OpenRaiser/Intern-Atlas huggingface dataset:
AbstractBeam: Enhancing Bottom-Up Program Synthesis using Library Learning
Venue: arXiv cs.SE 2024
Year: 2024

LambdaBeam is a state-of-the-art, execution-guided algorithm for program synthesis that utilizes higher-order functions, lambda functions, and iterative loops within a Domain-Specific Language (DSL). LambdaBeam generates each program from scratch but does not take advantage of the frequent recurrence of program blocks or subprograms commonly found in specific domains, such as loops for list traversal. To address this limitation, we introduce AbstractBeam: a novel program synthesis framework designed to enhance LambdaBeam by leveraging Library Learning. AbstractBeam identifies and integrates recurring program structures into the DSL, optimizing the synthesis process. Our experimental evaluations demonstrate that AbstractBeam statistically significantly (p < 0.05) outperforms LambdaBeam in the integer list manipulation domain. Beyond solving more tasks, AbstractBeam's program synthesis is also more efficient, requiring less time and fewer candidate programs to generate a solution. Furthermore, our findings indicate that Library Learning effectively enhances program synthesis in domains that are not explicitly designed to showcase its advantages, thereby highlighting the broader applicability of Library Learning.
WASH: Train your Ensemble with Communication-Efficient Weight Shuffling, then Average
Venue: arXiv cs.LG 2024
Year: 2024

The performance of deep neural networks is enhanced by ensemble methods, which average the output of several models. However, this comes at an increased cost at inference. Weight averaging methods aim at balancing the generalization of ensembling and the inference speed of a single model by averaging the parameters of an ensemble of models. Yet, naive averaging results in poor performance as models converge to different loss basins, and aligning the models to improve the performance of the average is challenging. Alternatively, inspired by distributed training, methods like DART and PAPA have been proposed to train several models in parallel such that they will end up in the same basin, resulting in good averaging accuracy. However, these methods either compromise ensembling accuracy or demand significant communication between models during training. In this paper, we introduce WASH, a novel distributed method for training model ensembles for weight averaging that achieves state-of-the-art image classification accuracy. WASH maintains models within the same basin by randomly shuffling a small percentage of weights during training, resulting in diverse models and lower communication costs compared to standard parameter averaging methods.
Efficient Model Compression for Hierarchical Federated Learning
Venue: arXiv cs.LG 2024
Year: 2024

Federated learning (FL), as an emerging collaborative learning paradigm, has garnered significant attention due to its capacity to preserve privacy within distributed learning systems. In these systems, clients collaboratively train a unified neural network model using their local datasets and share model parameters rather than raw data, enhancing privacy. Predominantly, FL systems are designed for mobile and edge computing environments where training typically occurs over wireless networks. Consequently, as model sizes increase, the conventional FL frameworks increasingly consume substantial communication resources. To address this challenge and improve communication efficiency, this paper introduces a novel hierarchical FL framework that integrates the benefits of clustered FL and model compression. We present an adaptive clustering algorithm that identifies a core client and dynamically organizes clients into clusters. Furthermore, to enhance transmission efficiency, each core client implements a local aggregation with compression (LC aggregation) algorithm after collecting compressed models from other clients within the same cluster. Simulation results affirm that our proposed algorithms not only maintain comparable predictive accuracy but also significantly reduce energy consumption relative to existing FL mechanisms.
Optimizing Layout of Recursive Datatypes with Marmoset
Venue: arXiv cs.PL 2024
Year: 2024

While programmers know that the low-level memory representation of data structures can have significant effects on performance, compiler support to optimize the layout of those structures is an under-explored field. Prior work has optimized the layout of individual, non-recursive structures without considering how collections of those objects in linked or recursive data structures are laid out. This work introduces Marmoset, a compiler that optimizes the layouts of algebraic datatypes, with a special focus on producing highly optimized, packed data layouts where recursive structures can be traversed with minimal pointer chasing. Marmoset performs an analysis of how a recursive ADT is used across functions to choose a global layout that promotes simple, strided access for that ADT in memory. It does so by building and solving a constraint system to minimize an abstract cost model, yielding a predicted efficient layout for the ADT. Marmoset then builds on top of Gibbon, a prior compiler for packed, mostly-serial representations, to synthesize optimized ADTs. We show experimentally that Marmoset is able to choose optimal layouts across a series of microbenchmarks and case studies, outperforming both Gibbons baseline approach, as well as MLton, a Standard ML compiler that uses traditional pointer-heavy representations.
Matrix Low-Rank Trust Region Policy Optimization
Venue: arXiv cs.LG 2024
Year: 2024

