Miracle-dz/UniFAR-FTUs huggingface dataset:
Pre-training Language Models for Surface Realization

Surface Realization in Natural Language Generation (NLG) is the task of deriving the surface form of a sentence (the actual words) from an underlying representation. Following recent advances in deep learning, several models have been proposed for different NLG sub-tasks including surface realization. Most of these models require a large amount of training data, however, acquiring accurately labeled data is laborious and expensive. In this work, we study how synthetically generated labeled data can be leveraged to improve the performance of a surface realization model. By pre-training a language model on automatically labeled data and then fine-tuning it on manually labeled data, our approach improved the state-of-the-art performance on the standard English datasets from the deep track of the Multilingual Surface Re-alization (MSR) workshop (Belz et al., 2020) by more than 10% BLEU score. 1
The Elementary Scenario Component Metric for Summarization Evaluation

Evaluation is a fundamental step in the development of novel automatic summarization methods. The correlation between commonly used automatic evaluation metrics and golden standard human evaluations is often modest at best. Automatic evaluation metrics have thus not proven an alternative to human evaluation. This presents a problem to the progress of automatic summarization because evaluations conducted by people are time-consuming, inconsistent, and costly. We introduce the Elementary Scenario Component Metric (ESCM), which draws on the creative arts and scenario modelling literature. This metric does not require reference summaries, but uses twelve elementary scenario components, or a sub-selection thereof, to estimate the relevance of summaries instead. We show that the ESCM achieves a correlation of 0.89 with human evaluations and is less time-consuming than the creation of reference summaries.
Detecting Security Patches in Java Projects Using NLP Technology

Known vulnerabilities in software are solved through security patches; thus, applying such patches as soon as they are released is crucial to protect from cyber-attacks. The diffusion of open source software allowed to inspect the patches to understand whether they are security related or not. In this paper, we propose some solutions based on state-of-the-art deep learning technologies for Natural Language Processing for security patches detection. In the experiments, we benchmarked our solutions on two data sets for Java security patches detection. Our models showed promising results, outper-forming all the others we used for comparison. Interestingly, we achieved better results training the classifiers from scratch than fine tuning existing models.
Modeling the Ordering of English Adjectives using Collaborative Filtering

We present a novel procedure for acquiring productive knowledge of restrictions on adjective ordering from counts of adjective-bigrams, which are readily obtainable from nat-ural language corpora. The procedure uses a model-based Collaborative Filtering (CF) algo-rithm, and is the first computational model of adjective-ordering to do so. We consider two widely-used model-based CF algorithms, Singular Value Decomposition and Non-negative Matrix Factorization. We evaluated the procedure by first training the underlying CF model on subsets of the largest publicly available dataset of English adjective-bigrams, the Google Books NGram database, and then measuring the model’s capacity to predict the ordering of (unseen) pairs of adjectives. Our re-sults show that both CF models exhibit good performance on the task of predicting adjective ordering. Moreover, the CF models consistently outperform a baseline model that is grounded in a ranking of adjectives intended to align with a (linear) hierarchy of adjective-classes, suggesting that CF models make use adjective-ordering data that does not neatly fit into (proposed) hierarchies of adjective-classes.
Evaluating Large-Language Models for Dimensional Music Emotion Prediction from Social Media Discourse

The automatic prediction of emotional responses to music is a task of inherent interest to the ﬁeld of music information retrieval. These efforts are often hindered by the absence of large datasets available for this task. In this work, we investigate the use of sentiment analysis on online social media conversations as an alternate data source to train computational models to predict the emotive responses to a piece of music. Using two datasets annotated with valence and arousal values, we create a corpus of social media commentary for these songs extracted from YouTube, Twitter, and Reddit. We evaluate our approach with transformer models to predict the affective values of the 2402 songs in our dataset. We achieve a moderate Pearson’s correlation of 0.62 and 0.72 for valence and arousal, respectively, for discourse from YouTube. These promising results demonstrate that discourse about music may carry semantic information useful to making determinations about the music itself. Such an approach could potentially supplement music information retrieval systems to estimate emotion for pieces of music for which the audio is restricted by copyright or otherwise unavailable.
Efficient Large Integer Multiplication with Arm SVE Instructions

