OpenRaiser/Intern-Atlas huggingface dataset:
HALOS: Hallucination-free Organ Segmentation after Organ Resection Surgery
Venue: arXiv cs.CV 2023
Year: 2023

The wide range of research in deep learning-based medical image segmentation pushed the boundaries in a multitude of applications. A clinically relevant problem that received less attention is the handling of scans with irregular anatomy, e.g., after organ resection. State-of-the-art segmentation models often lead to organ hallucinations, i.e., false-positive predictions of organs, which cannot be alleviated by oversampling or post-processing. Motivated by the increasing need to develop robust deep learning models, we propose HALOS for abdominal organ segmentation in MR images that handles cases after organ resection surgery. To this end, we combine missing organ classification and multi-organ segmentation tasks into a multi-task model, yielding a classification-assisted segmentation pipeline. The segmentation network learns to incorporate knowledge about organ existence via feature fusion modules. Extensive experiments on a small labeled test set and large-scale UK Biobank data demonstrate the effectiveness of our approach in terms of higher segmentation Dice scores and near-to-zero false positive prediction rate.
Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors
Venue: arXiv cs.LG 2023
Year: 2023

The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem in a data-driven way, advancing the latest methods in machine learning for modeling complex spatial systems over time. In this edition, our dynamic road graph data combine information from road maps, $10^{12}$ probe data points, and stationary vehicle detectors in three cities over the span of two years. While stationary vehicle detectors are the most accurate way to capture traffic volume, they are only available in few locations. Traffic4cast 2022 explores models that have the ability to generalize loosely related temporal vertex data on just a few nodes to predict dynamic future traffic states on the edges of the entire road graph. In the core challenge, participants are invited to predict the likelihoods of three congestion classes derived from the speed levels in the GPS data for the entire road graph in three cities 15 min into the future. We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task. The data are aggregated in 15 min time bins for one hour prior to the prediction time. For the extended challenge, participants are tasked to predict the average travel times on super-segments 15 min into the future - super-segments are longer sequences of road segments in the graph. The competition results provide an important advance in the prediction of complex city-wide traffic states just from publicly available sparse vehicle data and without the need for large amounts of real-time floating vehicle data.
Cooperative Jamming and Relay Selection for Covert Communications
Venue: arXiv cs.IT 2023
Year: 2023

This paper investigates the covert communications via cooperative jamming and relay selection in a wireless relay system, where a source intends to transmit a message to its destination with the help of a selected relay, and a warden attempts to detect the existence of wireless transmissions from both the source and relay, while friendly jammers send jamming signals to prevent warden from detecting the transmission process. To this end, we first propose two relay selection schemes, namely random relay selection (RRS) and max-min relay selection (MMRS), as well as their corresponding cooperative jamming (CJ) schemes for ensuring covertness in the system. We then provide theoretical modeling for the covert rate performance under each relay selection scheme and its CJ scheme and further explore the optimal transmit power controls of both the source and relay for covert rate maximization. Finally, extensive simulation/numerical results are presented to validate our theoretical models and also to illustrate the covert rate performance of the relay system under cooperative jamming and relay selection.
Structure and Rank of Cyclic codes over a class of non-chain rings
Venue: arXiv cs.IT 2023
Year: 2023

The rings $Z_{4}+νZ_{4}$ have been classified into chain rings and non-chain rings on the basis of the values of $ν^{2} \in Z_{4}+νZ_{4}.$ In this paper, the structure of cyclic codes of arbitrary length over the rings $Z_{4}+νZ_{4}$ for those values of $ν^{2}$ for which these are non-chain rings has been established. A unique form of generators of these codes has also been obtained. Further, rank and cardinality of these codes have been established by finding minimal spanning sets for these codes.
Robot Grasping and Manipulation: A Prospective
Venue: arXiv cs.RO 2023
Year: 2023

``A simple handshake would give them away''. This is how Anthony Hopkins' fictional character, Dr Robert Ford, summarises a particular flaw of the 2016 science-fiction \emph{Westworld}'s hosts. In the storyline, Westworld is a futuristic theme park and the hosts are autonomous robots engineered to be indistinguishable from the human guests, except for their hands that have not been perfected yet. In another classic science-fiction saga, scientists unlock the secrets of full synthetic intelligence, Skynet, by reverse engineering a futuristic hand. In both storylines, reality inspires fiction on one crucial point: designing hands and reproducing robust and reliable manipulation actions is one of the biggest challenges in robotics. Solving this problem would lead us to a new, improved era of autonomy. A century ago, the third industrial revolution brought robots into the assembly lines, changing our way of working forever. The next revolution has already started by bringing us artificial intelligence (AI) assistants, enhancing our quality of life in our jobs and everyday lives--even combating worldwide pandemics.
Transfer Learning for Real-time Deployment of a Screening Tool for Depression Detection Using Actigraphy
Venue: arXiv cs.LG 2023
Year: 2023

