OpenRaiser/Intern-Atlas huggingface dataset:
Adaptive Perception Transformer for Temporal Action Localization
Venue: arXiv cs.CV 2022
Year: 2022

Temporal action localization aims to predict the boundary and category of each action instance in untrimmed long videos. Most of previous methods based on anchors or proposals neglect the global-local context interaction in entire video sequences. Besides, their multi-stage designs cannot generate action boundaries and categories straightforwardly. To address the above issues, this paper proposes a end-to-end model, called Adaptive Perception transformer (AdaPerFormer for short). Specifically, AdaPerFormer explores a dual-branch attention mechanism. One branch takes care of the global perception attention, which can model entire video sequences and aggregate global relevant contexts. While the other branch concentrates on the local convolutional shift to aggregate intra-frame and inter-frame information through our bidirectional shift operation. The end-to-end nature produces the boundaries and categories of video actions without extra steps. Extensive experiments together with ablation studies are provided to reveal the effectiveness of our design. Our method obtains competitive performance on the THUMOS14 and ActivityNet-1.3 dataset.
Massive Data Generation for Deep Learning-aided Wireless Systems Using Meta Learning and Generative Adversarial Network
Venue: arXiv cs.IT 2022
Year: 2022

As an entirely-new paradigm to design the communication systems, deep learning (DL), an approach that the machine learns the desired wireless function, has received much attention recently. In order to fully realize the benefit of DL-aided wireless system, we need to collect a large number of training samples. Unfortunately, collecting massive samples in the real environments is very challenging since it requires significant signal transmission overhead. In this paper, we propose a new type of data acquisition framework for DL-aided wireless systems. In our work, generative adversarial network (GAN) is used to generate samples approximating the real samples. To reduce the amount of training samples required for the wireless data generation, we train GAN with the help of the meta learning. From numerical experiments, we show that the DL model trained by the GAN generated samples performs close to that trained by the real samples.
Bottom-Up 2D Pose Estimation via Dual Anatomical Centers for Small-Scale Persons
Venue: arXiv cs.CV 2022
Year: 2022

In multi-person 2D pose estimation, the bottom-up methods simultaneously predict poses for all persons, and unlike the top-down methods, do not rely on human detection. However, the SOTA bottom-up methods' accuracy is still inferior compared to the existing top-down methods. This is due to the predicted human poses being regressed based on the inconsistent human bounding box center and the lack of human-scale normalization, leading to the predicted human poses being inaccurate and small-scale persons being missed. To push the envelope of the bottom-up pose estimation, we firstly propose multi-scale training to enhance the network to handle scale variation with single-scale testing, particularly for small-scale persons. Secondly, we introduce dual anatomical centers (i.e., head and body), where we can predict the human poses more accurately and reliably, especially for small-scale persons. Moreover, existing bottom-up methods use multi-scale testing to boost the accuracy of pose estimation at the price of multiple additional forward passes, which weakens the efficiency of bottom-up methods, the core strength compared to top-down methods. By contrast, our multi-scale training enables the model to predict high-quality poses in a single forward pass (i.e., single-scale testing). Our method achieves 38.4\% improvement on bounding box precision and 39.1\% improvement on bounding box recall over the state of the art (SOTA) on the challenging small-scale persons subset of COCO. For the human pose AP evaluation, we achieve a new SOTA (71.0 AP) on the COCO test-dev set with the single-scale testing. We also achieve the top performance (40.3 AP) on OCHuman dataset in cross-dataset evaluation.
Learning to Prune Instances of Steiner Tree Problem in Graphs
Venue: arXiv cs.DS 2022
Year: 2022

We consider the Steiner tree problem on graphs where we are given a set of nodes and the goal is to find a tree sub-graph of minimum weight that contains all nodes in the given set, potentially including additional nodes. This is a classical NP-hard combinatorial optimisation problem. In recent years, a machine learning framework called learning-to-prune has been successfully used for solving a diverse range of combinatorial optimisation problems. In this paper, we use this learning framework on the Steiner tree problem and show that even on this problem, the learning-to-prune framework results in computing near-optimal solutions at a fraction of the time required by commercial ILP solvers. Our results underscore the potential of the learning-to-prune framework in solving various combinatorial optimisation problems.
Seamless Tracking of Group Targets and Ungrouped Targets Using Belief Propagation
Venue: arXiv cs.LG 2022
Year: 2022

