OpenRaiser/Intern-Atlas huggingface dataset:
Kernelizing Temporal Exploration Problems
Venue: arXiv cs.CC 2023
Year: 2023

We study the kernelization of exploration problems on temporal graphs. A temporal graph consists of a finite sequence of snapshot graphs $\mathcal{G}=(G_1, G_2, \dots, G_L)$ that share a common vertex set but might have different edge sets. The non-strict temporal exploration problem (NS-TEXP for short) introduced by Erlebach and Spooner, asks if a single agent can visit all vertices of a given temporal graph where the edges traversed by the agent are present in non-strict monotonous time steps, i.e., the agent can move along the edges of a snapshot graph with infinite speed. The exploration must at the latest be completed in the last snapshot graph. The optimization variant of this problem is the $k$-arb NS-TEXP problem, where the agent's task is to visit at least $k$ vertices of the temporal graph. We show that under standard computational complexity assumptions, neither of the problems NS-TEXP nor $k$-arb NS-TEXP allow for polynomial kernels in the standard parameters: number of vertices $n$, lifetime $L$, number of vertices to visit $k$, and maximal number of connected components per time step $γ$; as well as in the combined parameters $L+k$, $L + γ$, and $k+γ$. On the way to establishing these lower bounds, we answer a couple of questions left open by Erlebach and Spooner. We also initiate the study of structural kernelization by identifying a new parameter of a temporal graph $p(\mathcal{G}) = \sum_{i=1}^{L} (|E(G_i)|) - |V(G)| +1$. Informally, this parameter measures how dynamic the temporal graph is. Our main algorithmic result is the construction of a polynomial (in $p(\mathcal{G})$) kernel for the more general Weighted $k$-arb NS-TEXP problem, where weights are assigned to the vertices and the task is to find a temporal walk of weight at least $k$.
A modest proposal: explicit support for foundational pluralism
Venue: arXiv cs.LO 2023
Year: 2023

Whilst mathematicians assume classical reasoning principles by default they often context switch when working, restricting themselves to various forms of subclassical reasoning. This pattern is especially common amongst logicians and set theorists, but workaday mathematicians also commonly do this too, witnessed by narrative notes accompanying a proof -- "the following proof is constructive", or "the following proof does not use choice", for example. Yet, current proof assistants provide poor support for capturing these narrative notes formally, an observation that is especially true of systems based on Gordon's HOL, a classical higher-order logic. Consequently, HOL and its many implementations seem ironically more committed to classical reasoning than mainstream mathematicians are themselves, limiting the mathematical content that one may easily formalise. To facilitate these context switches, we propose that mathematicians mentally employ a simple tainting system when temporarily working subclassically -- an idea not currently explored in proof assistants. We introduce a series of modest but far-reaching changes to HOL, extending the standard two-place Natural Deduction relation to incorporate a taint-label, taken from a particular lattice, and which describes or limits the "amount" of classical reasoning used within a proof. Taint can be seen either as a simple typing system on HOL proofs, or as a form of static analysis on proof trees, and partitions our logic into various fragments of differing expressivity, sitting side-by-side. Results may pass from a "less classical" fragment into a "more classical" fragment of the logic without modification, but not vice versa, with the flow of results between worlds controlled by an inference rule akin to a subtyping or subsumption rule.
Paparazzi: A Deep Dive into the Capabilities of Language and Vision Models for Grounding Viewpoint Descriptions
Venue: arXiv cs.CV 2023
Year: 2023

Existing language and vision models achieve impressive performance in image-text understanding. Yet, it is an open question to what extent they can be used for language understanding in 3D environments and whether they implicitly acquire 3D object knowledge, e.g. about different views of an object. In this paper, we investigate whether a state-of-the-art language and vision model, CLIP, is able to ground perspective descriptions of a 3D object and identify canonical views of common objects based on text queries. We present an evaluation framework that uses a circling camera around a 3D object to generate images from different viewpoints and evaluate them in terms of their similarity to natural language descriptions. We find that a pre-trained CLIP model performs poorly on most canonical views and that fine-tuning using hard negative sampling and random contrasting yields good results even under conditions with little available training data.
Dynamic Named Entity Recognition
Venue: arXiv cs.CL 2023
Year: 2023

Named Entity Recognition (NER) is a challenging and widely studied task that involves detecting and typing entities in text. So far,NER still approaches entity typing as a task of classification into universal classes (e.g. date, person, or location). Recent advances innatural language processing focus on architectures of increasing complexity that may lead to overfitting and memorization, and thus, underuse of context. Our work targets situations where the type of entities depends on the context and cannot be solved solely by memorization. We hence introduce a new task: Dynamic Named Entity Recognition (DNER), providing a framework to better evaluate the ability of algorithms to extract entities by exploiting the context. The DNER benchmark is based on two datasets, DNER-RotoWire and DNER-IMDb. We evaluate baseline models and present experiments reflecting issues and research axes related to this novel task.
A3Test: Assertion-Augmented Automated Test Case Generation
Venue: arXiv cs.SE 2023
Year: 2023

