVT, a federated transformer learning approach for BEV perception. We address two common data heterogeneity issues in FedBEVT: (i) diverse sensor poses, and (ii) varying sensor numbers in perception systems. We present federated learning with camera-attentive personalization (FedCaP) and adaptive multi-camera masking (AMCM) to enhance the performance in real-world scenarios. To evaluate our method in real-world settings, we create a dataset consisting of four typical federated use cases. Our findings suggest that FedBEVT outperforms the baseline approaches in all four use cases, demonstrating the potential of our approach for improving BEV perception in autonomous driving. We will make all codes and data publicly available.

Sgdr: Stochastic Gradient Descent With Warm Restarts

Abstract: Restart techniques are common in gradient-free optimization to deal with multimodal functions. Partial warm restarts are also gaining popularity in gradient-based optimization to improve the rate of convergence in accelerated gradient schemes to deal with ill-conditioned functions. In this paper, we propose a simple warm restart technique for stochastic gradient descent to improve its anytime performance when training deep neural networks. We empirically study its performance on the CIFAR-10 and CIFAR-100 datasets, where we demonstrate new state-of-the-art results at 3.14% and 16.21%, respectively. We also demonstrate its advantages on a dataset of EEG recordings and on a downsampled version of the ImageNet dataset. Our source code is available at https://github.com/loshchil/SGDR


Rq2: Can Existing Data Augmentation Methods Produce Robust Code Models?

This phenomenon is consistent with the conclusion drawn by previous work #REFR that simply increasing the training data is not sufficient for improving the robustness of code models.

Adversarial robustness reflects how models handle the data with noise, which is an important characteristic that should be evaluated by the models.

Table 5 presents the attack success rate of two state-of-the-art attack methods on our trained models.

First, the results demonstrate that data augmentation can not always enhance the robustness of models.

Compared to the No Aug models, only four out of eight models trained by using data augmentation have higher robustness.

Also importantly, in some cases, although data augmentation can help train a more robust model, the robustness improvement is insignificant, e.g., the greatest improvement is 5.98% (GraphCodeBERT-Refactor-BigCloneBench-ALERT). Then, we compare each data augmentation method.

In CodeBERT, RS performs the best and has a relatively better robustness improvement in five (out of 12) cases, and reduces ASR by up to 5.10% under MHM attack and 3.69% under ALERT attack compared to No Aug.

Besides, the second best one, RD reduces ASR by up to 5.67% in MHM and 2.66% in ALERT compared to No Aug.

In GraphCodeBERT, RS still is the best choice for robustness improvement in five (out of 12) cases, which deduces ASR by up to 4.10% in MHM and 3.02% in ALERT compared to No Aug.

Interestingly, based on pre-trained PL models, compared to Refactor, methods like RS that could sightly break the syntax of programs can produce more accurate and robust code models to solve downstream tasks.

Boosting Source Code Learning With Data Augmentation: An Empirical Study

Abstract: The next era of program understanding is being propelled by the use of machine learning to solve software problems. Recent studies have shown surprising results of source code learning, which applies deep neural networks (DNNs) to various critical software tasks, e.g., bug detection and clone detection. This success can be greatly attributed to the utilization of massive high-quality training data, and in practice, data augmentation, which is a technique used to produce additional training data, has been widely adopted in various domains, such as computer vision. However, in source code learning, data augmentation has not been extensively studied, and existing practice is limited to simple syntax-preserved methods, such as code refactoring. Essentially, source code is often represented in two ways, namely, sequentially as text data and structurally as graph data, when it is used as training data in source code learning. Inspired by these analogy relations, we take an early step to investigate whether data augmentation methods that are originally used for text and graphs are effective in improving the training quality of source code learning. To that end, we first collect and categorize data augmentation methods in the literature. Second, we conduct a comprehensive empirical study on four critical tasks and 11 DNN architectures to explore the effectiveness of 12 data augmentation methods (including code refactoring and 11 other methods for text and graph data). Our results identify the data augmentation methods that can produce more accurate and robust models for source code learning, including those based on mixup (e.g., SenMixup for texts and Manifold-Mixup for graphs), and those that slightly break the syntax of source code (e.g., random swap and random deletion for texts).

Adversarial Examples For Models Of Code

Abstract: Neural models of code have shown impressive performance for tasks such as predicting method names and identifying certain kinds of bugs. In this paper, we show that these models are vulnerable to adversarial examples, and introduce a novel approach for attacking trained models of code with adversarial examples. The main idea is to force a given trained model to make an incorrect prediction as specified by the adversary by introducing small perturbations that do not change the program's semantics. To find such perturbations, we present a new technique for Discrete Adversarial Manipulation of Programs (DAMP). DAMP works by deriving the desired prediction with respect to the model's inputs while holding the model weights constant and following the gradients to slightly modify the code. ::: To defend a model against such attacks, we propose placing a defensive model (Anti-DAMP) in front of it. Anti-DAMP detects unlikely mutations and masks them before feeding the input to the downstream model. ::: We show that our DAMP attack is effective across three neural architectures: code2vec, GGNN, and GNN-FiLM, in both Java and C#. We show that DAMP has up to 89% success rate in changing a prediction to the adversary's choice ("targeted attack"), and a success rate of up to 94% in changing a given prediction to any incorrect prediction ("non-targeted attack"). By using Anti-DAMP, the success rate of the attack drops drastically for both targeted and non-targeted attacks, with a minor penalty of 2% relative degradation in accuracy while not performing under attack.


