Miracle-dz/UniFAR-FTUs huggingface dataset:
Health and productivity management initiatives to promote worker health and improve the workplace environment at the Fukushima Daiichi nuclear power plant

Abstract Background: Since the Fukushima Daiichi nuclear power plant (FDNPP) accident in 2011, measures have been taken to address occupational health risks, such as heat illness and infectious diseases, and ensure fitness for duty with the Tokyo Electric Power Company and its contractors. However, with the decommissioning operations set to span several decades and an aging workforce, there arose a need for proactive occupational health strategies that not only addressed these risks but also promoted employee health and created a comfortable work environment. With the Japanese government’s promotion of health and productivity management (HPM) for corporations, an HPM-based initiative was launched at the FDNPP in 2019. Case: We designed an HPM questionnaire tailored to the unique conditions at the FDNPP for contractors in 2019. Subsequently, we adjusted the questionnaire annually in light of evolving societal contexts and insights derived from contractors’ feedback. This initiative also involved the annual recognition of outstanding contractors. These efforts have led to a steady increase in the number of contractors participating in the HPM survey, with respondents’ scores continually improving. We also emphasized dissemination of HPM practices from contractors to their subcontractors due to the complex subcontracting structure at FDNPP, and our results showed that more contractors have been extending these efforts to their subcontractors. Conclusions: Our findings suggest that individual contractors are steadily enhancing their HPM efforts. We are committed to continually offering support to advance HPM throughout the FDNPP.
Caractérisation morphologique de la population caprine locale des régions arides tunisiennes

Une caracterisation morphologique de la population caprine locale, basee sur le profilage des phenotypes et de l'anatomie generale de l'animal, a ete realisee dans la region aride tunisienne. L'objectif de l'etude est d'analyser la variabilite morphologique de la population, d'expliquer ses aptitudes d'adaptation au stress climatique et, aussi, de comprendre le determinisme genetique des types pigmentaires presents. A cet egard, des comptages directs de certains caracteres visibles ont ete realises au niveau de 180 troupeaux pour interesser plus de 6000 chevres et 500 boucs repartis sur les differentes variantes geographiques et naturelles de la region aride. Les analyses des frequences phenotypiques ont permis d'identifier la repartition des types pigmentaires par sexe et par zone naturelle. L'etude confirme la large variabilite de la population locale et illustre ses aptitudes d'adaptation au climat hostile par ses amplitudes thermiques et ses pics de chaleur. La population est composee d'au moins 8 principaux types pigmentaires assez differencies et differemment repartis entre les zones geographiques. La longue selection intra troupeau, operee par les eleveurs sur les aspects morphologique et synergique avec la selection environnementale, a conduit a une specialisation des troupeaux au niveau des patrons phenotypiques visibles. La chevre locale est de couleur noire avec une tete coloree qui definit son type pigmentaire, elle porte des cornes et une barbiche. Les pampilles n'existent que chez environ 28% de chevres et des boucs. Le determinisme genetique du profil de la chevre locale classe notre population parmi les groupes animaux irreguliers.
Contrastive Unlearning: A Contrastive Approach to Machine Unlearning

Machine unlearning aims to eliminate the influence of a subset of training samples (i.e., unlearning samples) from a trained model. Effectively and efficiently removing the unlearning samples without negatively impacting the overall model performance is still challenging. In this paper, we propose a contrastive unlearning framework, leveraging the concept of representation learning for more effective unlearning. It removes the influence of unlearning samples by contrasting their embeddings against the remaining samples so that they are pushed away from their original classes and pulled toward other classes. By directly optimizing the representation space, it effectively removes the influence of unlearning samples while maintaining the representations learned from the remaining samples. Experiments on a variety of datasets and models on both class unlearning and sample unlearning showed that contrastive unlearning achieves the best unlearning effects and efficiency with the lowest performance loss compared with the state-of-the-art algorithms.
Pruning for Protection: Increasing Jailbreak Resistance in Aligned LLMs Without Fine-Tuning

Large Language Models (LLMs) are vulnerable to `Jailbreaking' prompts, a type of attack that can coax these models into generating harmful and illegal content. In this paper, we show that pruning up to 20% of LLM parameters markedly increases their resistance to such attacks without additional training and without sacrificing their performance in standard benchmarks. Intriguingly, we discovered that the enhanced safety observed post-pruning correlates to the initial safety training level of the model, hinting that the effect of pruning could be more general and may hold for other LLM behaviors beyond safety. Additionally, we introduce a curated dataset of 225 harmful tasks across five categories, inserted into ten different Jailbreaking prompts, showing that pruning aids LLMs in concentrating attention on task-relevant tokens in jailbreaking prompts. Lastly, our experiments reveal that the prominent chat models, such as LLaMA-2 Chat, Vicuna, and Mistral Instruct exhibit high susceptibility to jailbreaking attacks, with some categories achieving nearly 70-100% success rate. These insights underline the potential of pruning as a generalizable approach for improving LLM safety, reliability, and potentially other desired behaviors.
I-SplitEE: Image classification in Split Computing DNNs with Early Exits

