OpenRaiser/Intern-Atlas huggingface dataset:
Lotteries for Shared Experiences
Venue: arXiv cs.GT 2022
Year: 2022

We study a setting where tickets for an experience are allocated by lottery. Each agent belongs to a group, and a group is successful if and only if its members receive enough tickets for everyone. A lottery is efficient if it maximizes the number of agents in successful groups, and fair if it gives every group the same chance of success. We study the efficiency and fairness of existing approaches, and propose practical alternatives. If agents must identify the members of their group, a natural solution is the Group Lottery, which orders groups uniformly at random and processes them sequentially. We provide tight bounds on the inefficiency and unfairness of this mechanism, and describe modifications that obtain a fairer allocation. If agents may request multiple tickets without identifying members of their group, the most common mechanism is the Individual Lottery, which orders agents uniformly at random and awards each their request until no tickets remain. Because each member of a group may apply for (and win) tickets, this approach can yield arbitrarily unfair and inefficient outcomes. As an alternative, we propose the Weighted Individual Lottery, in which the processing order is biased against agents with large requests. Although it is still possible to have multiple winners in a group, this simple modification makes this event much less likely. As a result, the Weighted Individual Lottery is approximately fair and approximately efficient, and similar to the Group Lottery when there are many more agents than tickets.
Evaluating deep tracking models for player tracking in broadcast ice hockey video
Venue: arXiv cs.CV 2022
Year: 2022

Tracking and identifying players is an important problem in computer vision based ice hockey analytics. Player tracking is a challenging problem since the motion of players in hockey is fast-paced and non-linear. There is also significant player-player and player-board occlusion, camera panning and zooming in hockey broadcast video. Prior published research perform player tracking with the help of handcrafted features for player detection and re-identification. Although commercial solutions for hockey player tracking exist, to the best of our knowledge, no network architectures used, training data or performance metrics are publicly reported. There is currently no published work for hockey player tracking making use of the recent advancements in deep learning while also reporting the current accuracy metrics used in literature. Therefore, in this paper, we compare and contrast several state-of-the-art tracking algorithms and analyze their performance and failure modes in ice hockey.
Incentivizing Federated Learning
Venue: arXiv cs.GT 2022
Year: 2022

Federated Learning is an emerging distributed collaborative learning paradigm used by many of applications nowadays. The effectiveness of federated learning relies on clients' collective efforts and their willingness to contribute local data. However, due to privacy concerns and the costs of data collection and model training, clients may not always contribute all the data they possess, which would negatively affect the performance of the global model. This paper presents an incentive mechanism that encourages clients to contribute as much data as they can obtain. Unlike previous incentive mechanisms, our approach does not monetize data. Instead, we implicitly use model performance as a reward, i.e., significant contributors are paid off with better models. We theoretically prove that clients will use as much data as they can possibly possess to participate in federated learning under certain conditions with our incentive mechanism
An Automated System for Detecting Visual Damages of Wind Turbine Blades
Venue: arXiv cs.AI 2022
Year: 2022

Wind energy's ability to compete with fossil fuels on a market level depends on lowering wind's high operational costs. Since damages on wind turbine blades are the leading cause for these operational problems, identifying blade damages is critical. However, recent works in visual identification of blade damages are still experimental and focus on optimizing the traditional machine learning metrics such as IoU. In this paper, we argue that pushing models to production long before achieving the "optimal" model performance can still generate real value for this use case. We discuss the performance of our damage's suggestion model in production and how this system works in coordination with humans as part of a commercialized product and how it can contribute towards lowering wind energy's operational costs.
Investigating classification learning curves for automatically generated and labelled plant images
Venue: arXiv cs.LG 2022
Year: 2022

In the context of supervised machine learning a learning curve describes how a model's performance on unseen data relates to the amount of samples used to train the model. In this paper we present a dataset of plant images with representatives of crops and weeds common to the Manitoba prairies at different growth stages. We determine the learning curve for a classification task on this data with the ResNet architecture. Our results are in accordance with previous studies and add to the evidence that learning curves are governed by power-law relationships over large scales, applications, and models. We further investigate how label noise and the reduction of trainable parameters impacts the learning curve on this dataset. Both effects lead to the model requiring disproportionally larger training sets to achieve the same classification performance as observed without these effects.
Wasserstein Generative Adversarial Networks for Online Test Generation for Cyber Physical Systems
Venue: arXiv cs.LG 2022
Year: 2022

We propose a novel online test generation algorithm WOGAN based on Wasserstein Generative Adversarial Networks. WOGAN is a general-purpose black-box test generator applicable to any system under test having a fitness function for determining failing tests. As a proof of concept, we evaluate WOGAN by generating roads such that a lane assistance system of a car fails to stay on the designated lane. We find that our algorithm has a competitive performance respect to previously published algorithms.
FAST: A Fully-Concurrent Access Technique to All SRAM Rows for Enhanced Speed and Energy Efficiency in Data-Intensive Applications
Venue: arXiv cs.AR 2022
Year: 2022

