OpenRaiser/Intern-Atlas huggingface dataset:
Learning Human Body Motions from Skeleton-Based Observations for Robot-Assisted Therapy
Venue: arXiv cs.RO 2022
Year: 2022

Robots applied in therapeutic scenarios, for instance in the therapy of individuals with Autism Spectrum Disorder, are sometimes used for imitation learning activities in which a person needs to repeat motions by the robot. To simplify the task of incorporating new types of motions that a robot can perform, it is desirable that the robot has the ability to learn motions by observing demonstrations from a human, such as a therapist. In this paper, we investigate an approach for acquiring motions from skeleton observations of a human, which are collected by a robot-centric RGB-D camera. Given a sequence of observations of various joints, the joint positions are mapped to match the configuration of a robot before being executed by a PID position controller. We evaluate the method, in particular the reproduction error, by performing a study with QTrobot in which the robot acquired different upper-body dance moves from multiple participants. The results indicate the method's overall feasibility, but also indicate that the reproduction quality is affected by noise in the skeleton observations.
Towards Systems Education for Artificial Intelligence: A Course Practice in Intelligent Computing Architectures
Venue: arXiv cs.AI 2022
Year: 2022

With the rapid development of artificial intelligence (AI) community, education in AI is receiving more and more attentions. There have been many AI related courses in the respects of algorithms and applications, while not many courses in system level are seriously taken into considerations. In order to bridge the gap between AI and computing systems, we are trying to explore how to conduct AI education from the perspective of computing systems. In this paper, a course practice in intelligent computing architectures are provided to demonstrate the system education in AI era. The motivation for this course practice is first introduced as well as the learning orientations. The main goal of this course aims to teach students for designing AI accelerators on FPGA platforms. The elaborated course contents include lecture notes and related technical materials. Especially several practical labs and projects are detailed illustrated. Finally, some teaching experiences and effects are discussed as well as some potential improvements in the future.
Mind the hubris: complexity can misfire
Venue: arXiv cs.GL 2022
Year: 2022

Here we briefly reflect on the philosophical foundations that ground the quest towards ever-detailed models and identify four practical dangers derived from this pursuit: explosion of the model's uncertainty space, model black-boxing, computational exhaustion and model attachment. We argue that the growth of a mathematical model should be carefully and continuously pondered lest models become extraneous constructs chasing the Cartesian dream.
Advancing Semi-Supervised Task Oriented Dialog Systems by JSA Learning of Discrete Latent Variable Models
Venue: arXiv cs.CL 2022
Year: 2022

Developing semi-supervised task-oriented dialog (TOD) systems by leveraging unlabeled dialog data has attracted increasing interests. For semi-supervised learning of latent state TOD models, variational learning is often used, but suffers from the annoying high-variance of the gradients propagated through discrete latent variables and the drawback of indirectly optimizing the target log-likelihood. Recently, an alternative algorithm, called joint stochastic approximation (JSA), has emerged for learning discrete latent variable models with impressive performances. In this paper, we propose to apply JSA to semi-supervised learning of the latent state TOD models, which is referred to as JSA-TOD. To our knowledge, JSA-TOD represents the first work in developing JSA based semi-supervised learning of discrete latent variable conditional models for such long sequential generation problems like in TOD systems. Extensive experiments show that JSA-TOD significantly outperforms its variational learning counterpart. Remarkably, semi-supervised JSA-TOD using 20% labels performs close to the full-supervised baseline on MultiWOZ2.1.
Multisymplectic Formulation of Deep Learning Using Mean--Field Type Control and Nonlinear Stability of Training Algorithm
Venue: arXiv cs.LG 2022
Year: 2022

