OpenRaiser/Intern-Atlas huggingface dataset:
HybridOcc: NeRF Enhanced Transformer-based Multi-Camera 3D Occupancy Prediction
Venue: arXiv cs.CV 2024
Year: 2024

Vision-based 3D semantic scene completion (SSC) describes autonomous driving scenes through 3D volume representations. However, the occlusion of invisible voxels by scene surfaces poses challenges to current SSC methods in hallucinating refined 3D geometry. This paper proposes HybridOcc, a hybrid 3D volume query proposal method generated by Transformer framework and NeRF representation and refined in a coarse-to-fine SSC prediction framework. HybridOcc aggregates contextual features through the Transformer paradigm based on hybrid query proposals while combining it with NeRF representation to obtain depth supervision. The Transformer branch contains multiple scales and uses spatial cross-attention for 2D to 3D transformation. The newly designed NeRF branch implicitly infers scene occupancy through volume rendering, including visible and invisible voxels, and explicitly captures scene depth rather than generating RGB color. Furthermore, we present an innovative occupancy-aware ray sampling method to orient the SSC task instead of focusing on the scene surface, further improving the overall performance. Extensive experiments on nuScenes and SemanticKITTI datasets demonstrate the effectiveness of our HybridOcc on the SSC task.
Vanilla Gradient Descent for Oblique Decision Trees
Venue: arXiv cs.LG 2024
Year: 2024

Decision Trees (DTs) constitute one of the major highly non-linear AI models, valued, e.g., for their efficiency on tabular data. Learning accurate DTs is, however, complicated, especially for oblique DTs, and does take a significant training time. Further, DTs suffer from overfitting, e.g., they proverbially "do not generalize" in regression tasks. Recently, some works proposed ways to make (oblique) DTs differentiable. This enables highly efficient gradient-descent algorithms to be used to learn DTs. It also enables generalizing capabilities by learning regressors at the leaves simultaneously with the decisions in the tree. Prior approaches to making DTs differentiable rely either on probabilistic approximations at the tree's internal nodes (soft DTs) or on approximations in gradient computation at the internal node (quantized gradient descent). In this work, we propose DTSemNet, a novel semantically equivalent and invertible encoding for (hard, oblique) DTs as Neural Networks (NNs), that uses standard vanilla gradient descent. Experiments across various classification and regression benchmarks show that oblique DTs learned using DTSemNet are more accurate than oblique DTs of similar size learned using state-of-the-art techniques. Further, DT training time is significantly reduced. We also experimentally demonstrate that DTSemNet can learn DT policies as efficiently as NN policies in the Reinforcement Learning (RL) setup with physical inputs (dimensions $\leq32$). The code is available at https://github.com/CPS-research-group/dtsemnet.
GSLAMOT: A Tracklet and Query Graph-based Simultaneous Locating, Mapping, and Multiple Object Tracking System
Venue: arXiv cs.CV 2024
Year: 2024

For interacting with mobile objects in unfamiliar environments, simultaneously locating, mapping, and tracking the 3D poses of multiple objects are crucially required. This paper proposes a Tracklet Graph and Query Graph-based framework, i.e., GSLAMOT, to address this challenge. GSLAMOT utilizes camera and LiDAR multimodal information as inputs and divides the representation of the dynamic scene into a semantic map for representing the static environment, a trajectory of the ego-agent, and an online maintained Tracklet Graph (TG) for tracking and predicting the 3D poses of the detected mobile objects. A Query Graph (QG) is constructed in each frame by object detection to query and update TG. For accurate object association, a Multi-criteria Star Graph Association (MSGA) method is proposed to find matched objects between the detections in QG and the predicted tracklets in TG. Then, an Object-centric Graph Optimization (OGO) method is proposed to simultaneously optimize the TG, the semantic map, and the agent trajectory. It triangulates the detected objects into the map to enrich the map's semantic information. We address the efficiency issues to handle the three tightly coupled tasks in parallel. Experiments are conducted on KITTI, Waymo, and an emulated Traffic Congestion dataset that highlights challenging scenarios. Experiments show that GSLAMOT enables accurate crowded object tracking while conducting SLAM accurately in challenging scenarios, demonstrating more excellent performances than the state-of-the-art methods. The code and dataset are at https://gslamot.github.io.
AI Managed Emergency Documentation with a Pretrained Model
Venue: arXiv cs.AI 2024
Year: 2024

