OpenRaiser/Intern-Atlas huggingface dataset:
A new perspective on probabilistic image modeling
Venue: arXiv cs.LG 2022
Year: 2022

We present the Deep Convolutional Gaussian Mixture Model (DCGMM), a new probabilistic approach for image modeling capable of density estimation, sampling and tractable inference. DCGMM instances exhibit a CNN-like layered structure, in which the principal building blocks are convolutional Gaussian Mixture (cGMM) layers. A key innovation w.r.t. related models like sum-product networks (SPNs) and probabilistic circuits (PCs) is that each cGMM layer optimizes an independent loss function and therefore has an independent probabilistic interpretation. This modular approach permits intervening transformation layers to harness the full spectrum of (potentially non-invertible) mappings available to CNNs, e.g., max-pooling or half-convolutions. DCGMM sampling and inference are realized by a deep chain of hierarchical priors, where a sample generated by a given cGMM layer defines the parameters of sampling in the next-lower cGMM layer. For sampling through non-invertible transformation layers, we introduce a new gradient-based sharpening technique that exploits redundancy (overlap) in, e.g., half-convolutions. DCGMMs can be trained end-to-end by SGD from random initial conditions, much like CNNs. We show that DCGMMs compare favorably to several recent PC and SPN models in terms of inference, classification and sampling, the latter particularly for challenging datasets such as SVHN. We provide a public TF2 implementation.
A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware
Venue: arXiv cs.NE 2022
Year: 2022

Neuromorphic systems open up opportunities to enlarge the explorative space for computational research. However, it is often challenging to unite efficiency and usability. This work presents the software aspects of this endeavor for the BrainScaleS-2 system, a hybrid accelerated neuromorphic hardware architecture based on physical modeling. We introduce key aspects of the BrainScaleS-2 Operating System: experiment workflow, API layering, software design, and platform operation. We present use cases to discuss and derive requirements for the software and showcase the implementation. The focus lies on novel system and software features such as multi-compartmental neurons, fast re-configuration for hardware-in-the-loop training, applications for the embedded processors, the non-spiking operation mode, interactive platform access, and sustainable hardware/software co-development. Finally, we discuss further developments in terms of hardware scale-up, system usability and efficiency.
L-MAC: Location-aware MAC Protocol for Wireless Sensor Networks
Venue: arXiv cs.NI 2022
Year: 2022

This paper presents the design, implementation and performance evaluation of a location MAC protocol, called L-MAC, for wireless sensor networks. L-MAC is a combination of TDMA and CSMA while offsetting the high overhead of time slot assignment by allocating the time slots to sensor nodes based on their location information. This design avoids high computation complexity of time slot assignment incurred by node mobility and node failure. The area which the wireless sensor network occupies is divided into blocks and each block is associated with an inter-block time slot and an intra-block time slot. In the inter-block time slot, the sensor nodes stay active and receive the packets from nodes outside of the block. In the intra-block time slot, the sensor nodes communicate with peer nodes in the same block under CSMA. Sensor nodes stay sleep in all other time slots unless they have traffic to send. L-MAC is implemented and evaluated in NS-2.
Review of Disentanglement Approaches for Medical Applications -- Towards Solving the Gordian Knot of Generative Models in Healthcare
Venue: arXiv cs.LG 2022
Year: 2022

Deep neural networks are commonly used for medical purposes such as image generation, segmentation, or classification. Besides this, they are often criticized as black boxes as their decision process is often not human interpretable. Encouraging the latent representation of a generative model to be disentangled offers new perspectives of control and interpretability. Understanding the data generation process could help to create artificial medical data sets without violating patient privacy, synthesizing different data modalities, or discovering data generating characteristics. These characteristics might unravel novel relationships that can be related to genetic traits or patient outcomes. In this paper, we give a comprehensive overview of popular generative models, like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Flow-based Models. Furthermore, we summarize the different notions of disentanglement, review approaches to disentangle latent space representations and metrics to evaluate the degree of disentanglement. After introducing the theoretical frameworks, we give an overview of recent medical applications and discuss the impact and importance of disentanglement approaches for medical applications.
Can we integrate spatial verification methods into neural-network loss functions for atmospheric science?
Venue: arXiv cs.LG 2022
Year: 2022

