OpenRaiser/Intern-Atlas huggingface dataset:
Graph state-space models
Venue: arXiv cs.LG 2023
Year: 2023

State-space models constitute an effective modeling tool to describe multivariate time series and operate by maintaining an updated representation of the system state from which predictions are made. Within this framework, relational inductive biases, e.g., associated with functional dependencies existing among signals, are not explicitly exploited leaving unattended great opportunities for effective modeling approaches. The manuscript aims, for the first time, at filling this gap by matching state-space modeling and spatio-temporal data where the relational information, say the functional graph capturing latent dependencies, is learned directly from data and is allowed to change over time. Within a probabilistic formulation that accounts for the uncertainty in the data-generating process, an encoder-decoder architecture is proposed to learn the state-space model end-to-end on a downstream task. The proposed methodological framework generalizes several state-of-the-art methods and demonstrates to be effective in extracting meaningful relational information while achieving optimal forecasting performance in controlled environments.
Chatbots as Problem Solvers: Playing Twenty Questions with Role Reversals
Venue: arXiv cs.AI 2023
Year: 2023

New chat AI applications like ChatGPT offer an advanced understanding of question context and memory across multi-step tasks, such that experiments can test its deductive reasoning. This paper proposes a multi-role and multi-step challenge, where ChatGPT plays the classic twenty-questions game but innovatively switches roles from the questioner to the answerer. The main empirical result establishes that this generation of chat applications can guess random object names in fewer than twenty questions (average, 12) and correctly guess 94% of the time across sixteen different experimental setups. The research introduces four novel cases where the chatbot fields the questions, asks the questions, both question-answer roles, and finally tries to guess appropriate contextual emotions. One task that humans typically fail but trained chat applications complete involves playing bilingual games of twenty questions (English answers to Spanish questions). Future variations address direct problem-solving using a similar inquisitive format to arrive at novel outcomes deductively, such as patentable inventions or combination thinking. Featured applications of this dialogue format include complex protein designs, neuroscience metadata, and child development educational materials.
Network Utility Maximization with Unknown Utility Functions: A Distributed, Data-Driven Bilevel Optimization Approach
Venue: arXiv cs.LG 2023
Year: 2023

Fair resource allocation is one of the most important topics in communication networks. Existing solutions almost exclusively assume each user utility function is known and concave. This paper seeks to answer the following question: how to allocate resources when utility functions are unknown, even to the users? This answer has become increasingly important in the next-generation AI-aware communication networks where the user utilities are complex and their closed-forms are hard to obtain. In this paper, we provide a new solution using a distributed and data-driven bilevel optimization approach, where the lower level is a distributed network utility maximization (NUM) algorithm with concave surrogate utility functions, and the upper level is a data-driven learning algorithm to find the best surrogate utility functions that maximize the sum of true network utility. The proposed algorithm learns from data samples (utility values or gradient values) to autotune the surrogate utility functions to maximize the true network utility, so works for unknown utility functions. For the general network, we establish the nonasymptotic convergence rate of the proposed algorithm with nonconcave utility functions. The simulations validate our theoretical results and demonstrate the great effectiveness of the proposed method in a real-world network.
Significant Digits: Using Large-Scale Blockchain Data to Predict Fraudulent Addresses
Venue: arXiv cs.CR 2023
Year: 2023

Blockchain systems and cryptocurrencies have exploded in popularity over the past decade, and with this growing user base, the number of cryptocurrency scams has also surged. Given the graphical structure of blockchain networks and the abundance of data generated on these networks, we use graph mining techniques to extract essential information on transactions and apply Benford's Law to extract distributional information on address transactions. We then apply a gradient-boosting tree model to predict fraudulent addresses. Our results show that our method can detect scams with reasonable accuracy and that the features generated based on Benford's Law are the most significant features.
InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval
Venue: arXiv cs.IR 2023
Year: 2023

Recently, InPars introduced a method to efficiently use large language models (LLMs) in information retrieval tasks: via few-shot examples, an LLM is induced to generate relevant queries for documents. These synthetic query-document pairs can then be used to train a retriever. However, InPars and, more recently, Promptagator, rely on proprietary LLMs such as GPT-3 and FLAN to generate such datasets. In this work we introduce InPars-v2, a dataset generator that uses open-source LLMs and existing powerful rerankers to select synthetic query-document pairs for training. A simple BM25 retrieval pipeline followed by a monoT5 reranker finetuned on InPars-v2 data achieves new state-of-the-art results on the BEIR benchmark. To allow researchers to further improve our method, we open source the code, synthetic data, and finetuned models: https://github.com/zetaalphavector/inPars/tree/master/tpu
Learning Feature Recovery Transformer for Occluded Person Re-identification
Venue: arXiv cs.CV 2023
Year: 2023

