OpenRaiser/Intern-Atlas huggingface dataset:
Checkpoint and Restart: An Energy Consumption Characterization in Clusters
Venue: arXiv cs.DC 2024
Year: 2024

The fault tolerance method currently used in High Performance Computing (HPC) is the rollback-recovery method by using checkpoints. This, like any other fault tolerance method, adds an additional energy consumption to that of the execution of the application. The objective of this work is to determine the factors that affect the energy consumption of the computing nodes on homogeneous cluster, when performing checkpoint and restart operations, on SPMD (Single Program Multiple Data) applications. We have focused on the energetic study of compute nodes, contemplating different configurations of hardware and software parameters. We studied the effect of performance states (states P) and power states (states C) of processors, application problem size, checkpoint software (DMTCP) and distributed file system (NFS) configuration. The results analysis allowed to identify opportunities to reduce the energy consumption of checkpoint and restart operations.
Quantifying Liveness and Safety of Avalanche's Snowball
Venue: arXiv cs.DC 2024
Year: 2024

This work examines the resilience properties of the Snowball and Avalanche protocols that underlie the popular Avalanche blockchain. We experimentally quantify the resilience of Snowball using a simulation implemented in Rust, where the adversary strategically rebalances the network to delay termination. We show that in a network of $n$ nodes of equal stake, the adversary is able to break liveness when controlling $Ω(\sqrt{n})$ nodes. Specifically, for $n = 2000$, a simple adversary controlling $5.2\%$ of stake can successfully attack liveness. When the adversary is given additional information about the state of the network (without any communication or other advantages), the stake needed for a successful attack is as little as $2.8\%$. We show that the adversary can break safety in time exponentially dependent on their stake, and inversely linearly related to the size of the network, e.g. in 265 rounds in expectation when the adversary controls $25\%$ of a network of 3000. We conclude that Snowball and Avalanche are akin to Byzantine reliable broadcast protocols as opposed to consensus.
Temporal Order Preserved Optimal Transport-based Cross-modal Knowledge Transfer Learning for ASR
Venue: arXiv cs.SD 2024
Year: 2024

Transferring linguistic knowledge from a pretrained language model (PLM) to an acoustic model has been shown to greatly improve the performance of automatic speech recognition (ASR). However, due to the heterogeneous feature distributions in cross-modalities, designing an effective model for feature alignment and knowledge transfer between linguistic and acoustic sequences remains a challenging task. Optimal transport (OT), which efficiently measures probability distribution discrepancies, holds great potential for aligning and transferring knowledge between acoustic and linguistic modalities. Nonetheless, the original OT treats acoustic and linguistic feature sequences as two unordered sets in alignment and neglects temporal order information during OT coupling estimation. Consequently, a time-consuming pretraining stage is required to learn a good alignment between the acoustic and linguistic representations. In this paper, we propose a Temporal Order Preserved OT (TOT)-based Cross-modal Alignment and Knowledge Transfer (CAKT) (TOT-CAKT) for ASR. In the TOT-CAKT, local neighboring frames of acoustic sequences are smoothly mapped to neighboring regions of linguistic sequences, preserving their temporal order relationship in feature alignment and matching. With the TOT-CAKT model framework, we conduct Mandarin ASR experiments with a pretrained Chinese PLM for linguistic knowledge transfer. Our results demonstrate that the proposed TOT-CAKT significantly improves ASR performance compared to several state-of-the-art models employing linguistic knowledge transfer, and addresses the weaknesses of the original OT-based method in sequential feature alignment for ASR.
LSTMSE-Net: Long Short Term Speech Enhancement Network for Audio-visual Speech Enhancement
Venue: arXiv cs.SD 2024
Year: 2024

