OpenRaiser/Intern-Atlas huggingface dataset:
A Virtual Reality Tool for Representing, Visualizing and Updating Deep Learning Models
Venue: arXiv cs.HC 2023
Year: 2023

Deep learning is ubiquitous, but its lack of transparency limits its impact on several potential application areas. We demonstrate a virtual reality tool for automating the process of assigning data inputs to different categories. A dataset is represented as a cloud of points in virtual space. The user explores the cloud through movement and uses hand gestures to categorise portions of the cloud. This triggers gradual movements in the cloud: points of the same category are attracted to each other, different groups are pushed apart, while points are globally distributed in a way that utilises the entire space. The space, time, and forces observed in virtual reality can be mapped to well-defined machine learning concepts, namely the latent space, the training epochs and the backpropagation. Our tool illustrates how the inner workings of deep neural networks can be made tangible and transparent. We expect this approach to accelerate the autonomous development of deep learning applications by end users in novel areas.
DeepCollide: Scalable Data-Driven High DoF Configuration Space Modeling using Implicit Neural Representations
Venue: arXiv cs.RO 2023
Year: 2023

Collision detection is essential to virtually all robotics applications. However, traditional geometric collision detection methods generally require pre-existing workspace geometry representations; thus, they are unable to infer the collision detection function from sampled data when geometric information is unavailable. Learning-based approaches can overcome this limitation. Following this line of research, we present DeepCollide, an implicit neural representation method for approximating the collision detection function from sampled collision data. As shown by our theoretical analysis and empirical evidence, DeepCollide presents clear benefits over the state-of-the-art, as it relates to time cost scalability with respect to training data and DoF, as well as the ability to accurately express complex workspace geometries. We publicly release our code.
A Neural Space-Time Representation for Text-to-Image Personalization
Venue: arXiv cs.CV 2023
Year: 2023

A key aspect of text-to-image personalization methods is the manner in which the target concept is represented within the generative process. This choice greatly affects the visual fidelity, downstream editability, and disk space needed to store the learned concept. In this paper, we explore a new text-conditioning space that is dependent on both the denoising process timestep (time) and the denoising U-Net layers (space) and showcase its compelling properties. A single concept in the space-time representation is composed of hundreds of vectors, one for each combination of time and space, making this space challenging to optimize directly. Instead, we propose to implicitly represent a concept in this space by optimizing a small neural mapper that receives the current time and space parameters and outputs the matching token embedding. In doing so, the entire personalized concept is represented by the parameters of the learned mapper, resulting in a compact, yet expressive, representation. Similarly to other personalization methods, the output of our neural mapper resides in the input space of the text encoder. We observe that one can significantly improve the convergence and visual fidelity of the concept by introducing a textual bypass, where our neural mapper additionally outputs a residual that is added to the output of the text encoder. Finally, we show how one can impose an importance-based ordering over our implicit representation, providing users control over the reconstruction and editability of the learned concept using a single trained model. We demonstrate the effectiveness of our approach over a range of concepts and prompts, showing our method's ability to generate high-quality and controllable compositions without fine-tuning any parameters of the generative model itself.
Evaluating The Impact Of Cloud-Based Microservices Architecture On Application Performance
Venue: arXiv cs.DC 2023
Year: 2023

The study assesses the impact of cloud-based microservices architectures on application performance. Several aspects of performance evaluation are discussed, including response time, throughput, scalability, and reliability. This article examines the advantages and challenges of adopting a cloud-based approach. It explores potential bottlenecks and issues in a microservices architecture and presents optimization techniques. With the help of case studies and empirical studies, it compares cloud-based microservices architectures with traditional monolithic architectures. Furthermore, the paper examines the challenges of monitoring and troubleshooting distributed microservices. In conclusion, it emphasizes the importance of planning, designing, and testing during the adoption of cloud-based microservices.
Sound Design Strategies for Latent Audio Space Explorations Using Deep Learning Architectures
Venue: arXiv cs.SD 2023
Year: 2023