Automated depression screening and diagnosis is a highly relevant problem today. There are a number of limitations of the traditional depression detection methods, namely, high dependence on clinicians and biased self-reporting. In recent years, research has suggested strong potential in machine learning (ML) based methods that make use of the user's passive data collected via wearable devices. However, ML is data hungry. Especially in the healthcare domain primary data collection is challenging. In this work, we present an approach based on transfer learning, from a model trained on a secondary dataset, for the real time deployment of the depression screening tool based on the actigraphy data of users. This approach enables machine learning modelling even with limited primary data samples. A modified version of leave one out cross validation approach performed on the primary set resulted in mean accuracy of 0.96, where in each iteration one subject's data from the primary set was set aside for testing.
Learning Adaptable Risk-Sensitive Policies to Coordinate in Multi-Agent General-Sum Games
Venue: arXiv cs.MA 2023
Year: 2023

In general-sum games, the interaction of self-interested learning agents commonly leads to socially worse outcomes, such as defect-defect in the iterated stag hunt (ISH). Previous works address this challenge by sharing rewards or shaping their opponents' learning process, which require too strong assumptions. In this paper, we demonstrate that agents trained to optimize expected returns are more likely to choose a safe action that leads to guaranteed but lower rewards. However, there typically exists a risky action that leads to higher rewards in the long run only if agents cooperate, e.g., cooperate-cooperate in ISH. To overcome this, we propose using action value distribution to characterize the decision's risk and corresponding potential payoffs. Specifically, we present Adaptable Risk-Sensitive Policy (ARSP). ARSP learns the distributions over agent's return and estimates a dynamic risk-seeking bonus to discover risky coordination strategies. Furthermore, to avoid overfitting training opponents, ARSP learns an auxiliary opponent modeling task to infer opponents' types and dynamically alter corresponding strategies during execution. Empirically, agents trained via ARSP can achieve stable coordination during training without accessing opponent's rewards or learning process, and can adapt to non-cooperative opponents during execution. To the best of our knowledge, it is the first method to learn coordination strategies between agents both in iterated prisoner's dilemma (IPD) and iterated stag hunt (ISH) without shaping opponents or rewards, and can adapt to opponents with distinct strategies during execution. Furthermore, we show that ARSP can be scaled to high-dimensional settings.
ReFit: A Framework for Refinement of Weakly Supervised Semantic Segmentation using Object Border Fitting for Medical Images
Venue: arXiv cs.CV 2023
Year: 2023

Weakly Supervised Semantic Segmentation (WSSS) relying only on image-level supervision is a promising approach to deal with the need for Segmentation networks, especially for generating a large number of pixel-wise masks in a given dataset. However, most state-of-the-art image-level WSSS techniques lack an understanding of the geometric features embedded in the images since the network cannot derive any object boundary information from just image-level labels. We define a boundary here as the line separating an object and its background, or two different objects. To address this drawback, we are proposing our novel ReFit framework, which deploys state-of-the-art class activation maps combined with various post-processing techniques in order to achieve fine-grained higher-accuracy segmentation masks. To achieve this, we investigate a state-of-the-art unsupervised segmentation network that can be used to construct a boundary map, which enables ReFit to predict object locations with sharper boundaries. By applying our method to WSSS predictions, we achieved up to 10% improvement over the current state-of-the-art WSSS methods for medical imaging. The framework is open-source, to ensure that our results are reproducible, and accessible online at https://github.com/bharathprabakaran/ReFit.
Bayes Complexity of Learners vs Overfitting
Venue: arXiv cs.LG 2023
Year: 2023

We introduce a new notion of complexity of functions and we show that it has the following properties: (i) it governs a PAC Bayes-like generalization bound, (ii) for neural networks it relates to natural notions of complexity of functions (such as the variation), and (iii) it explains the generalization gap between neural networks and linear schemes. While there is a large set of papers which describes bounds that have each such property in isolation, and even some that have two, as far as we know, this is a first notion that satisfies all three of them. Moreover, in contrast to previous works, our notion naturally generalizes to neural networks with several layers. Even though the computation of our complexity is nontrivial in general, an upper-bound is often easy to derive, even for higher number of layers and functions with structure, such as period functions. An upper-bound we derive allows to show a separation in the number of samples needed for good generalization between 2 and 4-layer neural networks for periodic functions.
Solar Power Prediction Using Machine Learning
Venue: arXiv cs.LG 2023
Year: 2023

