Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Line-Separable Unit-Disk Coverage and Related Problems

Given a set $P$ of $n$ points and a set $S$ of $m$ disks in the plane, the disk coverage problem asks for a smallest subset of disks that together cover all points of $P$. The problem is NP-hard. In this paper, we consider a line-separable unit-disk version of the problem where all disks have the same radius and their centers are separated from the points of $P$ by a line $\ell$. We present an $O((n+m)\log(n+m))$ time algorithm for the problem. This improves the previously best result of $O(nm+ n\log n)$ time. Our techniques also solve the line-constrained version of the problem, where centers of all disks of $S$ are located on a line $\ell$ while points of $P$ can be anywhere in the plane. Our algorithm runs in $O((n+m)\log (m+ n)+m \log m\log n)$ time, which improves the previously best result of $O(nm\log(m+n))$ time. In addition, our results lead to an algorithm of $O(n^3\log n)$ time for a half-plane coverage problem (given $n$ half-planes and $n$ points, find a smallest subset of half-planes covering all points); this improves the previously best algorithm of $O(n^4\log n)$ time. Further, if all half-planes are lower ones, our algorithm runs in $O(n\log n)$ time while the previously best algorithm takes $O(n^2\log n)$ time.
INCEPTNET: Precise And Early Disease Detection Application For Medical Images Analyses

In view of the recent paradigm shift in deep AI based image processing methods, medical image processing has advanced considerably. In this study, we propose a novel deep neural network (DNN), entitled InceptNet, in the scope of medical image processing, for early disease detection and segmentation of medical images in order to enhance precision and performance. We also investigate the interaction of users with the InceptNet application to present a comprehensive application including the background processes, and foreground interactions with users. Fast InceptNet is shaped by the prominent Unet architecture, and it seizes the power of an Inception module to be fast and cost effective while aiming to approximate an optimal local sparse structure. Adding Inception modules with various parallel kernel sizes can improve the network's ability to capture the variations in the scaled regions of interest. To experiment, the model is tested on four benchmark datasets, including retina blood vessel segmentation, lung nodule segmentation, skin lesion segmentation, and breast cancer cell detection. The improvement was more significant on images with small scale structures. The proposed method improved the accuracy from 0.9531, 0.8900, 0.9872, and 0.9881 to 0.9555, 0.9510, 0.9945, and 0.9945 on the mentioned datasets, respectively, which show outperforming of the proposed method over the previous works. Furthermore, by exploring the procedure from start to end, individuals who have utilized a trial edition of InceptNet, in the form of a complete application, are presented with thirteen multiple choice questions in order to assess the proposed method. The outcomes are evaluated through the means of Human Computer Interaction.
Exploring magnetic coupling of solar atmosphere through frequency modulations of 3-min slow magnetoacoustic waves

Coronal fan loops rooted in sunspot umbra show outward propagating waves with subsonic phase speed and period around 3-min. However, their source region in the lower atmosphere is still ambiguous. We performed multi-wavelength observations of a clean fan loop system rooted in sunspot observed by Interface Region Imaging Spectrograph (IRIS) and Solar Dynamics Observatory (SDO). We utilised less explored property of frequency modulation of these 3-min waves from the photosphere to corona, and found them to be periodic with the ranges in 14-20 min, and 24-35 min. Based on our findings, we interpret that 3-min slow waves observed in the coronal fan loops are driven by 3-min oscillations observed at the photospheric footpoints of these fan loops in the umbral region. We also explored any connection between 3-min and 5-min oscillations observed at the photosphere, and found them to be poorly understood. Results provide clear evidence of magnetic coupling of the solar umbral atmosphere through propagation of 3-min waves along the fan loops at different atmospheric heights.
DEEPBEAS3D: Deep Learning and B-Spline EXPLICIT Active Surfaces

Deep learning-based automatic segmentation methods have become state-of-the-art. However, they are often not robust enough for direct clinical application, as domain shifts between training and testing data affect their performance. Failure in automatic segmentation can cause sub-optimal results that require correction. To address these problems, we propose a novel 3D extension of an interactive segmentation framework that represents a segmentation from a convolutional neural network (CNN) as a B-spline explicit active surface (BEAS). BEAS ensures segmentations are smooth in 3D space, increasing anatomical plausibility, while allowing the user to precisely edit the 3D surface. We apply this framework to the task of 3D segmentation of the anal sphincter complex (AS) from transperineal ultrasound (TPUS) images, and compare it to the clinical tool used in the pelvic floor disorder clinic (4D View VOCAL, GE Healthcare; Zipf, Austria). Experimental results show that: 1) the proposed framework gives the user explicit control of the surface contour; 2) the perceived workload calculated via the NASA-TLX index was reduced by 30% compared to VOCAL; and 3) it required 70% (170 seconds) less user time than VOCAL (p< 0.00001).
Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model

