OpenRaiser/Intern-Atlas huggingface dataset:
MashUp: Scaling TCAM-based IP Lookup to Larger Databases by Tiling Trees
Venue: arXiv cs.NI 2022
Year: 2022

Ternary content addressable memories (TCAMs) are commonly used to implement IP lookup, but suffer from high power and area costs. Thus TCAM included in modern chips is limited and can support moderately large datasets in data centers and enterprises, but fails to scale to backbone WAN databases of millions of prefixes. IPv6 deployment also makes it harder to deploy TCAMs because of the larger prefixes used in the 128-bit address space. While the combination of algorithmic techniques and TCAM has been proposed before for reducing power consumption or update costs(e.g., CoolCAM [32] and TreeCAM [28]), we focus on reducing TCAM bits using a scheme we call MashUp that can easily be implemented in modern reconfigurable pipeline chips such as Tofino-3. MashUp uses a new technique, tiling trees, which takes into account TCAM grain (tile) sizes. When applied to a publicly available IPv6 dataset using Tofino-3 TCAM grain sizes (44 by 512), there was a 2X reduction in TCAM required. Further, if we mix TCAM and SRAM using a new technique we call node hybridization, MashUp decreases TCAM bits by 4.5X for IPv6, and by 7.5X for IPv4, allowing wide area databases of 900,000 prefixes to be supported by Tofino-3 and similar chips
A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection Algorithms
Venue: arXiv cs.LG 2022
Year: 2022

Anomaly detection has many applications ranging from bank-fraud detection and cyber-threat detection to equipment maintenance and health monitoring. However, choosing a suitable algorithm for a given application remains a challenging design decision, often informed by the literature on anomaly detection algorithms. We extensively reviewed twelve of the most popular unsupervised anomaly detection methods. We observed that, so far, they have been compared using inconsistent protocols - the choice of the class of interest or the positive class, the split of training and test data, and the choice of hyperparameters - leading to ambiguous evaluations. This observation led us to define a coherent evaluation protocol which we then used to produce an updated and more precise picture of the relative performance of the twelve methods on five widely used tabular datasets. While our evaluation cannot pinpoint a method that outperforms all the others on all datasets, it identifies those that stand out and revise misconceived knowledge about their relative performances.
Relevance-guided Unsupervised Discovery of Abilities with Quality-Diversity Algorithms
Venue: arXiv cs.NE 2022
Year: 2022

Quality-Diversity algorithms provide efficient mechanisms to generate large collections of diverse and high-performing solutions, which have shown to be instrumental for solving downstream tasks. However, most of those algorithms rely on a behavioural descriptor to characterise the diversity that is hand-coded, hence requiring prior knowledge about the considered tasks. In this work, we introduce Relevance-guided Unsupervised Discovery of Abilities; a Quality-Diversity algorithm that autonomously finds a behavioural characterisation tailored to the task at hand. In particular, our method introduces a custom diversity metric that leads to higher densities of solutions near the areas of interest in the learnt behavioural descriptor space. We evaluate our approach on a simulated robotic environment, where the robot has to autonomously discover its abilities based on its full sensory data. We evaluated the algorithms on three tasks: navigation to random targets, moving forward with a high velocity, and performing half-rolls. The experimental results show that our method manages to discover collections of solutions that are not only diverse, but also well-adapted to the considered downstream task.
Self-Supervised Learning to Guide Scientifically Relevant Categorization of Martian Terrain Images
Venue: arXiv cs.CV 2022
Year: 2022

Automatic terrain recognition in Mars rover images is an important problem not just for navigation, but for scientists interested in studying rock types, and by extension, conditions of the ancient Martian paleoclimate and habitability. Existing approaches to label Martian terrain either involve the use of non-expert annotators producing taxonomies of limited granularity (e.g. soil, sand, bedrock, float rock, etc.), or rely on generic class discovery approaches that tend to produce perceptual classes such as rover parts and landscape, which are irrelevant to geologic analysis. Expert-labeled datasets containing granular geological/geomorphological terrain categories are rare or inaccessible to public, and sometimes require the extraction of relevant categorical information from complex annotations. In order to facilitate the creation of a dataset with detailed terrain categories, we present a self-supervised method that can cluster sedimentary textures in images captured from the Mast camera onboard the Curiosity rover (Mars Science Laboratory). We then present a qualitative analysis of these clusters and describe their geologic significance via the creation of a set of granular terrain categories. The precision and geologic validation of these automatically discovered clusters suggest that our methods are promising for the rapid classification of important geologic features and will therefore facilitate our long-term goal of producing a large, granular, and publicly available dataset for Mars terrain recognition.
Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local Information
Venue: arXiv cs.CV 2022
Year: 2022

