OpenRaiser/Intern-Atlas huggingface dataset:
Why you don't overfit, and don't need Bayes if you only train for one epoch
Venue: arXiv cs.LG 2024
Year: 2024

Here, we show that in the data-rich setting where you only train on each datapoint once (or equivalently, you only train for one epoch), standard "maximum likelihood" training optimizes the true data generating process (DGP) loss, which is equivalent to the test loss. Further, we show that the Bayesian model average optimizes the same objective, albeit while taking the expectation over uncertainty induced by finite data. As standard maximum likelihood training in the single-epoch setting optimizes the same objective as Bayesian inference, we argue that we do not expect Bayesian inference to offer any advantages in terms of overfitting or calibration in these settings. This explains the diminishing importance of Bayes in areas such as LLMs, which are often trained with one (or very few) epochs.
Multi-agent reinforcement learning strategy to maximize the lifetime of Wireless Rechargeable
Venue: arXiv cs.LG 2024
Year: 2024

The thesis proposes a generalized charging framework for multiple mobile chargers to maximize the network lifetime and ensure target coverage and connectivity in large scale WRSNs. Moreover, a multi-point charging model is leveraged to enhance charging efficiency, where the MC can charge multiple sensors simultaneously at each charging location. The thesis proposes an effective Decentralized Partially Observable Semi-Markov Decision Process (Dec POSMDP) model that promotes Mobile Chargers (MCs) cooperation and detects optimal charging locations based on realtime network information. Furthermore, the proposal allows reinforcement algorithms to be applied to different networks without requiring extensive retraining. To solve the Dec POSMDP model, the thesis proposes an Asynchronous Multi Agent Reinforcement Learning algorithm (AMAPPO) based on the Proximal Policy Optimization algorithm (PPO).
Exploring Accuracy-Fairness Trade-off in Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) have made significant strides in the field of artificial intelligence, showcasing their ability to interact with humans and influence human cognition through information dissemination. However, recent studies have brought to light instances of bias inherent within these LLMs, presenting a critical issue that demands attention. In our research, we delve deeper into the intricate challenge of harmonising accuracy and fairness in the enhancement of LLMs. While improving accuracy can indeed enhance overall LLM performance, it often occurs at the expense of fairness. Overemphasising optimisation of one metric invariably leads to a significant degradation of the other. This underscores the necessity of taking into account multiple considerations during the design and optimisation phases of LLMs. Therefore, we advocate for reformulating the LLM training process as a multi-objective learning task. Our investigation reveals that multi-objective evolutionary learning (MOEL) methodologies offer promising avenues for tackling this challenge. Our MOEL framework enables the simultaneous optimisation of both accuracy and fairness metrics, resulting in a Pareto-optimal set of LLMs. In summary, our study sheds valuable lights on the delicate equilibrium between accuracy and fairness within LLMs, which is increasingly significant for their real-world applications. By harnessing MOEL, we present a promising pathway towards fairer and more efficacious AI technologies.
LLaVA-MR: Large Language-and-Vision Assistant for Video Moment Retrieval
Venue: arXiv cs.CV 2024
Year: 2024

Multimodal Large Language Models (MLLMs) are widely used for visual perception, understanding, and reasoning. However, long video processing and precise moment retrieval remain challenging due to LLMs' limited context size and coarse frame extraction. We propose the Large Language-and-Vision Assistant for Moment Retrieval (LLaVA-MR), which enables accurate moment retrieval and contextual grounding in videos using MLLMs. LLaVA-MR combines Dense Frame and Time Encoding (DFTE) for spatial-temporal feature extraction, Informative Frame Selection (IFS) for capturing brief visual and motion patterns, and Dynamic Token Compression (DTC) to manage LLM context limitations. Evaluations on benchmarks like Charades-STA and QVHighlights demonstrate that LLaVA-MR outperforms 11 state-of-the-art methods, achieving an improvement of 1.82% in R1@0.5 and 1.29% in mAP@0.5 on the QVHighlights dataset. Our implementation will be open-sourced upon acceptance.
Are Anomaly Scores Telling the Whole Story? A Benchmark for Multilevel Anomaly Detection
Venue: arXiv cs.LG 2024
Year: 2024