Most methods in reinforcement learning use a Policy Gradient (PG) approach to learn a parametric stochastic policy that maps states to actions. The standard approach is to implement such a mapping via a neural network (NN) whose parameters are optimized using stochastic gradient descent. However, PG methods are prone to large policy updates that can render learning inefficient. Trust region algorithms, like Trust Region Policy Optimization (TRPO), constrain the policy update step, ensuring monotonic improvements. This paper introduces low-rank matrix-based models as an efficient alternative for estimating the parameters of TRPO algorithms. By gathering the stochastic policy's parameters into a matrix and applying matrix-completion techniques, we promote and enforce low rank. Our numerical studies demonstrate that low-rank matrix-based policy models effectively reduce both computational and sample complexities compared to NN models, while maintaining comparable aggregated rewards.
Enhancing Resiliency of Integrated Space-Air-Ground-Sea Networks with Renewable Energies: A Use Case After the 2023 Türkiye Earthquake
Venue: arXiv cs.NI 2024
Year: 2024

Natural disasters can have catastrophic consequences, a poignant example is the series of $7.7$ and $7.6$ magnitude earthquakes that devastated Türkiye on February 6, 2023. To limit the damage, it is essential to maintain the communications infrastructure to ensure individuals impacted by the disaster can receive critical information. The disastrous earthquakes in Türkiye have revealed the importance of considering communications and energy solutions together to build resilient and sustainable infrastructure. Thus, this paper proposes an integrated space-air-ground-sea network architecture that utilizes various communications and energy-enabling technologies. This study aims to contribute to the development of robust and sustainable disaster-response frameworks. In light of the Türkiye earthquakes, two methods for network management are proposed: the first aims to ensure sustainability in the pre-disaster phase and the second aims to maintain communications during the in-disaster phase. In these frameworks, communications technologies such as High Altitude Platform Station(s)(HAPS), which are among the key enablers to unlock the potential of 6G networks, and energy technologies such as Renewable Energy Sources (RES), Battery Energy Storage Systems (BESSs), and Electric Vehicles (EVs) have been used as the prominent technologies. By simulating a case study, we demonstrate the performance of a proposed framework for providing network resiliency. The paper concludes with potential challenges and future directions to achieve a disaster-resilient network architecture solution.
Deployment of Large Language Models to Control Mobile Robots at the Edge
Venue: arXiv cs.RO 2024
Year: 2024

This paper investigates the possibility of intuitive human-robot interaction through the application of Natural Language Processing (NLP) and Large Language Models (LLMs) in mobile robotics. This work aims to explore the feasibility of using these technologies for edge-based deployment, where traditional cloud dependencies are eliminated. The study specifically contrasts the performance of GPT-4-Turbo, which requires cloud connectivity, with an offline-capable, quantized version of LLaMA 2 (LLaMA 2-7B.Q5 K M). These results show that GPT-4-Turbo delivers superior performance in interpreting and executing complex commands accurately, whereas LLaMA 2 exhibits significant limitations in consistency and reliability of command execution. Communication between the control computer and the mobile robot is established via a Raspberry Pi Pico W, which wirelessly receives commands from the computer without internet dependency and transmits them through a wired connection to the robot's Arduino controller. This study highlights the potential and challenges of implementing LLMs and NLP at the edge, providing groundwork for future research into fully autonomous and network-independent robotic systems. For video demonstrations and source code, please refer to: https://tinyurl.com/MobileRobotGPT4LLaMA2024.
P4: Towards private, personalized, and Peer-to-Peer learning
Venue: arXiv cs.LG 2024
Year: 2024

Personalized learning is a proposed approach to address the problem of data heterogeneity in collaborative machine learning. In a decentralized setting, the two main challenges of personalization are client clustering and data privacy. In this paper, we address these challenges by developing P4 (Personalized Private Peer-to-Peer) a method that ensures that each client receives a personalized model while maintaining differential privacy guarantee of each client's local dataset during and after the training. Our approach includes the design of a lightweight algorithm to identify similar clients and group them in a private, peer-to-peer (P2P) manner. Once grouped, we develop differentially-private knowledge distillation for clients to co-train with minimal impact on accuracy. We evaluate our proposed method on three benchmark datasets (FEMNIST or Federated EMNIST, CIFAR-10 and CIFAR-100) and two different neural network architectures (Linear and CNN-based networks) across a range of privacy parameters. The results demonstrate the potential of P4, as it outperforms the state-of-the-art of differential private P2P by up to 40 percent in terms of accuracy. We also show the practicality of P4 by implementing it on resource constrained devices, and validating that it has minimal overhead, e.g., about 7 seconds to run collaborative training between two clients.
Compression and In-Situ Query Processing for Fine-Grained Array Lineage
Venue: arXiv cs.DB 2024
Year: 2024