The research in Deep Learning applications in sound and music computing have gathered an interest in the recent years; however, there is still a missing link between these new technologies and on how they can be incorporated into real-world artistic practices. In this work, we explore a well-known Deep Learning architecture called Variational Autoencoders (VAEs). These architectures have been used in many areas for generating latent spaces where data points are organized so that similar data points locate closer to each other. Previously, VAEs have been used for generating latent timbre spaces or latent spaces of symbolic music excepts. Applying VAE to audio features of timbre requires a vocoder to transform the timbre generated by the network to an audio signal, which is computationally expensive. In this work, we apply VAEs to raw audio data directly while bypassing audio feature extraction. This approach allows the practitioners to use any audio recording while giving flexibility and control over the aesthetics through dataset curation. The lower computation time in audio signal generation allows the raw audio approach to be incorporated into real-time applications. In this work, we propose three strategies to explore latent spaces of audio and timbre for sound design applications. By doing so, our aim is to initiate a conversation on artistic approaches and strategies to utilize latent audio spaces in sound and music practices.
Balancing Effect of Training Dataset Distribution of Multiple Styles for Multi-Style Text Transfer
Venue: arXiv cs.CL 2023
Year: 2023

Text style transfer is an exciting task within the field of natural language generation that is often plagued by the need for high-quality paired datasets. Furthermore, training a model for multi-attribute text style transfer requires datasets with sufficient support across all combinations of the considered stylistic attributes, adding to the challenges of training a style transfer model. This paper explores the impact of training data input diversity on the quality of the generated text from the multi-style transfer model. We construct a pseudo-parallel dataset by devising heuristics to adjust the style distribution in the training samples. We balance our training dataset using marginal and joint distributions to train our style transfer models. We observe that a balanced dataset produces more effective control effects over multiple styles than an imbalanced or skewed one. Through quantitative analysis, we explore the impact of multiple style distributions in training data on style-transferred output. These findings will better inform the design of style-transfer datasets.
Automated Driving Architecture and Operation of a Light Commercial Vehicle
Venue: arXiv cs.RO 2023
Year: 2023

This paper is on the automated driving architecture and operation of a light commercial vehicle. Simple longitudinal and lateral dynamic models of the vehicle and a more detailed CarSim model are developed and used in simulations and controller design and evaluation. Experimental validation is used to make sure that the models used represent the actual response of the vehicle as closely as possible. The vehicle is made drive-by-wire by interfacing with the existing throttle-by-wire, by adding an active vacuum booster for brake-by-wire and by adding a steering actuator for steer-by-wire operation. Vehicle localization is achieved by using a GPS sensor integrated with six axes IMU with a built-in INS algorithm and a digital compass for heading information. Front looking radar, lidar and camera are used for environmental sensing. Communication with the road infrastructure and other vehicles is made possible by a vehicle to vehicle communication modem. A dedicated computer under real time Linux is used to collect, process and distribute sensor information. A dSPACE MicroAutoBox is used for drive-by-wire controls. CACC based longitudinal control and path tracking of a map of GPS waypoints are used to present the operation of this automated driving vehicle.
Revisiting Sentence Union Generation as a Testbed for Text Consolidation
Venue: arXiv cs.CL 2023
Year: 2023

Tasks involving text generation based on multiple input texts, such as multi-document summarization, long-form question answering and contemporary dialogue applications, challenge models for their ability to properly consolidate partly-overlapping multi-text information. However, these tasks entangle the consolidation phase with the often subjective and ill-defined content selection requirement, impeding proper assessment of models' consolidation capabilities. In this paper, we suggest revisiting the sentence union generation task as an effective well-defined testbed for assessing text consolidation capabilities, decoupling the consolidation challenge from subjective content selection. To support research on this task, we present refined annotation methodology and tools for crowdsourcing sentence union, create the largest union dataset to date and provide an analysis of its rich coverage of various consolidation aspects. We then propose a comprehensive evaluation protocol for union generation, including both human and automatic evaluation. Finally, as baselines, we evaluate state-of-the-art language models on the task, along with a detailed analysis of their capacity to address multi-text consolidation challenges and their limitations.
Matrix Estimation for Offline Reinforcement Learning with Low-Rank Structure
Venue: arXiv cs.LG 2023
Year: 2023

