OpenRaiser/Intern-Atlas huggingface dataset:
Commonly Interesting Images
Venue: arXiv cs.CV 2024
Year: 2024

Images tell stories, trigger emotions, and let us recall memories -- they make us think. Thus, they have the ability to attract and hold one's attention, which is the definition of being "interesting". Yet, the appeal of an image is highly subjective. Looking at the image of my son taking his first steps will always bring me back to this emotional moment, while it is just a blurry, quickly taken snapshot to most others. Preferences vary widely: some adore cats, others are dog enthusiasts, and a third group may not be fond of either. We argue that every image can be interesting to a particular observer under certain circumstances. This work particularly emphasizes subjective preferences. However, our analysis of 2.5k image collections from diverse users of the photo-sharing platform Flickr reveals that specific image characteristics make them commonly more interesting. For instance, images, including professionally taken landscapes, appeal broadly due to their aesthetic qualities. In contrast, subjectively interesting images, such as those depicting personal or niche community events, resonate on a more individual level, often evoking personal memories and emotions.
Scalable Ensemble Diversification for OOD Generalization and Detection
Venue: arXiv cs.LG 2024
Year: 2024

Training a diverse ensemble of models has several practical applications such as providing candidates for model selection with better out-of-distribution (OOD) generalization, and enabling the detection of OOD samples via Bayesian principles. An existing approach to diverse ensemble training encourages the models to disagree on provided OOD samples. However, the approach is computationally expensive and it requires well-separated ID and OOD examples, such that it has only been demonstrated in small-scale settings. $\textbf{Method.}$ This work presents a method for Scalable Ensemble Diversification (SED) applicable to large-scale settings (e.g. ImageNet) that does not require OOD samples. Instead, SED identifies hard training samples on the fly and encourages the ensemble members to disagree on these. To improve scaling, we show how to avoid the expensive computations in existing methods of exhaustive pairwise disagreements across models. $\textbf{Results.}$ We evaluate the benefits of diversification with experiments on ImageNet. First, for OOD generalization, we observe large benefits from the diversification in multiple settings including output-space (classical) ensembles and weight-space ensembles (model soups). Second, for OOD detection, we turn the diversity of ensemble hypotheses into a novel uncertainty score estimator that surpasses a large number of OOD detection baselines. Code is available here: https://github.com/AlexanderRubinstein/diverse-universe-public.
Spotlight Text Detector: Spotlight on Candidate Regions Like a Camera
Venue: arXiv cs.CV 2024
Year: 2024

The irregular contour representation is one of the tough challenges in scene text detection. Although segmentation-based methods have achieved significant progress with the help of flexible pixel prediction, the overlap of geographically close texts hinders detecting them separately. To alleviate this problem, some shrink-based methods predict text kernels and expand them to restructure texts. However, the text kernel is an artificial object with incomplete semantic features that are prone to incorrect or missing detection. In addition, different from the general objects, the geometry features (aspect ratio, scale, and shape) of scene texts vary significantly, which makes it difficult to detect them accurately. To consider the above problems, we propose an effective spotlight text detector (STD), which consists of a spotlight calibration module (SCM) and a multivariate information extraction module (MIEM). The former concentrates efforts on the candidate kernel, like a camera focus on the target. It obtains candidate features through a mapping filter and calibrates them precisely to eliminate some false positive samples. The latter designs different shape schemes to explore multiple geometric features for scene texts. It helps extract various spatial relationships to improve the model's ability to recognize kernel regions. Ablation studies prove the effectiveness of the designed SCM and MIEM. Extensive experiments verify that our STD is superior to existing state-of-the-art methods on various datasets, including ICDAR2015, CTW1500, MSRA-TD500, and Total-Text.
Learning phase-space flows using time-discrete implicit Runge-Kutta PINNs
Venue: arXiv cs.LG 2024
Year: 2024

We present a computational framework for obtaining multidimensional phase-space solutions of systems of non-linear coupled differential equations, using high-order implicit Runge-Kutta Physics- Informed Neural Networks (IRK-PINNs) schemes. Building upon foundational work originally solving differential equations for fields depending on coordinates [J. Comput. Phys. 378, 686 (2019)], we adapt the scheme to a context where the coordinates are treated as functions. This modification enables us to efficiently solve equations of motion for a particle in an external field. Our scheme is particularly useful for explicitly time-independent and periodic fields. We apply this approach to successfully solve the equations of motion for a mass particle placed in a central force field and a charged particle in a periodic electric field.
Explicitly Modeling Pre-Cortical Vision with a Neuro-Inspired Front-End Improves CNN Robustness
Venue: arXiv cs.CV 2024
Year: 2024