One major issue that challenges person re-identification (Re-ID) is the ubiquitous occlusion over the captured persons. There are two main challenges for the occluded person Re-ID problem, i.e., the interference of noise during feature matching and the loss of pedestrian information brought by the occlusions. In this paper, we propose a new approach called Feature Recovery Transformer (FRT) to address the two challenges simultaneously, which mainly consists of visibility graph matching and feature recovery transformer. To reduce the interference of the noise during feature matching, we mainly focus on visible regions that appear in both images and develop a visibility graph to calculate the similarity. In terms of the second challenge, based on the developed graph similarity, for each query image, we propose a recovery transformer that exploits the feature sets of its $k$-nearest neighbors in the gallery to recover the complete features. Extensive experiments across different person Re-ID datasets, including occluded, partial and holistic datasets, demonstrate the effectiveness of FRT. Specifically, FRT significantly outperforms state-of-the-art results by at least 6.2\% Rank-1 accuracy and 7.2\% mAP scores on the challenging Occluded-Duke dataset. The code is available at https://github.com/xbq1994/Feature-Recovery-Transformer.
Urban-Semantic Computer Vision: A Framework for Contextual Understanding of People in Urban Spaces
Venue: arXiv cs.CY 2023
Year: 2023

Increasing computational power and improving deep learning methods have made computer vision technologies pervasively common in urban environments. Their applications in policing, traffic management, and documenting public spaces are increasingly common. Despite the often-discussed biases in the algorithms' training and unequally borne benefits, almost all applications similarly reduce urban experiences to simplistic, reductive, and mechanistic measures. There is a lack of context, depth, and specificity in these practices that enables semantic knowledge or analysis within urban contexts, especially within the context of using and occupying urban space. This paper will critique existing uses of artificial intelligence and computer vision in urban practices to propose a new framework for understanding people, action, and public space. This paper revisits Geertz's use of thick descriptions in generating interpretive theories of culture and activity and uses this lens to establish a framework to evaluate the varied uses of computer vision technologies that weigh meaning. We discuss how the framework's positioning may differ (and conflict) between different users of the technology. This paper also discusses the current use and training of deep learning algorithms and how this process limits semantic learning and proposes three potential methodologies for gaining a more contextually specific, urban-semantic, description of urban space relevant to urbanists. This paper contributes to the critical conversations regarding the proliferation of artificial intelligence by challenging the current applications of these technologies in the urban environment by highlighting their failures within this context while also proposing an evolution of these algorithms that may ultimately make them sensitive and useful within this spatial and cultural milieu.
TAC+: Optimizing Error-Bounded Lossy Compression for 3D AMR Simulations
Venue: arXiv cs.DC 2023
Year: 2023

Today's scientific simulations require significant data volume reduction because of the enormous amounts of data produced and the limited I/O bandwidth and storage space. Error-bounded lossy compression has been considered one of the most effective solutions to the above problem. However, little work has been done to improve error-bounded lossy compression for Adaptive Mesh Refinement (AMR) simulation data. Unlike the previous work that only leverages 1D compression, in this work, we propose an approach (TAC) to leverage high-dimensional SZ compression for each refinement level of AMR data. To remove the data redundancy across different levels, we propose several pre-process strategies and adaptively use them based on the data features. We further optimize TAC to TAC+ by improving the lossless encoding stage of SZ compression to handle many small AMR data blocks after the pre-processing efficiently. Experiments on 10 AMR datasets from three real-world large-scale AMR simulations demonstrate that TAC+ can improve the compression ratio by up to 4.9$\times$ under the same data distortion, compared to the state-of-the-art method. In addition, we leverage the flexibility of our approach to tune the error bound for each level, which achieves much lower data distortion on two application-specific metrics.
Piloting Virtual Reality Photo-Based Tours among Students of a Filipino Language Class: A Case of Emergency Remote Teaching in Japan
Venue: arXiv cs.CY 2023
Year: 2023

