OpenRaiser/Intern-Atlas huggingface dataset:
Language-Image Models with 3D Understanding
Venue: arXiv cs.CV 2024
Year: 2024

Multi-modal large language models (MLLMs) have shown incredible capabilities in a variety of 2D vision and language tasks. We extend MLLMs' perceptual capabilities to ground and reason about images in 3-dimensional space. To that end, we first develop a large-scale pre-training dataset for 2D and 3D called LV3D by combining multiple existing 2D and 3D recognition datasets under a common task formulation: as multi-turn question-answering. Next, we introduce a new MLLM named Cube-LLM and pre-train it on LV3D. We show that pure data scaling makes a strong 3D perception capability without 3D specific architectural design or training objective. Cube-LLM exhibits intriguing properties similar to LLMs: (1) Cube-LLM can apply chain-of-thought prompting to improve 3D understanding from 2D context information. (2) Cube-LLM can follow complex and diverse instructions and adapt to versatile input and output formats. (3) Cube-LLM can be visually prompted such as 2D box or a set of candidate 3D boxes from specialists. Our experiments on outdoor benchmarks demonstrate that Cube-LLM significantly outperforms existing baselines by 21.3 points of AP-BEV on the Talk2Car dataset for 3D grounded reasoning and 17.7 points on the DriveLM dataset for complex reasoning about driving scenarios, respectively. Cube-LLM also shows competitive results in general MLLM benchmarks such as refCOCO for 2D grounding with (87.0) average score, as well as visual question answering benchmarks such as VQAv2, GQA, SQA, POPE, etc. for complex reasoning. Our project is available at https://janghyuncho.github.io/Cube-LLM.
Evaluating Large Language Models for Material Selection
Venue: arXiv cs.CL 2024
Year: 2024

Material selection is a crucial step in conceptual design due to its significant impact on the functionality, aesthetics, manufacturability, and sustainability impact of the final product. This study investigates the use of Large Language Models (LLMs) for material selection in the product design process and compares the performance of LLMs against expert choices for various design scenarios. By collecting a dataset of expert material preferences, the study provides a basis for evaluating how well LLMs can align with expert recommendations through prompt engineering and hyperparameter tuning. The divergence between LLM and expert recommendations is measured across different model configurations, prompt strategies, and temperature settings. This approach allows for a detailed analysis of factors influencing the LLMs' effectiveness in recommending materials. The results from this study highlight two failure modes, and identify parallel prompting as a useful prompt-engineering method when using LLMs for material selection. The findings further suggest that, while LLMs can provide valuable assistance, their recommendations often vary significantly from those of human experts. This discrepancy underscores the need for further research into how LLMs can be better tailored to replicate expert decision-making in material selection. This work contributes to the growing body of knowledge on how LLMs can be integrated into the design process, offering insights into their current limitations and potential for future improvements.
Leafy Spurge Dataset: Real-world Weed Classification Within Aerial Drone Imagery
Venue: arXiv cs.CV 2024
Year: 2024

Invasive plant species are detrimental to the ecology of both agricultural and wildland areas. Euphorbia esula, or leafy spurge, is one such plant that has spread through much of North America from Eastern Europe. When paired with contemporary computer vision systems, unmanned aerial vehicles, or drones, offer the means to track expansion of problem plants, such as leafy spurge, and improve chances of controlling these weeds. We gathered a dataset of leafy spurge presence and absence in grasslands of western Montana, USA, then surveyed these areas with a commercial drone. We trained image classifiers on these data, and our best performing model, a pre-trained DINOv2 vision transformer, identified leafy spurge with 0.84 accuracy (test set). This result indicates that classification of leafy spurge is tractable, but not solved. We release this unique dataset of labelled and unlabelled, aerial drone imagery for the machine learning community to explore. Improving classification performance of leafy spurge would benefit the fields of ecology, conservation, and remote sensing alike. Code and data are available at our website: leafy-spurge-dataset.github.io.
Update Rate, Accuracy, and Age of Information in a Wireless Sensor Network
Venue: arXiv cs.IT 2024
Year: 2024

Age of Information (AoI), namely the time that has elapsed since the most recently delivered packet was generated, is receiving increasing attention with the emergence of many real-time applications that rely on the exchange of time-sensitive information. AoI captures the freshness of the information from the perspective of the destination. The term "accuracy of information" is used to assess how close the estimate at the destination is to the parameter value measured by the sensor. In this paper, the mean square error (MSE) is used to evaluate the accuracy of information. We focus on a single sensor that monitors a time-sensitive physical process, which is modelled as a random walk. Whenever the state of the random walk changes by more than a specified threshold, the sensor generates a status update packet and transmits it to the destination. When no update packet is received, the destination assumes that the state of the process has not changed. We study the problem of finding the minimum update rate under AoI and accuracy of information constraints. More specifically, we derive analytical expressions for the update rate, the AoI, and the MSE.
False Sense of Security in Explainable Artificial Intelligence (XAI)
Venue: arXiv cs.CY 2024
Year: 2024