The recent advances in Deep Neural Networks (DNNs) stem from their exceptional performance across various domains. However, their inherent large size hinders deploying these networks on resource-constrained devices like edge, mobile, and IoT platforms. Strategies have emerged, from partial cloud computation offloading (split computing) to integrating early exits within DNN layers. Our work presents an innovative unified approach merging early exits and split computing. We determine the 'splitting layer', the optimal depth in the DNN for edge device computations, and whether to infer on edge device or be offloaded to the cloud for inference considering accuracy, computational efficiency, and communication costs. Also, Image classification faces diverse environmental distortions, influenced by factors like time of day, lighting, and weather. To adapt to these distortions, we introduce I-SplitEE, an online unsupervised algorithm ideal for scenarios lacking ground truths and with sequential data. Experimental validation using Caltech-256 and Cifar-10 datasets subjected to varied distortions showcases I-SplitEE's ability to reduce costs by a minimum of 55% with marginal performance degradation of at most 5%.
Investigating Color-Blind User-Interface Accessibility via Simulated Interfaces

Over 300 million people who live with color vision deficiency (CVD) have a decreased ability to distinguish between colors, limiting their ability to interact with websites and software packages. User-interface designers have taken various approaches to tackle the issue, with most offering a high-contrast mode. The Web Content Accessibility Guidelines (WCAG) outline some best practices for maintaining accessibility that have been adopted and recommended by several governments; however, it is currently uncertain how this impacts perceived user functionality and if this could result in a reduced aesthetic look. In the absence of subjective data, we aim to investigate how a CVD observer might rate the functionality and aesthetics of existing UIs. However, the design of a comparative study of CVD vs. non-CVD populations is inherently hard; therefore, we build on the successful field of physiologically based CVD models and propose a novel simulation-based experimental protocol, where non-CVD observers rate the relative aesthetics and functionality of screenshots of 20 popular websites as seen in full color vs. with simulated CVD. Our results show that relative aesthetics and functionality correlate positively and that an operating-system-wide high-contrast mode can reduce both aesthetics and functionality. While our results are only valid in the context of simulated CVD screenshots, the approach has the benefit of being easily deployable, and can help to spot a number of common pitfalls in production. Finally, we propose a AAA–A classification of the interfaces we analyzed.
Ground surface and poly-urethane (PU) filling alter the pressure distribution pattern in square standing horses

Summary This study was conducted to evaluate the effects of Pour-In, Poly-Urethane (PIPU) products on pressure distribution underneath the hoof and shoe, as many farriers report the successful use of these products in cases where horses suffer from a reduced bearing surface. The tested hypotheses were that shoes filled with PU would increase the bearing surface and thus provide lower mean pressures and that these effects would be the same despite the deformability capacities of the surface the horses stood on. Data were collected from 6 clinically sound Warmblood horses while standing on top of a pressure/force measuring device. The horses had been shod at random with regular steel shoes and the same shoes filled with PU. Data were collected on 2 different ground surfaces, a firm, rubber surface, and a deformable, sand surface. With PU filling the bearing surface underneath the shoe did not increase significantly, nor did the mean pressure decrease. On a firm surface the pressure distribution pattern is similar in both shoeing conditions. On a deformable surface the shoe filled with PU has a significantly different (p<0.05) pressure distribution pattern and mainly the sole and the frog became loaded. With the use of PIPU the pressure distribution pattern underneath the shoe and hoof , is altered. These findings will aid to explain why so many farriers use PU products successfully in horses with a defect in their hoof wall or loss of bearing surface. On the other hand there seems no reason to shoe sound horses without problems in their feet with PIPU to provide ‘total foot support’. It might even have negative effects on horses with normal healthy feet which are trained on a deformable, sand surface, since normal loading of the hoof should occur mainly on the hoof wall instead of the frog and sole being the only weight bearing structures.
Weakly Supervised Gaussian Contrastive Grounding with Large Multimodal Models for Video Question Answering

