OpenRaiser/Intern-Atlas huggingface dataset:
Tuning the activation function to optimize the forecast horizon of a reservoir computer
Venue: arXiv cs.NE 2023
Year: 2023

Reservoir computing is a machine learning framework where the readouts from a nonlinear system (the reservoir) are trained so that the output from the reservoir, when forced with an input signal, reproduces a desired output signal. A common implementation of reservoir computers is to use a recurrent neural network as the reservoir. The design of this network can have significant effects on the performance of the reservoir computer. In this paper we study the effect of the node activation function on the ability of reservoir computers to learn and predict chaotic time series. We find that the Forecast Horizon (FH), the time during which the reservoir's predictions remain accurate, can vary by an order of magnitude across a set of 16 activation functions used in machine learning. By using different functions from this set, and by modifying their parameters, we explore whether the entropy of node activation levels or the curvature of the activation functions determine the predictive ability of the reservoirs. We find that the FH is low when the activation function is used in a region where it has low curvature, and a positive correlation between curvature and FH. For the activation functions studied we find that the largest FH generally occurs at intermediate levels of the entropy of node activation levels. Our results show that the performance of reservoir computers is very sensitive to the activation function shape. Therefore, modifying this shape in hyperparameter optimization algorithms can lead to improvements in reservoir computer performance.
DSFormer: Effective Compression of Text-Transformers by Dense-Sparse Weight Factorization
Venue: arXiv cs.CL 2023
Year: 2023

With the tremendous success of large transformer models in natural language understanding, down-sizing them for cost-effective deployments has become critical. Recent studies have explored the low-rank weight factorization techniques which are efficient to train, and apply out-of-the-box to any transformer architecture. Unfortunately, the low-rank assumption tends to be over-restrictive and hinders the expressiveness of the compressed model. This paper proposes, DSFormer, a simple alternative factorization scheme which expresses a target weight matrix as the product of a small dense and a semi-structured sparse matrix. The resulting approximation is more faithful to the weight distribution in transformers and therefore achieves a stronger efficiency-accuracy trade-off. Another concern with existing factorizers is their dependence on a task-unaware initialization step which degrades the accuracy of the resulting model. DSFormer addresses this issue through a novel Straight-Through Factorizer (STF) algorithm that jointly learns all the weight factorizations to directly maximize the final task accuracy. Extensive experiments on multiple natural language understanding benchmarks demonstrate that DSFormer obtains up to 40% better compression than the state-of-the-art low-rank factorizers, leading semi-structured sparsity baselines and popular knowledge distillation approaches. Our approach is also orthogonal to mainstream compressors and offers up to 50% additional compression when added to popular distilled, layer-shared and quantized transformers. We empirically evaluate the benefits of STF over conventional optimization practices.
Zero-Shot Metric Depth with a Field-of-View Conditioned Diffusion Model
Venue: arXiv cs.CV 2023
Year: 2023

While methods for monocular depth estimation have made significant strides on standard benchmarks, zero-shot metric depth estimation remains unsolved. Challenges include the joint modeling of indoor and outdoor scenes, which often exhibit significantly different distributions of RGB and depth, and the depth-scale ambiguity due to unknown camera intrinsics. Recent work has proposed specialized multi-head architectures for jointly modeling indoor and outdoor scenes. In contrast, we advocate a generic, task-agnostic diffusion model, with several advancements such as log-scale depth parameterization to enable joint modeling of indoor and outdoor scenes, conditioning on the field-of-view (FOV) to handle scale ambiguity and synthetically augmenting FOV during training to generalize beyond the limited camera intrinsics in training datasets. Furthermore, by employing a more diverse training mixture than is common, and an efficient diffusion parameterization, our method, DMD (Diffusion for Metric Depth) achieves a 25\% reduction in relative error (REL) on zero-shot indoor and 33\% reduction on zero-shot outdoor datasets over the current SOTA using only a small number of denoising steps. For an overview see https://diffusion-vision.github.io/dmd
Conditional Image Generation with Pretrained Generative Model
Venue: arXiv cs.CV 2023
Year: 2023

