OpenRaiser/Intern-Atlas huggingface dataset:
Methodological monotheism across fields of science in contemporary quantitative research
Venue: arXiv cs.DL 2022
Year: 2022

The importance of research teams' diversity for the progress of science is highlighted extensively. Despite the seemingly hegemonic role of hypothesis testing in modern quantitative research, little attention has been devoted to the diversity of quantitative methods, epitomized by the linear model framework of analysis. Using bibliometric data from the Web of Science, we conduct a large-scale and cross-disciplinary assessment of the prevalence of linear-model-based research from 1990 to 2022. In absolute terms, linear models are widely used across all fields of science. In relative terms, three patterns suggest linear models are hegemonic among Social Sciences. First, there is a high and growing prevalence of linear-model-based research. Second, global patterns of linear-model-based research prevalence align with global inequalities in knowledge production. Third, there was a citation premium to linear-model-based research until 2012 for articles' number of citations and for the entire period in terms of having at least one citation. Previous research suggests that the confluence of these patterns may be detrimental to the Social Sciences as it potentially marginalizes theories incompatible with the linear models' framework, lowers the diversity of narratives about social phenomena, and prevents innovative and path-breaking research, limiting the breadth of research.
Exploring Anchor-based Detection for Ego4D Natural Language Query
Venue: arXiv cs.CV 2022
Year: 2022

In this paper we provide the technique report of Ego4D natural language query challenge in CVPR 2022. Natural language query task is challenging due to the requirement of comprehensive understanding of video contents. Most previous works address this task based on third-person view datasets while few research interest has been placed in the ego-centric view by far. Great progress has been made though, we notice that previous works can not adapt well to ego-centric view datasets e.g., Ego4D mainly because of two reasons: 1) most queries in Ego4D have a excessively small temporal duration (e.g., less than 5 seconds); 2) queries in Ego4D are faced with much more complex video understanding of long-term temporal orders. Considering these, we propose our solution of this challenge to solve the above issues.
DSM-MoC as Baseline: Reliability Assurance via Redundant Cellular Connectivity in Connected Cars
Venue: arXiv cs.NI 2022
Year: 2022

Connected Cars (CCs) and vehicle-to-everything (V2X) use cases require stringent reliability for safety and non-safety uses. With increasing network softwarisation, it has become easier to use multiple, redundant connectivity options instead of relying on a single network connectivity. But where should these redundant connections be managed? Is it at a network provider's core network - i.e. supply side managed (SSM) - or at the CC - i.e. demand side managed (DSM)? In our work, we investigate the use of SSM and DSM for CCs on four separate days and across 800 kilometers of major / minor roads in South East England. For Day 1, we captured performance indicators, and determined hypothetical multi-operator configurations for four UK providers and a global Universal SIM. For Day 2, 3& 4, we built and deployed a test-bed to actually implement network switching and understand performance (incl. for TCP & UDP) either on the road or in a stationary location. Based on our results, we make three contributions. First, we show that DSM can deliver superior performance for CCs more than any individual network (up to 28 percentage points in a hypothetical scenario), or SSM which had up to 4.8x longer page load times. Second, unlike other smartphone-only studies, our system-level study demonstrates that improvements of at least 12% can be obtained in a practical DSM field implementation for a CC. Third, we confirm that the advantage of DSM in a field implementation is higher for UDP traffic (23% better latency) compared to TCP (13%).
Diversifying Message Aggregation in Multi-Agent Communication via Normalized Tensor Nuclear Norm Regularization
Venue: arXiv cs.MA 2022
Year: 2022

Aggregating messages is a key component for the communication of multi-agent reinforcement learning (Comm-MARL). Recently, it has witnessed the prevalence of graph attention networks (GAT) in Comm-MARL, where agents can be represented as nodes and messages can be aggregated via the weighted passing. While successful, GAT can lead to homogeneity in the strategies of message aggregation, and the ``core'' agent may excessively influence other agents' behaviors, which can severely limit the multi-agent coordination. To address this challenge, we first study the adjacency tensor of the communication graph and demonstrate that the homogeneity of message aggregation could be measured by the normalized tensor rank. Since the rank optimization problem is known to be NP-hard, we define a new nuclear norm, which is a convex surrogate of normalized tensor rank, to replace the rank. Leveraging the norm, we further propose a plug-and-play regularizer on the adjacency tensor, named Normalized Tensor Nuclear Norm Regularization (NTNNR), to actively enrich the diversity of message aggregation during the training stage. We extensively evaluate GAT with the proposed regularizer in both cooperative and mixed cooperative-competitive scenarios. The results demonstrate that aggregating messages using NTNNR-enhanced GAT can improve the efficiency of the training and achieve higher asymptotic performance than existing message aggregation methods. When NTNNR is applied to existing graph-attention Comm-MARL methods, we also observe significant performance improvements on the StarCraft II micromanagement benchmarks.
EvolveHypergraph: Group-Aware Dynamic Relational Reasoning for Trajectory Prediction
Venue: arXiv cs.CV 2022
Year: 2022

