OpenRaiser/Intern-Atlas huggingface dataset:
Fine-grained classification of journal articles by relying on multiple layers of information through similarity network fusion: the case of the Cambridge Journal of Economics
Venue: arXiv cs.DL 2023
Year: 2023

In order to explore the suitability of a fine-grained classification of journal articles by exploiting multiple sources of information, articles are organized in a two-layer multiplex. The first layer conveys similarities based on the full-text of articles, and the second similarities based on cited references. The information of the two layers are only weakly associated. The Similarity Network Fusion process is adopted to combine the two layers into a new single-layer network. A clustering algorithm is applied to the fused network and the classification of articles is obtained. In order to evaluate its coherence, this classification is compared with the ones obtained by applying the same algorithm to each of two layers. Moreover, the classification obtained for the fused network is also compared with the classifications obtained when the layers of information are integrated using different methods available in literature. In the case of the Cambridge Journal of Economics, Similarity Network Fusion appears to be the best option. Moreover, the achieved classification appears to be fine-grained enough to represent the extreme heterogeneity characterizing the contributions published in the journal.
Text-Blueprint: An Interactive Platform for Plan-based Conditional Generation
Venue: arXiv cs.CL 2023
Year: 2023

While conditional generation models can now generate natural language well enough to create fluent text, it is still difficult to control the generation process, leading to irrelevant, repetitive, and hallucinated content. Recent work shows that planning can be a useful intermediate step to render conditional generation less opaque and more grounded. We present a web browser-based demonstration for query-focused summarization that uses a sequence of question-answer pairs, as a blueprint plan for guiding text generation (i.e., what to say and in what order). We illustrate how users may interact with the generated text and associated plan visualizations, e.g., by editing and modifying the blueprint in order to improve or control the generated output. A short video demonstrating our system is available at https://goo.gle/text-blueprint-demo.
ViP-NeRF: Visibility Prior for Sparse Input Neural Radiance Fields
Venue: arXiv cs.CV 2023
Year: 2023

Neural radiance fields (NeRF) have achieved impressive performances in view synthesis by encoding neural representations of a scene. However, NeRFs require hundreds of images per scene to synthesize photo-realistic novel views. Training them on sparse input views leads to overfitting and incorrect scene depth estimation resulting in artifacts in the rendered novel views. Sparse input NeRFs were recently regularized by providing dense depth estimated from pre-trained networks as supervision, to achieve improved performance over sparse depth constraints. However, we find that such depth priors may be inaccurate due to generalization issues. Instead, we hypothesize that the visibility of pixels in different input views can be more reliably estimated to provide dense supervision. In this regard, we compute a visibility prior through the use of plane sweep volumes, which does not require any pre-training. By regularizing the NeRF training with the visibility prior, we successfully train the NeRF with few input views. We reformulate the NeRF to also directly output the visibility of a 3D point from a given viewpoint to reduce the training time with the visibility constraint. On multiple datasets, our model outperforms the competing sparse input NeRF models including those that use learned priors. The source code for our model can be found on our project page: https://nagabhushansn95.github.io/publications/2023/ViP-NeRF.html.
Verification against in-situ observations for Data-Driven Weather Prediction
Venue: arXiv cs.LG 2023
Year: 2023

Data-driven weather prediction models (DDWPs) have made rapid strides in recent years, demonstrating an ability to approximate Numerical Weather Prediction (NWP) models to a high degree of accuracy. The fast, accurate, and low-cost DDWP forecasts make their use in operational forecasting an attractive proposition, however, there remains work to be done in rigorously evaluating DDWPs in a true operational setting. Typically trained and evaluated using ERA5 reanalysis data, DDWPs have been tested only in a simulation, which cannot represent the real world with complete accuracy even if it is of a very high quality. The safe use of DDWPs in operational forecasting requires more thorough "real-world" verification, as well as a careful examination of how DDWPs are currently trained and evaluated. It is worth asking, for instance, how well do the reanalysis datasets, used for training, simulate the real world? With an eye towards climate justice and the uneven availability of weather data: is the simulation equally good for all regions of the world, and would DDWPs exacerbate biases present in the training data? Does a good performance in simulation correspond to good performance in operational settings? In addition to approximating the physics of NWP models, how can ML be uniquely deployed to provide more accurate weather forecasts? As a first step towards answering such questions, we present a robust dataset of in-situ observations derived from the NOAA MADIS program to serve as a benchmark to validate DDWPs in an operational setting. By providing a large corpus of quality-controlled, in-situ observations, this dataset provides a meaningful real-world task that all NWPs and DDWPs can be tested against. We hope that this data can be used not only to rigorously and fairly compare operational weather models but also to spur future research in new directions.
Click-Feedback Retrieval
Venue: arXiv cs.CV 2023
Year: 2023