This study investigates the use of a large language model system to improve efficiency and quality in emergency department (ED) discharge letter writing. Time constraints and infrastructural deficits make compliance with current discharge letter targets difficult. We explored potential efficiencies from an artificial intelligence software in the generation of ED discharge letters and the attitudes of doctors toward this technology. The evaluated system leverages advanced techniques to fine-tune a model to generate discharge summaries from short-hand inputs, including voice, text, and electronic health record data. Nineteen physicians with emergency medicine experience evaluated the system text and voice-to-text interfaces against manual typing. The results showed significant time savings with MedWrite LLM interfaces compared to manual methods.
TC-RAG:Turing-Complete RAG's Case study on Medical LLM Systems
Venue: arXiv cs.IR 2024
Year: 2024

In the pursuit of enhancing domain-specific Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) emerges as a promising solution to mitigate issues such as hallucinations, outdated knowledge, and limited expertise in highly specialized queries. However, existing approaches to RAG fall short by neglecting system state variables, which are crucial for ensuring adaptive control, retrieval halting, and system convergence. In this paper, we introduce the TC-RAG through rigorous proof, a novel framework that addresses these challenges by incorporating a Turing Complete System to manage state variables, thereby enabling more efficient and accurate knowledge retrieval. By leveraging a memory stack system with adaptive retrieval, reasoning, and planning capabilities, TC-RAG not only ensures the controlled halting of retrieval processes but also mitigates the accumulation of erroneous knowledge via Push and Pop actions. In the case study of the medical domain, our extensive experiments on real-world healthcare datasets demonstrate the superiority of TC-RAG over existing methods in accuracy by over 7.20\%. Our dataset and code have been available at https://https://github.com/Artessay/SAMA.git.
A Deep Neural Network Framework for Solving Forward and Inverse Problems in Delay Differential Equations
Venue: arXiv cs.LG 2024
Year: 2024

We propose a unified framework for delay differential equations (DDEs) based on deep neural networks (DNNs) - the neural delay differential equations (NDDEs), aimed at solving the forward and inverse problems of delay differential equations. This framework could embed delay differential equations into neural networks to accommodate the diverse requirements of DDEs in terms of initial conditions, control equations, and known data. NDDEs adjust the network parameters through automatic differentiation and optimization algorithms to minimize the loss function, thereby obtaining numerical solutions to the delay differential equations without the grid dependence and polynomial interpolation typical of traditional numerical methods. In addressing inverse problems, the NDDE framework can utilize observational data to perform precise estimation of single or multiple delay parameters, which is very important in practical mathematical modeling. The results of multiple numerical experiments have shown that NDDEs demonstrate high precision in both forward and inverse problems, proving their effectiveness and promising potential in dealing with delayed differential equation issues.
On the Improvement of Generalization and Stability of Forward-Only Learning via Neural Polarization
Venue: arXiv cs.LG 2024
Year: 2024

