OpenRaiser/Intern-Atlas huggingface dataset:
Reward Selection with Noisy Observations
Venue: arXiv cs.GT 2023
Year: 2023

We study a fundamental problem in optimization under uncertainty. There are $n$ boxes; each box $i$ contains a hidden reward $x_i$. Rewards are drawn i.i.d. from an unknown distribution $\mathcal{D}$. For each box $i$, we see $y_i$, an unbiased estimate of its reward, which is drawn from a Normal distribution with known standard deviation $σ_i$ (and an unknown mean $x_i$). Our task is to select a single box, with the goal of maximizing our reward. This problem captures a wide range of applications, e.g. ad auctions, where the hidden reward is the click-through rate of an ad. Previous work in this model [BKMR12] proves that the naive policy, which selects the box with the largest estimate $y_i$, is suboptimal, and suggests a linear policy, which selects the box $i$ with the largest $y_i - c \cdot σ_i$, for some $c > 0$. However, no formal guarantees are given about the performance of either policy (e.g., whether their expected reward is within some factor of the optimal policy's reward). In this work, we prove that both the naive policy and the linear policy are arbitrarily bad compared to the optimal policy, even when $\mathcal{D}$ is well-behaved, e.g. has monotone hazard rate (MHR), and even under a "small tail" condition, which requires that not too many boxes have arbitrarily large noise. On the flip side, we propose a simple threshold policy that gives a constant approximation to the reward of a prophet (who knows the realized values $x_1, \dots, x_n$) under the same "small tail" condition. We prove that when this condition is not satisfied, even an optimal clairvoyant policy (that knows $\mathcal{D}$) cannot get a constant approximation to the prophet, even for MHR distributions, implying that our threshold policy is optimal against the prophet benchmark, up to constants.
PolyLM: An Open Source Polyglot Large Language Model
Venue: arXiv cs.CL 2023
Year: 2023

Large language models (LLMs) demonstrate remarkable ability to comprehend, reason, and generate following nature language instructions. However, the development of LLMs has been primarily focused on high-resource languages, such as English, thereby limiting their applicability and research in other languages. Consequently, we present PolyLM, a multilingual LLM trained on 640 billion (B) tokens, avaliable in two model sizes: 1.7B and 13B. To enhance its multilingual capabilities, we 1) integrate bilingual data into training data; and 2) adopt a curriculum learning strategy that increases the proportion of non-English data from 30% in the first stage to 60% in the final stage during pre-training. Further, we propose a multilingual self-instruct method which automatically generates 132.7K diverse multilingual instructions for model fine-tuning. To assess the model's performance, we collect several existing multilingual tasks, including multilingual understanding, question answering, generation, and translation. Extensive experiments show that PolyLM surpasses other open-source models such as LLaMA and BLOOM on multilingual tasks while maintaining comparable performance in English. Our models, alone with the instruction data and multilingual benchmark, are available at: \url{https://modelscope.cn/models/damo/nlp_polylm_13b_text_generation}.
Semantic Communications System with Model Division Multiple Access and Controllable Coding Rate for Point Cloud
Venue: arXiv cs.MM 2023
Year: 2023

Point cloud, as a 3D representation, is widely used in autonomous driving, virtual reality (VR), and augmented reality (AR). However, traditional communication systems think that the point cloud's semantic information is irrelevant to communication, which hinders the efficient transmission of point clouds in the era of artificial intelligence (AI). This paper proposes a point cloud based semantic communication system (PCSC), which uses AI-based encoding techniques to extract the semantic information of the point cloud and joint source-channel coding (JSCC) technology to overcome the distortion caused by noise channels and solve the "cliff effect" in traditional communication. In addition, the system realizes the controllable coding rate without fine-tuning the network. The method analyzes the coded semantic vector's importance and discards semantically-unimportant information, thereby improving the transmission efficiency. Besides, PCSC and the recently proposed non-orthogonal model division multiple access (MDMA) technology are combined to design a point cloud MDMA transmission system (M-PCSC) for multi-user transmission. Relevant experimental results show that the proposed method outperforms the traditional method 10dB in the same channel bandwidth ratio under the PSNR D1 and PSNR D2 metrics. In terms of transmission, the proposed method can effectively solve the "cliff effect" in the traditional methods.
A Multi-Task Perspective for Link Prediction with New Relation Types and Nodes
Venue: arXiv cs.LG 2023
Year: 2023