The State of Emergency declaration in Japan due to the COVID-19 pandemic affected many aspects of society in the country, much like the rest of the world. One sector that felt its disruptive impact was education. As educational institutions raced to implement emergency remote teaching (ERT) to continue providing the learning needs of students, some have opened to innovative interventions. This paper describes a case of ERT where Filipino vocabulary was taught to a class of Japanese students taking Philippine Studies in a Japanese university using a cognitive innovation based on virtual reality, an immer-sive technology often researched for immersion and presence. Students were divided into three groups to experience six lessons designed around virtual reality photo-based tours at different immersion levels. While the effect of immersion on satisfaction was not found to be statistically significant, presence and satisfaction were found to be correlated. Despite challenges that were encountered, benefits like enjoyment, increased engagement , and perceived learning were reported by the students. Our findings exemplify how emerging multisensory technologies can be used to enhance affective and cognitive dimensions of human experience while responding to gaps created by the spatial limitations of remote learning.
Virtual Reality Photo-based Tours for Teaching Filipino Vocabulary in an Online Class in Japan: Transitioning into the New Normal
Venue: arXiv cs.CY 2023
Year: 2023

When educational institutions worldwide scrambled for ways to continue their classes during lockdowns caused by the COVID-19 pandemic, the use of information and communication technology (ICT) for remote teaching has become widely considered to be a potential solution. As universities raced to implement emergency remote teaching (ERT) strategies in Japan, some have explored innovative interventions other than webinar platforms and learning management systems to bridge the gap caused by restricted mobility among teachers and learners. One such innovation is virtual reality (VR). VR has been changing the landscape of higher education because of its ability to "teleport" learners to various places by simulating real-world environments in the virtual world. Some teachers, including the authors of this paper, explored integrating VR into their activities to address issues caused by geographical limitations brought about by the heightened restrictions in 2020. Results were largely encouraging. However, rules started relaxing in the succeeding years as more people got vaccinated. Thus, some fully online classes in Japan shifted to blended learning as they moved toward fully returning to in-person classes prompting educators to modify how they implemented their VR-based interventions. This paper describes how a class of university students in Japan who were taking a Filipino language course experienced a VR-based intervention in blended mode, which was originally prototyped during the peak of the ERT era. Moreover, adjustments and comparisons regarding methodological idiosyncrasies and findings between the fully online iteration and the recently implemented blended one are reported in detail.
Flying Bird Object Detection Algorithm in Surveillance Video Based on Motion Information
Venue: arXiv cs.CV 2023
Year: 2023

A Flying Bird Object Detection algorithm Based on Motion Information (FBOD-BMI) is proposed to solve the problem that the features of the object are not obvious in a single frame, and the size of the object is small (low Signal-to-Noise Ratio (SNR)) in surveillance video. Firstly, a ConvLSTM-PAN model structure is designed to capture suspicious flying bird objects, in which the Convolutional Long and Short Time Memory (ConvLSTM) network aggregated the Spatio-temporal features of the flying bird object on adjacent multi-frame before the input of the model and the Path Aggregation Network (PAN) located the suspicious flying bird objects. Then, an object tracking algorithm is used to track suspicious flying bird objects and calculate their Motion Range (MR). At the same time, the size of the MR of the suspicious flying bird object is adjusted adaptively according to its speed of movement (specifically, if the bird moves slowly, its MR will be expanded according to the speed of the bird to ensure the environmental information needed to detect the flying bird object). Adaptive Spatio-temporal Cubes (ASt-Cubes) of the flying bird objects are generated to ensure that the SNR of the flying bird objects is improved, and the necessary environmental information is retained adaptively. Finally, a LightWeight U-Shape Net (LW-USN) based on ASt-Cubes is designed to detect flying bird objects, which rejects the false detections of the suspicious flying bird objects and returns the position of the real flying bird objects. The monitoring video including the flying birds is collected in the unattended traction substation as the experimental dataset to verify the performance of the algorithm. The experimental results show that the flying bird object detection method based on motion information proposed in this paper can effectively detect the flying bird object in surveillance video.
Two-dimensional tile displacement can simulate cellular automata
Venue: arXiv cs.ET 2023
Year: 2023

Tile displacement is a newly-recognized mechanism in DNA nanotechnology that exploits principles analogous to toehold-mediated strand displacement but within the context of self-assembled DNA origami tile arrays. Here, we formulate an abstract model of tile displacement for the simplest case: individual assemblies interacting with monomer tiles in solution. We give several constructions for programmable computation by tile displacement, from circuits to cellular automata, that vary in how they use energy (or not) to drive the system forward (or not), how much space and how many tile types they require, and whether their computational power is limited to PTIME or PSPACE with respect to the size of the system. In particular, we show that tile displacement systems are Turing universal and can simulate arbitrary two-dimensional synchronous block cellular automata, where each transition rule for updating the state of a 2 by 2 neighborhood is implemented by just a single tile.
PA-GM: Position-Aware Learning of Embedding Networks for Deep Graph Matching
Venue: arXiv cs.CV 2023
Year: 2023