Compute-in-memory (CiM) is a promising approach to improving the computing speed and energy efficiency in dataintensive applications. Beyond existing CiM techniques of bitwise logic-in-memory operations and dot product operations, this paper extends the CiM paradigm with FAST, a new shift-based inmemory computation technique to handle high-concurrency operations on multiple rows in an SRAM. Such high-concurrency operations are widely seen in both conventional applications (e.g. the table update in a database), and emerging applications (e.g. the parallel weight update in neural network accelerators), in which low latency and low energy consumption are critical. The proposed shift-based CiM architecture is enabled by integrating the shifter function into each SRAM cell, and by creating a datapath that exploits the high-parallelism of shifting operations in multiple rows in the array. A 128-row 16-column shiftable SRAM in 65nm CMOS is designed to evaluate the proposed architecture. Postlayout SPICE simulations show average improvements of 4.4x energy efficiency and 96.0x speed over a conventional fully-digital memory-computing-separated scheme, when performing the 8-bit weight update task in a VGG-7 framework.
The k-Server with Preferences Problem
Venue: arXiv cs.DS 2022
Year: 2022

The $k$-Server Problem covers plenty of resource allocation scenarios, and several variations have been studied extensively for decades. We present a model generalizing the $k$-Server Problem by preferences of the requests, where the servers are not identical and requests can express which specific servers should serve them. In our model, requests can either be answered by any server (general requests) or by a specific one (specific requests). If only general requests appear, the instance is one of the original $k$-Server Problem, and a lower bound for the competitive ratio of $k$ applies. If only specific requests appear, a solution with a competitive ratio of $1$ becomes trivial. We show that if both kinds of requests appear, the lower bound raises to $2k-1$. We study deterministic online algorithms and present two algorithms for uniform metrics. The first one has a competitive ratio dependent on the frequency of specific requests. It achieves a worst-case competitive ratio of $3k-2$ while it is optimal when only general requests appear or when specific requests dominate the input sequence. The second has a worst-case competitive ratio of $2k+14$. For the first algorithm, we show a lower bound of $3k-2$, while the second algorithm has a lower bound of $2k-1$ when only general requests appear. The two algorithms differ in only one behavioral rule that significantly influences the competitive ratio. We show that there is a trade-off between performing well against instances of the $k$-Server Problem and mixed instances based on the rule. Additionally, no deterministic online algorithm can be optimal for both kinds of instances simultaneously. Regarding non-uniform metrics, we present an adaption of the Double Coverage algorithm for $2$ servers on the line achieving a competitive ratio of $6$, and an adaption of the Work-Function-Algorithm achieving a competitive ratio of $4k$.
A Self-Paced Mixed Distillation Method for Non-Autoregressive Generation
Venue: arXiv cs.CL 2022
Year: 2022

Non-Autoregressive generation is a sequence generation paradigm, which removes the dependency between target tokens. It could efficiently reduce the text generation latency with parallel decoding in place of token-by-token sequential decoding. However, due to the known multi-modality problem, Non-Autoregressive (NAR) models significantly under-perform Auto-regressive (AR) models on various language generation tasks. Among the NAR models, BANG is the first large-scale pre-training model on English un-labeled raw text corpus. It considers different generation paradigms as its pre-training tasks including Auto-regressive (AR), Non-Autoregressive (NAR), and semi-Non-Autoregressive (semi-NAR) information flow with multi-stream strategy. It achieves state-of-the-art performance without any distillation techniques. However, AR distillation has been shown to be a very effective solution for improving NAR performance. In this paper, we propose a novel self-paced mixed distillation method to further improve the generation quality of BANG. Firstly, we propose the mixed distillation strategy based on the AR stream knowledge. Secondly, we encourage the model to focus on the samples with the same modality by self-paced learning. The proposed self-paced mixed distillation algorithm improves the generation quality and has no influence on the inference latency. We carry out extensive experiments on summarization and question generation tasks to validate the effectiveness. To further illustrate the commercial value of our approach, we conduct experiments on three generation tasks in real-world advertisements applications. Experimental results on commercial data show the effectiveness of the proposed model. Compared with BANG, it achieves significant BLEU score improvement. On the other hand, compared with auto-regressive generation method, it achieves more than 7x speedup.
Self-Adaptive RISs Beyond Free Space: Convergence of Localization, Sensing and Communication under Rich-Scattering Conditions
Venue: arXiv cs.IT 2022
Year: 2022

