OpenRaiser/Intern-Atlas huggingface dataset:
From Space-Time to Space-Order: Directly Planning a Temporal Planning Graph by Redefining CBS
Venue: arXiv cs.MA 2024
Year: 2024

The majority of multi-agent path finding (MAPF) methods compute collision-free space-time paths which require agents to be at a specific location at a specific discretized timestep. However, executing these space-time paths directly on robotic systems is infeasible due to real-time execution differences (e.g. delays) which can lead to collisions. To combat this, current methods translate the space-time paths into a temporal plan graph (TPG) that only requires that agents observe the order in which they navigate through locations where their paths cross. However, planning space-time paths and then post-processing them into a TPG does not reduce the required agent-to-agent coordination, which is fixed once the space-time paths are computed. To that end, we propose a novel algorithm Space-Order CBS that can directly plan a TPG and explicitly minimize coordination. Our main theoretical insight is our novel perspective on viewing a TPG as a set of space-visitation order paths where agents visit locations in relative orders (e.g. 1st vs 2nd) as opposed to specific timesteps. We redefine unique conflicts and constraints for adapting CBS for space-order planning. We experimentally validate how Space-Order CBS can return TPGs which significantly reduce coordination, thus subsequently reducing the amount of agent-agent communication and leading to more robustness to delays during execution.
Performance Characterization of Expert Router for Scalable LLM Inference
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) have experienced widespread adoption across scientific and industrial domains due to their versatility and utility for diverse tasks. Nevertheless, deploying and serving these models at scale with optimal throughput and latency remains a significant challenge, primarily because of LLMs' high computational and memory demands. Specialized models optimized for specific tasks can be combined through a routing mechanism to address these challenges, creating a modular inference system. This paper introduces Expert Router, a scalable routing architecture that directs prompts to specialized expert models. We characterize multiple Expert Router configurations, including different LLama 3 models with quantized and non-quantized weights under up to 1,000 concurrent users. Our findings reveal that Expert Router introduces minimal latency overhead, with the configuration of expert models being a dominating factor in performance outcomes. High-parameter expert models deliver stable throughput and latency under moderate concurrency levels. In contrast, smaller expert models maintain competitive performance across a wider range of concurrent users compared to tensor-parallelized baseline models. This highlights the potential of Expert Router for efficient and scalable LLM deployment.
Pixels and Predictions: Potential of GPT-4V in Meteorological Imagery Analysis and Forecast Communication
Venue: arXiv cs.CL 2024
Year: 2024

Generative AI, such as OpenAI's GPT-4V large-language model, has rapidly entered mainstream discourse. Novel capabilities in image processing and natural-language communication may augment existing forecasting methods. Large language models further display potential to better communicate weather hazards in a style honed for diverse communities and different languages. This study evaluates GPT-4V's ability to interpret meteorological charts and communicate weather hazards appropriately to the user, despite challenges of hallucinations, where generative AI delivers coherent, confident, but incorrect responses. We assess GPT-4V's competence via its web interface ChatGPT in two tasks: (1) generating a severe-weather outlook from weather-chart analysis and conducting self-evaluation, revealing an outlook that corresponds well with a Storm Prediction Center human-issued forecast; and (2) producing hazard summaries in Spanish and English from weather charts. Responses in Spanish, however, resemble direct (not idiomatic) translations from English to Spanish, yielding poorly translated summaries that lose critical idiomatic precision required for optimal communication. Our findings advocate for cautious integration of tools like GPT-4V in meteorology, underscoring the necessity of human oversight and development of trustworthy, explainable AI.
Tailors: New Music Timbre Visualizer to Entertain Music Through Imagery
Venue: arXiv cs.SD 2024
Year: 2024

In this paper, I have implemented a timbre visualization system called Tailors. Through the experiment with 27 MIR users, Tailors was found to be effective in conveying timbral warmth, brightness, depth, shallowness, hardness, roughness, and sharpness features of music compared to the only music condition and basic visualization. All scores of Tailors in the music imagery and music entertainment surveys were valued highest among the three conditions. Multiple linear regression analysis between timbre-imagery and imagery-entertainment shows significant and positive correlations. Coefficients comparing results from Fisher Transformation show that Tailors made user's music entertainment better through improved music visual imagery. The post-survey result represents that Tailors ranked first for the best timbre expression, music experience, and willingness to use it again. While some users felt a burden in the eye, Tailors left the future work of the data-driven approach of the mapping rule of timbre visualization to gain consent from many users. Furthermore, reducing timbre features to focus on features that Tailors can express well was also discussed, with future work of Tailors in a more artistic way using the sense of space.
PIVOT- Input-aware Path Selection for Energy-efficient ViT Inference
Venue: arXiv cs.AR 2024
Year: 2024

