OpenRaiser/Intern-Atlas huggingface dataset:
Efficient N:M Sparse DNN Training Using Algorithm, Architecture, and Dataflow Co-Design
Venue: arXiv cs.LG 2023
Year: 2023

Sparse training is one of the promising techniques to reduce the computational cost of DNNs while retaining high accuracy. In particular, N:M fine-grained structured sparsity, where only N out of consecutive M elements can be nonzero, has attracted attention due to its hardware-friendly pattern and capability of achieving a high sparse ratio. However, the potential to accelerate N:M sparse DNN training has not been fully exploited, and there is a lack of efficient hardware supporting N:M sparse training. To tackle these challenges, this paper presents a computation-efficient training scheme for N:M sparse DNNs using algorithm, architecture, and dataflow co-design. At the algorithm level, a bidirectional weight pruning method, dubbed BDWP, is proposed to leverage the N:M sparsity of weights during both forward and backward passes of DNN training, which can significantly reduce the computational cost while maintaining model accuracy. At the architecture level, a sparse accelerator for DNN training, namely SAT, is developed to neatly support both the regular dense operations and the computation-efficient N:M sparse operations. At the dataflow level, multiple optimization methods ranging from interleave mapping, pre-generation of N:M sparse weights, and offline scheduling, are proposed to boost the computational efficiency of SAT. Finally, the effectiveness of our training scheme is evaluated on a Xilinx VCU1525 FPGA card using various DNN models and datasets. Experimental results show the SAT accelerator with the BDWP sparse training method under 2:8 sparse ratio achieves an average speedup of 1.75x over that with the dense training, accompanied by a negligible accuracy loss of 0.56% on average. Furthermore, our proposed training scheme significantly improves the training throughput by 2.97~25.22x and the energy efficiency by 1.36~3.58x over prior FPGA-based accelerators.
Graph Neural Network for Stress Predictions in Stiffened Panels Under Uniform Loading
Venue: arXiv cs.LG 2023
Year: 2023

Machine learning (ML) and deep learning (DL) techniques have gained significant attention as reduced order models (ROMs) to computationally expensive structural analysis methods, such as finite element analysis (FEA). Graph neural network (GNN) is a particular type of neural network which processes data that can be represented as graphs. This allows for efficient representation of complex geometries that can change during conceptual design of a structure or a product. In this study, we propose a novel graph embedding technique for efficient representation of 3D stiffened panels by considering separate plate domains as vertices. This approach is considered using Graph Sampling and Aggregation (GraphSAGE) to predict stress distributions in stiffened panels with varying geometries. A comparison between a finite-element-vertex graph representation is conducted to demonstrate the effectiveness of the proposed approach. A comprehensive parametric study is performed to examine the effect of structural geometry on the prediction performance. Our results demonstrate the immense potential of graph neural networks with the proposed graph embedding method as robust reduced-order models for 3D structures.
A numerical framework for simulating progressive failure in composite laminates under high-cycle fatigue loading
Venue: arXiv cs.CE 2023
Year: 2023

In this work, a recently proposed high-cycle fatigue cohesive zone model, which covers crack initiation and propagation with limited input parameters, is embedded in a robust and efficient numerical framework for simulating progressive failure in composite laminates under fatigue loading. The fatigue cohesive zone model is enhanced with an implicit time integration scheme of the fatigue damage variable which allows for larger cycle increments and more efficient analyses. The method is combined with an adaptive strategy for determining the cycle increment based on global convergence rates. Moreover, a consistent material tangent stiffness matrix has been derived by fully linearizing the underlying mixed-mode quasi-static model and the fatigue damage update. The enhanced fatigue cohesive zone model is used to describe matrix cracking and delamination in laminates. In order to allow for matrix cracks to initiate at arbitrary locations and to avoid complex and costly mesh generation, the phantom node version of the eXtended finite element method (XFEM) is employed. For the insertion of new crack segments, an XFEM fatigue crack insertion criterion is presented, which is consistent with the fatigue cohesive zone formulation. It is shown with numerical examples that the improved fatigue damage update enhances the accuracy, efficiency and robustness of the numerical simulations significantly. The numerical framework is applied to the simulation of progressive fatigue failure in an open-hole [$\pm$45]-laminate. It is demonstrated that the numerical model is capable of accurately and efficiently simulating the complete failure process from distributed damage to localized failure.
NeRRF: 3D Reconstruction and View Synthesis for Transparent and Specular Objects with Neural Refractive-Reflective Fields
Venue: arXiv cs.CV 2023
Year: 2023