This paper considers the problem of tracking a large-scale number of group targets. Usually, multi-target in most tracking scenarios are assumed to have independent motion and are well-separated. However, for group target tracking (GTT), the targets within groups are closely spaced and move in a coordinated manner, the groups can split or merge, and the numbers of targets in groups may be large, which lead to more challenging data association, filtering and computation problems. Within the belief propagation (BP) framework, we propose a scalable group target belief propagation (GTBP) method by jointly inferring target existence variables, group structure, data association and target states. The method can efficiently calculate the approximations of the marginal posterior distributions of these variables by performing belief propagation on the devised factor graph. As a consequence, GTBP is capable of capturing the changes in group structure, e.g., group splitting and merging. Furthermore, we model the evolution of targets as the co-action of the group or single-target motions specified by the possible group structures and corresponding probabilities. This flexible modeling enables seamless and simultaneous tracking of multiple group targets and ungrouped targets. Particularly, GTBP has excellent scalability and low computational complexity. It not only maintains the same scalability as BP, i.e., scaling linearly in the number of sensor measurements and quadratically in the number of targets, but also only scales linearly in the number of preserved group partitions. Finally, numerical experiments are presented to demonstrate the effectiveness and scalability of the proposed GTBP method.
Reduced-PINN: An Integration-Based Physics-Informed Neural Networks for Stiff ODEs
Venue: arXiv cs.LG 2022
Year: 2022

Physics-informed neural networks (PINNs) have recently received much attention due to their capabilities in solving both forward and inverse problems. For training a deep neural network associated with a PINN, one typically constructs a total loss function using a weighted sum of different loss terms and then tries to minimize that. This approach often becomes problematic for solving stiff equations since it cannot consider adaptive increments. Many studies reported the poor performance of the PINN and its challenges in simulating stiff chemical active issues with administering conditions of stiff ordinary differential conditions (ODEs). Studies show that stiffness is the primary cause of the failure of the PINN in simulating stiff kinetic systems. Here, we address this issue by proposing a reduced weak-form of the loss function, which led to a new PINN architecture, further named as Reduced-PINN, that utilizes a reduced-order integration method to enable the PINN to solve stiff chemical kinetics. The proposed Reduced-PINN can be applied to various reaction-diffusion systems involving stiff dynamics. To this end, we transform initial value problems (IVPs) to their equivalent integral forms and solve the resulting integral equations using physics-informed neural networks. In our derived integral-based optimization process, there is only one term without explicitly incorporating loss terms associated with ordinary differential equation (ODE) and initial conditions (ICs). To illustrate the capabilities of Reduced-PINN, we used it to simulate multiple stiff/mild second-order ODEs. We show that Reduced-PINN captures the solution accurately for a stiff scalar ODE. We also validated the Reduced-PINN against a stiff system of linear ODEs.
Even vertex $ζ$-graceful labeling on Rough Graph
Venue: arXiv cs.AI 2022
Year: 2022

Rough graph is the graphical structure of information system with imprecise knowledge. Tong He designed the properties of rough graph in 2006[6] and following that He and Shi introduced the notion of edge rough graph[7]. He et al developed the concept of weighted rough graph with weighted attributes[6]. In this paper, we introduce a new type of labeling called Even vertex ζ- graceful labeling as weight value for edges. We investigate this labeling for some special graphs like rough path graph, rough cycle graph, rough comb graph, rough ladder graph and rough star graph.
Runtime reliability monitoring for complex fault-tolerance policies
Venue: arXiv cs.SE 2022
Year: 2022

Reliability of complex Cyber-Physical Systems is necessary to guarantee availability and/or safety of the provided services. Diverse and complex fault tolerance policies are adopted to enhance reliability, that include a varied mix of redundancy and dynamic reconfiguration to address hardware reliability, as well as specific software reliability techniques like diversity or software rejuvenation. These complex policies call for flexible runtime health checks of system executions that go beyond conventional runtime monitoring of pre-programmed health conditions, also in order to minimize maintenance costs. Defining a suitable monitoring model in the application of this method in complex systems is still a challenge. In this paper we propose a novel approach, Reliability Based Monitoring (RBM), for a flexible runtime monitoring of reliability in complex systems, that exploits a hierarchical reliability model periodically applied to runtime diagnostics data: this allows to dynamically plan maintenance activities aimed at prevent failures. As a proof of concept, we show how to apply RBM to a 2oo3 software system implementing different fault-tolerant policies.
Clustering Egocentric Images in Passive Dietary Monitoring with Self-Supervised Learning
Venue: arXiv cs.CV 2022
Year: 2022