Retrieving target information based on input query is of fundamental importance in many real-world applications. In practice, it is not uncommon for the initial search to fail, where additional feedback information is needed to guide the searching process. In this work, we study a setting where the feedback is provided through users clicking liked and disliked searching results. We believe this form of feedback is of great practical interests for its convenience and efficiency. To facilitate future work in this direction, we construct a new benchmark termed click-feedback retrieval based on a large-scale dataset in fashion domain. We demonstrate that incorporating click-feedback can drastically improve the retrieval performance, which validates the value of the proposed setting. We also introduce several methods to utilize click-feedback during training, and show that click-feedback-guided training can significantly enhance the retrieval quality. We hope further exploration in this direction can bring new insights on building more efficient and user-friendly search engines.
Logipedia: a multi-system encyclopedia of formal proofs
Venue: arXiv cs.LO 2023
Year: 2023

Libraries of formal proofs are an important part of our mathematical heritage, but their usability and sustainability is poor. Indeed, each library is specific to a proof system, sometimes even to some version of this system. Thus, a library developed in one system cannot, in general, be used in another and when the system is no more maintained, the library may be lost. This impossibility of using a proof developed in one system in another has been noted for long and a remediation has been proposed: as we have empirical evidence that most of the formal proofs developed in one of these systems can also be developed in another, we can develop a standard language, in which these proofs can be translated, and then used in all systems supporting this standard. Logipedia is an attempt to build such a multi-system online encyclopedia of formal proofs expressed in such as standard language. It is based on two main ideas: the use of a logical framework and of reverse mathematics.
Unsupervised Discovery of 3D Hierarchical Structure with Generative Diffusion Features
Venue: arXiv cs.CV 2023
Year: 2023

Inspired by recent findings that generative diffusion models learn semantically meaningful representations, we use them to discover the intrinsic hierarchical structure in biomedical 3D images using unsupervised segmentation. We show that features of diffusion models from different stages of a U-Net-based ladder-like architecture capture different hierarchy levels in 3D biomedical images. We design three losses to train a predictive unsupervised segmentation network that encourages the decomposition of 3D volumes into meaningful nested subvolumes that represent a hierarchy. First, we pretrain 3D diffusion models and use the consistency of their features across subvolumes. Second, we use the visual consistency between subvolumes. Third, we use the invariance to photometric augmentations as a regularizer. Our models achieve better performance than prior unsupervised structure discovery approaches on challenging biologically-inspired synthetic datasets and on a real-world brain tumor MRI dataset.
Exploring Emerging Technologies for Requirements Elicitation Interview Training: Empirical Assessment of Robotic and Virtual Tutors
Venue: arXiv cs.SE 2023
Year: 2023

Requirements elicitation interviews are a widely adopted technique, where the interview success heavily depends on the interviewer's preparedness and communication skills. Students can enhance these skills through practice interviews. However, organizing practice interviews for many students presents scalability challenges, given the time and effort required to involve stakeholders in each session. To address this, we propose REIT, an extensible architecture for Requirements Elicitation Interview Training system based on emerging educational technologies. REIT has components to support both the interview phase, wherein students act as interviewers while the system assumes the role of an interviewee, and the feedback phase, during which the system assesses students' performance and offers contextual and behavioral feedback to enhance their interviewing skills. We demonstrate the applicability of REIT through two implementations: RoREIT with a physical robotic agent and VoREIT with a virtual voice-only agent. We empirically evaluated both instances with a group of graduate students. The participants appreciated both systems. They demonstrated higher learning gain when trained with RoREIT, but they found VoREIT more engaging and easier to use. These findings indicate that each system has distinct benefits and drawbacks, suggesting that REIT can be realized for various educational settings based on preferences and available resources.
ViewFormer: View Set Attention for Multi-view 3D Shape Understanding
Venue: arXiv cs.CV 2023
Year: 2023

This paper presents ViewFormer, a simple yet effective model for multi-view 3d shape recognition and retrieval. We systematically investigate the existing methods for aggregating multi-view information and propose a novel ``view set" perspective, which minimizes the relation assumption about the views and releases the representation flexibility. We devise an adaptive attention model to capture pairwise and higher-order correlations of the elements in the view set. The learned multi-view correlations are aggregated into an expressive view set descriptor for recognition and retrieval. Experiments show the proposed method unleashes surprising capabilities across different tasks and datasets. For instance, with only 2 attention blocks and 4.8M learnable parameters, ViewFormer reaches 98.8% recognition accuracy on ModelNet40 for the first time, exceeding previous best method by 1.1% . On the challenging RGBD dataset, our method achieves 98.4% recognition accuracy, which is a 4.1% absolute improvement over the strongest baseline. ViewFormer also sets new records in several evaluation dimensions of 3D shape retrieval defined on the SHREC'17 benchmark.
TAPE: Temporal Attention-based Probabilistic human pose and shape Estimation
Venue: arXiv cs.CV 2023
Year: 2023