In recent years, diffusion models have gained popularity for their ability to generate higher-quality images in comparison to GAN models. However, like any other large generative models, these models require a huge amount of data, computational resources, and meticulous tuning for successful training. This poses a significant challenge, rendering it infeasible for most individuals. As a result, the research community has devised methods to leverage pre-trained unconditional diffusion models with additional guidance for the purpose of conditional image generative. These methods enable conditional image generations on diverse inputs and, most importantly, circumvent the need for training the diffusion model. In this paper, our objective is to reduce the time-required and computational overhead introduced by the addition of guidance in diffusion models -- while maintaining comparable image quality. We propose a set of methods based on our empirical analysis, demonstrating a reduction in computation time by approximately threefold.
Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity
Venue: arXiv cs.LG 2023
Year: 2023

Motivated by high resource costs of centralized machine learning schemes as well as data privacy concerns, federated learning (FL) emerged as an efficient alternative that relies on aggregating locally trained models rather than collecting clients' potentially private data. In practice, available resources and data distributions vary from one client to another, creating an inherent system heterogeneity that leads to deterioration of the performance of conventional FL algorithms. In this work, we present a federated quantization-based self-supervised learning scheme (Fed-QSSL) designed to address heterogeneity in FL systems. At clients' side, to tackle data heterogeneity we leverage distributed self-supervised learning while utilizing low-bit quantization to satisfy constraints imposed by local infrastructure and limited communication resources. At server's side, Fed-QSSL deploys de-quantization, weighted aggregation and re-quantization, ultimately creating models personalized to both data distribution as well as specific infrastructure of each client's device. We validated the proposed algorithm on real world datasets, demonstrating its efficacy, and theoretically analyzed impact of low-bit training on the convergence and robustness of the learned models.
Generating Forms via Informed Motion, a Flight Inspired Method Based on Wind and Topography Data
Venue: arXiv cs.GR 2023
Year: 2023

Generative systems are becoming a crucial part of current design practice. There exist gaps however, between the digital processes, field data and designer's input. To solve this problem, multiple processes were developed in order to generate emergent and self-organizing design solutions that combine the designer's input with surface models acquired via photogrammetry and generative design tools. Different generative design methods were utilized for trials, including surface scattering based on UV coordinates, animation snapshots (similar to long exposure photography) and a particle swarm algorithm on arbitrary data, interpolated within GIS software. A large volume of adaptive forms that are complex, yet responsive to changes in parameters, user input, topography and/or various spatial data were acquired. Resulting outputs were rendered and projection mapped onto the original physical model and evaluated for further iterations.
Design Systems for Closing Gaps with Rheotomic Surfaces and Allometry
Venue: arXiv cs.GR 2023
Year: 2023

This study aims to present a material based, second order design method that makes the rapid creation of bridging structures in order to connect two or more horizontal planes which are currently separated and three design instances created via such method. The first element of the presented method is a generative system that creates circulation surfaces through the interpolation of the curves defined on the current surfaces, and also creates the structural volumes via rheotomic (minimal) surfaces. The second constituent is the instantiation of the exposed variable, connected to a displacement algorithm inspired by allometry based on user input, contextual data, or simulation results. The method was created with applicability through additive manufacturing in consideration. The design process - similar to manufacturing - proceeds in a vertical manner, in order to reduce the generation of support geometry as much as possible. The system includes a raster data input viable for simulation results, feeding the accumulation variable in order to modify material amount or quality with the aim of improving structural performance where stress is greater. Through the application of the method, three physical models which connect different horizontal planes were obtained. The models can be evaluated with digital of physical simulations, and results can be utilized in an iterative manner, improving the results by each recursion.
Packed-Ensemble Surrogate Models for Fluid Flow Estimation Arround Airfoil Geometries
Venue: arXiv cs.LG 2023
Year: 2023