Forward-only learning algorithms have recently gained attention as alternatives to gradient backpropagation, replacing the backward step of this latter solver with an additional contrastive forward pass. Among these approaches, the so-called Forward-Forward Algorithm (FFA) has been shown to achieve competitive levels of performance in terms of generalization and complexity. Networks trained using FFA learn to contrastively maximize a layer-wise defined goodness score when presented with real data (denoted as positive samples) and to minimize it when processing synthetic data (corr. negative samples). However, this algorithm still faces weaknesses that negatively affect the model accuracy and training stability, primarily due to a gradient imbalance between positive and negative samples. To overcome this issue, in this work we propose a novel implementation of the FFA algorithm, denoted as Polar-FFA, which extends the original formulation by introducing a neural division (\emph{polarization}) between positive and negative instances. Neurons in each of these groups aim to maximize their goodness when presented with their respective data type, thereby creating a symmetric gradient behavior. To empirically gauge the improved learning capabilities of our proposed Polar-FFA, we perform several systematic experiments using different activation and goodness functions over image classification datasets. Our results demonstrate that Polar-FFA outperforms FFA in terms of accuracy and convergence speed. Furthermore, its lower reliance on hyperparameters reduces the need for hyperparameter tuning to guarantee optimal generalization capabilities, thereby allowing for a broader range of neural network configurations.
Intuitive Human-Robot Interface: A 3-Dimensional Action Recognition and UAV Collaboration Framework
Venue: arXiv cs.RO 2024
Year: 2024

Harnessing human movements to command an Unmanned Aerial Vehicle (UAV) holds the potential to revolutionize their deployment, rendering it more intuitive and user-centric. In this research, we introduce a novel methodology adept at classifying three-dimensional human actions, leveraging them to coordinate on-field with a UAV. Utilizing a stereo camera, we derive both RGB and depth data, subsequently extracting three-dimensional human poses from the continuous video feed. This data is then processed through our proposed k-nearest neighbour classifier, the results of which dictate the behaviour of the UAV. It also includes mechanisms ensuring the robot perpetually maintains the human within its visual purview, adeptly tracking user movements. We subjected our approach to rigorous testing involving multiple tests with real robots. The ensuing results, coupled with comprehensive analysis, underscore the efficacy and inherent advantages of our proposed methodology.
Adaptify: A Refined Adaptation Scheme for Frame Classification in Atrophic Gastritis Videos
Venue: arXiv cs.CV 2024
Year: 2024

Atrophic gastritis is a significant risk factor for developing gastric cancer. The incorporation of machine learning algorithms can efficiently elevate the possibility of accurately detecting atrophic gastritis. Nevertheless, when the trained model is applied in real-life circumstances, its output is often not consistently reliable. In this paper, we propose Adaptify, an adaptation scheme in which the model assimilates knowledge from its own classification decisions. Our proposed approach includes keeping the primary model constant, while simultaneously running and updating the auxiliary model. By integrating the knowledge gleaned by the auxiliary model into the primary model and merging their outputs, we have observed a notable improvement in output stability and consistency compared to relying solely on either the main model or the auxiliary model.
Establishment of a Blockchain-based Architecture for Fake News Detection
Venue: arXiv cs.SE 2024
Year: 2024

Fake News are a contemporary phenomenon with potential devastating effects. For inquiry and auditability purposes, it is essential that the news, once classified as false, can be persisted in an immutable means so that interested parties can query it. Although Blockchain clearly satisfies the main requirements for Fake News Management Software Systems, the prescriptive architectural solutions for that domain that cohabit Blockchain with other technologies in a single proposal still need to be made available. This paper's main contribution is presenting a prescriptive architectural solution for blockchain-based fake news management software systems. The Hoffmeister process for software architecture design is systematically followed to culminate in a software solution for that domain. The implementation of two candidate architectures and a brief simulation-based evaluation show the feasibility of the solution to satisfy the functional and quality requirements.
Developing a Llama-Based Chatbot for CI/CD Question Answering: A Case Study at Ericsson
Venue: arXiv cs.SE 2024
Year: 2024

This paper presents our experience developing a Llama-based chatbot for question answering about continuous integration and continuous delivery (CI/CD) at Ericsson, a multinational telecommunications company. Our chatbot is designed to handle the specificities of CI/CD documents at Ericsson, employing a retrieval-augmented generation (RAG) model to enhance accuracy and relevance. Our empirical evaluation of the chatbot on industrial CI/CD-related questions indicates that an ensemble retriever, combining BM25 and embedding retrievers, yields the best performance. When evaluated against a ground truth of 72 CI/CD questions and answers at Ericsson, our most accurate chatbot configuration provides fully correct answers for 61.11% of the questions, partially correct answers for 26.39%, and incorrect answers for 12.50%. Through an error analysis of the partially correct and incorrect answers, we discuss the underlying causes of inaccuracies and provide insights for further refinement. We also reflect on lessons learned and suggest future directions for further improving our chatbot's accuracy.
Social VR for Professional Networking: A Spatial Perspective
Venue: arXiv cs.HC 2024
Year: 2024