In our recent dietary assessment field studies on passive dietary monitoring in Ghana, we have collected over 250k in-the-wild images. The dataset is an ongoing effort to facilitate accurate measurement of individual food and nutrient intake in low and middle income countries with passive monitoring camera technologies. The current dataset involves 20 households (74 subjects) from both the rural and urban regions of Ghana, and two different types of wearable cameras were used in the studies. Once initiated, wearable cameras continuously capture subjects' activities, which yield massive amounts of data to be cleaned and annotated before analysis is conducted. To ease the data post-processing and annotation tasks, we propose a novel self-supervised learning framework to cluster the large volume of egocentric images into separate events. Each event consists of a sequence of temporally continuous and contextually similar images. By clustering images into separate events, annotators and dietitians can examine and analyze the data more efficiently and facilitate the subsequent dietary assessment processes. Validated on a held-out test set with ground truth labels, the proposed framework outperforms baselines in terms of clustering quality and classification accuracy.
Repairing Activity Start Times to Improve Business Process Simulation
Venue: arXiv cs.OH 2022
Year: 2022

Business Process Simulation (BPS) is a common technique to estimate the impact of business process changes, e.g. what would be the cycle time of a process if the number of traces increases? The starting point of BPS is a business process model annotated with simulation parameters (a BPS model). Several studies have proposed methods to automatically discover BPS models from event logs -- extracted from enterprise information systems -- via process mining techniques. These approaches model the processing time of each activity based on the start and end timestamps recorded in the event log. In practice, however, it is common that the recorded start times do not precisely reflect the actual start of the activities. For example, a resource starts working on an activity, but its start time is not recorded until she/he interacts with the system. If not corrected, these situations induce waiting times in which the resource is considered to be free, while she/he is actually working. To address this limitation, this article proposes a technique to identify the waiting time previous to each activity instance in which the resource is actually working on them, and repair their start time so that they reflect the actual processing time. The idea of the proposed technique is that, as far as simulation is concerned, an activity instance may start once it is enabled and the corresponding resource is available. Accordingly, for each activity instance, the proposed technique estimates the activity enablement and the resource availability time based on the information available in the event log, and repairs the start time to include the non-recorded processing time. An empirical evaluation involving eight real-life event logs shows that the proposed approach leads to BPS models that closely reflect the temporal dynamics of the process.
Continuous Deep Learning: A Workflow to Bring Models into Production
Venue: arXiv cs.SE 2022
Year: 2022

Researchers have been highly active to investigate the classical machine learning workflow and integrate best practices from the software engineering lifecycle. However, deep learning exhibits deviations that are not yet covered in this conceptual development process. This includes the requirement of dedicated hardware, dispensable feature engineering, extensive hyperparameter optimization, large-scale data management, and model compression to reduce size and inference latency. Individual problems of deep learning are under thorough examination, and numerous concepts and implementations have gained traction. Unfortunately, the complete end-to-end development process still remains unspecified. In this paper, we define a detailed deep learning workflow that incorporates the aforementioned characteristics on the baseline of the classical machine learning workflow. We further transferred the conceptual idea into practice by building a prototypic deep learning system using some of the latest technologies on the market. To examine the feasibility of the workflow, two use cases are applied to the prototype.
Bokeh-Loss GAN: Multi-Stage Adversarial Training for Realistic Edge-Aware Bokeh
Venue: arXiv cs.CV 2022
Year: 2022

In this paper, we tackle the problem of monocular bokeh synthesis, where we attempt to render a shallow depth of field image from a single all-in-focus image. Unlike in DSLR cameras, this effect can not be captured directly in mobile cameras due to the physical constraints of the mobile aperture. We thus propose a network-based approach that is capable of rendering realistic monocular bokeh from single image inputs. To do this, we introduce three new edge-aware Bokeh Losses based on a predicted monocular depth map, that sharpens the foreground edges while blurring the background. This model is then finetuned using an adversarial loss to generate a realistic Bokeh effect. Experimental results show that our approach is capable of generating a pleasing, natural Bokeh effect with sharp edges while handling complicated scenes.
Understanding the Power of Evolutionary Computation for GPU Code Optimization
Venue: arXiv cs.SE 2022
Year: 2022

Achieving high performance for GPU codes requires developers to have significant knowledge in parallel programming and GPU architectures, and in-depth understanding of the application. This combination makes it challenging to find performance optimizations for GPU-based applications, especially in scientific computing. This paper shows that significant speedups can be achieved on two quite different scientific workloads using the tool, GEVO, to improve performance over human-optimized GPU code. GEVO uses evolutionary computation to find code edits that improve the runtime of a multiple sequence alignment kernel and a SARS-CoV-2 simulation by 28.9% and 29% respectively. Further, when GEVO begins with an early, unoptimized version of the sequence alignment program, it finds an impressive 30 times speedup -- a performance improvement similar to that of the hand-tuned version. This work presents an in-depth analysis of the discovered optimizations, revealing that the primary sources of improvement vary across applications; that most of the optimizations generalize across GPU architectures; and that several of the most important optimizations involve significant code interdependencies. The results showcase the potential of automated program optimization tools to help reduce the optimization burden for scientific computing developers and enhance performance portability for domain-specific accelerators.
Fundamentals of Task-Agnostic Data Valuation
Venue: arXiv cs.LG 2022
Year: 2022