BACKGROUND Prior research has associated spoken language use with depression, yet studies often involve small or non-clinical samples and face challenges in the manual transcription of speech. This paper aimed to automatically identify depression-related topics in speech recordings collected from clinical samples. METHODS The data included 3919 English free-response speech recordings collected via smartphones from 265 participants with a depression history. We transcribed speech recordings via automatic speech recognition (Whisper tool, OpenAI) and identified principal topics from transcriptions using a deep learning topic model (BERTopic). To identify depression risk topics and understand the context, we compared participants' depression severity and behavioral (extracted from wearable devices) and linguistic (extracted from transcribed texts) characteristics across identified topics. RESULTS From the 29 topics identified, we identified 6 risk topics for depression: 'No Expectations', 'Sleep', 'Mental Therapy', 'Haircut', 'Studying', and 'Coursework'. Participants mentioning depression risk topics exhibited higher sleep variability, later sleep onset, and fewer daily steps and used fewer words, more negative language, and fewer leisure-related words in their speech recordings. LIMITATIONS Our findings were derived from a depressed cohort with a specific speech task, potentially limiting the generalizability to non-clinical populations or other speech tasks. Additionally, some topics had small sample sizes, necessitating further validation in larger datasets. CONCLUSION This study demonstrates that specific speech topics can indicate depression severity. The employed data-driven workflow provides a practical approach for analyzing large-scale speech data collected from real-world settings.
Advancing Text-to-GLOSS Neural Translation Using a Novel Hyper-parameter Optimization Technique

In this paper, we investigate the use of transformers for Neural Machine Translation of text-to-GLOSS for Deaf and Hard-of-Hearing communication. Due to the scarcity of available data and limited resources for text-to-GLOSS translation, we treat the problem as a low-resource language task. We use our novel hyper-parameter exploration technique to explore a variety of architectural parameters and build an optimal transformer-based architecture specifically tailored for text-to-GLOSS translation. The study aims to improve the accuracy and fluency of Neural Machine Translation generated GLOSS. This is achieved by examining various architectural parameters including layer count, attention heads, embedding dimension, dropout, and label smoothing to identify the optimal architecture for improving text-to-GLOSS translation performance. The experiments conducted on the PHOENIX14T dataset reveal that the optimal transformer architecture outperforms previous work on the same dataset. The best model reaches a ROUGE (Recall-Oriented Understudy for Gisting Evaluation) score of 55.18% and a BLEU-1 (BiLingual Evaluation Understudy 1) score of 63.6%, outperforming state-of-the-art results on the BLEU1 and ROUGE score by 8.42 and 0.63 respectively.
Towards data-driven mass spectrometry in atmospheric science

Aerosols found in the atmosphere affect the climate and worsen air quality. To mitigate these adverse impacts, aerosol formation and aerosol chemistry in the atmosphere need to be better mapped out and understood. Currently, mass spectrometry is the single most important analytical technique in atmospheric chemistry and is used to track and identify compounds and processes. Vast amounts of data are collected in each measurement of current time-of-flight and orbitrap mass spectrometers using modern rapid data acquisition practices. However, compound identification remains as a major bottleneck during data analysis due to lacking reference libraries and analysis tools. Data-driven compound identification approaches could alleviate the problem, yet remain rare to non-existent in atmospheric science. In this perspective, we review the current state of data-driven compound identification with mass spectrometry in atmospheric science, and discuss current challenges and possible future steps towards a digital mass spectrometry era in atmospheric science.
Improving Query-Focused Meeting Summarization with Query-Relevant Knowledge

