OpenRaiser/Intern-Atlas huggingface dataset:
Context Neural Networks: A Scalable Multivariate Model for Time Series Forecasting
Venue: arXiv cs.LG 2024
Year: 2024

Real-world time series often exhibit complex interdependencies that cannot be captured in isolation. Global models that model past data from multiple related time series globally while producing series-specific forecasts locally are now common. However, their forecasts for each individual series remain isolated, failing to account for the current state of its neighbouring series. Multivariate models like multivariate attention and graph neural networks can explicitly incorporate inter-series information, thus addressing the shortcomings of global models. However, these techniques exhibit quadratic complexity per timestep, limiting scalability. This paper introduces the Context Neural Network, an efficient linear complexity approach for augmenting time series models with relevant contextual insights from neighbouring time series without significant computational overhead. The proposed method enriches predictive models by providing the target series with real-time information from its neighbours, addressing the limitations of global models, yet remaining computationally tractable for large datasets.
5G Virtual Reality Manipulator Teleoperation using a Mobile Phone
Venue: arXiv cs.RO 2024
Year: 2024

This paper presents an approach to teleoperate a manipulator using a mobile phone as a leader device. Using its IMU and camera, the phone estimates its Cartesian pose which is then used to to control the Cartesian pose of the robot's tool. The user receives visual feedback in the form of multi-view video - a point cloud rendered in a virtual reality environment. This enables the user to observe the scene from any position. To increase immersion, the robot's estimate of external forces is relayed using the phone's haptic actuator. Leader and follower are connected through wireless networks such as 5G or Wi-Fi. The paper describes the setup and analyzes its performance.
Cross-Domain Continual Learning via CLAMP
Venue: arXiv cs.LG 2024
Year: 2024

Artificial neural networks, celebrated for their human-like cognitive learning abilities, often encounter the well-known catastrophic forgetting (CF) problem, where the neural networks lose the proficiency in previously acquired knowledge. Despite numerous efforts to mitigate CF, it remains the significant challenge particularly in complex changing environments. This challenge is even more pronounced in cross-domain adaptation following the continual learning (CL) setting, which is a more challenging and realistic scenario that is under-explored. To this end, this article proposes a cross-domain CL approach making possible to deploy a single model in such environments without additional labelling costs. Our approach, namely continual learning approach for many processes (CLAMP), integrates a class-aware adversarial domain adaptation strategy to align a source domain and a target domain. An assessor-guided learning process is put forward to navigate the learning process of a base model assigning a set of weights to every sample controlling the influence of every sample and the interactions of each loss function in such a way to balance the stability and plasticity dilemma thus preventing the CF problem. The first assessor focuses on the negative transfer problem rejecting irrelevant samples of the source domain while the second assessor prevents noisy pseudo labels of the target domain. Both assessors are trained in the meta-learning approach using random transformation techniques and similar samples of the source domain. Theoretical analysis and extensive numerical validations demonstrate that CLAMP significantly outperforms established baseline algorithms across all experiments by at least $10\%$ margin.
3D Hand Mesh Recovery from Monocular RGB in Camera Space
Venue: arXiv cs.CV 2024
Year: 2024

With the rapid advancement of technologies such as virtual reality, augmented reality, and gesture control, users expect interactions with computer interfaces to be more natural and intuitive. Existing visual algorithms often struggle to accomplish advanced human-computer interaction tasks, necessitating accurate and reliable absolute spatial prediction methods. Moreover, dealing with complex scenes and occlusions in monocular images poses entirely new challenges. This study proposes a network model that performs parallel processing of root-relative grids and root recovery tasks. The model enables the recovery of 3D hand meshes in camera space from monocular RGB images. To facilitate end-to-end training, we utilize an implicit learning approach for 2D heatmaps, enhancing the compatibility of 2D cues across different subtasks. Incorporate the Inception concept into spectral graph convolutional network to explore relative mesh of root, and integrate it with the locally detailed and globally attentive method designed for root recovery exploration. This approach improves the model's predictive performance in complex environments and self-occluded scenes. Through evaluation on the large-scale hand dataset FreiHAND, we have demonstrated that our proposed model is comparable with state-of-the-art models. This study contributes to the advancement of techniques for accurate and reliable absolute spatial prediction in various human-computer interaction applications.
Challenges and Opportunities for Large-Scale Exploration with Air-Ground Teams using Semantics
Venue: arXiv cs.RO 2024
Year: 2024

One common and desirable application of robots is exploring potentially hazardous and unstructured environments. Air-ground collaboration offers a synergistic approach to addressing such exploration challenges. In this paper, we demonstrate a system for large-scale exploration using a team of aerial and ground robots. Our system uses semantics as lingua franca, and relies on fully opportunistic communications. We highlight the unique challenges from this approach, explain our system architecture and showcase lessons learned during our experiments. All our code is open-source, encouraging researchers to use it and build upon.
On-Demand Model and Client Deployment in Federated Learning with Deep Reinforcement Learning
Venue: arXiv cs.LG 2024
Year: 2024

