OpenRaiser/Intern-Atlas huggingface dataset:
Conversational Complexity for Assessing Risk in Large Language Models
Venue: arXiv cs.AI 2024
Year: 2024

Large Language Models (LLMs) present a dual-use dilemma: they enable beneficial applications while harboring potential for harm, particularly through conversational interactions. Despite various safeguards, advanced LLMs remain vulnerable. A watershed case in early 2023 involved journalist Kevin Roose's extended dialogue with Bing, an LLM-powered search engine, which revealed harmful outputs after probing questions, highlighting vulnerabilities in the model's safeguards. This contrasts with simpler early jailbreaks, like the "Grandma Jailbreak," where users framed requests as innocent help for a grandmother, easily eliciting similar content. This raises the question: How much conversational effort is needed to elicit harmful information from LLMs? We propose two measures to quantify this effort: Conversational Length (CL), which measures the number of conversational turns needed to obtain a specific harmful response, and Conversational Complexity (CC), defined as the Kolmogorov complexity of the user's instruction sequence leading to the harmful response. To address the incomputability of Kolmogorov complexity, we approximate CC using a reference LLM to estimate the compressibility of the user instructions. Applying this approach to a large red-teaming dataset, we perform a quantitative analysis examining the statistical distribution of harmful and harmless conversational lengths and complexities. Our empirical findings suggest that this distributional analysis and the minimization of CC serve as valuable tools for understanding AI safety, offering insights into the accessibility of harmful information. This work establishes a foundation for a new perspective on LLM safety, centered around the algorithmic complexity of pathways to harm.
GAS: Generative Activation-Aided Asynchronous Split Federated Learning
Venue: arXiv cs.LG 2024
Year: 2024

Split Federated Learning (SFL) splits and collaboratively trains a shared model between clients and server, where clients transmit activations and client-side models to server for updates. Recent SFL studies assume synchronous transmission of activations and client-side models from clients to server. However, due to significant variations in computational and communication capabilities among clients, activations and client-side models arrive at server asynchronously. The delay caused by asynchrony significantly degrades the performance of SFL. To address this issue, we consider an asynchronous SFL framework, where an activation buffer and a model buffer are embedded on the server to manage the asynchronously transmitted activations and client-side models, respectively. Furthermore, as asynchronous activation transmissions cause the buffer to frequently receive activations from resource-rich clients, leading to biased updates of the server-side model, we propose Generative activations-aided Asynchronous SFL (GAS). In GAS, the server maintains an activation distribution for each label based on received activations and generates activations from these distributions according to the degree of bias. These generative activations are then used to assist in updating the server-side model, ensuring more accurate updates. We derive a tighter convergence bound, and our experiments demonstrate the effectiveness of the proposed method. The code is available at https://github.com/eejiarong/GAS.
Refined Statistical Bounds for Classification Error Mismatches with Constrained Bayes Error
Venue: arXiv cs.IT 2024
Year: 2024

In statistical classification/multiple hypothesis testing and machine learning, a model distribution estimated from the training data is usually applied to replace the unknown true distribution in the Bayes decision rule, which introduces a mismatch between the Bayes error and the model-based classification error. In this work, we derive the classification error bound to study the relationship between the Kullback-Leibler divergence and the classification error mismatch. We first reconsider the statistical bounds based on classification error mismatch derived in previous works, employing a different method of derivation. Then, motivated by the observation that the Bayes error is typically low in machine learning tasks like speech recognition and pattern recognition, we derive a refined Kullback-Leibler-divergence-based bound on the error mismatch with the constraint that the Bayes error is lower than a threshold.
What Could Possibly Go Wrong: Undesirable Patterns in Collective Development
Venue: arXiv cs.SE 2024
Year: 2024

Software development, often perceived as a technical endeavor, is fundamentally a social activity requiring collaboration among team members. Acknowledging this, the software development community has devised strategies to address possible collaboration-related shortcomings. Various studies have attempted to capture the social dynamics within software engineering. In these studies, the authors developed methods to identify numerous teamwork issues and proposed various approaches to address them. However, certain teamwork issues remain unstudied, necessitating a comprehensive bottom-up exploration from practitioner's perceptions to common patterns. This paper introduces the concept of undesirable patterns in collective development, referring to potential teamwork problems that may escalate if unaddressed. Through 38 in-depth exploratory interviews, we identify and classify 42 patterns, revealing their origins and consequences. Subsequent surveys, 436 and 968 participants each, explore the significance and frequency of the undesirable patterns, and evaluate potential tools and features to manage these patterns. The study contributes a nuanced understanding of undesirable patterns, evaluating their impact and proposing pragmatic tools and features for industrial application. The findings provide a valuable foundation for further in-depth studies and the development of tools to enhance collaborative software engineering practices.
Constraint-Based Breakpoints for Responsive Visualization Design and Development
Venue: arXiv cs.HC 2024
Year: 2024