The attention module in vision transformers(ViTs) performs intricate spatial correlations, contributing significantly to accuracy and delay. It is thereby important to modulate the number of attentions according to the input feature complexity for optimal delay-accuracy tradeoffs. To this end, we propose PIVOT - a co-optimization framework which selectively performs attention skipping based on the input difficulty. For this, PIVOT employs a hardware-in-loop co-search to obtain optimal attention skip configurations. Evaluations on the ZCU102 MPSoC FPGA show that PIVOT achieves 2.7x lower EDP at 0.2% accuracy reduction compared to LVViT-S ViT. PIVOT also achieves 1.3% and 1.8x higher accuracy and throughput than prior works on traditional CPUs and GPUs. The PIVOT project can be found at https://github.com/Intelligent-Computing-Lab-Yale/PIVOT.
Evaluating Physician-AI Interaction for Cancer Management: Paving the Path towards Precision Oncology
Venue: arXiv cs.HC 2024
Year: 2024

We evaluated how clinicians approach clinical decision-making when given findings from both randomized controlled trials (RCTs) and machine learning (ML) models. To do so, we designed a clinical decision support system (CDSS) that displays survival curves and adverse event information from a synthetic RCT and ML model for 12 patients with multiple myeloma. We conducted an interventional study in a simulated setting to evaluate how clinicians synthesized the available data to make treatment decisions. Participants were invited to participate in a follow-up interview to discuss their choices in an open-ended format. When ML model results were concordant with RCT results, physicians had increased confidence in treatment choice compared to when they were given RCT results alone. When ML model results were discordant with RCT results, the majority of physicians followed the ML model recommendation in their treatment selection. Perceived reliability of the ML model was consistently higher after physicians were provided with data on how it was trained and validated. Follow-up interviews revealed four major themes: (1) variability in what variables participants used for decision-making, (2) perceived advantages to an ML model over RCT data, (3) uncertainty around decision-making when the ML model quality was poor, and (4) perception that this type of study is an important thought exercise for clinicians. Overall, ML-based CDSSs have the potential to change treatment decisions in cancer management. However, meticulous development and validation of these systems as well as clinician training are required before deployment.
CORE-BEHRT: A Carefully Optimized and Rigorously Evaluated BEHRT
Venue: arXiv cs.LG 2024
Year: 2024

The widespread adoption of Electronic Health Records (EHR) has significantly increased the amount of available healthcare data. This has allowed models inspired by Natural Language Processing (NLP) and Computer Vision, which scale exceptionally well, to be used in EHR research. Particularly, BERT-based models have surged in popularity following the release of BEHRT and Med-BERT. Subsequent models have largely built on these foundations despite the fundamental design choices of these pioneering models remaining underexplored. Through incremental optimization, we study BERT-based EHR modeling and isolate the sources of improvement for key design choices, giving us insights into the effect of data representation, individual technical components, and training procedure. Evaluating this across a set of generic tasks (death, pain treatment, and general infection), we showed that improving data representation can increase the average downstream performance from 0.785 to 0.797 AUROC ($p<10^{-7}$), primarily when including medication and timestamps. Improving the architecture and training protocol on top of this increased average downstream performance to 0.801 AUROC ($p<10^{-7}$). We then demonstrated the consistency of our optimization through a rigorous evaluation across 25 diverse clinical prediction tasks. We observed significant performance increases in 17 out of 25 tasks and improvements in 24 tasks, highlighting the generalizability of our results. Our findings provide a strong foundation for future work and aim to increase the trustworthiness of BERT-based EHR models.
Towards a high-performance AI compiler with upstream MLIR
Venue: arXiv cs.PL 2024
Year: 2024

This work proposes a compilation flow using open-source compiler passes to build a framework to achieve ninja performance from a generic linear algebra high-level abstraction. We demonstrate this flow with a proof-of-concept MLIR project that uses input IR in Linalg-on-Tensor from TensorFlow and PyTorch, performs cache-level optimizations and lowering to micro-kernels for efficient vectorization, achieving over 90% of the performance of ninja-written equivalent programs. The contributions of this work include: (1) Packing primitives on the tensor dialect and passes for cache-aware distribution of tensors (single and multi-core) and type-aware instructions (VNNI, BFDOT, BFMMLA), including propagation of shapes across the entire function; (2) A linear algebra pipeline, including tile, fuse and bufferization strategies to get model-level IR into hardware friendly tile calls; (3) A mechanism for micro-kernel lowering to an open source library that supports various CPUs.
Analysis and Visualization of Musical Structure using Networks
Venue: arXiv cs.SI 2024
Year: 2024

