Miracle-dz/UniFAR-FTUs huggingface dataset:
The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysis

We conduct an inquiry into the sociotechnical aspects of sentiment analysis (SA) by critically examining 189 peer-reviewed papers on their applications, models, and datasets. Our investigation stems from the recognition that SA has become an integral component of diverse sociotechnical systems, exerting influence on both social and technical users. By delving into sociological and technological literature on sentiment, we unveil distinct conceptualizations of this term in domains such as finance, government, and medicine. Our study exposes a lack of explicit definitions and frameworks for characterizing sentiment, resulting in potential challenges and biases. To tackle this issue, we propose an ethics sheet encompassing critical inquiries to guide practitioners in ensuring equitable utilization of SA. Our findings underscore the significance of adopting an interdisciplinary approach to defining sentiment in SA and offer a pragmatic solution for its implementation.
Redundant String Symmetry-Based Error Correction: Experiments on Quantum Devices

Computational power in measurement-based quantum computing (MBQC) stems from symmetry protected topological (SPT) order of the entangled resource state. But resource states are prone to preparation errors. We introduce a quantum error correction (QEC) approach using redundant non-local symmetry of the resource state. We demonstrate it within a teleportation protocol based on extending the $\mathbb{Z}_2 \times \mathbb{Z}_2$ symmetry of one-dimensional cluster states to other graph states. Qubit ZZ-crosstalk errors, which are prominent in quantum devices, degrade the teleportation fidelity of the usual cluster state. However, as we demonstrate experimentally, once we grow graph states with redundant symmetry, perfect teleportation fidelity is restored. We identify the underlying redundant-SPT order as error-protected degeneracies in the entanglement spectrum.
A New Time Series Similarity Measure and Its Smart Grid Applications

Many smart grid applications involve data mining, clustering, classification, identification, and anomaly detection, among others. These applications primarily depend on the measurement of similarity, which is the distance between different time series or subsequences of a time series. The commonly used time series distance measures, namely Euclidean Distance (ED) and Dynamic Time Warping (DTW), do not quantify the flexible nature of electricity usage data in terms of temporal dynamics. As a result, there is a need for a new distance measure that can quantify both the amplitude and temporal changes of electricity time series for smart grid applications, e.g., demand response and load profiling. This paper introduces a novel distance measure to compare electricity usage patterns. The method consists of two phases that quantify the effort required to reshape one time series into another, considering both amplitude and temporal changes. The proposed method is evaluated against ED and DTW using real-world data in three smart grid applications. Overall, the proposed measure outperforms ED and DTW in accurately identifying the best load scheduling strategy, anomalous days with irregular electricity usage, and determining electricity users' behind-the-meter (BTM) equipment.
Empirical Risk Minimization over Artificial Neural Networks Overcomes the Curse of Dimensionality in the Numerical Approximation of Linear Kolmogorov Partial Differential Equations with Unbounded Initial Functions

Deep learning algorithms have been successfully applied to numerically solve linear Kolmogorov partial differential equations (PDEs). A recent research shows that the empirical risk minimization~(ERM) over deep artificial neural networks overcomes the curse of dimensionality in the numerical approximation of linear Kolmogorov PDEs with bounded initial functions. However, the initial functions may be unbounded in many applications such as the Black Scholes PDEs in pricing call options. In this paper, we extend this result to the cases involving unbounded initial functions. We prove that for $d$-dimensional linear Kolmogorov PDEs with unbounded initial functions, under suitable assumptions, the number of training data and the size of the artificial neural network required to achieve an accuracy $\varepsilon$ for the ERM grow polynomially in both $d$ and $\varepsilon^{-1}$. Moreover, we verify that the required assumptions hold for Black-Scholes PDEs and heat equations which are two important cases of linear Kolmogorov PDEs.
SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation

With evolving data regulations, machine unlearning (MU) has become an important tool for fostering trust and safety in today's AI models. However, existing MU methods focusing on data and/or weight perspectives often grapple with limitations in unlearning accuracy, stability, and cross-domain applicability. To address these challenges, we introduce the concept of 'weight saliency' in MU, drawing parallels with input saliency in model explanation. This innovation directs MU's attention toward specific model weights rather than the entire model, improving effectiveness and efficiency. The resultant method that we call saliency unlearning (SalUn) narrows the performance gap with 'exact' unlearning (model retraining from scratch after removing the forgetting dataset). To the best of our knowledge, SalUn is the first principled MU approach adaptable enough to effectively erase the influence of forgetting data, classes, or concepts in both image classification and generation. For example, SalUn yields a stability advantage in high-variance random data forgetting, e.g., with a 0.2% gap compared to exact unlearning on the CIFAR-10 dataset. Moreover, in preventing conditional diffusion models from generating harmful images, SalUn achieves nearly 100% unlearning accuracy, outperforming current state-of-the-art baselines like Erased Stable Diffusion and Forget-Me-Not.
Compiler Testing with Relaxed Memory Models

