OpenRaiser/Intern-Atlas huggingface dataset:
Which Tokens to Use? Investigating Token Reduction in Vision Transformers
Venue: arXiv cs.CV 2023
Year: 2023

Since the introduction of the Vision Transformer (ViT), researchers have sought to make ViTs more efficient by removing redundant information in the processed tokens. While different methods have been explored to achieve this goal, we still lack understanding of the resulting reduction patterns and how those patterns differ across token reduction methods and datasets. To close this gap, we set out to understand the reduction patterns of 10 different token reduction methods using four image classification datasets. By systematically comparing these methods on the different classification tasks, we find that the Top-K pruning method is a surprisingly strong baseline. Through in-depth analysis of the different methods, we determine that: the reduction patterns are generally not consistent when varying the capacity of the backbone model, the reduction patterns of pruning-based methods significantly differ from fixed radial patterns, and the reduction patterns of pruning-based methods are correlated across classification datasets. Finally we report that the similarity of reduction patterns is a moderate-to-strong proxy for model performance. Project page at https://vap.aau.dk/tokens.
Evaluating and Optimizing the Effectiveness of Neural Machine Translation in Supporting Code Retrieval Models: A Study on the CAT Benchmark
Venue: arXiv cs.SE 2023
Year: 2023

Neural Machine Translation (NMT) is widely applied in software engineering tasks. The effectiveness of NMT for code retrieval relies on the ability to learn from the sequence of tokens in the source language to the sequence of tokens in the target language. While NMT performs well in pseudocode-to-code translation, it might have challenges in learning to translate from natural language query to source code in newly curated real-world code documentation/ implementation datasets. In this work, we analyze the performance of NMT in natural language-to-code translation in the newly curated CAT benchmark that includes the optimized versions of three Java datasets TLCodeSum, CodeSearchNet, Funcom, and a Python dataset PCSD. Our evaluation shows that NMT has low accuracy, measured by CrystalBLEU and Meteor metrics in this task. To alleviate the duty of NMT in learning complex representation of source code, we propose ASTTrans Representation, a tailored representation of an Abstract Syntax Tree (AST) using a subset of non-terminal nodes. We show that the classical approach NMT performs significantly better in learning ASTTrans Representation over code tokens with up to 36% improvement on Meteor score. Moreover, we leverage ASTTrans Representation to conduct combined code search processes from the state-of-the-art code search processes using GraphCodeBERT and UniXcoder. Our NMT models of learning ASTTrans Representation can boost the Mean Reciprocal Rank of these state-of-the-art code search processes by up to 3.08% and improve 23.08% of queries' results over the CAT benchmark.
Explainable AI in Orthopedics: Challenges, Opportunities, and Prospects
Venue: arXiv cs.AI 2023
Year: 2023

While artificial intelligence (AI) has made many successful applications in various domains, its adoption in healthcare lags a little bit behind other high-stakes settings. Several factors contribute to this slower uptake, including regulatory frameworks, patient privacy concerns, and data heterogeneity. However, one significant challenge that impedes the implementation of AI in healthcare, particularly in orthopedics, is the lack of explainability and interpretability around AI models. Addressing the challenge of explainable AI (XAI) in orthopedics requires developing AI models and algorithms that prioritize transparency and interpretability, allowing clinicians, surgeons, and patients to understand the contributing factors behind any AI-powered predictive or descriptive models. The current contribution outlines several key challenges and opportunities that manifest in XAI in orthopedic practice. This work emphasizes the need for interdisciplinary collaborations between AI practitioners, orthopedic specialists, and regulatory entities to establish standards and guidelines for the adoption of XAI in orthopedics.
Generative Perturbation Analysis for Probabilistic Black-Box Anomaly Attribution
Venue: arXiv cs.LG 2023
Year: 2023

We address the task of probabilistic anomaly attribution in the black-box regression setting, where the goal is to compute the probability distribution of the attribution score of each input variable, given an observed anomaly. The training dataset is assumed to be unavailable. This task differs from the standard XAI (explainable AI) scenario, since we wish to explain the anomalous deviation from a black-box prediction rather than the black-box model itself. We begin by showing that mainstream model-agnostic explanation methods, such as the Shapley values, are not suitable for this task because of their ``deviation-agnostic property.'' We then propose a novel framework for probabilistic anomaly attribution that allows us to not only compute attribution scores as the predictive mean but also quantify the uncertainty of those scores. This is done by considering a generative process for perturbations that counter-factually bring the observed anomalous observation back to normalcy. We introduce a variational Bayes algorithm for deriving the distributions of per variable attribution scores. To the best of our knowledge, this is the first probabilistic anomaly attribution framework that is free from being deviation-agnostic.
Slot Induction via Pre-trained Language Model Probing and Multi-level Contrastive Learning
Venue: arXiv cs.CL 2023
Year: 2023