In this paper, we propose long short term memory speech enhancement network (LSTMSE-Net), an audio-visual speech enhancement (AVSE) method. This innovative method leverages the complementary nature of visual and audio information to boost the quality of speech signals. Visual features are extracted with VisualFeatNet (VFN), and audio features are processed through an encoder and decoder. The system scales and concatenates visual and audio features, then processes them through a separator network for optimized speech enhancement. The architecture highlights advancements in leveraging multi-modal data and interpolation techniques for robust AVSE challenge systems. The performance of LSTMSE-Net surpasses that of the baseline model from the COG-MHEAR AVSE Challenge 2024 by a margin of 0.06 in scale-invariant signal-to-distortion ratio (SISDR), $0.03$ in short-time objective intelligibility (STOI), and $1.32$ in perceptual evaluation of speech quality (PESQ). The source code of the proposed LSTMSE-Net is available at \url{https://github.com/mtanveer1/AVSEC-3-Challenge}.
SlipNet: Enhancing Slip Cost Mapping for Autonomous Navigation on Heterogeneous and Deformable Terrains
Venue: arXiv cs.RO 2024
Year: 2024

Autonomous space rovers face significant challenges when navigating deformable and heterogeneous terrains due to variability in soil properties, which can lead to severe wheel slip, compromising navigation efficiency and increasing the risk of entrapment. To address this problem, we introduce SlipNet, a novel approach for predicting wheel slip in segmented regions of diverse terrain surfaces without relying on prior terrain classification. SlipNet employs dynamic terrain segmentation and slip assignment techniques on previously unseen data, enhancing rover navigation capabilities in uncertain environments. We developed a synthetic data generation framework using the high-fidelity Vortex Studio simulator to create realistic datasets that replicate a wide range of deformable terrain conditions for training and evaluation. Extensive simulation results demonstrate that our model, combining DeepLab v3+ with SlipNet, significantly outperforms the state-of-the-art TerrainNet method, achieving lower mean absolute error (MAE) across five distinct terrain samples. These findings highlight the effectiveness of SlipNet in improving rover navigation in challenging terrains.
ADHD diagnosis based on action characteristics recorded in videos using machine learning
Venue: arXiv cs.CV 2024
Year: 2024

Demand for ADHD diagnosis and treatment is increasing significantly and the existing services are unable to meet the demand in a timely manner. In this work, we introduce a novel action recognition method for ADHD diagnosis by identifying and analysing raw video recordings. Our main contributions include 1) designing and implementing a test focusing on the attention and hyperactivity/impulsivity of participants, recorded through three cameras; 2) implementing a novel machine learning ADHD diagnosis system based on action recognition neural networks for the first time; 3) proposing classification criteria to provide diagnosis results and analysis of ADHD action characteristics.
Wikipedia in Wartime: Experiences of Wikipedians Maintaining Articles About the Russia-Ukraine War
Venue: arXiv cs.SI 2024
Year: 2024

How do Wikipedians maintain an accurate encyclopedia during an ongoing geopolitical conflict where state actors might seek to spread disinformation or conduct an information operation? In the context of the Russia-Ukraine War, this question becomes more pressing, given the Russian government's extensive history of orchestrating information campaigns. We conducted an interview study with 13 expert Wikipedians involved in the Russo-Ukrainian War topic area on the English-language edition of Wikipedia. While our participants did not perceive there to be clear evidence of a state-backed information operation, they agreed that war-related articles experienced high levels of disruptive editing from both Russia-aligned and Ukraine-aligned accounts. The English-language edition of Wikipedia had existing policies and processes at its disposal to counter such disruption. State-backed or not, the disruptive activity created time-intensive maintenance work for our participants. Finally, participants considered English-language Wikipedia to be more resilient than social media in preventing the spread of false information online. We conclude by discussing sociotechnical implications for Wikipedia and social platforms.
Investigating Mixed Reality for Communication Between Humans and Mobile Manipulators
Venue: arXiv cs.RO 2024
Year: 2024