The task of inductive link prediction in (discrete) attributed multigraphs infers missing attributed links (relations) between nodes in new test multigraphs. Traditional relational learning methods face the challenge of limited generalization to test multigraphs containing both novel nodes and novel relation types not seen in training. Recently, under the only assumption that all relation types share the same structural predictive patterns (single task), Gao et al. (2023) proposed a link prediction method using the theoretical concept of double equivariance (equivariance for nodes & relation types), in contrast to the (single) equivariance (only for nodes) used to design Graph Neural Networks (GNNs). In this work we further extend the double equivariance concept to multi-task double equivariance, where we define link prediction in attributed multigraphs that can have distinct and potentially conflicting predictive patterns for different sets of relation types (multiple tasks). Our empirical results on real-world datasets demonstrate that our approach can effectively generalize to test graphs with multi-task structures without access to additional information.
A Study on the Appropriate size of the Mongolian general corpus
Venue: arXiv cs.CL 2023
Year: 2023

This study aims to determine the appropriate size of the Mongolian general corpus. This study used the Heaps function and Type Token Ratio to determine the appropriate size of the Mongolian general corpus. The sample corpus of 906,064 tokens comprised texts from 10 domains of newspaper politics, economy, society, culture, sports, world articles and laws, middle and high school literature textbooks, interview articles, and podcast transcripts. First, we estimated the Heaps function with this sample corpus. Next, we observed changes in the number of types and TTR values while increasing the number of tokens by one million using the estimated Heaps function. As a result of observation, we found that the TTR value hardly changed when the number of tokens exceeded from 39 to 42 million. Thus, we conclude that an appropriate size for a Mongolian general corpus is from 39 to 42 million tokens.
Visualization for Multivariate Gaussian Anomaly Detection in Images
Venue: arXiv cs.CV 2023
Year: 2023

This paper introduces a simplified variation of the PaDiM (Pixel-Wise Anomaly Detection through Instance Modeling) method for anomaly detection in images, fitting a single multivariate Gaussian (MVG) distribution to the feature vectors extracted from a backbone convolutional neural network (CNN) and using their Mahalanobis distance as the anomaly score. We introduce an intermediate step in this framework by applying a whitening transformation to the feature vectors, which enables the generation of heatmaps capable of visually explaining the features learned by the MVG. The proposed technique is evaluated on the MVTec-AD dataset, and the results show the importance of visual model validation, providing insights into issues in this framework that were otherwise invisible. The visualizations generated for this paper are publicly available at https://doi.org/10.5281/zenodo.7937978.
Function-Space Regularization for Deep Bayesian Classification
Venue: arXiv cs.LG 2023
Year: 2023

Bayesian deep learning approaches assume model parameters to be latent random variables and infer posterior distributions to quantify uncertainty, increase safety and trust, and prevent overconfident and unpredictable behavior. However, weight-space priors are model-specific, can be difficult to interpret and are hard to specify. Instead, we apply a Dirichlet prior in predictive space and perform approximate function-space variational inference. To this end, we interpret conventional categorical predictions from stochastic neural network classifiers as samples from an implicit Dirichlet distribution. By adapting the inference, the same function-space prior can be combined with different models without affecting model architecture or size. We illustrate the flexibility and efficacy of such a prior with toy experiments and demonstrate scalability, improved uncertainty quantification and adversarial robustness with large-scale image classification experiments.
SpComp: A Sparsity Structure-Specific Compilation of Matrix Operations
Venue: arXiv cs.MS 2023
Year: 2023

Sparse matrix operations involve a large number of zero operands which makes most of the operations redundant. The amount of redundancy magnifies when a matrix operation repeatedly executes on sparse data. Optimizing matrix operations for sparsity involves either reorganization of data or reorganization of computations, performed either at compile-time or run-time. Although compile-time techniques avert from introducing run-time overhead, their application either is limited to simple sparse matrix operations generating dense output and handling immutable sparse matrices or requires manual intervention to customize the technique to different matrix operations. We contribute a compile time technique called SpComp that optimizes a sparse matrix operation by automatically customizing its computations to the positions of non-zero values of the data. Our approach neither incurs any run-time overhead nor requires any manual intervention. It is also applicable to complex matrix operations generating sparse output and handling mutable sparse matrices. We introduce a data-flow analysis, named Essential Indices Analysis, that statically collects the symbolic information about the computations and helps the code generator to reorganize the computations. The generated code includes piecewise-regular loops, free from indirect references and amenable to further optimization. We see a substantial performance gain by SpComp-generated SpMSpV code when compared against the state-of-the-art TACO compiler and piecewise-regular code generator. On average, we achieve 79% performance gain against TACO and 83% performance gain against the piecewise-regular code generator. When compared against the CHOLMOD library, SpComp generated sparse Cholesky decomposition code showcases 65% performance gain on average.
Learning Kernel-Modulated Neural Representation for Efficient Light Field Compression
Venue: arXiv cs.CV 2023
Year: 2023

