OpenRaiser/Intern-Atlas huggingface dataset:
TRR360D: A dataset for 360 degree rotated rectangular box table detection
Venue: arXiv cs.CV 2023
Year: 2023

To address the problem of scarcity and high annotation costs of rotated image table detection datasets, this paper proposes a method for building a rotated image table detection dataset. Based on the ICDAR2019MTD modern table detection dataset, we refer to the annotation format of the DOTA dataset to create the TRR360D rotated table detection dataset. The training set contains 600 rotated images and 977 annotated instances, and the test set contains 240 rotated images and 499 annotated instances. The AP50(T<90) evaluation metric is defined, and this dataset is available for future researchers to study rotated table detection algorithms and promote the development of table detection technology. The TRR360D rotated table detection dataset was created by constraining the starting point and annotation direction, and is publicly available at https://github.com/vansin/TRR360D.
Calibration of Quantum Decision Theory: Aversion to Large Losses and Predictability of Probabilistic Choices
Venue: arXiv cs.AI 2023
Year: 2023

We present the first calibration of quantum decision theory (QDT) to a dataset of binary risky choice. We quantitatively account for the fraction of choice reversals between two repetitions of the experiment, using a probabilistic choice formulation in the simplest form without model assumption or adjustable parameters. The prediction of choice reversal is then refined by introducing heterogeneity between decision makers through their differentiation into two groups: ``majoritarian'' and ``contrarian'' (in proportion 3:1). This supports the first fundamental tenet of QDT, which models choice as an inherent probabilistic process, where the probability of a prospect can be expressed as the sum of its utility and attraction factors. We propose to parameterise the utility factor with a stochastic version of cumulative prospect theory (logit-CPT), and the attraction factor with a constant absolute risk aversion (CARA) function. For this dataset, and penalising the larger number of QDT parameters via the Wilks test of nested hypotheses, the QDT model is found to perform significantly better than logit-CPT at both the aggregate and individual levels, and for all considered fit criteria for the first experiment iteration and for predictions (second ``out-of-sample'' iteration). The distinctive QDT effect captured by the attraction factor is mostly appreciable (i.e., most relevant and strongest in amplitude) for prospects with big losses. Our quantitative analysis of the experimental results supports the existence of an intrinsic limit of predictability, which is associated with the inherent probabilistic nature of choice. The results of the paper can find applications both in the prediction of choice of human decision makers as well as for organizing the operation of artificial intelligence.
How To Guide Your Learner: Imitation Learning with Active Adaptive Expert Involvement
Venue: arXiv cs.LG 2023
Year: 2023

Imitation learning aims to mimic the behavior of experts without explicit reward signals. Passive imitation learning methods which use static expert datasets typically suffer from compounding error, low sample efficiency, and high hyper-parameter sensitivity. In contrast, active imitation learning methods solicit expert interventions to address the limitations. However, recent active imitation learning methods are designed based on human intuitions or empirical experience without theoretical guarantee. In this paper, we propose a novel active imitation learning framework based on a teacher-student interaction model, in which the teacher's goal is to identify the best teaching behavior and actively affect the student's learning process. By solving the optimization objective of this framework, we propose a practical implementation, naming it AdapMen. Theoretical analysis shows that AdapMen can improve the error bound and avoid compounding error under mild conditions. Experiments on the MetaDrive benchmark and Atari 2600 games validate our theoretical analysis and show that our method achieves near-expert performance with much less expert involvement and total sampling steps than previous methods. The code is available at https://github.com/liuxhym/AdapMen.
Filtering Eye-Tracking Data From an EyeLink 1000: Comparing Heuristic, Savitzky-Golay, IIR and FIR Digital Filters
Venue: arXiv cs.HC 2023
Year: 2023

In a previous report (Raju et al.,2023) we concluded that, if the goal was to preserve events such as saccades, microsaccades, and smooth pursuit in eye-tracking recordings, data with sine wave frequencies less than 100 Hz (-3db) were the signal and data above 100 Hz were noise. We compare 5 filters in their ability to preserve signal and remove noise. Specifically, we compared the proprietary STD and EXTRA heuristic filters provided by our EyeLink 1000 (SR-Research, Ottawa, Canada), a Savitzky-Golay (SG) filter, an infinite impulse response (IIR) filter (low-pass Butterworth), and a finite impulse filter (FIR). For each of the non-heuristic filters, we systematically searched for optimal parameters. Both the IIR and the FIR filters were zero-phase filters. Mean frequency response profiles and amplitude spectra for all 5 filters are provided. In addition, we examined the effect of our filters on a noisy recording. Our FIR filter had the sharpest roll-off of any filter. Therefore, it maintained the signal and removed noise more effectively than any other filter. On this basis, we recommend the use of our FIR filter. Several reports have shown that filtering increased the temporal autocorrelation of a signal. To address this, the present filters were also evaluated in terms of autocorrelation (specifically the first 3 lags). Of all our filters, the STD filter introduced the least amount of autocorrelation.
CoRL: Environment Creation and Management Focused on System Integration
Venue: arXiv cs.LG 2023
Year: 2023