While the modeling of pair-wise relations has been widely studied in multi-agent interacting systems, its ability to capture higher-level and larger-scale group-wise activities is limited. In this paper, we propose a group-aware relational reasoning approach (named EvolveHypergraph) with explicit inference of the underlying dynamically evolving relational structures, and we demonstrate its effectiveness for multi-agent trajectory prediction. In addition to the edges between a pair of nodes (i.e., agents), we propose to infer hyperedges that adaptively connect multiple nodes to enable group-aware relational reasoning in an unsupervised manner without fixing the number of hyperedges. The proposed approach infers the dynamically evolving relation graphs and hypergraphs over time to capture the evolution of relations, which are used by the trajectory predictor to obtain future states. Moreover, we propose to regularize the smoothness of the relation evolution and the sparsity of the inferred graphs or hypergraphs, which effectively improves training stability and enhances the explainability of inferred relations. The proposed approach is validated on both synthetic crowd simulations and multiple real-world benchmark datasets. Our approach infers explainable, reasonable group-aware relations and achieves state-of-the-art performance in long-term prediction.
IRS-Based Integrated Location Sensing and Communication for mmWave SIMO Systems
Venue: arXiv cs.IT 2022
Year: 2022

In this paper, we establish an integrated sensing and communication (ISAC) system based on a distributed semi-passive intelligent reflecting surface (IRS), which allows location sensing and data transmission to be carried out simultaneously, sharing the same frequency and time resources. The detailed working process of the proposed IRS-based ISAC system is designed, including the transmission protocol, location sensing and beamforming optimization. Specifically, each coherence block consists of two periods, the ISAC period with two time blocks and the pure communication (PC) period. During each time block of the ISAC period, data transmission and user positioning are carried out simultaneously. The estimated user location in the first time block will be used for beamforming design in the second time block. During the PC period, only data transmission is conducted, by invoking the user location estimated in the second time block of the ISAC period for beamforming design. {\color{black}Simulation results show that a millimeter-level positioning accuracy can be achieved by the proposed location sensing scheme, demonstrating the advantage of the proposed IRS-based ISAC framework. Besides, the proposed two beamforming schemes based on the estimated location information achieve similar performance to the benchmark schemes assuming perfect channel state information (CSI), which verifies the effectiveness of beamforming design using sensed location information.
Towards Automating Retinoscopy for Refractive Error Diagnosis
Venue: arXiv cs.HC 2022
Year: 2022

Refractive error is the most common eye disorder and is the key cause behind correctable visual impairment, responsible for nearly 80% of the visual impairment in the US. Refractive error can be diagnosed using multiple methods, including subjective refraction, retinoscopy, and autorefractors. Although subjective refraction is the gold standard, it requires cooperation from the patient and hence is not suitable for infants, young children, and developmentally delayed adults. Retinoscopy is an objective refraction method that does not require any input from the patient. However, retinoscopy requires a lens kit and a trained examiner, which limits its use for mass screening. In this work, we automate retinoscopy by attaching a smartphone to a retinoscope and recording retinoscopic videos with the patient wearing a custom pair of paper frames. We develop a video processing pipeline that takes retinoscopic videos as input and estimates the net refractive error based on our proposed extension of the retinoscopy mathematical model. Our system alleviates the need for a lens kit and can be performed by an untrained examiner. In a clinical trial with 185 eyes, we achieved a sensitivity of 91.0% and specificity of 74.0% on refractive error diagnosis. Moreover, the mean absolute error of our approach was 0.75$\pm$0.67D on net refractive error estimation compared to subjective refraction measurements. Our results indicate that our approach has the potential to be used as a retinoscopy-based refractive error screening tool in real-world medical settings.
Diverse Generative Perturbations on Attention Space for Transferable Adversarial Attacks
Venue: arXiv cs.CV 2022
Year: 2022

Adversarial attacks with improved transferability - the ability of an adversarial example crafted on a known model to also fool unknown models - have recently received much attention due to their practicality. Nevertheless, existing transferable attacks craft perturbations in a deterministic manner and often fail to fully explore the loss surface, thus falling into a poor local optimum and suffering from low transferability. To solve this problem, we propose Attentive-Diversity Attack (ADA), which disrupts diverse salient features in a stochastic manner to improve transferability. Primarily, we perturb the image attention to disrupt universal features shared by different models. Then, to effectively avoid poor local optima, we disrupt these features in a stochastic manner and explore the search space of transferable perturbations more exhaustively. More specifically, we use a generator to produce adversarial perturbations that each disturbs features in different ways depending on an input latent code. Extensive experimental evaluations demonstrate the effectiveness of our method, outperforming the transferability of state-of-the-art methods. Codes are available at https://github.com/wkim97/ADA.
Runtime Analysis of the (1+1) EA on Weighted Sums of Transformed Linear Functions
Venue: arXiv cs.NE 2022
Year: 2022