Light field is a type of image data that captures the 3D scene information by recording light rays emitted from a scene at various orientations. It offers a more immersive perception than classic 2D images but at the cost of huge data volume. In this paper, we draw inspiration from the visual characteristics of Sub-Aperture Images (SAIs) of light field and design a compact neural network representation for the light field compression task. The network backbone takes randomly initialized noise as input and is supervised on the SAIs of the target light field. It is composed of two types of complementary kernels: descriptive kernels (descriptors) that store scene description information learned during training, and modulatory kernels (modulators) that control the rendering of different SAIs from the queried perspectives. To further enhance compactness of the network meanwhile retain high quality of the decoded light field, we accordingly introduce modulator allocation and kernel tensor decomposition mechanisms, followed by non-uniform quantization and lossless entropy coding techniques, to finally form an efficient compression pipeline. Extensive experiments demonstrate that our method outperforms other state-of-the-art (SOTA) methods by a significant margin in the light field compression task. Moreover, after aligning descriptors, the modulators learned from one light field can be transferred to new light fields for rendering dense views, indicating a potential solution for view synthesis task.
Smart Infrastructure: A Research Junction
Venue: arXiv cs.CV 2023
Year: 2023

Complex inner-city junctions are among the most critical traffic areas for injury and fatal accidents. The development of highly automated driving (HAD) systems struggles with the complex and hectic everyday life within those areas. Sensor-equipped smart infrastructures, which can communicate and cooperate with vehicles, are essential to enable a holistic scene understanding to resolve occlusions drivers and vehicle perception systems for themselves can not cover. We introduce an intelligent research infrastructure equipped with visual sensor technology, located at a public inner-city junction in Aschaffenburg, Germany. A multiple-view camera system monitors the traffic situation to perceive road users' behavior. Both motorized and non-motorized traffic is considered. The system is used for research in data generation, evaluating new HAD sensors systems, algorithms, and Artificial Intelligence (AI) training strategies using real-, synthetic- and augmented data. In addition, the junction features a highly accurate digital twin. Real-world data can be taken into the digital twin for simulation purposes and synthetic data generation.
Large Class Separation is not what you need for Relational Reasoning-based OOD Detection
Venue: arXiv cs.CV 2023
Year: 2023

Standard recognition approaches are unable to deal with novel categories at test time. Their overconfidence on the known classes makes the predictions unreliable for safety-critical applications such as healthcare or autonomous driving. Out-Of-Distribution (OOD) detection methods provide a solution by identifying semantic novelty. Most of these methods leverage a learning stage on the known data, which means training (or fine-tuning) a model to capture the concept of normality. This process is clearly sensitive to the amount of available samples and might be computationally expensive for on-board systems. A viable alternative is that of evaluating similarities in the embedding space produced by large pre-trained models without any further learning effort. We focus exactly on such a fine-tuning-free OOD detection setting. This works presents an in-depth analysis of the recently introduced relational reasoning pre-training and investigates the properties of the learned embedding, highlighting the existence of a correlation between the inter-class feature distance and the OOD detection accuracy. As the class separation depends on the chosen pre-training objective, we propose an alternative loss function to control the inter-class margin, and we show its advantage with thorough experiments.
B-CLEAN-SC: CLEAN-SC for broadband sources
Venue: arXiv cs.SD 2023
Year: 2023

This paper presents B-CLEAN-SC, a variation of CLEAN-SC for broadband sources. Opposed to CLEAN-SC, which ``deconvolves'' the beamforming map for each frequency individually, B-CLEAN-SC processes frequency intervals. Instead of performing a deconvolution iteration at the location of the maximum level, B-CLEAN-SC performs it at the location of the over-frequency-averaged maximum to improve the location estimation. The method is validated and compared to standard CLEAN-SC on synthetic cases, and real-world experiments, for broad- and narrowband sources. It improves the source reconstruction at low and high frequencies and suppresses noise, while it only increases the need for memory but not computational effort.
Online Information Acquisition: Hiring Multiple Agents
Venue: arXiv cs.GT 2023
Year: 2023

We investigate the mechanism design problem faced by a principal who hires \emph{multiple} agents to gather and report costly information. Then, the principal exploits the information to make an informed decision. We model this problem as a game, where the principal announces a mechanism consisting in action recommendations and a payment function, a.k.a. scoring rule. Then, each agent chooses an effort level and receives partial information about an underlying state of nature based on the effort. Finally, the agents report the information (possibly non-truthfully), the principal takes a decision based on this information, and the agents are paid according to the scoring rule. While previous work focuses on single-agent problems, we consider multi-agents settings. This poses the challenge of coordinating the agents' efforts and aggregating correlated information. Indeed, we show that optimal mechanisms must correlate agents' efforts, which introduces externalities among the agents, and hence complex incentive compatibility constraints and equilibrium selection problems. First, we design a polynomial-time algorithm to find an optimal incentive compatible mechanism. Then, we study an online problem, where the principal repeatedly interacts with a group of unknown agents. We design a no-regret algorithm that provides $\widetilde{\mathcal{O}}(T^{2/3})$ regret with respect to an optimal mechanism, matching the state-of-the-art bound for single-agent settings.
52 Weeks Later: Attitudes Towards COVID-19 Apps for Different Purposes Over Time
Venue: arXiv cs.HC 2023
Year: 2023