Learning And Reasoning Poverty Estimation

The data merging can be done in various ways, such as early fusion, late fusion, naive concatenation, attention mechanism #REFR , or gating-based fusion.

However, as we have limited training data, training visual or textual embeddings from scratch will, in all likelihood, not yield desirable results.

Thus, we propose to use a transfer learning approach wherein we use pre-trained models trained on large corpora and fine-tune them to our task-specific dataset.

For the visual inputs of satellite images, we can utilize pre-trained CNN-based models, such as VGG Net [Simonyan and Zisserman, 2014], or ResNet #OTHEREFR .

For the textual inputs, we prefer the language models, such as BERT [Devlin et al., 2019] or RoBERTa #OTHEREFR .

Each of the representations goes through a non-linear transformation before the data merging operation.

We treat our problem as a classification task wherein we have four ground-truth labels at the district level -'advanced', 'catching up', 'falling behind', and 'lagged'.

This is calculated based on aggregating the individual MDPI scores at the district level.

Correlation, Association, and Causality Analysing the relationship of the input variables with the target output can be a challenging step but it is important for strategic actions.

Such insights are important to determine the driving factors (causative), factors that exhibit linear relationships (correlative), and factors that co-occur (associa- Figure 2 : Proposed multi-input deep learning model for aggregating and processing data from proxy and traditional data sources. tive).

We propose to use the Bayesian Networks for identifying causative factors, Pearson's correlation to determine the correlative factors, and the Hypergeometric test to discover the associative factors.

Learning And Reasoning Multifaceted And Longitudinal Data For Poverty Estimates And Livelihood Capabilities Of Lagged Regions In Rural India

Abstract: Poverty is a multifaceted phenomenon linked to the lack of capabilities of households to earn a sustainable livelihood, increasingly being assessed using multidimensional indicators. Its spatial pattern depends on social, economic, political, and regional variables. Artificial intelligence has shown immense scope in analyzing the complexities and nuances of poverty. The proposed project aims to examine the poverty situation of rural India for the period of 1990-2022 based on the quality of life and livelihood indicators. The districts will be classified into 'advanced', 'catching up', 'falling behind', and 'lagged' regions. The project proposes to integrate multiple data sources, including conventional national-level large sample household surveys, census surveys, and proxy variables like daytime, and nighttime data from satellite images, and communication networks, to name a few, to provide a comprehensive view of poverty at the district level. The project also intends to examine causation and longitudinal analysis to examine the reasons for poverty. Poverty and inequality could be widening in developing countries due to demographic and growth-agglomerating policies. Therefore, targeting the lagging regions and the vulnerable population is essential to eradicate poverty and improve the quality of life to achieve the goal of 'zero poverty'. Thus, the study also focuses on the districts with a higher share of the marginal section of the population compared to the national average to trace the performance of development indicators and their association with poverty in these regions.

Neural Machine Translation By Jointly Learning To Align And Translate

Abstract: Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition.


In contrast to semantic word embeddings #REFR , we show that intrinsic and extrinsic metrics for phonetic word embeddings generally correlate with each other.

They range from intuitive baselines to more complex techniques using metric and contrastive learning.

More importantly, however, we include an evaluation suite for testing the performance of phonetic embeddings. The motivations for this are two-fold. First, prior works are inconsistent in evaluating their models.

This prevents the field from observing long-term improvements of such embeddings and from making fair comparisons across different approaches.

Secondly, when a practitioner is deciding which phonetic word embedding method to use, the go-to approach is to first apply the embeddings (generally fast) and then train a downstream model on those embeddings (compute and time intensive).

Instead, intrinsic embedding evaluation metrics (cheap)-if shown to correlate well with extrinsic metrics-could provide useful signals in embedding method selection prior to training of downstream models (expensive).

While some work on evaluating acoustic word embeddings exists #OTHEREFR , this work specializes in phonetic word embeddings for text, not speech.

• a survey of existing phonetic embeddings, • four novel methods for phonetic word embedding, ranging from simple baselines to complex models, and • an evaluation suite for such embeddings.

Pwesuite: Phonetic Word Embeddings And Tasks They Facilitate

Abstract: Word embeddings that map words into a fixeddimensional vector space are the backbone of modern NLP. Most word embedding methods encode semantic information. However, phonetic information, which is important for some tasks, is often overlooked. In this work, we develop several novel methods which leverage articulatory features to build phonetically informed word embeddings, and present a set of phonetic word embeddings to encourage their community development, evaluation and use. While several methods for learning phonetic word embeddings already exist, there is a lack of consistency in evaluating their effectiveness. Thus, we also proposes several ways to evaluate both intrinsic aspects of phonetic word embeddings, such as word retrieval and correlation with sound similarity, and extrinsic performances, such as rhyme and cognate detection and sound analogies. We hope that our suite of tasks will promote reproducibility and provide direction for future research on phonetic word embeddings.