Cross-modal remote sensing text-image retrieval (RSCTIR) has recently become an urgent research hotspot due to its ability of enabling fast and flexible information extraction on remote sensing (RS) images. However, current RSCTIR methods mainly focus on global features of RS images, which leads to the neglect of local features that reflect target relationships and saliency. In this article, we first propose a novel RSCTIR framework based on global and local information (GaLR), and design a multi-level information dynamic fusion (MIDF) module to efficaciously integrate features of different levels. MIDF leverages local information to correct global information, utilizes global information to supplement local information, and uses the dynamic addition of the two to generate prominent visual representation. To alleviate the pressure of the redundant targets on the graph convolution network (GCN) and to improve the model s attention on salient instances during modeling local features, the de-noised representation matrix and the enhanced adjacency matrix (DREA) are devised to assist GCN in producing superior local representations. DREA not only filters out redundant features with high similarity, but also obtains more powerful local features by enhancing the features of prominent objects. Finally, to make full use of the information in the similarity matrix during inference, we come up with a plug-and-play multivariate rerank (MR) algorithm. The algorithm utilizes the k nearest neighbors of the retrieval results to perform a reverse search, and improves the performance by combining multiple components of bidirectional retrieval. Extensive experiments on public datasets strongly demonstrate the state-of-the-art performance of GaLR methods on the RSCTIR task. The code of GaLR method, MR algorithm, and corresponding files have been made available at https://github.com/xiaoyuan1996/GaLR .
Exploring a Fine-Grained Multiscale Method for Cross-Modal Remote Sensing Image Retrieval
Venue: arXiv cs.CV 2022
Year: 2022

Remote sensing (RS) cross-modal text-image retrieval has attracted extensive attention for its advantages of flexible input and efficient query. However, traditional methods ignore the characteristics of multi-scale and redundant targets in RS image, leading to the degradation of retrieval accuracy. To cope with the problem of multi-scale scarcity and target redundancy in RS multimodal retrieval task, we come up with a novel asymmetric multimodal feature matching network (AMFMN). Our model adapts to multi-scale feature inputs, favors multi-source retrieval methods, and can dynamically filter redundant features. AMFMN employs the multi-scale visual self-attention (MVSA) module to extract the salient features of RS image and utilizes visual features to guide the text representation. Furthermore, to alleviate the positive samples ambiguity caused by the strong intraclass similarity in RS image, we propose a triplet loss function with dynamic variable margin based on prior similarity of sample pairs. Finally, unlike the traditional RS image-text dataset with coarse text and higher intraclass similarity, we construct a fine-grained and more challenging Remote sensing Image-Text Match dataset (RSITMD), which supports RS image retrieval through keywords and sentence separately and jointly. Experiments on four RS text-image datasets demonstrate that the proposed model can achieve state-of-the-art performance in cross-modal RS text-image retrieval task.
STFT-Domain Neural Speech Enhancement with Very Low Algorithmic Latency
Venue: arXiv cs.SD 2022
Year: 2022

Deep learning based speech enhancement in the short-time Fourier transform (STFT) domain typically uses a large window length such as 32 ms. A larger window can lead to higher frequency resolution and potentially better enhancement. This however incurs an algorithmic latency of 32 ms in an online setup, because the overlap-add algorithm used in the inverse STFT (iSTFT) is also performed using the same window size. To reduce this inherent latency, we adapt a conventional dual-window-size approach, where a regular input window size is used for STFT but a shorter output window is used for overlap-add, for STFT-domain deep learning based frame-online speech enhancement. Based on this STFT-iSTFT configuration, we employ complex spectral mapping for frame-online enhancement, where a deep neural network (DNN) is trained to predict the real and imaginary (RI) components of target speech from the mixture RI components. In addition, we use the DNN-predicted RI components to conduct frame-online beamforming, the results of which are used as extra features for a second DNN to perform frame-online post-filtering. The frequency-domain beamformer can be easily integrated with our DNNs and is designed to not incur any algorithmic latency. Additionally, we propose a future-frame prediction technique to further reduce the algorithmic latency. Evaluation on noisy-reverberant speech enhancement shows the effectiveness of the proposed algorithms. Compared with Conv-TasNet, our STFT-domain system can achieve better enhancement performance for a comparable amount of computation, or comparable performance with less computation, maintaining strong performance at an algorithmic latency as low as 2 ms.
SinTra: Learning an inspiration model from a single multi-track music segment
Venue: arXiv cs.SD 2022
Year: 2022