Neural radiance fields (NeRF) have revolutionized the field of image-based view synthesis. However, NeRF uses straight rays and fails to deal with complicated light path changes caused by refraction and reflection. This prevents NeRF from successfully synthesizing transparent or specular objects, which are ubiquitous in real-world robotics and A/VR applications. In this paper, we introduce the refractive-reflective field. Taking the object silhouette as input, we first utilize marching tetrahedra with a progressive encoding to reconstruct the geometry of non-Lambertian objects and then model refraction and reflection effects of the object in a unified framework using Fresnel terms. Meanwhile, to achieve efficient and effective anti-aliasing, we propose a virtual cone supersampling technique. We benchmark our method on different shapes, backgrounds and Fresnel terms on both real-world and synthetic datasets. We also qualitatively and quantitatively benchmark the rendering results of various editing applications, including material editing, object replacement/insertion, and environment illumination estimation. Codes and data are publicly available at https://github.com/dawning77/NeRRF.
Using Curriculum Theory to Inform Approaches to Generative AI in Schools
Venue: arXiv cs.CY 2023
Year: 2023

In an educational landscape dramatically altered by the swift proliferation of Large Language Models, this essay interrogates the urgent this essay interrogates the urgent pedagogical modifications required in secondary schooling. Anchored in Madeline Grumet's triadic framework of curriculum inquiry, the study delineates the multifaceted relationship between Generative AI and Elliot Eisner's explicit, implicit, and null curriculum concepts. It scrutinizes the logistical and ethical challenges, such as the reliability of AI detectors, that educators confront when attempting to assimilate this nascent technology into long-standing curricular structures. Engaging with Ted Aoki's theory of the "zone of between", the essay illuminates educators' dilemmas in reconciling prescriptive curricular aims with the fluid realities of classroom life, all within an educational milieu in constant flux due to Generative AI. The paper culminates in a reflective analysis by the researcher, identifying avenues for further scholarly investigation within each of Grumet's constitutive strands of curriculum theory, thereby providing a roadmap for future research on Generative AI's transformative impact on educational practice.
Book Chapter in Computational Demography and Health
Venue: arXiv cs.CY 2023
Year: 2023

Recent developments in computing, data entry and generation, and analytic tools have changed the landscape of modern demography and health research. These changes have come to be known as computational demography, big data, and precision health in the field. This emerging interdisciplinary research comprises social scientists, physical scientists, engineers, data scientists, and disease experts. This work has changed how we use administrative data, conduct surveys, and allow for complex behavioral studies via big data (electronic trace data from mobile phones, apps, etc.). This chapter reviews this emerging field's new data sources, methods, and applications.
Machine Learning Technique Based Fake News Detection
Venue: arXiv cs.CL 2023
Year: 2023

False news has received attention from both the general public and the scholarly world. Such false information has the ability to affect public perception, giving nefarious groups the chance to influence the results of public events like elections. Anyone can share fake news or facts about anyone or anything for their personal gain or to cause someone trouble. Also, information varies depending on the part of the world it is shared on. Thus, in this paper, we have trained a model to classify fake and true news by utilizing the 1876 news data from our collected dataset. We have preprocessed the data to get clean and filtered texts by following the Natural Language Processing approaches. Our research conducts 3 popular Machine Learning (Stochastic gradient descent, Naïve Bayes, Logistic Regression,) and 2 Deep Learning (Long-Short Term Memory, ASGD Weight-Dropped LSTM, or AWD-LSTM) algorithms. After we have found our best Naive Bayes classifier with 56% accuracy and an F1-macro score of an average of 32%.
SCREWS: A Modular Framework for Reasoning with Revisions
Venue: arXiv cs.AI 2023
Year: 2023

