OpenRaiser/Intern-Atlas huggingface dataset:
MRGazer: Decoding Eye Gaze Points from Functional Magnetic Resonance Imaging in Individual Space
Venue: arXiv cs.CV 2023
Year: 2023

Eye-tracking research has proven valuable in understanding numerous cognitive functions. Recently, Frey et al. provided an exciting deep learning method for learning eye movements from fMRI data. However, it needed to co-register fMRI into standard space to obtain eyeballs masks, and thus required additional templates and was time consuming. To resolve this issue, in this paper, we propose a framework named MRGazer for predicting eye gaze points from fMRI in individual space. The MRGazer consisted of eyeballs extraction module and a residual network-based eye gaze prediction. Compared to the previous method, the proposed framework skips the fMRI co-registration step, simplifies the processing protocol and achieves end-to-end eye gaze regression. The proposed method achieved superior performance in a variety of eye movement tasks than the co-registration-based method, and delivered objective results within a shorter time (~ 0.02 Seconds for each volume) than prior method (~0.3 Seconds for each volume).
Conflict Management in the Near-RT-RIC of Open RAN: A Game Theoretic Approach
Venue: arXiv cs.NI 2023
Year: 2023

Open Radio Access Network (RAN) was introduced recently to incorporate intelligence and openness into the upcoming generation of RAN. Open RAN offers standardized interfaces and the capacity to accommodate network applications from external vendors through extensible applications (xApps), which enhance network management flexibility. The Near-Real-Time Radio Intelligent Controller (Near-RT-RIC) employs specialized and intelligent xApps for achieving time-critical optimization objectives, but conflicts may arise due to different vendors' xApps modifying the same parameters or indirectly affecting each others' performance. A standardized Conflict Management System (CMS) is absent in most of the popular Open RAN architectures including the most prominent O-RAN Alliance architecture. To address this, we propose a CMS with independent controllers for conflict detection and mitigation between xApps in the Near-RT-RIC. We utilize cooperative bargain game theory, including Nash Social Welfare Function (NSWF) and the Equal Gains (EG) solution, to find optimal configurations for conflicting parameters. Experimental results demonstrate the effectiveness of the proposed Conflict Management Controller (CMC) in balancing conflicting parameters and mitigating adverse impacts in the Near-RT-RIC on a theoretical example scenario.
Revisiting Machine Learning based Test Case Prioritization for Continuous Integration
Venue: arXiv cs.SE 2023
Year: 2023

To alleviate the cost of regression testing in continuous integration (CI), a large number of machine learning-based (ML-based) test case prioritization techniques have been proposed. However, it is yet unknown how they perform under the same experimental setup, because they are evaluated on different datasets with different metrics. To bridge this gap, we conduct the first comprehensive study on these ML-based techniques in this paper. We investigate the performance of 11 representative ML-based prioritization techniques for CI on 11 open-source subjects and obtain a series of findings. For example, the performance of the techniques changes across CI cycles, mainly resulting from the changing amount of training data, instead of code evolution and test removal/addition. Based on the findings, we give some actionable suggestions on enhancing the effectiveness of ML-based techniques, e.g., pretraining a prioritization technique with cross-subject data to get it thoroughly trained and then finetuning it with within-subject data dramatically improves its performance. In particular, the pretrained MART achieves state-of-the-art performance, producing the optimal sequence on 80% subjects, while the existing best technique, the original MART, only produces the optimal sequence on 50% subjects.
Transfer Attacks and Defenses for Large Language Models on Coding Tasks
Venue: arXiv cs.LG 2023
Year: 2023

