OpenRaiser/Intern-Atlas huggingface dataset:
On Learning the Structure of Clusters in Graphs
Venue: arXiv cs.DS 2022
Year: 2022

Graph clustering is a fundamental problem in unsupervised learning, with numerous applications in computer science and in analysing real-world data. In many real-world applications, we find that the clusters have a significant high-level structure. This is often overlooked in the design and analysis of graph clustering algorithms which make strong simplifying assumptions about the structure of the graph. This thesis addresses the natural question of whether the structure of clusters can be learned efficiently and describes four new algorithmic results for learning such structure in graphs and hypergraphs. All of the presented theoretical results are extensively evaluated on both synthetic and real-word datasets of different domains, including image classification and segmentation, migration networks, co-authorship networks, and natural language processing. These experimental results demonstrate that the newly developed algorithms are practical, effective, and immediately applicable for learning the structure of clusters in real-world data.
Stateful Logic using Phase Change Memory
Venue: arXiv cs.ET 2022
Year: 2022

Stateful logic is a digital processing-in-memory technique that could address von Neumann memory bottleneck challenges while maintaining backward compatibility with standard von Neumann architectures. In stateful logic, memory cells are used to perform the logic operations without reading or moving any data outside the memory array. Stateful logic has been previously demonstrated using several resistive memory types, mostly by resistive RAM (RRAM). Here we present a new method to design stateful logic using a different resistive memory - phase change memory (PCM). We propose and experimentally demonstrate four logic gate types (NOR, IMPLY, OR, NIMP) using commonly used PCM materials. Our stateful logic circuits are different than previously proposed circuits due to the different switching mechanism and functionality of PCM compared to RRAM. Since the proposed stateful logic form a functionally complete set, these gates enable sequential execution of any logic function within the memory, paving the way to PCM-based digital processing-in-memory systems.
GPT Takes the Bar Exam
Venue: arXiv cs.CL 2022
Year: 2022

Nearly all jurisdictions in the United States require a professional license exam, commonly referred to as "the Bar Exam," as a precondition for law practice. To even sit for the exam, most jurisdictions require that an applicant completes at least seven years of post-secondary education, including three years at an accredited law school. In addition, most test-takers also undergo weeks to months of further, exam-specific preparation. Despite this significant investment of time and capital, approximately one in five test-takers still score under the rate required to pass the exam on their first try. In the face of a complex task that requires such depth of knowledge, what, then, should we expect of the state of the art in "AI?" In this research, we document our experimental evaluation of the performance of OpenAI's `text-davinci-003` model, often-referred to as GPT-3.5, on the multistate multiple choice (MBE) section of the exam. While we find no benefit in fine-tuning over GPT-3.5's zero-shot performance at the scale of our training data, we do find that hyperparameter optimization and prompt engineering positively impacted GPT-3.5's zero-shot performance. For best prompt and parameters, GPT-3.5 achieves a headline correct rate of 50.3% on a complete NCBE MBE practice exam, significantly in excess of the 25% baseline guessing rate, and performs at a passing rate for both Evidence and Torts. GPT-3.5's ranking of responses is also highly-correlated with correctness; its top two and top three choices are correct 71% and 88% of the time, respectively, indicating very strong non-entailment performance. While our ability to interpret these results is limited by nascent scientific understanding of LLMs and the proprietary nature of GPT, we believe that these results strongly suggest that an LLM will pass the MBE component of the Bar Exam in the near future.
On the utility of feature selection in building two-tier decision trees
Venue: arXiv cs.LG 2022
Year: 2022

Nowadays, feature selection is frequently used in machine learning when there is a risk of performance degradation due to overfitting or when computational resources are limited. During the feature selection process, the subset of features that are most relevant and least redundant is chosen. In recent years, it has become clear that, in addition to relevance and redundancy, features' complementarity must be considered. Informally, if the features are weak predictors of the target variable separately and strong predictors when combined, then they are complementary. It is demonstrated in this paper that the synergistic effect of complementary features mutually amplifying each other in the construction of two-tier decision trees can be interfered with by another feature, resulting in a decrease in performance. It is demonstrated using cross-validation on both synthetic and real datasets, regression and classification, that removing or eliminating the interfering feature can improve performance by up to 24 times. It has also been discovered that the lesser the domain is learned, the greater the increase in performance. More formally, it is demonstrated that there is a statistically significant negative rank correlation between performance on the dataset prior to the elimination of the interfering feature and performance growth after the elimination of the interfering feature. It is concluded that this broadens the scope of feature selection methods for cases where data and computational resources are sufficient.
Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats
Venue: arXiv cs.CV 2022
Year: 2022

