OpenRaiser/Intern-Atlas huggingface dataset:
The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
Venue: arXiv cs.AI 2024
Year: 2024

One of the grand challenges of artificial general intelligence is developing agents capable of conducting scientific research and discovering new knowledge. While frontier models have already been used as aides to human scientists, e.g. for brainstorming ideas, writing code, or prediction tasks, they still conduct only a small part of the scientific process. This paper presents the first comprehensive framework for fully automatic scientific discovery, enabling frontier large language models to perform research independently and communicate their findings. We introduce The AI Scientist, which generates novel research ideas, writes code, executes experiments, visualizes results, describes its findings by writing a full scientific paper, and then runs a simulated review process for evaluation. In principle, this process can be repeated to iteratively develop ideas in an open-ended fashion, acting like the human scientific community. We demonstrate its versatility by applying it to three distinct subfields of machine learning: diffusion modeling, transformer-based language modeling, and learning dynamics. Each idea is implemented and developed into a full paper at a cost of less than $15 per paper. To evaluate the generated papers, we design and validate an automated reviewer, which we show achieves near-human performance in evaluating paper scores. The AI Scientist can produce papers that exceed the acceptance threshold at a top machine learning conference as judged by our automated reviewer. This approach signifies the beginning of a new era in scientific discovery in machine learning: bringing the transformative benefits of AI agents to the entire research process of AI itself, and taking us closer to a world where endless affordable creativity and innovation can be unleashed on the world's most challenging problems. Our code is open-sourced at https://github.com/SakanaAI/AI-Scientist
OWL2Vec4OA: Tailoring Knowledge Graph Embeddings for Ontology Alignment
Venue: arXiv cs.AI 2024
Year: 2024

Ontology alignment is integral to achieving semantic interoperability as the number of available ontologies covering intersecting domains is increasing. This paper proposes OWL2Vec4OA, an extension of the ontology embedding system OWL2Vec*. While OWL2Vec* has emerged as a powerful technique for ontology embedding, it currently lacks a mechanism to tailor the embedding to the ontology alignment task. OWL2Vec4OA incorporates edge confidence values from seed mappings to guide the random walk strategy. We present the theoretical foundations, implementation details, and experimental evaluation of our proposed extension, demonstrating its potential effectiveness for ontology alignment tasks.
EqNIO: Subequivariant Neural Inertial Odometry
Venue: arXiv cs.RO 2024
Year: 2024

Neural networks are seeing rapid adoption in purely inertial odometry, where accelerometer and gyroscope measurements from commodity inertial measurement units (IMU) are used to regress displacements and associated uncertainties. They can learn informative displacement priors, which can be directly fused with the raw data with off-the-shelf non-linear filters. Nevertheless, these networks do not consider the physical roto-reflective symmetries inherent in IMU data, leading to the need to memorize the same priors for every possible motion direction, which hinders generalization. In this work, we characterize these symmetries and show that the IMU data and the resulting displacement and covariance transform equivariantly, when rotated around the gravity vector and reflected with respect to arbitrary planes parallel to gravity. We design a neural network that respects these symmetries by design through equivariant processing in three steps: First, it estimates an equivariant gravity-aligned frame from equivariant vectors and invariant scalars derived from IMU data, leveraging expressive linear and non-linear layers tailored to commute with the underlying symmetry transformation. We then map the IMU data into this frame, thereby achieving an invariant canonicalization that can be directly used with off-the-shelf inertial odometry networks. Finally, we map these network outputs back into the original frame, thereby obtaining equivariant covariances and displacements. We demonstrate the generality of our framework by applying it to the filter-based approach based on TLIO, and the end-to-end RONIN architecture, and show better performance on the TLIO, Aria, RIDI and OxIOD datasets than existing methods.
Is it a work or leisure travel? Applying text classification to identify work-related travel on social networks
Venue: arXiv cs.SI 2024
Year: 2024

In today's digital era, the use of Social Networks (SNs) and Location-Based SNs (LBSNs) has become integral for travelers seeking Points of Interest (POI) and sharing travel experiences. This trend is supported by the fact that a significant majority of American travelers utilize SNs during their trips. However, the abundance of information available on these platforms presents a challenge in identifying the best options. To address this issue, Recommender Systems (RS) are commonly employed to suggest POIs based on user history, with the integration of contextual information enhancing the quality of recommendations. Notably, incorporating user travel purpose, which is often overlooked but holds potential in characterizing travelers' behavior, can lead to more tailored recommendations. In this study, we propose a model to predict whether a trip is leisure or work-related, utilizing state-of-the-art Automatic Text Classification (ATC) models such as BERT, RoBERTa, and BART to enhance the understanding of user travel purposes and improve recommendation accuracy in specific travel scenarios.
A GRASP algorithm for the Meal Delivery Routing Problem
Venue: arXiv cs.RO 2024
Year: 2024