Anomaly detection (AD) is a machine learning task that identifies anomalies by learning patterns from normal training data. In many real-world scenarios, anomalies vary in severity, from minor anomalies with little risk to severe abnormalities requiring immediate attention. However, existing models primarily operate in a binary setting, and the anomaly scores they produce are usually based on the deviation of data points from normal data, which may not accurately reflect practical severity. In this paper, we address this gap by making three key contributions. First, we propose a novel setting, Multilevel AD (MAD), in which the anomaly score represents the severity of anomalies in real-world applications, and we highlight its diverse applications across various domains. Second, we introduce a novel benchmark, MAD-Bench, that evaluates models not only on their ability to detect anomalies, but also on how effectively their anomaly scores reflect severity. This benchmark incorporates multiple types of baselines and real-world applications involving severity. Finally, we conduct a comprehensive performance analysis on MAD-Bench. We evaluate models on their ability to assign severity-aligned scores, investigate the correspondence between their performance on binary and multilevel detection, and study their robustness. This analysis offers key insights into improving AD models for practical severity alignment. The code framework and datasets used for the benchmark will be made publicly available.
An Experimental Study on Data Augmentation Techniques for Named Entity Recognition on Low-Resource Domains
Venue: arXiv cs.CL 2024
Year: 2024

Named Entity Recognition (NER) is a machine learning task that traditionally relies on supervised learning and annotated data. Acquiring such data is often a challenge, particularly in specialized fields like medical, legal, and financial sectors. Those are commonly referred to as low-resource domains, which comprise long-tail entities, due to the scarcity of available data. To address this, data augmentation techniques are increasingly being employed to generate additional training instances from the original dataset. In this study, we evaluate the effectiveness of two prominent text augmentation techniques, Mention Replacement and Contextual Word Replacement, on two widely-used NER models, Bi-LSTM+CRF and BERT. We conduct experiments on four datasets from low-resource domains, and we explore the impact of various combinations of training subset sizes and number of augmented examples. We not only confirm that data augmentation is particularly beneficial for smaller datasets, but we also demonstrate that there is no universally optimal number of augmented examples, i.e., NER practitioners must experiment with different quantities in order to fine-tune their projects.
Swift: A Multi-FPGA Framework for Scaling Up Accelerated Graph Analytics
Venue: arXiv cs.AR 2024
Year: 2024

Graph analytics are vital in fields such as social networks, biomedical research, and graph neural networks (GNNs). However, traditional CPUs and GPUs struggle with the memory bottlenecks caused by large graph datasets and their fine-grained memory accesses. While specialized graph accelerators address these challenges, they often support only moderate-sized graphs (under 500 million edges). Our paper proposes Swift, a novel scale-up graph accelerator framework that processes large graphs by leveraging the flexibility of FPGA custom datapath and memory resources, and optimizes utilization of high-bandwidth 3D memory (HBM). Swift supports up to 8 FPGAs in a node. Swift introduces a decoupled, asynchronous model based on the Gather-Apply-Scatter (GAS) scheme. It subgraphs across FPGAs, and each subgraph into intervals based on source vertex IDs. Processing on these intervals is decoupled and executed asynchronously, instead of bulk-synchonous operation, where throughput is limited by the slowest task. This enables simultaneous processing within each multi-FPGA node and optimizes the utilization of communication (PCIe), off-chip (HBM), and on-chip BRAM/URAM resources. Swift demonstrates significant performance improvements compared to prior scalable FPGA-based frameworks, performing 12.8 times better than the ForeGraph. Performance against Gunrock on NVIDIA A40 GPUs is mixed, because NVlink gives the GPU system a nearly 5X bandwidth advantage, but the FPGA system nevertheless achieves 2.6x greater energy efficiency.
Functional Array Programming in an Extended Pi-Calculus
Venue: arXiv cs.PL 2024
Year: 2024