Recent advanced methods in Natural Language Understanding for Task-oriented Dialogue (TOD) Systems (e.g., intent detection and slot filling) require a large amount of annotated data to achieve competitive performance. In reality, token-level annotations (slot labels) are time-consuming and difficult to acquire. In this work, we study the Slot Induction (SI) task whose objective is to induce slot boundaries without explicit knowledge of token-level slot annotations. We propose leveraging Unsupervised Pre-trained Language Model (PLM) Probing and Contrastive Learning mechanism to exploit (1) unsupervised semantic knowledge extracted from PLM, and (2) additional sentence-level intent label signals available from TOD. Our approach is shown to be effective in SI task and capable of bridging the gaps with token-level supervised models on two NLU benchmark datasets. When generalized to emerging intents, our SI objectives also provide enhanced slot label representations, leading to improved performance on the Slot Filling tasks.
A Computational Design Pipeline to Fabricate Sensing Network Physicalizations
Venue: arXiv cs.HC 2023
Year: 2023

Interaction is critical for data analysis and sensemaking. However, designing interactive physicalizations is challenging as it requires cross-disciplinary knowledge in visualization, fabrication, and electronics. Interactive physicalizations are typically produced in an unstructured manner, resulting in unique solutions for a specific dataset, problem, or interaction that cannot be easily extended or adapted to new scenarios or future physicalizations. To mitigate these challenges, we introduce a computational design pipeline to 3D print network physicalizations with integrated sensing capabilities. Networks are ubiquitous, yet their complex geometry also requires significant engineering considerations to provide intuitive, effective interactions for exploration. Using our pipeline, designers can readily produce network physicalizations supporting selection-the most critical atomic operation for interaction-by touch through capacitive sensing and computational inference. Our computational design pipeline introduces a new design paradigm by concurrently considering the form and interactivity of a physicalization into one cohesive fabrication workflow. We evaluate our approach using (i) computational evaluations, (ii) three usage scenarios focusing on general visualization tasks, and (iii) expert interviews. The design paradigm introduced by our pipeline can lower barriers to physicalization research, creation, and adoption.
JiangJun: Mastering Xiangqi by Tackling Non-Transitivity in Two-Player Zero-Sum Games
Venue: arXiv cs.AI 2023
Year: 2023

This paper presents an empirical exploration of non-transitivity in perfect-information games, specifically focusing on Xiangqi, a traditional Chinese board game comparable in game-tree complexity to chess and shogi. By analyzing over 10,000 records of human Xiangqi play, we highlight the existence of both transitive and non-transitive elements within the game's strategic structure. To address non-transitivity, we introduce the JiangJun algorithm, an innovative combination of Monte-Carlo Tree Search (MCTS) and Policy Space Response Oracles (PSRO) designed to approximate a Nash equilibrium. We evaluate the algorithm empirically using a WeChat mini program and achieve a Master level with a 99.41\% win rate against human players. The algorithm's effectiveness in overcoming non-transitivity is confirmed by a plethora of metrics, such as relative population performance and visualization results. Our project site is available at \url{https://sites.google.com/view/jiangjun-site/}.
How Does the Inner Geometry of Soft Actuators Modulate the Dynamic and Hysteretic Response?
Venue: arXiv cs.RO 2023
Year: 2023

This paper investigates the influence of the internal geometrical structure of soft pneu-nets on the dynamic response and hysteresis of the actuators. The research findings indicate that by strategically manipulating the stress distribution within soft robots, it is possible to enhance the dynamic response while reducing hysteresis. The study utilizes the Finite Element Method (FEM) and includes experimental validation through markerless motion tracking of the soft robot. In particular, the study examines actuator bending angles up to 500% strain while achieving 95% accuracy in predicting the bending angle. The results demonstrate that the particular design with the minimum air chamber width in the center significantly improves both high- and low-frequency hysteresis behavior by 21.5% while also enhancing dynamic response by 60% to 112% across various frequencies and peak-to-peak pressures. Consequently, the paper evaluates the effectiveness of "mechanically programming" stress distribution and distributed energy storage within soft robots to maximize their dynamic performance, offering direct benefits for control.
Understanding Auto-Scheduling Optimizations for Model Deployment via Visualizations
Venue: arXiv cs.HC 2023
Year: 2023

After completing the design and training phases, deploying a deep learning model onto specific hardware is essential before practical implementation. Targeted optimizations are necessary to enhance the model's performance by reducing inference latency. Auto-scheduling, an automated technique offering various optimization options, proves to be a viable solution for large-scale auto-deployment. However, the low-level code generated by auto-scheduling resembles hardware coding, potentially hindering human comprehension and impeding manual optimization efforts. In this ongoing study, we aim to develop an enhanced visualization that effectively addresses the extensive profiling metrics associated with auto-scheduling. This visualization will illuminate the intricate scheduling process, enabling further advancements in latency optimization through insights derived from the schedule.
Do You Trust What You See? Toward A Multidimensional Measure of Trust in Visualization
Venue: arXiv cs.HC 2023
Year: 2023