One essential function of professional events, such as industry trade shows and academic conferences, is to foster and extend a person's connections to others within the community of their interest. In this paper, we delve into the emerging practice transitioning these events from physical venues to social VR as a new medium. Specifically, we ask: how does the spatial design in social VR affect the attendee's networking behaviors and experiences at these events? To answer this question, we conducted in-situ observations and in-depth interviews with 13 participants. Each of them had attended or hosted at least one real-world professional event taking place in social VR. We identified four elements of VR spatial design that shaped social interactions at these events: area size, which influenced a person's perceived likelihood of encountering others; pathways connecting areas, which guided their planning of the next activity to perform; magnets in areas, which facilitated spontaneous gatherings among people; and conventionality, which affected the assessment of a person's behavior appropriateness. Some of these elements were interpreted differently depending on the role of the participant, i.e., event hosts vs. attendees. We concluded this paper with multiple design implications derived from our findings.
A Probabilistic Framework for Adapting to Changing and Recurring Concepts in Data Streams
Venue: arXiv cs.LG 2024
Year: 2024

The distribution of streaming data often changes over time as conditions change, a phenomenon known as concept drift. Only a subset of previous experience, collected in similar conditions, is relevant to learning an accurate classifier for current data. Learning from irrelevant experience describing a different concept can degrade performance. A system learning from streaming data must identify which recent experience is irrelevant when conditions change and which past experience is relevant when concepts reoccur, \textit{e.g.,} when weather events or financial patterns repeat. Existing streaming approaches either do not consider experience to change in relevance over time and thus cannot handle concept drift, or only consider the recency of experience and thus cannot handle recurring concepts, or only sparsely evaluate relevance and thus fail when concept drift is missed. To enable learning in changing conditions, we propose SELeCT, a probabilistic method for continuously evaluating the relevance of past experience. SELeCT maintains a distinct internal state for each concept, representing relevant experience with a unique classifier. We propose a Bayesian algorithm for estimating state relevance, combining the likelihood of drawing recent observations from a given state with a transition pattern prior based on the system's current state.
Elite360M: Efficient 360 Multi-task Learning via Bi-projection Fusion and Cross-task Collaboration
Venue: arXiv cs.CV 2024
Year: 2024

360 cameras capture the entire surrounding environment with a large FoV, exhibiting comprehensive visual information to directly infer the 3D structures, e.g., depth and surface normal, and semantic information simultaneously. Existing works predominantly specialize in a single task, leaving multi-task learning of 3D geometry and semantics largely unexplored. Achieving such an objective is, however, challenging due to: 1) inherent spherical distortion of planar equirectangular projection (ERP) and insufficient global perception induced by 360 image's ultra-wide FoV; 2) non-trivial progress in effectively merging geometry and semantics among different tasks to achieve mutual benefits. In this paper, we propose a novel end-to-end multi-task learning framework, named Elite360M, capable of inferring 3D structures via depth and surface normal estimation, and semantics via semantic segmentation simultaneously. Our key idea is to build a representation with strong global perception and less distortion while exploring the inter- and cross-task relationships between geometry and semantics. We incorporate the distortion-free and spatially continuous icosahedron projection (ICOSAP) points and combine them with ERP to enhance global perception. With a negligible cost, a Bi-projection Bi-attention Fusion module is thus designed to capture the semantic- and distance-aware dependencies between each pixel of the region-aware ERP feature and the ICOSAP point feature set. Moreover, we propose a novel Cross-task Collaboration module to explicitly extract task-specific geometric and semantic information from the learned representation to achieve preliminary predictions. It then integrates the spatial contextual information among tasks to realize cross-task fusion. Extensive experiments demonstrate the effectiveness and efficacy of Elite360M.
Behavioral Learning of Dish Rinsing and Scrubbing based on Interruptive Direct Teaching Considering Assistance Rate
Venue: arXiv cs.RO 2024
Year: 2024