Physical based simulations can be very time and computationally demanding tasks. One way of accelerating these processes is by making use of data-driven surrogate models that learn from existing simulations. Ensembling methods are particularly relevant in this domain as their smoothness properties coincide with the smoothness of physical phenomena. The drawback is that they can remain costly. This research project focused on studying Packed-Ensembles that generalize Deep Ensembles but remain faster to train. Several models have been trained and compared in terms of multiple important metrics. PE(8,4,1) has been identified as the clear winner in this particular task, beating down its Deep Ensemble conterpart while accelerating the training time by 25%.
What Do You Mean by Memory? When Engineers Are Lost in the Maze of Complexity
Venue: arXiv cs.SE 2023
Year: 2023

An accepted practice to decrease applications' memory usage is to reduce the amount and frequency of memory allocations. Factors such as (a) the prevalence of out-of-memory (OOM) killers, (b) memory allocations in modern programming languages done implicitly, (c) overcommitting being a default strategy in the Linux kernel, and (d) the rise in complexity and terminology related to memory management makes the existing guidance inefficient. The industry needs detailed guidelines for optimizing memory usage targeting specific operating systems (OS) and programming language types.
Embedded Shape Matching in Photogrammetry Data for Modeling Making Knowledge
Venue: arXiv cs.CV 2023
Year: 2023

In three-dimensional models obtained by photogrammetry of existing structures, all of the shapes that the eye can select cannot always find their equivalents in the geometric components of the model. However, the matching of meaningful parts and assemblages with the records acquired with rapid and detailed documentation methods will provide an advantage for the creation of information models of existing structures. While aiming to produce answers to this problem and in order to overcome the difficulties of pattern recognition in three-dimensional models, we used two-dimensional samples obtained by projection. Processing techniques such as ambient occlusion, curvature and normal maps are commonly used in modern computer graphics applications that enable the representation of three-dimensional surface properties in two-dimensional data sets. The method we propose is based on the recognition of patterns through these mappings instead of the usual light-based visualization. The first stage of the application is photogrammetric capture of a few examples of Zeugma mosaics and three-dimensional digital modeling of a set of Seljuk era brick walls based on knowledge obtained through architectural history literature. The second stage covers the creation of digital models byprocessing the surface representation obtained from this data using Alice Vision, OpenCV-Python, and Autodesk Maya to include information on aspects of the making of the walls. What is envisioned for the next stages is that the mapping data contributes and supports the knowledge for rule-based design and making processesof cultural heritage.
Sequential Multiuser Scheduling and Power Allocation for Cell-Free Multiple-Antenna Networks
Venue: arXiv cs.IT 2023
Year: 2023

Resource allocation is a fundamental task in cell-free (CF) massive multi-input multi-output (MIMO) systems, which can effectively improve the network performance. In this paper, we study the downlink of CF MIMO networks with network clustering and linear precoding, and develop a sequential multiuser scheduling and power allocation scheme. In particular, we present a multiuser scheduling algorithm based on greedy techniques and a gradient ascent {(GA)} power allocation algorithm for sum-rate maximization when imperfect channel state information (CSI) is considered. Numerical results show the superiority of the proposed sequential scheduling and power allocation scheme and algorithms to existing approaches while reducing the computational complexity and the signaling load.
CR-SAM: Curvature Regularized Sharpness-Aware Minimization
Venue: arXiv cs.LG 2023
Year: 2023