As it stands, a robust mathematical framework to analyse and study various topics in deep learning is yet to come to the fore. Nonetheless, viewing deep learning as a dynamical system allows the use of established theories to investigate the behaviour of deep neural networks. In order to study the stability of the training process, in this article, we formulate training of deep neural networks as a hydrodynamics system, which has a multisymplectic structure. For that, the deep neural network is modelled using a stochastic differential equation and, thereby, mean-field type control is used to train it. The necessary conditions for optimality of the mean--field type control reduce to a system of Euler-Poincare equations, which has the a similar geometric structure to that of compressible fluids. The mean-field type control is solved numerically using a multisymplectic numerical scheme that takes advantage of the underlying geometry. Moreover, the numerical scheme, yields an approximated solution which is also an exact solution of a hydrodynamics system with a multisymplectic structure and it can be analysed using backward error analysis. Further, nonlinear stability yields the condition for selecting the number of hidden layers and the number of nodes per layer, that makes the training stable while approximating the solution of a residual neural network with a number of hidden layers approaching infinity.
Decentralized digital twins of complex dynamical systems
Venue: arXiv cs.LG 2022
Year: 2022

In this paper, we introduce a decentralized digital twin (DDT) framework for dynamical systems and discuss the prospects of the DDT modeling paradigm in computational science and engineering applications. The DDT approach is built on a federated learning concept, a branch of machine learning that encourages knowledge sharing without sharing the actual data. This approach enables clients to collaboratively learn an aggregated model while keeping all the training data on each client. We demonstrate the feasibility of the DDT framework with various dynamical systems, which are often considered prototypes for modeling complex transport phenomena in spatiotemporally extended systems. Our results indicate that federated machine learning might be a key enabler for designing highly accurate decentralized digital twins in complex nonlinear spatiotemporal systems.
RSG-Net: Towards Rich Sematic Relationship Prediction for Intelligent Vehicle in Complex Environments
Venue: arXiv cs.CV 2022
Year: 2022

Behavioral and semantic relationships play a vital role on intelligent self-driving vehicles and ADAS systems. Different from other research focused on trajectory, position, and bounding boxes, relationship data provides a human understandable description of the object's behavior, and it could describe an object's past and future status in an amazingly brief way. Therefore it is a fundamental method for tasks such as risk detection, environment understanding, and decision making. In this paper, we propose RSG-Net (Road Scene Graph Net): a graph convolutional network designed to predict potential semantic relationships from object proposals, and produces a graph-structured result, called "Road Scene Graph". The experimental results indicate that this network, trained on Road Scene Graph dataset, could efficiently predict potential semantic relationships among objects around the ego-vehicle.
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification
Venue: arXiv cs.LG 2022
Year: 2022

Model agnostic meta-learning algorithms aim to infer priors from several observed tasks that can then be used to adapt to a new task with few examples. Given the inherent diversity of tasks arising in existing benchmarks, recent methods use separate, learnable structure, such as hierarchies or graphs, for enabling task-specific adaptation of the prior. While these approaches have produced significantly better meta learners, our goal is to improve their performance when the heterogeneous task distribution contains challenging distribution shifts and semantic disparities. To this end, we introduce CAML (Contrastive Knowledge-Augmented Meta Learning), a novel approach for knowledge-enhanced few-shot learning that evolves a knowledge graph to effectively encode historical experience, and employs a contrastive distillation strategy to leverage the encoded knowledge for task-aware modulation of the base learner. Using standard benchmarks, we evaluate the performance of CAML in different few-shot learning scenarios. In addition to the standard few-shot task adaptation, we also consider the more challenging multi-domain task adaptation and few-shot dataset generalization settings in our empirical studies. Our results shows that CAML consistently outperforms best known approaches and achieves improved generalization.
C3-SL: Circular Convolution-Based Batch-Wise Compression for Communication-Efficient Split Learning
Venue: arXiv cs.LG 2022
Year: 2022