Tracking data lineage is important for data integrity, reproducibility, and debugging data science workflows. However, fine-grained lineage (i.e., at a cell level) is challenging to store, even for the smallest datasets. This paper introduces DSLog, a storage system that efficiently stores, indexes, and queries array data lineage, agnostic to capture methodology. A main contribution is our new compression algorithm, named ProvRC, that compresses captured lineage relationships. Using ProvRC for lineage compression result in a significant storage reduction over functions with simple spatial regularity, beating alternative columnar-store baselines by up to 2000x}. We also show that ProvRC facilitates in-situ query processing that allows forward and backward lineage queries without decompression - in the optimal case, surpassing baselines by 20x in query latency on random numpy pipelines.
MindFormer: Semantic Alignment of Multi-Subject fMRI for Brain Decoding
Venue: arXiv cs.CV 2024
Year: 2024

Research efforts for visual decoding from fMRI signals have attracted considerable attention in research community. Still multi-subject fMRI decoding with one model has been considered intractable due to the drastic variations in fMRI signals between subjects and even within the same subject across different trials. To address current limitations in multi-subject brain decoding, here we introduce a novel semantic alignment method of multi-subject fMRI signals using so-called MindFormer. This model is specifically designed to generate fMRI-conditioned feature vectors that can be used for conditioning Stable Diffusion model for fMRI- to-image generation or large language model (LLM) for fMRI-to-text generation. More specifically, MindFormer incorporates two key innovations: 1) a subject specific token that effectively capture individual differences in fMRI signals while synergistically combines multi subject fMRI data for training, and 2) a novel feature embedding and training scheme based on the IP-Adapter to extract semantically meaningful features from fMRI signals. Our experimental results demonstrate that MindFormer generates semantically consistent images and text across different subjects. Since our MindFormer maintains semantic fidelity by fully utilizing the training data across different subjects by significantly surpassing existing models in multi-subject brain decoding, this may help deepening our understanding of neural processing variations among individuals.
Evaluating NoSQL Databases for OLAP Workloads: A Benchmarking Study of MongoDB, Redis, Kudu and ArangoDB
Venue: arXiv cs.DB 2024
Year: 2024

In the era of big data, conventional RDBMS models have become impractical for handling colossal workloads. Consequently, NoSQL databases have emerged as the preferred storage solutions for executing processing-intensive Online Analytical Processing (OLAP) tasks. Within the realm of NoSQL databases, various classifications exist based on their data storage mechanisms, making it challenging to select the most suitable one for a given OLAP workload. While each NoSQL database boasts distinct advantages, inherent scalability, adaptability to diverse data formats, and high data availability are universally recognized benefits crucial for managing OLAP workloads effectively. Existing research predominantly evaluates individual databases within custom data pipeline setups, lacking a standardized approach for comparative analysis across different databases to identify the optimal data pipeline for OLAP workloads. In this paper, we present our experimental insights into how various NoSQL databases handle OLAP workloads within a standardized data processing pipeline. Our experimental pipeline comprises Apache Spark for large-scale transformations, data cleansing, and schema normalization, diverse NoSQL databases as data stores, and a Business Intelligence tool for data analysis and visualization.
MobileConvRec: A Conversational Dataset for Mobile Apps Recommendations
Venue: arXiv cs.CL 2024
Year: 2024

Existing recommendation systems have focused on two paradigms: 1- historical user-item interaction-based recommendations and 2- conversational recommendations. Conversational recommendation systems facilitate natural language dialogues between users and the system, allowing the system to solicit users' explicit needs while enabling users to inquire about recommendations and provide feedback. Due to substantial advancements in natural language processing, conversational recommendation systems have gained prominence. Existing conversational recommendation datasets have greatly facilitated research in their respective domains. Despite the exponential growth in mobile users and apps in recent years, research in conversational mobile app recommender systems has faced substantial constraints. This limitation can primarily be attributed to the lack of high-quality benchmark datasets specifically tailored for mobile apps. To facilitate research for conversational mobile app recommendations, we introduce MobileConvRec. MobileConvRec simulates conversations by leveraging real user interactions with mobile apps on the Google Play store, originally captured in large-scale mobile app recommendation dataset MobileRec. The proposed conversational recommendation dataset synergizes sequential user-item interactions, which reflect implicit user preferences, with comprehensive multi-turn conversations to effectively grasp explicit user needs. MobileConvRec consists of over 12K multi-turn recommendation-related conversations spanning 45 app categories. Moreover, MobileConvRec presents rich metadata for each app such as permissions data, security and privacy-related information, and binary executables of apps, among others. We demonstrate that MobileConvRec can serve as an excellent testbed for conversational mobile app recommendation through a comparative study of several pre-trained large language models.
Magnitude-based Neuron Pruning for Backdoor Defens
Venue: arXiv cs.LG 2024
Year: 2024