A Survey Of Word Embeddings Evaluation Methods

Abstract: Word embeddings are real-valued word representations able to capture lexical semantics and trained on natural language corpora. Models proposing these representations have gained popularity in the recent years, but the issue of the most adequate evaluation method still remains open. This paper presents an extensive overview of the field of word embeddings evaluation, highlighting main problems and proposing a typology of approaches to evaluation, summarizing 16 intrinsic methods and 12 extrinsic methods. I describe both widely-used and experimental methods, systematize information about evaluation datasets and discuss some key challenges.

phonetic word embeddings
semantic word embeddings


Also, the author in #REFR considers the synchronization of multi-dimensional mechanical and electrical systems where the subsystems interact via positive semidefinite matrix weights.

Corresponding to a matrix-weighted consensus system, one may define a matrix-weighted graph and a matrix-weighted Laplacian.

It has been shown that algebraic graph properties of the matrix-weighted Laplacian determine the consensus and clustering behaviors of the whole system #OTHEREFR .

Several applications of the matrix-weighted consensus algorithm can be found in the literature.

For example, the authors in #OTHEREFR propose multi-dimensional opinion dynamics, where the in-teractions and inter-logical dependencies between several considered topics are captured by matrix weights, respectively.

In bearing-based formation control and network localization #OTHEREFR , the interactions between neighboring agents are modeled by an orthogonal projection matrix obtained from the directional (or bearing) vector between neighboring agents.

It is noteworthy that most existing works on matrixweighted consensus assumed that the agents update their states synchronously or following a deterministic update sequence.

Continuous-time matrix-weighted consensus with switching graph topologies was studied in #OTHEREFR .

Discrete-time matrix-weighted consensus with fixed or switching topologies was studied in #OTHEREFR .

Matrixweighted consensus with hybrid continuous-discrete time updates was examined in #OTHEREFR .

The randomized consensus algorithms, which refer to a family of consensus algorithms in which the edges between agents are randomly selected according to some stochastic model for every discrete instant, have received a lot of attention in the literature #OTHEREFR .

Randomized Matrix Weighted Consensus ⋆

Abstract: In this paper, a randomized gossip-type matrix-weighted consensus algorithm is proposed for both leaderless and leaderfollower topologies. Under some mild assumptions, the proposed pairwise asynchronous update algorithm achieves a consensus in expectation. Moreover, the probability distribution, the weighting matrices, and the updating step size jointly determine the upper bound of the ǫ-convergence time of the algorithm. The theoretical result is verified by several simulation examples.

Synchronization Under Matrix-Weighted Laplacian

Abstract: Synchronization in a group of linear time-invariant systems is studied where the coupling between each pair of systems is characterized by a different output matrix. Simple methods are proposed to generate a (separate) linear coupling gain for each pair of systems, which ensures that all the solutions converge to a common trajectory.

positive semidefinite matrix
synchronization


For example, the record in validation dataset with 'celex_id' of '31999L0010' extracts 'foodstuff' but gets 'foodstuff' and 'processed foodstuff' with a reordered categories list. #REFR Ko et al.

And Legal-BERT-FP and BERT-base-uncased gain competitive f-scores in validation and testing dataset.

At epoch 5, Legal-BERT-FP performs slightly better than BERT in validation and slightly worse in testing.

The extracted answers are not independent of the positions of the category in categories list.

The Table 6 : validation and testing results (%) in the concept multi-label classification for multi-answer questioning downstream task with proposed classification metric extracted answers might be different if the same title and same domain-tree randomly reorders the categories in the categories list.

However, in classification, the order of the categories is not important.

(2020) also mentions the position bias in extractive question answering.

The other issue in the proposed multi-label classification task is the long input problem since BERT has limited input length.

Table 7 shows validation and testing in domain multi-label classification with the downstream task utilizing the multi-answer questioning task proposed in methodology.

The domain multi-label classification has the constant domain label group which is different from concept multi-label classification having various lengths of label groups.

The evaluation metrics include training loss, validation loss, and overall metrics -precision, recall, f1 score and accuracy in seqeval.

Adopting The Multi-Answer Questioning Task With An Auxiliary Metric For Extreme Multi-Label Text Classification Utilizing The Label Hierarchy

Abstract: Extreme multi-label text classification utilizes the label hierarchy to partition extreme labels into multiple label groups, turning the task into simple multi-group multi-label classification tasks. Current research encodes labels as a vector with fixed length which needs establish multiple classifiers for different label groups. The problem is how to build only one classifier without sacrificing the label relationship in the hierarchy. This paper adopts the multi-answer questioning task for extreme multi-label classification. This paper also proposes an auxiliary classification evaluation metric. This study adopts the proposed method and the evaluation metric to the legal domain. The utilization of legal Berts and the study on task distribution are discussed. The experiment results show that the proposed hierarchy and multianswer questioning task can do extreme multilabel classification for EURLEX dataset. And in minor/fine-tuning the multi-label classification task, the domain adapted BERT models could not show apparent advantages in this experiment. The method is also theoretically applicable to zero-shot learning. * corresponding author 1 https://analyticsindiamag.com/ what-is-extreme-multilabel-text-classification/ 4 sklearn.preprocessing. MultiLabelBinarizer\T1\ textemdashscikit-learn1.0.2documentation 5 https:

Large-Scale Multi-Label Text Classification On Eu Legislation

Abstract: We consider Large-Scale Multi-Label Text Classification (LMTC) in the legal domain. We release a new dataset of 57k legislative documents from EURLEX, annotated with ~4.3k EUROVOC labels, which is suitable for LMTC, few- and zero-shot learning. Experimenting with several neural classifiers, we show that BIGRUs with label-wise attention perform better than other current state of the art methods. Domain-specific WORD2VEC and context-sensitive ELMO embeddings further improve performance. We also find that considering only particular zones of the documents is sufficient. This allows us to bypass BERT's maximum text length limit and fine-tune BERT, obtaining the best results in all but zero-shot learning cases.


