Miracle-dz/UniFAR-FTUs huggingface dataset:
Experiencing Dublin’s Docklands: perceptions of employment and amenity changes in the Sheriff Street community

Flagship projects and gentrification have a profound impact on the neighbourhoods which surround them. At present, most studies focus on their housing implications. We seek to engage in a broader understanding of their effects by examining the ways in which residents of a low-income neighbourhood adjacent to a new waterfront development perceive and experience changes in employment opportunities and local amenities which have arisen because of this project. We will examine the discourses of long-term residents (those residing for more than 25 years) of the Sheriff Street neighbourhood one of the city’s most deprived towards the new Dublin Docklands and its International Financial Services Centre (IFSC). This type of engagement with non-gentrifying groups is currently lacking from academic research and consequently limits our full understanding of the impact of these spaces.What we have found is a complex and nuanced picture, where residents are both optimistic and disappointed by the impact the project has had on their lives. Through the addition of new amenities catering to middleclass users, the Docklands reinforces old, and creates new, socio-economic divisions within the area.
There are different shades of green: heterogeneous environmental innovations and their effects on firm performance

Using a firm-level dataset from the Spanish Technological Innovation Panel (2003-2016), this study explores the characteristics of environmentally innovative firms and quantifies the effects of pursuing different types of environmental innovation strategies (resource-saving, pollution-reducing, and regulation-driven innovations) on sales, employment, and productivity dynamics.
Kinetic description and convergence analysis of genetic algorithms for global optimization

Genetic Algorithms (GA) are a class of metaheuristic global optimization methods inspired by the process of natural selection among individuals in a population. Despite their widespread use, a comprehensive theoretical analysis of these methods remains challenging due to the complexity of the heuristic mechanisms involved. In this work, relying on the tools of statistical physics, we take a first step towards a mathematical understanding of GA by showing how their behavior for a large number of individuals can be approximated through a time-discrete kinetic model. This allows us to prove the convergence of the algorithm towards a global minimum under mild assumptions on the objective function for a popular choice of selection mechanism. Furthermore, we derive a time-continuous model of GA, represented by a Boltzmann-like partial differential equation, and establish relations with other kinetic and mean-field dynamics in optimization. Numerical experiments support the validity of the proposed kinetic approximation and investigate the asymptotic configurations of the GA particle system for different selection mechanisms and benchmark problems.
XIMAGENET-12: An Explainable AI Benchmark Dataset for Model Robustness Evaluation

The lack of standardized robustness metrics and the widespread reliance on numerous unrelated benchmark datasets for testing have created a gap between academically validated robust models and their often problematic practical adoption. To address this, we introduce XIMAGENET-12, an explainable benchmark dataset with over 200K images and 15,600 manual semantic annotations. Covering 12 categories from ImageNet to represent objects commonly encountered in practical life and simulating six diverse scenarios, including overexposure, blurring, color changing, etc., we further propose a novel robustness criterion that extends beyond model generation ability assessment. This benchmark dataset, along with related code, is available at https://sites.google. com/view/ximagenet-12/home. Researchers and practitioners can leverage this resource to evaluate the robustness of their visual models under challenging conditions and ultimately benefit from the demands of practical computer vision systems.
Non-autoregressive Text Editing with Copy-aware Latent Alignments

Recent work has witnessed a paradigm shift from Seq2Seq to Seq2Edit in the field of text editing, with the aim of addressing the slow autoregressive inference problem posed by the former. Despite promising results, Seq2Edit approaches still face several challenges such as inflexibility in generation and difficulty in generalizing to other languages. In this work, we propose a novel non-autoregressive text editing method to circumvent the above issues, by modeling the edit process with latent CTC alignments. We make a crucial extension to CTC by introducing the copy operation into the edit space, thus enabling more efficient management of textual overlap in editing. We conduct extensive experiments on GEC and sentence fusion tasks, showing that our proposed method significantly outperforms existing Seq2Edit models and achieves similar or even better results than Seq2Seq with over $4\times$ speedup. Moreover, it demonstrates good generalizability on German and Russian. In-depth analyses reveal the strengths of our method in terms of the robustness under various scenarios and generating fluent and flexible outputs.
Debias the Training of Diffusion Models