Modern large language models (LLMs), such as ChatGPT, have demonstrated impressive capabilities for coding tasks including writing and reasoning about code. They improve upon previous neural network models of code, such as code2seq or seq2seq, that already demonstrated competitive results when performing tasks such as code summarization and identifying code vulnerabilities. However, these previous code models were shown vulnerable to adversarial examples, i.e. small syntactic perturbations that do not change the program's semantics, such as the inclusion of "dead code" through false conditions or the addition of inconsequential print statements, designed to "fool" the models. LLMs can also be vulnerable to the same adversarial perturbations but a detailed study on this concern has been lacking so far. In this paper we aim to investigate the effect of adversarial perturbations on coding tasks with LLMs. In particular, we study the transferability of adversarial examples, generated through white-box attacks on smaller code models, to LLMs. Furthermore, to make the LLMs more robust against such adversaries without incurring the cost of retraining, we propose prompt-based defenses that involve modifying the prompt to include additional information such as examples of adversarially perturbed code and explicit instructions for reversing adversarial perturbations. Our experiments show that adversarial examples obtained with a smaller code model are indeed transferable, weakening the LLMs' performance. The proposed defenses show promise in improving the model's resilience, paving the way to more robust defensive solutions for LLMs in code-related applications.
Multi-Objective Bayesian Optimization with Active Preference Learning
Venue: arXiv cs.LG 2023
Year: 2023

There are a lot of real-world black-box optimization problems that need to optimize multiple criteria simultaneously. However, in a multi-objective optimization (MOO) problem, identifying the whole Pareto front requires the prohibitive search cost, while in many practical scenarios, the decision maker (DM) only needs a specific solution among the set of the Pareto optimal solutions. We propose a Bayesian optimization (BO) approach to identifying the most preferred solution in the MOO with expensive objective functions, in which a Bayesian preference model of the DM is adaptively estimated by an interactive manner based on the two types of supervisions called the pairwise preference and improvement request. To explore the most preferred solution, we define an acquisition function in which the uncertainty both in the objective functions and the DM preference is incorporated. Further, to minimize the interaction cost with the DM, we also propose an active learning strategy for the preference estimation. We empirically demonstrate the effectiveness of our proposed method through the benchmark function optimization and the hyper-parameter optimization problems for machine learning models.
Experimentation in Early-Stage Video Game Startups: Practices and Challenges
Venue: arXiv cs.SE 2023
Year: 2023

Experimentation has been considered critical for successful software product and business development, including in video game startups. Video game startups need "wow" qualities that distinguish them from the competition. Thus, they need to continuously experiment to find these qualities before running out of time and resources. In this study, we aimed to explore how these companies perform experimentation. We interviewed four co-founders of video game startups. Our findings identify six practices, or scenarios, through which video game startups conduct experiments and challenges associated with these. The initial results could inform these startups about the possibilities and challenges and guide future research.
ADriver-I: A General World Model for Autonomous Driving
Venue: arXiv cs.CV 2023
Year: 2023

Typically, autonomous driving adopts a modular design, which divides the full stack into perception, prediction, planning and control parts. Though interpretable, such modular design tends to introduce a substantial amount of redundancy. Recently, multimodal large language models (MLLM) and diffusion techniques have demonstrated their superior performance on comprehension and generation ability. In this paper, we first introduce the concept of interleaved vision-action pair, which unifies the format of visual features and control signals. Based on the vision-action pairs, we construct a general world model based on MLLM and diffusion model for autonomous driving, termed ADriver-I. It takes the vision-action pairs as inputs and autoregressively predicts the control signal of the current frame. The generated control signals together with the historical vision-action pairs are further conditioned to predict the future frames. With the predicted next frame, ADriver-I performs further control signal prediction. Such a process can be repeated infinite times, ADriver-I achieves autonomous driving in the world created by itself. Extensive experiments are conducted on nuScenes and our large-scale private datasets. ADriver-I shows impressive performance compared to several constructed baselines. We hope our ADriver-I can provide some new insights for future autonomous driving and embodied intelligence.
Ball Mill Fault Prediction Based on Deep Convolutional Auto-Encoding Network
Venue: arXiv cs.LG 2023
Year: 2023

