Miracle-dz/UniFAR-FTUs huggingface dataset:
Hygroscopic phase field fracture modelling of composite materials

This paper investigates the effect of moisture content upon the degradation behaviour of composite materials. A coupled phase field framework considering moisture diffusion, hygroscopic expansion, and fracture behaviour is developed. This multi-physics framework is used to explore the damage evolution of composite materials, spanning the micro-, meso- and macro-scales. The micro-scale unit-cell model shows how the mismatch between the hygroscopic expansion of fibre and matrix leads to interface debonding. From the meso-scale ply-level model, we learn that the distribution of fibres has a minor influence on the material properties, while increasing moisture content facilitates interface debonding. The macro-scale laminate-level model shows that moisture induces a higher degree of damage on the longitudinal ply relative to the transverse ply. This work opens a new avenue to understand and predict environmentally assisted degradation in composite materials.
Mini‐VLAT: A Short and Effective Measure of Visualization Literacy

The visualization community regards visualization literacy as a necessary skill. Yet, despite the recent increase in research into visualization literacy by the education and visualization communities, we lack practical and time‐effective instruments for the widespread measurements of people's comprehension and interpretation of visual designs. We present Mini‐VLAT, a brief but practical visualization literacy test. The Mini‐VLAT is a 12‐item short form of the 53‐item Visualization Literacy Assessment Test (VLAT). The Mini‐VLAT is reliable (coefficient omega = 0.72) and strongly correlates with the VLAT. Five visualization experts validated the Mini‐VLAT items, yielding an average content validity ratio (CVR) of 0.6. We further validate Mini‐VLAT by demonstrating a strong positive correlation between study participants' Mini‐VLAT scores and their aptitude for learning an unfamiliar visualization using a Parallel Coordinate Plot test. Overall, the Mini‐VLAT items showed a similar pattern of validity and reliability as the 53‐item VLAT. The results show that Mini‐VLAT is a psychometrically sound and practical short measure of visualization literacy.
Finiteness criteria for Gorenstein flat dimension and stability

This paper proves necessary and sufficient conditions for a module of finite Gorenstein flat dimension to be projectively coresolved Gorenstein flat, or of finite flat dimension. The special case of modules over group rings is studied and various characterizations of finiteness of Gorenstein homological dimension of groups are obtained. Stability results for the class of projectively coresolved Gorenstein flat modules are also established.
Analysis of photovoltaic modules with protection diodes in presence of mismatching

Simulation was used to investigate the electrical status of photovoltaic (PV) array components in mismatching conditions, leading to general rules for shaded PV array modules with bypass diodes. In particular, it is shown that, for a given PV array, load, and shading ratio (SR), there is a critical load point that identifies a load current (or load voltage) beyond (or below) which the diode securely goes into operation. The values of dissipated power and reverse voltage of shaded cell are lower when the diode does not go into operation. With the diode in operation the worst condition for the shaded cell occurs for minimum SR, there is a current split between diode and module, and the diode current increases with increasing SR. If the array is short circuited, the diode goes into operation with minimum SR. With the diode in operation, the electrical state of the shaded module is practically independent of array configuration. <<ETX>>
From ligands to behavioral outcomes: understanding the role of mineralocorticoid receptors in brain function

Abstract Stress is a normal response to situational pressures or demands. Exposure to stress activates the hypothalamic-pituitary-adrenal (HPA) axis and leads to the release of corticosteroids, which act in the brain via two distinct receptors: mineralocorticoid receptors (MR) and glucocorticoid receptors (GR). Persistent HPA axis overactivation or dysregulation can disrupt an individual’s homeostasis, thereby contributing to an increased risk for mental illness. On the other hand, successful coping with stressful events involves adaptive and cognitive processes in the brain that render individuals more resilient to similar stressors in the future. Here we review the role of the MR in these processes, starting with an overview of the physiological structure, ligand binding, and expression of MR, and further summarizing its role in the brain, its relevance to psychiatric disorders, and related rodent studies. Given the central role of MR in cognitive and emotional functioning, and its importance as a target for promoting resilience, future research should investigate how MR modulation can be used to alleviate disturbances in emotion and behavior, as well as cognitive impairment, in patients with stress-related psychiatric disorders.
Collaborative Multi-BS Power Management for Dense Radio Access Network Using Deep Reinforcement Learning