Reconstructing 3D human pose and shape from monocular videos is a well-studied but challenging problem. Common challenges include occlusions, the inherent ambiguities in the 2D to 3D mapping and the computational complexity of video processing. Existing methods ignore the ambiguities of the reconstruction and provide a single deterministic estimate for the 3D pose. In order to address these issues, we present a Temporal Attention based Probabilistic human pose and shape Estimation method (TAPE) that operates on an RGB video. More specifically, we propose to use a neural network to encode video frames to temporal features using an attention-based neural network. Given these features, we output a per-frame but temporally-informed probability distribution for the human pose using Normalizing Flows. We show that TAPE outperforms state-of-the-art methods in standard benchmarks and serves as an effective video-based prior for optimization-based human pose and shape estimation. Code is available at: https: //github.com/nikosvasilik/TAPE
Examining European Press Coverage of the Covid-19 No-Vax Movement: An NLP Framework
Venue: arXiv cs.CL 2023
Year: 2023

This paper examines how the European press dealt with the no-vax reactions against the Covid-19 vaccine and the dis- and misinformation associated with this movement. Using a curated dataset of 1786 articles from 19 European newspapers on the anti-vaccine movement over a period of 22 months in 2020-2021, we used Natural Language Processing techniques including topic modeling, sentiment analysis, semantic relationship with word embeddings, political analysis, named entity recognition, and semantic networks, to understand the specific role of the European traditional press in the disinformation ecosystem. The results of this multi-angle analysis demonstrate that the European well-established press actively opposed a variety of hoaxes mainly spread on social media, and was critical of the anti-vax trend, regardless of the political orientation of the newspaper. This confirms the relevance of studying the role of high-quality press in the disinformation ecosystem.
Large-Scale Assessment of Labour Market Dynamics in China during the COVID-19 Pandemic
Venue: arXiv cs.CY 2023
Year: 2023

The outbreak of the COVID-19 pandemic has had an unprecedented impact on China's labour market, and has largely changed the structure of labour supply and demand in different regions. It becomes critical for policy makers to understand the emerging dynamics of the post-pandemic labour market and provide the right policies for supporting the sustainable development of regional economies. To this end, in this paper, we provide a data-driven approach to assess and understand the evolving dynamics in regions' labour markets with large-scale online job search queries and job postings. In particular, we model the spatial-temporal patterns of labour flow and labour demand which reflect the attractiveness of regional labour markets. Our analysis shows that regional labour markets suffered from dramatic changes and demonstrated unusual signs of recovery during the pandemic. Specifically, the intention of labour flow quickly recovered with a trend of migrating from large to small cities and from northern to southern regions, respectively. Meanwhile, due to the pandemic, the demand of blue-collar workers has been substantially reduced compared to that of white-collar workers. In addition, the demand structure of blue-collar jobs also changed from manufacturing to service industries. Our findings reveal that the pandemic can cause varied impacts on regions with different structures of labour demand and control policies. This analysis provides timely information for both individuals and organizations in confronting the dynamic change in job markets during the extreme events, such as pandemics. Also, the governments can be better assisted for providing the right policies on job markets in facilitating the sustainable development of regions' economies.
Towards Discovering Erratic Behavior in Robotic Process Automation with Statistical Process Control
Venue: arXiv cs.RO 2023
Year: 2023

Companies that use robotic process automation very often deal with problems maintaining the bots in their RPA portfolio. Current key performance indicators do not track the behavior of RPA bots or processes. For better maintainability of RPA bots, it is crucial to easily identify problematic behavior in RPA bots. Therefore, we propose a strategy that tracks and measures the behavior of processes to increase the maintainability of RPA bots. We selected indicators of statistical dispersion for measuring variability to analyze the behavior of RPA bots. We analyzed how well statistical dispersion can describe the behavior of RPA bots on 12 processes. The results provide evidence that, by using statistical dispersion for behavioral analysis, the unwanted behavior of RPA bots can be described. Our results showed that statistical dispersion can describe the success rate with a correlation of -0.91 and outliers in the data with a correlation of 0.42. Also, the results demonstrate that the outliers do not influence the success rate of RPA bots. This research implies that we can describe the behavior of RPA bots with variable analysis. Furthermore, with high probability, it can also be used for analyzing other processes, as a tool for gaining insights into performance and as a benchmark tool for comparing or selecting a process to rework.
Leveraging Unlabelled Data in Multiple-Instance Learning Problems for Improved Detection of Parkinsonian Tremor in Free-Living Conditions
Venue: arXiv cs.LG 2023
Year: 2023