Test case generation is an important activity, yet a time-consuming and laborious task. Recently, AthenaTest -- a deep learning approach for generating unit test cases -- is proposed. However, AthenaTest can generate less than one-fifth of the test cases correctly, due to a lack of assertion knowledge and test signature verification. In this paper, we propose A3Test, a DL-based test case generation approach that is augmented by assertion knowledge with a mechanism to verify naming consistency and test signatures. A3Test leverages the domain adaptation principles where the goal is to adapt the existing knowledge from an assertion generation task to the test case generation task. We also introduce a verification approach to verify naming consistency and test signatures. Through an evaluation of 5,278 focal methods from the Defects4j dataset, we find that our A3Test (1) achieves 147% more correct test cases and 15% more method coverage, with a lower number of generated test cases than AthenaTest; (2) still outperforms the existing pre-trained models for the test case generation task; (3) contributes substantially to performance improvement via our own proposed assertion pre-training and the verification components; (4) is 97.2% much faster while being more accurate than AthenaTest.
On Interpretable Approaches to Cluster, Classify and Represent Multi-Subspace Data via Minimum Lossy Coding Length based on Rate-Distortion Theory
Venue: arXiv cs.CV 2023
Year: 2023

To cluster, classify and represent are three fundamental objectives of learning from high-dimensional data with intrinsic structure. To this end, this paper introduces three interpretable approaches, i.e., segmentation (clustering) via the Minimum Lossy Coding Length criterion, classification via the Minimum Incremental Coding Length criterion and representation via the Maximal Coding Rate Reduction criterion. These are derived based on the lossy data coding and compression framework from the principle of rate distortion in information theory. These algorithms are particularly suitable for dealing with finite-sample data (allowed to be sparse or almost degenerate) of mixed Gaussian distributions or subspaces. The theoretical value and attractive features of these methods are summarized by comparison with other learning methods or evaluation criteria. This summary note aims to provide a theoretical guide to researchers (also engineers) interested in understanding 'white-box' machine (deep) learning methods.
Democratizing Making: Scaffolding Participation Using e-Waste to Engage Under-resourced Communities in Technology Design
Venue: arXiv cs.HC 2023
Year: 2023

Maker culture and DIY practices are central to democratizing the design of technology; enabling non-designers (future end-users) to actively participate in the design process. However, little is known about how individuals from under-resourced communities and low socioeconomic status (SES) backgrounds, can practically leverage maker practices to design technology, creating value for themselves or their communities. To investigate this, we collaborated with an e-waste recycling centre, involving 24 participants (staff and low-SES volunteers) in two participatory maker workshop activities. Participants were provided with a generative e-waste toolkit, through which they repurposed e-waste materials and developed novel technology prototypes that created value from their perspectives and agendas. Our findings unpack three factors that influenced their making: balancing personal and community needs; incorporating convenience and productivity; and re-thinking sustainability and connection; and discuss strategies for scaffolding participation and engagement of under-resourced communities in making using an e-waste generative toolkit to democratize technology design.
Instance-incremental Scene Graph Generation from Real-world Point Clouds via Normalizing Flows
Venue: arXiv cs.CV 2023
Year: 2023

This work introduces a new task of instance-incremental scene graph generation: Given a scene of the point cloud, representing it as a graph and automatically increasing novel instances. A graph denoting the object layout of the scene is finally generated. It is an important task since it helps to guide the insertion of novel 3D objects into a real-world scene in vision-based applications like augmented reality. It is also challenging because the complexity of the real-world point cloud brings difficulties in learning object layout experiences from the observation data (non-empty rooms with labeled semantics). We model this task as a conditional generation problem and propose a 3D autoregressive framework based on normalizing flows (3D-ANF) to address it. First, we represent the point cloud as a graph by extracting the label semantics and contextual relationships. Next, a model based on normalizing flows is introduced to map the conditional generation of graphic elements into the Gaussian process. The mapping is invertible. Thus, the real-world experiences represented in the observation data can be modeled in the training phase, and novel instances can be autoregressively generated based on the Gaussian process in the testing phase. To evaluate the performance of our method sufficiently, we implement this new task on the indoor benchmark dataset 3DSSG-O27R16 and our newly proposed graphical dataset of outdoor scenes GPL3D. Experiments show that our method generates reliable novel graphs from the real-world point cloud and achieves state-of-the-art performance on the datasets.
A Deforestation of Reducts: Refocusing
Venue: arXiv cs.LO 2023
Year: 2023