Astudillo and Frazier #REFR model the constituent functions of the composition by a single multioutput function f(x) = (f 1 (x), . . .

is the posterior mean function and K (n) (•, •) is the posterior covariance function, see #OTHEREFR for more details.

The acquisition function then uses this posterior model to identify the next query location x n+1 .

In doing so, vanilla BO ignores the values of the member functions in the composition h.

BO for composite function, on the other hand, takes advantage of the available information about h, and its easy-to-compute nature.

, f M (x)) and then model the uncertainty in f(x) using a multi-output Gaussian process to optimize h(f(x)).

Since the prior over f is modelled as a MOGP, the proposed method tries to capture the correlations between different components of the multi-output function f(x).

Note that the proposed EI and PI-based acquisition functions are required to be computed using Monte Carlo sampling.

Furthermore, a sample from the posterior distribution is obtained by first sampling an n variate normal distribution, then scaling it by the lower Cholesky factor and then centering it with the mean of the posterior GP. Two problems arise due to this: 1.

Such simulation based averaging approach increases the time complexity of the procedure linearly with the number of samples taken for averaging and 2.

Bayesian Optimization For Function Compositions With Applications To Dynamic Pricing

Abstract: Bayesian Optimization (BO) is used to find the global optima of black box functions. In this work, we propose a practical BO method of function compositions where the form of the composition is known but the constituent functions are expensive to evaluate. By assuming an independent Gaussian process (GP) model for each of the constituent black-box function, we propose EI and UCB based BO algorithms and demonstrate their ability to outperform vanilla BO and the current state-of-art algorithms. We demonstrate a novel application of the proposed methods to dynamic pricing in revenue management when the underlying demand function is expensive to evaluate.

Bayesian Optimization Of Composite Functions

Abstract: We consider optimization of composite objective functions, i.e., of the form $f(x)=g(h(x))$, where $h$ is a black-box derivative-free expensive-to-evaluate function with vector-valued outputs, and $g$ is a cheap-to-evaluate real-valued function. While these problems can be solved with standard Bayesian optimization, we propose a novel approach that exploits the composite structure of the objective function to substantially improve sampling efficiency. Our approach models $h$ using a multi-output Gaussian process and chooses where to sample using the expected improvement evaluated on the implied non-Gaussian posterior on $f$, which we call expected improvement for composite functions (\ei). Although \ei\ cannot be computed in closed form, we provide a novel stochastic gradient estimator that allows its efficient maximization. We also show that our approach is asymptotically consistent, i.e., that it recovers a globally optimal solution as sampling effort grows to infinity, generalizing previous convergence results for classical expected improvement. Numerical experiments show that our approach dramatically outperforms standard Bayesian optimization benchmarks, reducing simple regret by several orders of magnitude.

constituent functions
single multioutput function


Though the main benefit may be further reduction in overall communication cost, it is not clear whether an advantage over pairwise approaches can be achieved when an average pairwise intersection is large compared to the total intersection (∩ i S i ) #REFR .

As a result, it is easier to formally analyze its performance, and, indeed, we have shown that it completes in time bounded by the network diameter (or logarithmic in network size for the "small-world" networks that reasonably model blockchain networks).

We have also validated our analytical findings against a novel event-based simulator that we have developed.

We run the simulator on real-world transaction pool statistics drawn from our own measurement campaign.

In our simulations, SREP incurs only tens of gigabytes of overall bandwidth overhead to synchronize networks with ten thousand nodes, which is several times better than the current approach in the literature.

For future work, we propose to consider multi-party set reconciliation #OTHEREFR , #OTHEREFR in the context of transaction pool sync.

Srep: Out-Of-Band Sync Of Transaction Pools For Large-Scale Blockchains

Abstract: Synchronization of transaction pools (mempools) has shown potential for improving the performance and block propagation delay of state-of-the-art blockchains. Indeed, various heuristics have been proposed in the literature to this end, all of which incorporate exchanges of unconfirmed transactions into their block propagation protocol. In this work, we take a different approach, maintaining transaction synchronization outside (and independently) of the block propagation channel. In the process, we formalize the synchronization problem within a graph theoretic framework and introduce a novel algorithm (SREP-Set Reconciliation-Enhanced Propagation) with quantifiable guarantees. We analyze the algorithm's performance for various realistic network topologies, and show that it converges on any connected graph in a number of steps that is bounded by the diameter of the graph. We confirm our analytical findings through extensive simulations that include comparison with MempoolSync, a recent approach from the literature. Our simulations show that SREP incurs reasonable overall bandwidth overhead and, unlike MempoolSync, scales gracefully with the size of the network.