In this article, a framework for defining and analysing a family of graphs or networks from symbolic music information is discussed. Such graphs concern different types of elements, such as pitches, chords and rhythms, and the relations among them, and are built from quantitative or categorical data contained in digital music scores. They are helpful in visualizing musical features at once, thus leading to a computational tool for understanding the general structural elements of a music fragment. Data obtained from a digital score undergoes different analytical procedures from graph and network theory, such as computing their centrality measures and entropy, and detecting their communities. We analyze pieces of music coming from different styles, and compare some of our results with conclusions from traditional music analysis techniques.
Revisiting Unnaturalness for Automated Program Repair in the Era of Large Language Models
Venue: arXiv cs.SE 2024
Year: 2024

Language models have improved by orders of magnitude with the recent emergence of Transformer-based Large Language Models (LLMs). LLMs have demonstrated their ability to generate natural code that is highly similar to code written by professional developers. One intermediate value an LLM can emit is entropy, which measures the naturalness of a token of code. We hypothesize that entropy can be used to improve the performance of Automated Program Repair (APR) tasks. While much progress has been made in Automated Program Repair (APR), fault localization techniques suffer from a lack of diversity in ranking scores, patch generation tools tend to be inefficient as all tests need to run before determining if a patch is likely to be correct, and patch ranking often suffers from the test-suite over-fitting problem. However, using an LLM directly for APR introduces concerns for training data leakage. In this work, we introduce a novel way of using the entropy of LLMs in combination with prior APR tools to improve all stages of APR. We show that entropy is highly complementary with prior fault localization tools. Our proposed re-ranking method achieves a 50% Top-5 score improvement over SBFL. We propose a patch-naturalness measurement, entropy-delta, to improve the efficiency of template-based repair techniques by ranking plausible patches before undergoing testing. When using entropy-delta for patch ranking and classification, our proposed method can rank correct patches more effectively than state-of-the-art machine learning tools with an 49% improvement in Top-1. Our work suggests that LLMs can be an effective addition to compliment prior APR tasks while minimizing both the test-suite overfitting problem and the LLM data leakage problem.
Efficient Multi-Processor Scheduling in Increasingly Realistic Models
Venue: arXiv cs.DC 2024
Year: 2024

We study the problem of efficiently scheduling a computational DAG on multiple processors. The majority of previous works have developed and compared algorithms for this problem in relatively simple models; in contrast to this, we analyze this problem in a more realistic model that captures many real-world aspects, such as communication costs, synchronization costs, and the hierarchical structure of modern processing architectures. For this we extend the well-established BSP model of parallel computing with non-uniform memory access (NUMA) effects. We then develop a range of new scheduling algorithms to minimize the scheduling cost in this more complex setting: several initialization heuristics, a hill-climbing local search method, and several approaches that formulate (and solve) the scheduling problem as an Integer Linear Program (ILP). We combine these algorithms into a single framework, and conduct experiments on a diverse set of real-world computational DAGs to show that the resulting scheduler significantly outperforms both academic and practical baselines. In particular, even without NUMA effects, our scheduler finds solutions of 24%-44% smaller cost on average than the baselines, and in case of NUMA effects, it achieves up to a factor $2.5\times$ improvement compared to the baselines. Finally, we also develop a multilevel scheduling algorithm, which provides up to almost a factor $5\times$ improvement in the special case when the problem is dominated by very high communication costs.
FlowMap: High-Quality Camera Poses, Intrinsics, and Depth via Gradient Descent
Venue: arXiv cs.CV 2024
Year: 2024

This paper introduces FlowMap, an end-to-end differentiable method that solves for precise camera poses, camera intrinsics, and per-frame dense depth of a video sequence. Our method performs per-video gradient-descent minimization of a simple least-squares objective that compares the optical flow induced by depth, intrinsics, and poses against correspondences obtained via off-the-shelf optical flow and point tracking. Alongside the use of point tracks to encourage long-term geometric consistency, we introduce differentiable re-parameterizations of depth, intrinsics, and pose that are amenable to first-order optimization. We empirically show that camera parameters and dense depth recovered by our method enable photo-realistic novel view synthesis on 360-degree trajectories using Gaussian Splatting. Our method not only far outperforms prior gradient-descent based bundle adjustment methods, but surprisingly performs on par with COLMAP, the state-of-the-art SfM method, on the downstream task of 360-degree novel view synthesis (even though our method is purely gradient-descent based, fully differentiable, and presents a complete departure from conventional SfM).
Facilitating Human Feedback for GenAI Prompt Optimization
Venue: arXiv cs.HC 2024
Year: 2024