In a small-step semantics with a deterministic reduction strategy, refocusing is a transformation that connects a reduction-based normalization function (i.e., a normalization function that enumerates the successive terms in a reduction sequence -- the successive reducts) and a reduction-free normalization function (i.e., a normalization function that does not construct any reduct because all the reducts are deforested). This transformation was introduced by Nielsen and the author in the early 2000's with an informal correctness proof. Since then, it has been used in a variety of settings, starting with Biernacka and the author's syntactic correspondence between calculi and abstract machines, and several formal proofs of it have been put forward. This article presents a simple, if overdue, formal proof of refocusing that uses the Coq Proof Assistant and is aligned with the simplicity of the original idea.
Classification with Trust: A Supervised Approach based on Sequential Ellipsoidal Partitioning
Venue: arXiv cs.LG 2023
Year: 2023

Standard metrics of performance of classifiers, such as accuracy and sensitivity, do not reveal the trust or confidence in the predicted labels of data. While other metrics such as the computed probability of a label or the signed distance from a hyperplane can act as a trust measure, these are subjected to heuristic thresholds. This paper presents a convex optimization-based supervised classifier that sequentially partitions a dataset into several ellipsoids, where each ellipsoid contains nearly all points of the same label. By stating classification rules based on this partitioning, Bayes' formula is then applied to calculate a trust score to a label assigned to a test datapoint determined from these rules. The proposed Sequential Ellipsoidal Partitioning Classifier (SEP-C) exposes dataset irregularities, such as degree of overlap, without requiring a separate exploratory data analysis. The rules of classification, which are free of hyperparameters, are also not affected by class-imbalance, the underlying data distribution, or number of features. SEP-C does not require the use of non-linear kernels when the dataset is not linearly separable. The performance, and comparison with other methods, of SEP-C is demonstrated on the XOR-problem, circle dataset, and other open-source datasets.
Creating Disasters: Recession Forecasting with GAN-Generated Synthetic Time Series Data
Venue: arXiv cs.LG 2023
Year: 2023

A common problem when forecasting rare events, such as recessions, is limited data availability. Recent advancements in deep learning and generative adversarial networks (GANs) make it possible to produce high-fidelity synthetic data in large quantities. This paper uses a model called DoppelGANger, a GAN tailored to producing synthetic time series data, to generate synthetic Treasury yield time series and associated recession indicators. It is then shown that short-range forecasting performance for Treasury yields is improved for models trained on synthetic data relative to models trained only on real data. Finally, synthetic recession conditions are produced and used to train classification models to predict the probability of a future recession. It is shown that training models on synthetic recessions can improve a model's ability to predict future recessions over a model trained only on real data.
Intergenerational Test Generation for Natural Language Processing Applications
Venue: arXiv cs.SE 2023
Year: 2023

The development of modern NLP applications often relies on various benchmark datasets containing plenty of manually labeled tests to evaluate performance. While constructing datasets often costs many resources, the performance on the held-out data may not properly reflect their capability in real-world application scenarios and thus cause tremendous misunderstanding and monetary loss. To alleviate this problem, in this paper, we propose an automated test generation method for detecting erroneous behaviors of various NLP applications. Our method is designed based on the sentence parsing process of classic linguistics, and thus it is capable of assembling basic grammatical elements and adjuncts into a grammatically correct test with proper oracle information. We implement this method into NLPLego, which is designed to fully exploit the potential of seed sentences to automate the test generation. NLPLego disassembles the seed sentence into the template and adjuncts and then generates new sentences by assembling context-appropriate adjuncts with the template in a specific order. Unlike the taskspecific methods, the tests generated by NLPLego have derivation relations and different degrees of variation, which makes constructing appropriate metamorphic relations easier. Thus, NLPLego is general, meaning it can meet the testing requirements of various NLP applications. To validate NLPLego, we experiment with three common NLP tasks, identifying failures in four state-of-art models. Given seed tests from SQuAD 2.0, SST, and QQP, NLPLego successfully detects 1,732, 5301, and 261,879 incorrect behaviors with around 95.7% precision in three tasks, respectively.
USR: Unsupervised Separated 3D Garment and Human Reconstruction via Geometry and Semantic Consistency
Venue: arXiv cs.CV 2023
Year: 2023