Video Question Answering (VideoQA) aims to answer natural language questions based on the information observed in videos. Despite the recent success of Large Multimodal Models (LMMs) in image-language understanding and reasoning, they deal with VideoQA insufficiently by simply taking uniformly sampled frames as visual inputs, which ignores question-relevant visual clues. Moreover, there are no human annotations for question-critical timestamps in existing VideoQA datasets. In light of this, we propose a novel weakly supervised framework to enforce the LMMs to reason out the answers with question-critical moments as visual inputs. Specifically, we fuse the question and answer pairs as event descriptions to find multiple keyframes as target moments, which will be pseudo-labels. With these pseudo-labels as additionally weak supervision, we devise a lightweight Gaussian-based Contrastive Grounding (GCG) module. GCG learns multiple Gaussian functions to characterize the temporal structure of the video, and sample question-critical frames as positive moments to be the visual inputs of LMMs. Extensive experiments on several VideoQA benchmarks verify the effectiveness of our framework, and we achieve substantial improvements compared to previous state-of-the-art methods.
Generative Dense Retrieval: Memory Can Be a Burden

Generative Retrieval (GR), autoregressively decoding relevant document identifiers given a query, has been shown to perform well under the setting of small-scale corpora. By memorizing the document corpus with model parameters, GR implicitly achieves deep interaction between query and document. However, such a memorizing mechanism faces three drawbacks: (1) Poor memory accuracy for fine-grained features of documents; (2) Memory confusion gets worse as the corpus size increases; (3) Huge memory update costs for new documents. To alleviate these problems, we propose the Generative Dense Retrieval (GDR) paradigm. Specifically, GDR first uses the limited memory volume to achieve inter-cluster matching from query to relevant document clusters. Memorizing-free matching mechanism from Dense Retrieval (DR) is then introduced to conduct fine-grained intra-cluster matching from clusters to relevant documents. The coarse-to-fine process maximizes the advantages of GR’s deep interaction and DR’s scalability. Besides, we design a cluster identifier constructing strategy to facilitate corpus memory and a cluster-adaptive negative sampling strategy to enhance the intra-cluster mapping ability. Empirical results show that GDR obtains an average of 3.0 R@100 improvement on NQ dataset under multiple settings and has better scalability.
Nowcasting Madagascar's real GDP using machine learning algorithms

We investigate the predictive power of different machine learning algorithms to nowcast Madagascar's gross domestic product (GDP). We trained popular regression models, including linear regularized regression (Ridge, Lasso, Elastic-net), dimensionality reduction model (principal component regression), k-nearest neighbors algorithm (k-NN regression), support vector regression (linear SVR), and tree-based ensemble models (Random forest and XGBoost regressions), on 10 Malagasy quarterly macroeconomic leading indicators over the period 2007Q1--2022Q4, and we used simple econometric models as a benchmark. We measured the nowcast accuracy of each model by calculating the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Our findings reveal that the Ensemble Model, formed by aggregating individual predictions, consistently outperforms traditional econometric models. We conclude that machine learning models can deliver more accurate and timely nowcasts of Malagasy economic performance and provide policymakers with additional guidance for data-driven decision making.
Population Dynamics of Pometia for The Period of Post-Selective Logging in Tropical Rainforest, Southern Papua, Indonesia

Tropical rainforest changed in term of structure, composition and population, resulting from logging. One of target trees during logging is Pometia . Our goals of this research were to answer that (1) How were survival, growth, fecundity and population growth rate (λ) of Pometia within remnant stands? ; (2) what were driving factors to explain population dynamics of Pometia within logged forest? Data were derived from three hectares-permanent sample plot (PSP) in logged forest of PT Tunas Timber Lestari. Integral Projection Models (IPMs) were applied to analyze population dynamics. The study showed dynamically that probability of the individuals of Pometia survived in this area did not significantly differ among their size, while stands showed the significant difference between small and large individuals. The growth of both individuals of Pometia and stands varied significantly in different size. The probability of fecundity both Pometia and stands were significantly different among size. The population of Pometia has been increasing for about a decade as the population of all stands was growing as well. Moreover, the regular documentation of species composition and structure before and after logging is necessary as a control of biodiversity. Therefore, a proper protocol to monitor the biodiversity should be designed by government and disseminated it to logging companies. How to Cite Murdjoko, A. , Marsono, D. , Sadono, R. & Hadisusanto, S. (2016). Population Dynamics of Pometia for The Period of Post-Selective Logging in Tropical Rainforest, Southern Papua, Indonesia. Biosaintifika: Journal of Biology & Biology Education , 8(3), 321-330.
Neglected Hessian component explains mysteries in Sharpness regularization