In this study, we implement large integer multiplication with the Arm Scalable Vector Extension (SVE) instructions. SVE is a single instruction, multiple data (SIMD) instruction set for the Arm AArch64 architecture. We use a reduced-radix representation technique because SIMD instructions do not retain the carry that occurs when partial products are added in large integer multiplication computations. Furthermore, we develop and implement a multiplication algorithm based on the Basecase method, which allows the application of ordinary multiplication instructions to special integers in reduced-radix representation. To evaluate performance, we compare our multiplication implementation on an A64FX processor with the GNU Multiple Precision Arithmetic Library (GMP). We show that processing with SVE was faster than GMP for multiplication with operands larger than 2,048 bits. The performance gain was up to 36%. These results suggest that SVE instructions have the potential to be faster than scalar instructions for large integer multiplication, especially for large operands.
ESSPER: Elastic and Scalable FPGA-Cluster System for High-Performance Reconfigurable Computing with Supercomputer Fugaku

FPGA clusters have yet to be a mainstream of HPC, even for accelerators, and several challenges exist in their architecture and system organization. This work presents ESSPER, a flexible and scalable FPGA cluster prototype system for reconfigurable HPC to meet the concept of customizability, scalability, and interoperability with existing HPC systems. Based on our classification of FPGA cluster architectures, we propose a new category of FPGA clusters with a host-FPGA bridging network using software-bridged APIs for the use of remote FPGAs. We have designed, implemented, verified, and demonstrated a proof-of-concept system of ESSPER, as a functional extension of the supercomputer Fugaku.
ACTS: A Near-Memory FPGA Graph Processing Framework

Despite the high off-chip bandwidth and on-chip parallelism offered by today's near-memory accelerators, software-based (CPU and GPU) graph processing frameworks still suffer performance degradation from under-utilization of available memory bandwidth because graph traversal often exhibits poor locality. Emerging FPGAbased graph accelerators tackle this challenge by designing specialized graph processing pipelines and application-specific memory subsystems to maximize bandwidth utilization and efficiently utilize high-speed on-chip memory. To use the limited on-chip (BRAM) memory effectively while handling larger graph sizes, several FPGAbased solutions resort to some form of graph slicing or partitioning during preprocessing to stage vertex property data into the BRAM. While this has demonstrated performance superiority for small graphs, this approach breaks down with larger graph sizes. For example, GraphLily [19], a recent high-performance FPGA-based graph accelerator, experiences up to 11X performance degradation between graphs having 3M vertices and 28M vertices. This makes prior FPGA approaches impractical for large graphs. We propose ACTS, an HBM-enabled FPGA graph accelerator, to address this problem. Rather than partitioning the graph offline to improve spatial locality, we partition vertex-update messages (based on destination vertex IDs) generated online after active edges have been processed. This optimizes read bandwidth even as the graph size scales. We compare ACTS against Gunrock, a state-of-the-art graph processing accelerator for the GPU, and GraphLily, a recent FPGA-based graph accelerator also utilizing HBM memory. Our results show a geometric mean speedup of 1.5X, with a maximum speedup of 4.6X over Gunrock, and a geometric speedup of 3.6X, with a maximum speedup of 16.5X, over GraphLily. Our results also showed a geometric mean power reduction of 50% and a mean reduction of energy-delay product of 88% over Gunrock.
Numerical implementation of the symmetric Galerkin boundary element method in 2D elastodynamics