A cautious interpretation of AI regulations and policy in the EU and the USA place explainability as a central deliverable of compliant AI systems. However, from a technical perspective, explainable AI (XAI) remains an elusive and complex target where even state of the art methods often reach erroneous, misleading, and incomplete explanations. "Explainability" has multiple meanings which are often used interchangeably, and there are an even greater number of XAI methods - none of which presents a clear edge. Indeed, there are multiple failure modes for each XAI method, which require application-specific development and continuous evaluation. In this paper, we analyze legislative and policy developments in the United States and the European Union, such as the Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, the AI Act, the AI Liability Directive, and the General Data Protection Regulation (GDPR) from a right to explanation perspective. We argue that these AI regulations and current market conditions threaten effective AI governance and safety because the objective of trustworthy, accountable, and transparent AI is intrinsically linked to the questionable ability of AI operators to provide meaningful explanations. Unless governments explicitly tackle the issue of explainability through clear legislative and policy statements that take into account technical realities, AI governance risks becoming a vacuous "box-ticking" exercise where scientific standards are replaced with legalistic thresholds, providing only a false sense of security in XAI.
Thoughtful Things: Building Human-Centric Smart Devices with Small Language Models
Venue: arXiv cs.HC 2024
Year: 2024

Everyday devices like light bulbs and kitchen appliances are now embedded with so many features and automated behaviors that they have become complicated to actually use. While such "smart" capabilities can better support users' goals, the task of learning the "ins and outs" of different devices is daunting. Voice assistants aim to solve this problem by providing a natural language interface to devices, yet such assistants cannot understand loosely-constrained commands, they lack the ability to reason about and explain devices' behaviors to users, and they rely on connectivity to intrusive cloud infrastructure. Toward addressing these issues, we propose thoughtful things: devices that leverage lightweight, on-device language models to take actions and explain their behaviors in response to unconstrained user commands. We propose an end-to-end framework that leverages formal modeling, automated training data synthesis, and generative language models to create devices that are both capable and thoughtful in the presence of unconstrained user goals and inquiries. Our framework requires no labeled data and can be deployed on-device, with no cloud dependency. We implement two thoughtful things (a lamp and a thermostat) and deploy them on real hardware, evaluating their practical performance.
The Trajectory of Romance Scams in the U.S
Venue: arXiv cs.CR 2024
Year: 2024

Romance scams (RS) inflict financial and emotional damage by defrauding victims under the guise of meaningful relationships. This research study examines RS trends in the U.S. through a quantitative analysis of web searches, news articles, research publications, and government reports from 2004 to 2023. This is the first study to use multiple sources for RS trend analysis. Results reveal increasing public interest and media coverage contrasted by a recent decrease in incidents reported to authorities. The frequency of research dedicated to RS has steadily grown but focuses predominantly on documenting the problem rather than developing solutions. Overall, findings suggest RS escalation despite declining official reports, which are likely obscured by low victim reporting rates. This highlights the need for greater awareness to encourage reporting enabling accurate data-driven policy responses. Additionally, more research must focus on techniques to counter these crimes. With improved awareness and prevention, along with responses informed by more accurate data, the rising RS threat can perhaps be mitigated.
End-to-End Autoencoder for Drill String Acoustic Communications
Venue: arXiv cs.LG 2024
Year: 2024

Drill string communications are important for drilling efficiency and safety. The design of a low latency drill string communication system with high throughput and reliability remains an open challenge. In this paper a deep learning autoencoder (AE) based end-to-end communication system, where transmitter and receiver implemented as feed forward neural networks, is proposed for acousticdrill string communications. Simulation shows that the AE system is able to outperform a baseline non-contiguous OFDM system in terms of BER and PAPR, operating with lower latency.
On the Convergence of Malleability and the HPC PowerStack: Exploiting Dynamism in Over-Provisioned and Power-Constrained HPC Systems
Venue: arXiv cs.DC 2024
Year: 2024

Recent High-Performance Computing (HPC) systems are facing important challenges, such as massive power consumption, while at the same time significantly under-utilized system resources. Given the power consumption trends, future systems will be deployed in an over-provisioned manner where more resources are installed than they can afford to power simultaneously. In such a scenario, maximizing resource utilization and energy efficiency, while keeping a given power constraint, is pivotal. Driven by this observation, in this position paper we first highlight the recent trends of resource management techniques, with a particular focus on malleability support (i.e., dynamically scaling resource allocations/requirements for a job), co-scheduling (i.e., co-locating multiple jobs within a node), and power management. Second, we consider putting them together, assess their relationships/synergies, and discuss the functionality requirements in each software component for future over-provisioned and power-constrained HPC systems. Third, we briefly introduce our ongoing efforts on the integration of software tools, which will ultimately lead to the convergence of malleability and power management, as it is designed in the HPC PowerStack initiative.
Strategies for Increasing Corporate Responsible AI Prioritization
Venue: arXiv cs.CY 2024
Year: 2024