We study the data-parallel language BUTF, inspired by the Futhark language for array programming. We give a translation of BUTF into a version of the pi-calculus with broadcasting and labeled names. The translation is both complete and sound. Moreover, we propose a cost model by annotating translated BUTF processes. This is used for a complexity analysis of the translation.
One Energy Game for the Spectrum between Branching Bisimilarity and Weak Trace Semantics
Venue: arXiv cs.LO 2024
Year: 2024

We provide the first generalized game characterization of van Glabbeek's linear-time--branching-time spectrum with silent steps. Thereby, one multi-dimensional energy game can be used to characterize and decide a wide array of weak behavioral equivalences between stability-respecting branching bisimilarity and weak trace equivalence in one go. To establish correctness, we relate attacker-winning energy budgets and distinguishing sublanguages of Hennessy--Milner logic that we characterize by eight dimensions of formula expressiveness.
G-RAG: Knowledge Expansion in Material Science
Venue: arXiv cs.IR 2024
Year: 2024

In the field of Material Science, effective information retrieval systems are essential for facilitating research. Traditional Retrieval-Augmented Generation (RAG) approaches in Large Language Models (LLMs) often encounter challenges such as outdated information, hallucinations, limited interpretability due to context constraints, and inaccurate retrieval. To address these issues, Graph RAG integrates graph databases to enhance the retrieval process. Our proposed method processes Material Science documents by extracting key entities (referred to as MatIDs) from sentences, which are then utilized to query external Wikipedia knowledge bases (KBs) for additional relevant information. We implement an agent-based parsing technique to achieve a more detailed representation of the documents. Our improved version of Graph RAG called G-RAG further leverages a graph database to capture relationships between these entities, improving both retrieval accuracy and contextual understanding. This enhanced approach demonstrates significant improvements in performance for domains that require precise information retrieval, such as Material Science.
Does Open Access Foster Interdisciplinary Citation? Decomposing Open Access Citation Advantage
Venue: arXiv cs.DL 2024
Year: 2024

The existence of an open access (OA) citation advantage, that is, whether OA increases citations, has been a topic of interest for many years. Although numerous previous studies have focused on whether OA increases citations, expectations for OA go beyond that. One such expectation is the promotion of knowledge transfer across various fields. This study aimed to clarify whether OA, especially gold OA, increases interdisciplinary citations in various natural science fields. Specifically, we measured the effect of OA on interdisciplinary and within-discipline citation counts by decomposing an existing metric of the OA citation advantage. The results revealed that OA increases both interdisciplinary and within-discipline citations in many fields and increases only interdisciplinary citations in chemistry, computer science, and clinical medicine. Among these fields, clinical medicine tends to obtain more interdisciplinary citations without being influenced by specific journals or papers. The findings indicate that OA fosters knowledge transfer to different fields, which extends our understanding of its effects.
Construction and Preliminary Validation of a Dynamic Programming Concept Inventory
Venue: arXiv cs.DS 2024
Year: 2024

Concept inventories are standardized assessments that evaluate student understanding of key concepts within academic disciplines. While prevalent across STEM fields, their development lags for advanced computer science topics like dynamic programming (DP) -- an algorithmic technique that poses significant conceptual challenges for undergraduates. To fill this gap, we developed and validated a Dynamic Programming Concept Inventory (DPCI). We detail the iterative process used to formulate multiple-choice questions targeting known student misconceptions about DP concepts identified through prior research studies. We discuss key decisions, tradeoffs, and challenges faced in crafting probing questions to subtly reveal these conceptual misunderstandings. We conducted a preliminary psychometric validation by administering the DPCI to 172 undergraduate CS students finding our questions to be of appropriate difficulty and effectively discriminating between differing levels of student understanding. Taken together, our validated DPCI will enable instructors to accurately assess student mastery of DP. Moreover, our approach for devising a concept inventory for an advanced theoretical computer science concept can guide future efforts to create assessments for other under-evaluated areas currently lacking coverage.
Multiset Transformer: Advancing Representation Learning in Persistence Diagrams
Venue: arXiv cs.LG 2024
Year: 2024