While convolutional neural networks (CNNs) excel at clean image classification, they struggle to classify images corrupted with different common corruptions, limiting their real-world applicability. Recent work has shown that incorporating a CNN front-end block that simulates some features of the primate primary visual cortex (V1) can improve overall model robustness. Here, we expand on this approach by introducing two novel biologically-inspired CNN model families that incorporate a new front-end block designed to simulate pre-cortical visual processing. RetinaNet, a hybrid architecture containing the novel front-end followed by a standard CNN back-end, shows a relative robustness improvement of 12.3% when compared to the standard model; and EVNet, which further adds a V1 block after the pre-cortical front-end, shows a relative gain of 18.5%. The improvement in robustness was observed for all the different corruption categories, though accompanied by a small decrease in clean image accuracy, and generalized to a different back-end architecture. These findings show that simulating multiple stages of early visual processing in CNN early layers provides cumulative benefits for model robustness.
IRASNet: Improved Feature-Level Clutter Reduction for Domain Generalized SAR-ATR
Venue: arXiv cs.CV 2024
Year: 2024

Recently, computer-aided design models and electromagnetic simulations have been used to augment synthetic aperture radar (SAR) data for deep learning. However, an automatic target recognition (ATR) model struggles with domain shift when using synthetic data because the model learns specific clutter patterns present in such data, which disturbs performance when applied to measured data with different clutter distributions. This study proposes a framework particularly designed for domain-generalized SAR-ATR called IRASNet, enabling effective feature-level clutter reduction and domain-invariant feature learning. First, we propose a clutter reduction module (CRM) that maximizes the signal-to-clutter ratio on feature maps. The module reduces the impact of clutter at the feature level while preserving target and shadow information, thereby improving ATR performance. Second, we integrate adversarial learning with CRM to extract clutter-reduced domain-invariant features. The integration bridges the gap between synthetic and measured datasets without requiring measured data during training. Third, we improve feature extraction from target and shadow regions by implementing a positional supervision task using mask ground truth encoding. The improvement enhances the ability of the model to discriminate between classes. Our proposed IRASNet presents new state-of-the-art public SAR datasets utilizing target and shadow information to achieve superior performance across various test conditions. IRASNet not only enhances generalization performance but also significantly improves feature-level clutter reduction, making it a valuable advancement in the field of radar image pattern recognition.
The Role of Language Models in Modern Healthcare: A Comprehensive Review
Venue: arXiv cs.CV 2024
Year: 2024

The application of large language models (LLMs) in healthcare has gained significant attention due to their ability to process complex medical data and provide insights for clinical decision-making. These models have demonstrated substantial capabilities in understanding and generating natural language, which is crucial for medical documentation, diagnostics, and patient interaction. This review examines the trajectory of language models from their early stages to the current state-of-the-art LLMs, highlighting their strengths in healthcare applications and discussing challenges such as data privacy, bias, and ethical considerations. The potential of LLMs to enhance healthcare delivery is explored, alongside the necessary steps to ensure their ethical and effective integration into medical practice.
RESAA: A Removal and Structural Analysis Attack Against Compound Logic Locking
Venue: arXiv cs.CR 2024
Year: 2024

The semiconductor industry's paradigm shift towards fabless integrated circuit (IC) manufacturing has introduced security threats, including piracy, counterfeiting, hardware Trojans, and overproduction. In response to these challenges, various countermeasures, including Logic locking (LL), have been proposed to protect designs and mitigate security risks. LL is likely the most researched form of intellectual property (IP) protection for ICs. A significant advance has been made with the introduction of compound logic locking (CLL), where two LL techniques are concurrently utilized for improved resiliency against attacks. However, the vulnerabilities of LL techniques, particularly CLL, need to be explored further. This paper presents a novel framework, RESAA, designed to classify CLL-locked designs, identify critical gates, and execute various attacks to uncover secret keys. RESAA is agnostic to specific LL techniques, offering comprehensive insights into CLL's security scenarios. Experimental results demonstrate RESAA's efficacy in identifying critical gates, distinguishing segments corresponding to different LL techniques, and determining associated keys based on different threat models. In particular, for the oracle-less threat model, RESAA can achieve up to 92.6% accuracy on a relatively complex ITC'99 benchmark circuit. The results reported in this paper emphasize the significance of evaluation and thoughtful selection of LL techniques, as all studied CLL variants demonstrated vulnerability to our framework. RESAA is also open-sourced for the community at large.
Harnessing Diversity for Important Data Selection in Pretraining Large Language Models
Venue: arXiv cs.AI 2024
Year: 2024