We discuss the need for a confluence of localization, sensing and communications if RISs are to be deployed in a self-adaptive manner in the dynamically evolving rich-scattering settings that are typical for 6G deployment scenarios such as factories. We establish that in such problems the rich-scattering wireless channels are subject to a highly nonlinear deterministic double-parametrization through both the RIS and uncontrolled moving objects. Therefore, acquiring full context-awareness through localization and sensing is a prerequisite for RIS-empowered communications. Yet, the byproducts of this daunting communications overhead can feed many appliances that require context awareness, such that overhead concerns may vanish. We illustrate the essential steps for operating a self-adaptive RIS under rich scattering based on a prototypical case study. We discover that self-adaptive RISs outperform context-ignorant RISs only below a certain noise threshold that depends, among other factors, on how strongly uncontrolled perturbers impact the wireless channel. We also discuss ensuing future research directions that will determine the conditions under which RISs may serve as technological enabler of 6G networks.
Feature-Distribution Perturbation and Calibration for Generalized Person ReID
Venue: arXiv cs.CV 2022
Year: 2022

Person Re-identification (ReID) has been advanced remarkably over the last 10 years along with the rapid development of deep learning for visual recognition. However, the i.i.d. (independent and identically distributed) assumption commonly held in most deep learning models is somewhat non-applicable to ReID considering its objective to identify images of the same pedestrian across cameras at different locations often of variable and independent domain characteristics that are also subject to view-biased data distribution. In this work, we propose a Feature-Distribution Perturbation and Calibration (PECA) method to derive generic feature representations for person ReID, which is not only discriminative across cameras but also agnostic and deployable to arbitrary unseen target domains. Specifically, we perform per-domain feature-distribution perturbation to refrain the model from overfitting to the domain-biased distribution of each source (seen) domain by enforcing feature invariance to distribution shifts caused by perturbation. Furthermore, we design a global calibration mechanism to align feature distributions across all the source domains to improve the model generalization capacity by eliminating domain bias. These local perturbation and global calibration are conducted simultaneously, which share the same principle to avoid models overfitting by regularization respectively on the perturbed and the original distributions. Extensive experiments were conducted on eight person ReID datasets and the proposed PECA model outperformed the state-of-the-art competitors by significant margins.
Denoising-based image reconstruction from pixels located at non-integer positions
Venue: arXiv cs.CV 2022
Year: 2022

Digital images are commonly represented as regular 2D arrays, so pixels are organized in form of a matrix addressed by integers. However, there are many image processing operations, such as rotation or motion compensation, that produce pixels at non-integer positions. Typically, image reconstruction techniques cannot handle samples at non-integer positions. In this paper, we propose to use triangulation-based reconstruction as initial estimate that is later refined by a novel adaptive denoising framework. Simulations reveal that improvements of up to more than 1.8 dB (in terms of PSNR) are achieved with respect to the initial estimate.
Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation
Venue: arXiv cs.IR 2022
Year: 2022

Sequential recommendation (SR) learns users' preferences by capturing the sequential patterns from users' behaviors evolution. As discussed in many works, user-item interactions of SR generally present the intrinsic power-law distribution, which can be ascended to hierarchy-like structures. Previous methods usually handle such hierarchical information by making user-item sectionalization empirically under Euclidean space, which may cause distortion of user-item representation in real online scenarios. In this paper, we propose a Poincaré-based heterogeneous graph neural network named PHGR to model the sequential pattern information as well as hierarchical information contained in the data of SR scenarios simultaneously. Specifically, for the purpose of explicitly capturing the hierarchical information, we first construct a weighted user-item heterogeneous graph by aliening all the user-item interactions to improve the perception domain of each user from a global view. Then the output of the global representation would be used to complement the local directed item-item homogeneous graph convolution. By defining a novel hyperbolic inner product operator, the global and local graph representation learning are directly conducted in Poincaré ball instead of commonly used projection operation between Poincaré ball and Euclidean space, which could alleviate the cumulative error issue of general bidirectional translation process. Moreover, for the purpose of explicitly capturing the sequential dependency information, we design two types of temporal attention operations under Poincaré ball space. Empirical evaluations on datasets from the public and financial industry show that PHGR outperforms several comparison methods.
Exploring the stimulative effect on following drivers in a consecutive lane-change using microscopic vehicle trajectory data
Venue: arXiv cs.LG 2022
Year: 2022