This study investigates the optimization of Generative AI (GenAI) systems through human feedback, focusing on how varying feedback mechanisms influence the quality of GenAI outputs. We devised a Human-AI training loop where 32 students, divided into two groups, evaluated AI-generated responses based on a single prompt. One group assessed a single output, while the other compared two outputs. Preliminary results from this small-scale experiment suggest that comparative feedback might encourage more nuanced evaluations, highlighting the potential for improved human-AI collaboration in prompt optimization. Future research with larger samples is recommended to validate these findings and further explore effective feedback strategies for GenAI systems.
Clustering of timed sequences -- Application to the analysis of care pathways
Venue: arXiv cs.LG 2024
Year: 2024

Improving the future of healthcare starts by better understanding the current actual practices in hospital settings. This motivates the objective of discovering typical care pathways from patient data. Revealing typical care pathways can be achieved through clustering. The difficulty in clustering care pathways, represented by sequences of timestamped events, lies in defining a semantically appropriate metric and clustering algorithms. In this article, we adapt two methods developed for time series to the clustering of timed sequences: the drop-DTW metric and the DBA approach for the construction of averaged time sequences. These methods are then applied in clustering algorithms to propose original and sound clustering algorithms for timed sequences. This approach is experimented with and evaluated on synthetic and real-world data.
Insufficient Statistics Perturbation: Stable Estimators for Private Least Squares
Venue: arXiv cs.LG 2024
Year: 2024

We present a sample- and time-efficient differentially private algorithm for ordinary least squares, with error that depends linearly on the dimension and is independent of the condition number of $X^\top X$, where $X$ is the design matrix. All prior private algorithms for this task require either $d^{3/2}$ examples, error growing polynomially with the condition number, or exponential time. Our near-optimal accuracy guarantee holds for any dataset with bounded statistical leverage and bounded residuals. Technically, we build on the approach of Brown et al. (2023) for private mean estimation, adding scaled noise to a carefully designed stable nonprivate estimator of the empirical regression vector.
Deep multi-prototype capsule networks
Venue: arXiv cs.CV 2024
Year: 2024

Capsule networks are a type of neural network that identify image parts and form the instantiation parameters of a whole hierarchically. The goal behind the network is to perform an inverse computer graphics task, and the network parameters are the mapping weights that transform parts into a whole. The trainability of capsule networks in complex data with high intra-class or intra-part variation is challenging. This paper presents a multi-prototype architecture for guiding capsule networks to represent the variations in the image parts. To this end, instead of considering a single capsule for each class and part, the proposed method employs several capsules (co-group capsules), capturing multiple prototypes of an object. In the final layer, co-group capsules compete, and their soft output is considered the target for a competitive cross-entropy loss. Moreover, in the middle layers, the most active capsules map to the next layer with a shared weight among the co-groups. Consequently, due to the reduction in parameters, implicit weight-sharing makes it possible to have more deep capsule network layers. The experimental results on MNIST, SVHN, C-Cube, CEDAR, MCYT, and UTSig datasets reveal that the proposed model outperforms others regarding image classification accuracy.
Multi-scale Intervention Planning based on Generative Design
Venue: arXiv cs.AI 2024
Year: 2024

The scarcity of green spaces, in urban environments, consists a critical challenge. There are multiple adverse effects, impacting the health and well-being of the citizens. Small scale interventions, e.g. pocket parks, is a viable solution, but comes with multiple constraints, involving the design and implementation over a specific area. In this study, we harness the capabilities of generative AI for multi-scale intervention planning, focusing on nature based solutions. By leveraging image-to-image and image inpainting algorithms, we propose a methodology to address the green space deficit in urban areas. Focusing on two alleys in Thessaloniki, where greenery is lacking, we demonstrate the efficacy of our approach in visualizing NBS interventions. Our findings underscore the transformative potential of emerging technologies in shaping the future of urban intervention planning processes.
The Algebras for Automatic Relations
Venue: arXiv cs.FL 2024
Year: 2024

We introduce "synchronous algebras", an algebraic structure tailored to recognize automatic relations (aka. synchronous relations, or regular relations). They are the equivalent of monoids for regular languages, however they conceptually differ in two points: first, they are typed and second, they are equipped with a dependency relation expressing constraints between elements of different types. The interest of the proposed definition is that it allows to lift, in an effective way, pseudovarieties of regular languages to that of synchronous relations, and we show how algebraic characterizations of pseudovarieties of regular languages can be lifted to the pseudovarieties of synchronous relations that they induce. A typical example of such a pseudovariety is the class of "group relations", defined as the relations recognized by finite-state synchronous permutation automata. In order to prove this result, we adapt two pillars of algebraic language to synchronous algebras: (a) any relation admits a syntactic synchronous algebra recognizing it, and moreover, the relation is synchronous if, and only if, its syntactic algebra is finite and (b) classes of synchronous relations with desirable closure properties (i.e. pseudovarieties) correspond to pseudovarieties of synchronous algebras.