Network energy efficiency is a main pillar in the design and operation of wireless communication systems. In this paper, we investigate a dense radio access network (dense-RAN) capable of radiated power management at the base station (BS). Aiming to improve the long-term network energy efficiency, an optimization problem is formulated by collaboratively managing multi-BSs’ radiated power levels with constraints on the users’ traffic volume and achievable rate. Considering stochastic traffic arrivals at the users and time-varying network interference, we first formulate the problem as a Markov decision process (MDP) and then develop a novel deep reinforcement learning (DRL) framework based on the cloud-RAN operation scheme. To tackle the trade-off between complexity and performance, the overall optimization of multi-BSs’ energy efficiency with the multiplicative complexity constraint is modeled to achieve near-optimal performance by using a deep Q-network (DQN). In DQN, each BS first maximizes its individual energy efficiency, and then cooperates with other BSs to maximize the overall multi-BSs’ energy efficiency. Simulation results demonstrate that the proposed algorithm can converge faster and enjoy a network energy efficiency improvement by 5% and 10% compared with the benchmarks of the Q-learning and sleep schemes, respectively.
The role of impact parameter in typical close galaxy flybys

Abstract Close galaxy flybys, interactions during which two galaxies inter-penetrate, are frequent and can significantly affect the evolution of individual galaxies. Equal-mass flybys are extremely rare and almost exclusively distant, while frequent flybys have mass ratios $q=0.1$ or lower, with a secondary galaxy penetrating deep into the primary. This can result in comparable strengths of interaction between the two classes of flybys and lead to essentially the same effects. To demonstrate this, emphasise and explore the role of the impact parameter further, we performed a series of N-body simulations of typical flybys with varying relative impact parameters $b/R_{\mathrm{vir},1}$ ranging from $0.114$ to $0.272$ of the virial radius of the primary galaxy. Two-armed spirals form during flybys, with radii of origin correlated with the impact parameter and strengths well approximated with an inverted S-curve. The impact parameter does not affect the shape of induced spirals, and the lifetimes of a distinguished spiral structure appear to be constant, $T_\mathrm{LF} \sim 2$ Gyr. Bars, with strengths anti-correlated with the impact parameter, form after the encounter is over in simulations with $b/R_{\mathrm{vir},1} \leq 0.178$ and interaction strengths $S\geq0.076$ , but they are short-lived except for the stronger interactions with $S\geq0.129$ . We showcase an occurrence of multiple structures (ring-like, double bar) that survives for an exceptionally long time in one of the simulations. Effects on the pre-existing bar instability, that develops much later, are diverse: from an acceleration of bar formation, little to no effect, to even bar suppression. There is no uniform correlation between these effects and the impact parameter, as they are secondary effects, happening later in a post-flyby stage. Classical bulges are resilient to flyby interactions, while dark matter halos can significantly spin up in the amount anti-correlated with the impact parameter. There is an offset angle between the angular momentum vector of the dark matter halo and that of a disc, and it correlates linearly with the impact parameter. Thus, flybys remain an important pathway for structural evolution within galaxies in the local Universe.
LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models

The recent advancements in self-supervised learning, combined with the Transformer architecture, have enabled natural language processing (NLP) to achieve remarkably low perplexity. However, powerful NLP models necessitate increasing model size, leading to substantial computational and memory requirements. In this paper, we introduce an efficient inference framework tailored for large-scale generative language models. To reduce the model size, we employ a weight-only quantization strategy while preserving full precision for activations. As a result, we attain sub-4-bit quantization for each weight through non-uniform or uniform quantization techniques. Our proposed kernel, called LUT-GEMM, then accelerates quantized matrix multiplications, offering a flexible balance between compression ratio and accuracy. Unlike earlier matrix multiplication kernels that accommodated weight-only quantization, LUT-GEMM efficiently eliminates the resource-demanding dequantization process for both uniform and non-uniform quantization methods. By reducing the latency of individual GPUs and the overall inference process for large-scale language models, LUT-GEMM provides significant performance improvements in inference. The impact of LUT-GEMM is facilitated by implementing high compression ratios through low-bit quantization and efficient LUT-based operations, which decreases the number of required GPUs. For the OPT-175B model with 3-bit quantization, we show that LUT-GEMM accelerates the latency for generating each token by 2.1x compared to OPTQ, which requires costly dequantization. Consequently, LUT-GEMM enables inference of the OPT-175B model on a single GPU without noticeable degradation in accuracy or performance, while the non-quantized OPT-175B model requires a minimum of 8 GPUs.
A Comprehensive Evaluation of Neural SPARQL Query Generation from Natural Language Questions