This paper presents a machine learning-based approach for predicting solar power generation with high accuracy using a 99% AUC (Area Under the Curve) metric. The approach includes data collection, pre-processing, feature selection, model selection, training, evaluation, and deployment. High-quality data from multiple sources, including weather data, solar irradiance data, and historical solar power generation data, are collected and pre-processed to remove outliers, handle missing values, and normalize the data. Relevant features such as temperature, humidity, wind speed, and solar irradiance are selected for model training. Support Vector Machines (SVM), Random Forest, and Gradient Boosting are used as machine learning algorithms to produce accurate predictions. The models are trained on a large dataset of historical solar power generation data and other relevant features. The performance of the models is evaluated using AUC and other metrics such as precision, recall, and F1-score. The trained machine learning models are then deployed in a production environment, where they can be used to make real-time predictions about solar power generation. The results show that the proposed approach achieves a 99% AUC for solar power generation prediction, which can help energy companies better manage their solar power systems, reduce costs, and improve energy efficiency.
Comfortable Priority Handling with Predictive Velocity Optimization for Intersection Crossings
Venue: arXiv cs.RO 2023
Year: 2023

We address the problem of motion planning for four-way intersection crossings with right-of-ways. Road safety typically assigns liability to the follower in rear-end collisions and to the approaching vehicle required to yield in side crashes. As an alternative to previous models based on heuristic state machines, we propose a planning framework which changes the prediction model of other cars (e.g. their prototypical accelerations and decelerations) depending on the given longitudinal or lateral priority rules. Combined with a state-of-the-art trajectory optimization approach ROPT (Risk Optimization Method) this allows to find ego velocity profiles minimizing risks from curves and all involved vehicles while maximizing utility (needed time to arrive at a goal) and comfort (change and duration of acceleration) under the presence of regulatory conditions. Analytical and statistical evaluations show that our method is able to follow right-of-ways for a wide range of other vehicle behaviors and path geometries. Even when the other cars drive in a non-priority-compliant way, ROPT achieves good risk-comfort tradeoffs.
MetaMixer: A Regularization Strategy for Online Knowledge Distillation
Venue: arXiv cs.CV 2023
Year: 2023

Online knowledge distillation (KD) has received increasing attention in recent years. However, while most existing online KD methods focus on developing complicated model structures and training strategies to improve the distillation of high-level knowledge like probability distribution, the effects of the multi-level knowledge in the online KD are greatly overlooked, especially the low-level knowledge. Thus, to provide a novel viewpoint to online KD, we propose MetaMixer, a regularization strategy that can strengthen the distillation by combining the low-level knowledge that impacts the localization capability of the networks, and high-level knowledge that focuses on the whole image. Experiments under different conditions show that MetaMixer can achieve significant performance gains over state-of-the-art methods.
Automatic summarisation of Instagram social network posts Combining semantic and statistical approaches
Venue: arXiv cs.AI 2023
Year: 2023

The proliferation of data and text documents such as articles, web pages, books, social network posts, etc. on the Internet has created a fundamental challenge in various fields of text processing under the title of "automatic text summarisation". Manual processing and summarisation of large volumes of textual data is a very difficult, expensive, time-consuming and impossible process for human users. Text summarisation systems are divided into extractive and abstract categories. In the extractive summarisation method, the final summary of a text document is extracted from the important sentences of the same document without any modification. In this method, it is possible to repeat a series of sentences and to interfere with pronouns. However, in the abstract summarisation method, the final summary of a textual document is extracted from the meaning and significance of the sentences and words of the same document or other documents. Many of the works carried out have used extraction methods or abstracts to summarise the collection of web documents, each of which has advantages and disadvantages in the results obtained in terms of similarity or size. In this work, a crawler has been developed to extract popular text posts from the Instagram social network with appropriate preprocessing, and a set of extraction and abstraction algorithms have been combined to show how each of the abstraction algorithms can be used. Observations made on 820 popular text posts on the social network Instagram show the accuracy (80%) of the proposed system.
A Theory of Emergent In-Context Learning as Implicit Structure Induction
Venue: arXiv cs.CL 2023
Year: 2023