Most existing studies improve the efficiency of Split learning (SL) by compressing the transmitted features. However, most works focus on dimension-wise compression that transforms high-dimensional features into a low-dimensional space. In this paper, we propose circular convolution-based batch-wise compression for SL (C3-SL) to compress multiple features into one single feature. To avoid information loss while merging multiple features, we exploit the quasi-orthogonality of features in high-dimensional space with circular convolution and superposition. To the best of our knowledge, we are the first to explore the potential of batch-wise compression under the SL scenario. Based on the simulation results on CIFAR-10 and CIFAR-100, our method achieves a 16x compression ratio with negligible accuracy drops compared with the vanilla SL. Moreover, C3-SL significantly reduces 1152x memory and 2.25x computation overhead compared to the state-of-the-art dimension-wise compression method.
Gacs-Kucera Theorem
Venue: arXiv cs.CC 2022
Year: 2022

Gacs-Kucera Theorem, tightened by Barmpalias and Lewis-Pye, w.t.t.-reduces each infinite sequence to a Kolmogorov--Martin-Lof random one and is broadly used in various Math and CS areas. Its early proofs are somewhat cumbersome, but using some general concepts yields significant simplification illustrated here.
Interference and Need Aware Workload Colocation in Hyperscale Datacenters
Venue: arXiv cs.DC 2022
Year: 2022

Datacenters suffer from resource utilization inefficiencies due to the conflicting goals of service owners and platform providers. Service owners intending to maintain Service Level Objectives (SLO) for themselves typically request a conservative amount of resources. Platform providers want to increase operational efficiency to reduce capital and operating costs. Achieving both operational efficiency and SLO for individual services at the same time is challenging due to the diversity in service workload characteristics, resource usage patterns that are dependent on input load, heterogeneity in platform, memory, I/O, and network architecture, and resource bundling. This paper presents a tunable approach to resource allocation that accounts for both dynamic service resource needs and platform heterogeneity. In addition, an online K-Means-based service classification method is used in conjunction with an offline sensitivity component. Our tunable approach allows trading resource utilization efficiency for absolute SLO guarantees based on the service owners' sensitivity to its SLO. We evaluate our tunable resource allocator at scale in a private cloud environment with mostly latency-critical workloads. When tuning for operational efficiency, we demonstrate up to ~50% reduction in required machines; ~40% reduction in Total-Cost-of-Ownership (TCO); and ~60% reduction in CPU and memory fragmentation, but at the cost of increasing the number of tasks experiencing degradation of SLO by up to ~25% compared to the baseline. When tuning for SLO, by introducing interference-aware colocation, we can tune the solver to reduce tasks experiencing degradation of SLO by up to ~22% compared to the baseline, but at an additional cost of ~30% in terms of the number of hosts. We highlight this trade-off between TCO and SLO violations, and offer tuning based on the requirements of the platform owners.
On the benefits of non-linear weight updates
Venue: arXiv cs.LG 2022
Year: 2022

Recent work has suggested that the generalisation performance of a DNN is related to the extent to which the Signal-to-Noise Ratio is optimised at each of the nodes. In contrast, Gradient Descent methods do not always lead to SNR-optimal weight configurations. One way to improve SNR performance is to suppress large weight updates and amplify small weight updates. Such balancing is already implicit in some common optimizers, but we propose an approach that makes this explicit. The method applies a non-linear function to gradients prior to making DNN parameter updates. We investigate the performance with such non-linear approaches. The result is an adaptation to existing optimizers that improves performance for many problem types.
A Pilot Study on The Impact of Stereoscopic Display Type on User Interactions Within A Immersive Analytics Environment
Venue: arXiv cs.HC 2022
Year: 2022

Immersive Analytics (IA) and consumer adoption of augmented reality (AR) and virtual reality (VR) head-mounted displays (HMDs) are both rapidly growing. When used in conjunction, stereoscopic IA environments can offer improved user understanding and engagement; however, it is unclear how the choice of stereoscopic display impacts user interactions within an IA environment. This paper presents a pilot study that examines the impact of stereoscopic display type on object manipulation and environmental navigation using consumer-available AR and VR displays. This work finds that the display type can impact how users manipulate virtual content, how they navigate the environment, and how able they are to answer questions about the represented data.
Static Hand Gesture Recognition for American Sign Language using Neuromorphic Hardware
Venue: arXiv cs.LG 2022
Year: 2022