Dressed people reconstruction from images is a popular task with promising applications in the creative media and game industry. However, most existing methods reconstruct the human body and garments as a whole with the supervision of 3D models, which hinders the downstream interaction tasks and requires hard-to-obtain data. To address these issues, we propose an unsupervised separated 3D garments and human reconstruction model (USR), which reconstructs the human body and authentic textured clothes in layers without 3D models. More specifically, our method proposes a generalized surface-aware neural radiance field to learn the mapping between sparse multi-view images and geometries of the dressed people. Based on the full geometry, we introduce a Semantic and Confidence Guided Separation strategy (SCGS) to detect, segment, and reconstruct the clothes layer, leveraging the consistency between 2D semantic and 3D geometry. Moreover, we propose a Geometry Fine-tune Module to smooth edges. Extensive experiments on our dataset show that comparing with state-of-the-art methods, USR achieves improvements on both geometry and appearance reconstruction while supporting generalizing to unseen people in real time. Besides, we also introduce SMPL-D model to show the benefit of the separated modeling of clothes and the human body that allows swapping clothes and virtual try-on.
Feature selection algorithm based on incremental mutual information and cockroach swarm optimization
Venue: arXiv cs.AI 2023
Year: 2023

Feature selection is an effective preprocessing technique to reduce data dimension. For feature selection, rough set theory provides many measures, among which mutual information is one of the most important attribute measures. However, mutual information based importance measures are computationally expensive and inaccurate, especially in hypersample instances, and it is undoubtedly a NP-hard problem in high-dimensional hyperhigh-dimensional data sets. Although many representative group intelligent algorithm feature selection strategies have been proposed so far to improve the accuracy, there is still a bottleneck when using these feature selection algorithms to process high-dimensional large-scale data sets, which consumes a lot of performance and is easy to select weakly correlated and redundant features. In this study, we propose an incremental mutual information based improved swarm intelligent optimization method (IMIICSO), which uses rough set theory to calculate the importance of feature selection based on mutual information. This method extracts decision table reduction knowledge to guide group algorithm global search. By exploring the computation of mutual information of supersamples, we can not only discard the useless features to speed up the internal and external computation, but also effectively reduce the cardinality of the optimal feature subset by using IMIICSO method, so that the cardinality is minimized by comparison. The accuracy of feature subsets selected by the improved cockroach swarm algorithm based on incremental mutual information is better or almost the same as that of the original swarm intelligent optimization algorithm. Experiments using 10 datasets derived from UCI, including large scale and high dimensional datasets, confirmed the efficiency and effectiveness of the proposed algorithm.
I2V: Towards Texture-Aware Self-Supervised Blind Denoising using Self-Residual Learning for Real-World Images
Venue: arXiv cs.CV 2023
Year: 2023

Although the advances of self-supervised blind denoising are significantly superior to conventional approaches without clean supervision in synthetic noise scenarios, it shows poor quality in real-world images due to spatially correlated noise corruption. Recently, pixel-shuffle downsampling (PD) has been proposed to eliminate the spatial correlation of noise. A study combining a blind spot network (BSN) and asymmetric PD (AP) successfully demonstrated that self-supervised blind denoising is applicable to real-world noisy images. However, PD-based inference may degrade texture details in the testing phase because high-frequency details (e.g., edges) are destroyed in the downsampled images. To avoid such an issue, we propose self-residual learning without the PD process to maintain texture information. We also propose an order-variant PD constraint, noise prior loss, and an efficient inference scheme (progressive random-replacing refinement ($\text{PR}^3$)) to boost overall performance. The results of extensive experiments show that the proposed method outperforms state-of-the-art self-supervised blind denoising approaches, including several supervised learning methods, in terms of PSNR, SSIM, LPIPS, and DISTS in real-world sRGB images.
Denotational Semantics and a Fast Interpreter for jq
Venue: arXiv cs.LO 2023
Year: 2023

jq is a widely used tool that provides a programming language to manipulate JSON data. However, its semantics are currently only specified by its implementation, making it difficult to reason about its behaviour. To this end, I provide a syntax and denotational semantics for a subset of the jq language. In particular, the semantics provide a new way to interpret updates. I implement an extended version of the semantics in a novel interpreter for the jq language called jaq. Although jaq uses a significantly simpler approach to execute jq programs than jq, jaq is faster than jq on ten out of thirteen benchmarks.
Self-improving object detection via disagreement reconciliation
Venue: arXiv cs.CV 2023
Year: 2023

Object detectors often experience a drop in performance when new environmental conditions are insufficiently represented in the training data. This paper studies how to automatically fine-tune a pre-existing object detector while exploring and acquiring images in a new environment without relying on human intervention, i.e., in a self-supervised fashion. In our setting, an agent initially explores the environment using a pre-trained off-the-shelf detector to locate objects and associate pseudo-labels. By assuming that pseudo-labels for the same object must be consistent across different views, we devise a novel mechanism for producing refined predictions from the consensus among observations. Our approach improves the off-the-shelf object detector by 2.66% in terms of mAP and outperforms the current state of the art without relying on ground-truth annotations.