Large language models (LLMs) can improve their accuracy on various tasks through iteratively refining and revising their output based on feedback. We observe that these revisions can introduce errors, in which case it is better to roll back to a previous result. Further, revisions are typically homogeneous: they use the same reasoning method that produced the initial answer, which may not correct errors. To enable exploration in this space, we present SCREWS, a modular framework for reasoning with revisions. It is comprised of three main modules: Sampling, Conditional Resampling, and Selection, each consisting of sub-modules that can be hand-selected per task. We show that SCREWS not only unifies several previous approaches under a common framework, but also reveals several novel strategies for identifying improved reasoning chains. We evaluate our framework with state-of-the-art LLMs (ChatGPT and GPT-4) on a diverse set of reasoning tasks and uncover useful new reasoning strategies for each: arithmetic word problems, multi-hop question answering, and code debugging. Heterogeneous revision strategies prove to be important, as does selection between original and revised candidates.
Transitioning To The Digital Generation Case Studies (Previous Digital Point Studies In Japan Cases:1993-2023)
Venue: arXiv cs.CY 2023
Year: 2023

In this paper, we discuss at The 8th International Workshop on Application of Big Data for Computational Social Science, October 26-29, 2023, Venice, Italy. To achieve the realization of the Global and Innovation Gateway for All (GIGA) initiative (2019), proposed in December 2019 by the Primary and Secondary Education Planning Division of the Elementary and Secondary Education Bureau of the Ministry of Education, Culture, Sports, Science and Technology, a movement has emerged to utilize information and communication technology (ICT) in the field of education. The history of ICT education in Japan dates back to the 100 Schools Project (1994), which aimed to provide network access environments, and the New 100 Schools Project (1997), which marked the beginning of full-scale ICT education in Japan. In this paper, we discuss the usage dynamics of smartphone-based learning applications among young people (analyzing data from January to September 2020) and their current status. Further, the results are summarized and future research topics and issues are discussed. The results show that there are situations in which ICT learning environments can be effectively utilized and others in which they cannot, depending on the differences between digital students and analog students who utilize ICT in their studies; this indicates that we are currently in a transition to a generation of digital natives. ICT education has both advantages and disadvantages, and it is expected that it will be used in combination with conventional educational methods while assessing the characteristics of ICT education in the future. Of course, there are many challenges. We plan to discuss how to appeal in this regard at the Workshop.
Does My Dog ''Speak'' Like Me? The Acoustic Correlation between Pet Dogs and Their Human Owners
Venue: arXiv cs.SD 2023
Year: 2023

How hosts language influence their pets' vocalization is an interesting yet underexplored problem. This paper presents a preliminary investigation into the possible correlation between domestic dog vocal expressions and their human host's language environment. We first present a new dataset of Shiba Inu dog vocals from YouTube, which provides 7500 clean sound clips, including their contextual information of these vocals and their owner's speech clips with a carefully-designed data processing pipeline. The contextual information includes the scene category in which the vocal was recorded, the dog's location and activity. With a classification task and prominent factor analysis, we discover significant acoustic differences in the dog vocals from the two language environments. We further identify some acoustic features from dog vocalizations that are potentially correlated to their host language patterns.
Learning algorithms for identification of whisky using portable Raman spectroscopy
Venue: arXiv cs.LG 2023
Year: 2023