To improve persistence diagram representation learning, we propose Multiset Transformer. This is the first neural network that utilizes attention mechanisms specifically designed for multisets as inputs and offers rigorous theoretical guarantees of permutation invariance. The architecture integrates multiset-enhanced attentions with a pool-decomposition scheme, allowing multiplicities to be preserved across equivariant layers. This capability enables full leverage of multiplicities while significantly reducing both computational and spatial complexity compared to the Set Transformer. Additionally, our method can greatly benefit from clustering as a preprocessing step to further minimize complexity, an advantage not possessed by the Set Transformer. Experimental results demonstrate that the Multiset Transformer outperforms existing neural network methods in the realm of persistence diagram representation learning.
Brain-Computer Interfaces for Emotional Regulation in Patients with Various Disorders
Venue: arXiv cs.HC 2024
Year: 2024

Neurological and Physiological Disorders that impact emotional regulation each have their own unique characteristics which are important to understand in order to create a generalized solution to all of them. The purpose of this experiment is to explore the potential applications of EEG-based Brain-Computer Interfaces (BCIs) in enhancing emotional regulation for individuals with neurological and physiological disorders. The research focuses on the development of a novel neural network algorithm for understanding EEG data, with a particular emphasis on recognizing and regulating emotional states. The procedure involves the collection of EEG-based emotion data from open-Neuro. Using novel data modification techniques, information from the dataset can be altered to create a dataset that has neural patterns of patients with disorders whilst showing emotional change. The data analysis reveals promising results, as the algorithm is able to successfully classify emotional states with a high degree of accuracy. This suggests that EEG-based BCIs have the potential to be a valuable tool in aiding individuals with a range of neurological and physiological disorders in recognizing and regulating their emotions. To improve upon this work, data collection on patients with neurological disorders should be done to improve overall sample diversity.
Self-Supervised Learning for Ordered Three-Dimensional Structures
Venue: arXiv cs.LG 2024
Year: 2024

Recent work has proven that training large language models with self-supervised tasks and fine-tuning these models to complete new tasks in a transfer learning setting is a powerful idea, enabling the creation of models with many parameters, even with little labeled data; however, the number of domains that have harnessed these advancements has been limited. In this work, we formulate a set of geometric tasks suitable for the large-scale study of ordered three-dimensional structures, without requiring any human intervention in data labeling. We build deep rotation- and permutation-equivariant neural networks based on geometric algebra and use them to solve these tasks on both idealized and simulated three-dimensional structures. Quantifying order in complex-structured assemblies remains a long-standing challenge in materials physics; these models can elucidate the behavior of real self-assembling systems in a variety of ways, from distilling insights from learned tasks without further modification to solving new tasks with smaller amounts of labeled data via transfer learning.
EV-PINN: A Physics-Informed Neural Network for Predicting Electric Vehicle Dynamics
Venue: arXiv cs.LG 2024
Year: 2024

An onboard prediction of dynamic parameters (e.g. Aerodynamic drag, rolling resistance) enables accurate path planning for EVs. This paper presents EV-PINN, a Physics-Informed Neural Network approach in predicting instantaneous battery power and cumulative energy consumption during cruising while generalizing to the nonlinear dynamics of an EV. Our method learns real-world parameters such as motor efficiency, regenerative braking efficiency, vehicle mass, coefficient of aerodynamic drag, and coefficient of rolling resistance using automatic differentiation based on dynamics and ensures consistency with ground truth vehicle data. EV-PINN was validated using 15 and 35 minutes of in-situ battery log data from the Tesla Model 3 Long Range and Tesla Model S, respectively. With only vehicle speed and time as inputs, our model achieves high accuracy and generalization to dynamics, with validation losses of 0.002195 and 0.002292, respectively. This demonstrates EV-PINN's effectiveness in estimating parameters and predicting battery usage under actual driving conditions without the need for additional sensors.
K-GBS3FCM -- KNN Graph-Based Safe Semi-Supervised Fuzzy C-Means
Venue: arXiv cs.LG 2024
Year: 2024