Data selection is of great significance in pre-training large language models, given the variation in quality within the large-scale available training corpora. To achieve this, researchers are currently investigating the use of data influence to measure the importance of data instances, $i.e.,$ a high influence score indicates that incorporating this instance to the training set is likely to enhance the model performance. Consequently, they select the top-$k$ instances with the highest scores. However, this approach has several limitations. (1) Computing the influence of all available data is time-consuming. (2) The selected data instances are not diverse enough, which may hinder the pre-trained model's ability to generalize effectively to various downstream tasks. In this paper, we introduce \texttt{Quad}, a data selection approach that considers both quality and diversity by using data influence to achieve state-of-the-art pre-training results. In particular, noting that attention layers capture extensive semantic details, we have adapted the accelerated $iHVP$ computation methods for attention layers, enhancing our ability to evaluate the influence of data, $i.e.,$ its quality. For the diversity, \texttt{Quad} clusters the dataset into similar data instances within each cluster and diverse instances across different clusters. For each cluster, if we opt to select data from it, we take some samples to evaluate the influence to prevent processing all instances. To determine which clusters to select, we utilize the classic Multi-Armed Bandit method, treating each cluster as an arm. This approach favors clusters with highly influential instances (ensuring high quality) or clusters that have been selected less frequently (ensuring diversity), thereby well balancing between quality and diversity.
Models Can and Should Embrace the Communicative Nature of Human-Generated Math
Venue: arXiv cs.AI 2024
Year: 2024

Math is constructed by people for people: just as natural language corpora reflect not just propositions but the communicative goals of language users, the math data that models are trained on reflects not just idealized mathematical entities but rich communicative intentions. While there are important advantages to treating math in a purely symbolic manner, we here hypothesize that there are benefits to treating math as situated linguistic communication and that language models are well suited for this goal, in ways that are not fully appreciated. We illustrate these points with two case studies. First, we ran an experiment in which we found that language models interpret the equals sign in a humanlike way -- generating systematically different word problems for the same underlying equation arranged in different ways. Second, we found that language models prefer proofs to be ordered in naturalistic ways, even though other orders would be logically equivalent. We advocate for AI systems that learn from and represent the communicative intentions latent in human-generated math.
AI-Driven Risk-Aware Scheduling for Active Debris Removal Missions
Venue: arXiv cs.AI 2024
Year: 2024

The proliferation of debris in Low Earth Orbit (LEO) represents a significant threat to space sustainability and spacecraft safety. Active Debris Removal (ADR) has emerged as a promising approach to address this issue, utilising Orbital Transfer Vehicles (OTVs) to facilitate debris deorbiting, thereby reducing future collision risks. However, ADR missions are substantially complex, necessitating accurate planning to make the missions economically viable and technically effective. Moreover, these servicing missions require a high level of autonomous capability to plan under evolving orbital conditions and changing mission requirements. In this paper, an autonomous decision-planning model based on Deep Reinforcement Learning (DRL) is developed to train an OTV to plan optimal debris removal sequencing. It is shown that using the proposed framework, the agent can find optimal mission plans and learn to update the planning autonomously to include risk handling of debris with high collision risk.
Investigations on Algorithm Selection for Interval-Based Coding Methods
Venue: arXiv cs.IT 2024
Year: 2024

There is a class of entropy-coding methods which do not substitute symbols by code words (such as Huffman coding), but operate on intervals or ranges. This class includes three prominent members: conventional arithmetic coding, range coding, and coding based on asymmetric numeral systems. To determine the correct symbol in the decoder, each of these methods requires the comparison of a state variable with subinterval boundaries. In adaptive operation, considering varying symbol statistics, an array of interval boundaries must additionally be kept up to date. The larger the symbol alphabet, the more time-consuming both the search for the correct subinterval and the updating of interval borders become. Detailed pseudo-code is used to discuss different approaches to speed up the symbol search in the decoder and the adaptation of the array of interval borders, both depending on the chosen alphabet size. It is shown that reducing the $\mathcal{O}$-complexity does not lead to an acceleration in practical implementations if the alphabet size is too small. In adaptive compression mode, the binary indexing method proves to be superior when considering the overall processing time. Although the symbol search (in the decoder) takes longer than with other algorithms, the faster updating of the array of interval borders more than compensates for this disadvantage. A variant of the binary indexing method is proposed, which is more flexible and has a partially lower complexity than the original approach.
CNN Mixture-of-Depths
Venue: arXiv cs.CV 2024
Year: 2024

We introduce Mixture-of-Depths (MoD) for Convolutional Neural Networks (CNNs), a novel approach that enhances the computational efficiency of CNNs by selectively processing channels based on their relevance to the current prediction. This method optimizes computational resources by dynamically selecting key channels in feature maps for focused processing within the convolutional blocks (Conv-Blocks), while skipping less relevant channels. Unlike conditional computation methods that require dynamic computation graphs, CNN MoD uses a static computation graph with fixed tensor sizes which improve hardware efficiency. It speeds up the training and inference processes without the need for customized CUDA kernels, unique loss functions, or finetuning. CNN MoD either matches the performance of traditional CNNs with reduced inference times, GMACs, and parameters, or exceeds their performance while maintaining similar inference times, GMACs, and parameters. For example, on ImageNet, ResNet86-MoD exceeds the performance of the standard ResNet50 by 0.45% with a 6% speedup on CPU and 5% on GPU. Moreover, ResNet75-MoD achieves the same performance as ResNet50 with a 25% speedup on CPU and 15% on GPU.
Locally Regularized Sparse Graph by Fast Proximal Gradient Descent
Venue: arXiv cs.LG 2024
Year: 2024