In the last decade, much work in atmospheric science has focused on spatial verification (SV) methods for gridded prediction, which overcome serious disadvantages of pixelwise verification. However, neural networks (NN) in atmospheric science are almost always trained to optimize pixelwise loss functions, even when ultimately assessed with SV methods. This establishes a disconnect between model verification during vs. after training. To address this issue, we develop spatially enhanced loss functions (SELF) and demonstrate their use for a real-world problem: predicting the occurrence of thunderstorms (henceforth, "convection") with NNs. In each SELF we use either a neighbourhood filter, which highlights convection at scales larger than a threshold, or a spectral filter (employing Fourier or wavelet decomposition), which is more flexible and highlights convection at scales between two thresholds. We use these filters to spatially enhance common verification scores, such as the Brier score. We train each NN with a different SELF and compare their performance at many scales of convection, from discrete storm cells to tropical cyclones. Among our many findings are that (a) for a low (high) risk threshold, the ideal SELF focuses on small (large) scales; (b) models trained with a pixelwise loss function perform surprisingly well; (c) however, models trained with a spectral filter produce much better-calibrated probabilities than a pixelwise model. We provide a general guide to using SELFs, including technical challenges and the final Python code, as well as demonstrating their use for the convection problem. To our knowledge this is the most in-depth guide to SELFs in the geosciences.
Multispectral Satellite Data Classification using Soft Computing Approach
Venue: arXiv cs.CV 2022
Year: 2022

A satellite image is a remotely sensed image data, where each pixel represents a specific location on earth. The pixel value recorded is the reflection radiation from the earth's surface at that location. Multispectral images are those that capture image data at specific frequencies across the electromagnetic spectrum as compared to Panchromatic images which are sensitive to all wavelength of visible light. Because of the high resolution and high dimensions of these images, they create difficulties for clustering techniques to efficiently detect clusters of different sizes, shapes and densities as a trade off for fast processing time. In this paper we propose a grid-density based clustering technique for identification of objects. We also introduce an approach to classify a satellite image data using a rule induction based machine learning algorithm. The object identification and classification methods have been validated using several synthetic and benchmark datasets.
Short Text Topic Modeling: Application to tweets about Bitcoin
Venue: arXiv cs.IR 2022
Year: 2022

Understanding the semantic of a collection of texts is a challenging task. Topic models are probabilistic models that aims at extracting "topics" from a corpus of documents. This task is particularly difficult when the corpus is composed of short texts, such as posts on social networks. Following several previous research papers, we explore in this paper a set of collected tweets about bitcoin. In this work, we train three topic models and evaluate their output with several scores. We also propose a concrete application of the extracted topics.
Distinguishing Non-natural from Natural Adversarial Samples for More Robust Pre-trained Language Model
Venue: arXiv cs.LG 2022
Year: 2022

Recently, the problem of robustness of pre-trained language models (PrLMs) has received increasing research interest. Latest studies on adversarial attacks achieve high attack success rates against PrLMs, claiming that PrLMs are not robust. However, we find that the adversarial samples that PrLMs fail are mostly non-natural and do not appear in reality. We question the validity of current evaluation of robustness of PrLMs based on these non-natural adversarial samples and propose an anomaly detector to evaluate the robustness of PrLMs with more natural adversarial samples. We also investigate two applications of the anomaly detector: (1) In data augmentation, we employ the anomaly detector to force generating augmented data that are distinguished as non-natural, which brings larger gains to the accuracy of PrLMs. (2) We apply the anomaly detector to a defense framework to enhance the robustness of PrLMs. It can be used to defend all types of attacks and achieves higher accuracy on both adversarial samples and compliant samples than other defense frameworks.
ReCCoVER: Detecting Causal Confusion for Explainable Reinforcement Learning
Venue: arXiv cs.LG 2022
Year: 2022

Despite notable results in various fields over the recent years, deep reinforcement learning (DRL) algorithms lack transparency, affecting user trust and hindering their deployment to high-risk tasks. Causal confusion refers to a phenomenon where an agent learns spurious correlations between features which might not hold across the entire state space, preventing safe deployment to real tasks where such correlations might be broken. In this work, we examine whether an agent relies on spurious correlations in critical states, and propose an alternative subset of features on which it should base its decisions instead, to make it less susceptible to causal confusion. Our goal is to increase transparency of DRL agents by exposing the influence of learned spurious correlations on its decisions, and offering advice to developers about feature selection in different parts of state space, to avoid causal confusion. We propose ReCCoVER, an algorithm which detects causal confusion in agent's reasoning before deployment, by executing its policy in alternative environments where certain correlations between features do not hold. We demonstrate our approach in taxi and grid world environments, where ReCCoVER detects states in which an agent relies on spurious correlations and offers a set of features that should be considered instead.
Temporal Abstractions-Augmented Temporally Contrastive Learning: An Alternative to the Laplacian in RL
Venue: arXiv cs.LG 2022
Year: 2022