Clustering data using prior domain knowledge, starting from a partially labeled set, has recently been widely investigated. Often referred to as semi-supervised clustering, this approach leverages labeled data to enhance clustering accuracy. To maximize algorithm performance, it is crucial to ensure the safety of this prior knowledge. Methods addressing this concern are termed safe semi-supervised clustering (S3C) algorithms. This paper introduces the KNN graph-based safety-aware semi-supervised fuzzy c-means algorithm (K-GBS3FCM), which dynamically assesses neighborhood relationships between labeled and unlabeled data using the K-Nearest Neighbors (KNN) algorithm. This approach aims to optimize the use of labeled data while minimizing the adverse effects of incorrect labels. Additionally, it is proposed a mechanism that adjusts the influence of labeled data on unlabeled ones through regularization parameters and the average safety degree. Experimental results on multiple benchmark datasets demonstrate that the graph-based approach effectively leverages prior knowledge to enhance clustering accuracy. The proposed method was significantly superior in 64% of the 56 test configurations, obtaining higher levels of clustering accuracy when compared to other semi-supervised and traditional unsupervised methods. This research highlights the potential of integrating graph-based approaches, such as KNN, with established techniques to develop advanced clustering algorithms, offering significant applications in fields that rely on both labeled and unlabeled data for more effective clustering.
Funhouse Mirror or Echo Chamber? A Methodological Approach to Teaching Critical AI Literacy Through Metaphors
Venue: arXiv cs.CY 2024
Year: 2024

As educational institutions grapple with teaching students about increasingly complex Artificial Intelligence (AI) systems, finding effective methods for explaining these technologies and their societal implications remains a major challenge. This study proposes a methodological approach combining Conceptual Metaphor Theory (CMT) with UNESCO's AI competency framework to develop Critical AI Literacy (CAIL). Through a systematic analysis of metaphors commonly used to describe AI systems, we develop criteria for selecting pedagogically appropriate metaphors and demonstrate their alignment with established AI literacy competencies, as well as UNESCO's AI competency framework. Our method identifies and suggests four key metaphors for teaching CAIL. This includes GenAI as an echo chamber, GenAI as a funhouse mirror, GenAI as a black box magician, and GenAI as a map. Each of these seeks to address specific aspects of understanding characteristics of AI, from filter bubbles to algorithmic opacity. We present these metaphors alongside interactive activities designed to engage students in experiential learning of AI concepts. In doing so, we offer educators a structured approach to teaching CAIL that bridges technical understanding with societal implications. This work contributes to the growing field of AI education by demonstrating how carefully selected metaphors can make complex technological concepts more accessible while promoting critical engagement with AI systems.
Automatic Inference of Relational Object Invariants
Venue: arXiv cs.PL 2024
Year: 2024

Relational object invariants (or representation invariants) are relational properties held by the fields of a (memory) object throughout its lifetime. For example, the length of a buffer never exceeds its capacity. Automatic inference of these invariants is particularly challenging because they are often broken temporarily during field updates. In this paper, we present an Abstract Interpretation-based solution to infer object invariants. Our key insight is a new object abstraction for memory objects, where memory is divided into multiple memory banks, each containing several objects. Within each bank, the objects are further abstracted by separating the most recently used (MRU) object, represented precisely with strong updates, while the rest are summarized. For an effective implementation of this approach, we introduce a new composite abstract domain, which forms a reduced product of numerical and equality sub-domains. This design efficiently expresses relationships between a small number of variables (e.g., fields of the same abstract object). We implement the new domain in the CRAB abstract interpreter and evaluate it on several benchmarks for memory safety. We show that our approach is significantly more scalable for relational properties than the existing implementation of CRAB. For evaluating precision, we have integrated our analysis as a pre-processing step to SEABMC bounded model checker, and show that it is effective at both discharging assertions during pre-processing, and significantly improving the run-time of SEABMC.