We consider offline Reinforcement Learning (RL), where the agent does not interact with the environment and must rely on offline data collected using a behavior policy. Previous works provide policy evaluation guarantees when the target policy to be evaluated is covered by the behavior policy, that is, state-action pairs visited by the target policy must also be visited by the behavior policy. We show that when the MDP has a latent low-rank structure, this coverage condition can be relaxed. Building on the connection to weighted matrix completion with non-uniform observations, we propose an offline policy evaluation algorithm that leverages the low-rank structure to estimate the values of uncovered state-action pairs. Our algorithm does not require a known feature representation, and our finite-sample error bound involves a novel discrepancy measure quantifying the discrepancy between the behavior and target policies in the spectral space. We provide concrete examples where our algorithm achieves accurate estimation while existing coverage conditions are not satisfied. Building on the above evaluation algorithm, we further design an offline policy optimization algorithm and provide non-asymptotic performance guarantees.
New constructions of cyclic subspace codes
Venue: arXiv cs.DM 2023
Year: 2023

A subspace of a finite field is called a Sidon space if the product of any two of its nonzero elements is unique up to a scalar multiplier from the base field. Sidon spaces, introduced by Roth et al. (IEEE Trans Inf Theory 64(6): 4412-4422, 2018), have a close connection with optimal full-length orbit codes. In this paper, we present two constructions of Sidon spaces. The union of Sidon spaces from the first construction yields cyclic subspace codes in $\mathcal{G}_{q}(n,k)$ with minimum distance $2k-2$ and size $r(\lceil \frac{n}{2rk} \rceil -1)((q^{k}-1)^{r}(q^{n}-1)+\frac{(q^{k}-1)^{r-1}(q^{n}-1)}{q-1})$, where $k|n$, $r\geq 2$ and $n\geq (2r+1)k$, $\mathcal{G}_{q}(n,k)$ is the set of all $k$-dimensional subspaces of $\mathbb{F}_{q}^{n}$. The union of Sidon spaces from the second construction gives cyclic subspace codes in $\mathcal{G}_{q}(n,k)$ with minimum distance $2k-2$ and size $\lfloor \frac{(r-1)(q^{k}-2)(q^{k}-1)^{r-1}(q^{n}-1)}{2}\rfloor$ where $n= 2rk$ and $r\geq 2$. Our cyclic subspace codes have larger sizes than those in the literature, in particular, in the case of $n=4k$, the size of our resulting code is within a factor of $\frac{1}{2}+o_{k}(1)$ of the sphere-packing bound as $k$ goes to infinity.
Robotic LEGO Assembly and Disassembly from Human Demonstration
Venue: arXiv cs.RO 2023
Year: 2023

This paper studies automatic prototyping using LEGO. To satisfy individual needs and self-sustainability, this paper presents a framework that learns the assembly and disassembly sequences from human demonstrations. In addition, a digital twin is developed to verify the correctness of robot learning before deploying to the real world. Moreover, an end-effector tool (EOT) is designed, which allows large industrial robots to easily manipulate LEGO bricks. The proposed system is deployed to a FANUC LR-mate 200id/7L robot. Experiments demonstrate that the proposed system can effectively learn the assembly and disassembly tasks from human demonstrations. And the learned tasks are realized by the FANUC robot.
BookGPT: A General Framework for Book Recommendation Empowered by Large Language Model
Venue: arXiv cs.IR 2023
Year: 2023