Existing reinforcement learning environment libraries use monolithic environment classes, provide shallow methods for altering agent observation and action spaces, and/or are tied to a specific simulation environment. The Core Reinforcement Learning library (CoRL) is a modular, composable, and hyper-configurable environment creation tool. It allows minute control over agent observations, rewards, and done conditions through the use of easy-to-read configuration files, pydantic validators, and a functor design pattern. Using integration pathways allows agents to be quickly implemented in new simulation environments, encourages rapid exploration, and enables transition of knowledge from low-fidelity to high-fidelity simulations. Natively multi-agent design and integration with Ray/RLLib (Liang et al., 2018) at release allow for easy scalability of agent complexity and computing power. The code is publicly released and available at https://github.com/act3-ace/CoRL.
Domain Specific Question Answering Over Knowledge Graphs Using Logical Programming and Large Language Models
Venue: arXiv cs.LG 2023
Year: 2023

Answering questions over domain-specific graphs requires a tailored approach due to the limited number of relations and the specific nature of the domain. Our approach integrates classic logical programming languages into large language models (LLMs), enabling the utilization of logical reasoning capabilities to tackle the KGQA task. By representing the questions as Prolog queries, which are readable and near close to natural language in representation, we facilitate the generation of programmatically derived answers. To validate the effectiveness of our approach, we evaluate it using a well-known benchmark dataset, MetaQA. Our experimental results demonstrate that our method achieves accurate identification of correct answer entities for all test questions, even when trained on a small fraction of annotated data. Overall, our work presents a promising approach to addressing question answering over domain-specific graphs, offering an explainable and robust solution by incorporating logical programming languages.
Denoise Pretraining on Nonequilibrium Molecules for Accurate and Transferable Neural Potentials
Venue: arXiv cs.LG 2023
Year: 2023

Recent advances in equivariant graph neural networks (GNNs) have made deep learning amenable to developing fast surrogate models to expensive ab initio quantum mechanics (QM) approaches for molecular potential predictions. However, building accurate and transferable potential models using GNNs remains challenging, as the data is greatly limited by the expensive computational costs and level of theory of QM methods, especially for large and complex molecular systems. In this work, we propose denoise pretraining on nonequilibrium molecular conformations to achieve more accurate and transferable GNN potential predictions. Specifically, atomic coordinates of sampled nonequilibrium conformations are perturbed by random noises and GNNs are pretrained to denoise the perturbed molecular conformations which recovers the original coordinates. Rigorous experiments on multiple benchmarks reveal that pretraining significantly improves the accuracy of neural potentials. Furthermore, we show that the proposed pretraining approach is model-agnostic, as it improves the performance of different invariant and equivariant GNNs. Notably, our models pretrained on small molecules demonstrate remarkable transferability, improving performance when fine-tuned on diverse molecular systems, including different elements, charged molecules, biomolecules, and larger systems. These results highlight the potential for leveraging denoise pretraining approaches to build more generalizable neural potentials for complex molecular systems.
An efficient algorithm for integer lattice reduction
Venue: arXiv cs.CR 2023
Year: 2023

A lattice of integers is the collection of all linear combinations of a set of vectors for which all entries of the vectors are integers and all coefficients in the linear combinations are also integers. Lattice reduction refers to the problem of finding a set of vectors in a given lattice such that the collection of all integer linear combinations of this subset is still the entire original lattice and so that the Euclidean norms of the subset are reduced. The present paper proposes simple, efficient iterations for lattice reduction which are guaranteed to reduce the Euclidean norms of the basis vectors (the vectors in the subset) monotonically during every iteration. Each iteration selects the basis vector for which projecting off (with integer coefficients) the components of the other basis vectors along the selected vector minimizes the Euclidean norms of the reduced basis vectors. Each iteration projects off the components along the selected basis vector and efficiently updates all information required for the next iteration to select its best basis vector and perform the associated projections.
Online simulator-based experimental design for cognitive model selection
Venue: arXiv cs.LG 2023
Year: 2023

The problem of model selection with a limited number of experimental trials has received considerable attention in cognitive science, where the role of experiments is to discriminate between theories expressed as computational models. Research on this subject has mostly been restricted to optimal experiment design with analytically tractable models. However, cognitive models of increasing complexity, with intractable likelihoods, are becoming more commonplace. In this paper, we propose BOSMOS: an approach to experimental design that can select between computational models without tractable likelihoods. It does so in a data-efficient manner, by sequentially and adaptively generating informative experiments. In contrast to previous approaches, we introduce a novel simulator-based utility objective for design selection, and a new approximation of the model likelihood for model selection. In simulated experiments, we demonstrate that the proposed BOSMOS technique can accurately select models in up to 2 orders of magnitude less time than existing LFI alternatives for three cognitive science tasks: memory retention, sequential signal detection and risky choice.
Building Floorspace in China: A Dataset and Learning Pipeline
Venue: arXiv cs.CV 2023
Year: 2023