Ball mills play a critical role in modern mining operations, making their bearing failures a significant concern due to the potential loss of production efficiency and economic consequences. This paper presents an anomaly detection method based on Deep Convolutional Auto-encoding Neural Networks (DCAN) for addressing the issue of ball mill bearing fault detection. The proposed approach leverages vibration data collected during normal operation for training, overcoming challenges such as labeling issues and data imbalance often encountered in supervised learning methods. DCAN includes the modules of convolutional feature extraction and transposed convolutional feature reconstruction, demonstrating exceptional capabilities in signal processing and feature extraction. Additionally, the paper describes the practical deployment of the DCAN-based anomaly detection model for bearing fault detection, utilizing data from the ball mill bearings of Wuhan Iron & Steel Resources Group and fault data from NASA's bearing vibration dataset. Experimental results validate the DCAN model's reliability in recognizing fault vibration patterns. This method holds promise for enhancing bearing fault detection efficiency, reducing production interruptions, and lowering maintenance costs.
TRIDENT: The Nonlinear Trilogy for Implicit Neural Representations
Venue: arXiv cs.CV 2023
Year: 2023

Implicit neural representations (INRs) have garnered significant interest recently for their ability to model complex, high-dimensional data without explicit parameterisation. In this work, we introduce TRIDENT, a novel function for implicit neural representations characterised by a trilogy of nonlinearities. Firstly, it is designed to represent high-order features through order compactness. Secondly, TRIDENT efficiently captures frequency information, a feature called frequency compactness. Thirdly, it has the capability to represent signals or images such that most of its energy is concentrated in a limited spatial region, denoting spatial compactness. We demonstrated through extensive experiments on various inverse problems that our proposed function outperforms existing implicit neural representation functions.
TDiffDe: A Truncated Diffusion Model for Remote Sensing Hyperspectral Image Denoising
Venue: arXiv cs.CV 2023
Year: 2023

Hyperspectral images play a crucial role in precision agriculture, environmental monitoring or ecological analysis. However, due to sensor equipment and the imaging environment, the observed hyperspectral images are often inevitably corrupted by various noise. In this study, we proposed a truncated diffusion model, called TDiffDe, to recover the useful information in hyperspectral images gradually. Rather than starting from a pure noise, the input data contains image information in hyperspectral image denoising. Thus, we cut the trained diffusion model from small steps to avoid the destroy of valid information.
On the Size Overhead of Pairwise Spanners
Venue: arXiv cs.DS 2023
Year: 2023

Given an undirected possibly weighted $n$-vertex graph $G=(V,E)$ and a set $\mathcal{P}\subseteq V^2$ of pairs, a subgraph $S=(V,E')$ is called a ${\cal P}$-pairwise $α$-spanner of $G$, if for every pair $(u,v)\in\mathcal{P}$ we have $d_S(u,v)\leqα\cdot d_G(u,v)$. The parameter $α$ is called the stretch of the spanner, and its size overhead is define as $\frac{|E'|}{|{\cal P}|}$. A surprising connection was recently discussed between the additive stretch of $(1+ε,β)$-spanners, to the hopbound of $(1+ε,β)$-hopsets. A long sequence of works showed that if the spanner/hopset has size $\approx n^{1+1/k}$ for some parameter $k\ge 1$, then $β\approx\left(\frac1ε\right)^{\log k}$. In this paper we establish a new connection to the size overhead of pairwise spanners. In particular, we show that if $|{\cal P}|\approx n^{1+1/k}$, then a ${\cal P}$-pairwise $(1+ε)$-spanner must have size at least $β\cdot |{\cal P}|$ with $β\approx\left(\frac1ε\right)^{\log k}$ (a near matching upper bound was recently shown in \cite{ES23}). We also extend the connection between pairwise spanners and hopsets to the large stretch regime, by showing nearly matching upper and lower bounds for ${\cal P}$-pairwise $α$-spanners. In particular, we show that if $|{\cal P}|\approx n^{1+1/k}$, then the size overhead is $β\approx\frac kα$. A source-wise spanner is a special type of pairwise spanner, for which ${\cal P}=A\times V$ for some $A\subseteq V$. A prioritized spanner is given also a ranking of the vertices $V=(v_1,\dots,v_n)$, and is required to provide improved stretch for pairs containing higher ranked vertices. By using a sequence of reductions, we improve on the state-of-the-art results for source-wise and prioritized spanners.
Beat-Aligned Spectrogram-to-Sequence Generation of Rhythm-Game Charts
Venue: arXiv cs.LG 2023
Year: 2023