Responsible artificial intelligence (RAI) is increasingly recognized as a critical concern. However, the level of corporate RAI prioritization has not kept pace. In this work, we conduct 16 semi-structured interviews with practitioners to investigate what has historically motivated companies to increase the prioritization of RAI. What emerges is a complex story of conflicting and varied factors, but we bring structure to the narrative by highlighting the different strategies available to employ, and point to the actors with access to each. While there are no guaranteed steps for increasing RAI prioritization, we paint the current landscape of motivators so that practitioners can learn from each other, and put forth our own selection of promising directions forward.
Persona Inconstancy in Multi-Agent LLM Collaboration: Conformity, Confabulation, and Impersonation
Venue: arXiv cs.AI 2024
Year: 2024

Multi-agent AI systems can be used for simulating collective decision-making in scientific and practical applications. They can also be used to introduce a diverse group discussion step in chatbot pipelines, enhancing the cultural sensitivity of the chatbot's responses. These applications, however, are predicated on the ability of AI agents to reliably adopt assigned personas and mimic human interactions. To see whether LLM agents satisfy these requirements, we examine AI agent ensembles engaged in cross-national collaboration and debate by analyzing their private responses and chat transcripts. Our findings suggest that multi-agent discussions can support collective AI decisions that more often reflect diverse perspectives, yet this effect is tempered by the agents' susceptibility to conformity due to perceived peer pressure and occasional challenges in maintaining consistent personas and opinions. Instructions that encourage debate in support of one's opinions rather than collaboration increase the rate of inconstancy. Without addressing the factors we identify, the full potential of multi-agent frameworks for producing more culturally diverse AI outputs or more realistic simulations of group decision-making may remain untapped.
Trio-ViT: Post-Training Quantization and Acceleration for Softmax-Free Efficient Vision Transformer
Venue: arXiv cs.CV 2024
Year: 2024

Motivated by the huge success of Transformers in the field of natural language processing (NLP), Vision Transformers (ViTs) have been rapidly developed and achieved remarkable performance in various computer vision tasks. However, their huge model sizes and intensive computations hinder ViTs' deployment on embedded devices, calling for effective model compression methods, such as quantization. Unfortunately, due to the existence of hardware-unfriendly and quantization-sensitive non-linear operations, particularly {Softmax}, it is non-trivial to completely quantize all operations in ViTs, yielding either significant accuracy drops or non-negligible hardware costs. In response to challenges associated with \textit{standard ViTs}, we focus our attention towards the quantization and acceleration for \textit{efficient ViTs}, which not only eliminate the troublesome Softmax but also integrate linear attention with low computational complexity, and propose Trio-ViT accordingly. Specifically, at the algorithm level, we develop a {tailored post-training quantization engine} taking the unique activation distributions of Softmax-free efficient ViTs into full consideration, aiming to boost quantization accuracy. Furthermore, at the hardware level, we build an accelerator dedicated to the specific Convolution-Transformer hybrid architecture of efficient ViTs, thereby enhancing hardware efficiency. Extensive experimental results consistently prove the effectiveness of our Trio-ViT framework. {Particularly, we can gain up to $\uparrow$$\mathbf{3.6}\times$, $\uparrow$$\mathbf{5.0}\times$, and $\uparrow$$\mathbf{7.3}\times$ FPS under comparable accuracy over state-of-the-art ViT accelerators, as well as $\uparrow$$\mathbf{6.0}\times$, $\uparrow$$\mathbf{1.5}\times$, and $\uparrow$$\mathbf{2.1}\times$ DSP efficiency.} Codes are available at \url{https://github.com/shihuihong214/Trio-ViT}.
sqlelf: a SQL-centric Approach to ELF Analysis
Venue: arXiv cs.SE 2024
Year: 2024

The exploration and understanding of Executable and Linkable Format (ELF) objects underpin various critical activities in computer systems, from debugging to reverse engineering. Traditional UNIX tooling like readelf, nm, and objdump have served the community reliably over the years. However, as the complexity and scale of software projects has grown, there arises a need for more intuitive, flexible, and powerful methods to investigate ELF objects. In this paper, we introduce sqlelf, an innovative tool that empowers users to probe ELF objects through the expressive power of SQL. By modeling ELF objects as relational databases, sqlelf offers the following advantages over conventional methods. Our evaluations demonstrate that sqlelf not only provides more nuanced and comprehensive insights into ELF objects but also significantly reduces the effort and time traditionally required for ELF exploration tasks
Transformer models as an efficient replacement for statistical test suites to evaluate the quality of random numbers
Venue: arXiv cs.LG 2024
Year: 2024