The symmetric Galerkin boundary element method (SGBEM) employs both the displacement integral equation and the traction integral equation which lead to a symmetric system of equations. A two‐dimensional SGBEM is implemented in this paper, with emphasis on the special treatments of singular integrals. The integrals in the time domain are carried out by an analytical method. In order to evaluate the strong singular double integrals and the hypersingular double integrals in the space domain which are associated with the fundamental solutions Gpu and Gpp, artificial body forces are introduced which can be used to indirectly derive the singular terms. Thus, those singular integrals which behave like 1/r and 1/r2 are all avoided in the proposed SGEBM implementation. An artificial body force scheme is proposed to evaluate the body force term effectively. Two numerical examples are presented to assess the accuracy of the numerical implementation, and show similar accuracy when compared with the FEM and the analytical solutions. Copyright © 2003 John Wiley & Sons, Ltd.
A Study of Early Aggregation in Database Query Processing on FPGAs

In database query processing, aggregation is an operator by which data with a common property is grouped and expressed in a summary form. Early aggregation is a popular method for improving the performance of the aggregation operator. In this paper, we study early aggregation algorithms in the context of query processing acceleration in database systems on FPGAs. The comparative study leads us to set-associative caches with a low inter-reference recency set (LIRS) replacement policy. They show both great performance and modest implementation complexity compared to some of the most prominent early aggregation algorithms. We also present a novel application-specific architecture for implementing set-associative caches. Benchmarks of our implementation show speedups of up to 3x for end-to-end aggregation compared to a state-of-the-art FPGA-based query engine.
A new data conversion method for mixed precision Krylov solvers with FP16/BF16 Jacobi preconditioners

Mixed precision Krylov solvers with the Jacobi preconditioner often show significant convergence degradation when the Jacobi preconditioner is computed in low precision such as FP16 and BF16. It is found that this convergence degradation is attributed to loss of diagonal dominance due to roundoff errors in data conversion. To resolve this issue, we propose a new data conversion method, which is designed to keep diagonal dominance of the original matrix data. The proposed method is tested by computing the Poisson equation using the conjugate gradient method, the general minimum residual method, and the biconjugate gradient stabilized method with the FP16/BF16 Jacobi preconditioner on NVIDIA V100 GPUs. Here, the new data conversion is implemented by switching the round-nearest, round-up, round-down, and round-towards-zero intrinsics in CUDA, and is called once before the main iteration. Therefore, the cost of the new data conversion is negligible. When the coefficients of matrix is continuously changed by scaling the linear system, the conventional data conversion based on the round-nearest intrinsic shows periodic changes of the convergence property depending on the difference of the roundoff errors between diagonal and off-diagonal coefficients. Here, the period and magnitude of the convergence degradation depend on the bit length of significand. On the other hand, the proposed data conversion method is shown to fully avoid the convergence degradation, and robust mixed precision computing is enabled for the Jacobi preconditioner without extra overheads.
Quantitative analysis of electron-beam-induced current profiles across p-n junctions in GaAs/Al0.4Ga0.6As heterostructures

We propose a method for the quantitative evaluation of electron‐beam‐induced current profiles measured in normal‐collector geometry across p–n junctions in GaAs/Al0.4Ga0. 6As heterostructures. We obtain a theoretical expression, valid for all distances from the junction, and which takes into account the spatial extension of the electron–hole pairs generation. From the comparison of calculated with experimental profiles, we evaluate the minority carrier diffusion length and to some extent the surface recombination velocity.
Non-Wars and Emergence of New Organizational Forms Combining States, Markets and Civil Society