In the heart of "rhythm games" - games where players must perform actions in sync with a piece of music - are "charts", the directives to be given to players. We newly formulate chart generation as a sequence generation task and train a Transformer using a large dataset. We also introduce tempo-informed preprocessing and training procedures, some of which are suggested to be integral for a successful training. Our model is found to outperform the baselines on a large dataset, and is also found to benefit from pretraining and finetuning.
Molly: A Verified Compiler for Cryptoprotocol Roles
Venue: arXiv cs.CR 2023
Year: 2023

Molly is a program that compiles cryptographic protocol roles written in a high-level notation into straight-line programs in an intermediate-level imperative language, suitable for implementation in a conventional programming language. We define a denotational semantics for protocol roles based on an axiomatization of the runtime. A notable feature of our approach is that we assume that encryption is randomized. Thus, at the runtime level we treat encryption as a relation rather than a function. Molly is written in Coq, and generates a machine-checked proof that the procedure it constructs is correct with respect to the runtime semantics. Using Coq's extraction mechanism, one can build an efficient functional program for compilation.
Scalable CP Decomposition for Tensor Learning using GPU Tensor Cores
Venue: arXiv cs.LG 2023
Year: 2023

CP decomposition is a powerful tool for data science, especially gene analysis, deep learning, and quantum computation. However, the application of tensor decomposition is largely hindered by the exponential increment of the computational complexity and storage consumption with the size of tensors. While the data in our real world is usually presented as trillion- or even exascale-scale tensors, existing work can only support billion-scale scale tensors. In our work, we propose the Exascale-Tensor to mitigate the significant gap. Specifically, we propose a compression-based tensor decomposition framework, namely the exascale-tensor, to support exascale tensor decomposition. Then, we carefully analyze the inherent parallelism and propose a bag of strategies to improve computational efficiency. Last, we conduct experiments to decompose tensors ranging from million-scale to trillion-scale for evaluation. Compared to the baselines, the exascale-tensor supports 8,000x larger tensors and a speedup up to 6.95x. We also apply our method to two real-world applications, including gene analysis and tensor layer neural networks, of which the numeric results demonstrate the scalability and effectiveness of our method.
Studying Artist Sentiments around AI-generated Artwork
Venue: arXiv cs.HC 2023
Year: 2023

Art created using generated Artificial Intelligence has taken the world by storm and generated excitement for many digital creators and technologists. However, the reception and reaction from artists have been mixed. Concerns about plagiarizing their artworks and styles for datasets and uncertainty around the future of digital art sparked movements in artist communities shunning the use of AI for generating art and protecting artists' rights. Collaborating with these tools for novel creative use cases also sparked hope from some creators. Artists are an integral stakeholder in the rapidly evolving digital creativity industry and understanding their concerns and hopes inform responsible development and use of creativity support tools. In this work, we study artists' sentiments about AI-generated art. We interviewed 7 artists and analyzed public posts from artists on social media platforms Reddit, Twitter and Artstation. We report artists' main concerns and hopes around AI-generated artwork, informing a way forward for inclusive development of these tools.
AdaTyper: Adaptive Semantic Column Type Detection
Venue: arXiv cs.DB 2023
Year: 2023