Scaling large language models (LLMs) leads to an emergent capacity to learn in-context from example demonstrations. Despite progress, theoretical understanding of this phenomenon remains limited. We argue that in-context learning relies on recombination of compositional operations found in natural language data. We derive an information-theoretic bound showing how in-context learning abilities arise from generic next-token prediction when the pretraining distribution has sufficient amounts of compositional structure, under linguistically motivated assumptions. A second bound provides a theoretical justification for the empirical success of prompting LLMs to output intermediate steps towards an answer. To validate theoretical predictions, we introduce a controlled setup for inducing in-context learning; unlike previous approaches, it accounts for the compositional nature of language. Trained transformers can perform in-context learning for a range of tasks, in a manner consistent with the theoretical results. Mirroring real-world LLMs in a miniature setup, in-context learning emerges when scaling parameters and data, and models perform better when prompted to output intermediate steps. Probing shows that in-context learning is supported by a representation of the input's compositional structure. Taken together, these results provide a step towards theoretical understanding of emergent behavior in large language models.
GoNet: An Approach-Constrained Generative Grasp Sampling Network
Venue: arXiv cs.RO 2023
Year: 2023

This work addresses the problem of learning approach-constrained data-driven grasp samplers. To this end, we propose GoNet: a generative grasp sampler that can constrain the grasp approach direction to a subset of SO(3). The key insight is to discretize SO(3) into a predefined number of bins and train GoNet to generate grasps whose approach directions are within those bins. At run-time, the bin aligning with the second largest principal component of the observed point cloud is selected. GoNet is benchmarked against GraspNet, a state-of-the-art unconstrained grasp sampler, in an unconfined grasping experiment in simulation and on an unconfined and confined grasping experiment in the real world. The results demonstrate that GoNet achieves higher success-over-coverage in simulation and a 12%-18% higher success rate in real-world table-picking and shelf-picking tasks than the baseline.
Class-level Multiple Distributions Representation are Necessary for Semantic Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

Existing approaches focus on using class-level features to improve semantic segmentation performance. How to characterize the relationships of intra-class pixels and inter-class pixels is the key to extract the discriminative representative class-level features. In this paper, we introduce for the first time to describe intra-class variations by multiple distributions. Then, multiple distributions representation learning(\textbf{MDRL}) is proposed to augment the pixel representations for semantic segmentation. Meanwhile, we design a class multiple distributions consistency strategy to construct discriminative multiple distribution representations of embedded pixels. Moreover, we put forward a multiple distribution semantic aggregation module to aggregate multiple distributions of the corresponding class to enhance pixel semantic information. Our approach can be seamlessly integrated into popular segmentation frameworks FCN/PSPNet/CCNet and achieve 5.61\%/1.75\%/0.75\% mIoU improvements on ADE20K. Extensive experiments on the Cityscapes, ADE20K datasets have proved that our method can bring significant performance improvement.
Beyond Demographic Parity: Redefining Equal Treatment
Venue: arXiv cs.LG 2023
Year: 2023

Liberalism-oriented political philosophy reasons that all individuals should be treated equally independently of their protected characteristics. Related work in machine learning has translated the concept of \emph{equal treatment} into terms of \emph{equal outcome} and measured it as \emph{demographic parity} (also called \emph{statistical parity}). Our analysis reveals that the two concepts of equal outcome and equal treatment diverge; therefore, demographic parity does not faithfully represent the notion of \emph{equal treatment}. We propose a new formalization for equal treatment by (i) considering the influence of feature values on predictions, such as computed by Shapley values decomposing predictions across its features, (ii) defining distributions of explanations, and (iii) comparing explanation distributions between populations with different protected characteristics. We show the theoretical properties of our notion of equal treatment and devise a classifier two-sample test based on the AUC of an equal treatment inspector. We study our formalization of equal treatment on synthetic and natural data. We release \texttt{explanationspace}, an open-source Python package with methods and tutorials.
Interpretable ODE-style Generative Diffusion Model via Force Field Construction
Venue: arXiv cs.CV 2023
Year: 2023

For a considerable time, researchers have focused on developing a method that establishes a deep connection between the generative diffusion model and mathematical physics. Despite previous efforts, progress has been limited to the pursuit of a single specialized method. In order to advance the interpretability of diffusion models and explore new research directions, it is essential to establish a unified ODE-style generative diffusion model. Such a model should draw inspiration from physical models and possess a clear geometric meaning. This paper aims to identify various physical models that are suitable for constructing ODE-style generative diffusion models accurately from a mathematical perspective. We then summarize these models into a unified method. Additionally, we perform a case study where we use the theoretical model identified by our method to develop a range of new diffusion model methods, and conduct experiments. Our experiments on CIFAR-10 demonstrate the effectiveness of our approach. We have constructed a computational framework that attains highly proficient results with regards to image generation speed, alongside an additional model that demonstrates exceptional performance in both Inception score and FID score. These results underscore the significance of our method in advancing the field of diffusion models.