OpenRaiser/Intern-Atlas huggingface dataset:
Fighting over-fitting with quantization for learning deep neural networks on noisy labels
Venue: arXiv cs.CV 2023
Year: 2023

The rising performance of deep neural networks is often empirically attributed to an increase in the available computational power, which allows complex models to be trained upon large amounts of annotated data. However, increased model complexity leads to costly deployment of modern neural networks, while gathering such amounts of data requires huge costs to avoid label noise. In this work, we study the ability of compression methods to tackle both of these problems at once. We hypothesize that quantization-aware training, by restricting the expressivity of neural networks, behaves as a regularization. Thus, it may help fighting overfitting on noisy data while also allowing for the compression of the model at inference. We first validate this claim on a controlled test with manually introduced label noise. Furthermore, we also test the proposed method on Facial Action Unit detection, where labels are typically noisy due to the subtlety of the task. In all cases, our results suggests that quantization significantly improve the results compared with existing baselines, regularization as well as other compression methods.
Addressing Class Variable Imbalance in Federated Semi-supervised Learning
Venue: arXiv cs.LG 2023
Year: 2023

Federated Semi-supervised Learning (FSSL) combines techniques from both fields of federated and semi-supervised learning to improve the accuracy and performance of models in a distributed environment by using a small fraction of labeled data and a large amount of unlabeled data. Without the need to centralize all data in one place for training, it collect updates of model training after devices train models at local, and thus can protect the privacy of user data. However, during the federal training process, some of the devices fail to collect enough data for local training, while new devices will be included to the group training. This leads to an unbalanced global data distribution and thus affect the performance of the global model training. Most of the current research is focusing on class imbalance with a fixed number of classes, while little attention is paid to data imbalance with a variable number of classes. Therefore, in this paper, we propose Federated Semi-supervised Learning for Class Variable Imbalance (FCVI) to solve class variable imbalance. The class-variable learning algorithm is used to mitigate the data imbalance due to changes of the number of classes. Our scheme is proved to be significantly better than baseline methods, while maintaining client privacy.
Protective Self-Adaptive Pruning to Better Compress DNNs
Venue: arXiv cs.CV 2023
Year: 2023

Adaptive network pruning approach has recently drawn significant attention due to its excellent capability to identify the importance and redundancy of layers and filters and customize a suitable pruning solution. However, it remains unsatisfactory since current adaptive pruning methods rely mostly on an additional monitor to score layer and filter importance, and thus faces high complexity and weak interpretability. To tackle these issues, we have deeply researched the weight reconstruction process in iterative prune-train process and propose a Protective Self-Adaptive Pruning (PSAP) method. First of all, PSAP can utilize its own information, weight sparsity ratio, to adaptively adjust pruning ratio of layers before each pruning step. Moreover, we propose a protective reconstruction mechanism to prevent important filters from being pruned through supervising gradients and to avoid unrecoverable information loss as well. Our PSAP is handy and explicit because it merely depends on weights and gradients of model itself, instead of requiring an additional monitor as in early works. Experiments on ImageNet and CIFAR-10 also demonstrate its superiority to current works in both accuracy and compression ratio, especially for compressing with a high ratio or pruning from scratch.
Geometrical Properties of Balls in Sum-Rank Metric
Venue: arXiv cs.IT 2023
Year: 2023

The sum-rank metric arises as an algebraic approach for coding in MIMO block-fading channels and multishot network coding. Codes designed in the sum-rank metric have raised interest in applications such as streaming codes, robust coded distributed storage systems and post-quantum secure cryptosystems. The sum-rank metric can be seen as a generalization of the well-known Hamming metric and the rank metric. As a relatively new metric, there are still many open theoretical problems for codes in the sum-rank metric. In this paper we investigate the geometrical properties of the balls with sum-rank radii motivated by investigating covering properties of codes.
Large-Scale Traffic Signal Control Using Constrained Network Partition and Adaptive Deep Reinforcement Learning
Venue: arXiv cs.AI 2023
Year: 2023