In Federated Learning (FL), the limited accessibility of data from diverse locations and user types poses a significant challenge due to restricted user participation. Expanding client access and diversifying data enhance models by incorporating diverse perspectives, thereby enhancing adaptability. However, challenges arise in dynamic and mobile environments where certain devices may become inaccessible as FL clients, impacting data availability and client selection methods. To address this, we propose an On-Demand solution, deploying new clients using Docker Containers on-the-fly. Our On-Demand solution, employing Deep Reinforcement Learning (DRL), targets client availability and selection, while considering data shifts, and container deployment complexities. It employs an autonomous end-to-end solution for handling model deployment and client selection. The DRL strategy uses a Markov Decision Process (MDP) framework, with a Master Learner and a Joiner Learner. The designed cost functions represent the complexity of the dynamic client deployment and selection. Simulated tests show that our architecture can easily adjust to changes in the environment and respond to On-Demand requests. This underscores its ability to improve client availability, capability, accuracy, and learning efficiency, surpassing heuristic and tabular reinforcement learning solutions.
A hybrid meta-heuristic approach for channel estimation in OFDM MIMO
Venue: arXiv cs.NI 2024
Year: 2024

In wireless communication Multiple Input Multiple Output (MIMO) technology has brought significant improvement in service by adopting Orthogonal Frequency Division Multiplexing (OFDM), a digital modulation technique. To achieve great performance with MIMO efficiently gathering channel state information (CSI) plays a vital role. Among different approach of channel estimation techniques data-aided channel estimation is more reliable. The existing methods of data-aided channel estimation are Least Square (LS) and Minimum Mean Square Error (MMSE) methods which do not achieve a great performance. Moreover, MMSE is little complex and has higher computational cost. That is why many attempts have been done previously to optimize the methods with help of meta heuristics and also other ways. In this paper we have tried to optimize LS estimation with a combined algorithm of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The proposed algorithm has outperformed LS and MMSE. And it gives similar result if we optimize LS with standard PSO but in less numbers of iteration.
Learning Design Preferences through Design Feature Extraction and Weighted Ensemble
Venue: arXiv cs.HC 2024
Year: 2024

Design is a factor that plays an important role in consumer purchase decisions. As the need for understanding and predicting various preferences for each customer increases along with the importance of mass customization, predicting individual design preferences has become a critical factor in product development. However, current methods for predicting design preferences have some limitations. Product design involves a vast amount of high-dimensional information, and personal design preference is a complex and heterogeneous area of emotion unique to each individual. To address these challenges, we propose an approach that utilizes dimensionality reduction model to transform design samples into low-dimensional feature vectors, enabling us to extract the key representational features of each design. For preference prediction models using feature vectors, by referring to the design preference tendencies of others, we can predict the individual-level design preferences more accurately. Our proposed framework overcomes the limitations of traditional methods to determine design preferences, allowing us to accurately identify design features and predict individual preferences for specific products. Through this framework, we can improve the effectiveness of product development and create personalized product recommendations that cater to the unique needs of each consumer.
InsightNet: Structured Insight Mining from Customer Feedback
Venue: arXiv cs.CL 2024
Year: 2024

We propose InsightNet, a novel approach for the automated extraction of structured insights from customer reviews. Our end-to-end machine learning framework is designed to overcome the limitations of current solutions, including the absence of structure for identified topics, non-standard aspect names, and lack of abundant training data. The proposed solution builds a semi-supervised multi-level taxonomy from raw reviews, a semantic similarity heuristic approach to generate labelled data and employs a multi-task insight extraction architecture by fine-tuning an LLM. InsightNet identifies granular actionable topics with customer sentiments and verbatim for each topic. Evaluations on real-world customer review data show that InsightNet performs better than existing solutions in terms of structure, hierarchy and completeness. We empirically demonstrate that InsightNet outperforms the current state-of-the-art methods in multi-label topic classification, achieving an F1 score of 0.85, which is an improvement of 11% F1-score over the previous best results. Additionally, InsightNet generalises well for unseen aspects and suggests new topics to be added to the taxonomy.
Permissioned Blockchain-based Framework for Ranking Synthetic Data Generators
Venue: arXiv cs.DB 2024
Year: 2024