With the continuous development and change exhibited by large language model (LLM) technology, represented by generative pretrained transformers (GPTs), many classic scenarios in various fields have re-emerged with new opportunities. This paper takes ChatGPT as the modeling object, incorporates LLM technology into the typical book resource understanding and recommendation scenario for the first time, and puts it into practice. By building a ChatGPT-like book recommendation system (BookGPT) framework based on ChatGPT, this paper attempts to apply ChatGPT to recommendation modeling for three typical tasks, book rating recommendation, user rating recommendation, and book summary recommendation, and explores the feasibility of LLM technology in book recommendation scenarios. At the same time, based on different evaluation schemes for book recommendation tasks and the existing classic recommendation models, this paper discusses the advantages and disadvantages of the BookGPT in book recommendation scenarios and analyzes the opportunities and improvement directions for subsequent LLMs in these scenarios.
CUEING: a lightweight model to Capture hUman attEntion In driviNG
Venue: arXiv cs.CV 2023
Year: 2023

Discrepancies in decision-making between Autonomous Driving Systems (ADS) and human drivers underscore the need for intuitive human gaze predictors to bridge this gap, thereby improving user trust and experience. Existing gaze datasets, despite their value, suffer from noise that hampers effective training. Furthermore, current gaze prediction models exhibit inconsistency across diverse scenarios and demand substantial computational resources, restricting their on-board deployment in autonomous vehicles. We propose a novel adaptive cleansing technique for purging noise from existing gaze datasets, coupled with a robust, lightweight convolutional self-attention gaze prediction model. Our approach not only significantly enhances model generalizability and performance by up to 12.13% but also ensures a remarkable reduction in model complexity by up to 98.2% compared to the state-of-the art, making in-vehicle deployment feasible to augment ADS decision visualization and performance.
Towards Higher Pareto Frontier in Multilingual Machine Translation
Venue: arXiv cs.CL 2023
Year: 2023

Multilingual neural machine translation has witnessed remarkable progress in recent years. However, the long-tailed distribution of multilingual corpora poses a challenge of Pareto optimization, i.e., optimizing for some languages may come at the cost of degrading the performance of others. Existing balancing training strategies are equivalent to a series of Pareto optimal solutions, which trade off on a Pareto frontier. In this work, we propose a new training framework, Pareto Mutual Distillation (Pareto-MD), towards pushing the Pareto frontier outwards rather than making trade-offs. Specifically, Pareto-MD collaboratively trains two Pareto optimal solutions that favor different languages and allows them to learn from the strengths of each other via knowledge distillation. Furthermore, we introduce a novel strategy to enable stronger communication between Pareto optimal solutions and broaden the applicability of our approach. Experimental results on the widely-used WMT and TED datasets show that our method significantly pushes the Pareto frontier and outperforms baselines by up to +2.46 BLEU.
Comparative Study of Pre-Trained BERT Models for Code-Mixed Hindi-English Data
Venue: arXiv cs.CL 2023
Year: 2023

The term "Code Mixed" refers to the use of more than one language in the same text. This phenomenon is predominantly observed on social media platforms, with an increasing amount of adaptation as time goes on. It is critical to detect foreign elements in a language and process them correctly, as a considerable number of individuals are using code-mixed languages that could not be comprehended by understanding one of those languages. In this work, we focus on low-resource Hindi-English code-mixed language and enhancing the performance of different code-mixed natural language processing tasks such as sentiment analysis, emotion recognition, and hate speech identification. We perform a comparative analysis of different Transformer-based language Models pre-trained using unsupervised approaches. We have included the code-mixed models like HingBERT, HingRoBERTa, HingRoBERTa-Mixed, mBERT, and non-code-mixed models like AlBERT, BERT, and RoBERTa for comparative analysis of code-mixed Hindi-English downstream tasks. We report state-of-the-art results on respective datasets using HingBERT-based models which are specifically pre-trained on real code-mixed text. Our HingBERT-based models provide significant improvements thus highlighting the poor performance of vanilla BERT models on code-mixed text.
A Tutorial on Holographic MIMO Communications--Part II: Performance Analysis and Holographic Beamforming
Venue: arXiv cs.IT 2023
Year: 2023

