Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved representation of surface‐groundwater interaction in the Canadian land surface scheme

The improvement of surface‐groundwater interactions in land surface models are necessary to determine the evolution of hydrologic variables such as surface runoff, evapotranspiration, soil moisture, and streamflows, especially during dry conditions, when streamflows are largely derived from water releases from groundwater storage. Despite its importance, investigations of the effects of surface‐groundwater interactions on streamflows simulated by large‐scale land surface models are lacking. In this paper, we implement a new parameterization to represent groundwater dynamics in the Canadian Land Surface Scheme (CLASS), which is used for modelling the land surface component in the Canadian regional and global climate models. We compare offline simulations performed with the original and modified versions of CLASS to find the impact of these modifications on the regional hydrology. The offline simulations are driven by ERA‐Interim atmospheric forcing data from the European Centre for Medium‐Range Weather Forecasts reanalysis (for the 1980–2011 period), over a northeast Canadian domain. The original and modified versions of CLASS differ in the soil bottom boundary conditions, with free (gravitational) drainage in the former, while an unconfined aquifer at the depth of bedrock is considered in the latter. Results suggest higher soil moisture levels in the simulation with modified CLASS compared to the original version, particularly for regions with shallow water table. At these locations, summer surface runoff, evapotranspiration and streamflows are also higher in the simulation with modified CLASS, and the simulated low flow values are in better agreement to those observed. This study thus demonstrates the need to account for surface‐groundwater interactions in land surface models for realistic simulation of hydrological processes and streamflows.
Thread Detection and Response Generation using Transformers with Prompt Optimisation

Conversational systems are crucial for human-computer interaction, managing complex dialogues by identifying threads and prioritising responses. This is especially vital in multi-party conversations, where precise identification of threads and strategic response prioritisation ensure efficient dialogue management. To address these challenges an end-to-end model that identifies threads and prioritises their response generation based on the importance was developed, involving a systematic decomposition of the problem into discrete components - thread detection, prioritisation, and performance optimisation which was meticulously analysed and optimised. These refined components seamlessly integrate into a unified framework, in conversational systems. Llama2 7b is used due to its high level of generalisation but the system can be updated with any open source Large Language Model(LLM). The computational capabilities of the Llama2 model was augmented by using fine tuning methods and strategic prompting techniques to optimise the model's performance, reducing computational time and increasing the accuracy of the model. The model achieves up to 10x speed improvement, while generating more coherent results compared to existing models.
LinearAPT: An Adaptive Algorithm for the Fixed-Budget Thresholding Linear Bandit Problem

In this study, we delve into the Thresholding Linear Bandit (TLB) problem, a nuanced domain within stochastic Multi-Armed Bandit (MAB) problems, focusing on maximizing decision accuracy against a linearly defined threshold under resource constraints. We present LinearAPT, a novel algorithm designed for the fixed budget setting of TLB, providing an efficient solution to optimize sequential decision-making. This algorithm not only offers a theoretical upper bound for estimated loss but also showcases robust performance on both synthetic and real-world datasets. Our contributions highlight the adaptability, simplicity, and computational efficiency of LinearAPT, making it a valuable addition to the toolkit for addressing complex sequential decision-making challenges.
Addressing the risks of induced seismicity in subsurface energy operations

Shale gas could help address the insatiable global demand for energy. However, in addition to risks of environmental pollution, the risk of induced seismicity during the hydraulic fracturing process is often considered as the major showstopper in the public acceptability of shale gas as an alternative source of fossil fuel. Other types of subsurface energy development have also demonstrated similar induced seismicity risks. This article presents an interdisciplinary review of notable cases of suspected induced seismicity relating to subsurface energy operations, covering operations for hydraulic fracturing, wastewater injection, conventional gas extraction, enhanced geothermal systems and water impoundment. Possible causal mechanisms of induced seismicity are described and illustrated, then methods to mitigate induced seismicity, encompassing regulations, including so‐called traffic light systems, monitoring and assessment, and numerical modeling approaches for predicting the occurrence of induced seismicity are outlined. Issues relating to public perception of energy technologies in regards to induced seismicity potential are also discussed.
Prevalence and Factors Associated with Grade of Obesity in Selected Health Areas in Yaoundé: A Cross-Sectional Study