Few concepts are as ubiquitous in computational fields as trust. However, in the case of information visualization, there are several unique and complex challenges, chief among them: defining and measuring trust. In this paper, we investigate the factors that influence trust in visualizations. We draw on the literature to identify five factors likely to affect trust: credibility, clarity, reliability, familiarity, and confidence. We then conduct two studies investigating these factors' relationship with visualization design features. In the first study, participants' credibility, understanding, and reliability ratings depended on the visualization design and its source. In the second study, we find these factors also align with subjective trust rankings. Our findings suggest that these five factors are important considerations for the design of trustworthy visualizations.
Automated Driving Without Ethics: Meaning, Design and Real-World Implementation
Venue: arXiv cs.AI 2023
Year: 2023

The ethics of automated vehicles (AV) has received a great amount of attention in recent years, specifically in regard to their decisional policies in accident situations in which human harm is a likely consequence. After a discussion about the pertinence and cogency of the term 'artificial moral agent' to describe AVs that would accomplish these sorts of decisions, and starting from the assumption that human harm is unavoidable in some situations, a strategy for AV decision making is proposed using only pre-defined parameters to characterize the risk of possible accidents and also integrating the Ethical Valence Theory, which paints AV decision-making as a type of claim mitigation, into multiple possible decision rules to determine the most suitable action given the specific environment and decision context. The goal of this approach is not to define how moral theory requires vehicles to behave, but rather to provide a computational approach that is flexible enough to accommodate a number of human 'moral positions' concerning what morality demands and what road users may expect, offering an evaluation tool for the social acceptability of an automated vehicle's decision making.
Induction Network: Audio-Visual Modality Gap-Bridging for Self-Supervised Sound Source Localization
Venue: arXiv cs.CV 2023
Year: 2023

Self-supervised sound source localization is usually challenged by the modality inconsistency. In recent studies, contrastive learning based strategies have shown promising to establish such a consistent correspondence between audio and sound sources in visual scenarios. Unfortunately, the insufficient attention to the heterogeneity influence in the different modality features still limits this scheme to be further improved, which also becomes the motivation of our work. In this study, an Induction Network is proposed to bridge the modality gap more effectively. By decoupling the gradients of visual and audio modalities, the discriminative visual representations of sound sources can be learned with the designed Induction Vector in a bootstrap manner, which also enables the audio modality to be aligned with the visual modality consistently. In addition to a visual weighted contrastive loss, an adaptive threshold selection strategy is introduced to enhance the robustness of the Induction Network. Substantial experiments conducted on SoundNet-Flickr and VGG-Sound Source datasets have demonstrated a superior performance compared to other state-of-the-art works in different challenging scenarios. The code is available at https://github.com/Tahy1/AVIN
Is unsafe an Achilles' Heel? A Comprehensive Study of Safety Requirements in Unsafe Rust Programming
Venue: arXiv cs.SE 2023
Year: 2023

Rust is an emerging, strongly-typed programming language focusing on efficiency and memory safety. With increasing projects adopting Rust, knowing how to use Unsafe Rust is crucial for Rust security. We observed that the description of safety requirements needs to be unified in Unsafe Rust programming. Current unsafe API documents in the standard library exhibited variations, including inconsistency and insufficiency. To enhance Rust security, we suggest unsafe API documents to list systematic descriptions of safety requirements for users to follow. In this paper, we conducted the first comprehensive empirical study on safety requirements across unsafe boundaries. We studied unsafe API documents in the standard library and defined 19 safety properties (SP). We then completed the data labeling on 416 unsafe APIs while analyzing their correlation to find interpretable results. To validate the practical usability and SP coverage, we categorized existing Rust CVEs until 2023-07-08 and performed a statistical analysis of std unsafe API usage toward the crates.io ecosystem. In addition, we conducted a user survey to gain insights into four aspects from experienced Rust programmers. We finally received 50 valid responses and confirmed our classification with statistical significance.
Induced-Minor-Free Graphs: Separator Theorem, Subexponential Algorithms, and Improved Hardness of Recognition
Venue: arXiv cs.DS 2023
Year: 2023