Diffusion models have demonstrated compelling generation quality by optimizing the variational lower bound through a simple denoising score matching loss. In this paper, we provide theoretical evidence that the prevailing practice of using a constant loss weight strategy in diffusion models leads to biased estimation during the training phase. Simply optimizing the denoising network to predict Gaussian noise with constant weighting may hinder precise estimations of original images. To address the issue, we propose an elegant and effective weighting strategy grounded in the theoretically unbiased principle. Moreover, we conduct a comprehensive and systematic exploration to dissect the inherent bias problem deriving from constant weighting loss from the perspectives of its existence, impact and reasons. These analyses are expected to advance our understanding and demystify the inner workings of diffusion models. Through empirical evaluation, we demonstrate that our proposed debiased estimation method significantly enhances sample quality without the reliance on complex techniques, and exhibits improved efficiency compared to the baseline method both in training and sampling processes.
Quantum random access memory architectures using superconducting cavities

Quantum random access memory (QRAM) is a common architecture resource for algorithms with many proposed applications, including quantum chemistry, windowed quantum arithmetic, unstructured search, machine learning, and quantum cryptography. Here we propose two bucket-brigade QRAM architectures based on high-coherence superconducting resonators, which differ in their realizations of the conditional-routing operations. In the first, we directly construct controlled-$\mathsf{SWAP}$ ($\textsf{CSWAP}$) operations, while in the second we utilize the properties of giant-unidirectional emitters (GUEs). For both architectures we analyze single-rail and dual-rail implementations of a bosonic qubit. In the single-rail encoding we can detect first-order ancilla errors, while the dual-rail encoding additionally allows for the detection of photon losses. For parameter regimes of interest the post-selected infidelity of a QRAM query in a dual-rail architecture is nearly an order of magnitude below that of a corresponding query in a single-rail architecture. These findings suggest that dual-rail encodings are particularly attractive as architectures for QRAM devices in the era before fault tolerance.
A Convolutional Network Adaptation for Cortical Classification During Mobile Brain Imaging

Deep neural networks (DNN) have become increasingly utilized in brain-computer interface (BCI) technologies with the outset goal of classifying human physiological signals in computer-readable format. While our present understanding of DNN usage for BCI is promising, we have little experience in deciphering neural events from dynamic freely-mobile situations. Using an improved version of EEGNet, our goal was to classify cognitive events from electroencephalography (EEG) signals while subjects simultaneously walked on a treadmill, sometimes while carrying a rucksack equivalent to 40% of their body weight. Walking subjects simultaneously performed a visual oddball target detection task, eliciting the P300 event-related potential (ERP), which then served as the DNN classification target. We found the base EEGNet to reach classification levels well above chance, with similar performance to previously reported P300 results. We found performance to be robust to noise, with classification similar for walking and loaded walking, with respect to standard seated condition with minimal movement. With additional architectural search and tuning to the EEGNet model (termed Cog-Neuro, herein; CN-EEGNet), we reached classification accuracy of greater than 95%, similar to previously reported state of the art levels in seated P300 tasks. To our knowledge, these results are the first documented implementation of a DNN for the classification of cognitive neural state during dual-task walking. The classification of one's ongoing cognitive state during a demanding physical task establishes the utility for BCI in complex environments.
BaSAL: Size Balanced Warm Start Active Learning for LiDAR Semantic Segmentation

Active learning strives to reduce the need for costly data annotation, by repeatedly querying an annotator to label the most informative samples from a pool of unlabeled data, and then training a model from these samples. We identify two problems with existing active learning methods for LiDAR semantic segmentation. First, they overlook the severe class imbalance inherent in LiDAR semantic segmentation datasets. Second, to bootstrap the active learning loop when there is no labeled data available, they train their initial model from randomly selected data samples, leading to low performance. This situation is referred to as the cold start problem. To address these problems we propose BaSAL, a size-balanced warm start active learning model, based on the observation that each object class has a characteristic size. By sampling object clusters according to their size, we can thus create a size-balanced dataset that is also more class-balanced. Furthermore, in contrast to existing information measures like entropy or CoreSet, size-based sampling does not require a pretrained model, thus addressing the cold start problem effectively. Results show that we are able to improve the performance of the initial model by a large margin. Combining warm start and size-balanced sampling with established information measures, our approach achieves comparable performance to training on the entire SemanticKITTI dataset, despite using only 5% of the annotations, outperforming existing active learning methods. We also match the existing state-of-the-art in active learning on nuScenes. Our code is available at: https://github.com/Tony-WJR/BaSAL.
Algorithms of inversion of Microlab-1 satellite data including effects of multipath propagation

The first results of the GPS/MET programme of sounding the Earth's atmosphere by means of a Low Earth Orbit satellite implemented with a Global Positioning System (GPS) receiver indicated that in the lower troposphere, the phase and amplitude of the signal received undergo strong oscillations. This effect is a consequence of multipath propagation resulting from the complicated structure of the atmospheric refractivity in the tropical regions. In this paper, we propose a method of processing of the phase measurements based on the theory of diffraction. The method was tested in processing GPS/MET radio soundings. Comparison of the reconstructed profiles of the temperature and humidity with those derived from radio soundings and extracted from the NMC Global Atmospheric Circulation Model indicates validity of the method proposed.
Cross-Episodic Curriculum for Transformer Agents