Multi-agent Deep Reinforcement Learning (MADRL) based traffic signal control becomes a popular research topic in recent years. To alleviate the scalability issue of completely centralized RL techniques and the non-stationarity issue of completely decentralized RL techniques on large-scale traffic networks, some literature utilizes a regional control approach where the whole network is firstly partitioned into multiple disjoint regions, followed by applying the centralized RL approach to each region. However, the existing partitioning rules either have no constraints on the topology of regions or require the same topology for all regions. Meanwhile, no existing regional control approach explores the performance of optimal joint action in an exponentially growing regional action space when intersections are controlled by 4-phase traffic signals (EW, EWL, NS, NSL). In this paper, we propose a novel RL training framework named RegionLight to tackle the above limitations. Specifically, the topology of regions is firstly constrained to a star network which comprises one center and an arbitrary number of leaves. Next, the network partitioning problem is modeled as an optimization problem to minimize the number of regions. Then, an Adaptive Branching Dueling Q-Network (ABDQ) model is proposed to decompose the regional control task into several joint signal control sub-tasks corresponding to particular intersections. Subsequently, these sub-tasks maximize the regional benefits cooperatively. Finally, the global control strategy for the whole network is obtained by concatenating the optimal joint actions of all regions. Experimental results demonstrate the superiority of our proposed framework over all baselines under both real and synthetic datasets in all evaluation metrics.
Expressivity Landscape for Logics with Probabilistic Interventionist Counterfactuals
Venue: arXiv cs.LO 2023
Year: 2023

Causal multiteam semantics is a framework where probabilistic dependencies arising from data and causation between variables can be together formalized and studied logically. We consider several logics in the setting of causal multiteam semantics that can express probability comparisons concerning formulae and constants, and encompass interventionist counterfactuals and selective implications that describe consequences of actions and consequences of learning from observations, respectively. We discover complete characterizations of expressivity of the logics in terms of families of linear equations that define the corresponding classes of causal multiteams (together with some closure conditions). The characterizations yield a strict hierarchy of expressive power. Finally, we present some undefinability results based on the characterizations.
Automatic evaluation of herding behavior in towed fishing gear using end-to-end training of CNN and attention-based networks
Venue: arXiv cs.CV 2023
Year: 2023

This paper considers the automatic classification of herding behavior in the cluttered low-visibility environment that typically surrounds towed fishing gear. The paper compares three convolutional and attention-based deep action recognition network architectures trained end-to-end on a small set of video sequences captured by a remotely controlled camera and classified by an expert in fishing technology. The sequences depict a scene in front of a fishing trawl where the conventional herding mechanism has been replaced by directed laser light. The goal is to detect the presence of a fish in the sequence and classify whether or not the fish reacts to the lasers. A two-stream CNN model, a CNN-transformer hybrid, and a pure transformer model were trained end-to-end to achieve 63%, 54%, and 60% 10-fold classification accuracy on the three-class task when compared to the human expert. Inspection of the activation maps learned by the three networks raises questions about the attributes of the sequences the models may be learning, specifically whether changes in viewpoint introduced by human camera operators that affect the position of laser lines in the video frames may interfere with the classification. This underlines the importance of careful experimental design when capturing scientific data for automatic end-to-end evaluation and the usefulness of inspecting the trained models.
Motion Matters: Neural Motion Transfer for Better Camera Physiological Measurement
Venue: arXiv cs.CV 2023
Year: 2023

Machine learning models for camera-based physiological measurement can have weak generalization due to a lack of representative training data. Body motion is one of the most significant sources of noise when attempting to recover the subtle cardiac pulse from a video. We explore motion transfer as a form of data augmentation to introduce motion variation while preserving physiological changes of interest. We adapt a neural video synthesis approach to augment videos for the task of remote photoplethysmography (rPPG) and study the effects of motion augmentation with respect to 1) the magnitude and 2) the type of motion. After training on motion-augmented versions of publicly available datasets, we demonstrate a 47% improvement over existing inter-dataset results using various state-of-the-art methods on the PURE dataset. We also present inter-dataset results on five benchmark datasets to show improvements of up to 79% using TS-CAN, a neural rPPG estimation method. Our findings illustrate the usefulness of motion transfer as a data augmentation technique for improving the generalization of models for camera-based physiological sensing. We release our code for using motion transfer as a data augmentation technique on three publicly available datasets, UBFC-rPPG, PURE, and SCAMPS, and models pre-trained on motion-augmented data here: https://motion-matters.github.io/
Understand Legal Documents with Contextualized Large Language Models
Venue: arXiv cs.CL 2023
Year: 2023

The growth of pending legal cases in populous countries, such as India, has become a major issue. Developing effective techniques to process and understand legal documents is extremely useful in resolving this problem. In this paper, we present our systems for SemEval-2023 Task 6: understanding legal texts (Modi et al., 2023). Specifically, we first develop the Legal-BERT-HSLN model that considers the comprehensive context information in both intra- and inter-sentence levels to predict rhetorical roles (subtask A) and then train a Legal-LUKE model, which is legal-contextualized and entity-aware, to recognize legal entities (subtask B). Our evaluations demonstrate that our designed models are more accurate than baselines, e.g., with an up to 15.0% better F1 score in subtask B. We achieved notable performance in the task leaderboard, e.g., 0.834 micro F1 score, and ranked No.5 out of 27 teams in subtask A.
Mapping Language Literacy At Scale: A Case Study on Facebook
Venue: arXiv cs.CY 2023
Year: 2023