In recent years, the field of neural machine translation (NMT) for SPARQL query generation has witnessed significant growth. Incorporating the copy mechanism with traditional encoder-decoder architectures and using pre-trained encoder-decoders and large language models have set new performance benchmarks. This paper presents various experiments that replicate and expand upon recent NMT-based SPARQL generation studies, comparing pre-trained language models (PLMs), non-pre-trained language models (NPLMs), and large language models (LLMs), highlighting the impact of question annotation and the copy mechanism and testing various fine-tuning methods using LLMs. In particular, we provide a systematic error analysis of the models and test their generalization ability. Our study demonstrates that the copy mechanism yields significant performance enhancements for most PLMs and NPLMs. Annotating the data is pivotal to generating correct URIs, with the"tag-within"strategy emerging as the most effective approach. Additionally, our findings reveal that the primary source of errors stems from incorrect URIs in SPARQL queries that are sometimes replaced with hallucinated URIs when using base models. This does not happen using the copy mechanism, but it sometimes leads to selecting wrong URIs among candidates. Finally, the performance of the tested LLMs fell short of achieving the desired outcomes.
Cohomology operations for moment-angle complexes and resolutions of Stanley-Reisner rings

A fundamental result in toric topology identifies the cohomology ring of the moment-angle complex $\mathcal{Z}_K$ associated to a simplicial complex $K$ with the Koszul homology of the Stanley--Reisner ring of $K$. By studying cohomology operations induced by the standard torus action on the moment-angle complex, we extend this to a topological interpretation of the minimal free resolution of the Stanley-Reisner ring. The exterior algebra module structure in cohomology induced by the torus action recovers the linear part of the minimal free resolution, and we show that higher cohomology operations induced by the action (in the sense of Goresky-Kottwitz-MacPherson) can be assembled into an explicit differential on the resolution. Describing these operations in terms of Hochster's formula, we recover and extend a result due to Katth\"an. We then apply all of this to study the equivariant formality of torus actions on moment-angle complexes. For these spaces, we obtain complete algebraic and combinatorial characterisations of which subtori of the naturally acting torus act equivariantly formally.
Automated Program Repair Based on Code Review: How do Pre-trained Transformer Models Perform?

—Sequence-to-sequence models have been used to transform erroneous programs into correct ones when trained with a large enough dataset. Some recent studies also demonstrated strong empirical evidence that code review (natural language instruction about suggestive changes in code) can improve the program repair further. Large language models, trained with Natural Language (NL) and computer program corpora, have the capacity to contain inherent knowledge of both. In this study, we investigate if this inherent knowledge of code and NL can be utilized to improve automated program repair. We applied PLBART and CodeT5, two state-of-the-art language models that are pre-trained with both Programming Language (PL) and Natural Language (NL), on two such natural language-based program repair datasets and found that the pre-trained language models ﬁne-tuned with datasets containing both code review and subsequent code changes notably outperform each of the previous models. We observed that the pre-trained models improve the previously best-reported results by 9.91% on the Review4Repair dataset and by 24.72% on the dataset by Tufano et al. This suggests that a pre-trained sequential model has a better understanding of natural language and can utilize it much better. We performed an ablation study to assess the contribution of the pre-training mechanism and the model architecture. We found that pre-training was signiﬁcantly more important in the performance gain than the model architecture. The practical application of using pre-trained transformer models in the context of automated program repair is still a long way off. However, our study demonstrates the substantial value of employing pre-trained models, paving the path for future studies to use more of these.
New Product Development (NPD) through Social Media-based Analysis by Comparing Word2Vec and BERT Word Embeddings

This study introduces novel methods for sentiment and opinion classification of tweets to support the New Product Development (NPD) process. Two popular word embedding techniques, Word2Vec and BERT, were evaluated as inputs for classic Machine Learning and Deep Learning algorithms to identify the best-performing approach in sentiment analysis and opinion detection with limited data. The results revealed that BERT word embeddings combined with Balanced Random Forest yielded the most accurate single model for both sentiment analysis and opinion detection on a use case. Additionally, the paper provides feedback for future product development performing word graph analysis of the tweets with same sentiment to highlight potential areas of improvement.
Unrolled three-operator splitting for parameter-map learning in Low Dose X-ray CT reconstruction

We propose a method for fast and automatic estimation of spatially dependent regularization maps for total variation-based (TV) tomography reconstruction. The estimation is based on two distinct sub-networks, with the first sub-network estimating the regularization parameter-map from the input data while the second one unrolling T iterations of the Primal-Dual Three-Operator Splitting (PD3O) algorithm. The latter approximately solves the corresponding TV-minimization problem incorporating the previously estimated regularization parameter-map. The overall network is then trained end-to-end in a supervised learning fashion using pairs of clean-corrupted data but crucially without the need of having access to labels for the optimal regularization parameter-maps.
AI driven shadow model detection in agropv farms

