Miracle-dz/UniFAR-FTUs huggingface dataset:
Advancing Relation Extraction through Language Probing with Exemplars from Set Co-Expansion

Relation Extraction (RE) is a pivotal task in automatically extracting structured information from unstructured text. In this paper, we present a multi-faceted approach that integrates representative examples and through co-set expansion. The primary goal of our method is to enhance relation classification accuracy and mitigating confusion between contrastive classes. Our approach begins by seeding each relationship class with representative examples. Subsequently, our co-set expansion algorithm enriches training objectives by incorporating similarity measures between target pairs and representative pairs from the target class. Moreover, the co-set expansion process involves a class ranking procedure that takes into account exemplars from contrastive classes. Contextual details encompassing relation mentions are harnessed via context-free Hearst patterns to ascertain contextual similarity. Empirical evaluation demonstrates the efficacy of our co-set expansion approach, resulting in a significant enhancement of relation classification performance. Our method achieves an observed margin of at least 1 percent improvement in accuracy in most settings, on top of existing fine-tuning approaches. To further refine our approach, we conduct an in-depth analysis that focuses on tuning contrastive examples. This strategic selection and tuning effectively reduce confusion between classes sharing similarities, leading to a more precise classification process. Experimental results underscore the effectiveness of our proposed framework for relation extraction. The synergy between co-set expansion and context-aware prompt tuning substantially contributes to improved classification accuracy. Furthermore, the reduction in confusion between contrastive classes through contrastive examples tuning validates the robustness and reliability of our method.
Double winding condensate CFT

We conjecture a two-dimensional conformal field theory built out of a linear dilaton and a compact $\beta\gamma$ system deformed by winding condensates on each of the compact cycles. In particular, explicit expressions of the residues of the correlation functions are provided. As a worldsheet theory, it describes a stringy black hole in AdS$_3$ (supported by the Kalb-Ramond flux) at the Hawking-Page temperature. It is expected to be connected to the Horowitz-Polchinski-like solution near the Hagedorn temperature of thermal AdS$_3$ and Euclidean BTZ blackhole as we vary the temperature.
An Analysis of Initial Training Strategies for Exemplar-Free Class-Incremental Learning

Class-Incremental Learning (CIL) aims to build classification models from data streams. At each step of the CIL process, new classes must be integrated into the model. Due to catastrophic forgetting, CIL is particularly challenging when examples from past classes cannot be stored, the case on which we focus here. To date, most approaches are based exclusively on the target dataset of the CIL process. However, the use of models pre-trained in a self-supervised way on large amounts of data has recently gained momentum. The initial model of the CIL process may only use the first batch of the target dataset, or also use pre-trained weights obtained on an auxiliary dataset. The choice between these two initial learning strategies can significantly influence the performance of the incremental learning model, but has not yet been studied in depth. Performance is also influenced by the choice of the CIL algorithm, the neural architecture, the nature of the target task, the distribution of classes in the stream and the number of examples available for learning. We conduct a comprehensive experimental study to assess the roles of these factors. We present a statistical analysis framework that quantifies the relative contribution of each factor to incremental performance. Our main finding is that the initial training strategy is the dominant factor influencing the average incremental accuracy, but that the choice of CIL algorithm is more important in preventing forgetting. Based on this analysis, we propose practical recommendations for choosing the right initial training strategy for a given incremental learning use case. These recommendations are intended to facilitate the practical deployment of incremental learning.
Aligning Language Models with Offline Learning from Human Feedback

Learning from human preferences is crucial for language models (LMs) to effectively cater to human needs and societal values. Previous research has made notable progress by leveraging human feedback to follow instructions. However, these approaches rely primarily on online learning techniques like Proximal Policy Optimization (PPO), which have been proven unstable and challenging to tune for language models. Moreover, PPO requires complex distributed system implementation, hindering the efficiency of large-scale distributed training. In this study, we propose an offline learning from human feedback framework to align LMs without interacting with environments. Specifically, we explore filtering alignment (FA), reward-weighted regression (RWR), and conditional alignment (CA) to align language models to human preferences. By employing a loss function similar to supervised fine-tuning, our methods ensure more stable model training than PPO with a simple machine learning system~(MLSys) and much fewer (around 9\%) computing resources. Experimental results demonstrate that conditional alignment outperforms other offline alignment methods and is comparable to PPO.
$\mathcal{P}\text{ARseL}$: Towards a Verified Root-of-Trust Over seL4