Background: According to the WHO, obesity is an abnormal or excessive accumulation of body fat that poses a health risk. It is a major public health problem with growing numbers (1,400,000,000 overweight adults and more than 500,000,000 obese). Every year, there are at least 2,800,000 associated deaths. The prevalence of obesity in Africa is above 61% in the adult population. Despite control strategies, obesity is still a real problem in Cameroon. Methodology: This study aimed to determine the relationship between various risk factors and the prevalence of different grades of obesity in selected health areas in Yaounde. This work consisted of a descriptive cross-sectional study carried out in Yaoundé, in Central Cameroon. Data were collected via a questionnaire administered face-to-face to obese patients in the target hospitals and the anthropometric parameters of these were determined. These data were analyzed using CSPRO 7.1 and SPSS 21.0 software. Results: The total number of participants in this study was 267 patients, most of whom were women (82.4%). The predominant type of obesity was moderate obesity; its frequency was 53%, followed by severe obesity, 37%. The factors associated with all grades of obesity were female gender (73% moderate obesity, 89% severe obesity, 100% morbid obesity), and genetic factors (54.6% moderate obesity, 73.5% severe obesity, 54.6% morbid obesity). Age was associated with moderate obesity: the age group most affected by moderate obesity was 55–65 years (37.5%). Conclusion: These results show that it is of paramount importance to reeducate the population on diet and lifestyle to prevent obesity, including among genetically predisposed people and even women, whatever their conditions (Menopause, multipara).
Data Analytics: Unlocking Multivision’s Future Perspectives

This research examines the problem of making data analysis understandable to non-technical consumers in today’s world of extensive data. We present a project that combines a user-friendly web application built on the Django framework with simple data analysis using linear regression. Through the use of the simple statistical technique of linear regression, even non-technical people may obtain valuable insights from large-scale datasets. Easy-to-use online application that makes data uploading, analysis, and interpretation a breeze. Our method uses built-in linear regression methods in the Django framework to automate dataset analysis. In this paper, the user interface design and the use of linear regression are explained, together with technical architecture insights. Facilitating inclusion across several areas, the major objective is to democratize data analysis. Our goal is to empower non-technical individuals and enable a wider audience to understand and apply data for informed decision-making by providing a tool that combines the efficiency of linear regression with an easy-to-use online interface.
Stochastic and Deterministic Study of Ridge Regression

The present paper is concerned with studying the ridge parameters through deterministic and stochastic approach in the case of ordinary ridge regression (ORR). In the deterministic approach, some new formulas for the ridge parameters are proposed and compared with the formula suggested by Hoerl and Kennard (1970a). The performance of the proposed ridge parameters is evaluated through a simulation study in the presence of multicollinearity. An application using real data is given. The evaluation is based on the mean square error (MSE) and relative MSE (RMSE). In the stochastic approach, the main properties of the proposed new formulas and the formula suggested by Hoerl and Kennard (1970a) are studied. The probability density function (pdf) and distribution function of the formulas are derived. The empirical distributions of these formulas are derived using Pearson’s method. It is found that the performance of the new formulas are better than ordinary least squares (OLS) and the formula suggested by Hoerl and Kennard (1970 a) for all selected distributions.
Pemberdayaan Mahasiswa Pada Uji Coba Produk Acne Care Tanpa Reapply Dalam Menumbuhkan Kemandirian Wirausaha