Agro-photovoltaic (APV) is a growing farming practice that combines agriculture and solar photovoltaic projects within the same area. This emerging market is expected to experience significant growth in the next few years, with a projected investment of $9 billion in 2030. Identifying shadows is crucial to understanding the APV environment, as they impact plant growth, microclimate, and evapotranspiration. In this study, we use state-of-the-art CNN and GAN-based neural networks to detect shadows in agro-PV farms, demonstrating their effectiveness. However, challenges remain, including partial shadowing from moving objects and real-time monitoring. Future research should focus on developing more sophisticated neural network-based shadow detection algorithms and integrating them with control systems for APV farms. Overall, shadow detection is crucial to increase productivity and profitability while supporting the environment, soil, and farmers.
Generalized Automatic Least Squares: Efficiency Gains from Misspecified Heteroscedasticity Models

It is well known that in the presence of heteroscedasticity ordinary least squares estimator is not efficient. I propose a generalized automatic least squares estimator (GALS) that makes partial correction of heteroscedasticity based on a (potentially) misspecified model without a pretest. Such an estimator is guaranteed to be at least as efficient as either OLS or WLS but can provide some asymptotic efficiency gains over OLS if the misspecified model is approximately correct. If the heteroscedasticity model is correct, the proposed estimator achieves full asymptotic efficiency. The idea is to frame moment conditions corresponding to OLS and WLS squares based on miss-specified heteroscedasticity as a joint generalized method of moments estimation problem. The resulting optimal GMM estimator is equivalent to a feasible GLS with estimated weight matrix. I also propose an optimal GMM variance-covariance estimator for GALS to account for any remaining heteroscedasticity in the residuals.
The Self-Perception and Political Biases of ChatGPT

This contribution analyzes the self-perception and political biases of OpenAI’s Large Language Model ChatGPT. Considering the first small-scale reports and studies that have emerged, claiming that ChatGPT is politically biased towards progressive and libertarian points of view, this contribution is aimed at providing further clarity on this subject. Although the concept of political bias and affiliation is hard to define, lacking an agreed-upon measure for its quantification, this contribution attempts to examine this issue by having ChatGPT respond to questions on commonly used measures of political bias. In addition, further measures for personality traits that have previously been linked to political affiliations were examined. More specifically, ChatGPT was asked to answer the questions posed by the political compass test as well as similar questionnaires that are specific to the respective politics of the G7 member states. These eight tests were repeated ten times each and indicate that ChatGPT seems to hold a bias towards progressive views. The political compass test revealed a bias towards progressive and libertarian views, supporting the claims of prior research. The political questionnaires for the G7 member states indicated a bias towards progressive views but no significant bias between authoritarian and libertarian views, contradicting the findings of prior reports. In addition, ChatGPT’s Big Five personality traits were tested using the OCEAN test, and its personality type was queried using the Myers-Briggs Type Indicator (MBTI) test. Finally, the maliciousness of ChatGPT was evaluated using the Dark Factor test. These three tests were also repeated ten times each, revealing that ChatGPT perceives itself as highly open and agreeable, has the Myers-Briggs personality type ENFJ, and is among the test-takers with the least pronounced dark traits.
Autoencoders with Intrinsic Dimension Constraints for Learning Low Dimensional Image Representations

Autoencoders have achieved great success in various computer vision applications. The autoencoder learns appropriate low dimensional image representations through the self-supervised paradigm, i.e., reconstruction. Existing studies mainly focus on the minimizing the reconstruction error on pixel level of image, while ignoring the preservation of Intrinsic Dimension (ID), which is a fundamental geometric property of data representations in Deep Neural Networks (DNNs). Motivated by the important role of ID, in this paper, we propose a novel deep representation learning approach with autoencoder, which incorporates regularization of the global and local ID constraints into the reconstruction of data representations. This approach not only preserves the global manifold structure of the whole dataset, but also maintains the local manifold structure of the feature maps of each point, which makes the learned low-dimensional features more discriminant and improves the performance of the downstream algorithms. To our best knowledge, existing works are rare and limited on exploiting both global and local ID invariant properties on the regularization of autoencoders. Numerical experimental results on benchmark datasets (Extended Yale B, Caltech101 and ImageNet) show that the resulting regularized learning models achieve better discriminative representations for downstream tasks including image classification and clustering.