A graph $G$ contains a graph $H$ as an induced minor if $H$ can be obtained from $G$ by vertex deletions and edge contractions. The class of $H$-induced-minor-free graphs generalizes the class of $H$-minor-free graphs, but unlike $H$-minor-free graphs, it can contain dense graphs. We show that if an $n$-vertex $m$-edge graph $G$ does not contain a graph $H$ as an induced minor, then it has a balanced vertex separator of size $O_{H}(\sqrt{m})$, where the $O_{H}(\cdot)$-notation hides factors depending on $H$. More precisely, our upper bound for the size of the balanced separator is $O(\min(|V(H)|^2, \log n) \cdot \sqrt{|V(H)|+|E(H)|} \cdot \sqrt{m})$. We give an algorithm for finding either an induced minor model of $H$ in $G$ or such a separator in randomized polynomial-time. We apply this to obtain subexponential $2^{O_{H}(n^{2/3} \log n)}$ time algorithms on $H$-induced-minor-free graphs for a large class of problems including maximum independent set, minimum feedback vertex set, 3-coloring, and planarization. For graphs $H$ where every edge is incident to a vertex of degree at most 2, our results imply a $2^{O_{H}(n^{2/3} \log n)}$ time algorithm for testing if $G$ contains $H$ as an induced minor. Our second main result is that there exists a fixed tree $T$, so that there is no $2^{o(n/\log^3 n)}$ time algorithm for testing if a given $n$-vertex graph contains $T$ as an induced minor unless the Exponential Time Hypothesis (ETH) fails. Our reduction also gives NP-hardness, which solves an open problem asked by Fellows, Kratochvíl, Middendorf, and Pfeiffer [Algorithmica, 1995], who asked if there exists a fixed planar graph $H$ so that testing for $H$ as an induced minor is NP-hard.
Bayes Risk Consistency of Nonparametric Classification Rules for Spike Trains Data
Venue: arXiv cs.IT 2023
Year: 2023

Spike trains data find a growing list of applications in computational neuroscience, imaging, streaming data and finance. Machine learning strategies for spike trains are based on various neural network and probabilistic models. The probabilistic approach is relying on parametric or nonparametric specifications of the underlying spike generation model. In this paper we consider the two-class statistical classification problem for a class of spike train data characterized by nonparametrically specified intensity functions. We derive the optimal Bayes rule and next form the plug-in nonparametric kernel classifier. Asymptotical properties of the rules are established including the limit with respect to the increasing recording time interval and the size of a training set. In particular the convergence of the kernel classifier to the Bayes rule is proved. The obtained results are supported by a finite sample simulation studies.
Scientific modeling of Optical 3D Measuring Devices based on GPU-accelerated Ray Tracing using the NVIDIA OptiX Engine
Venue: arXiv cs.CE 2023
Year: 2023

Scientific optical 3D modeling requires the possibility to implement highly flexible and customizable mathematical models as well as high computing power. However, established ray tracing software for optical design and modeling purposes often has limitations in terms of access to underlying mathematical models and the possibility of accelerating the mostly CPU-based computation. To address these limitations, we propose the use of NVIDIA's OptiX Ray Tracing Engine as a highly flexible and high-performing alternative. OptiX offers a highly customizable ray tracing framework with onboard GPU support for parallel computing, as well as access to optimized ray tracing algorithms for accelerated computation. To demonstrate the capabilities of our approach, a realistic focus variation instrument is modeled, describing optical instrument components (light sources, lenses, detector, etc.) as well as the measuring sample surface mathematically or as meshed files. Using this focus variation instrument model, exemplary virtual measurements of arbitrary and standardized sample surfaces are carried out, generating image stacks of more than 100 images and tracing more than 1E9 light rays per image. The performance and accuracy of the simulations are qualitatively evaluated, and virtually generated detector images are compared with images acquired by a respective physical measuring device.
Strategic Interactions in Multi-modal Mobility Systems: A Game-Theoretic Perspective
Venue: arXiv cs.MA 2023
Year: 2023

The evolution of existing transportation systems,mainly driven by urbanization and increased availability of mobility options, such as private, profit-maximizing ride-hailing companies, calls for tools to reason about their design and regulation. To study this complex socio-technical problem, one needs to account for the strategic interactions of the heterogeneous stakeholders involved in the mobility ecosystem and analyze how they influence the system. In this paper, we focus on the interactions between citizens who compete for the limited resources of a mobility system to complete their desired trip. Specifically, we present a game-theoretic framework for multi-modal mobility systems, where citizens, characterized by heterogeneous preferences, have access to various mobility options and seek individually-optimal decisions. We study the arising game and prove the existence of an equilibrium, which can be efficiently computed via a convex optimization problem. Through both an analytical and a numerical case study for the classic scenario of Sioux Falls, USA, we illustrate the capabilities of our model and perform sensitivity analyses. Importantly, we show how to embed our framework into a "larger" game among stakeholders of the mobility ecosystem (e.g., municipality, Mobility Service Providers, and citizens), effectively giving rise to tools to inform strategic interventions and policy-making in the mobility ecosystem.