In reinforcement learning, the graph Laplacian has proved to be a valuable tool in the task-agnostic setting, with applications ranging from skill discovery to reward shaping. Recently, learning the Laplacian representation has been framed as the optimization of a temporally-contrastive objective to overcome its computational limitations in large (or continuous) state spaces. However, this approach requires uniform access to all states in the state space, overlooking the exploration problem that emerges during the representation learning process. In this work, we propose an alternative method that is able to recover, in a non-uniform-prior setting, the expressiveness and the desired properties of the Laplacian representation. We do so by combining the representation learning with a skill-based covering policy, which provides a better training distribution to extend and refine the representation. We also show that a simple augmentation of the representation objective with the learned temporal abstractions improves dynamics-awareness and helps exploration. We find that our method succeeds as an alternative to the Laplacian in the non-uniform setting and scales to challenging continuous control environments. Finally, even if our method is not optimized for skill discovery, the learned skills can successfully solve difficult continuous navigation tasks with sparse rewards, where standard skill discovery approaches are no so effective.
HyperShot: Few-Shot Learning by Kernel HyperNetworks
Venue: arXiv cs.LG 2022
Year: 2022

Few-shot models aim at making predictions using a minimal number of labeled examples from a given task. The main challenge in this area is the one-shot setting where only one element represents each class. We propose HyperShot - the fusion of kernels and hypernetwork paradigm. Compared to reference approaches that apply a gradient-based adjustment of the parameters, our model aims to switch the classification module parameters depending on the task's embedding. In practice, we utilize a hypernetwork, which takes the aggregated information from support data and returns the classifier's parameters handcrafted for the considered problem. Moreover, we introduce the kernel-based representation of the support examples delivered to hypernetwork to create the parameters of the classification module. Consequently, we rely on relations between embeddings of the support examples instead of direct feature values provided by the backbone models. Thanks to this approach, our model can adapt to highly different tasks.
A Hybrid Lagrangian-Eulerian Model for the Structural Analysis of Multifield Datasets
Venue: arXiv cs.GR 2022
Year: 2022

Multifields datasets are common in a large number of research and engineering applications of computational science. The effective visualization of the corresponding datasets can facilitate their analysis by elucidating the complex and dynamic interactions that exist between the attributes that describe the physics of the phenomenon. We present in this paper a new hybrid Lagrangian-Eulerian model that extends existing Lagrangian visualization techniques to the analysis of multifields problems. In particular, our approach factors in the entire data space to reveal the structure of multifield datasets, thereby combining both Eulerian and Lagrangian perspectives. We evaluate our technique in the context of several fluid dynamics applications. Our results indicate that our proposed approach is able to characterize important structural features that are missed by existing methods.
VLSP 2021 - ViMRC Challenge: Vietnamese Machine Reading Comprehension
Venue: arXiv cs.CL 2022
Year: 2022

One of the emerging research trends in natural language understanding is machine reading comprehension (MRC) which is the task to find answers to human questions based on textual data. Existing Vietnamese datasets for MRC research concentrate solely on answerable questions. However, in reality, questions can be unanswerable for which the correct answer is not stated in the given textual data. To address the weakness, we provide the research community with a benchmark dataset named UIT-ViQuAD 2.0 for evaluating the MRC task and question answering systems for the Vietnamese language. We use UIT-ViQuAD 2.0 as a benchmark dataset for the challenge on Vietnamese MRC at the Eighth Workshop on Vietnamese Language and Speech Processing (VLSP 2021). This task attracted 77 participant teams from 34 universities and other organizations. In this article, we present details of the organization of the challenge, an overview of the methods employed by shared-task participants, and the results. The highest performances are 77.24% in F1-score and 67.43% in Exact Match on the private test set. The Vietnamese MRC systems proposed by the top 3 teams use XLM-RoBERTa, a powerful pre-trained language model based on the transformer architecture. The UIT-ViQuAD 2.0 dataset motivates researchers to further explore the Vietnamese machine reading comprehension task and related tasks such as question answering, question generation, and natural language inference.
Causal inference in time series in terms of Rényi transfer entropy
Venue: arXiv cs.IT 2022
Year: 2022

Uncovering causal interdependencies from observational data is one of the great challenges of nonlinear time series analysis. In this paper, we discuss this topic with the help of information-theoretic concept known as Rényi information measure. In particular, we tackle the directional information flow between bivariate time series in terms of Rényi transfer entropy. We show that by choosing Rényi $α$ parameter appropriately we can control information that is transferred only between selected parts of underlying distributions. This, in turn, provides particularly potent tool for quantifying causal interdependencies in time series, where the knowledge of "black swan" events such as spikes or sudden jumps are of a key importance. In this connection, we first prove that for Gaussian variables, Granger causality and Rényi transfer entropy are entirely equivalent. Moreover, we also partially extend this results to heavy-tailed $α$-Gaussian variables. These results allow to establish connection between autoregressive and Rényi entropy based information-theoretic approaches to data-driven causal inference. To aid our intuition we employ Leonenko et al. entropy estimator and analyze Rényi information flow between bivariate time series generated from two unidirectionally coupled Rössler systems. Notably, we find that Rényi transfer entropy not only allowed us to detect a threshold of synchronization but it also provided a non-trivial insight into the structure of a transient regime that exists between region of chaotic correlations and synchronization threshold. In addition, from Rényi transfer entropy we could reliably infer the direction of coupling - and hence causality, only for coupling strengths smaller that the onset value of transient regime, i.e. when two Rössler systems were coupled, but have not yet entered a synchronization.
A Primer on Maximum Causal Entropy Inverse Reinforcement Learning
Venue: arXiv cs.LG 2022
Year: 2022