This article investigates mixed reality (MR) to enhance human-robot collaboration (HRC). The proposed solution adopts MR as a communication layer to convey a mobile manipulator's intentions and upcoming actions to the humans with whom it interacts, thus improving their collaboration. A user study involving 20 participants demonstrated the effectiveness of this MR-focused approach in facilitating collaborative tasks, with a positive effect on overall collaboration performances and human satisfaction.
Double Machine Learning at Scale to Predict Causal Impact of Customer Actions
Venue: arXiv cs.LG 2024
Year: 2024

Causal Impact (CI) of customer actions are broadly used across the industry to inform both short- and long-term investment decisions of various types. In this paper, we apply the double machine learning (DML) methodology to estimate the CI values across 100s of customer actions of business interest and 100s of millions of customers. We operationalize DML through a causal ML library based on Spark with a flexible, JSON-driven model configuration approach to estimate CI at scale (i.e., across hundred of actions and millions of customers). We outline the DML methodology and implementation, and associated benefits over the traditional potential outcomes based CI model. We show population-level as well as customer-level CI values along with confidence intervals. The validation metrics show a 2.2% gain over the baseline methods and a 2.5X gain in the computational time. Our contribution is to advance the scalable application of CI, while also providing an interface that allows faster experimentation, cross-platform support, ability to onboard new use cases, and improves accessibility of underlying code for partner teams.
The Hidden Costs of Automation: An Empirical Study on GitHub Actions Workflow Maintenance
Venue: arXiv cs.SE 2024
Year: 2024

GitHub Actions (GA) is an orchestration platform that streamlines the automatic execution of software engineering tasks such as building, testing, and deployment. Although GA workflows are the primary means for automation, according to our experience and observations, human intervention is necessary to correct defects, update dependencies, or refactor existing workflow files. In fact, previous research has shown that software artifacts similar to workflows, such as build files and bots, can introduce additional maintenance tasks in software projects. This suggests that workflow files, which are also used to automate repetitive tasks in professional software production, may generate extra workload for developers. However, the nature of such effort has not been well studied. This paper presents a large-scale empirical investigation towards characterizing the maintenance of GA workflows by studying the evolution of workflow files in almost 200 mature GitHub projects across ten programming languages. Our findings largely confirm the results of previous studies on the maintenance of similar artifacts, while also revealing GA-specific insights such as bug fixing and CI/CD improvement being among the major drivers of GA maintenance. A direct implication is that practitioners should be aware of proper resource planning and allocation for maintaining GA workflows, thus exposing the ``hidden costs of automation.'' Our findings also call for identifying and documenting best practices for such maintenance, and for enhanced tool features supporting dependency tracking and better error reporting of workflow specifications.
Pluralistic Salient Object Detection
Venue: arXiv cs.CV 2024
Year: 2024

We introduce pluralistic salient object detection (PSOD), a novel task aimed at generating multiple plausible salient segmentation results for a given input image. Unlike conventional SOD methods that produce a single segmentation mask for salient objects, this new setting recognizes the inherent complexity of real-world images, comprising multiple objects, and the ambiguity in defining salient objects due to different user intentions. To study this task, we present two new SOD datasets "DUTS-MM" and "DUS-MQ", along with newly designed evaluation metrics. DUTS-MM builds upon the DUTS dataset but enriches the ground-truth mask annotations from three aspects which 1) improves the mask quality especially for boundary and fine-grained structures; 2) alleviates the annotation inconsistency issue; and 3) provides multiple ground-truth masks for images with saliency ambiguity. DUTS-MQ consists of approximately 100K image-mask pairs with human-annotated preference scores, enabling the learning of real human preferences in measuring mask quality. Building upon these two datasets, we propose a simple yet effective pluralistic SOD baseline based on a Mixture-of-Experts (MOE) design. Equipped with two prediction heads, it simultaneously predicts multiple masks using different query prompts and predicts human preference scores for each mask candidate. Extensive experiments and analyses underscore the significance of our proposed datasets and affirm the effectiveness of our PSOD framework.
Exploring Low-Dimensional Subspaces in Diffusion Models for Controllable Image Editing
Venue: arXiv cs.CV 2024
Year: 2024