The capacity to generalize to future unseen data stands as one of the utmost crucial attributes of deep neural networks. Sharpness-Aware Minimization (SAM) aims to enhance the generalizability by minimizing worst-case loss using one-step gradient ascent as an approximation. However, as training progresses, the non-linearity of the loss landscape increases, rendering one-step gradient ascent less effective. On the other hand, multi-step gradient ascent will incur higher training cost. In this paper, we introduce a normalized Hessian trace to accurately measure the curvature of loss landscape on {\em both} training and test sets. In particular, to counter excessive non-linearity of loss landscape, we propose Curvature Regularized SAM (CR-SAM), integrating the normalized Hessian trace as a SAM regularizer. Additionally, we present an efficient way to compute the trace via finite differences with parallelism. Our theoretical analysis based on PAC-Bayes bounds establishes the regularizer's efficacy in reducing generalization error. Empirical evaluation on CIFAR and ImageNet datasets shows that CR-SAM consistently enhances classification performance for ResNet and Vision Transformer (ViT) models across various datasets. Our code is available at https://github.com/TrustAIoT/CR-SAM.
Peer-to-Peer Learning + Consensus with Non-IID Data
Venue: arXiv cs.LG 2023
Year: 2023

Peer-to-peer deep learning algorithms are enabling distributed edge devices to collaboratively train deep neural networks without exchanging raw training data or relying on a central server. Peer-to-Peer Learning (P2PL) and other algorithms based on Distributed Local-Update Stochastic/mini-batch Gradient Descent (local DSGD) rely on interleaving epochs of training with distributed consensus steps. This process leads to model parameter drift/divergence amongst participating devices in both IID and non-IID settings. We observe that model drift results in significant oscillations in test performance evaluated after local training and consensus phases. We then identify factors that amplify performance oscillations and demonstrate that our novel approach, P2PL with Affinity, dampens test performance oscillations in non-IID settings without incurring any additional communication cost.
A Comprehensive End-to-End Computer Vision Framework for Restoration and Recognition of Low-Quality Engineering Drawings
Venue: arXiv cs.CV 2023
Year: 2023

The digitization of engineering drawings is crucial for efficient reuse, distribution, and archiving. Existing computer vision approaches for digitizing engineering drawings typically assume the input drawings have high quality. However, in reality, engineering drawings are often blurred and distorted due to improper scanning, storage, and transmission, which may jeopardize the effectiveness of existing approaches. This paper focuses on restoring and recognizing low-quality engineering drawings, where an end-to-end framework is proposed to improve the quality of the drawings and identify the graphical symbols on them. The framework uses K-means clustering to classify different engineering drawing patches into simple and complex texture patches based on their gray level co-occurrence matrix statistics. Computer vision operations and a modified Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) model are then used to improve the quality of the two types of patches, respectively. A modified Faster Region-based Convolutional Neural Network (Faster R-CNN) model is used to recognize the quality-enhanced graphical symbols. Additionally, a multi-stage task-driven collaborative learning strategy is proposed to train the modified ESRGAN and Faster R-CNN models to improve the resolution of engineering drawings in the direction that facilitates graphical symbol recognition, rather than human visual perception. A synthetic data generation method is also proposed to construct quality-degraded samples for training the framework. Experiments on real-world electrical diagrams show that the proposed framework achieves an accuracy of 98.98% and a recall of 99.33%, demonstrating its superiority over previous approaches. Moreover, the framework is integrated into a widely-used power system software application to showcase its practicality.
Learning Rhythmic Trajectories with Geometric Constraints for Laser-Based Skincare Procedures
Venue: arXiv cs.RO 2023
Year: 2023

The increasing deployment of robots has significantly enhanced the automation levels across a wide and diverse range of industries. This paper investigates the automation challenges of laser-based dermatology procedures in the beauty industry; This group of related manipulation tasks involves delivering energy from a cosmetic laser onto the skin with repetitive patterns. To automate this procedure, we propose to use a robotic manipulator and endow it with the dexterity of a skilled dermatology practitioner through a learning-from-demonstration framework. To ensure that the cosmetic laser can properly deliver the energy onto the skin surface of an individual, we develop a novel structured prediction-based imitation learning algorithm with the merit of handling geometric constraints. Notably, our proposed algorithm effectively tackles the imitation challenges associated with quasi-periodic motions, a common feature of many laser-based cosmetic tasks. The conducted real-world experiments illustrate the performance of our robotic beautician in mimicking realistic dermatological procedures; Our new method is shown to not only replicate the rhythmic movements from the provided demonstrations but also to adapt the acquired skills to previously unseen scenarios and subjects.
Conciliating Privacy and Utility in Data Releases via Individual Differential Privacy and Microaggregation
Venue: arXiv cs.CR 2023
Year: 2023