We study valuing the data of a data owner/seller for a data seeker/buyer. Data valuation is often carried out for a specific task assuming a particular utility metric, such as test accuracy on a validation set, that may not exist in practice. In this work, we focus on task-agnostic data valuation without any validation requirements. The data buyer has access to a limited amount of data (which could be publicly available) and seeks more data samples from a data seller. We formulate the problem as estimating the differences in the statistical properties of the data at the seller with respect to the baseline data available at the buyer. We capture these statistical differences through second moment by measuring diversity and relevance of the seller's data for the buyer; we estimate these measures through queries to the seller without requesting raw data. We design the queries with the proposed approach so that the seller is blind to the buyer's raw data and has no knowledge to fabricate responses to queries to obtain a desired outcome of the diversity and relevance trade-off.We will show through extensive experiments on real tabular and image datasets that the proposed estimates capture the diversity and relevance of the seller's data for the buyer.
A Compact Pretraining Approach for Neural Language Models
Venue: arXiv cs.CL 2022
Year: 2022

Domain adaptation for large neural language models (NLMs) is coupled with massive amounts of unstructured data in the pretraining phase. In this study, however, we show that pretrained NLMs learn in-domain information more effectively and faster from a compact subset of the data that focuses on the key information in the domain. We construct these compact subsets from the unstructured data using a combination of abstractive summaries and extractive keywords. In particular, we rely on BART to generate abstractive summaries, and KeyBERT to extract keywords from these summaries (or the original unstructured text directly). We evaluate our approach using six different settings: three datasets combined with two distinct NLMs. Our results reveal that the task-specific classifiers trained on top of NLMs pretrained using our method outperform methods based on traditional pretraining, i.e., random masking on the entire data, as well as methods without pretraining. Further, we show that our strategy reduces pretraining time by up to five times compared to vanilla pretraining. The code for all of our experiments is publicly available at https://github.com/shahriargolchin/compact-pretraining.
Static Seeding and Clustering of LSTM Embeddings to Learn from Loosely Time-Decoupled Events
Venue: arXiv cs.LG 2022
Year: 2022

Humans learn from the occurrence of events in a different place and time to predict similar trajectories of events. We define Loosely Decoupled Timeseries (LDT) phenomena as two or more events that could happen in different places and across different timelines but share similarities in the nature of the event and the properties of the location. In this work we improve on the use of Recurring Neural Networks (RNN), in particular Long Short-Term Memory (LSTM) networks, to enable AI solutions that generate better timeseries predictions for LDT. We use similarity measures between timeseries based on the trends and introduce embeddings representing those trends. The embeddings represent properties of the event which, coupled with the LSTM structure, can be clustered to identify similar temporally unaligned events. In this paper, we explore methods of seeding a multivariate LSTM from time-invariant data related to the geophysical and demographic phenomena being modeled by the LSTM. We apply these methods on the timeseries data derived from the COVID-19 detected infection and death cases. We use publicly available socio-economic data to seed the LSTM models, creating embeddings, to determine whether such seeding improves case predictions. The embeddings produced by these LSTMs are clustered to identify best-matching candidates for forecasting an evolving timeseries. Applying this method, we show an improvement in 10-day moving average predictions of disease propagation at the US County level.
Toward Robust Graph Semi-Supervised Learning against Extreme Data Scarcity
Venue: arXiv cs.LG 2022
Year: 2022

The success of graph neural networks on graph-based web mining highly relies on abundant human-annotated data, which is laborious to obtain in practice. When only few labeled nodes are available, how to improve their robustness is a key to achieve replicable and sustainable graph semi-supervised learning. Though self-training has been shown to be powerful for semi-supervised learning, its application on graph-structured data may fail because (1) larger receptive fields are not leveraged to capture long-range node interactions, which exacerbates the difficulty of propagating feature-label patterns from labeled nodes to unlabeled nodes; and (2) limited labeled data makes it challenging to learn well-separated decision boundaries for different node classes without explicitly capturing the underlying semantic structure. To address the challenges of capturing informative structural and semantic knowledge, we propose a new graph data augmentation framework, AGST (Augmented Graph Self-Training), which is built with two new (i.e., structural and semantic) augmentation modules on top of a decoupled GST backbone. In this work, we investigate whether this novel framework can learn a robust graph predictive model under the low-data context. We conduct comprehensive evaluations on semi-supervised node classification under different scenarios of limited labeled-node data. The experimental results demonstrate the unique contributions of the novel data augmentation framework for node classification with few labeled data.