Widespread adoption and growing popularity of embedded/IoT/CPS devices make them attractive attack targets. On low-to-mid-range devices, security features are typically few or none due to various constraints. Such devices are thus subject to malware-based compromise. One popular defensive measure is Remote Attestation $(\mathcal{R}\mathrm{A})$ which allows a trusted entity to determine the current software integrity of an untrusted remote device. For higher-end devices, $\mathcal{R}\mathrm{A}$ is achievable via secure hardware components. For low-end (bare metal) devices, minimalistic hybrid (hardware/-software) $\mathcal{R}\mathrm{A}$ is effective, which incurs some hardware modifications. That leaves certain mid-range devices (e.g., ARM Cortex-A family) equipped with standard hardware components, e.g., a memory management unit (MMU) and perhaps a secure boot facility. In this space, seL4 (a verified microkernel with guaranteed process isolation) is a promising platform for attaining $\mathcal{R}\mathrm{A}$. HYDRA [1] made a first step towards this, albeit without achieving any verifiability or provable guarantees. This paper picks up where HYDRA left off by constructing a $\mathcal{P}\text{ARseL}$ architecture, that separates all user-dependent components from the TCB. This leads to much stronger isolation guarantees, based on seL4 alone, and facilitates formal verification. In $\mathcal{P}\text{ARseL}$, We use formal verification to obtain several security properties for the isolated $\mathcal{R}\mathrm{A}$ TCB, including: memory safety, functional correctness, and secret independence. We implement $\mathcal{P}\text{ARseL}$ in $F^{\ast}$ and specify/prove expected properties using Hoare logic. Next, we automatically translate the $F^{\ast}$ implementation to C using KaRaM eL, which preserves verified properties of $\mathcal{P}\text{ARseL}$, C implementation (atop seL4). Finally, we instantiate and evaluate $\mathcal{P}\text{ARseL}$ on a commodity platform - a SabreLite embedded device.
Will More Expressive Graph Neural Networks do Better on Generative Tasks?

Graph generation poses a significant challenge as it involves predicting a complete graph with multiple nodes and edges based on simply a given label. This task also carries fundamental importance to numerous real-world applications, including de-novo drug and molecular design. In recent years, several successful methods have emerged in the field of graph generation. However, these approaches suffer from two significant shortcomings: (1) the underlying Graph Neural Network (GNN) architectures used in these methods are often underexplored; and (2) these methods are often evaluated on only a limited number of metrics. To fill this gap, we investigate the expressiveness of GNNs under the context of the molecular graph generation task, by replacing the underlying GNNs of graph generative models with more expressive GNNs. Specifically, we analyse the per- formance of six GNNs in two different generative frameworks -- autoregressive generation models, such as GCPN and GraphAF, and one-shot generation models, such as GraphEBM -- on six different molecular generative objectives on the ZINC-250k dataset. Through our extensive experiments, we demonstrate that advanced GNNs can indeed improve the performance of GCPN, GraphAF, and GraphEBM on molecular generation tasks, but GNN expressiveness is not a necessary condition for a good GNN-based generative model. Moreover, we show that GCPN and GraphAF with advanced GNNs can achieve state-of-the-art results across 17 other non-GNN-based graph generative approaches, such as variational autoencoders and Bayesian optimisation models, on the proposed molecular generative objectives (DRD2, Median1, Median2), which are impor- tant metrics for de-novo molecular design.
INS/DVL Fusion with DVL Based Acceleration Measurements

Autonomous underwater vehicles (AUVs) are increasingly used in many applications such as oceanographic surveys, mapping, and inspection of underwater structures. To successfully complete those tasks, a Doppler velocity log (DVL) and an inertial navigation system (INS) are utilized to determine the AUV navigation solution. In such fusion, DVL velocity measurement is used to update the navigation states. In this paper, we propose calculating the AUV acceleration vector based on past DVL measurements and using it as an additional update to increase the system's accuracy. Simulations and sea experiments were conducted to demonstrate the efficiency of our approach. The results indicate that the proposed method exhibits rapid convergence and significantly improves the overall performance compared to the baseline INS and DVL fusion approach.
Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders

Sequential recommenders have been widely used in industry due to their strength in modeling user preferences. While these models excel at learning a user’s positive interests, less attention has been paid to learning from negative user feedback. Negative user feedback is an important lever of user control, and comes with an expectation that recommenders should respond quickly and reduce similar recommendations to the user. However, negative feedback signals are often ignored in the training objective of sequential retrieval models, which primarily aim at predicting positive user interactions. In this work, we incorporate explicit and implicit negative user feedback into the training objective of sequential recommenders in the retrieval stage using a "not-to-recommend" loss function that optimizes for the log-likelihood of not recommending items with negative feedback. We demonstrate the effectiveness of this approach using live experiments on a large-scale industrial recommender system. Furthermore, we address a challenge in measuring recommender responsiveness to negative feedback by developing a counterfactual simulation framework to compare recommender responses between different user actions, showing improved responsiveness from the modeling change.
Local Distortion Aware Efficient Transformer Adaptation for Image Quality Assessment