In this paper, we develop four spiking neural network (SNN) models for two static American Sign Language (ASL) hand gesture classification tasks, i.e., the ASL Alphabet and ASL Digits. The SNN models are deployed on Intel's neuromorphic platform, Loihi, and then compared against equivalent deep neural network (DNN) models deployed on an edge computing device, the Intel Neural Compute Stick 2 (NCS2). We perform a comprehensive comparison between the two systems in terms of accuracy, latency, power consumption, and energy. The best DNN model achieves an accuracy of 99.93% on the ASL Alphabet dataset, whereas the best performing SNN model has an accuracy of 99.30%. For the ASL-Digits dataset, the best DNN model achieves an accuracy of 99.76% accuracy while the SNN achieves 99.03%. Moreover, our obtained experimental results show that the Loihi neuromorphic hardware implementations achieve up to 20.64x and 4.10x reduction in power consumption and energy, respectively, when compared to NCS2.
PTGCF: Printing Texture Guided Color Fusion for Impressionism Oil Painting Style Rendering
Venue: arXiv cs.CV 2022
Year: 2022

As a major branch of Non-Photorealistic Rendering (NPR), image stylization mainly uses the computer algorithms to render a photo into an artistic painting. Recent work has shown that the extraction of style information such as stroke texture and color of the target style image is the key to image stylization. Given its stroke texture and color characteristics, a new stroke rendering method is proposed, which fully considers the tonal characteristics and the representative color of the original oil painting, in order to fit the tone of the original oil painting image into the stylized image and make it close to the artist's creative effect. The experiments have validated the efficacy of the proposed model. This method would be more suitable for the works of pointillism painters with a relatively uniform sense of direction, especially for natural scenes. When the original painting brush strokes have a clearer sense of direction, using this method to simulate brushwork texture features can be less satisfactory.
Adaptive Asymmetric Label-guided Hashing for Multimedia Search
Venue: arXiv cs.IR 2022
Year: 2022

With the rapid growth of multimodal media data on the Web in recent years, hash learning methods as a way to achieve efficient and flexible cross-modal retrieval of massive multimedia data have received a lot of attention from the current Web resource retrieval research community. Existing supervised hashing methods simply transform label information into pairwise similarity information to guide hash learning, leading to a potential risk of semantic error in the face of multi-label data. In addition, most existing hash optimization methods solve NP-hard optimization problems by employing approximate approximation strategies based on relaxation strategies, leading to a large quantization error. In order to address above obstacles, we present a simple yet efficient Adaptive Asymmetric Label-guided Hashing, named A2LH, for Multimedia Search. Specifically, A2LH is a two-step hashing method. In the first step, we design an association representation model between the different modality representations and semantic label representation separately, and use the semantic label representation as an intermediate bridge to solve the semantic gap existing between different modalities. In addition, we present an efficient discrete optimization algorithm for solving the quantization error problem caused by relaxation-based optimization algorithms. In the second step, we leverage the generated hash codes to learn the hash mapping functions. The experimental results show that our proposed method achieves optimal performance on all compared baseline methods.
Can Deep Learning Assist Automatic Identification of Layered Pigments From XRF Data?
Venue: arXiv cs.CV 2022
Year: 2022