Deep learning-based 3D human pose estimation performs best when trained on large amounts of labeled data, making combined learning from many datasets an important research direction. One obstacle to this endeavor are the different skeleton formats provided by different datasets, i.e., they do not label the same set of anatomical landmarks. There is little prior research on how to best supervise one model with such discrepant labels. We show that simply using separate output heads for different skeletons results in inconsistent depth estimates and insufficient information sharing across skeletons. As a remedy, we propose a novel affine-combining autoencoder (ACAE) method to perform dimensionality reduction on the number of landmarks. The discovered latent 3D points capture the redundancy among skeletons, enabling enhanced information sharing when used for consistency regularization. Our approach scales to an extreme multi-dataset regime, where we use 28 3D human pose datasets to supervise one model, which outperforms prior work on a range of benchmarks, including the challenging 3D Poses in the Wild (3DPW) dataset. Our code and models are available for research purposes.
Performance Evaluations of C-Band 5G NR FR1 (Sub-6 GHz) Uplink MIMO on Urban Train
Venue: arXiv cs.NI 2022
Year: 2022

Due to the recent demand for huge Uplink throughput on Mobile networks driven by the rapid development of social media platforms, UHD 4K/8K video, and VR/AR contents, Uplink MIMO (UL-MIMO) has now been deployed on commercial 5G networks with reasonable availability of supported User Equipment (UE) for consumers. By utilizing up to 2 Tx antenna ports, UL-MIMO-capable UE promised to achieve up to two times the uplink throughput in ideal conditions, while providing improved uplink performance over UE with 1Tx in challenging conditions. In Japan, SoftBank, one of the carriers, introduced 5G Standalone (SA) services for the Fixed Wireless Access (FWA) application back in October 2021. Mobile services were commenced in May 2022, which provide UL-MIMO for supported UE on C-Band or Band n77 (3.7 GHz). In this paper, the uplink performance of UL-MIMO-capable UE will be compared against the conventional UL-1Tx UE on trains, which is the most popular method of transportation for the Japanese. The results show that UL-MIMO-capable UE delivers an average of 19.8% better throughput on moving trains with up to 33.5% in the more favorable signal conditions. A moderate relationship between downlink 5G NR SS-RSRP and uplink throughput also has been observed.
Mixture of von Mises-Fisher distribution with sparse prototypes
Venue: arXiv cs.LG 2022
Year: 2022

Mixtures of von Mises-Fisher distributions can be used to cluster data on the unit hypersphere. This is particularly adapted for high-dimensional directional data such as texts. We propose in this article to estimate a von Mises mixture using a l 1 penalized likelihood. This leads to sparse prototypes that improve clustering interpretability. We introduce an expectation-maximisation (EM) algorithm for this estimation and explore the trade-off between the sparsity term and the likelihood one with a path following algorithm. The model's behaviour is studied on simulated data and, we show the advantages of the approach on real data benchmark. We also introduce a new data set on financial reports and exhibit the benefits of our method for exploratory analysis.
ComplAI: Theory of A Unified Framework for Multi-factor Assessment of Black-Box Supervised Machine Learning Models
Venue: arXiv cs.LG 2022
Year: 2022

The advances in Artificial Intelligence are creating new opportunities to improve lives of people around the world, from business to healthcare, from lifestyle to education. For example, some systems profile the users using their demographic and behavioral characteristics to make certain domain-specific predictions. Often, such predictions impact the life of the user directly or indirectly (e.g., loan disbursement, determining insurance coverage, shortlisting applications, etc.). As a result, the concerns over such AI-enabled systems are also increasing. To address these concerns, such systems are mandated to be responsible i.e., transparent, fair, and explainable to developers and end-users. In this paper, we present ComplAI, a unique framework to enable, observe, analyze and quantify explainability, robustness, performance, fairness, and model behavior in drift scenarios, and to provide a single Trust Factor that evaluates different supervised Machine Learning models not just from their ability to make correct predictions but from overall responsibility perspective. The framework helps users to (a) connect their models and enable explanations, (b) assess and visualize different aspects of the model, such as robustness, drift susceptibility, and fairness, and (c) compare different models (from different model families or obtained through different hyperparameter settings) from an overall perspective thereby facilitating actionable recourse for improvement of the models. It is model agnostic and works with different supervised machine learning scenarios (i.e., Binary Classification, Multi-class Classification, and Regression) and frameworks. It can be seamlessly integrated with any ML life-cycle framework. Thus, this already deployed framework aims to unify critical aspects of Responsible AI systems for regulating the development process of such real systems.
HPointLoc: Point-based Indoor Place Recognition using Synthetic RGB-D Images
Venue: arXiv cs.CV 2022
Year: 2022