This paper introduces constraint-based breakpoints, a technique for designing responsive visualizations for a wide variety of screen sizes and datasets. Breakpoints in responsive visualization define when different visualization designs are shown. Conventionally, breakpoints are static, pre-defined widths, and as such do not account for changes to the visualized dataset or visualization parameters. To guarantee readability and efficient use of space across datasets, these static breakpoints would require manual updates. Constraint-based breakpoints solve this by evaluating visualization-specific constraints on the size of visual elements, overlapping elements, and the aspect ratio of the visualization and available space. Once configured, a responsive visualization with constraint-based breakpoints can adapt to different screen sizes for any dataset. We describe a framework that guides designers in creating a stack of visualization designs for different display sizes and defining constraints for each of these designs. We demonstrate constraint-based breakpoints for different data types and their visualizations: geographic data (choropleth map, proportional circle map, Dorling cartogram, hexagonal grid map, bar chart, waffle chart), network data (node-link diagram, adjacency matrix, arc diagram), and multivariate data (scatterplot, heatmap). Interactive demos and supplemental material are available at https://responsive-vis.github.io/breakpoints/.
Mutual Benefit: The Case for Sharing Autonomous Vehicle Data with the Public
Venue: arXiv cs.CY 2024
Year: 2024

Autonomous driving is a widely researched technology that is frequently tested on public roads. The data generated from these tests represent an essential competitive element for the respective companies moving this technology forward. In this paper, we argue for the normative idea that a part of this data should more explicitly benefit the general public by sharing it through a trusted entity as a form of compensation and control for the communities that are being experimented upon. To support this argument, we highlight what data is available to be shared, make the ethical case for sharing autonomous vehicle data, present case studies in how AV data is currently shared, draw from existing data-sharing platforms from similar transportation industries to make recommendations on how data should be shared and conclude with arguments as to why such data-sharing should be encouraged.
Serverless Query Processing with Flexible Performance SLAs and Prices
Venue: arXiv cs.DB 2024
Year: 2024

Serverless query processing has become increasingly popular due to its auto-scaling, high elasticity, and pay-as-you-go pricing. It allows cloud data warehouse (or lakehouse) users to focus on data analysis without the burden of managing systems and resources. Accordingly, in serverless query services, users become more concerned about cost-efficiency under acceptable performance than performance under fixed resources. This poses new challenges for serverless query engine design in providing flexible performance service-level agreements (SLAs) and cost-efficiency (i.e., prices). In this paper, we first define the problem of flexible performance SLAs and prices in serverless query processing and discuss its significance. Then, we envision the challenges and solutions for solving this problem and the opportunities it raises for other database research. Finally, we present PixelsDB, an open-source prototype with three service levels supported by dedicated architectural designs. Evaluations show that PixelsDB reduces resource costs by 65.5\% for near-real-world workloads generated by Cloud Analytics Benchmark (CAB) while not violating the pending time guarantees.
Erasure Coded Neural Network Inference via Fisher Averaging
Venue: arXiv cs.LG 2024
Year: 2024

Erasure-coded computing has been successfully used in cloud systems to reduce tail latency caused by factors such as straggling servers and heterogeneous traffic variations. A majority of cloud computing traffic now consists of inference on neural networks on shared resources where the response time of inference queries is also adversely affected by the same factors. However, current erasure coding techniques are largely focused on linear computations such as matrix-vector and matrix-matrix multiplications and hence do not work for the highly non-linear neural network functions. In this paper, we seek to design a method to code over neural networks, that is, given two or more neural network models, how to construct a coded model whose output is a linear combination of the outputs of the given neural networks. We formulate the problem as a KL barycenter problem and propose a practical algorithm COIN that leverages the diagonal Fisher information to create a coded model that approximately outputs the desired linear combination of outputs. We conduct experiments to perform erasure coding over neural networks trained on real-world vision datasets and show that the accuracy of the decoded outputs using COIN is significantly higher than other baselines while being extremely compute-efficient.
Kvasir-VQA: A Text-Image Pair GI Tract Dataset
Venue: arXiv cs.CV 2024
Year: 2024