X-ray fluorescence spectroscopy (XRF) plays an important role for elemental analysis in a wide range of scientific fields, especially in cultural heritage. XRF imaging, which uses a raster scan to acquire spectra across artworks, provides the opportunity for spatial analysis of pigment distributions based on their elemental composition. However, conventional XRF-based pigment identification relies on time-consuming elemental mapping by expert interpretations of measured spectra. To reduce the reliance on manual work, recent studies have applied machine learning techniques to cluster similar XRF spectra in data analysis and to identify the most likely pigments. Nevertheless, it is still challenging for automatic pigment identification strategies to directly tackle the complex structure of real paintings, e.g. pigment mixtures and layered pigments. In addition, pixel-wise pigment identification based on XRF imaging remains an obstacle due to the high noise level compared with averaged spectra. Therefore, we developed a deep-learning-based end-to-end pigment identification framework to fully automate the pigment identification process. In particular, it offers high sensitivity to the underlying pigments and to the pigments with a low concentration, therefore enabling satisfying results in mapping the pigments based on single-pixel XRF spectrum. As case studies, we applied our framework to lab-prepared mock-up paintings and two 19th-century paintings: Paul Gauguin's Poèmes Barbares (1896) that contains layered pigments with an underlying painting, and Paul Cezanne's The Bathers (1899-1904). The pigment identification results demonstrated that our model achieved comparable results to the analysis by elemental mapping, suggesting the generalizability and stability of our model.
A formalization of the change of variables formula for integrals in mathlib
Venue: arXiv cs.LO 2022
Year: 2022

We report on a formalization of the change of variables formula in integrals, in the mathlib library for Lean. Our version of this theorem is extremely general, and builds on developments in linear algebra, analysis, measure theory and descriptive set theory. The interplay between these domains is transparent thanks to the highly integrated development model of mathlib.
Criteria Comparative Learning for Real-scene Image Super-Resolution
Venue: arXiv cs.CV 2022
Year: 2022

Real-scene image super-resolution aims to restore real-world low-resolution images into their high-quality versions. A typical RealSR framework usually includes the optimization of multiple criteria which are designed for different image properties, by making the implicit assumption that the ground-truth images can provide a good trade-off between different criteria. However, this assumption could be easily violated in practice due to the inherent contrastive relationship between different image properties. Contrastive learning (CL) provides a promising recipe to relieve this problem by learning discriminative features using the triplet contrastive losses. Though CL has achieved significant success in many computer vision tasks, it is non-trivial to introduce CL to RealSR due to the difficulty in defining valid positive image pairs in this case. Inspired by the observation that the contrastive relationship could also exist between the criteria, in this work, we propose a novel training paradigm for RealSR, named Criteria Comparative Learning (Cria-CL), by developing contrastive losses defined on criteria instead of image patches. In addition, a spatial projector is proposed to obtain a good view for Cria-CL in RealSR. Our experiments demonstrate that compared with the typical weighted regression strategy, our method achieves a significant improvement under similar parameter settings.
Using clarification questions to improve software developers' Web search
Venue: arXiv cs.SE 2022
Year: 2022

Context: Recent research indicates that Web queries written by software developers are not very successful in retrieving relevant results, performing measurably worse compared to general purpose Web queries. Most approaches up to this point have addressed this problem with software engineering-specific automated query reformulation techniques, which work without developer involvement but are limited by the content of the original query. In other words, these techniques automatically improve the existing query but can not contribute new, previously unmentioned, concepts. Objective: In this paper, we propose a technique to guide software developers in manually improving their own Web search queries. We examine a conversational approach that follows unsuccessful queries with a clarification question aimed at eliciting additional query terms, thus providing to the developer a clear dimension along which the query could be improved. Methods: We describe a set of clarification questions derived from a corpus of software developer queries and a neural approach to recommending them for a newly issued query. Results: Our evaluation indicates that the recommendation technique is accurate, predicting a valid clarification question 80% of the time and outperforms simple baselines, as well as, state-of-the-art Learning To Rank (LTR) baselines. Conclusion: As shown in the experimental results, the described approach is capable at recommending appropriate clarification questions to software developers and considered useful by a sample of developers ranging from novices to experienced professionals.