OpenRaiser/Intern-Atlas huggingface dataset:
A Continual Learning Approach for Cross-Domain White Blood Cell Classification
Venue: arXiv cs.CV 2023
Year: 2023

Accurate classification of white blood cells in peripheral blood is essential for diagnosing hematological diseases. Due to constantly evolving clinical settings, data sources, and disease classifications, it is necessary to update machine learning classification models regularly for practical real-world use. Such models significantly benefit from sequentially learning from incoming data streams without forgetting previously acquired knowledge. However, models can suffer from catastrophic forgetting, causing a drop in performance on previous tasks when fine-tuned on new data. Here, we propose a rehearsal-based continual learning approach for class incremental and domain incremental scenarios in white blood cell classification. To choose representative samples from previous tasks, we employ exemplar set selection based on the model's predictions. This involves selecting the most confident samples and the most challenging samples identified through uncertainty estimation of the model. We thoroughly evaluated our proposed approach on three white blood cell classification datasets that differ in color, resolution, and class composition, including scenarios where new domains or new classes are introduced to the model with every task. We also test a long class incremental experiment with both new domains and new classes. Our results demonstrate that our approach outperforms established baselines in continual learning, including existing iCaRL and EWC methods for classifying white blood cells in cross-domain environments.
Match-And-Deform: Time Series Domain Adaptation through Optimal Transport and Temporal Alignment
Venue: arXiv cs.LG 2023
Year: 2023

While large volumes of unlabeled data are usually available, associated labels are often scarce. The unsupervised domain adaptation problem aims at exploiting labels from a source domain to classify data from a related, yet different, target domain. When time series are at stake, new difficulties arise as temporal shifts may appear in addition to the standard feature distribution shift. In this paper, we introduce the Match-And-Deform (MAD) approach that aims at finding correspondences between the source and target time series while allowing temporal distortions. The associated optimization problem simultaneously aligns the series thanks to an optimal transport loss and the time stamps through dynamic time warping. When embedded into a deep neural network, MAD helps learning new representations of time series that both align the domains and maximize the discriminative power of the network. Empirical studies on benchmark datasets and remote sensing data demonstrate that MAD makes meaningful sample-to-sample pairing and time shift estimation, reaching similar or better classification performance than state-of-the-art deep time series domain adaptation strategies.
Block-Level Interference Exploitation Precoding for MU-MISO: An ADMM Approach
Venue: arXiv cs.IT 2023
Year: 2023

We study constructive interference based block-level precoding (CI-BLP) in the downlink of multi-user multiple-input single-output (MU-MISO) systems. Specifically, our aim is to extend the analysis on CI-BLP to the case where the considered number of symbol slots is smaller than that of the users. To this end, we mathematically prove the feasibility of using the pseudo-inverse to obtain the optimal CI-BLP precoding matrix in a closed form. Similar to the case when the number of users is small, we show that a quadratic programming (QP) optimization on simplex can be constructed. We also design a low-complexity algorithm based on the alternating direction method of multipliers (ADMM) framework, which can efficiently solve large-scale QP problems. We further analyze the convergence and complexity of the proposed algorithm. Numerical results validate our analysis and the optimality of the proposed algorithm, and further show that the proposed algorithm offers a flexible performance-complexity tradeoff by limiting the maximum number of iterations, which motivates the use of CI-BLP in practical wireless systems.
On Offline Evaluation of 3D Object Detection for Autonomous Driving
Venue: arXiv cs.CV 2023
Year: 2023

Prior work in 3D object detection evaluates models using offline metrics like average precision since closed-loop online evaluation on the downstream driving task is costly. However, it is unclear how indicative offline results are of driving performance. In this work, we perform the first empirical evaluation measuring how predictive different detection metrics are of driving performance when detectors are integrated into a full self-driving stack. We conduct extensive experiments on urban driving in the CARLA simulator using 16 object detection models. We find that the nuScenes Detection Score has a higher correlation to driving performance than the widely used average precision metric. In addition, our results call for caution on the exclusive reliance on the emerging class of `planner-centric' metrics.
Software Startups -- A Research Agenda
Venue: arXiv cs.SE 2023
Year: 2023