Deep Neural Networks (DNNs) are known to be vulnerable to backdoor attacks, posing concerning threats to their reliable deployment. Recent research reveals that backdoors can be erased from infected DNNs by pruning a specific group of neurons, while how to effectively identify and remove these backdoor-associated neurons remains an open challenge. In this paper, we investigate the correlation between backdoor behavior and neuron magnitude, and find that backdoor neurons deviate from the magnitude-saliency correlation of the model. The deviation inspires us to propose a Magnitude-based Neuron Pruning (MNP) method to detect and prune backdoor neurons. Specifically, MNP uses three magnitude-guided objective functions to manipulate the magnitude-saliency correlation of backdoor neurons, thus achieving the purpose of exposing backdoor behavior, eliminating backdoor neurons and preserving clean neurons, respectively. Experiments show our pruning strategy achieves state-of-the-art backdoor defense performance against a variety of backdoor attacks with a limited amount of clean data, demonstrating the crucial role of magnitude for guiding backdoor defenses.
Microsaccade-inspired Event Camera for Robotics
Venue: arXiv cs.RO 2024
Year: 2024

Neuromorphic vision sensors or event cameras have made the visual perception of extremely low reaction time possible, opening new avenues for high-dynamic robotics applications. These event cameras' output is dependent on both motion and texture. However, the event camera fails to capture object edges that are parallel to the camera motion. This is a problem intrinsic to the sensor and therefore challenging to solve algorithmically. Human vision deals with perceptual fading using the active mechanism of small involuntary eye movements, the most prominent ones called microsaccades. By moving the eyes constantly and slightly during fixation, microsaccades can substantially maintain texture stability and persistence. Inspired by microsaccades, we designed an event-based perception system capable of simultaneously maintaining low reaction time and stable texture. In this design, a rotating wedge prism was mounted in front of the aperture of an event camera to redirect light and trigger events. The geometrical optics of the rotating wedge prism allows for algorithmic compensation of the additional rotational motion, resulting in a stable texture appearance and high informational output independent of external motion. The hardware device and software solution are integrated into a system, which we call Artificial MIcrosaccade-enhanced EVent camera (AMI-EV). Benchmark comparisons validate the superior data quality of AMI-EV recordings in scenarios where both standard cameras and event cameras fail to deliver. Various real-world experiments demonstrate the potential of the system to facilitate robotics perception both for low-level and high-level vision tasks.
Learn to be Fair without Labels: a Distribution-based Learning Framework for Fair Ranking
Venue: arXiv cs.IR 2024
Year: 2024

Ranking algorithms as an essential component of retrieval systems have been constantly improved in previous studies, especially regarding relevance-based utilities. In recent years, more and more research attempts have been proposed regarding fairness in rankings due to increasing concerns about potential discrimination and the issue of echo chamber. These attempts include traditional score-based methods that allocate exposure resources to different groups using pre-defined scoring functions or selection strategies and learning-based methods that learn the scoring functions based on data samples. Learning-based models are more flexible and achieve better performance than traditional methods. However, most of the learning-based models were trained and tested on outdated datasets where fairness labels are barely available. State-of-art models utilize relevance-based utility scores as a substitute for the fairness labels to train their fairness-aware loss, where plugging in the substitution does not guarantee the minimum loss. This inconsistency challenges the model's accuracy and performance, especially when learning is achieved by gradient descent. Hence, we propose a distribution-based fair learning framework (DLF) that does not require labels by replacing the unavailable fairness labels with target fairness exposure distributions. Experimental studies on TREC fair ranking track dataset confirm that our proposed framework achieves better fairness performance while maintaining better control over the fairness-relevance trade-off than state-of-art fair ranking frameworks.
Hyperspectral and multispectral image fusion with arbitrary resolution through self-supervised representations
Venue: arXiv cs.CV 2024
Year: 2024

The fusion of a low-resolution hyperspectral image (LR-HSI) with a high-resolution multispectral image (HR-MSI) has emerged as an effective technique for achieving HSI super-resolution (SR). Previous studies have mainly concentrated on estimating the posterior distribution of the latent high-resolution hyperspectral image (HR-HSI), leveraging an appropriate image prior and likelihood computed from the discrepancy between the latent HSI and observed images. Low rankness stands out for preserving latent HSI characteristics through matrix factorization among the various priors. However, the primary limitation in previous studies lies in the generalization of a fusion model with fixed resolution scales, which necessitates retraining whenever output resolutions are changed. To overcome this limitation, we propose a novel continuous low-rank factorization (CLoRF) by integrating two neural representations into the matrix factorization, capturing spatial and spectral information, respectively. This approach enables us to harness both the low rankness from the matrix factorization and the continuity from neural representation in a self-supervised manner.Theoretically, we prove the low-rank property and Lipschitz continuity in the proposed continuous low-rank factorization. Experimentally, our method significantly surpasses existing techniques and achieves user-desired resolutions without the need for neural network retraining. Code is available at https://github.com/wangting1907/CLoRF-Fusion.