With the escalating demand for meal delivery services, this study delves into the Meal Delivery Routing Problem (MDRP) within the context of last-mile logis-tics. Focusing on the critical aspects of courier allocation and order fulfillment, we introduce a novel approach utilizing a GRASP metaheuristic. The algorithm optimizes the assignment of couriers to orders, considering dynamic factors such as courier availability, order demands, and geographical locations. Real-world in-stances from a Colombian delivery app form the basis of our computational anal-ysis. Calibration of GRASP parameters reveals a delicate trade-off between solu-tion quality and computational time. Comparative results with a simulation-optimization based study underscore GRASP's competitive performance, demon-strating strengths in fulfilling orders and routing efficiency across diverse in-stances. This research enhances operational efficiency in the burgeoning food de-livery industry, shedding light on practical algorithms for last-mile logistics opti-mization.
Automated Romberg Test: Leveraging a CNN and Centre of Mass Analysis for Sensory Ataxia Diagnosis
Venue: arXiv cs.CV 2024
Year: 2024

This paper proposes a novel method to diagnose sensory ataxia via an automated Romberg Test - the current de facto medical procedure used to diagnose this condition. It utilizes a convolutional neural network to predict joint locations, used for the calculation of various bio-mechanical markers such as the center of mass of the subject and various joint angles. This information is used in combination with data filtering techniques such as Kalman Filters, and center of mass analysis which helped make accurate inferences about the relative weight distribution in the lateral and anterior-posterior axes, and provide an objective, mathematically based diagnosis of this condition. In order to evaluate the performance of this method, testing was performed using dual weight scales and pre-annotated diagnosis videos taken from medical settings. These two methods both quantified the veritable weight distribution upon the ground surface with a ground truth and provided a real-world estimate of accuracy for the proposed method. A mean absolute error of 0.2912 percent was found for the calculated relative weight distribution difference, and an accuracy of 83.33 percent was achieved on diagnoses.
On Representing Humans' Soft-Ethics Preferences As Dispositions
Venue: arXiv cs.HC 2024
Year: 2024

The aim of this paper is to represent humans' soft-ethical preferences by means of dispositional properties. We begin by examining real-life situations, termed as scenarios, that involve ethical dilemmas. Users engage with these scenarios, making decisions on how to act and providing justifications for their choices. We adopt a dispositional approach to represent these scenarios and the interaction with the users. Dispositions are properties that are instantiated by any kind of entity and that may manifest if properly triggered. In particular, the dispositional properties we are interested in are the ethical and behavioural ones. The approach will be described by means of examples. The ultimate goal is to implement the results of this work into a software exoskeleton solution aimed at augmenting human capabilities by preserving their soft-ethical preferences in interactions with autonomous systems.
Using deep learning to enhance electronic service quality: Application to real estate websites
Venue: arXiv cs.CV 2024
Year: 2024

Electronic service quality (E-SQ) is a strategic metric for successful e-services.Among the service quality dimensions, tangibility is overlooked. However, by incorporating visuals or tangible tools, the intangible nature of e-services can be balanced. Thanks to advancements in Deep Learning for computer vision, tangible visual features can now be leveraged to enhance the browsing and searching experience of electronic services. Users usually have specific search criteria to meet, but most services will not offer flexible search filters. This research emphasizes the importance of integrating visual and descriptive features to improve the tangibility and efficiency of e-services. A prime example of an electronic service that can benefit from this is real-estate websites. Searching for real estate properties that match user preferences is usually demanding and lacks visual filters, such as the Damage Level to the property. The research introduces a novel visual descriptive feature, the Damage Level, which utilizes a deep learning network known as Mask-RCNN to estimate damage in real estate images. Additionally, a model is developed to incorporate the Damage Level as a tangible feature in electronic real estate services, with the aim of enhancing the tangible customer experience.
ViC: Virtual Compiler Is All You Need For Assembly Code Search
Venue: arXiv cs.SE 2024
Year: 2024

Assembly code search is vital for reducing the burden on reverse engineers, allowing them to quickly identify specific functions using natural language within vast binary programs. Despite its significance, this critical task is impeded by the complexities involved in building high-quality datasets. This paper explores training a Large Language Model (LLM) to emulate a general compiler. By leveraging Ubuntu packages to compile a dataset of 20 billion tokens, we further continue pre-train CodeLlama as a Virtual Compiler (ViC), capable of compiling any source code of any language to assembly code. This approach allows for virtual compilation across a wide range of programming languages without the need for a real compiler, preserving semantic equivalency and expanding the possibilities for assembly code dataset construction. Furthermore, we use ViC to construct a sufficiently large dataset for assembly code search. Employing this extensive dataset, we achieve a substantial improvement in assembly code search performance, with our model surpassing the leading baseline by 26%.
Wavelet based inpainting detection
Venue: arXiv cs.CV 2024
Year: 2024