We present a novel dataset named as HPointLoc, specially designed for exploring capabilities of visual place recognition in indoor environment and loop detection in simultaneous localization and mapping. The loop detection sub-task is especially relevant when a robot with an on-board RGB-D camera can drive past the same place (``Point") at different angles. The dataset is based on the popular Habitat simulator, in which it is possible to generate photorealistic indoor scenes using both own sensor data and open datasets, such as Matterport3D. To study the main stages of solving the place recognition problem on the HPointLoc dataset, we proposed a new modular approach named as PNTR. It first performs an image retrieval with the Patch-NetVLAD method, then extracts keypoints and matches them using R2D2, LoFTR or SuperPoint with SuperGlue, and finally performs a camera pose optimization step with TEASER++. Such a solution to the place recognition problem has not been previously studied in existing publications. The PNTR approach has shown the best quality metrics on the HPointLoc dataset and has a high potential for real use in localization systems for unmanned vehicles. The proposed dataset and framework are publicly available: https://github.com/metra4ok/HPointLoc.
Novel Architecture to Create and Maintain Personal Blockchains
Venue: arXiv cs.CY 2022
Year: 2022

Blockchain has been touted as a revolutionary technology. However, despite the excitement, blockchain has not been adopted in many fields. Many are hesitant to adopt blockchain technology due to privacy concerns, barriers to use, or lack of practical use cases. In this work, we outline a potential blockchain use case for tracking financial transactions across multiple financial institutions. We show the downsides of traditional centralized approaches and that blockchain approaches fail to give all the privacy and accessibility required for this use case. Thus we propose a novel blockchain architecture to support our use case. This novel architecture combines the ease of use of public blockchains with the privacy of private blockchains by allowing users to create personal blockchains. We believe this novel personal blockchain architecture will lead to more blockchain adoption, particularly in use cases handling private data.
A Comprehensive Gold Standard and Benchmark for Comics Text Detection and Recognition
Venue: arXiv cs.CL 2022
Year: 2022

This study focuses on improving the optical character recognition (OCR) data for panels in the COMICS dataset, the largest dataset containing text and images from comic books. To do this, we developed a pipeline for OCR processing and labeling of comic books and created the first text detection and recognition datasets for western comics, called "COMICS Text+: Detection" and "COMICS Text+: Recognition". We evaluated the performance of state-of-the-art text detection and recognition models on these datasets and found significant improvement in word accuracy and normalized edit distance compared to the text in COMICS. We also created a new dataset called "COMICS Text+", which contains the extracted text from the textboxes in the COMICS dataset. Using the improved text data of COMICS Text+ in the comics processing model from resulted in state-of-the-art performance on cloze-style tasks without changing the model architecture. The COMICS Text+ dataset can be a valuable resource for researchers working on tasks including text detection, recognition, and high-level processing of comics, such as narrative understanding, character relations, and story generation. All the data and inference instructions can be accessed in https://github.com/gsoykan/comics_text_plus.
1st Place Solution for YouTubeVOS Challenge 2022: Referring Video Object Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

The task of referring video object segmentation aims to segment the object in the frames of a given video to which the referring expressions refer. Previous methods adopt multi-stage approach and design complex pipelines to obtain promising results. Recently, the end-to-end method based on Transformer has proved its superiority. In this work, we draw on the advantages of the above methods to provide a simple and effective pipeline for RVOS. Firstly, We improve the state-of-the-art one-stage method ReferFormer to obtain mask sequences that are strongly correlated with language descriptions. Secondly, based on a reliable and high-quality keyframe, we leverage the superior performance of video object segmentation model to further enhance the quality and temporal consistency of the mask results. Our single model reaches 70.3 J &F on the Referring Youtube-VOS validation set and 63.0 on the test set. After ensemble, we achieve 64.1 on the final leaderboard, ranking 1st place on CVPR2022 Referring Youtube-VOS challenge. Code will be available at https://github.com/Zhiweihhh/cvpr2022-rvos-challenge.git.
A Learning-Based Optimal Uncertainty Quantification Method and Its Application to Ballistic Impact Problems
Venue: arXiv cs.LG 2022
Year: 2022

This paper concerns the study of optimal (supremum and infimum) uncertainty bounds for systems where the input (or prior) probability measure is only partially/imperfectly known (e.g., with only statistical moments and/or on a coarse topology) rather than fully specified. Such partial knowledge provides constraints on the input probability measures. The theory of Optimal Uncertainty Quantification allows us to convert the task into a constraint optimization problem where one seeks to compute the least upper/greatest lower bound of the system's output uncertainties by finding the extremal probability measure of the input. Such optimization requires repeated evaluation of the system's performance indicator (input to performance map) and is high-dimensional and non-convex by nature. Therefore, it is difficult to find the optimal uncertainty bounds in practice. In this paper, we examine the use of machine learning, especially deep neural networks, to address the challenge. We achieve this by introducing a neural network classifier to approximate the performance indicator combined with the stochastic gradient descent method to solve the optimization problem. We demonstrate the learning based framework on the uncertainty quantification of the impact of magnesium alloys, which are promising light-weight structural and protective materials. Finally, we show that the approach can be used to construct maps for the performance certificate and safety design in engineering practice.
Deep Hierarchy Quantization Compression algorithm based on Dynamic Sampling
Venue: arXiv cs.LG 2022
Year: 2022