Finding bugs is key to the correctness of compilers in wide use today. If the behaviour of a compiled program, as allowed by its architecture memory model, is not a behaviour of the source program under its source model, then there is a bug. This holds for all programs, but we focus on concurrency bugs that occur only with two or more threads of execution. We focus on testing techniques that detect such bugs in C/C++ compilers. We seek a testing technique that automatically covers concurrency bugs up to fixed bounds on program sizes and that scales to find bugs in compiled programs with many lines of code. Otherwise, a testing technique can miss bugs. Unfortunately, the state-of-the-art techniques are yet to satisfy all of these properties. We present the Téléchat compiler testing tool for concurrent programs. Téléchat compiles a concurrent C/C++ program and compares source and compiled program behaviours using source and architecture memory models. We make three claims: Téléchat improves the state-of-the-art at finding bugs in code generation for multi-threaded execution, it is the first public description of a compiler testing tool for concurrency that is deployed in industry, and it is the first tool that takes a significant step towards the desired properties. We provide experimental evidence suggesting Téléchat finds bugs missed by other state-of-the-art techniques, case studies indicating that Téléchat satisfies the properties, and reports of our experience deploying Téléchat in industry regression testing.
Large Language Model for Multi-objective Evolutionary Optimization

Multiobjective evolutionary algorithms (MOEAs) are major methods for solving multiobjective optimization problems (MOPs). Many MOEAs have been proposed in the past decades, of which the search operators need a carefully handcrafted design with domain knowledge. Recently, some attempts have been made to replace the manually designed operators in MOEAs with learning-based operators (e.g., neural network models). However, much effort is still required for designing and training such models, and the learned operators might not generalize well on new problems. To tackle the above challenges, this work investigates a novel approach that leverages the powerful large language model (LLM) to design MOEA operators. With proper prompt engineering, we successfully let a general LLM serve as a black-box search operator for decomposition-based MOEA (MOEA/D) in a zero-shot manner. In addition, by learning from the LLM behavior, we further design an explicit white-box operator with randomness and propose a new version of decomposition-based MOEA, termed MOEA/D-LO. Experimental studies on different test benchmarks show that our proposed method can achieve competitive performance with widely used MOEAs. It is also promising to see the operator only learned from a few instances can have robust generalization performance on unseen problems with quite different patterns and settings. The results reveal the potential benefits of using pre-trained LLMs in the design of MOEAs.To foster reproducibility and accessibility, the source code is https://github.com/FeiLiu36/LLM4MOEA.
DepWiGNN: A Depth-wise Graph Neural Network for Multi-hop Spatial Reasoning in Text

Spatial reasoning in text plays a crucial role in various real-world applications. Existing approaches for spatial reasoning typically infer spatial relations from pure text, which overlooks the gap between natural language and symbolic structures. Graph neural networks (GNNs) have showcased exceptional proficiency in inducing and aggregating symbolic structures. However, classical GNNs face challenges in handling multi-hop spatial reasoning due to the over-smoothing issue, i.e., the performance decreases substantially as the number of graph layers increases. To cope with these challenges, we propose a novel Depth-Wise Graph Neural Network (DepWiGNN). Specifically, we design a novel node memory scheme and aggregate the information over the depth dimension instead of the breadth dimension of the graph, which empowers the ability to collect long dependencies without stacking multiple layers. Experimental results on two challenging multi-hop spatial reasoning datasets show that DepWiGNN outperforms existing spatial reasoning methods. The comparisons with the other three GNNs further demonstrate its superiority in capturing long dependency in the graph.
$X_0(2900)$ production in the $B^+$ decay process

In the present work, we investigate the $X_0(2900)$ production process $B^+ \to D^+ X_0(2900)$ in the molecular frame, where $X_0(2900)$ is considered as a $\bar{D}^{*}K^*$ molecular state. We infer that the initial $B^+$ meson and the final $X_0(2900)$ are connected through $D_{s1}(2460)^+ \bar{D}^{*0}$ mesons by exchanging a $K^{*0}$ meson. The contributions from such a meson loop to the process $B^+ \to D^+ X_0(2900)$ are evaluated and our estimations indicate that the measured branching fraction of $B^+ \to D^+ X_0(2900)$ could be well reproduced in a reasonable model parameter range, which indicates that $X_0(2900)$ could be interpreted as a $\bar{D}^{*}K^*$ molecular state.
On the Hidden Waves of Image