Simple Multi-Party Set Reconciliation

Abstract: As users migrate information to cloud storage, many distributed cloud-based services use multiple loosely consistent replicas of user information to avoid the high overhead of more tightly coupled synchronization. Periodically, the information must be synchronized, or reconciled. One can place this problem in the theoretical framework of {\em set reconciliation}: two parties $A_1$ and $A_2$ each hold a set of keys, named $S_1$ and $S_2$ respectively, and the goal is for both parties to obtain $S_1 \cup S_2$. Typically, set reconciliation is interesting algorithmically when sets are large but the set difference $|S_1-S_2|+|S_2-S_1|$ is small. In this setting the focus is on accomplishing reconciliation efficiently in terms of communication; ideally, the communication should depend on the size of the set difference, and not on the size of the sets. ::: In this paper, we extend recent approaches using Invertible Bloom Lookup Tables (IBLTs) for set reconciliation to the multi-party setting. In this setting there are three or more parties $A_1,A_2,\ldots,A_n$ holding sets of keys $S_1,S_2,\ldots,S_n$ respectively, and the goal is for all parties to obtain $\cup_i S_i$. This could of course be done by pairwise reconciliations, but we seek more effective methods. ::: Our methodology uses network coding techniques in conjunction with IBLTs, allowing efficiency in network utilization along with efficiency obtained by passing messages of size $O(|\cup_i S_i - \cap_i S_i|)$. Further, our approach can function even if the number of parties is not exactly known in advance, and in many cases can be used to determine which parties contain keys not in the joint union. By connecting reconciliation with network coding, we can allow for substantially more efficient reconciliation methods that apply to a number of natural distributed computing problems.

overall communication cost
average pairwise intersection


D. Federated Learning And Autonomous Driving

To further overcome this performance gap, authors in #REFR proposed modifications to the existing approach through deep gradient compression.

However, the challenge is to choose the favorable lower-limit value, as similar to soft-filter pruning, the quantization and selection of the wrong lower-limit value can directly impact the overall model aggregation, which may provide an overall reduced model size but decreases the accuracy.

To overcome the previous challenge, stochastic gradient descent with k-sparsification is proposed in #OTHEREFR , by reducing the data and model size and also improving convergence through error compensation for the transmission taking place between edge and server.

A similar approach is used in #OTHEREFR , the method proposes to fix the sparsity rate.

The communication or transmission of the gradient is only enabled for a fraction of the gradient with the highest magnitudes and keeping the unused gradient in the container.

The sparsity rate used by the authors is p = 0.001, and this approach has relatively less impact the learned model overall accuracy and performance.

Deep gradient compression uses approaches such as: momentum correction, local gradient clipping, for the convolutional neural network and recurrent neural network.

Results show that gradients are compressed by ratio of 270-660 following a hierarchical approach, without slowing down the model convergence.

Sparsification methods were initially proposed with the function of improving and promoting distributed and parallel training among the cloud and data-centers.

However, these methods lacked model convergence and aggregation as a scope which is currently a most essential metrics for the federated and distributed machine learning.

Similarly, attention should be given to the number of edge devices participating in the transmission and the server participating in collaborative training.

A Survey On Approximate Edge Ai For Energy Efficient Autonomous Driving Services

Abstract: Autonomous driving services rely heavily on sensors such as cameras, LiDAR, radar, and communication modules. A common practice of processing the sensed data is using a high-performance computing unit placed inside the vehicle, which deploys AI models and algorithms to act as the brain or administrator of the vehicle. The vehicular data generated from average hours of driving can be up to 20 Terabytes depending on the data rate and specification of the sensors. Given the scale and fast growth of services for autonomous driving, it is essential to improve the overall energy and environmental efficiency, especially in the trend towards vehicular electrification (e.g., battery-powered). Although the areas have seen significant advancements in sensor technologies, wireless communications, computing and AI/ML algorithms, the challenge still exists in how to apply and integrate those technology innovations to achieve energy efficiency. This survey reviews and compares the connected vehicular applications, vehicular communications, approximation and Edge AI techniques. The focus is on energy efficiency by covering newly proposed approximation and enabling frameworks. To the best of our knowledge, this survey is the first to review the latest approximate Edge AI frameworks and publicly available datasets in energy-efficient autonomous driving. The insights and vision from this survey can be beneficial for the collaborative driving service development on low-power and memory-constrained systems and also for the energy optimization of autonomous vehicles.

Deep Gradient Compression: Reducing The Communication Bandwidth For Distributed Training

Abstract: Large-scale distributed training requires significant communication bandwidth for gradient exchange that limits the scalability of multi-node training, and requires expensive high-bandwidth network infrastructure. The situation gets even worse with distributed training on mobile devices (federated learning), which suffers from higher latency, lower throughput, and intermittent poor connections. In this paper, we find 99.9% of the gradient exchange in distributed SGD is redundant, and propose Deep Gradient Compression (DGC) to greatly reduce the communication bandwidth. To preserve accuracy during compression, DGC employs four methods: momentum correction, local gradient clipping, momentum factor masking, and warm-up training. We have applied Deep Gradient Compression to image classification, speech recognition, and language modeling with multiple datasets including Cifar10, ImageNet, Penn Treebank, and Librispeech Corpus. On these scenarios, Deep Gradient Compression achieves a gradient compression ratio from 270x to 600x without losing accuracy, cutting the gradient size of ResNet-50 from 97MB to 0.35MB, and for DeepSpeech from 488MB to 0.74MB. Deep gradient compression enables large-scale distributed training on inexpensive commodity 1Gbps Ethernet and facilitates distributed training on mobile.