Literacy is one of the most fundamental skills for people to access and navigate today's digital environment. This work systematically studies the language literacy skills of online populations for more than 160 countries and regions across the world, including many low-resourced countries where official literacy data are particularly sparse. Leveraging public data on Facebook, we develop a population-level literacy estimate for the online population that is based on aggregated and de-identified public posts written by adult Facebook users globally, significantly improving both the coverage and resolution of existing literacy tracking data. We found that, on Facebook, women collectively show higher language literacy than men in many countries, but substantial gaps remain in Africa and Asia. Further, our analysis reveals a considerable regional gap within a country that is associated with multiple socio-technical inequalities, suggesting an "inequality paradox" -- where the online language skill disparity interacts with offline socioeconomic inequalities in complex ways. These findings have implications for global women's empowerment and socioeconomic inequalities.
Community detection in complex networks via node similarity, graph representation learning, and hierarchical clustering
Venue: arXiv cs.SI 2023
Year: 2023

Community detection is a critical challenge in analysing real graphs, including social, transportation, citation, cybersecurity, and many other networks. This article proposes three new, general, hierarchical frameworks to deal with this task. The introduced approach supports various linkage-based clustering algorithms, vertex proximity matrices, and graph representation learning models. We compare over a hundred module combinations on the Stochastic Block Model graphs and real-life datasets. We observe that our best pipelines (Wasserman-Faust and the mutual information-based PPMI proximity, as well as the deep learning-based DNGR representations) perform competitively to the state-of-the-art Leiden and Louvain algorithms. At the same time, unlike the latter, they remain hierarchical. Thus, they output a series of nested partitions of all possible cardinalities which are compatible with each other. This feature is crucial when the number of correct partitions is unknown in advance.
Automated deep learning segmentation of high-resolution 7 T postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases
Venue: arXiv cs.CV 2023
Year: 2023

Postmortem MRI allows brain anatomy to be examined at high resolution and to link pathology measures with morphometric measurements. However, automated segmentation methods for brain mapping in postmortem MRI are not well developed, primarily due to limited availability of labeled datasets, and heterogeneity in scanner hardware and acquisition protocols. In this work, we present a high resolution of 135 postmortem human brain tissue specimens imaged at 0.3 mm$^{3}$ isotropic using a T2w sequence on a 7T whole-body MRI scanner. We developed a deep learning pipeline to segment the cortical mantle by benchmarking the performance of nine deep neural architectures, followed by post-hoc topological correction. We then segment four subcortical structures (caudate, putamen, globus pallidus, and thalamus), white matter hyperintensities, and the normal appearing white matter. We show generalizing capabilities across whole brain hemispheres in different specimens, and also on unseen images acquired at 0.28 mm^3 and 0.16 mm^3 isotropic T2*w FLASH sequence at 7T. We then compute localized cortical thickness and volumetric measurements across key regions, and link them with semi-quantitative neuropathological ratings. Our code, Jupyter notebooks, and the containerized executables are publicly available at: https://pulkit-khandelwal.github.io/exvivo-brain-upenn
Practical compilation of fexprs using partial evaluation: Fexprs can performantly replace macros in purely-functional Lisp
Venue: arXiv cs.PL 2023
Year: 2023

Macros are a common part of Lisp languages, and one of their most lauded features. Much research has gone into making macros both safer and more powerful resulting in developments in multiple areas, including maintaining hygiene, and typed program staging. However, macros do suffer from various downsides, including being second-class. Particularly egregious for eager functional programming, they are unable to be passed to higher-order functions or freely composed. Fexprs, as reformulated by John Shutt, provide a first-class and more powerful alternative to macros that meshes well with pure functional programming. Unfortunately, naive execution of fexprs is much slower than macros due to re-executing unoptimized operative combiner code at runtime that, in a macro-based language, would have been expanded and then optimized at compile time. To show that fexprs can be practical replacements for macros, we formulate a small purely functional fexpr based Lisp, Kraken, with an online partial evaluation and compilation framework that supports first-class, partially-static-data environments and can completely optimize away fexprs that are used and written in the style of macros. We show our partial evaluation and compilation framework produces code that is more than 70,000 times faster than naive interpretation due to the elimination of repeated work and exposure of static information enabling additional optimization. In addition, our Kraken compiler performs better compared to existing interpreted languages that support fexprs, including improving on NewLisp's fexpr performance by 233x on one benchmark.
Reducing Air Pollution through Machine Learning
Venue: arXiv cs.LG 2023
Year: 2023