In this paper, we introduce an intriguing phenomenon-the successful reconstruction of images using a set of one-way wave equations with hidden and learnable speeds. Each individual image corresponds to a solution with a unique initial condition, which can be computed from the original image using a visual encoder (e.g., a convolutional neural network). Furthermore, the solution for each image exhibits two noteworthy mathematical properties: (a) it can be decomposed into a collection of special solutions of the same one-way wave equations that are first-order autoregressive, with shared coefficient matrices for autoregression, and (b) the product of these coefficient matrices forms a diagonal matrix with the speeds of the wave equations as its diagonal elements. We term this phenomenon hidden waves, as it reveals that, although the speeds of the set of wave equations and autoregressive coefficient matrices are latent, they are both learnable and shared across images. This represents a mathematical invariance across images, providing a new mathematical perspective to understand images.
Knowledge from Uncertainty in Evidential Deep Learning

This work reveals an evidential signal that emerges from the uncertainty value in Evidential Deep Learning (EDL). EDL is one example of a class of uncertainty-aware deep learning approaches designed to provide confidence (or epistemic uncertainty) about the current test sample. In particular for computer vision and bidirectional encoder large language models, the `evidential signal' arising from the Dirichlet strength in EDL can, in some cases, discriminate between classes, which is particularly strong when using large language models. We hypothesise that the KL regularisation term causes EDL to couple aleatoric and epistemic uncertainty. In this paper, we empirically investigate the correlations between misclassification and evaluated uncertainty, and show that EDL's `evidential signal' is due to misclassification bias. We critically evaluate EDL with other Dirichlet-based approaches, namely Generative Evidential Neural Networks (EDL-GEN) and Prior Networks, and show theoretically and empirically the differences between these loss functions. We conclude that EDL's coupling of uncertainty arises from these differences due to the use (or lack) of out-of-distribution samples during training.
causalBETA: An R Package for Bayesian Semiparametric Causal Inference with Event-Time Outcomes

Observational studies are often conducted to estimate causal effects of treatments or exposures on event-time outcomes. Since treatments are not randomized in observational studies, techniques from causal inference are required to adjust for confounding. Bayesian approaches to causal estimates are desirable because they provide 1) prior smoothing provides useful regularization of causal effect estimates, 2) flexible models that are robust to misspecification, 3) full inference (i.e. both point and uncertainty estimates) for causal estimands. However, Bayesian causal inference is difficult to implement manually and there is a lack of user-friendly software, presenting a significant barrier to wide-spread use. We address this gap by developing causalBETA (Bayesian Event Time Analysis) - an open-source R package for estimating causal effects on event-time outcomes using Bayesian semiparametric models. The package provides a familiar front-end to users, with syntax identical to existing survival analysis R packages such as survival. At the same time, it back-ends to Stan - a popular platform for Bayesian modeling and high performance statistical computing - for efficient posterior computation. To improve user experience, the package is built using customized S3 class objects and methods to facilitate visualizations and summaries of results using familiar generic functions like plot() and summary(). In this paper, we provide the methodological details of the package, a demonstration using publicly-available data, and computational guidance.
Protection from Evil and Good: The Differential Effects of Page Protection on Wikipedia Article Quality

Wikipedia, the Web's largest encyclopedia, frequently faces content disputes or malicious users seeking to subvert its integrity. Administrators can mitigate such disruptions by enforcing"page protection"that selectively limits contributions to specific articles to help prevent the degradation of content. However, this practice contradicts one of Wikipedia's fundamental principles$-$that it is open to all contributors$-$and may hinder further improvement of the encyclopedia. In this paper, we examine the effect of page protection on article quality to better understand whether and when page protections are warranted. Using decade-long data on page protections from the English Wikipedia, we conduct a quasi-experimental study analyzing pages that received"requests for page protection"$-$written appeals submitted by Wikipedia editors to administrators to impose page protections. We match pages that indeed received page protection with similar pages that did not and quantify the causal effect of the interventions on a well-established measure of article quality. Our findings indicate that the effect of page protection on article quality depends on the characteristics of the page prior to the intervention: high-quality articles are affected positively as opposed to low-quality articles that are impacted negatively. Subsequent analysis suggests that high-quality articles degrade when left unprotected, whereas low-quality articles improve. Overall, with our study, we outline page protections on Wikipedia and inform best practices on whether and when to protect an article.
CCIL: Continuity-based Data Augmentation for Corrective Imitation Learning