Jerawat merupakan masalah umum dalam perawatan kulit yang tidak hanya berdampak secara kosmetik tetapi juga psikologis. Dalam rangka mengatasi kendala produk perawatan kulit yang memerlukan pengaplikasian ulang, inovasi produk acne care tanpa reapply dengan kandungan DNA Salmon telah diciptakan. Studi ini melibatkan mahasiswa dalam pengembangan produk tersebut, memberikan mereka pengalaman riset serta kesempatan untuk mengembangkan keterampilan wirausaha. Tujuan kegiatan pengabdian ini adalah untuk mengevaluasi efektivitas produk acne care tanpa reapply dalam mengatasi jerawat dibandingkan dengan produk perawatan kulit yang sudah ada. Selain itu, untuk memberdayakan mahasiswa melalui uji coba reformulasi produk acne care, menumbuhkan kemandirian usaha dan mempersiapkan mereka untuk tantangan di dunia bisnis. Metode ini dilakukan melalui beberapa tahap, mulai dari persiapan dan pemantapan rencana kegiatan, pembelian peralatan dan bahan, proses penciptaan reformulasi produk, uji coba produk, hingga proses pengemasan. Proses pengembangan produk ini melibatkan kolaborasi antara tim dosen, mahasiswa, dan mitra industri. Hasil dari pengabdian kepada masyarakat ini yaitu evaluasi efektivitas produk acne care tanpa reapply menunjukkan bahwa produk dengan kandungan DNA Salmon memiliki keunggulan dalam merangsang regenerasi kulit dan mengatasi jerawat tanpa reapply. Dibandingkan dengan produk perawatan kulit yang sudah ada, produk ini lebih cepat dan efektif dalam mengatasi masalah jerawat. Pemberdayaan mahasiswa dalam uji coba produk acne care tidak hanya memberikan perspektif segar dan ide kreatif, tetapi juga meningkatkan keterampilan wirausaha mereka. Mahasiswa mendapatkan pengalaman praktis dalam riset, meningkatkan kemandirian, dan memahami proses pengembangan produk inovasi.
Topologically Regularized Multiple Instance Learning to Harness Data Scarcity

In biomedical data analysis, Multiple Instance Learning (MIL) models have emerged as a powerful tool to classify patients' microscopy samples. However, the data-intensive requirement of these models poses a significant challenge in scenarios with scarce data availability, e.g., in rare diseases. We introduce a topological regularization term to MIL to mitigate this challenge. It provides a shape-preserving inductive bias that compels the encoder to maintain the essential geometrical-topological structure of input bags during projection into latent space. This enhances the performance and generalization of the MIL classifier regardless of the aggregation function, particularly for scarce training data. The effectiveness of our method is confirmed through experiments across a range of datasets, showing an average enhancement of 2.8% for MIL benchmarks, 15.3% for synthetic MIL datasets, and 5.5% for real-world biomedical datasets over the current state-of-the-art.
Penerapan Strategi Pemasaran Pada UMKM Minimarket Bintang Jaya

Situs jejaring sosial dalam beberapa tahun terakhir ini telah menyaksikan pertumbuhan eksponensial di tingkat global, terutama karena peluang yang telah diberikan kepada orang untuk terkait antara yang satu dengan lainnya melalui cara yang mudah, tepat waktu untuk berbagi dan bertukar berbagai macam informasi. Sehingga pengaruh media sosial dalam kehidupan pribadi dan profesional semakin meningkat. UMKM Minimarket Bintang Jaya merupakan umkm yang terletak di kota Batam yang sampai saat ini pemasaran yang dilakukan hanya dengan word of mouth sehingga terdapat kurangnya strategi promosi atau pemasaran yang dilakukan secara online maupun offline. Maka dari itu, penelitian ini bertujuan untuk membantu mempromosikan usaha umkm agar dapat mengikuti dan beradaptasi dengan perkembangan teknologi saat ini dengan menggunakan media digital/online dan advertisement. Metode dalam penelitian ini menggunakan teknik wawancara dan observasi dalam mengumpulkan informasi dan data yang sekiranya dapat digunakan untuk penelitian. Untuk hasil implementasi dari luaran, dapat dilihat bahwa terdapat kenaikan followers dan direct message dari pengguna media sosial yang menanyakan tentang umkm dan juga membuat umkm lebih dikenal luas oleh masyarakat dan menarik perhatian calon pelanggan baru. Adapun rekomendasi untuk penelitian selanjutnya yaitu dapat memperluas dan memperdalam kegiatan pemasaran di media sosial menggunakan aplikasi atau fitur yang ada seperti iklan guna menarik perhatian pelanggan yang lebih luas.
From Agent Autonomy to Casual Collaboration: A Design Investigation on Help-Seeking Urban Robots