Image Quality Assessment (IQA) constitutes a fundamental task within the field of computer vision, yet it remains an unresolved challenge, owing to the intricate distortion conditions, diverse image contents, and limited availability of data. Recently, the community has witnessed the emergence of numerous large-scale pretrained foundation models, which greatly benefit from dramatically increased data and parameter capacities. However, it remains an open problem whether the scaling law in high-level tasks is also applicable to IQA task which is closely related to low-level clues. In this paper, we demonstrate that with proper injection of local distortion features, a larger pretrained and fixed foundation model performs better in IQA tasks. Specifically, for the lack of local distortion structure and inductive bias of vision transformer (ViT), alongside the large-scale pretrained ViT, we use another pretrained convolution neural network (CNN), which is well known for capturing the local structure, to extract multi-scale image features. Further, we propose a local distortion extractor to obtain local distortion features from the pretrained CNN and a local distortion injector to inject the local distortion features into ViT. By only training the extractor and injector, our method can benefit from the rich knowledge in the powerful foundation models and achieve state-of-the-art performance on popular IQA datasets, indicating that IQA is not only a low-level problem but also benefits from stronger high-level features drawn from large-scale pretrained models.
Unraveling the Neural Network: Identifying Temporal Labeling of Visual Events through EEG-Based Functional Connectivity Analysis of Brain Regions

Understanding the complex interplay between the brain and a dynamic environment necessitates the continuous generation and updating of expectations for forthcoming events and their corresponding sensory and motor responses. This study investigates the interconnectivity patterns associated with time perception in predictable and unpredictable conditions. EEG signals were obtained from an existing database, encompassing an experiment conducted on healthy participants subjected to two conditions: predictable and unpredictable, across various time delays. Functional connectivity between brain regions was estimated using the phase lag index method, allowing for the identification of differences in time perception between conditions. Comparative analysis revealed significant variations, particularly in the gamma, beta, and theta frequency bands, with more pronounced differences observed in the predictable condition. Subsequent exploration of the dissimilarities within each delay demonstrated significant differences across all delays. Notably, the unpredictable condition exhibited increased connectivity within the alpha band during the 400-ms delay, specifically between occipital and temporal regions, with higher mean connectivity compared to the predictable condition. In the delta band, distinct connectivity patterns emerged, involving connections between central and frontal regions across different delays. Notably, heightened connectivity between central and prefrontal regions was observed during the 83-ms delay. The right hemisphere of the prefrontal cortex played a pivotal role in time perception. Furthermore, a decline in connectivity across the delta, theta, and beta bands was observed during the longest delay (800 ms) in both conditions, relative to other delays.
Generalised Jeffery's equations for rapidly spinning particles. Part 1. Spheroids

Abstract The observed behaviour of passive objects in simple flows can be surprisingly intricate, and is complicated further by object activity. Inspired by the motility of bacterial swimmers, in this two-part study we examine the three-dimensional motion of rigid active particles in shear Stokes flow, focusing on bodies that induce rapid rotation as part of their activity. In Part 1 we develop a multiscale framework to investigate these emergent dynamics and apply it to simple spheroidal objects. In Part 2 (Dalwadi et al., J. Fluid Mech., vol. 979, 2024, A2) we apply our framework to understand the emergent dynamics of more complex shapes; helicoidal objects with chirality. Via a multiple scales asymptotic analysis for nonlinear systems, we systematically derive emergent equations of motion for long-term trajectories that explicitly account for the strong (leading-order) effects of fast spinning. Supported by numerical examples, we constructively link these effective dynamics to the well-known Jeffery's orbits for passive spheroids, deriving an explicit closed-form expression for the effective shape of the active particle, broadening the scope of Jeffery's seminal study to spinning spheroids.
Compressed Sensing: OMP2D Based Algorithm for Fully Polarimetric ISAR

One of the main issues that arises when using CS techniques in real-time applications is related to the high computational burden and the memory footprint. In the case of separable sampling, the 2D-OMP algorithm [1] provides a consistent solution, achieving a considerable reduction in both computational complexity and memory occupation. In this document an extension of the 2D-OMP algorithm is proposed to cope with multiple polarimetric channels. For this purpose, an Objective Function, to weight data collected from all the available channels, has been introduced in the OMP2D algorithm. By proceeding in this way, decisions are taken by exploiting an increased amount of measures (and information), and they result to be more accurate and reliable. The simulations results show how the proposed algorithm (OMP2D-FP) outperforms the simple parallelization of the conventional algorithm (OMP2D), in terms of False Alarm and Detection probability (ROC curves) as well as in terms of Mean Square Error between the recovered and the observed scenes.
A Comparative Study of Satisfaction of Chinese and Japanese Transit Passengers at Incheon International Airport