Software startup companies develop innovative, software-intensive products within limited time frames and with few resources, searching for sustainable and scalable business models. Software startups are quite distinct from traditional mature software companies, but also from micro-, small-, and medium-sized enterprises, introducing new challenges relevant for software engineering research. This paper's research agenda focuses on software engineering in startups, identifying, in particular, 70+ research questions in the areas of supporting startup engineering activities, startup evolution models and patterns, ecosystems and innovation hubs, human aspects in software startups, applying startup concepts in non-startup environments, and methodologies and theories for startup research. We connect and motivate this research agenda with past studies in software startup research, while pointing out possible future directions. While all authors of this research agenda have their main background in Software Engineering or Computer Science, their interest in software startups broadens the perspective to the challenges, but also to the opportunities that emerge from multi-disciplinary research. Our audience is therefore primarily software engineering researchers, even though we aim at stimulating collaborations and research that crosses disciplinary boundaries. We believe that with this research agenda we cover a wide spectrum of the software startup industry current needs.
MixNet: Toward Accurate Detection of Challenging Scene Text in the Wild
Venue: arXiv cs.CV 2023
Year: 2023

Detecting small scene text instances in the wild is particularly challenging, where the influence of irregular positions and nonideal lighting often leads to detection errors. We present MixNet, a hybrid architecture that combines the strengths of CNNs and Transformers, capable of accurately detecting small text from challenging natural scenes, regardless of the orientations, styles, and lighting conditions. MixNet incorporates two key modules: (1) the Feature Shuffle Network (FSNet) to serve as the backbone and (2) the Central Transformer Block (CTBlock) to exploit the 1D manifold constraint of the scene text. We first introduce a novel feature shuffling strategy in FSNet to facilitate the exchange of features across multiple scales, generating high-resolution features superior to popular ResNet and HRNet. The FSNet backbone has achieved significant improvements over many existing text detection methods, including PAN, DB, and FAST. Then we design a complementary CTBlock to leverage center line based features similar to the medial axis of text regions and show that it can outperform contour-based approaches in challenging cases when small scene texts appear closely. Extensive experimental results show that MixNet, which mixes FSNet with CTBlock, achieves state-of-the-art results on multiple scene text detection datasets.
Inducing Causal Structure for Abstractive Text Summarization
Venue: arXiv cs.CL 2023
Year: 2023

The mainstream of data-driven abstractive summarization models tends to explore the correlations rather than the causal relationships. Among such correlations, there can be spurious ones which suffer from the language prior learned from the training corpus and therefore undermine the overall effectiveness of the learned model. To tackle this issue, we introduce a Structural Causal Model (SCM) to induce the underlying causal structure of the summarization data. We assume several latent causal factors and non-causal factors, representing the content and style of the document and summary. Theoretically, we prove that the latent factors in our SCM can be identified by fitting the observed training data under certain conditions. On the basis of this, we propose a Causality Inspired Sequence-to-Sequence model (CI-Seq2Seq) to learn the causal representations that can mimic the causal factors, guiding us to pursue causal information for summary generation. The key idea is to reformulate the Variational Auto-encoder (VAE) to fit the joint distribution of the document and summary variables from the training corpus. Experimental results on two widely used text summarization datasets demonstrate the advantages of our approach.
On Popularity Bias of Multimodal-aware Recommender Systems: a Modalities-driven Analysis
Venue: arXiv cs.IR 2023
Year: 2023

Multimodal-aware recommender systems (MRSs) exploit multimodal content (e.g., product images or descriptions) as items' side information to improve recommendation accuracy. While most of such methods rely on factorization models (e.g., MFBPR) as base architecture, it has been shown that MFBPR may be affected by popularity bias, meaning that it inherently tends to boost the recommendation of popular (i.e., short-head) items at the detriment of niche (i.e., long-tail) items from the catalog. Motivated by this assumption, in this work, we provide one of the first analyses on how multimodality in recommendation could further amplify popularity bias. Concretely, we evaluate the performance of four state-of-the-art MRSs algorithms (i.e., VBPR, MMGCN, GRCN, LATTICE) on three datasets from Amazon by assessing, along with recommendation accuracy metrics, performance measures accounting for the diversity of recommended items and the portion of retrieved niche items. To better investigate this aspect, we decide to study the separate influence of each modality (i.e., visual and textual) on popularity bias in different evaluation dimensions. Results, which demonstrate how the single modality may augment the negative effect of popularity bias, shed light on the importance to provide a more rigorous analysis of the performance of such models.
An Efficient Distributed Multi-Agent Reinforcement Learning for EV Charging Network Control
Venue: arXiv cs.MA 2023
Year: 2023

The increasing trend in adopting electric vehicles (EVs) will significantly impact the residential electricity demand, which results in an increased risk of transformer overload in the distribution grid. To mitigate such risks, there are urgent needs to develop effective EV charging controllers. Currently, the majority of the EV charge controllers are based on a centralized approach for managing individual EVs or a group of EVs. In this paper, we introduce a decentralized Multi-agent Reinforcement Learning (MARL) charging framework that prioritizes the preservation of privacy for EV owners. We employ the Centralized Training Decentralized Execution-Deep Deterministic Policy Gradient (CTDE-DDPG) scheme, which provides valuable information to users during training while maintaining privacy during execution. Our results demonstrate that the CTDE framework improves the performance of the charging network by reducing the network costs. Moreover, we show that the Peak-to-Average Ratio (PAR) of the total demand is reduced, which, in turn, reduces the risk of transformer overload during the peak hours.
Low-count Time Series Anomaly Detection
Venue: arXiv cs.LG 2023
Year: 2023