This paper provides a first milestone in measuring the floorspace of buildings (that is, building footprint and height) for 40 major Chinese cities. The intent is to maximize city coverage and, eventually provide longitudinal data. Doing so requires building on imagery that is of a medium-fine-grained granularity, as larger cross sections of cities and longer time series for them are only available in such format. We use a multi-task object segmenter approach to learn the building footprint and height in the same framework in parallel: (1) we determine the surface area is covered by any buildings (the square footage of occupied land); (2) we determine floorspace from multi-image representations of buildings from various angles to determine the height of buildings. We use Sentinel-1 and -2 satellite images as our main data source. The benefits of these data are their large cross-sectional and longitudinal scope plus their unrestricted accessibility. We provide a detailed description of our data, algorithms, and evaluations. In addition, we analyze the quality of reference data and their role for measuring the building floorspace with minimal error. We conduct extensive quantitative and qualitative analyses with Shenzhen as a case study using our multi-task learner. Finally, we conduct correlation studies between our results (on both pixel and aggregated urban area levels) and nightlight data to gauge the merits of our approach in studying urban development. Our data and codebase are publicly accessible under https://gitlab.ethz.ch/raox/urban-satellite-public-v2.
The Type to Take Out a Loan? A Study of Developer Personality and Technical Debt
Venue: arXiv cs.SE 2023
Year: 2023

Background: Technical debt (TD) has been widely discussed in software engineering research, and there is an emerging literature linking it to developer characteristics. However, developer personality has not yet been studied in this context. Aims and Method: We explore the relationship between various personality traits (Five Factor Model, regulatory focus, and narcissism) of developers and the introduction and removal of TD. To this end, we complement an existing TD dataset with novel self-report personality data gathered by surveying developers, and analyze 2,145 commits from 19 developers. Results: We find that conscientiousness, emotional stability, openness to experience, and prevention focus are negatively associated with TD. There were no significant results for extraversion, agreeableness, promotion focus, or narcissism. Conclusions: We take our results as first evidence that developer personality has a systematic influence on the introduction and removal of TD. This has implications not only for future research, which could, for example, study the effects of personality on downstream consequences of TD like defects, but also for software engineering practitioners who may, for example, consider developer personality in staffing decisions.
Towards Safety Assured End-to-End Vision-Based Control for Autonomous Racing
Venue: arXiv cs.RO 2023
Year: 2023

Autonomous car racing is a challenging task, as it requires precise applications of control while the vehicle is operating at cornering speeds. Traditional autonomous pipelines require accurate pre-mapping, localization, and planning which make the task computationally expensive and environment-dependent. Recent works propose use of imitation and reinforcement learning to train end-to-end deep neural networks and have shown promising results for high-speed racing. However, the end-to-end models may be dangerous to be deployed on real systems, as the neural networks are treated as black-box models devoid of any provable safety guarantees. In this work we propose a decoupled approach where an optimal end-to-end controller and a state prediction end-to-end model are learned together, and the predicted state of the vehicle is used to formulate a control barrier function for safeguarding the vehicle to stay within lane boundaries. We validate our algorithm both on a high-fidelity Carla driving simulator and a 1/10-scale RC car on a real track. The evaluation results suggest that using an explicit safety controller helps to learn the task safely with fewer iterations and makes it possible to safely navigate the vehicle on the track along the more challenging racing line.
On Maximum Bipartite Matching with Separation
Venue: arXiv cs.DS 2023
Year: 2023

Maximum bipartite matching is a fundamental algorithmic problem which can be solved in polynomial time. We consider a natural variant in which there is a separation constraint: the vertices on one side lie on a path or a grid, and two vertices that are close to each other are not allowed to be matched simultaneously. We show that the problem is hard to approximate even for paths, and provide constant-factor approximation algorithms for both paths and grids.
Fast American Option Pricing using Nonlinear Stencils
Venue: arXiv cs.CE 2023
Year: 2023