Recently, diffusion models have emerged as a powerful class of generative models. Despite their success, there is still limited understanding of their semantic spaces. This makes it challenging to achieve precise and disentangled image generation without additional training, especially in an unsupervised way. In this work, we improve the understanding of their semantic spaces from intriguing observations: among a certain range of noise levels, (1) the learned posterior mean predictor (PMP) in the diffusion model is locally linear, and (2) the singular vectors of its Jacobian lie in low-dimensional semantic subspaces. We provide a solid theoretical basis to justify the linearity and low-rankness in the PMP. These insights allow us to propose an unsupervised, single-step, training-free LOw-rank COntrollable image editing (LOCO Edit) method for precise local editing in diffusion models. LOCO Edit identified editing directions with nice properties: homogeneity, transferability, composability, and linearity. These properties of LOCO Edit benefit greatly from the low-dimensional semantic subspace. Our method can further be extended to unsupervised or text-supervised editing in various text-to-image diffusion models (T-LOCO Edit). Finally, extensive empirical experiments demonstrate the effectiveness and efficiency of LOCO Edit. The codes will be released at https://github.com/ChicyChen/LOCO-Edit.
STAB: Speech Tokenizer Assessment Benchmark
Venue: arXiv cs.CL 2024
Year: 2024

Representing speech as discrete tokens provides a framework for transforming speech into a format that closely resembles text, thus enabling the use of speech as an input to the widely successful large language models (LLMs). Currently, while several speech tokenizers have been proposed, there is ambiguity regarding the properties that are desired from a tokenizer for specific downstream tasks and its overall generalizability. Evaluating the performance of tokenizers across different downstream tasks is a computationally intensive effort that poses challenges for scalability. To circumvent this requirement, we present STAB (Speech Tokenizer Assessment Benchmark), a systematic evaluation framework designed to assess speech tokenizers comprehensively and shed light on their inherent characteristics. This framework provides a deeper understanding of the underlying mechanisms of speech tokenization, thereby offering a valuable resource for expediting the advancement of future tokenizer models and enabling comparative analysis using a standardized benchmark. We evaluate the STAB metrics and correlate this with downstream task performance across a range of speech tasks and tokenizer choices.
Adaptive Class Emergence Training: Enhancing Neural Network Stability and Generalization through Progressive Target Evolution
Venue: arXiv cs.LG 2024
Year: 2024

Recent advancements in artificial intelligence, particularly deep neural networks, have pushed the boundaries of what is achievable in complex tasks. Traditional methods for training neural networks in classification problems often rely on static target outputs, such as one-hot encoded vectors, which can lead to unstable optimization and difficulties in handling non-linearities within data. In this paper, we propose a novel training methodology that progressively evolves the target outputs from a null vector to one-hot encoded vectors throughout the training process. This gradual transition allows the network to adapt more smoothly to the increasing complexity of the classification task, maintaining an equilibrium state that reduces the risk of overfitting and enhances generalization. Our approach, inspired by concepts from structural equilibrium in finite element analysis, has been validated through extensive experiments on both synthetic and real-world datasets. The results demonstrate that our method achieves faster convergence, improved accuracy, and better generalization, especially in scenarios with high data complexity and noise. This progressive training framework offers a robust alternative to classical methods, opening new perspectives for more efficient and stable neural network training.
Abstractive Text Summarization: State of the Art, Challenges, and Improvements
Venue: arXiv cs.CL 2024
Year: 2024