We introduce Kvasir-VQA, an extended dataset derived from the HyperKvasir and Kvasir-Instrument datasets, augmented with question-and-answer annotations to facilitate advanced machine learning tasks in Gastrointestinal (GI) diagnostics. This dataset comprises 6,500 annotated images spanning various GI tract conditions and surgical instruments, and it supports multiple question types including yes/no, choice, location, and numerical count. The dataset is intended for applications such as image captioning, Visual Question Answering (VQA), text-based generation of synthetic medical images, object detection, and classification. Our experiments demonstrate the dataset's effectiveness in training models for three selected tasks, showcasing significant applications in medical image analysis and diagnostics. We also present evaluation metrics for each task, highlighting the usability and versatility of our dataset. The dataset and supporting artifacts are available at https://datasets.simula.no/kvasir-vqa.
Landscape-Aware Automated Algorithm Configuration using Multi-output Mixed Regression and Classification
Venue: arXiv cs.LG 2024
Year: 2024

In landscape-aware algorithm selection problem, the effectiveness of feature-based predictive models strongly depends on the representativeness of training data for practical applications. In this work, we investigate the potential of randomly generated functions (RGF) for the model training, which cover a much more diverse set of optimization problem classes compared to the widely-used black-box optimization benchmarking (BBOB) suite. Correspondingly, we focus on automated algorithm configuration (AAC), that is, selecting the best suited algorithm and fine-tuning its hyperparameters based on the landscape features of problem instances. Precisely, we analyze the performance of dense neural network (NN) models in handling the multi-output mixed regression and classification tasks using different training data sets, such as RGF and many-affine BBOB (MA-BBOB) functions. Based on our results on the BBOB functions in 5d and 20d, near optimal configurations can be identified using the proposed approach, which can most of the time outperform the off-the-shelf default configuration considered by practitioners with limited knowledge about AAC. Furthermore, the predicted configurations are competitive against the single best solver in many cases. Overall, configurations with better performance can be best identified by using NN models trained on a combination of RGF and MA-BBOB functions.
Revisiting SMoE Language Models by Evaluating Inefficiencies with Task Specific Expert Pruning
Venue: arXiv cs.LG 2024
Year: 2024

Sparse Mixture of Expert (SMoE) models have emerged as a scalable alternative to dense models in language modeling. These models use conditionally activated feedforward subnetworks in transformer blocks, allowing for a separation between total model parameters and per-example computation. However, large token-routed SMoE models face a significant challenge: during inference, the entire model must be used for a sequence or a batch, resulting in high latencies in a distributed setting that offsets the advantages of per-token sparse activation. Our research explores task-specific model pruning to inform decisions about designing SMoE architectures, mainly modulating the choice of expert counts in pretraining. We investigate whether such pruned models offer advantages over smaller SMoE models trained from scratch, when evaluating and comparing them individually on tasks. To that end, we introduce an adaptive task-aware pruning technique UNCURL to reduce the number of experts per MoE layer in an offline manner post-training. Our findings reveal a threshold pruning factor for the reduction that depends on the number of experts used in pretraining, above which, the reduction starts to degrade model performance. These insights contribute to our understanding of model design choices when pretraining with SMoE architectures, particularly useful when considering task-specific inference optimization for later stages.
DiversityMedQA: Assessing Demographic Biases in Medical Diagnosis using Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

As large language models (LLMs) gain traction in healthcare, concerns about their susceptibility to demographic biases are growing. We introduce {DiversityMedQA}, a novel benchmark designed to assess LLM responses to medical queries across diverse patient demographics, such as gender and ethnicity. By perturbing questions from the MedQA dataset, which comprises medical board exam questions, we created a benchmark that captures the nuanced differences in medical diagnosis across varying patient profiles. Our findings reveal notable discrepancies in model performance when tested against these demographic variations. Furthermore, to ensure the perturbations were accurate, we also propose a filtering strategy that validates each perturbation. By releasing DiversityMedQA, we provide a resource for evaluating and mitigating demographic bias in LLM medical diagnoses.
Effective Noise-aware Data Simulation for Domain-adaptive Speech Enhancement Leveraging Dynamic Stochastic Perturbation
Venue: arXiv cs.SD 2024
Year: 2024

Cross-domain speech enhancement (SE) is often faced with severe challenges due to the scarcity of noise and background information in an unseen target domain, leading to a mismatch between training and test conditions. This study puts forward a novel data simulation method to address this issue, leveraging noise-extractive techniques and generative adversarial networks (GANs) with only limited target noisy speech data. Notably, our method employs a noise encoder to extract noise embeddings from target-domain data. These embeddings aptly guide the generator to synthesize utterances acoustically fitted to the target domain while authentically preserving the phonetic content of the input clean speech. Furthermore, we introduce the notion of dynamic stochastic perturbation, which can inject controlled perturbations into the noise embeddings during inference, thereby enabling the model to generalize well to unseen noise conditions. Experiments on the VoiceBank-DEMAND benchmark dataset demonstrate that our domain-adaptive SE method outperforms an existing strong baseline based on data simulation.
TASL-Net: Tri-Attention Selective Learning Network for Intelligent Diagnosis of Bimodal Ultrasound Video
Venue: arXiv cs.CV 2024
Year: 2024