$ε$-Differential privacy (DP) is a well-known privacy model that offers strong privacy guarantees. However, when applied to data releases, DP significantly deteriorates the analytical utility of the protected outcomes. To keep data utility at reasonable levels, practical applications of DP to data releases have used weak privacy parameters (large $ε$), which dilute the privacy guarantees of DP. In this work, we tackle this issue by using an alternative formulation of the DP privacy guarantees, named $ε$-individual differential privacy (iDP), which causes less data distortion while providing the same protection as DP to subjects. We enforce iDP in data releases by relying on attribute masking plus a pre-processing step based on data microaggregation. The goal of this step is to reduce the sensitivity to record changes, which determines the amount of noise required to enforce iDP (and DP). Specifically, we propose data microaggregation strategies designed for iDP whose sensitivities are significantly lower than those used in DP. As a result, we obtain iDP-protected data with significantly better utility than with DP. We report on experiments that show how our approach can provide strong privacy (small $ε$) while yielding protected data that do not significantly degrade the accuracy of secondary data analysis.
Pose-based Tremor Type and Level Analysis for Parkinson's Disease from Video
Venue: arXiv cs.CV 2023
Year: 2023

Purpose:Current methods for diagnosis of PD rely on clinical examination. The accuracy of diagnosis ranges between 73% and 84%, and is influenced by the experience of the clinical assessor. Hence, an automatic, effective and interpretable supporting system for PD symptom identification would support clinicians in making more robust PD diagnostic decisions. Methods: We propose to analyze Parkinson's tremor (PT) to support the analysis of PD, since PT is one of the most typical symptoms of PD with broad generalizability. To realize the idea, we present SPA-PTA, a deep learning-based PT classification and severity estimation system that takes consumer-grade videos of front-facing humans as input. The core of the system is a novel attention module with a lightweight pyramidal channel-squeezing-fusion architecture that effectively extracts relevant PT information and filters noise. It enhances modeling performance while improving system interpretability. Results:We validate our system via individual-based leave-one-out cross-validation on two tasks: the PT classification task and the tremor severity rating estimation task. Our system presents a 91.3% accuracy and 80.0% F1-score in classifying PT with non-PT class, while providing a 76.4% accuracy and 76.7% F1-score in more complex multiclass tremor rating classification task. Conclusion: Our system offers a cost-effective PT classification and tremor severity estimation results as warning signs of PD for undiagnosed patients with PT symptoms. In addition, it provides a potential solution for supporting PD diagnosis in regions with limited clinical resources.
Arma: Byzantine Fault Tolerant consensus with Linear Scalability
Venue: arXiv cs.DC 2023
Year: 2023

Arma is a Byzantine Fault Tolerant (BFT) consensus system designed to achieve linear scalability across all hardware resources: network bandwidth, CPU, and disk I/O. As opposed to preceding BFT protocols, Arma separates the dissemination and validation of client transactions from the consensus process, restricting the latter to totally ordering only metadata of batches of transactions. This separation enables each party to distribute compute and storage resources for transaction validation, dissemination and disk I/O among multiple machines, resulting in linear scalability. Additionally, Arma ensures censorship resistance by imposing a maximum time limit for the inclusion of client transactions. We build a prototype implementation of Arma and evaluate its performance experimentally. Our results show that Arma totally orders over 100,000 transactions per second when deployed in a WAN setting and integrated into Hyperledger Fabric.