We present a new algorithm, Cross-Episodic Curriculum (CEC), to boost the learning efficiency and generalization of Transformer agents. Central to CEC is the placement of cross-episodic experiences into a Transformer's context, which forms the basis of a curriculum. By sequentially structuring online learning trials and mixed-quality demonstrations, CEC constructs curricula that encapsulate learning progression and proficiency increase across episodes. Such synergy combined with the potent pattern recognition capabilities of Transformer models delivers a powerful cross-episodic attention mechanism. The effectiveness of CEC is demonstrated under two representative scenarios: one involving multi-task reinforcement learning with discrete control, such as in DeepMind Lab, where the curriculum captures the learning progression in both individual and progressively complex settings; and the other involving imitation learning with mixed-quality data for continuous control, as seen in RoboMimic, where the curriculum captures the improvement in demonstrators' expertise. In all instances, policies resulting from CEC exhibit superior performance and strong generalization. Code is open-sourced at https://cec-agent.github. io/ to facilitate research on Transformer agent learning.
Non-Bloch band theory for non-Hermitian continuum systems

One of the most pronounced non-Hermitian phenomena is the non-Hermitian skin effect, which refers to the exponential localization of bulk eigenstates near the boundaries of non-Hermitian systems. Whereas non-Bloch band theory has been developed to describe the non-Hermitian skin effect in lattice systems, its counterpart in continuum systems still lacks a quantitative characterization. Here, we generalize the non-Bloch band theory to non-Hermitian continuum systems. In contrast to lattice systems for which the bulk Hamiltonian alone determines the non-Hermitian skin effect and energy spectrum, we find for continuum systems that the number of boundary conditions must also be included as essential information. We show that the appropriate discretization of continuum systems into lattice models requires matching the hopping range of the latter to the number of boundary conditions in the former. Furthermore, in periodic non-Hermitian continuum systems, we highlight the application of the transfer matrix in determining the generalized Brillouin zone. Our theory serves as a useful toolbox for investigating the rich non-Bloch physics in non-Hermitian continuum systems, such as photonic crystals, elastic media, and certain cold-atom systems.
Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization

Neural combinatorial optimization (NCO) is a promising learning-based approach for solving challenging combinatorial optimization problems without specialized algorithm design by experts. However, most constructive NCO methods cannot solve problems with large-scale instance sizes, which significantly diminishes their usefulness for real-world applications. In this work, we propose a novel Light Encoder and Heavy Decoder (LEHD) model with a strong generalization ability to address this critical issue. The LEHD model can learn to dynamically capture the relationships between all available nodes of varying sizes, which is beneficial for model generalization to problems of various scales. Moreover, we develop a data-efficient training scheme and a flexible solution construction mechanism for the proposed LEHD model. By training on small-scale problem instances, the LEHD model can generate nearly optimal solutions for the Travelling Salesman Problem (TSP) and the Capacitated Vehicle Routing Problem (CVRP) with up to 1000 nodes, and also generalizes well to solve real-world TSPLib and CVRPLib problems. These results confirm our proposed LEHD model can significantly improve the state-of-the-art performance for constructive NCO. The code is available at https://github.com/CIAM-Group/NCO_code/tree/main/single_objective/LEHD.
Evaluation of a wide range laser diffraction grain size analyser for use with sediments

The qualities of a laser diffraction grain size analyser, the Coulter LS-100 (range claimed by the manufacturer: 0.4-900 mum in a single measurement), are evaluated on sediments of fluvial and lacustrine origin. Accuracy and resolution of measurement on standard latex spheres are excellent. Reproducibility of the results on natural sediments appears to be satisfactory, but the method underestimates the fraction of clay particles with an efficiency of detection (36-70%) proportional to the clay content determined from pipette analysis. This efficiency is somewhat higher than those reported from other instruments of the same generation. Comparison of the Coulter LS-100 with other sizing techniques shows good agreement with the sieving method but some differences appear with the electroresistance particle technique: median and mean size values measured by the Coulter Counter TA1 are systematically lower than those obtained by the Coulter LS-100. Analyses show good correlation with those of a Malvern Laser particle analyser but a discrepancy appears with very fine silt and clay sized sediments. The Coulter LS-100 detects a higher clay content than that measured with the Malvern Laser Sizer 2600. Except when precise measurements of clay content are needed, the Coulter LS-100 produces precise and accurate results in size ranges required for geological and environmental studies.
Transmission line dynamics on inverter-dominated grids: analysis and simulations