Combining this with #REFR After rearranging terms, we conclude that Lemma 3.1 holds when G is connected. Suppose G is not connected.

Observe that no edge on the boundary of a face of length three is also in a face of length four, or else a triangle and 4-cycle are adjacent.

Because G is not a triangle and no two triangles are adjacent, no two length-3 faces share an edge.

Therefore, we conclude that at least 3|X| edges of G are on the boundary of a face in X.

Moreover, every edge on the boundary of a length-4 face is on the boundary of at most two length-4 faces, so at least 2|Y | edges of G are on the boundary of a face in Y .

Thus, we have |E(G)| ≥ 3|X| + 2|Y |, which implies #OTHEREFR .

Let J be the set of components of G with less than four vertices, and let K be the set of components of G with at least four vertices.

Note that for all j ∈ J, |E(j)| ≤ |V (j)|.

Therefore, if |K| ≥ 1, Lemma 3.1 holds by induction on |J|. Otherwise, |K| = 0.

Note that n ≤ 15(n − 2)/7 if n ≥ 4, and in this case |E(G)| ≤ |V (G)|.

Therefore, if the total number of vertices in the components in J is at least four, Lemma 3.1 holds.

The Independence Ratio Of 4-Cycle-Free Planar Graphs

Abstract: We prove that every n-vertex planar graph G with no triangle sharing an edge with a 4-cycle has independence ratio n/α(G) ≤ 4 − ε for ε = 1/30. This result implies that the same bound holds for 4-cycle-free planar graphs and planar graphs with no adjacent triangles and no triangle sharing an edge with a 5-cycle. For the latter case we strengthen the bound to ε = 2/9.

Extremal C4-Free/C5-Free Planar Graphs

Abstract: We study the topic of "extremal" planar graphs, defining $\mathrm{ex_{_{\mathcal{P}}}}(n,H)$ to be the maximum number of edges possible in a planar graph on $n$ vertices that does not contain a given graph $H$ as a subgraph. In particular,we examine the case when $H$ is a small cycle,obtaining $\mathrm{ex_{_{\mathcal{P}}}}(n,C_{4}) \leq \frac{15}{7}(n-2)$ for all $n \geq 4$ and $\mathrm{ex_{_{\mathcal{P}}}}(n,C_{5}) \leq \frac{12n-33}{5}$ for all $n \geq 11$, and showing that both of these bounds are tight.


ADGAN #REFR ameliorates this issue by extracting expressive textures vectors from different semantic entities to synthesize the target image.

(a) To investigate the domain gap between source and OOD datasets, we obtain the high-level feature by a person ReID model #OTHEREFR and use t-SNE for visualization. (b) The domain generalization of NTED #OTHEREFR .

Typical pose transfer model performs reasonably well on the source domain, however, the generated results could be easily violated with OOD input.

We first propose an Open-World Pose Transfer (OWPT) framework to investigate the domain generalization of a pre-defined model toward OOD appearance and skeleton.

The former presents a coarse-to-fine structure which utilizes coarse shape or foreground masks to ensure generalization on arbitrary poses.

However, these methods are not efficient at inference and require additional computing power.

CASD #OTHEREFR introduces attention-based methods to distribute the semantic vectors to the target poses.

Compared to these parser-based methods, NTED #OTHEREFR applies sparse attention-based operation to extract the semantic textures without the assistance of the external parser.

Although these methods have been validated well on the DeepFashion dataset #OTHEREFR , there is no relevant research to extend these pre-trained models to OOD dataset.

Our work first explores the performance of these models on OOD data.

However, the performance still remains a large gap between photo-realistic image due to the pre-trained models overfitting on DeepFashion dataset #OTHEREFR .

Open-World Pose Transfer Via Sequential Test-Time Adaption

Abstract: Figure 1. Visualization of open-world pose transfer (OWPT). With open-world references, it can be observed that typical pose transfer method (e.g. NTED [32]) exhibits a twisty pattern. In this sense, we call for a solid model that can handle open-world instances beyond a specific dataset.

Controllable Person Image Synthesis With Attribute-Decomposed Gan

Abstract: This paper introduces the Attribute-Decomposed GAN, a novel generative model for controllable person image synthesis, which can produce realistic person images with desired human attributes (e.g., pose, head, upper clothes and pants) provided in various source inputs. The core idea of the proposed model is to embed human attributes into the latent space as independent codes and thus achieve flexible and continuous control of attributes via mixing and interpolation operations in explicit style representations. Specifically, a new architecture consisting of two encoding pathways with style block connections is proposed to decompose the original hard mapping into multiple more accessible subtasks. In source pathway, we further extract component layouts with an off-the-shelf human parser and feed them into a shared global texture encoder for decomposed latent codes. This strategy allows for the synthesis of more realistic output images and automatic separation of un-annotated attributes. Experimental results demonstrate the proposed method's superiority over the state of the art in pose transfer and its effectiveness in the brand-new task of component attribute transfer.

target image
expressive textures vectors


Attribute representations is a method of describing the data semantically #REFR . An attribute vector a represents a set of soft-biometrics.