Low-count time series describe sparse or intermittent events, which are prevalent in large-scale online platforms that capture and monitor diverse data types. Several distinct challenges surface when modelling low-count time series, particularly low signal-to-noise ratios (when anomaly signatures are provably undetectable), and non-uniform performance (when average metrics are not representative of local behaviour). The time series anomaly detection community currently lacks explicit tooling and processes to model and reliably detect anomalies in these settings. We address this gap by introducing a novel generative procedure for creating benchmark datasets comprising of low-count time series with anomalous segments. Via a mixture of theoretical and empirical analysis, our work explains how widely-used algorithms struggle with the distribution overlap between normal and anomalous segments. In order to mitigate this shortcoming, we then leverage our findings to demonstrate how anomaly score smoothing consistently improves performance. The practical utility of our analysis and recommendation is validated on a real-world dataset containing sales data for retail stores.
A time multiscale based data-driven approach in cyclic elasto-plasticity
Venue: arXiv cs.CE 2023
Year: 2023

Within the framework of computational plasticity, recent advances show that the quasi-static response of an elasto-plastic structure under cyclic loadings may exhibit a time multiscale behaviour. In particular, the system response can be computed in terms of time microscale and macroscale modes using a weakly intrusive multi-time Proper Generalized Decomposition (MT-PGD). In this work, such micro-macro characterization of the time response is exploited to build a data-driven model of the elasto-plastic constitutive relation. This can be viewed as a predictor-corrector scheme where the prediction is driven by the macrotime evolution and the correction is performed via a sparse sampling in space. Once the nonlinear term is forecasted, the multi-time PGD algorithm allows the fast computation of the total strain. The algorithm shows considerable gains in terms of computational time, opening new perspectives in the numerical simulation of history-dependent problems defined in very large time intervals.
Panoptic-Depth Color Map for Combination of Depth and Image Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

Image segmentation and depth estimation are crucial tasks in computer vision, especially in autonomous driving scenarios. Although these tasks are typically addressed separately, we propose an innovative approach to combine them in our novel deep learning network, Panoptic-DepthLab. By incorporating an additional depth estimation branch into the segmentation network, it can predict the depth of each instance segment. Evaluating on Cityscape dataset, we demonstrate the effectiveness of our method in achieving high-quality segmentation results with depth and visualize it with a color map. Our proposed method demonstrates a new possibility of combining different tasks and networks to generate a more comprehensive image recognition result to facilitate the safety of autonomous driving vehicles.
POCO: 3D Pose and Shape Estimation with Confidence
Venue: arXiv cs.CV 2023
Year: 2023

The regression of 3D Human Pose and Shape (HPS) from an image is becoming increasingly accurate. This makes the results useful for downstream tasks like human action recognition or 3D graphics. Yet, no regressor is perfect, and accuracy can be affected by ambiguous image evidence or by poses and appearance that are unseen during training. Most current HPS regressors, however, do not report the confidence of their outputs, meaning that downstream tasks cannot differentiate accurate estimates from inaccurate ones. To address this, we develop POCO, a novel framework for training HPS regressors to estimate not only a 3D human body, but also their confidence, in a single feed-forward pass. Specifically, POCO estimates both the 3D body pose and a per-sample variance. The key idea is to introduce a Dual Conditioning Strategy (DCS) for regressing uncertainty that is highly correlated to pose reconstruction quality. The POCO framework can be applied to any HPS regressor and here we evaluate it by modifying HMR, PARE, and CLIFF. In all cases, training the network to reason about uncertainty helps it learn to more accurately estimate 3D pose. While this was not our goal, the improvement is modest but consistent. Our main motivation is to provide uncertainty estimates for downstream tasks; we demonstrate this in two ways: (1) We use the confidence estimates to bootstrap HPS training. Given unlabelled image data, we take the confident estimates of a POCO-trained regressor as pseudo ground truth. Retraining with this automatically-curated data improves accuracy. (2) We exploit uncertainty in video pose estimation by automatically identifying uncertain frames (e.g. due to occlusion) and inpainting these from confident frames. Code and models will be available for research at https://poco.is.tue.mpg.de.
Augmenting a Firefighters PPE -- Gas Mask SCBA
Venue: arXiv cs.HC 2023
Year: 2023