Synthetic data generation is increasingly recognized as a crucial solution to address data related challenges such as scarcity, bias, and privacy concerns. As synthetic data proliferates, the need for a robust evaluation framework to select a synthetic data generator becomes more pressing given the variety of options available. In this research study, we investigate two primary questions: 1) How can we select the most suitable synthetic data generator from a set of options for a specific purpose? 2) How can we make the selection process more transparent, accountable, and auditable? To address these questions, we introduce a novel approach in which the proposed ranking algorithm is implemented as a smart contract within a permissioned blockchain framework called Sawtooth. Through comprehensive experiments and comparisons with state-of-the-art baseline ranking solutions, our framework demonstrates its effectiveness in providing nuanced rankings that consider both desirable and undesirable properties. Furthermore, our framework serves as a valuable tool for selecting the optimal synthetic data generators for specific needs while ensuring compliance with data protection principles.
On Discovery of Local Independence over Continuous Variables via Neural Contextual Decomposition
Venue: arXiv cs.LG 2024
Year: 2024

Conditional independence provides a way to understand causal relationships among the variables of interest. An underlying system may exhibit more fine-grained causal relationships especially between a variable and its parents, which will be called the local independence relationships. One of the most widely studied local relationships is Context-Specific Independence (CSI), which holds in a specific assignment of conditioned variables. However, its applicability is often limited since it does not allow continuous variables: data conditioned to the specific value of a continuous variable contains few instances, if not none, making it infeasible to test independence. In this work, we define and characterize the local independence relationship that holds in a specific set of joint assignments of parental variables, which we call context-set specific independence (CSSI). We then provide a canonical representation of CSSI and prove its fundamental properties. Based on our theoretical findings, we cast the problem of discovering multiple CSSI relationships in a system as finding a partition of the joint outcome space. Finally, we propose a novel method, coined neural contextual decomposition (NCD), which learns such partition by imposing each set to induce CSSI via modeling a conditional distribution. We empirically demonstrate that the proposed method successfully discovers the ground truth local independence relationships in both synthetic dataset and complex system reflecting the real-world physical dynamics.
Limited Ability of LLMs to Simulate Human Psychological Behaviours: a Psychometric Analysis
Venue: arXiv cs.CL 2024
Year: 2024

The humanlike responses of large language models (LLMs) have prompted social scientists to investigate whether LLMs can be used to simulate human participants in experiments, opinion polls and surveys. Of central interest in this line of research has been mapping out the psychological profiles of LLMs by prompting them to respond to standardized questionnaires. The conflicting findings of this research are unsurprising given that mapping out underlying, or latent, traits from LLMs' text responses to questionnaires is no easy task. To address this, we use psychometrics, the science of psychological measurement. In this study, we prompt OpenAI's flagship models, GPT-3.5 and GPT-4, to assume different personas and respond to a range of standardized measures of personality constructs. We used two kinds of persona descriptions: either generic (four or five random person descriptions) or specific (mostly demographics of actual humans from a large-scale human dataset). We found that the responses from GPT-4, but not GPT-3.5, using generic persona descriptions show promising, albeit not perfect, psychometric properties, similar to human norms, but the data from both LLMs when using specific demographic profiles, show poor psychometrics properties. We conclude that, currently, when LLMs are asked to simulate silicon personas, their responses are poor signals of potentially underlying latent traits. Thus, our work casts doubt on LLMs' ability to simulate individual-level human behaviour across multiple-choice question answering tasks.
CiMLoop: A Flexible, Accurate, and Fast Compute-In-Memory Modeling Tool
Venue: arXiv cs.AR 2024
Year: 2024

Compute-In-Memory (CiM) is a promising solution to accelerate Deep Neural Networks (DNNs) as it can avoid energy-intensive DNN weight movement and use memory arrays to perform low-energy, high-density computations. These benefits have inspired research across the CiM stack, but CiM research often focuses on only one level of the stack (i.e., devices, circuits, architecture, workload, or mapping) or only one design point (e.g., one fabricated chip). There is a need for a full-stack modeling tool to evaluate design decisions in the context of full systems (e.g., see how a circuit impacts system energy) and to perform rapid early-stage exploration of the CiM co-design space. To address this need, we propose CiMLoop: an open-source tool to model diverse CiM systems and explore decisions across the CiM stack. CiMLoop introduces (1) a flexible specification that lets users describe, model, and map workloads to both circuits and architecture, (2) an accurate energy model that captures the interaction between DNN operand values, hardware data representations, and analog/digital values propagated by circuits, and (3) a fast statistical model that can explore the design space orders-of-magnitude more quickly than other high-accuracy models. Using CiMLoop, researchers can evaluate design choices at different levels of the CiM stack, co-design across all levels, fairly compare different implementations, and rapidly explore the design space.
Human-interpretable clustering of short-text using large language models
Venue: arXiv cs.CL 2024
Year: 2024