Prior to concatenation, the outputs of each convolutional blocks are processed by a fully connected layer or LSTM layer depending on the network type.

Concatenation is followed by a two-layered fully connected MLP and a classifier layer for tCNN-IMU M LP .

In the case of tCNN-IMU LST M , the concatenation is followed by two LSTM layers and a classifier layer.

The networks use a softmax classifier for person identification, whereas a sigmoid layer for soft-biometrics identification #OTHEREFR .

Soft-biometrics of individuals describe or categorise an individual or a group of individuals #OTHEREFR , e.g., Gender Identity, Age, Weight and Height.

A similar combination of soft-biometrics could represent different persons with similar features.

The Nearest Neighbour Approach (NNA) is used for soft-biometrics-based identification.

The NNA calculates the distance between a prediction attribute vector a and an attribute representation A, with all the different combinations of soft-biometrics.

The person identity is assigned to the one related with the least distance from A.

NNA is performed by computing a certain similarity between a A and the vector a from the network; typically, the cosine similarity #OTHEREFR and the Probabilistic Retrieval Model (PRM) similarity #OTHEREFR .

Multi-Channel Time-Series Person And Soft-Biometric Identification

Abstract: Multi-channel time-series datasets are popular in the context of human activity recognition (HAR). On-body device (OBD) recordings of human movements are often preferred for HAR applications not only for their reliability but as an approach for identity protection, e.g., in industrial settings. Contradictory, the gait activity is a biometric, as the cyclic movement is distinctive and collectable. In addition, the gait cycle has proven to contain soft-biometric information of human groups, such as age and height. Though general human movements have not been considered a biometric, they might contain identity information. This work investigates person and soft-biometrics identification from OBD recordings of humans performing different activities using deep architectures. Furthermore, we propose the use of attribute representation for soft-biometric identification. We evaluate the method on four datasets of multi-channel time-series HAR, measuring the performance of a person and soft-biometrics identification and its relation concerning performed activities. We find that person identification is not limited to gait activity. The impact of activities on the identification performance was found to be training and dataset specific. Soft-biometric based attribute representation shows promising results and emphasis the necessity of larger datasets.

Learning Attribute Representation For Human Activity Recognition

Abstract: Attribute representations became relevant in image recognition and word spotting, providing support under the presence of unbalance and disjoint datasets. However, for human activity recognition using sequential data from on-body sensors, human-labeled attributes are lacking. This paper introduces a search for attributes that represent favorably signal segments for recognizing human activities. It presents three deep architectures, including temporal-convolutions and an IMU centered design, for predicting attributes. An empiric evaluation of random and learned attribute representations, and as well as the networks is carried out on two datasets, outperforming the state-of-the art.


For instance, to simulate Google's Sycamore #REFR , which has 53 qubits, we would require 128 PB of memory.

In a quantum computer, all operations follow quantum mechanics: preparing qubits (a quantum version of classical bits), applying unitary gates, and measuring qubits to get classical data.

The goal of quantum circuit simulation is to reproduce the classical result obtained by these quantum operations only with a classical computer.

There exist various types of quantum simulators #OTHEREFR 29] .

For general circuits dominated by non-Clifford gates, the two types of simulators: 1) state vector and 2) tensor network methods are widely used. We choose these simulation methods according to the objectives.

The state vector simulations require 2 n complex values on memory, where n is the number of qubits.

Since the total memory capacity of the current largest supercomputers is in the order of a few PB, state vector methods are limited by memory capacity.

One advantage of state vector methods is that the computational complexity for a circuit of depth d is O(2 n ×d) and scales linearly with d.

Furthermore, there are some studies to reduce the required memory size, such as by splitting the circuit #OTHEREFR .

On the other hand, the tensor contraction method #OTHEREFR can simulate several thousands of qubits with low-depth layers at a slightly higher computational cost.

Therefore, tensor contraction is the method of choice for many recent studies that aim to validate quantum supremacy in both quantum computing and high performance computing #OTHEREFR .

Quantum Circuit Simulation By Sgemm Emulation On Tensor Cores And Automatic Precision Selection

Abstract: Quantum circuit simulation provides the foundation for the development of quantum algorithms and the verification of quantum supremacy. Among the various methods for quantum circuit simulation, tensor network contraction has been increasing in popularity due to its ability to simulate a larger number of qubits. During tensor contraction, the input tensors are reshaped to matrices and computed by a GEMM operation, where these GEMM operations could reach up to 90% of the total calculation time. GEMM throughput can be improved by utilizing mixed-precision hardware such as Tensor Cores, but straightforward implementation results in insufficient fidelity for deep and large quantum circuits. Prior work has demonstrated that compensated summation with special care of the rounding mode can fully recover the FP32 precision of SGEMM even when using TF32 or FP16 Tensor Cores. The exponent range is a critical issue when applying such techniques to quantum circuit simulation. While TF32 supports almost the same exponent range as FP32, FP16 supports a much smaller exponent range. In this work, we use the exponent range statistics of input tensor elements to select which Tensor Cores we use for the GEMM. We evaluate our method on Random Circuit Sampling (RCS), including Sycamore's quantum circuit, and show that the throughput is 1.86 times higher at maximum while maintaining accuracy.