Data-driven approaches for remote detection of Parkinson's Disease and its motor symptoms have proliferated in recent years, owing to the potential clinical benefits of early diagnosis. The holy grail of such approaches is the free-living scenario, in which data are collected continuously and unobtrusively during every day life. However, obtaining fine-grained ground-truth and remaining unobtrusive is a contradiction and therefore, the problem is usually addressed via multiple-instance learning. Yet for large scale studies, obtaining even the necessary coarse ground-truth is not trivial, as a complete neurological evaluation is required. In contrast, large scale collection of data without any ground-truth is much easier. Nevertheless, utilizing unlabelled data in a multiple-instance setting is not straightforward, as the topic has received very little research attention. Here we try to fill this gap by introducing a new method for combining semi-supervised with multiple-instance learning. Our approach builds on the Virtual Adversarial Training principle, a state-of-the-art approach for regular semi-supervised learning, which we adapt and modify appropriately for the multiple-instance setting. We first establish the validity of the proposed approach through proof-of-concept experiments on synthetic problems generated from two well-known benchmark datasets. We then move on to the actual task of detecting PD tremor from hand acceleration signals collected in-the-wild, but in the presence of additional completely unlabelled data. We show that by leveraging the unlabelled data of 454 subjects we can achieve large performance gains (up to 9% increase in F1-score) in per-subject tremor detection for a cohort of 45 subjects with known tremor ground-truth.
NSLF-OL: Online Learning of Neural Surface Light Fields alongside Real-time Incremental 3D Reconstruction
Venue: arXiv cs.CV 2023
Year: 2023

Immersive novel view generation is an important technology in the field of graphics and has recently also received attention for operator-based human-robot interaction. However, the involved training is time-consuming, and thus the current test scope is majorly on object capturing. This limits the usage of related models in the robotics community for 3D reconstruction since robots (1) usually only capture a very small range of view directions to surfaces that cause arbitrary predictions on unseen, novel direction, (2) requires real-time algorithms, and (3) work with growing scenes, e.g., in robotic exploration. The paper proposes a novel Neural Surface Light Fields model that copes with the small range of view directions while producing a good result in unseen directions. Exploiting recent encoding techniques, the training of our model is highly efficient. In addition, we design Multiple Asynchronous Neural Agents (MANA), a universal framework to learn each small region in parallel for large-scale growing scenes. Our model learns online the Neural Surface Light Fields (NSLF) aside from real-time 3D reconstruction with a sequential data stream as the shared input. In addition to online training, our model also provides real-time rendering after completing the data stream for visualization. We implement experiments using well-known RGBD indoor datasets, showing the high flexibility to embed our model into real-time 3D reconstruction and demonstrating high-fidelity view synthesis for these scenes. The code is available on github.
A preferential interpretation of MultiLayer Perceptrons in a conditional logic with typicality
Venue: arXiv cs.AI 2023
Year: 2023

In this paper we investigate the relationships between a multipreferential semantics for defeasible reasoning in knowledge representation and a multilayer neural network model. Weighted knowledge bases for a simple description logic with typicality are considered under a (many-valued) ``concept-wise" multipreference semantics. The semantics is used to provide a preferential interpretation of MultiLayer Perceptrons (MLPs). A model checking and an entailment based approach are exploited in the verification of conditional properties of MLPs.
Improved Complexity Analysis of Quasi-Polynomial Algorithms Solving Parity Games
Venue: arXiv cs.GT 2023
Year: 2023

We improve the complexity of solving parity games (with priorities in vertices) for $d=ω(\log n)$ by a factor of $θ(d^2)$: the best complexity known to date was $O(mdn^{1.45+\log_2(d/\log_2(n))})$, while we obtain $O(mn^{1.45+\log_2(d/\log_2(n))}/d)$, where $n$ is the number of vertices, $m$ is the number of edges, and $d$ is the number of priorities. We base our work on existing algorithms using universal trees, and we improve their complexity. We present two independent improvements. First, an improvement by a factor of $θ(d)$ comes from a more careful analysis of the width of universal trees. Second, we perform (or rather recall) a finer analysis of requirements for a universal tree: while for solving games with priorities on edges one needs an $n$-universal tree, in the case of games with priorities in vertices it is enough to use an $n/2$-universal tree. This way, we allow to solve games of size $2n$ in the time needed previously to solve games of size $n$; such a change divides the quasi-polynomial complexity again by a factor of $θ(d)$.