Random numbers are incredibly important in a variety of fields, and the need for their validation remains important for safety. A Quantum Random Number Generator (QRNG) can theoretically generate truly random numbers, however their quality still needs to be thoroughly validated. Generally, the task of validating random numbers has been delegated to different statistical tests such as the tests from the NIST Statistical Test Suite (STS), which are often slow and only perform one test at a time. Our work presents a deep learning model utilizing the Transformer architecture that 1) performs multiple NIST STS tests at once, and 2) runs much faster. This model outputs multi-label classification results on passing these statistical tests. We performed a thorough hyper-parameter optimization to converge on the best possible model and as a result, achieved a high degree of accuracy with a Macro F1-score of above 0.96. We also compared this model to a conventional deep learning method (Long Short Term Memory Recurrent Neural Networks) to quantify randomness and showed our model achieved similar performances while being much more efficient and scalable. The high performance and efficiency of this Transformer-based deep learning model showed that it can be a viable replacement for the NIST STS for validating random numbers.
Federated Graph Condensation with Information Bottleneck Principles
Venue: arXiv cs.LG 2024
Year: 2024

Graph condensation (GC), which reduces the size of a large-scale graph by synthesizing a small-scale condensed graph as its substitution, has benefited various graph learning tasks. However, existing GC methods rely on centralized data storage, which is unfeasible for real-world decentralized data distribution, and overlook data holders' privacy-preserving requirements. To bridge this gap, we propose and study the novel problem of federated graph condensation (FGC) for graph neural networks (GNNs). Specifically, we first propose a general framework for FGC, where we decouple the typical gradient matching process for GC into client-side gradient calculation and server-side gradient matching, integrating knowledge from multiple clients' subgraphs into one smaller condensed graph. Nevertheless, our empirical studies show that under the federated setting, the condensed graph will consistently leak data membership privacy, i.e., the condensed graph during federated training can be utilized to steal training data under the membership inference attack (MIA). To tackle this issue, we innovatively incorporate information bottleneck principles into the FGC, which only needs to extract partial node features in one local pre-training step and utilize the features during federated training. Theoretical and experimental analyses demonstrate that our framework consistently protects membership privacy during training. Meanwhile, it can achieve comparable and even superior performance against existing centralized GC and federated graph learning (FGL) methods.
NeurDB: An AI-powered Autonomous Data System
Venue: arXiv cs.DB 2024
Year: 2024

In the wake of rapid advancements in artificial intelligence (AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB (AIxDB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, self-driving capabilities for improved system performance, etc. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.
Codexity: Secure AI-assisted Code Generation
Venue: arXiv cs.SE 2024
Year: 2024

Despite the impressive performance of Large Language Models (LLMs) in software development activities, recent studies show the concern of introducing vulnerabilities into software codebase by AI programming assistants (e.g., Copilot, CodeWhisperer). In this work, we present Codexity, a security-focused code generation framework integrated with five LLMs. Codexity leverages the feedback of static analysis tools such as Infer and CppCheck to mitigate security vulnerabilities in LLM-generated programs. Our evaluation in a real-world benchmark with 751 automatically generated vulnerable subjects demonstrates Codexity can prevent 60% of the vulnerabilities being exposed to the software developer.
Collaborative Intelligence in Sequential Experiments: A Human-in-the-Loop Framework for Drug Discovery
Venue: arXiv cs.AI 2024
Year: 2024

Drug discovery is a complex process that involves sequentially screening and examining a vast array of molecules to identify those with the target properties. This process, also referred to as sequential experimentation, faces challenges due to the vast search space, the rarity of target molecules, and constraints imposed by limited data and experimental budgets. To address these challenges, we introduce a human-in-the-loop framework for sequential experiments in drug discovery. This collaborative approach combines human expert knowledge with deep learning algorithms, enhancing the discovery of target molecules within a specified experimental budget. The proposed algorithm processes experimental data to recommend both promising molecules and those that could improve its performance to human experts. Human experts retain the final decision-making authority based on these recommendations and their domain expertise, including the ability to override algorithmic recommendations. We applied our method to drug discovery tasks using real-world data and found that it consistently outperforms all baseline methods, including those which rely solely on human or algorithmic input. This demonstrates the complementarity between human experts and the algorithm. Our results provide key insights into the levels of humans' domain knowledge, the importance of meta-knowledge, and effective work delegation strategies. Our findings suggest that such a framework can significantly accelerate the development of new vaccines and drugs by leveraging the best of both human and artificial intelligence.