Linear functions play a key role in the runtime analysis of evolutionary algorithms and studies have provided a wide range of new insights and techniques for analyzing evolutionary computation methods. Motivated by studies on separable functions and the optimization behaviour of evolutionary algorithms as well as objective functions from the area of chance constrained optimization, we study the class of objective functions that are weighted sums of two transformed linear functions. Our results show that the (1+1) EA, with a mutation rate depending on the number of overlapping bits of the functions, obtains an optimal solution for these functions in expected time O(n log n), thereby generalizing a well-known result for linear functions to a much wider range of problems.
Modular Extremely Large-Scale Array Communication: Near-Field Modelling and Performance Analysis
Venue: arXiv cs.IT 2022
Year: 2022

This paper investigates wireless communications based on a new antenna array architecture, termed modular extremely large-scale array (XL-array), where an extremely large number of antenna elements are regularly arranged on a common platform in a modular manner. Each module consists of a flexible/moderate number of antenna elements, and different modules are separated with an inter-module spacing that is typically much larger than the inter-element spacing/signal wavelength for ease of deployment. By properly modelling the variations of signal phase, amplitude and projected aperture across different array modules/elements, we develop the new channel model and analyze the signal-to-noise ratio (SNR) performance of the modular XL-array based communications. Under the practical non-uniform spherical wave (NUSW) model, the closed-form expression of the maximum achievable SNR is derived in terms of key geometric parameters, including the total planar array size, module separation distances along each dimension, as well as the user's location in the three-dimensional (3D) space. Besides, the asymptotic SNR scaling laws are revealed as the number of modules along different dimensions goes to infinity. Moreover, we show that our developed near-field modelling and performance analysis include the existing ones for the collocated XL-array, the far-field uniform plane wave (UPW) model, as well as the one-dimensional (1D) modular extremely large-scale uniform linear array (XL-ULA) as special cases. Extensive simulation results are provided to validate our obtained results.
Learning Based Joint Coding-Modulation for Digital Semantic Communication Systems
Venue: arXiv cs.IT 2022
Year: 2022

In learning-based semantic communications, neural networks have replaced different building blocks in traditional communication systems. However, the digital modulation still remains a challenge for neural networks. The intrinsic mechanism of neural network based digital modulation is mapping continuous output of the neural network encoder into discrete constellation symbols, which is a non-differentiable function that cannot be trained with existing gradient descend algorithms. To overcome this challenge, in this paper we develop a joint coding-modulation scheme for digital semantic communications with BPSK modulation. In our method, the neural network outputs the likelihood of each constellation point, instead of having a concrete mapping. A random code rather than a deterministic code is hence used, which preserves more information for the symbols with a close likelihood on each constellation point. The joint coding-modulation design can match the modulation process with channel states, and hence improve the performance of digital semantic communications. Experiment results show that our method outperforms existing digital modulation methods in semantic communications over a wide range of SNR, and outperforms neural network based analog modulation method in low SNR regime.
Empirical investigations on WVA structural issues
Venue: arXiv cs.LG 2022
Year: 2022

In this paper we want to present the results of empirical verification of some issues concerning the methods for overcoming catastrophic forgetting in neural networks. First, in the introduction, we will try to describe in detail the problem of catastrophic forgetting and methods for overcoming it for those who are not yet familiar with this topic. Then we will discuss the essence and limitations of the WVA method which we presented in previous papers. Further, we will touch upon the issues of applying the WVA method to gradients or optimization steps of weights, choosing the optimal attenuation function in this method, as well as choosing the optimal hyper-parameters of the method depending on the number of tasks in sequential training of neural networks.
Regressing Relative Fine-Grained Change for Sub-Groups in Unreliable Heterogeneous Data Through Deep Multi-Task Metric Learning
Venue: arXiv cs.LG 2022
Year: 2022