Clustering short text is a difficult problem, due to the low word co-occurrence between short text documents. This work shows that large language models (LLMs) can overcome the limitations of traditional clustering approaches by generating embeddings that capture the semantic nuances of short text. In this study clusters are found in the embedding space using Gaussian Mixture Modelling (GMM). The resulting clusters are found to be more distinctive and more human-interpretable than clusters produced using the popular methods of doc2vec and Latent Dirichlet Allocation (LDA). The success of the clustering approach is quantified using human reviewers and through the use of a generative LLM. The generative LLM shows good agreement with the human reviewers, and is suggested as a means to bridge the `validation gap' which often exists between cluster production and cluster interpretation. The comparison between LLM-coding and human-coding reveals intrinsic biases in each, challenging the conventional reliance on human coding as the definitive standard for cluster validation.
Branching Narratives: Character Decision Points Detection
Venue: arXiv cs.CL 2024
Year: 2024

This paper presents the Character Decision Points Detection (CHADPOD) task, a task of identification of points within narratives where characters make decisions that may significantly influence the story's direction. We propose a novel dataset based on CYOA-like games graphs to be used as a benchmark for such a task. We provide a comparative analysis of different models' performance on this task, including a couple of LLMs and several MLMs as baselines, achieving up to 89% accuracy. This underscores the complexity of narrative analysis, showing the challenges associated with understanding character-driven story dynamics. Additionally, we show how such a model can be applied to the existing text to produce linear segments divided by potential branching points, demonstrating the practical application of our findings in narrative analysis.
VALID: a Validated Algorithm for Learning in Decentralized Networks with Possible Adversarial Presence
Venue: arXiv cs.LG 2024
Year: 2024

We introduce the paradigm of validated decentralized learning for undirected networks with heterogeneous data and possible adversarial infiltration. We require (a) convergence to a global empirical loss minimizer when adversaries are absent, and (b) either detection of adversarial presence of convergence to an admissible consensus irrespective of the adversarial configuration. To this end, we propose the VALID protocol which, to the best of our knowledge, is the first to achieve a validated learning guarantee. Moreover, VALID offers an O(1/T) convergence rate (under pertinent regularity assumptions), and computational and communication complexities comparable to non-adversarial distributed stochastic gradient descent. Remarkably, VALID retains optimal performance metrics in adversary-free environments, sidestepping the robustness penalties observed in prior byzantine-robust methods. A distinctive aspect of our study is a heterogeneity metric based on the norms of individual agents' gradients computed at the global empirical loss minimizer. This not only provides a natural statistic for detecting significant byzantine disruptions but also allows us to prove the optimality of VALID in wide generality. Lastly, our numerical results reveal that, in the absence of adversaries, VALID converges faster than state-of-the-art byzantine robust algorithms, while when adversaries are present, VALID terminates with each honest either converging to an admissible consensus of declaring adversarial presence in the network.
Multilingual Power and Ideology Identification in the Parliament: a Reference Dataset and Simple Baselines
Venue: arXiv cs.CL 2024
Year: 2024

We introduce a dataset on political orientation and power position identification. The dataset is derived from ParlaMint, a set of comparable corpora of transcribed parliamentary speeches from 29 national and regional parliaments. We introduce the dataset, provide the reasoning behind some of the choices during its creation, present statistics on the dataset, and, using a simple classifier, some baseline results on predicting political orientation on the left-to-right axis, and on power position identification, i.e., distinguishing between the speeches delivered by governing coalition party members from those of opposition party members.
Incorporating Anatomical Awareness for Enhanced Generalizability and Progression Prediction in Deep Learning-Based Radiographic Sacroiliitis Detection
Venue: arXiv cs.CV 2024
Year: 2024

Purpose: To examine whether incorporating anatomical awareness into a deep learning model can improve generalizability and enable prediction of disease progression. Methods: This retrospective multicenter study included conventional pelvic radiographs of 4 different patient cohorts focusing on axial spondyloarthritis (axSpA) collected at university and community hospitals. The first cohort, which consisted of 1483 radiographs, was split into training (n=1261) and validation (n=222) sets. The other cohorts comprising 436, 340, and 163 patients, respectively, were used as independent test datasets. For the second cohort, follow-up data of 311 patients was used to examine progression prediction capabilities. Two neural networks were trained, one on images cropped to the bounding box of the sacroiliac joints (anatomy-aware) and the other one on full radiographs. The performance of the models was compared using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Results: On the three test datasets, the standard model achieved AUC scores of 0.853, 0.817, 0.947, with an accuracy of 0.770, 0.724, 0.850. Whereas the anatomy-aware model achieved AUC scores of 0.899, 0.846, 0.957, with an accuracy of 0.821, 0.744, 0.906, respectively. The patients who were identified as high risk by the anatomy aware model had an odds ratio of 2.16 (95% CI: 1.19, 3.86) for having progression of radiographic sacroiliitis within 2 years. Conclusion: Anatomical awareness can improve the generalizability of a deep learning model in detecting radiographic sacroiliitis. The model is published as fully open source alongside this study.