As intelligent agents transition from controlled to uncontrolled environments, they face challenges that sometimes exceed their operational capabilities. In many scenarios, they rely on assistance from bystanders to overcome those challenges. Using robots that get stuck in urban settings as an example, we investigate how agents can prompt bystanders into providing assistance. We conducted four focus group sessions with 17 participants that involved bodystorming, where participants assumed the role of robots and bystander pedestrians in role-playing activities. Generating insights from both assumed robot and bystander perspectives, we were able to identify potential non-verbal help-seeking strategies (i.e., addressing bystanders, cueing intentions, and displaying emotions) and factors shaping the assistive behaviours of bystanders. Drawing on these findings, we offer design considerations for help-seeking urban robots and other agents operating in uncontrolled environments to foster casual collaboration, encompass expressiveness, align with agent social categories, and curate appropriate incentives.
Photonic Crystal Circular Defect (CirD) Laser

We describe the design of photonic crystal circular defect (CirD) lasers to construct a compact optical module with a wavelength division multiplexing function for the application of inter-chip or intra-chip optical interconnects. Subsequently, we investigated the characteristics of CirD lasers including the quality factor of the cavity, the lasing threshold, and the modulation speed with a three-dimensional finite-difference time-domain method and two-dimensional rate equations. Finally, we demonstrated the single mode lasing and wavelength tuning behaviors of the CirD lasers using optical pumping technology under room-temperature continuous-wave conditions.
Deep learning for multi-label classification of coral conditions in the Indo-Pacific via underwater photogrammetry

Since coral reef ecosystems face threats from human activities and climate change, coral conservation programs are implemented worldwide. Monitoring coral health provides references for guiding conservation activities. However, current labor-intensive methods result in a backlog of unsorted images, highlighting the need for automated classification. Few studies have simultaneously utilized accurate annotations along with updated algorithms and datasets. This study aimed to create a dataset representing common coral conditions and associated stressors in the Indo-Pacific. Concurrently, it assessed existing classification algorithms and proposed a new multi-label method for automatically detecting coral conditions and extracting ecological information. A dataset containing over 20,000 high-resolution coral images of different health conditions and stressors was constructed based on the field survey. Seven representative deep learning architectures were tested on this dataset, and their performance was quantitatively evaluated using the F1 metric and the match ratio. Based on this evaluation, a new method utilizing the ensemble learning approach was proposed. The proposed method accurately classified coral conditions as healthy, compromised, dead, and rubble; it also identified corresponding stressors, including competition, disease, predation, and physical issues. This method can help develop the coral image archive, guide conservation activities, and provide references for decision-making for reef managers and conservationists. The proposed ensemble learning approach outperforms others on the dataset, showing State-Of-The-Art (SOTA) performance. Future research should improve its generalizability and accuracy to support global coral conservation efforts.
TOI-1173 A $b$: The First Inflated Super-Neptune in a Wide Binary System

Among Neptunian mass planets exoplanets ($20-50$ M$_\oplus$), puffy hot Neptunes are extremely rare, and their unique combination of low mass and extended radii implies very low density ($\rho<0.3$ g cm$^{-3}$). Over the last decade, only a few puffy planets have been detected and precisely characterized with both transit and radial velocity observations, most notably including WASP-107 $b$, TOI-1420 $b$, and WASP-193 $b$. In this paper, we report the discovery of TOI-1173 A $b$, a low-density ($\rho = 0.269_{-0.024}^{+0.028}$ g cm$^{-3}$) super-Neptune with $P = 7.06$ days in a nearly circular orbit around the primary G-dwarf star in the wide binary system TOI-1173 A/B. Using radial velocity observations with the MAROON-X spectrograph and transit photometry from TESS, we determined a planet mass of $M_{\rm{p}} = 26.1\pm1.9\ M_{\oplus}$ and radius of $R_{\rm{p}} = 8.10\pm0.17\ R_{\oplus}$. TOI-1173 A $b$ is the first puffy Super-Neptune planet detected in a wide binary system (separation $\sim 11,400$ AU). We explored several mechanisms to understand the puffy nature of TOI-1173 A $b$, and showed that tidal heating is the most promising explanation. Furthermore, we demonstrate that TOI-1173 A $b$ likely has maintained its orbital stability over time and may have undergone von-Zeipel-Lidov-Kozai migration followed by tidal circularization given its present-day architecture, with important implications for planet migration theory and induced engulfment into the host star. Further investigation of the atmosphere of TOI-1173 A $b$ will shed light on the origin of close-in low-density Neptunian planets in field and binary systems, while spin-orbit analyses may elucidate the dynamical evolution of the system.
SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale SAR Object Detection