Robots are expected to manipulate objects in a safe and dexterous way. For example, washing dishes is a dexterous operation that involves scrubbing the dishes with a sponge and rinsing them with water. It is necessary to learn it safely without splashing water and without dropping the dishes. In this study, we propose a safe and dexterous manipulation system. The robot learns a dynamics model of the object by estimating the state of the object and the robot itself, the control input, and the amount of human assistance required (assistance rate) after the human corrects the initial trajectory of the robot's hands by interruptive direct teaching. By backpropagating the error between the estimated and the reference value using the acquired dynamics model, the robot can generate a control input that approaches the reference value, for example, so that human assistance is not required and the dish does not move excessively. This allows for adaptive rinsing and scrubbing of dishes with unknown shapes and properties. As a result, it is possible to generate safe actions that require less human assistance.
Gender Dynamics in Russian Online Political Discourse
Venue: arXiv cs.IR 2024
Year: 2024

The digital landscape provides a dynamic platform for political discourse crucial for understanding shifts in public opinion and engagement especially under authoritarian governments This study examines YouTube user behavior during the Russian-Ukrainian war analyzing 2168 videos with over 36000 comments from January 2022 to February 2024 We observe distinct patterns of participation and gender dynamics that correlate with major political and military events Notably females were more active in antigovernment channels especially during peak conflict periods Contrary to assumptions about online engagement in authoritarian contexts our findings suggest a complex interplay where women emerge as pivotal digital communicators This highlights online platforms role in facilitating political expression under authoritarian regimes demonstrating its potential as a barometer for public sentiment.
OVOSE: Open-Vocabulary Semantic Segmentation in Event-Based Cameras
Venue: arXiv cs.CV 2024
Year: 2024

Event cameras, known for low-latency operation and superior performance in challenging lighting conditions, are suitable for sensitive computer vision tasks such as semantic segmentation in autonomous driving. However, challenges arise due to limited event-based data and the absence of large-scale segmentation benchmarks. Current works are confined to closed-set semantic segmentation, limiting their adaptability to other applications. In this paper, we introduce OVOSE, the first Open-Vocabulary Semantic Segmentation algorithm for Event cameras. OVOSE leverages synthetic event data and knowledge distillation from a pre-trained image-based foundation model to an event-based counterpart, effectively preserving spatial context and transferring open-vocabulary semantic segmentation capabilities. We evaluate the performance of OVOSE on two driving semantic segmentation datasets DDD17, and DSEC-Semantic, comparing it with existing conventional image open-vocabulary models adapted for event-based data. Similarly, we compare OVOSE with state-of-the-art methods designed for closed-set settings in unsupervised domain adaptation for event-based semantic segmentation. OVOSE demonstrates superior performance, showcasing its potential for real-world applications. The code is available at https://github.com/ram95d/OVOSE.
FASST: Fast LLM-based Simultaneous Speech Translation
Venue: arXiv cs.CL 2024
Year: 2024

Simultaneous speech translation (SST) takes streaming speech input and generates text translation on the fly. Existing methods either have high latency due to recomputation of input representations, or fall behind of offline ST in translation quality. In this paper, we propose FASST, a fast large language model based method for streaming speech translation. We propose blockwise-causal speech encoding and consistency mask, so that streaming speech input can be encoded incrementally without recomputation. Furthermore, we develop a two-stage training strategy to optimize FASST for simultaneous inference. We evaluate FASST and multiple strong prior models on MuST-C dataset. Experiment results show that FASST achieves the best quality-latency trade-off. It outperforms the previous best model by an average of 1.5 BLEU under the same latency for English to Spanish translation.