This paper tried to find out what the main factors are to impact on satisfaction level of transit passengers at Incheon airport. Incheon airport has made lots of transit facilities and services to attract transit passengers from neighboring airports to increase transit rate as high as advanced foreign airports such as Heathrow, Schiphol and Hong Kong. A survey was performed on 450 transit passengers resting at airport transit lounge in airside area. According to the result, it was found out to be significant factors affecting satisfaction of transit that convenience of security screening, price of duty-free product, a direction board & signage and terminal orientation among Chinese transit passengers. On the other hands, it seemed that Japanese transit passengers thought diversity of airline routes, convenient transfer flight schedule, total transit time and terminal orientation are major variables affecting their satisfaction. Considering the effect of demographic factors, Chinese passenger's satisfaction was more affected by age, travel purpose, the number of travel per year and flight destinations. However, Japanese transit passenger's satisfaction was only affected by the flight destinations, especially willing to travel to North America and Europe.
Diagnoses and treatments for participants with interstitial lung abnormalities detected in the Yorkshire Lung Screening Trial

Introduction Interstitial lung abnormalities (ILA) are relatively common incidental findings in participants undergoing low-dose CT screening for lung cancer. Some ILA are transient and inconsequential, but others represent interstitial lung disease (ILD). Lung cancer screening therefore offers the opportunity of earlier diagnosis and treatment of ILD for some screening participants. Methods The prevalence of ILA in participants in the baseline screening round of the Yorkshire Lung Screening Trial is reported, along with the proportion referred to a regional ILD service, eventual diagnoses, outcomes and treatments. Results Of 6650 participants undergoing screening, ILA were reported in 169 (2.5%) participants. Following review in a screening review meeting, 56 participants were referred to the ILD service for further evaluation (0.8% of all screening participants). 2 participants declined referral, 1 is currently awaiting review and the remaining 53 were confirmed as having ILD. Eventual diagnoses were idiopathic pulmonary fibrosis (n=14), respiratory bronchiolitis ILD (n=4), chronic hypersensitivity pneumonitis (n=2), connective tissue disease/rheumatoid arthritis-related ILD (n=4), asbestosis (n=1), idiopathic non-specific interstitial pneumonia (n=1), sarcoidosis (n=1) and pleuroparenchymal fibroelastosis (n=1). Twenty five patients had unclassifiable idiopathic interstitial pneumonia. Overall, 10 people received pharmacotherapy (7 antifibrotics and 3 prednisolone) representing 18% of those referred to the ILD service and 0.15% of those undergoing screening. 32 people remain under surveillance in the ILD service, some of whom may require treatment in future. Discussion Lung cancer screening detects clinically significant cases of ILD allowing early commencement of disease-modifying treatment in a proportion of participants. This is the largest screening cohort to report eventual diagnoses and treatments and provides an estimate of the level of clinical activity to be expected by ILD services as lung cancer screening is implemented. Further research is needed to clarify the optimal management of screen-detected ILD. Trial registration number ISRCTN42704678.
The Resistance Analysis Attack and Security Enhancement of the IMC LUT Based on the Complementary Resistive Switch Cells

The resistive random access memory (RRAM) based in-memory computing (IMC) is an emerging architecture to address the challenge of the “memory wall” problem. The complementary resistive switch (CRS) cell connects two bipolar RRAM elements anti-serially to reduce the sneak current in the crossbar array. The CRS array is a generic computing platform, for the arbitrary logic functions can be implemented in it. The IMC CRS LUT consumes fewer CRS cells than the static CRS LUT. The CRS array has built-in polymorphic characteristics because the correct logic function cannot be distinguished based on the circuit layout. However, the logic state of every CRS cell can be readout after each operation. It helps the attacker to recover the correct function of the IMC CRS LUT. This work discusses the resistance analysis attack of the IMC LUT based on the CRS array. The proposed resistance analysis attack method is able to be applied to different computation styles based on the CRS array, such as the CRS IMPLY, CRS NOR-OR/NAND-AND, and so on. The attacker can recover the logic function of the LUT by tracing the states of CRS cells. Furthermore, an improved IMC CRS LUT method is proposed and discussed to enhance security. The simulation and analysis results show that the improved IMC CRS LUT can resist various attacks, and it maintains the polymorphic characteristics of the IMC CRS LUT. And the N-bit full adder circuit based on the improved IMC CRS NOR-OR LUTs achieves the best performance compared with the previous counterparts.