Fine-Grained Change Detection and Regression Analysis are essential in many applications of ArtificialIntelligence. In practice, this task is often challenging owing to the lack of reliable ground truth information andcomplexity arising from interactions between the many underlying factors affecting a system. Therefore,developing a framework which can represent the relatedness and reliability of multiple sources of informationbecomes critical. In this paper, we investigate how techniques in multi-task metric learning can be applied for theregression of fine-grained change in real data.The key idea is that if we incorporate the incremental change in a metric of interest between specific instancesof an individual object as one of the tasks in a multi-task metric learning framework, then interpreting thatdimension will allow the user to be alerted to fine-grained change invariant to what the overall metric isgeneralised to be. The techniques investigated are specifically tailored for handling heterogeneous data sources,i.e. the input data for each of the tasks might contain missing values, the scale and resolution of the values is notconsistent across tasks and the data contains non-independent and identically distributed (non-IID) instances. Wepresent the results of our initial experimental implementations of this idea and discuss related research in thisdomain which may offer direction for further research.
Empty Triangles in Generalized Twisted Drawings of $K_n$
Venue: arXiv cs.CG 2022
Year: 2022

Simple drawings are drawings of graphs in the plane or on the sphere such that vertices are distinct points, edges are Jordan arcs connecting their endpoints, and edges intersect at most once (either in a proper crossing or in a shared endpoint). Simple drawings are generalized twisted if there is a point $O$ such that every ray emanating from $O$ crosses every edge of the drawing at most once and there is a ray emanating from $O$ which crosses every edge exactly once. We show that all generalized twisted drawings of $K_n$ contain exactly $2n-4$ empty triangles, by this making a substantial step towards proving the conjecture that this is the case for every simple drawing of $K_n$.
K-UNN: k-Space Interpolation With Untrained Neural Network
Venue: arXiv cs.CV 2022
Year: 2022

Recently, untrained neural networks (UNNs) have shown satisfactory performances for MR image reconstruction on random sampling trajectories without using additional full-sampled training data. However, the existing UNN-based approach does not fully use the MR image physical priors, resulting in poor performance in some common scenarios (e.g., partial Fourier, regular sampling, etc.) and the lack of theoretical guarantees for reconstruction accuracy. To bridge this gap, we propose a safeguarded k-space interpolation method for MRI using a specially designed UNN with a tripled architecture driven by three physical priors of the MR images (or k-space data), including sparsity, coil sensitivity smoothness, and phase smoothness. We also prove that the proposed method guarantees tight bounds for interpolated k-space data accuracy. Finally, ablation experiments show that the proposed method can more accurately characterize the physical priors of MR images than existing traditional methods. Additionally, under a series of commonly used sampling trajectories, experiments also show that the proposed method consistently outperforms traditional parallel imaging methods and existing UNNs, and even outperforms the state-of-the-art supervised-trained k-space deep learning methods in some cases.
A Deformation-based Edit Distance for Merge Trees
Venue: arXiv cs.CG 2022
Year: 2022

In scientific visualization, scalar fields are often compared through edit distances between their merge trees. Typical tasks include ensemble analysis, feature tracking and symmetry or periodicity detection. Tree edit distances represent how one tree can be transformed into another through a sequence of simple edit operations: relabeling, insertion and deletion of nodes. In this paper, we present a new set of edit operations working directly on the merge tree as an geometrical or topological object: the represented operations are deformation retractions and inverse transformations on merge trees, which stands in contrast to other methods working on branch decomposition trees. We present a quartic time algorithm for the new edit distance, which is branch decomposition-independent and a metric on the set of all merge trees.
Language Tokens: A Frustratingly Simple Approach Improves Zero-Shot Performance of Multilingual Translation
Venue: arXiv cs.CL 2022
Year: 2022

This paper proposes a simple yet effective method to improve direct (X-to-Y) translation for both cases: zero-shot and when direct data is available. We modify the input tokens at both the encoder and decoder to include signals for the source and target languages. We show a performance gain when training from scratch, or finetuning a pretrained model with the proposed setup. In the experiments, our method shows nearly 10.0 BLEU points gain on in-house datasets depending on the checkpoint selection criteria. In a WMT evaluation campaign, From-English performance improves by 4.17 and 2.87 BLEU points, in the zero-shot setting, and when direct data is available for training, respectively. While X-to-Y improves by 1.29 BLEU over the zero-shot baseline, and 0.44 over the many-to-many baseline. In the low-resource setting, we see a 1.5~1.7 point improvement when finetuning on X-to-Y domain data.
Predicting Tornadoes days ahead with Machine Learning
Venue: arXiv cs.LG 2022
Year: 2022

Developing methods to predict disastrous natural phenomena is more important than ever, and tornadoes are among the most dangerous ones in nature. Due to the unpredictability of the weather, counteracting them is not an easy task and today it is mainly carried out by expert meteorologists, who interpret meteorological models. In this paper we propose a system for the early detection of a tornado, validating its effectiveness in a real-world context and exploiting meteorological data collection systems that are already widespread throughout the world. Our system was able to predict tornadoes with a maximum probability of 84% up to five days before the event on a novel dataset of more than 5000 tornadic and non-tornadic events. The dataset and the code to reproduce our results are available at: https://tinyurl.com/3brsfwpk