The COVID-19 pandemic has prompted countries around the world to introduce smartphone apps to support disease control efforts. Their purposes range from digital contact tracing to quarantine enforcement to vaccination passports, and their effectiveness often depends on widespread adoption. While previous work has identified factors that promote or hinder adoption, it has typically examined data collected at a single point in time or focused exclusively on digital contact tracing apps. In this work, we conduct the first representative study that examines changes in people's attitudes towards COVID-19-related smartphone apps for five different purposes over the first 1.5 years of the pandemic. In three survey rounds conducted between Summer 2020 and Summer 2021 in the United States and Germany, with approximately 1,000 participants per round and country, we investigate people's willingness to use such apps, their perceived utility, and people's attitudes towards them in different stages of the pandemic. Our results indicate that privacy is a consistent concern for participants, even in a public health crisis, and the collection of identity-related data significantly decreases acceptance of COVID-19 apps. Trust in authorities is essential to increase confidence in government-backed apps and foster citizens' willingness to contribute to crisis management. There is a need for continuous communication with app users to emphasize the benefits of health crisis apps both for individuals and society, thus counteracting decreasing willingness to use them and perceived usefulness as the pandemic evolves.
Machine learning and Topological data analysis identify unique features of human papillae in 3D scans
Venue: arXiv cs.LG 2023
Year: 2023

The tongue surface houses a range of papillae that are integral to the mechanics and chemistry of taste and textural sensation. Although gustatory function of papillae is well investigated, the uniqueness of papillae within and across individuals remains elusive. Here, we present the first machine learning framework on 3D microscopic scans of human papillae (n = 2092), uncovering the uniqueness of geometric and topological features of papillae. The finer differences in shapes of papillae are investigated computationally based on a number of features derived from discrete differential geometry and computational topology. Interpretable machine learning techniques show that persistent homology features of the papillae shape are the most effective in predicting the biological variables. Models trained on these features with small volumes of data samples predict the type of papillae with an accuracy of 85%. The papillae type classification models can map the spatial arrangement of filiform and fungiform papillae on a surface. Remarkably, the papillae are found to be distinctive across individuals and an individual can be identified with an accuracy of 48% among the 15 participants from a single papillae. Collectively, this is the first unprecedented evidence demonstrating that tongue papillae can serve as a unique identifier inspiring new research direction for food preferences and oral diagnostics.
Cosserat-Rod Based Dynamic Modeling of Soft Slender Robot Interacting with Environment
Venue: arXiv cs.RO 2023
Year: 2023

Soft slender robots have attracted more and more research attentions in these years due to their continuity and compliance natures. However, mechanics modeling for soft robots interacting with environment is still an academic challenge because of the non-linearity of deformation and the non-smooth property of the contacts. In this work, starting from a piece-wise local strain field assumption, we propose a nonlinear dynamic model for soft robot via Cosserat rod theory using Newtonian mechanics which handles the frictional contact with environment and transfer them into the nonlinear complementary constraint (NCP) formulation. Moreover, we smooth both the contact and friction constraints in order to convert the inequality equations of NCP to the smooth equality equations. The proposed model allows us to compute the dynamic deformation and frictional contact force under common optimization framework in real time when the soft slender robot interacts with other rigid or soft bodies. In the end, the corresponding experiments are carried out which valid our proposed dynamic model.
An Architecture for Control Plane Slicing in Beyond 5G Networks
Venue: arXiv cs.NI 2023
Year: 2023

To accommodate various use cases with differing characteristics, the Fifth Generation (5G) mobile communications system intends to utilize network slicing. Network slicing enables the creation of multiple logical networks over a shared physical network infrastructure. While the problems such as resource allocation for multiple slices in mobile networks have been explored in considerable detail in the existing literature, the suitability of the existing mobile network architecture to support network slicing has not been analysed adequately. We think the existing 5G System (5GS) architecture suffers from certain limitations, such as a lack of slice isolation in its control plane. This work focuses on the future evolution of the existing 5GS architecture from a slicing perspective, especially that of its control plane, addressing some of the limitations of the existing 5GS architecture. We propose a new network architecture which enables efficient slicing in beyond 5G networks. The proposed architecture results in enhanced modularity and scalability of the control plane in sliced mobile networks. In addition, it also brings slice isolation to the control plane, which is not feasible in the existing 5G system. We also present a performance evaluation that confirms the improved performance and scalability of the proposed system viz a viz the existing 5G system.