As Part II of a three-part tutorial on holographic multiple-input multiple-output (HMIMO), this Letter focuses on the state-of-the-art in performance analysis and on holographic beamforming for HMIMO communications. We commence by discussing the spatial degrees of freedom (DoF) and ergodic capacity of a point-to-point HMIMO system, based on the channel model presented in Part I. Additionally, we also consider the sum-rate analysis of multi-user HMIMO systems. Moreover, we review the recent progress in holographic beamforming techniques developed for various HMIMO scenarios. Finally, we evaluate both the spatial DoF and the channel capacity through numerical simulations.
TransWorldNG: Traffic Simulation via Foundation Model
Venue: arXiv cs.AI 2023
Year: 2023

Traffic simulation is a crucial tool for transportation decision-making and policy development. However, achieving realistic simulations in the face of the high dimensionality and heterogeneity of traffic environments is a longstanding challenge. In this paper, we present TransWordNG, a traffic simulator that uses Data-driven algorithms and Graph Computing techniques to learn traffic dynamics from real data. The functionality and structure of TransWorldNG are introduced, which utilize a foundation model for transportation management and control. The results demonstrate that TransWorldNG can generate more realistic traffic patterns compared to traditional simulators. Additionally, TransWorldNG exhibits better scalability, as it shows linear growth in computation time as the scenario scale increases. To the best of our knowledge, this is the first traffic simulator that can automatically learn traffic patterns from real-world data and efficiently generate accurate and realistic traffic environments.
T2TD: Text-3D Generation Model based on Prior Knowledge Guidance
Venue: arXiv cs.CV 2023
Year: 2023

In recent years, 3D models have been utilized in many applications, such as auto-driver, 3D reconstruction, VR, and AR. However, the scarcity of 3D model data does not meet its practical demands. Thus, generating high-quality 3D models efficiently from textual descriptions is a promising but challenging way to solve this problem. In this paper, inspired by the ability of human beings to complement visual information details from ambiguous descriptions based on their own experience, we propose a novel text-3D generation model (T2TD), which introduces the related shapes or textual information as the prior knowledge to improve the performance of the 3D generation model. In this process, we first introduce the text-3D knowledge graph to save the relationship between 3D models and textual semantic information, which can provide the related shapes to guide the target 3D model generation. Second, we integrate an effective causal inference model to select useful feature information from these related shapes, which removes the unrelated shape information and only maintains feature information that is strongly relevant to the textual description. Meanwhile, to effectively integrate multi-modal prior knowledge into textual information, we adopt a novel multi-layer transformer structure to progressively fuse related shape and textual information, which can effectively compensate for the lack of structural information in the text and enhance the final performance of the 3D generation model. The final experimental results demonstrate that our approach significantly improves 3D model generation quality and outperforms the SOTA methods on the text2shape datasets.
Concept-Centric Transformers: Enhancing Model Interpretability through Object-Centric Concept Learning within a Shared Global Workspace
Venue: arXiv cs.LG 2023
Year: 2023

Many interpretable AI approaches have been proposed to provide plausible explanations for a model's decision-making. However, configuring an explainable model that effectively communicates among computational modules has received less attention. A recently proposed shared global workspace theory showed that networks of distributed modules can benefit from sharing information with a bottlenecked memory because the communication constraints encourage specialization, compositionality, and synchronization among the modules. Inspired by this, we propose Concept-Centric Transformers, a simple yet effective configuration of the shared global workspace for interpretability, consisting of: i) an object-centric-based memory module for extracting semantic concepts from input features, ii) a cross-attention mechanism between the learned concept and input embeddings, and iii) standard classification and explanation losses to allow human analysts to directly assess an explanation for the model's classification reasoning. We test our approach against other existing concept-based methods on classification tasks for various datasets, including CIFAR100, CUB-200-2011, and ImageNet, and we show that our model achieves better classification accuracy than all baselines across all problems but also generates more consistent concept-based explanations of classification output.