This paper presents a data-driven approach to mitigate the effects of air pollution from industrial plants on nearby cities by linking operational decisions with weather conditions. Our method combines predictive and prescriptive machine learning models to forecast short-term wind speed and direction and recommend operational decisions to reduce or pause the industrial plant's production. We exhibit several trade-offs between reducing environmental impact and maintaining production activities. The predictive component of our framework employs various machine learning models, such as gradient-boosted tree-based models and ensemble methods, for time series forecasting. The prescriptive component utilizes interpretable optimal policy trees to propose multiple trade-offs, such as reducing dangerous emissions by 33-47% and unnecessary costs by 40-63%. Our deployed models significantly reduced forecasting errors, with a range of 38-52% for less than 12-hour lead time and 14-46% for 12 to 48-hour lead time compared to official weather forecasts. We have successfully implemented the predictive component at the OCP Safi site, which is Morocco's largest chemical industrial plant, and are currently in the process of deploying the prescriptive component. Our framework enables sustainable industrial development by eliminating the pollution-industrial activity trade-off through data-driven weather-based operational decisions, significantly enhancing factory optimization and sustainability. This modernizes factory planning and resource allocation while maintaining environmental compliance. The predictive component has boosted production efficiency, leading to cost savings and reduced environmental impact by minimizing air pollution.
PICASO: Enhancing API Recommendations with Relevant Stack Overflow Posts
Venue: arXiv cs.SE 2023
Year: 2023

While having options could be liberating, too many options could lead to the sub-optimal solution being chosen. This is not an exception in the software engineering domain. Nowadays, API has become imperative in making software developers' life easier. APIs help developers implement a function faster and more efficiently. However, given the large number of open-source libraries to choose from, choosing the right APIs is not a simple task. Previous studies on API recommendation leverage natural language (query) to identify which API would be suitable for the given task. However, these studies only consider one source of input, i.e., GitHub or Stack Overflow, independently. There are no existing approaches that utilize Stack Overflow to help generate better API sequence recommendations from queries obtained from GitHub. Therefore, in this study, we aim to provide a framework that could improve the result of the API sequence recommendation by leveraging information from Stack Overflow. In this work, we propose PICASO, which leverages a bi-encoder to do contrastive learning and a cross-encoder to build a classification model in order to find a semantically similar Stack Overflow post given an annotation (i.e., code comment). Subsequently, PICASO then uses the Stack Overflow's title as a query expansion. PICASO then uses the extended queries to fine-tune a CodeBERT, resulting in an API sequence generation model. Based on our experiments, we found that incorporating the Stack Overflow information into CodeBERT would improve the performance of API sequence generation's BLEU-4 score by 10.8%.
SiamTHN: Siamese Target Highlight Network for Visual Tracking
Venue: arXiv cs.CV 2023
Year: 2023

Siamese network based trackers develop rapidly in the field of visual object tracking in recent years. The majority of siamese network based trackers now in use treat each channel in the feature maps generated by the backbone network equally, making the similarity response map sensitive to background influence and hence challenging to focus on the target region. Additionally, there are no structural links between the classification and regression branches in these trackers, and the two branches are optimized separately during training. Therefore, there is a misalignment between the classification and regression branches, which results in less accurate tracking results. In this paper, a Target Highlight Module is proposed to help the generated similarity response maps to be more focused on the target region. To reduce the misalignment and produce more precise tracking results, we propose a corrective loss to train the model. The two branches of the model are jointly tuned with the use of corrective loss to produce more reliable prediction results. Experiments on 5 challenging benchmark datasets reveal that the method outperforms current models in terms of performance, and runs at 38 fps, proving its effectiveness and efficiency.
System and Design Technology Co-optimization of SOT-MRAM for High-Performance AI Accelerator Memory System
Venue: arXiv cs.AR 2023
Year: 2023

SoCs are now designed with their own AI accelerator segment to accommodate the ever-increasing demand of Deep Learning (DL) applications. With powerful MAC engines for matrix multiplications, these accelerators show high computing performance. However, because of limited memory resources (i.e., bandwidth and capacity), they fail to achieve optimum system performance during large batch training and inference. In this work, we propose a memory system with high on-chip capacity and bandwidth to shift the gear of AI accelerators from memory-bound to achieving system-level peak performance. We develop the memory system with DTCO-enabled customized SOT-MRAM as large on-chip memory through STCO and detailed characterization of the DL workloads. %We evaluate our workload-aware memory system on the CV and NLP benchmarks and observe significant PPA improvement compared to an SRAM-based in both inference and training modes. Our workload-aware memory system achieves 8X energy and 9X latency improvement on Computer Vision (CV) benchmarks in training and 8X energy and 4.5X latency improvement on Natural Language Processing (NLP) benchmarks in training while consuming only around 50% of SRAM area at iso-capacity.