Adler, Paul S. , Adly, Amr, Armanios, Daniel Erian, Battilana, Julie, Bodrožić, Zlatko, Clegg, Stewart et al. (2023). Authoritarianism, populism, and the global retreat of democracy: A curated discussion. Journal of Management Inquiry, 32, 3–20. Bachmann, Reinhard, Gillespie, Nicole, & Priem, Richard (2015). Repairing trust in organizations and institutions: Toward a conceptual framework. Organization Studies, 36, 1123–1142. Lounsbury, Michael, & Wang, Milo S. (2020). Into the clearing: Back to the future of constitutive institutional analysis. Organization Theory, 1. Lounsbury, Michael, Steele, Christopher W. J., Wang, Milo S. , & Toubiana, Madeline (2021). New directions in the study of institutional logics: From tools to phenomena. Annual Review of Sociology, 47, 261–280. Schilke, Oliver, Reimann, Martin, & Cook, Karen S. (2021). Trust in social relations. Annual Review of Sociology, 47, 239–259. Zucker, Lynne G. (1986). Production of trust: Institutional sources of economic structure, 1840–1920. Research in Organizational Behavior, 8, 53–111.
Cardiac arrest in myocardial infarction with non-obstructive coronary artery (MINOCA) secondary to thyroid dysfunction

A man in his 40s who was previously well had an out-of-hospital cardiac arrest. Postresuscitation ECG showed ST-elevation myocardial infarction (MI). Emergency coronary angiogram revealed MI with non-obstructive coronary arteries (MINOCA) with evidence of spasm in the right coronary artery. Both his echocardiogram and cardiac MRI revealed a normal heart. Further workup showed markedly elevated free T4 (99.5 pmol/L) and free T3 (26.7 pmol/L) with low thyroid stimulating hormone (<0.02 pmol/L) in keeping with thyroid storm. He also had an elevated adjusted calcium level (2.84 mmol/L), which could have contributed to his coronary artery spasm. His peak troponin T was elevated at 798 ng/L (<14) suggesting myocardial damage. He was treated with propylthiouracil, steroids, beta-blocker, calcium channel blocker and intravenous fluids. The patient achieved a full recovery and was discharged home. This is an unusual case of thyroid dysfunction resulting in coronary artery spasm, cardiac arrest and MINOCA.
Associations of remote mental healthcare with clinical outcomes: a natural language processing enriched electronic health record data study protocol

Introduction People often experience significant difficulties in receiving mental healthcare due to insufficient resources, stigma and lack of access to care. Remote care technology has the potential to overcome these barriers by reducing travel time and increasing frequency of contact with patients. However, the safe delivery of remote mental healthcare requires evidence on which aspects of care are suitable for remote delivery and which are better served by in-person care. We aim to investigate clinical and demographic associations with remote mental healthcare in a large electronic health record (EHR) dataset and the degree to which remote care is associated with differences in clinical outcomes using natural language processing (NLP) derived EHR data. Methods and analysis Deidentified EHR data, derived from the South London and Maudsley (SLaM) National Health Service Foundation Trust Biomedical Research Centre (BRC) Case Register, will be extracted using the Clinical Record Interactive Search tool for all patients receiving mental healthcare between 1 January 2019 and 31 March 2022. First, data on a retrospective, longitudinal cohort of around 80 000 patients will be analysed using descriptive statistics to investigate clinical and demographic associations with remote mental healthcare and multivariable Cox regression to compare clinical outcomes of remote versus in-person assessments. Second, NLP models that have been previously developed to extract mental health symptom data will be applied to around 5 million documents to analyse the variation in content of remote versus in-person assessments. Ethics and dissemination The SLaM BRC Case Register and Clinical Record Interactive Search (CRIS) tool have received ethical approval as a deidentified dataset (including NLP-derived data from unstructured free text documents) for secondary mental health research from Oxfordshire REC C (Ref: 18/SC/0372). The study has received approval from the SLaM CRIS Oversight Committee. Study findings will be disseminated through peer-reviewed, open access journal articles and service user and carer advisory groups.
A Bayesian hierarchical model for photometric red shifts

Summary. The Sloan digital sky survey is an extremely large astronomical survey that is conducted with the intention of mapping more than a quarter of the sky. Among the data that it is generating are spectroscopic and photometric measurements, both containing information about the red shift of galaxies. The former are precise and easy to interpret but expensive to gather; the latter are far cheaper but correspondingly more difficult to interpret. Recently, Csabai and co‐workers have described various calibration techniques aiming to predict red shift from photometric measurements. We investigate what a structured Bayesian approach to the problem can add. In particular, we are interested in providing uncertainty bounds that are associated with the underlying red shifts and the classifications of the galaxies. We find that quite a generic statistical modelling approach, using for the most part standard model ingredients, can compete with much more specific custom‐made and highly tuned techniques that are already available in the astronomical literature.
Double-resonant tunneling via surface plasmons in layered gratings