PPE (Personal Protective Equipment) has allowed firefighters to perform their everyday tasks without getting harmed since the mid 1800s. Now, the advancement of technology has given rise to the improvements of PPE. PPE can now include sensors to detect any number of environmental hazards (chemical, biological, temperature etc.). As the GT class of CS3750, we have decided to create a version of an interface design sensor that will help firefighters in two ways: navigation and communication. In order to augment a firefighter display when they are within a building, we chose to augment their SCBA (self-contained breathing apparatus). The gas mask will include a small screen that displays vital information directly towards the firefighter without need of any other support. We used the Google Glass to display vital information directly towards the eye in a minimalistic manner, while also augmenting that by adding LED lights to simulate someone calling their name or other auditory signals.While our prototype focuses on two main components of a firefighters search and rescue in a building, both of them combine to augment a firefighters display when searching throughout a building to help improve accuracy, speed and overall experience.
Lexical Diversity in Kinship Across Languages and Dialects
Venue: arXiv cs.CL 2023
Year: 2023

Languages are known to describe the world in diverse ways. Across lexicons, diversity is pervasive, appearing through phenomena such as lexical gaps and untranslatability. However, in computational resources, such as multilingual lexical databases, diversity is hardly ever represented. In this paper, we introduce a method to enrich computational lexicons with content relating to linguistic diversity. The method is verified through two large-scale case studies on kinship terminology, a domain known to be diverse across languages and cultures: one case study deals with seven Arabic dialects, while the other one with three Indonesian languages. Our results, made available as browseable and downloadable computational resources, extend prior linguistics research on kinship terminology, and provide insight into the extent of diversity even within linguistically and culturally close communities.
SurGNN: Explainable visual scene understanding and assessment of surgical skill using graph neural networks
Venue: arXiv cs.CV 2023
Year: 2023

This paper explores how graph neural networks (GNNs) can be used to enhance visual scene understanding and surgical skill assessment. By using GNNs to analyze the complex visual data of surgical procedures represented as graph structures, relevant features can be extracted and surgical skill can be predicted. Additionally, GNNs provide interpretable results, revealing the specific actions, instruments, or anatomical structures that contribute to the predicted skill metrics. This can be highly beneficial for surgical educators and trainees, as it provides valuable insights into the factors that contribute to successful surgical performance and outcomes. SurGNN proposes two concurrent approaches -- one supervised and the other self-supervised. The paper also briefly discusses other automated surgical skill evaluation techniques and highlights the limitations of hand-crafted features in capturing the intricacies of surgical expertise. We use the proposed methods to achieve state-of-the-art results on EndoVis19, and custom datasets. The working implementation of the code can be found at https://github.com/<redacted>.
Influences of Displaying Permission-related Information on Web Single Sign-On Login Decisions
Venue: arXiv cs.HC 2023
Year: 2023

Web users are increasingly presented with multiple login options, including password-based login and common web single sign-on (SSO) login options such as "Login with Google" and "Login with Facebook". There has been little focus in previous studies on how users choose from a list of login options and how to better inform users about privacy issues in web SSO systems. In this paper, we conducted a 200-participant study to understand factors that influence participants' login decisions, and how they are affected by displaying permission differences across login options; permissions in SSO result in release of user personal information to third-party web sites through SSO identity providers. We compare and report on login decisions made by participants before and after viewing permission-related information, examine self-reported responses for reasons related to their login decisions, and report on the factors that motivated their choices. We find that usability preferences and inertia (habituation) were among the dominant factors influencing login decisions. After participants viewed permission-related information, many prioritised privacy over other factors, changing their login decisions to more privacy-friendly alternatives. Displaying permission-related information also influenced some participants to make tradeoffs between privacy and usability preferences.
Interpretable Image Quality Assessment via CLIP with Multiple Antonym-Prompt Pairs
Venue: arXiv cs.CV 2023
Year: 2023

No reference image quality assessment (NR-IQA) is a task to estimate the perceptual quality of an image without its corresponding original image. It is even more difficult to perform this task in a zero-shot manner, i.e., without task-specific training. In this paper, we propose a new zero-shot and interpretable NRIQA method that exploits the ability of a pre-trained visionlanguage model to estimate the correlation between an image and a textual prompt. The proposed method employs a prompt pairing strategy and multiple antonym-prompt pairs corresponding to carefully selected descriptive features corresponding to the perceptual image quality. Thus, the proposed method is able to identify not only the perceptual quality evaluation of the image, but also the cause on which the quality evaluation is based. Experimental results show that the proposed method outperforms existing zero-shot NR-IQA methods in terms of accuracy and can evaluate the causes of perceptual quality degradation.