In this work, we study fast time scale dynamic interactions between inverters, synchronous machines, and transmission lines. The overlapping time scales between inverter controls and electromagnetic phenomena idfentified in recent years has necessitated a re-evaluation of assumptions made in power system dynamics studies. We utilize an open-source modeling platform to perform both small signal stability and dynamic time domain studies of networks containing inverters, machines, and loads. We use transmission line models of varying fidelity, including models with multiple segments and frequency dependence. Our results indicate that, for the cases we study, line dynamics are not important for characterizing small signal stability. However, while in many cases the high fidelity line models are unnecessary for dynamic simulations, there are some cases in which simpler models omit dynamics that could be important to the operation of inner inverter control loops.
DistillSpec: Improving Speculative Decoding via Knowledge Distillation

Speculative decoding (SD) accelerates large language model inference by employing a faster draft model for generating multiple tokens, which are then verified in parallel by the larger target model, resulting in the text generated according to the target model distribution. However, identifying a compact draft model that is well-aligned with the target model is challenging. To tackle this issue, we propose DistillSpec that uses knowledge distillation to better align the draft model with the target model, before applying SD. DistillSpec makes two key design choices, which we demonstrate via systematic study to be crucial to improving the draft and target alignment: utilizing on-policy data generation from the draft model, and tailoring the divergence function to the task and decoding strategy. Notably, DistillSpec yields impressive 10 - 45% speedups over standard SD on a range of standard benchmarks, using both greedy and non-greedy sampling. Furthermore, we combine DistillSpec with lossy SD to achieve fine-grained control over the latency vs. task performance trade-off. Finally, in practical scenarios with models of varying sizes, first using distillation to boost the performance of the target model and then applying DistillSpec to train a well-aligned draft model can reduce decoding latency by 6-10x with minimal performance drop, compared to standard decoding without distillation.
Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities

Robotics and Artificial Intelligence (AI) have been inextricably intertwined since their inception. Today, AI-Robotics systems have become an integral part of our daily lives, from robotic vacuum cleaners to semi-autonomous cars. These systems are built upon three fundamental architectural elements: perception, navigation and planning, and control. However, while the integration of AI in Robotics systems has enhanced the quality of our lives, it has also presented a serious problem - these systems are vulnerable to security attacks. The physical components, algorithms, and data that makeup AI-Robotics systems can be exploited by malicious actors, potentially leading to dire consequences. Motivated by the need to address the security concerns in AI-Robotics systems, this paper presents a comprehensive survey and taxonomy across three dimensions: attack surfaces, ethical and legal concerns, and Human-Robot Interaction (HRI) security. Our goal is to provide readers, developers and other stakeholders with a holistic understanding of these areas to enhance the overall AI-Robotics system security. We begin by identifying potential attack surfaces and provide mitigating defensive strategies. We then delve into ethical issues, such as dependency and psychological impact, as well as the legal concerns regarding accountability for these systems. Besides, emerging trends such as HRI are discussed, considering privacy, integrity, safety, trustworthiness, and explainability concerns. Finally, we present our vision for future research directions in this dynamic and promising field.
Performativity and Prospective Fairness

Deploying an algorithmically informed policy is a significant intervention in the structure of society. As is increasingly acknowledged, predictive algorithms have performative effects: using them can shift the distribution of social outcomes away from the one on which the algorithms were trained. Algorithmic fairness research is usually motivated by the worry that these performative effects will exacerbate the structural inequalities that gave rise to the training data. However, standard retrospective fairness methodologies are ill-suited to predict these effects. They impose static fairness constraints that hold after the predictive algorithm is trained, but before it is deployed and, therefore, before performative effects have had a chance to kick in. However, satisfying static fairness criteria after training is not sufficient to avoid exacerbating inequality after deployment. Addressing the fundamental worry that motivates algorithmic fairness requires explicitly comparing the change in relevant structural inequalities before and after deployment. We propose a prospective methodology for estimating this post-deployment change from pre-deployment data and knowledge about the algorithmic policy. That requires a strategy for distinguishing between, and accounting for, different kinds of performative effects. In this paper, we focus on the algorithmic effect on the causally downstream outcome variable. Throughout, we are guided by an application from public administration: the use of algorithms to (1) predict who among the recently unemployed will stay unemployed for the long term and (2) targeting them with labor market programs. We illustrate our proposal by showing how to predict whether such policies will exacerbate gender inequalities in the labor market.