Reliable identification of high-value products such as whisky is an increasingly important area, as issues such as brand substitution (i.e. fraudulent products) and quality control are critical to the industry. We have examined a range of machine learning algorithms and interfaced them directly with a portable Raman spectroscopy device to both identify and characterize the ethanol/methanol concentrations of commercial whisky samples. We demonstrate that machine learning models can achieve over 99% accuracy in brand identification across twenty-eight commercial samples. To demonstrate the flexibility of this approach we utilised the same samples and algorithms to quantify ethanol concentrations, as well as measuring methanol levels in spiked whisky samples. Our machine learning techniques are then combined with a through-the-bottle method to perform spectral analysis and identification without requiring the sample to be decanted from the original container, showing the practical potential of this approach to the detection of counterfeit or adulterated spirits and other high value liquid samples.
Trading-off Mutual Information on Feature Aggregation for Face Recognition
Venue: arXiv cs.CV 2023
Year: 2023

Despite the advances in the field of Face Recognition (FR), the precision of these methods is not yet sufficient. To improve the FR performance, this paper proposes a technique to aggregate the outputs of two state-of-the-art (SOTA) deep FR models, namely ArcFace and AdaFace. In our approach, we leverage the transformer attention mechanism to exploit the relationship between different parts of two feature maps. By doing so, we aim to enhance the overall discriminative power of the FR system. One of the challenges in feature aggregation is the effective modeling of both local and global dependencies. Conventional transformers are known for their ability to capture long-range dependencies, but they often struggle with modeling local dependencies accurately. To address this limitation, we augment the self-attention mechanism to capture both local and global dependencies effectively. This allows our model to take advantage of the overlapping receptive fields present in corresponding locations of the feature maps. However, fusing two feature maps from different FR models might introduce redundancies to the face embedding. Since these models often share identical backbone architectures, the resulting feature maps may contain overlapping information, which can mislead the training process. To overcome this problem, we leverage the principle of Information Bottleneck to obtain a maximally informative facial representation. This ensures that the aggregated features retain the most relevant and discriminative information while minimizing redundant or misleading details. To evaluate the effectiveness of our proposed method, we conducted experiments on popular benchmarks and compared our results with state-of-the-art algorithms. The consistent improvement we observed in these benchmarks demonstrates the efficacy of our approach in enhancing FR performance.
AI Risk Profiles: A Standards Proposal for Pre-Deployment AI Risk Disclosures
Venue: arXiv cs.AI 2023
Year: 2023

As AI systems' sophistication and proliferation have increased, awareness of the risks has grown proportionally (Sorkin et al. 2023). In response, calls have grown for stronger emphasis on disclosure and transparency in the AI industry (NTIA 2023; OpenAI 2023b), with proposals ranging from standardizing use of technical disclosures, like model cards (Mitchell et al. 2019), to yet-unspecified licensing regimes (Sindhu 2023). Since the AI value chain is complicated, with actors representing various expertise, perspectives, and values, it is crucial that consumers of a transparency disclosure be able to understand the risks of the AI system the disclosure concerns. In this paper we propose a risk profiling standard which can guide downstream decision-making, including triaging further risk assessment, informing procurement and deployment, and directing regulatory frameworks. The standard is built on our proposed taxonomy of AI risks, which reflects a high-level categorization of the wide variety of risks proposed in the literature. We outline the myriad data sources needed to construct informative Risk Profiles and propose a template-based methodology for collating risk information into a standard, yet flexible, structure. We apply this methodology to a number of prominent AI systems using publicly available information. To conclude, we discuss design decisions for the profiles and future work.
Diagnosing and exploiting the computational demands of videos games for deep reinforcement learning
Venue: arXiv cs.LG 2023
Year: 2023