With the advancement in image editing tools, manipulating digital images has become alarmingly easy. Inpainting, which is used to remove objects or fill in parts of an image, serves as a powerful tool for both image restoration and forgery. This paper introduces a novel approach for detecting image inpainting forgeries by combining DT-CWT with Hierarchical Feature segmentation and with noise inconsistency analysis. The DT-CWT offers several advantages for this task, including inherent shift-invariance, which makes it robust to minor manipulations during the inpainting process, and directional selectivity, which helps capture subtle artifacts introduced by inpainting in specific frequency bands and orientations. By first applying color image segmentation and then analyzing for each segment, noise inconsistency obtained via DT-CW we can identify patterns indicative of inpainting forgeries. The proposed method is evaluated on a benchmark dataset created for this purpose and is compared with existing forgery detection techniques. Our approach demonstrates superior results compared with SOTA in detecting inpainted images.
Towards Autonomous Agents: Adaptive-planning, Reasoning, and Acting in Language Models
Venue: arXiv cs.AI 2024
Year: 2024

We propose a novel in-context learning algorithm for building autonomous decision-making language agents. The language agent continuously attempts to solve the same task by self-correcting each time the task fails. Our selected language agent demonstrates the ability to solve tasks in a text-based game environment. Our results show that the gemma-2-9b-it language model, using our proposed method, can successfully complete two of six tasks that failed in the first attempt. This highlights the effectiveness of our approach in enhancing the problem-solving capabilities of a single language model through self-correction, paving the way for more advanced autonomous agents. The code is publicly available at https://github.com/YenCheHsiao/AutonomousLLMAgentwithAdaptingPlanning.
Generalization Enhancement Strategies to Enable Cross-year Cropland Mapping with Convolutional Neural Networks Trained Using Historical Samples
Venue: arXiv cs.CV 2024
Year: 2024

The accuracy of mapping agricultural fields across large areas is steadily improving with high-resolution satellite imagery and deep learning (DL) models, even in regions where fields are small and geometrically irregular. However, developing effective DL models often requires large, expensive label datasets, typically available only for specific years or locations. This limits the ability to create annual maps essential for agricultural monitoring, as domain shifts occur between years and regions due to changes in farming practices and environmental conditions. The challenge is to design a model flexible enough to account for these shifts without needing yearly labels. While domain adaptation techniques or semi-supervised training are common solutions, we explored enhancing the model's generalization power. Our results indicate that a holistic approach is essential, combining methods to improve generalization. Specifically, using an area-based loss function, such as Tversky-focal loss (TFL), significantly improved predictions across multiple years. The use of different augmentation techniques helped to encode different types of invariance, particularly photometric augmentations encoded invariance to brightness changes, though they increased false positives. The combination of photometric augmentation, TFL loss, and MC-dropout produced the best results, although dropout alone led to more false negatives in subsequent year predictions. Additionally, the choice of input normalization had a significant impact, with the best results obtained when statistics were calculated either locally or across the entire dataset over all bands (lab and gab). We developed a workflow that enabled a U-Net model to generate effective multi-year crop maps over large areas. Our code, available at: https://github.com/agroimpacts/cnn-generalization-enhancement, will be regularly updated with improvements.
Clock Auctions Augmented with Unreliable Advice
Venue: arXiv cs.GT 2024
Year: 2024

We provide the first analysis of (deferred acceptance) clock auctions in the learning-augmented framework. These auctions satisfy a unique list of appealing properties, including obvious strategyproofness, transparency, and unconditional winner privacy, making them particularly well-suited for real-world applications. However, early work that evaluated their performance from a worst-case analysis perspective concluded that no deterministic clock auction with $n$ bidders can achieve a $O(\log^{1-ε} n)$ approximation of the optimal social welfare for any $ε>0$, even in very simple settings. This overly pessimistic impossibility result heavily depends on the assumption that the designer has no information regarding the bidders' values. Leveraging the learning-augmented framework, we instead consider a designer equipped with some (machine-learned) advice regarding the optimal solution; this advice can provide useful guidance if accurate, but it may be unreliable. Our main results are learning-augmented clock auctions that use this advice to achieve much stronger guarantees whenever the advice is accurate (consistency), while maintaining worst-case guarantees even if this advice is arbitrarily inaccurate (robustness). Our first clock auction achieves the best of both worlds: $(1+ε)$-consistency for any $ε>0$ and $O(\log{n})$ robustness; we also extend this auction to achieve error tolerance. We then consider a much stronger notion of consistency, which we refer to as consistency$^\infty$, and provide auctions that achieves a near-optimal trade-off between consistency$^\infty$ and robustness. Finally, using our impossibility results regarding this trade-off, we prove lower bounds on the ``cost of smoothness,'' i.e., on the achievable robustness if we also require that the performance of the auction degrades smoothly as a function of the prediction error.
Rethinking the Alignment of Psychotherapy Dialogue Generation with Motivational Interviewing Strategies
Venue: arXiv cs.CL 2024
Year: 2024