Sparse graphs built by sparse representation has been demonstrated to be effective in clustering high-dimensional data. Albeit the compelling empirical performance, the vanilla sparse graph ignores the geometric information of the data by performing sparse representation for each datum separately. In order to obtain a sparse graph aligned with the local geometric structure of data, we propose a novel Support Regularized Sparse Graph, abbreviated as SRSG, for data clustering. SRSG encourages local smoothness on the neighborhoods of nearby data points by a well-defined support regularization term. We propose a fast proximal gradient descent method to solve the non-convex optimization problem of SRSG with the convergence matching the Nesterov's optimal convergence rate of first-order methods on smooth and convex objective function with Lipschitz continuous gradient. Extensive experimental results on various real data sets demonstrate the superiority of SRSG over other competing clustering methods.
Enhancing robot reliability for health-care facilities by means of Human-Aware Navigation Planning
Venue: arXiv cs.RO 2024
Year: 2024

With the aim of enabling robots to cooperate with humans, carry out human-like tasks, or navigate among humans, we need to ensure that they are equipped with the ability to comprehend human behaviors and use the extracted knowledge for intelligent decision-making. This ability is particularly important in the safety-critical and human-centred environment of health-care institutions. In the field of robotic navigation, the most cutting-edge approaches to enhancing robot reliability in the application domain of healthcare facilities and in general pertain to augmenting navigation systems with human-aware properties. To implement this in our work, the Co-operative Human-Aware Navigation planner has been integrated into the ROS-based differential-drive robot MARRtina and exhaustively challenged within various simulated contexts and scenarios (mainly modelling the situations relevant in the medical domain) to draw attention to the integrated system's benefits and identify its drawbacks or instances of poor performance while exploring the scope of system capabilities and creating a full characterization of its applicability. The simulation results are then presented to medical experts, and the enhanced robot acceptability within the domain is validated with them as the robot is further planned for deployment.
Adaptive Cost Model for Query Optimization
Venue: arXiv cs.DB 2024
Year: 2024

The principal component of conventional database query optimizers is a cost model that is used to estimate expected performance of query plans. The accuracy of the cost model has direct impact on the optimality of execution plans selected by the optimizer and thus, on the resulting query latency. Several common parameters of cost models in modern DBMS are related to the performance of CPU and I/O and are typically set by a database administrator upon system tuning. However these performance characteristics are not stable and therefore, a single point estimation may not suffice for all DB load regimes. In this paper, we propose an Adaptive Cost Model (ACM) which dynamically optimizes CPU- and I/O-related plan cost parameters at DB runtime. By continuously monitoring query execution statistics and the state of DB buffer cache ACM adjusts cost parameters without the need for manual intervention from a database administrator. This allows for responding to changes in the workload and system performance ensuring more optimal query execution plans. We describe the main ideas in the implementation of ACM and report on a preliminary experimental evaluation showing 20\% end-to-end latency improvement on TPC-H benchmark.
Confident Teacher, Confident Student? A Novel User Study Design for Investigating the Didactic Potential of Explanations and their Impact on Uncertainty
Venue: arXiv cs.HC 2024
Year: 2024

Evaluating the quality of explanations in Explainable Artificial Intelligence (XAI) is to this day a challenging problem, with ongoing debate in the research community. While some advocate for establishing standardized offline metrics, others emphasize the importance of human-in-the-loop (HIL) evaluation. Here we propose an experimental design to evaluate the potential of XAI in human-AI collaborative settings as well as the potential of XAI for didactics. In a user study with 1200 participants we investigate the impact of explanations on human performance on a challenging visual task - annotation of biological species in complex taxonomies. Our results demonstrate the potential of XAI in complex visual annotation tasks: users become more accurate in their annotations and demonstrate less uncertainty with AI assistance. The increase in accuracy was, however, not significantly different when users were shown the mere prediction of the model compared to when also providing an explanation. We also find negative effects of explanations: users tend to replicate the model's predictions more often when shown explanations, even when those predictions are wrong. When evaluating the didactic effects of explanations in collaborative human-AI settings, we find that users' annotations are not significantly better after performing annotation with AI assistance. This suggests that explanations in visual human-AI collaboration do not appear to induce lasting learning effects. All code and experimental data can be found in our GitHub repository: https://github.com/TeodorChiaburu/beexplainable.