Graph matching can be formalized as a combinatorial optimization problem, where there are corresponding relationships between pairs of nodes that can be represented as edges. This problem becomes challenging when there are potential ambiguities present due to nodes and edges with high similarity, and there is a need to find accurate results for similar content matching. In this paper, we introduce a novel end-to-end neural network that can map the linear assignment problem into a high-dimensional space augmented with node-level relative position information, which is crucial for improving the method's performance for similar content matching. Our model constructs the anchor set for the relative position of nodes and then aggregates the feature information of the target node and each anchor node based on a measure of relative position. It then learns the node feature representation by integrating the topological structure and the relative position information, thus realizing the linear assignment between the two graphs. To verify the effectiveness and generalizability of our method, we conduct graph matching experiments, including cross-category matching, on different real-world datasets. Comparisons with different baselines demonstrate the superiority of our method. Our source code is available under https://github.com/anonymous.
A Contact Proxy Splitting Method for Lagrangian Solid-Fluid Coupling
Venue: arXiv cs.GR 2023
Year: 2023

We present a robust and efficient method for simulating Lagrangian solid-fluid coupling based on a new operator splitting strategy. We use variational formulations to approximate fluid properties and solid-fluid interactions, and introduce a unified two-way coupling formulation for SPH fluids and FEM solids using interior point barrier-based frictional contact. We split the resulting optimization problem into a fluid phase and a solid-coupling phase using a novel time-splitting approach with augmented contact proxies, and propose efficient custom linear solvers. Our technique accounts for fluids interaction with nonlinear hyperelastic objects of different geometries and codimensions, while maintaining an algorithmically guaranteed non-penetrating criterion. Comprehensive benchmarks and experiments demonstrate the efficacy of our method.
TextDescriptives: A Python package for calculating a large variety of metrics from text
Venue: arXiv cs.CL 2023
Year: 2023

TextDescriptives is a Python package for calculating a large variety of metrics from text. It is built on top of spaCy and can be easily integrated into existing workflows. The package has already been used for analysing the linguistic stability of clinical texts, creating features for predicting neuropsychiatric conditions, and analysing linguistic goals of primary school students. This paper describes the package and its features.
Stoic Conceptual Modeling Applied to Business Process Modeling Notation (BPMN)
Venue: arXiv cs.SE 2023
Year: 2023

Basic abstraction principles are reached through ontology, which was traditionally conceived as a depiction of the world itself. Ontology is also described using conceptual modeling (CM) that defines fundamental concepts of reality. CM is one of the central activities in computer science, especially as it is mainly used in software engineering as an intermediate artifact for system construction. To achieve such a goal, we propose Stoic CM (SCM) as a description of what a system must do functionally with minimal ambiguity. As a case study, we apply SCM to investigate the ontology of BPMN (business process modeling notation). Such an undertaking would demonstrate SCM notions and simultaneously may offer a viable ontological foundation for BPMN. SCM defines the being of things and actions in reality based on Stoic notions of existence and subsistence. It has two levels of specification: (1) a subsistence static model where things and actions subsist and (2) an existence dynamic model where things and actions exist in time. From the Stoic ontological point of view, while a thing existing has a clear denotation, subsistence indicates the thing is being there, but it is inactive (does not participate in an event). We apply SCM to BPMN processes that involve buying a new car with many notions, such as activity, task, event, and message. The result indicates that SCM produces a tighter representation of reality, thus providing the necessary description of the part in the application world to be used as requirements for developing the software system.
Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach
Venue: arXiv cs.CL 2023
Year: 2023

Although remarkable progress on the neural table-to-text methods has been made, the generalization issues hinder the applicability of these models due to the limited source tables. Large-scale pretrained language models sound like a promising solution to tackle such issues. However, how to effectively bridge the gap between the structured table and the text input by fully leveraging table information to fuel the pretrained model is still not well explored. Besides, another challenge of integrating the deliberation mechanism into the text-to-text pretrained model for solving the table-to-text task remains seldom studied. In this paper, to implement the table-to-text generation with pretrained language model, we propose a table structure understanding and text deliberating approach, namely TASD. Specifically, we devise a three-layered multi-head attention network to realize the table-structure-aware text generation model with the help of the pretrained language model. Furthermore, a multi-pass decoder framework is adopted to enhance the capability of polishing generated text for table descriptions. The empirical studies, as well as human evaluation, on two public datasets, validate that our approach can generate faithful and fluent descriptive texts for different types of tables.