In the intelligent diagnosis of bimodal (gray-scale and contrast-enhanced) ultrasound videos, medical domain knowledge such as the way sonographers browse videos, the particular areas they emphasize, and the features they pay special attention to, plays a decisive role in facilitating precise diagnosis. Embedding medical knowledge into the deep learning network can not only enhance performance but also boost clinical confidence and reliability of the network. However, it is an intractable challenge to automatically focus on these person- and disease-specific features in videos and to enable networks to encode bimodal information comprehensively and efficiently. This paper proposes a novel Tri-Attention Selective Learning Network (TASL-Net) to tackle this challenge and automatically embed three types of diagnostic attention of sonographers into a mutual transformer framework for intelligent diagnosis of bimodal ultrasound videos. Firstly, a time-intensity-curve-based video selector is designed to mimic the temporal attention of sonographers, thus removing a large amount of redundant information while improving computational efficiency of TASL-Net. Then, to introduce the spatial attention of the sonographers for contrast-enhanced video analysis, we propose the earliest-enhanced position detector based on structural similarity variation, on which the TASL-Net is made to focus on the differences of perfusion variation inside and outside the lesion. Finally, by proposing a mutual encoding strategy that combines convolution and transformer, TASL-Net possesses bimodal attention to structure features on gray-scale videos and to perfusion variations on contrast-enhanced videos. These modules work collaboratively and contribute to superior performance. We conduct a detailed experimental validation of TASL-Net's performance on three datasets, including lung, breast, and liver.
Quantifying Emergence in Neural Networks: Insights from Pruning and Training Dynamics
Venue: arXiv cs.LG 2024
Year: 2024

Emergence, where complex behaviors develop from the interactions of simpler components within a network, plays a crucial role in enhancing neural network capabilities. We introduce a quantitative framework to measure emergence during the training process and examine its impact on network performance, particularly in relation to pruning and training dynamics. Our hypothesis posits that the degree of emergence, defined by the connectivity between active and inactive nodes, can predict the development of emergent behaviors in the network. Through experiments with feedforward and convolutional architectures on benchmark datasets, we demonstrate that higher emergence correlates with improved trainability and performance. We further explore the relationship between network complexity and the loss landscape, suggesting that higher emergence indicates a greater concentration of local minima and a more rugged loss landscape. Pruning, which reduces network complexity by removing redundant nodes and connections, is shown to enhance training efficiency and convergence speed, though it may lead to a reduction in final accuracy. These findings provide new insights into the interplay between emergence, complexity, and performance in neural networks, offering valuable implications for the design and optimization of more efficient architectures.
Large-scale Urban Facility Location Selection with Knowledge-informed Reinforcement Learning
Venue: arXiv cs.LG 2024
Year: 2024

The facility location problem (FLP) is a classical combinatorial optimization challenge aimed at strategically laying out facilities to maximize their accessibility. In this paper, we propose a reinforcement learning method tailored to solve large-scale urban FLP, capable of producing near-optimal solutions at superfast inference speed. We distill the essential swap operation from local search, and simulate it by intelligently selecting edges on a graph of urban regions, guided by a knowledge-informed graph neural network, thus sidestepping the need for heavy computation of local search. Extensive experiments on four US cities with different geospatial conditions demonstrate that our approach can achieve comparable performance to commercial solvers with less than 5\% accessibility loss, while displaying up to 1000 times speedup. We deploy our model as an online geospatial application at https://huggingface.co/spaces/randommmm/MFLP.
DAPONet: A Dual Attention and Partially Overparameterized Network for Real-Time Road Damage Detection
Venue: arXiv cs.CV 2024
Year: 2024

Current road damage detection methods, relying on manual inspections or sensor-mounted vehicles, are inefficient, limited in coverage, and often inaccurate, especially for minor damages, leading to delays and safety hazards. To address these issues and enhance real-time road damage detection using street view image data (SVRDD), we propose DAPONet, a model incorporating three key modules: a dual attention mechanism combining global and local attention, a multi-scale partial over-parameterization module, and an efficient downsampling module. DAPONet achieves a mAP50 of 70.1% on the SVRDD dataset, outperforming YOLOv10n by 10.4%, while reducing parameters to 1.6M and FLOPs to 1.7G, representing reductions of 41% and 80%, respectively. On the MS COCO2017 val dataset, DAPONet achieves an mAP50-95 of 33.4%, 0.8% higher than EfficientDet-D1, with a 74% reduction in both parameters and FLOPs.