Improper lane-changing behaviors may result in breakdown of traffic flow and the occurrence of various types of collisions. This study investigates lane-changing behaviors of multiple vehicles and the stimulative effect on following drivers in a consecutive lane-changing scenario. The microscopic trajectory data from the dataset are used for driving behavior analysis.Two discretionary lane-changing vehicle groups constitute a consecutive lane-changing scenario, and not only distance- and speed-related factors but also driving behaviors are taken into account to examine the impacts on the utility of following lane-changing vehicles.A random parameters logit model is developed to capture the driver psychological heterogeneity in the consecutive lane-changing situation.Furthermore, a lane-changing utility prediction model is established based on three supervised learning algorithms to detect the improper lane-changing decision. Results indicate that (1) the consecutive lane-changing behaviors have a significant negative effect on the following lane-changing vehicles after lane-change; (2) the stimulative effect exists in a consecutive lane-change situation and its influence is heterogeneous due to different psychological activities of drivers; and (3) the utility prediction model can be used to detect an improper lane-changing decision.
Real-time Collaborative Multi-Level Modeling by Conflict-Free Replicated Data Types
Venue: arXiv cs.SE 2022
Year: 2022

The need for real-time collaborative solutions in model-driven engineering has been increasing over the past years. Conflict-free replicated data types (CRDT) provide scalable and robust replication mechanisms that align well with the requirements of real-time collaborative environments. In this paper, we propose a real-time collaborative multi-level modeling framework to support advanced modeling scenarios, built on a collection of custom CRDT, specifically tailored for the needs of modeling environments. We demonstrate the benefits of the framework through an illustrative modeling case and compare it with other state-of-the-art modeling frameworks.
Rethinking Task-Incremental Learning Baselines
Venue: arXiv cs.AI 2022
Year: 2022

It is common to have continuous streams of new data that need to be introduced in the system in real-world applications. The model needs to learn newly added capabilities (future tasks) while retaining the old knowledge (past tasks). Incremental learning has recently become increasingly appealing for this problem. Task-incremental learning is a kind of incremental learning where task identity of newly included task (a set of classes) remains known during inference. A common goal of task-incremental methods is to design a network that can operate on minimal size, maintaining decent performance. To manage the stability-plasticity dilemma, different methods utilize replay memory of past tasks, specialized hardware, regularization monitoring etc. However, these methods are still less memory efficient in terms of architecture growth or input data costs. In this study, we present a simple yet effective adjustment network (SAN) for task incremental learning that achieves near state-of-the-art performance while using minimal architectural size without using memory instances compared to previous state-of-the-art approaches. We investigate this approach on both 3D point cloud object (ModelNet40) and 2D image (CIFAR10, CIFAR100, MiniImageNet, MNIST, PermutedMNIST, notMNIST, SVHN, and FashionMNIST) recognition tasks and establish a strong baseline result for a fair comparison with existing methods. On both 2D and 3D domains, we also observe that SAN is primarily unaffected by different task orders in a task-incremental setting.
Detection of Fights in Videos: A Comparison Study of Anomaly Detection and Action Recognition
Venue: arXiv cs.CV 2022
Year: 2022

Detection of fights is an important surveillance application in videos. Most existing methods use supervised binary action recognition. Since frame-level annotations are very hard to get for anomaly detection, weakly supervised learning using multiple instance learning is widely used. This paper explores the detection of fights in videos as one special type of anomaly detection and as binary action recognition. We use the UBI-Fight and NTU-CCTV-Fight datasets for most of the study since they have frame-level annotations. We find that the anomaly detection has similar or even better performance than the action recognition. Furthermore, we study to use anomaly detection as a toolbox to generate training datasets for action recognition in an iterative way conditioned on the performance of the anomaly detection. Experiment results should show that we achieve state-of-the-art performance on three fight detection datasets.
Variable-Input Deep Operator Networks
Venue: arXiv cs.LG 2022
Year: 2022

Existing architectures for operator learning require that the number and locations of sensors (where the input functions are evaluated) remain the same across all training and test samples, significantly restricting the range of their applicability. We address this issue by proposing a novel operator learning framework, termed Variable-Input Deep Operator Network (VIDON), which allows for random sensors whose number and locations can vary across samples. VIDON is invariant to permutations of sensor locations and is proved to be universal in approximating a class of continuous operators. We also prove that VIDON can efficiently approximate operators arising in PDEs. Numerical experiments with a diverse set of PDEs are presented to illustrate the robust performance of VIDON in learning operators.
Decoder Denoising Pretraining for Semantic Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

Semantic segmentation labels are expensive and time consuming to acquire. Hence, pretraining is commonly used to improve the label-efficiency of segmentation models. Typically, the encoder of a segmentation model is pretrained as a classifier and the decoder is randomly initialized. Here, we argue that random initialization of the decoder can be suboptimal, especially when few labeled examples are available. We propose a decoder pretraining approach based on denoising, which can be combined with supervised pretraining of the encoder. We find that decoder denoising pretraining on the ImageNet dataset strongly outperforms encoder-only supervised pretraining. Despite its simplicity, decoder denoising pretraining achieves state-of-the-art results on label-efficient semantic segmentation and offers considerable gains on the Cityscapes, Pascal Context, and ADE20K datasets.