The electromagnetic field distribution in the profiled metal-semiconductor and metal-oxide- metal systems was computed. When the reciprocal grating vector equals the difference of wavevectors of the surface modes at the two interfaces of the metal film, the effective resonant tunneling of the electromagnetic field through the metal film is possible. This may substantially increase the efficiency of the mentioned structures, used as selective photodetectors or light-emitting devices.
Mining above and below ground: timing the transition

Some mining operations eventually transition underground because surface mining becomes increasingly expensive as one progresses downward. Mining firms often delay this transition because large underground infrastructure costs are incurred up front, whereas underground extraction may occur over decades. When and how deep to install the underground infrastructure, as well as extraction schedules above and below ground, are decisions with a sizable impact on profits. This article addresses these questions while considering realistic factors, including choices of cutoff grades (minimum ore concentration at which the extracted material is processed to recover ore) and mining rates. We present a large longest-path representation of the problem and show that it can be solved via a series of small longest-path problems. The latter representation is not a decomposition of the original network but takes advantage of the structure of the problem. Together, the small networks require only a few seconds to solve. We illustrate our approach using data from a South African mine and provide insights regarding the effects of ore prices, discount rates, and their interactions on the characteristics of optimal solutions; we find that common wisdom is not always applicable. Our solutions have significantly higher profits than benchmark solutions, representing up to billions of dollars.
On the DL for Frequency

Difference limens for three frequencies (250, 1000, and 4000 cps) were determined for two trained observers by two psychophysical methods: the ABX procedure and the method of constant stimulus differences or AX procedure. Though the two observers differ considerably insensitivity, their DL's for the AX procedure are less than one‐half of their DL's for the ABX procedure. Comparison of these data with data in the literature on DL's for frequency indicates a wide range of values for different psychophysical procedures and for different subjects. Some observations are reported that illustrate the effect of such factors as practice and ensemble of stimulus conditions upon the size of the DL. The influence of the stimulus ensemble upon judgement time is discussed briefly. In view of all the experimental data, it would be rather imprudent to postulate a “true” DL, or to infer the behavior of the peripheral organ from the size of a DL measured under a given set of conditions. While there is undoubtedly some rela.
Analogy relations and inference

An approach to analogical reasoning is introduced which is based upon the so-called resemblance relations i.e. fuzzy binary relations which are reflexive, transitive, and monotonic with respect to the set inclusion. The analogical reasoning approach is given a complete characterization of resemblance relations using the representation theorems for fuzzy binary transitive relations. The analysis of some examples shows the relationship between this approach to analogical reasoning and fuzzy inference modeled through the compositional rule of inference. <<ETX>>
Attentional capture by fearful faces requires consciousness and is modulated by task-relevancy: A dot-probe EEG study

In the current EEG study, we used a dot-probe task in conjunction with backward masking to examine the neural activity underlying awareness and spatial processing of fearful faces and the neural processes for subsequent cued spatial targets. We presented face images under different viewing conditions (subliminal and supraliminal) and manipulated the relation between a fearful face in the pair and a subsequent target. Through both mass univariate analysis and multivariate pattern analysis, we found that fearful faces can be processed to an extent where they attract spatial attention only when they are presented supraliminally and when they are task-relevant. The spatial attention capture by fearful faces also modulated the processing of subsequent lateralised targets that were spatially congruent with the fearful face, in both behavioural and neural data. There was no evidence for nonconscious processing of the fearful faces in the current paradigm. We conclude that spatial attentional capture by fearful faces requires visual awareness and it is modulated by top-down task demands.