In this paper, we propose SinTra, an auto-regressive sequential generative model that can learn from a single multi-track music segment, to generate coherent, aesthetic, and variable polyphonic music of multi-instruments with an arbitrary length of bar. For this task, to ensure the relevance of generated samples and training music, we present a novel pitch-group representation. SinTra, consisting of a pyramid of Transformer-XL with a multi-scale training strategy, can learn both the musical structure and the relative positional relationship between notes of the single training music segment. Additionally, for maintaining the inter-track correlation, we use the convolution operation to process multi-track music, and when decoding, the tracks are independent to each other to prevent interference. We evaluate SinTra with both subjective study and objective metrics. The comparison results show that our framework can learn information from a single music segment more sufficiently than Music Transformer. Also the comparison between SinTra and its variant, i.e., the single-stage SinTra with the first stage only, shows that the pyramid structure can effectively suppress overly-fragmented notes.
Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR)
Venue: arXiv cs.CL 2022
Year: 2022

Text mining and information extraction for the medical domain has focused on scientific text generated by researchers. However, their direct access to individual patient experiences or patient-doctor interactions can be limited. Information provided on social media, e.g., by patients and their relatives, complements the knowledge in scientific text. It reflects the patient's journey and their subjective perspective on the process of developing symptoms, being diagnosed and offered a treatment, being cured or learning to live with a medical condition. The value of this type of data is therefore twofold: Firstly, it offers direct access to people's perspectives. Secondly, it might cover information that is not available elsewhere, including self-treatment or self-diagnoses. Named entity recognition and relation extraction are methods to structure information that is available in unstructured text. However, existing medical social media corpora focused on a comparably small set of entities and relations and particular domains, rather than putting the patient into the center of analyses. With this paper we contribute a corpus with a rich set of annotation layers following the motivation to uncover and model patients' journeys and experiences in more detail. We label 14 entity classes (incl. environmental factors, diagnostics, biochemical processes, patients' quality-of-life descriptions, pathogens, medical conditions, and treatments) and 20 relation classes (e.g., prevents, influences, interactions, causes) most of which have not been considered before for social media data. The publicly available dataset consists of 2,100 tweets with approx. 6,000 entity and 3,000 relation annotations. In a corpus analysis we find that over 80 % of documents contain relevant entities. Over 50 % of tweets express relations which we consider essential for uncovering patients' narratives about their journeys.
Domain Invariant Model with Graph Convolutional Network for Mammogram Classification
Venue: arXiv cs.CV 2022
Year: 2022

Due to its safety-critical property, the image-based diagnosis is desired to achieve robustness on out-of-distribution (OOD) samples. A natural way towards this goal is capturing only clinically disease-related features, which is composed of macroscopic attributes (e.g., margins, shapes) and microscopic image-based features (e.g., textures) of lesion-related areas. However, such disease-related features are often interweaved with data-dependent (but disease irrelevant) biases during learning, disabling the OOD generalization. To resolve this problem, we propose a novel framework, namely Domain Invariant Model with Graph Convolutional Network (DIM-GCN), which only exploits invariant disease-related features from multiple domains. Specifically, we first propose a Bayesian network, which explicitly decomposes the latent variables into disease-related and other disease-irrelevant parts that are provable to be disentangled from each other. Guided by this, we reformulate the objective function based on Variational Auto-Encoder, in which the encoder in each domain has two branches: the domain-independent and -dependent ones, which respectively encode disease-related and -irrelevant features. To better capture the macroscopic features, we leverage the observed clinical attributes as a goal for reconstruction, via Graph Convolutional Network (GCN). Finally, we only implement the disease-related features for prediction. The effectiveness and utility of our method are demonstrated by the superior OOD generalization performance over others on mammogram benign/malignant diagnosis.
TEAM-Atreides at SemEval-2022 Task 11: On leveraging data augmentation and ensemble to recognize complex Named Entities in Bangla
Venue: arXiv cs.CL 2022
Year: 2022

Many areas, such as the biological and healthcare domain, artistic works, and organization names, have nested, overlapping, discontinuous entity mentions that may even be syntactically or semantically ambiguous in practice. Traditional sequence tagging algorithms are unable to recognize these complex mentions because they may violate the assumptions upon which sequence tagging schemes are founded. In this paper, we describe our contribution to SemEval 2022 Task 11 on identifying such complex Named Entities. We have leveraged the ensemble of multiple ELECTRA-based models that were exclusively pretrained on the Bangla language with the performance of ELECTRA-based models pretrained on English to achieve competitive performance on the Track-11. Besides providing a system description, we will also present the outcomes of our experiments on architectural decisions, dataset augmentations, and post-competition findings.
Agile data management in NAV: A Case Study
Venue: arXiv cs.SE 2022
Year: 2022