Supplementary Information For"Quantum Supremacy Using A Programmable Superconducting Processor"

Abstract: The promise of quantum computers is that certain computational tasks might be executed exponentially faster on a quantum processor than on a classical processor1. A fundamental challenge is to build a high-fidelity processor capable of running quantum algorithms in an exponentially large computational space. Here we report the use of a processor with programmable superconducting qubits2–7 to create quantum states on 53 qubits, corresponding to a computational state-space of dimension 253 (about 1016). Measurements from repeated experiments sample the resulting probability distribution, which we verify using classical simulations. Our Sycamore processor takes about 200 seconds to sample one instance of a quantum circuit a million times—our benchmarks currently indicate that the equivalent task for a state-of-the-art classical supercomputer would take approximately 10,000 years. This dramatic increase in speed compared to all known classical algorithms is an experimental realization of quantum supremacy8–14 for this specific computational task, heralding a much-anticipated computing paradigm. Quantum supremacy is demonstrated using a programmable superconducting processor known as Sycamore, taking approximately 200 seconds to sample one instance of a quantum circuit a million times, which would take a state-of-the-art supercomputer around ten thousand years to compute.


For the UMi and UMa scenarios, 3GPP SCM had a higher probability of predicting LOS channel conditions at larger distances (several hundred meters) than NYUSIM #REFR .

The first step in generating a channel model for a UE is determining whether the channel condition is LOS/NLOS.

We extend the ChannelCondition class developed in #OTHEREFR with five different classes, namely, NYURmaChannelConditionModel, NYUUma-ChannelConditionModel, NYUUmiChannelConditionModel, NYU-InHChannelConditionModel, and NYUInFChannelConditionModel, each handling a different scenario for NYUSIM.

All the newly introduced NYUSIM classes mentioned above derive from the same base class, called NYUChannelCondition, which extends the Cha-nnelConditionModel interface.

The NYUSIM LOS probability models for outdoor scenarios were developed based on radio propagation measurements conducted at 28 and 73 GHz in New York City #OTHEREFR .

The Tx-Rx locations were selected from measurement data, and ray tracing was employed to determine whether the path between the Tx and Rx is in LOS/NLOS #OTHEREFR .

However, in a real-world UMi and UMa scenario, getting LOS propagation over several hundred meters is very challenging #OTHEREFR .

Thus, the UMi and UMa, LOS probability models, implemented in NYUUmiChannelConditionModel and NYU-UmaChannelConditionModel, use the NYU (squared) model based on Tables 1 and 2 in #OTHEREFR respectively and is given in (1) and #OTHEREFR .

In (1) 1 = 22m, 2 = 100m, in (2) 1 = 20m, 2 = 160m, is defined in Table 7 .4.2 in #OTHEREFR and ℎ is the height of the UE in meters.

Ns-3 Implementation Of Sub-Terahertz And Millimeter Wave Drop-Based Nyu Channel Model (Nyusim)

Abstract: The next generation of wireless networks will use sub-THz frequencies alongside mmWave frequencies to enable multi-Gbps and low latency applications. To enable different verticals and use cases, engineers must take a holistic approach to build, analyze, and study different parts of the network and the interplay among the lower and higher layers of the protocol stack. It is of paramount importance to accurately characterize the radio propagation in diverse scenarios such as urban microcell (UMi), urban macrocell (UMa), rural macrocell (RMa), indoor hotspot (InH), and indoor factory (InF) for a wide range of frequencies. The 3GPP statistical channel model (SCM) is oversimplified and restricted to the frequency range of 0.5-100 GHz. Thus, to overcome these limitations, this paper presents a detailed implementation of the drop-based NYU channel model (NYUSIM) for the frequency range of 0.5-150 GHz for the UMi, UMa, RMa, InH, and InF scenarios. NYUSIM allows researchers to design and evaluate new algorithms and protocols for future sub-THz wireless networks in ns-3. CCS CONCEPTS • Networks → Network simulation; Mobile networks;.

Investigation And Comparison Of 3Gpp And Nyusim Channel Models For 5G Wireless Communications

Abstract: Channel models describe how wireless channel parameters behave in a given scenario, and help evaluate link- and system-level performance. A proper channel model should be able to faithfully reproduce the channel parameters obtained in field measurements and accurately predict the spatial and temporal channel impulse response along with large-scale fading. This paper compares two popular channel models for next generation wireless communications: the 3rd Generation Partnership Project (3GPP) TR 38.900 Release 14 channel model and the statistical spatial channel model NYUSIM developed by New York University (NYU). The two channel models employ different modeling approaches in many aspects, such as the line-of-sight probability, path loss, and clustering methodology. Simulations are performed using the two channel models to analyze the channel eigenvalue distribution and spectral efficiency leveraging the analog/digital hybrid beamforming methods found in the literature. Simulation results show that the 3GPP model produces different eigenvalue and spectral efficiency distributions for mmWave bands, as compared to the outcome from NYUSIM that is based on massive amounts of real-world measured data in New York City. This work shows NYUSIM is more accurate for realistic simulations than 3GPP in urban environments.