We study the binomial, trinomial, and Black-Scholes-Merton models of option pricing. We present fast parallel discrete-time finite-difference algorithms for American call option pricing under the binomial and trinomial models and American put option pricing under the Black-Scholes-Merton model. For $T$-step finite differences, each algorithm runs in $O(\left(T\log^2{T}\right)/p + T)$ time under a greedy scheduler on $p$ processing cores, which is a significant improvement over the $Θ({T^2}/{p}) + Ω(T\log{T})$ time taken by the corresponding state-of-the-art parallel algorithm. Even when run on a single core, the $O(T\log^2{T})$ time taken by our algorithms is asymptotically much smaller than the $Θ(T^2)$ running time of the fastest known serial algorithms. Implementations of our algorithms significantly outperform the fastest implementations of existing algorithms in practice, e.g., when run for $T \approx 1000$ steps on a 48-core machine, our algorithm for the binomial model runs at least $15\times$ faster than the fastest existing parallel program for the same model with the speed-up factor gradually reaching beyond $500\times$ for $T \approx 0.5 \times 10^6$. It saves more than 80\% energy when $T \approx 4000$, and more than 99\% energy for $T > 60,000$. Our option pricing algorithms can be viewed as solving a class of nonlinear 1D stencil (i.e., finite-difference) computation problems efficiently using the Fast Fourier Transform (FFT). To our knowledge, ours are the first algorithms to handle such stencils in $o(T^2)$ time. These contributions are of independent interest as stencil computations have a wide range of applications beyond quantitative finance.
Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output Codes
Venue: arXiv cs.LG 2023
Year: 2023

Neural network ensembles have been studied extensively in the context of adversarial robustness and most ensemble-based approaches remain vulnerable to adaptive attacks. In this paper, we investigate the robustness of Error-Correcting Output Codes (ECOC) ensembles through architectural improvements and ensemble diversity promotion. We perform a comprehensive robustness assessment against adaptive attacks and investigate the relationship between ensemble diversity and robustness. Our results demonstrate the benefits of ECOC ensembles for adversarial robustness compared to regular ensembles of convolutional neural networks (CNNs) and show why the robustness of previous implementations is limited. We also propose an adversarial training method specific to ECOC ensembles that allows to further improve robustness to adaptive attacks.
PixMIM: Rethinking Pixel Reconstruction in Masked Image Modeling
Venue: arXiv cs.CV 2023
Year: 2023

Masked Image Modeling (MIM) has achieved promising progress with the advent of Masked Autoencoders (MAE) and BEiT. However, subsequent works have complicated the framework with new auxiliary tasks or extra pre-trained models, inevitably increasing computational overhead. This paper undertakes a fundamental analysis of MIM from the perspective of pixel reconstruction, which examines the input image patches and reconstruction target, and highlights two critical but previously overlooked bottlenecks. Based on this analysis, we propose a remarkably simple and effective method, {\ourmethod}, that entails two strategies: 1) filtering the high-frequency components from the reconstruction target to de-emphasize the network's focus on texture-rich details and 2) adopting a conservative data transform strategy to alleviate the problem of missing foreground in MIM training. {\ourmethod} can be easily integrated into most existing pixel-based MIM approaches (\ie, using raw images as reconstruction target) with negligible additional computation. Without bells and whistles, our method consistently improves three MIM approaches, MAE, ConvMAE, and LSMAE, across various downstream tasks. We believe this effective plug-and-play method will serve as a strong baseline for self-supervised learning and provide insights for future improvements of the MIM framework. Code and models are available at \url{https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/pixmim}.
Self-tuning hyper-parameters for unsupervised cross-lingual tokenization
Venue: arXiv cs.CL 2023
Year: 2023

We explore the possibility of meta-learning for the language-independent unsupervised tokenization problem for English, Russian, and Chinese. We implement the meta-learning approach for automatic determination of hyper-parameters of the unsupervised tokenization model proposed in earlier works, relying on various human-independent fitness functions such as normalised anti-entropy, compression factor and cross-split F1 score, as well as additive and multiplicative composite combinations of the three metrics, testing them against the conventional F1 tokenization score. We find a fairly good correlation between the latter and the additive combination of the former three metrics for English and Russian. In case of Chinese, we find a significant correlation between the F 1 score and the compression factor. Our results suggest the possibility of robust unsupervised tokenization of low-resource and dead languages and allow us to think about human languages in terms of the evolution of efficient symbolic communication codes with different structural optimisation schemes that have evolved in different human cultures.
Comparative Studies of Unsupervised and Supervised Learning Methods based on Multimedia Applications
Venue: arXiv cs.CV 2023
Year: 2023

In the mobile communication field, some of the video applications boosted the interest of robust methods for video quality assessment. Out of all existing methods, We Preferred, No Reference Video Quality Assessment is the one which is most needed in situations where the handiness of reference video is partially available. Our research interest lies in formulating and melding effective features into one model based on human visualizing characteristics. Our work explores comparative study between Supervised and unsupervised learning methods. Therefore, we implemented support vector regression algorithm as NR-based Video Quality Metric(VQM) for quality estimation with simplified input features. We concluded that our proposed model exhibited sparseness even after dimension reduction for objective scores of SSIM quality metric.