Recent advancements in large language models (LLMs) have shown promise in generating psychotherapeutic dialogues, particularly in the context of motivational interviewing (MI). However, the inherent lack of transparency in LLM outputs presents significant challenges given the sensitive nature of psychotherapy. Applying MI strategies, a set of MI skills, to generate more controllable therapeutic-adherent conversations with explainability provides a possible solution. In this work, we explore the alignment of LLMs with MI strategies by first prompting the LLMs to predict the appropriate strategies as reasoning and then utilizing these strategies to guide the subsequent dialogue generation. We seek to investigate whether such alignment leads to more controllable and explainable generations. Multiple experiments including automatic and human evaluations are conducted to validate the effectiveness of MI strategies in aligning psychotherapy dialogue generation. Our findings demonstrate the potential of LLMs in producing strategically aligned dialogues and suggest directions for practical applications in psychotherapeutic settings.
A Comparison of Imitation Learning Algorithms for Bimanual Manipulation
Venue: arXiv cs.RO 2024
Year: 2024

Amidst the wide popularity of imitation learning algorithms in robotics, their properties regarding hyperparameter sensitivity, ease of training, data efficiency, and performance have not been well-studied in high-precision industry-inspired environments. In this work, we demonstrate the limitations and benefits of prominent imitation learning approaches and analyze their capabilities regarding these properties. We evaluate each algorithm on a complex bimanual manipulation task involving an over-constrained dynamics system in a setting involving multiple contacts between the manipulated object and the environment. While we find that imitation learning is well suited to solve such complex tasks, not all algorithms are equal in terms of handling environmental and hyperparameter perturbations, training requirements, performance, and ease of use. We investigate the empirical influence of these key characteristics by employing a carefully designed experimental procedure and learning environment. Paper website: https://bimanual-imitation.github.io/
AquilaMoE: Efficient Training for MoE Models with Scale-Up and Scale-Out Strategies
Venue: arXiv cs.CL 2024
Year: 2024

In recent years, with the rapid application of large language models across various fields, the scale of these models has gradually increased, and the resources required for their pre-training have grown exponentially. Training an LLM from scratch will cost a lot of computation resources while scaling up from a smaller model is a more efficient approach and has thus attracted significant attention. In this paper, we present AquilaMoE, a cutting-edge bilingual 8*16B Mixture of Experts (MoE) language model that has 8 experts with 16 billion parameters each and is developed using an innovative training methodology called EfficientScale. This approach optimizes performance while minimizing data requirements through a two-stage process. The first stage, termed Scale-Up, initializes the larger model with weights from a pre-trained smaller model, enabling substantial knowledge transfer and continuous pretraining with significantly less data. The second stage, Scale-Out, uses a pre-trained dense model to initialize the MoE experts, further enhancing knowledge transfer and performance. Extensive validation experiments on 1.8B and 7B models compared various initialization schemes, achieving models that maintain and reduce loss during continuous pretraining. Utilizing the optimal scheme, we successfully trained a 16B model and subsequently the 8*16B AquilaMoE model, demonstrating significant improvements in performance and training efficiency.
An Infrastructure Cost Optimised Algorithm for Partitioning of Microservices
Venue: arXiv cs.SE 2024
Year: 2024

The evolution and advances made in the field of Cloud engineering influence the constant changes in software application development cycle and practices. Software architecture has evolved along with other domains and capabilities of software engineering. As migrating applications into the cloud is universally adopted by the software industry, microservices have proven to be the most suitable and widely accepted architecture pattern for applications deployed on distributed cloud. Their efficacy is enabled by both technical benefits like reliability, fault isolation, scalability and productivity benefits like ease of asset maintenance and clear ownership boundaries which in turn lead to fewer interdependencies and shorter development cycles thereby resulting in faster time to market. Though microservices have been established as an architecture pattern over the last decade, many organizations fail to optimize the architecture design to maximize efficiency. In some cases, the complexity of migrating an existing application into the microservices architecture becomes overwhelmingly complex and expensive. Additionally, automation and tool support for this problem are still at an early stage as there isn't a single well-acknowledged pattern or tool which could support the decomposition. This paper discusses a few impactful previous research and survey efforts to identify the lack of infrastructure cost optimization as a parameter in any of the approaches present. This paper proposes an Infrastructure-optimised predictive algorithm for partitioning monolithic software into microservices. It also summarizes the scope for future research opportunities within the area of microservices architecture and distributed cloud networks.