Humans learn by interacting with their environments and perceiving the outcomes of their actions. A landmark in artificial intelligence has been the development of deep reinforcement learning (dRL) algorithms capable of doing the same in video games, on par with or better than humans. However, it remains unclear whether the successes of dRL models reflect advances in visual representation learning, the effectiveness of reinforcement learning algorithms at discovering better policies, or both. To address this question, we introduce the Learning Challenge Diagnosticator (LCD), a tool that separately measures the perceptual and reinforcement learning demands of a task. We use LCD to discover a novel taxonomy of challenges in the Procgen benchmark, and demonstrate that these predictions are both highly reliable and can instruct algorithmic development. More broadly, the LCD reveals multiple failure cases that can occur when optimizing dRL algorithms over entire video game benchmarks like Procgen, and provides a pathway towards more efficient progress.
M$^3$CS: Multi-Target Masked Point Modeling with Learnable Codebook and Siamese Decoders
Venue: arXiv cs.CV 2023
Year: 2023

Masked point modeling has become a promising scheme of self-supervised pre-training for point clouds. Existing methods reconstruct either the original points or related features as the objective of pre-training. However, considering the diversity of downstream tasks, it is necessary for the model to have both low- and high-level representation modeling capabilities to capture geometric details and semantic contexts during pre-training. To this end, M$^3$CS is proposed to enable the model with the above abilities. Specifically, with masked point cloud as input, M$^3$CS introduces two decoders to predict masked representations and the original points simultaneously. While an extra decoder doubles parameters for the decoding process and may lead to overfitting, we propose siamese decoders to keep the amount of learnable parameters unchanged. Further, we propose an online codebook projecting continuous tokens into discrete ones before reconstructing masked points. In such way, we can enforce the decoder to take effect through the combinations of tokens rather than remembering each token. Comprehensive experiments show that M$^3$CS achieves superior performance at both classification and segmentation tasks, outperforming existing methods.
A Declarative Specification for Authoring Metrics Dashboards
Venue: arXiv cs.HC 2023
Year: 2023

Despite their ubiquity, authoring dashboards for metrics reporting in modern data analysis tools remains a manual, time-consuming process. Rather than focusing on interesting combinations of their data, users have to spend time creating each chart in a dashboard one by one. This makes dashboard creation slow and tedious. We conducted a review of production metrics dashboards and found that many dashboards contain a common structure: breaking down one or more metrics by different dimensions. In response, we developed a high-level specification for describing dashboards as sections of metrics repeated across the same dimensions and a graphical interface, Quick Dashboard, for authoring dashboards based on this specification. We present several usage examples that demonstrate the flexibility of this specification to create various kinds of dashboards and support a data-first approach to dashboard authoring.
Tackling the Incomplete Annotation Issue in Universal Lesion Detection Task By Exploratory Training
Venue: arXiv cs.CV 2023
Year: 2023

Universal lesion detection has great value for clinical practice as it aims to detect various types of lesions in multiple organs on medical images. Deep learning methods have shown promising results, but demanding large volumes of annotated data for training. However, annotating medical images is costly and requires specialized knowledge. The diverse forms and contrasts of objects in medical images make fully annotation even more challenging, resulting in incomplete annotations. Directly training ULD detectors on such datasets can yield suboptimal results. Pseudo-label-based methods examine the training data and mine unlabelled objects for retraining, which have shown to be effective to tackle this issue. Presently, top-performing methods rely on a dynamic label-mining mechanism, operating at the mini-batch level. However, the model's performance varies at different iterations, leading to inconsistencies in the quality of the mined labels and limits their performance enhancement. Inspired by the observation that deep models learn concepts with increasing complexity, we introduce an innovative exploratory training to assess the reliability of mined lesions over time. Specifically, we introduce a teacher-student detection model as basis, where the teacher's predictions are combined with incomplete annotations to train the student. Additionally, we design a prediction bank to record high-confidence predictions. Each sample is trained several times, allowing us to get a sequence of records for each sample. If a prediction consistently appears in the record sequence, it is likely to be a true object, otherwise it may just a noise. This serves as a crucial criterion for selecting reliable mined lesions for retraining. Our experimental results substantiate that the proposed framework surpasses state-of-the-art methods on two medical image datasets, demonstrating its superior performance.