Synthetic Aperture Radar (SAR) object detection has gained significant attention recently due to its irreplaceable all-weather imaging capabilities. However, this research field suffers from both limited public datasets (mostly comprising<2K images with only mono-category objects) and inaccessible source code. To tackle these challenges, we establish a new benchmark dataset and an open-source method for large-scale SAR object detection. Our dataset, SARDet-100K, is a result of intense surveying, collecting, and standardizing 10 existing SAR detection datasets, providing a large-scale and diverse dataset for research purposes. To the best of our knowledge, SARDet-100K is the first COCO-level large-scale multi-class SAR object detection dataset ever created. With this high-quality dataset, we conducted comprehensive experiments and uncovered a crucial challenge in SAR object detection: the substantial disparities between the pretraining on RGB datasets and finetuning on SAR datasets in terms of both data domain and model structure. To bridge these gaps, we propose a novel Multi-Stage with Filter Augmentation (MSFA) pretraining framework that tackles the problems from the perspective of data input, domain transition, and model migration. The proposed MSFA method significantly enhances the performance of SAR object detection models while demonstrating exceptional generalizability and flexibility across diverse models. This work aims to pave the way for further advancements in SAR object detection. The dataset and code is available at https://github.com/zcablii/SARDet_100K.
Towards Decoupling Frontend Enhancement and Backend Recognition in Monaural Robust ASR

It has been shown that the intelligibility of noisy speech can be improved by speech enhancement (SE) algorithms. However, monaural SE has not been established as an effective frontend for automatic speech recognition (ASR) in noisy conditions compared to an ASR model trained on noisy speech directly. The divide between SE and ASR impedes the progress of robust ASR systems, especially as SE has made major advances in recent years. This paper focuses on eliminating this divide with an ARN (attentive recurrent network) time-domain and a CrossNet time-frequency domain enhancement models. The proposed systems fully decouple frontend enhancement and backend ASR trained only on clean speech. Results on the WSJ, CHiME-2, LibriSpeech, and CHiME-4 corpora demonstrate that ARN and CrossNet enhanced speech both translate to improved ASR results in noisy and reverberant environments, and generalize well to real acoustic scenarios. The proposed system outperforms the baselines trained on corrupted speech directly. Furthermore, it cuts the previous best word error rate (WER) on CHiME-2 by $28.4\%$ relatively with a $5.57\%$ WER, and achieves $3.32/4.44\%$ WER on single-channel CHiME-4 simulated/real test data without training on CHiME-4.
Prepared for the Worst: A Learning-Based Adversarial Attack for Resilience Analysis of the ICP Algorithm

This paper presents a novel method to assess the resilience of the Iterative Closest Point (ICP) algorithm via deep-learning-based attacks on lidar point clouds. For safety-critical applications such as autonomous navigation, ensuring the resilience of algorithms prior to deployments is of utmost importance. The ICP algorithm has become the standard for lidar-based localization. However, the pose estimate it produces can be greatly affected by corruption in the measurements. Corruption can arise from a variety of scenarios such as occlusions, adverse weather, or mechanical issues in the sensor. Unfortunately, the complex and iterative nature of ICP makes assessing its resilience to corruption challenging. While there have been efforts to create challenging datasets and develop simulations to evaluate the resilience of ICP empirically, our method focuses on finding the maximum possible ICP pose error using perturbation-based adversarial attacks. The proposed attack induces significant pose errors on ICP and outperforms baselines more than 88% of the time across a wide range of scenarios. As an example application, we demonstrate that our attack can be used to identify areas on a map where ICP is particularly vulnerable to corruption in the measurements.