Inverse Reinforcement Learning (IRL) algorithms infer a reward function that explains demonstrations provided by an expert acting in the environment. Maximum Causal Entropy (MCE) IRL is currently the most popular formulation of IRL, with numerous extensions. In this tutorial, we present a compressed derivation of MCE IRL and the key results from contemporary implementations of MCE IRL algorithms. We hope this will serve both as an introductory resource for those new to the field, and as a concise reference for those already familiar with these topics.
Arithmetic crosscorrelation of pseudorandom binary sequences of coprime periods
Venue: arXiv cs.CR 2022
Year: 2022

The (classical) crosscorrelation is an important measure of pseudorandomness of two binary sequences for applications in communications. The arithmetic crosscorrelation is another figure of merit introduced by Goresky and Klapper generalizing Mandelbaum's arithmetic autocorrelation. First we observe that the arithmetic crosscorrelation is constant for two binary sequences of coprime periods which complements the analogous result for the classical crosscorrelation. Then we prove upper bounds for the constant arithmetic crosscorrelation of two Legendre sequences of different periods and of two binary $m$-sequences of coprime periods, respectively.
Demo of the Linguistic Field Data Management and Analysis System -- LiFE
Venue: arXiv cs.CL 2022
Year: 2022

In the proposed demo, we will present a new software - Linguistic Field Data Management and Analysis System - LiFE (https://github.com/kmi-linguistics/life) - an open-source, web-based linguistic data management and analysis application that allows for systematic storage, management, sharing and usage of linguistic data collected from the field. The application allows users to store lexical items, sentences, paragraphs, audio-visual content with rich glossing / annotation; generate interactive and print dictionaries; and also train and use natural language processing tools and models for various purposes using this data. Since its a web-based application, it also allows for seamless collaboration among multiple persons and sharing the data, models, etc with each other. The system uses the Python-based Flask framework and MongoDB in the backend and HTML, CSS and Javascript at the frontend. The interface allows creation of multiple projects that could be shared with the other users. At the backend, the application stores the data in RDF format so as to allow its release as Linked Data over the web using semantic web technologies - as of now it makes use of the OntoLex-Lemon for storing the lexical data and Ligt for storing the interlinear glossed text and then internally linking it to the other linked lexicons and databases such as DBpedia and WordNet. Furthermore it provides support for training the NLP systems using scikit-learn and HuggingFace Transformers libraries as well as make use of any model trained using these libraries - while the user interface itself provides limited options for tuning the system, an externally-trained model could be easily incorporated within the application; similarly the dataset itself could be easily exported into a standard machine-readable format like JSON or CSV that could be consumed by other programs and pipelines.
Manipulating UAV Imagery for Satellite Model Training, Calibration and Testing
Venue: arXiv cs.CV 2022
Year: 2022

Modern livestock farming is increasingly data driven and frequently relies on efficient remote sensing to gather data over wide areas. High resolution satellite imagery is one such data source, which is becoming more accessible for farmers as coverage increases and cost falls. Such images can be used to detect and track animals, monitor pasture changes, and understand land use. Many of the data driven models being applied to these tasks require ground truthing at resolutions higher than satellites can provide. Simultaneously, there is a lack of available aerial imagery focused on farmland changes that occur over days or weeks, such as herd movement. With this goal in mind, we present a new multi-temporal dataset of high resolution UAV imagery which is artificially degraded to match satellite data quality. An empirical blurring metric is used to calibrate the degradation process against actual satellite imagery of the area. UAV surveys were flown repeatedly over several weeks, for specific farm locations. This 5cm/pixel data is sufficiently high resolution to accurately ground truth cattle locations, and other factors such as grass cover. From 33 wide area UAV surveys, 1869 patches were extracted and artificially degraded using an accurate satellite optical model to simulate satellite data. Geographic patches from multiple time periods are aligned and presented as sets, providing a multi-temporal dataset that can be used for detecting changes on farms. The geo-referenced images and 27,853 manually annotated cattle labels are made publicly available.