Unlike traditional distributed machine learning, federated learning stores data locally for training and then aggregates the models on the server, which solves the data security problem that may arise in traditional distributed machine learning. However, during the training process, the transmission of model parameters can impose a significant load on the network bandwidth. It has been pointed out that the vast majority of model parameters are redundant during model parameter transmission. In this paper, we explore the data distribution law of selected partial model parameters on this basis, and propose a deep hierarchical quantization compression algorithm, which further compresses the model and reduces the network load brought by data transmission through the hierarchical quantization of model parameters. And we adopt a dynamic sampling strategy for the selection of clients to accelerate the convergence of the model. Experimental results on different public datasets demonstrate the effectiveness of our algorithm.
BlueCov: Integrating Test Coverage and Model Checking with JBMC
Venue: arXiv cs.SE 2022
Year: 2022

Automated test case generation tools help businesses to write tests and increase the safety net provided by high regression test coverage when making code changes. Test generation needs to cover as much as possible of the uncovered code while avoiding generating redundant tests for code that is already covered by an existing test-suite. In this paper we present our work on a tool for the real world application of integrating formal analysis with automatic test case generation. The test case generation is based on coverage analysis using the Java bounded model checker (JBMC). Counterexamples of the model checker can be translated into Java method calls with specific parameters. In order to avoid the generation of redundant tests, it is necessary to measure the coverage in the exact same way as JBMC generates its coverage goals. Each existing coverage measurement tool uses a slightly different instrumentation and thus a different coverage criterion. This makes integration with a test case generator based on formal analysis difficult. Therefore, we developed BlueCov as a specific runtime coverage measurement tool which uses the exact same coverage criteria as JBMC does. This approach also allows for incremental test-case generation, only generating test coverage for previously untested code, e.g., to complete existing test suites.
A deep real options policy for sequential service region design and timing
Venue: arXiv cs.LG 2022
Year: 2022

As various city agencies and mobility operators navigate toward innovative mobility solutions, there is a need for strategic flexibility in well-timed investment decisions in the design and timing of mobility service regions, i.e. cast as "real options" (RO). This problem becomes increasingly challenging with multiple interacting RO in such investments. We propose a scalable machine learning based RO framework for multi-period sequential service region design & timing problem for mobility-on-demand services, framed as a Markov decision process with non-stationary stochastic variables. A value function approximation policy from literature uses multi-option least squares Monte Carlo simulation to get a policy value for a set of interdependent investment decisions as deferral options (CR policy). The goal is to determine the optimal selection and timing of a set of zones to include in a service region. However, prior work required explicit enumeration of all possible sequences of investments. To address the combinatorial complexity of such enumeration, we propose a new variant "deep" RO policy using an efficient recurrent neural network (RNN) based ML method (CR-RNN policy) to sample sequences to forego the need for enumeration, making network design & timing policy tractable for large scale implementation. Experiments on multiple service region scenarios in New York City (NYC) shows the proposed policy substantially reduces the overall computational cost (time reduction for RO evaluation of > 90% of total investment sequences is achieved), with zero to near-zero gap compared to the benchmark. A case study of sequential service region design for expansion of MoD services in Brooklyn, NYC show that using the CR-RNN policy to determine optimal RO investment strategy yields a similar performance (0.5% within CR policy value) with significantly reduced computation time (about 5.4 times faster).
Evaluating Alternative Glyph Design for Showing Large-Magnitude-Range Quantum Spins
Venue: arXiv cs.HC 2022
Year: 2022

We present experimental results to explore a form of bivariate glyphs for representing large-magnitude-range vectors. The glyphs meet two conditions: (1) two visual dimensions are separable; and (2) one of the two visual dimensions uses a categorical representation (e.g., a categorical colormap). We evaluate how much these two conditions determine the bivariate glyphs' effectiveness. The first experiment asks participants to perform three local tasks requiring reading no more than two glyphs. The second experiment scales up the search space in global tasks when participants must look at the entire scene of hundreds of vector glyphs to get an answer. Our results support that the first condition is necessary for local tasks when a few items are compared. But it is not enough to understand a large amount of data. The second condition is necessary for perceiving global structures of examining very complex datasets. Participants' comments reveal that the categorical features in the bivariate glyphs trigger emergent optimal viewers' behaviors. This work contributes to perceptually accurate glyph representations for revealing patterns from large scientific results. We release source code, quantum physics data, training documents, participants' answers, and statistical analyses for reproducible science https://osf.io/4xcf5/?view_only=94123139df9c4ac984a1e0df811cd580.