Recent work has shown that methods like SAM which either explicitly or implicitly penalize second order information can improve generalization in deep learning. Seemingly similar methods like weight noise and gradient penalties often fail to provide such benefits. We show that these differences can be explained by the structure of the Hessian of the loss. First, we show that a common decomposition of the Hessian can be quantitatively interpreted as separating the feature exploitation from feature exploration. The feature exploration, which can be described by the Nonlinear Modeling Error matrix (NME), is commonly neglected in the literature since it vanishes at interpolation. Our work shows that the NME is in fact important as it can explain why gradient penalties are sensitive to the choice of activation function. Using this insight we design interventions to improve performance. We also provide evidence that challenges the long held equivalence of weight noise and gradient penalties. This equivalence relies on the assumption that the NME can be ignored, which we find does not hold for modern networks since they involve significant feature learning. We find that regularizing feature exploitation but not feature exploration yields performance similar to gradient penalties.
Ecosystem models cannot predict the consequences of conservation decisions

Ecosystem models are often used to predict the consequences of management decisions in applied ecology, including fisheries management and threatened species conservation. These models are high-dimensional, parameter-rich, and nonlinear, yet limited data is available to calibrate them, and they are rarely tested or validated. Consequently, the accuracy of their forecasts, and their utility as decision-support tools is a matter of debate. In this paper, we calibrate ecosystem models to time-series data from 110 different experimental microcosm ecosystems, each containing between three and five interacting species. We then assess how often these calibrated models offer accurate and useful predictions about how the ecosystem will respond to a set of standard management interventions. Our results show that for each timeseries dataset, a large number of very different parameter sets offer equivalent, good fits. However, these calibrated ecosystem models have poor predictive accuracy when forecasting future dynamics and offer ambiguous predictions about how species in the ecosystem will respond to management interventions. Closer inspection reveals that the ecosystem models fail because calibration cannot determine the types of interactions that occur within the ecosystem. Our findings call into question claims that ecosystem modelling can support applied ecological decision-making when they are calibrated against real-world datasets.
Emotion Classification In Software Engineering Texts: A Comparative Analysis of Pre-trained Transformers Language Models

Emotion recognition in software engineering texts is critical for understanding developer expressions and improving collaboration. This paper presents a comparative analysis of state-of-the-art Pre-trained Language Models (PTMs) for fine-grained emotion classification on two benchmark datasets from GitHub and Stack Overflow. We evaluate six transformer models - BERT, RoBERTa, ALBERT, DeBERTa, CodeBERT and GraphCodeBERT against the current best-performing tool SEntiMoji. Our analysis reveals consistent improvements ranging from 1.17% to 16.79% in terms of macro-averaged and micro-averaged F1 scores, with general domain models outperforming specialized ones. To further enhance PTMs, we incorporate polarity features in attention layer during training, demonstrating additional average gains of 1.0\% to 10.23\% over baseline PTMs approaches. Our work provides strong evidence for the advancements afforded by PTMs in recognizing nuanced emotions like Anger, Love, Fear, Joy, Sadness, and Surprise in software engineering contexts. Through comprehensive benchmarking and error analysis, we also outline scope for improvements to address contextual gaps.
Stylized Facts and Market Microstructure: An In-Depth Exploration of German Bond Futures Market

This paper presents an in-depth analysis of stylized facts in the context of futures on German bonds. The study examines four futures contracts on German bonds: Schatz, Bobl, Bund and Buxl, using tick-by-tick limit order book datasets. It uncovers a range of stylized facts and empirical observations, including the distribution of order sizes, patterns of order flow, and inter-arrival times of orders. The findings reveal both commonalities and unique characteristics across the different futures, thereby enriching our understanding of these markets. Furthermore, the paper introduces insightful realism metrics that can be used to benchmark market simulators. The study contributes to the literature on financial stylized facts by extending empirical observations to this class of assets, which has been relatively underexplored in existing research. This work provides valuable guidance for the development of more accurate and realistic market simulators.
Character Recognition in Byzantine Seals with Deep Neural Networks

Seals are small coin-shaped artifacts, mostly made of lead, held with strings to seal letters. This work presents the first attempt towards automatic reading of text on Byzantine seal images.Byzantine seals are generally decorated with iconography on the obverse side and Greek text on the reverse side. Text may include the sender's name, position in the Byzantine aristocracy, and elements of prayers. Both text and iconography are precious literary sources that wait to be exploited electronically, so the development of computerized systems for interpreting seals images is of paramount importance. This work's contribution is hence a deep, two-stages, character reading pipeline for transcribing Byzantine seal images. A first deep convolutional neural network (CNN) detects characters in the seal (character localization). A second convolutional network reads the localized characters (character classification). Finally, a diplomatic transcription of the seal is provided by post-processing the two network outputs. We provide an experimental evaluation of each CNN in isolation and both CNNs in combination. All performances are evaluated by cross-validation. Character localization achieves a mean average precision (mAP@0.5) greater than 0.9. Classification of characters cropped from ground truth bounding boxes achieves Top-1 accuracy greater than 0.92. End-to-end evaluation shows the efficiency of the proposed approach when compared to the SoTA for similar tasks.