Understanding the semantics of relational tables is instrumental for automation in data exploration and preparation systems. A key source for understanding a table is the semantics of its columns. With the rise of deep learning, learned table representations are now available, which can be applied for semantic type detection and achieve good performance on benchmarks. Nevertheless, we observe a gap between this performance and its applicability in practice. In this paper, we propose AdaTyper to address one of the most critical deployment challenges: adaptation. AdaTyper uses weak-supervision to adapt a hybrid type predictor towards new semantic types and shifted data distributions at inference time, using minimal human feedback. The hybrid type predictor of AdaTyper combines rule-based methods and a light machine learning model for semantic column type detection. We evaluate the adaptation performance of AdaTyper on real-world database tables hand-annotated with semantic column types through crowdsourcing and find that the f1-score improves for new and existing types. AdaTyper approaches an average precision of 0.6 after only seeing 5 examples, significantly outperforming existing adaptation methods based on human-provided regular expressions or dictionaries.
Responsive Hydrogel-based Modular Microrobots for Multi-functional Micromanipulation
Venue: arXiv cs.RO 2023
Year: 2023

Microrobots show great potential in biomedical applications such as drug delivery and cell manipulations. However, current microrobots are mostly fabricated as a single entity and type and the tasks they can perform are limited. In this paper, modular microrobots, with an overall size of 120 $μ$m $\times$ 200 $μ$m, are proposed with responsive mating components, made from stimuli-responsive hydrogels, and application specific end-effectors for microassembly tasks. The modular microrobots are fabricated based on photolithography and two-photon polymerization together or separately. Two types of modular microrobots are created based on the location of the responsive mating component. The first type of modular microrobot has a mating component that can shrink upon stimulation while the second type has a double bilayer structure that can realize an open and close motion. The exchange of end-effectors with an identical actuation base is demonstrated for both types of microrobots. Finally, different manipulation tasks are performed with different types of end-effectors.
HypUC: Hyperfine Uncertainty Calibration with Gradient-boosted Corrections for Reliable Regression on Imbalanced Electrocardiograms
Venue: arXiv cs.LG 2023
Year: 2023

The automated analysis of medical time series, such as the electrocardiogram (ECG), electroencephalogram (EEG), pulse oximetry, etc, has the potential to serve as a valuable tool for diagnostic decisions, allowing for remote monitoring of patients and more efficient use of expensive and time-consuming medical procedures. Deep neural networks (DNNs) have been demonstrated to process such signals effectively. However, previous research has primarily focused on classifying medical time series rather than attempting to regress the continuous-valued physiological parameters central to diagnosis. One significant challenge in this regard is the imbalanced nature of the dataset, as a low prevalence of abnormal conditions can lead to heavily skewed data that results in inaccurate predictions and a lack of certainty in such predictions when deployed. To address these challenges, we propose HypUC, a framework for imbalanced probabilistic regression in medical time series, making several contributions. (i) We introduce a simple kernel density-based technique to tackle the imbalanced regression problem with medical time series. (ii) Moreover, we employ a probabilistic regression framework that allows uncertainty estimation for the predicted continuous values. (iii) We also present a new approach to calibrate the predicted uncertainty further. (iv) Finally, we demonstrate a technique to use calibrated uncertainty estimates to improve the predicted continuous value and show the efficacy of the calibrated uncertainty estimates to flag unreliable predictions. HypUC is evaluated on a large, diverse, real-world dataset of ECGs collected from millions of patients, outperforming several conventional baselines on various diagnostic tasks, suggesting a potential use-case for the reliable clinical deployment of deep learning models.
Grammatical Error Correction via Mixed-Grained Weighted Training
Venue: arXiv cs.CL 2023
Year: 2023

The task of Grammatical Error Correction (GEC) aims to automatically correct grammatical errors in natural texts. Almost all previous works treat annotated training data equally, but inherent discrepancies in data are neglected. In this paper, the inherent discrepancies are manifested in two aspects, namely, accuracy of data annotation and diversity of potential annotations. To this end, we propose MainGEC, which designs token-level and sentence-level training weights based on inherent discrepancies in accuracy and potential diversity of data annotation, respectively, and then conducts mixed-grained weighted training to improve the training effect for GEC. Empirical evaluation shows that whether in the Seq2Seq or Seq2Edit manner, MainGEC achieves consistent and significant performance improvements on two benchmark datasets, demonstrating the effectiveness and superiority of the mixed-grained weighted training. Further ablation experiments verify the effectiveness of designed weights of both granularities in MainGEC.