Specifically focusing on the landscape of abstractive text summarization, as opposed to extractive techniques, this survey presents a comprehensive overview, delving into state-of-the-art techniques, prevailing challenges, and prospective research directions. We categorize the techniques into traditional sequence-to-sequence models, pre-trained large language models, reinforcement learning, hierarchical methods, and multi-modal summarization. Unlike prior works that did not examine complexities, scalability and comparisons of techniques in detail, this review takes a comprehensive approach encompassing state-of-the-art methods, challenges, solutions, comparisons, limitations and charts out future improvements - providing researchers an extensive overview to advance abstractive summarization research. We provide vital comparison tables across techniques categorized - offering insights into model complexity, scalability and appropriate applications. The paper highlights challenges such as inadequate meaning representation, factual consistency, controllable text summarization, cross-lingual summarization, and evaluation metrics, among others. Solutions leveraging knowledge incorporation and other innovative strategies are proposed to address these challenges. The paper concludes by highlighting emerging research areas like factual inconsistency, domain-specific, cross-lingual, multilingual, and long-document summarization, as well as handling noisy data. Our objective is to provide researchers and practitioners with a structured overview of the domain, enabling them to better understand the current landscape and identify potential areas for further research and improvement.
Relative-Translation Invariant Wasserstein Distance
Venue: arXiv cs.LG 2024
Year: 2024

We introduce a new family of distances, relative-translation invariant Wasserstein distances ($RW_p$), for measuring the similarity of two probability distributions under distribution shift. Generalizing it from the classical optimal transport model, we show that $RW_p$ distances are also real distance metrics defined on the quotient set $\mathcal{P}_p(\mathbb{R}^n)/\sim$ and invariant to distribution translations. When $p=2$, the $RW_2$ distance enjoys more exciting properties, including decomposability of the optimal transport model, translation-invariance of the $RW_2$ distance, and a Pythagorean relationship between $RW_2$ and the classical quadratic Wasserstein distance ($W_2$). Based on these properties, we show that a distribution shift, measured by $W_2$ distance, can be explained in the bias-variance perspective. In addition, we propose a variant of the Sinkhorn algorithm, named $RW_2$ Sinkhorn algorithm, for efficiently calculating $RW_2$ distance, coupling solutions, as well as $W_2$ distance. We also provide the analysis of numerical stability and time complexity for the proposed algorithm. Finally, we validate the $RW_2$ distance metric and the algorithm performance with three experiments. We conduct one numerical validation for the $RW_2$ Sinkhorn algorithm and show two real-world applications demonstrating the effectiveness of using $RW_2$ under distribution shift: digits recognition and similar thunderstorm detection. The experimental results report that our proposed algorithm significantly improves the computational efficiency of Sinkhorn in certain practical applications, and the $RW_2$ distance is robust to distribution translations compared with baselines.
Accelerating Large Language Model Training with Hybrid GPU-based Compression
Venue: arXiv cs.DC 2024
Year: 2024

Data Parallelism (DP), Tensor Parallelism (TP), and Pipeline Parallelism (PP) are the three strategies widely adopted to enable fast and efficient Large Language Model (LLM) training. However, these approaches rely on data-intensive communication routines to collect, aggregate, and re-distribute gradients, activations, and other important model information, which pose significant overhead. Co-designed with GPU-based compression libraries, MPI libraries have been proven to reduce message size significantly, and leverage interconnect bandwidth, thus increasing training efficiency while maintaining acceptable accuracy. In this work, we investigate the efficacy of compression-assisted MPI collectives under the context of distributed LLM training using 3D parallelism and ZeRO optimizations. We scaled up to 192 V100 GPUs on the Lassen supercomputer. First, we enabled a naïve compression scheme across all collectives and observed a 22.5\% increase in TFLOPS per GPU and a 23.6\% increase in samples per second for GPT-NeoX-20B training. Nonetheless, such a strategy ignores the sparsity discrepancy among messages communicated in each parallelism degree, thus introducing more errors and causing degradation in training loss. Therefore, we incorporated hybrid compression settings toward each parallel dimension and adjusted the compression intensity accordingly. Given their low-rank structure (arXiv:2301.02654), we apply aggressive compression on gradients when performing DP All-reduce. We adopt milder compression to preserve precision while communicating activations, optimizer states, and model parameters in TP and PP. Using the adjusted hybrid compression scheme, we demonstrate a 17.3\% increase in TFLOPS per GPU and a 12.7\% increase in samples per second while reaching baseline loss convergence.