We present a new technique to enhance the robustness of imitation learning methods by generating corrective data to account for compounding errors and disturbances. While existing methods rely on interactive expert labeling, additional offline datasets, or domain-specific invariances, our approach requires minimal additional assumptions beyond access to expert data. The key insight is to leverage local continuity in the environment dynamics to generate corrective labels. Our method first constructs a dynamics model from the expert demonstration, encouraging local Lipschitz continuity in the learned model. In locally continuous regions, this model allows us to generate corrective labels within the neighborhood of the demonstrations but beyond the actual set of states and actions in the dataset. Training on this augmented data enhances the agent's ability to recover from perturbations and deal with compounding errors. We demonstrate the effectiveness of our generated labels through experiments in a variety of robotics domains in simulation that have distinct forms of continuity and discontinuity, including classic control problems, drone flying, navigation with high-dimensional sensor observations, legged locomotion, and tabletop manipulation.
Liveness Properties in Geometric Logic for Domain-Theoretic Streams

We devise a version of Linear Temporal Logic (LTL) on a denotational domain of streams. We investigate this logic in terms of domain theory, (point-free) topology and geometric logic. This yields the first steps toward an extension of the"Domain Theory in Logical Form"paradigm to temporal liveness properties. We show that the negation-free formulae of LTL induce sober subspaces of streams, but that this is in general not the case in presence of negation. We propose a direct, inductive, translation of negation-free LTL to geometric logic. This translation reflects the approximations used to compute the usual fixpoint representations of LTL modalities. As a motivating example, we handle a natural input-output specification for the usual filter function on streams.
The diet of the Crested porcupine Hystrix cristata L., 1758 in a Mediterranean rural area

Studied was the diet of crested porcupines by faecal analysis in a rural hilly area in central Italy. Food categories were identified by comparison with either isolated sections of fresh plant material or digested fragments obtained from captive porcupines fed experimentally. Roots predominated in the diet and were consumed at a similar frequency all year round. Herbs were the most eaten epygeal category especially in winter and spring. Possibly the winter consumption of herbs was related to a decrease in the availability of other food categories, such as grass inflorescences and fruits. Consumption of these two food categories was positively correlated and inversely associated with herbs. Grass inflorescences (mainly Hordeum murinum) were an important diet component in summer. Porcupines fed on fruits mainly in summer and autumn. The consumption of storage organs increased in winter and in spring. The values of trophic niche breadth and overlap suggested that porcupines are generalist foragers. These feeding habits, combined with the abandonment by man of the countryside in the last few decades, may partly explain the recent range expansion of the crested porcupine in Italy.
Solving Expensive Optimization Problems in Dynamic Environments with Meta-learning

Dynamic environments pose great challenges for expensive optimization problems, as the objective functions of these problems change over time and thus require remarkable computational resources to track the optimal solutions. Although data-driven evolutionary optimization and Bayesian optimization (BO) approaches have shown promise in solving expensive optimization problems in static environments, the attempts to develop such approaches in dynamic environments remain rarely unexplored. In this paper, we propose a simple yet effective meta-learning-based optimization framework for solving expensive dynamic optimization problems. This framework is flexible, allowing any off-the-shelf continuously differentiable surrogate model to be used in a plug-in manner, either in data-driven evolutionary optimization or BO approaches. In particular, the framework consists of two unique components: 1) the meta-learning component, in which a gradient-based meta-learning approach is adopted to learn experience (effective model parameters) across different dynamics along the optimization process. 2) the adaptation component, where the learned experience (model parameters) is used as the initial parameters for fast adaptation in the dynamic environment based on few shot samples. By doing so, the optimization process is able to quickly initiate the search in a new environment within a strictly restricted computational budget. Experiments demonstrate the effectiveness of the proposed algorithm framework compared to several state-of-the-art algorithms on common benchmark test problems under different dynamic characteristics.
Resolving social dilemmas with minimal reward transfer

Multi-agent cooperation is an important topic, and is particularly challenging in mixed-motive situations where it does not pay to be nice to others. Consequently, self-interested agents often avoid collective behaviour, resulting in suboptimal outcomes for the group. In response, in this paper we introduce a metric to quantify the disparity between what is rational for individual agents and what is rational for the group, which we call the general self-interest level. This metric represents the maximum proportion of individual rewards that all agents can retain while ensuring that achieving social welfare optimum becomes a dominant strategy. By aligning the individual and group incentives, rational agents acting to maximise their own reward will simultaneously maximise the collective reward. As agents transfer their rewards to motivate others to consider their welfare, we diverge from traditional concepts of altruism or prosocial behaviours. The general self-interest level is a property of a game that is useful for assessing the propensity of players to cooperate and understanding how features of a game impact this. We illustrate the effectiveness of our method on several novel games representations of social dilemmas with arbitrary numbers of players.