To satisfy the need for analytical data in the development of digital services, many organizations use data warehouse, and, more recently, data lake architectures. These architectures have traditionally been accompanied by centralized organizational models, where a single team or department has been responsible for gathering, transforming, and giving access to analytical data. However, such centralized models presuppose stability and are incompatible with agile software development where applications and databases are continuously updated. To achieve more agile forms of data management, some organizations have therefore begun to experiment with distributed data management models such as 'data meshes'. Research on this topic is however limited. In this paper, we report findings from a case study of a public sector organization in Norway that has begun the transition from centralized to distributed data management, outlining both the benefits and challenges of a distributed approach.
Revisiting initial sets in abstract argumentation
Venue: arXiv cs.AI 2022
Year: 2022

We revisit the notion of initial sets by Xu and Cayrol, i.e., non-empty minimal admissible sets in abstract argumentation frameworks. Initial sets are a simple concept for analysing conflicts in an abstract argumentation framework and to explain why certain arguments can be accepted. We contribute with new insights on the structure of initial sets and devise a simple non-deterministic construction principle for any admissible set, based on iterative selection of initial sets of the original framework and its induced reducts. In particular, we characterise many existing admissibility-based semantics via this construction principle, thus providing a constructive explanation on the structure of extensions. We also investigate certain problems related to initial sets with respect to their computational complexity.
A data filling methodology for time series based on CNN and (Bi)LSTM neural networks
Venue: arXiv cs.LG 2022
Year: 2022

In the process of collecting data from sensors, several circumstances can affect their continuity and validity, resulting in alterations of the data or loss of information. Although classical methods of statistics, such as interpolation-like techniques, can be used to approximate the missing data in a time series, the recent developments in Deep Learning (DL) have given impetus to innovative and much more accurate forecasting techniques. In the present paper, we develop two DL models aimed at filling data gaps, for the specific case of internal temperature time series obtained from monitored apartments located in Bolzano, Italy. The DL models developed in the present work are based on the combination of Convolutional Neural Networks (CNNs), Long Short-Term Memory Neural Networks (LSTMs), and Bidirectional LSTMs (BiLSTMs). Two key features of our models are the use of both pre- and post-gap data, and the exploitation of a correlated time series (the external temperature) in order to predict the target one (the internal temperature). Our approach manages to capture the fluctuating nature of the data and shows good accuracy in reconstructing the target time series. In addition, our models significantly improve the already good results from another DL architecture that is used as a baseline for the present work.
SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding
Venue: arXiv cs.CL 2022
Year: 2022

This paper presents the shared task on Multilingual Idiomaticity Detection and Sentence Embedding, which consists of two subtasks: (a) a binary classification task aimed at identifying whether a sentence contains an idiomatic expression, and (b) a task based on semantic text similarity which requires the model to adequately represent potentially idiomatic expressions in context. Each subtask includes different settings regarding the amount of training data. Besides the task description, this paper introduces the datasets in English, Portuguese, and Galician and their annotation procedure, the evaluation metrics, and a summary of the participant systems and their results. The task had close to 100 registered participants organised into twenty five teams making over 650 and 150 submissions in the practice and evaluation phases respectively.
Working memory inspired hierarchical video decomposition with transformative representations
Venue: arXiv cs.CV 2022
Year: 2022

Video decomposition is very important to extract moving foreground objects from complex backgrounds in computer vision, machine learning, and medical imaging, e.g., extracting moving contrast-filled vessels from the complex and noisy backgrounds of X-ray coronary angiography (XCA). However, the challenges caused by dynamic backgrounds, overlapping heterogeneous environments and complex noises still exist in video decomposition. To solve these problems, this study is the first to introduce a flexible visual working memory model in video decomposition tasks to provide interpretable and high-performance hierarchical deep architecture, integrating the transformative representations between sensory and control layers from the perspective of visual and cognitive neuroscience. Specifically, robust PCA unrolling networks acting as a structure-regularized sensor layer decompose XCA into sparse/low-rank structured representations to separate moving contrast-filled vessels from noisy and complex backgrounds. Then, patch recurrent convolutional LSTM networks with a backprojection module embody unstructured random representations of the control layer in working memory, recurrently projecting spatiotemporally decomposed nonlocal patches into orthogonal subspaces for heterogeneous vessel retrieval and interference suppression. This video decomposition deep architecture effectively restores the heterogeneous profiles of intensity and the geometries of moving objects against the complex background interferences. Experiments show that the proposed method significantly outperforms state-of-the-art methods in accurate moving contrast-filled vessel extraction with excellent flexibility and computational efficiency.