Query-Focused Meeting Summarization (QFMS) aims to generate a summary of a given meeting transcript conditioned upon a query. The main challenges for QFMS are the long input text length and sparse query-relevant information in the meeting transcript. In this paper, we propose a knowledge-enhanced two-stage framework called Knowledge-Aware Summarizer (KAS) to tackle the challenges. In the first stage, we introduce knowledge-aware scores to improve the query-relevant segment extraction. In the second stage, we incorporate query-relevant knowledge in the summary generation. Experimental results on the QMSum dataset show that our approach achieves state-of-the-art performance. Further analysis proves the competency of our methods in generating relevant and faithful summaries.
Evaluation Kidney Layer Segmentation on Whole Slide Imaging using Convolutional Neural Networks and Transformers

The segmentation of kidney layer structures, including cortex, outer stripe, inner stripe, and inner medulla within human kidney whole slide images (WSI) plays an essential role in automated image analysis in renal pathology. However, the current manual segmentation process proves labor-intensive and infeasible for handling the extensive digital pathology images encountered at a large scale. In response, the realm of digital renal pathology has seen the emergence of deep learning-based methodologies. However, very few, if any, deep learning based approaches have been applied to kidney layer structure segmentation. Addressing this gap, this paper assesses the feasibility of performing deep learning based approaches on kidney layer structure segmetnation. This study employs the representative convolutional neural network (CNN) and Transformer segmentation approaches, including Swin-Unet, Medical-Transformer, TransUNet, U-Net, PSPNet, and DeepLabv3+. We quantitatively evaluated six prevalent deep learning models on renal cortex layer segmentation using mice kidney WSIs. The empirical results stemming from our approach exhibit compelling advancements, as evidenced by a decent Mean Intersection over Union (mIoU) index. The results demonstrate that Transformer models generally outperform CNN-based models. By enabling a quantitative evaluation of renal cortical structures, deep learning approaches are promising to empower these medical professionals to make more informed kidney layer segmentation.
Multilingual Text Representation

Modern NLP breakthrough includes large multilingual models capable of performing tasks across more than 100 languages. State-of-the-art language models came a long way, starting from the simple one-hot representation of words capable of performing tasks like natural language understanding, common-sense reasoning, or question-answering, thus capturing both the syntax and semantics of texts. At the same time, language models are expanding beyond our known language boundary, even competitively performing over very low-resource dialects of endangered languages. However, there are still problems to solve to ensure an equitable representation of texts through a unified modeling space across language and speakers. In this survey, we shed light on this iterative progression of multilingual text representation and discuss the driving factors that ultimately led to the current state-of-the-art. Subsequently, we discuss how the full potential of language democratization could be obtained, reaching beyond the known limits and what is the scope of improvement in that space.
Neural Networks for Fast Optimisation in Model Predictive Control: A Review

Model Predictive Control (MPC) is an optimal control algorithm with strong stability and robustness guarantees. Despite its popularity in robotics and industrial applications, the main challenge in deploying MPC is its high computation cost, stemming from the need to solve an optimisation problem at each control interval. There are several methods to reduce this cost. This survey focusses on approaches where a neural network is used to approximate an existing controller. Herein, relevant and unique neural approximation methods for linear, nonlinear, and robust MPC are presented and compared. Comparisons are based on the theoretical guarantees that are preserved, the factor by which the original controller is sped up, and the size of problem that a framework is applicable to. Research contributions include: a taxonomy that organises existing knowledge, a summary of literary gaps, discussion on promising research directions, and simple guidelines for choosing an approximation framework. The main conclusions are that (1) new benchmarking tools are needed to help prove the generalisability and scalability of approximation frameworks, (2) future breakthroughs most likely lie in the development of ties between control and learning, and (3) the potential and applicability of recently developed neural architectures and tools remains unexplored in this field.
Quantifying the limits of controllability for the nitrogen-vacancy electron spin defect

Solid-state electron spin qubits, like the nitrogen-vacancy center in diamond, rely on control sequences of population inversion to enhance sensitivity and improve device coherence. But even for this paradigmatic system, the fundamental limits of population inversion and potential impacts on applications like quantum sensing have not been assessed quantitatively. Here, we perform high accuracy simulations beyond the rotating wave approximation, including explicit unitary simulation of neighboring nuclear spins. Using quantum optimal control, we identify analytical pulses for the control of a qubit subspace within the spin-1 ground state and quantify the relationship between pulse complexity, control duration, and fidelity. We find exponentially increasing amplitude and bandwidth requirements with reduced control duration and further quantify the emergence of non-Markovian effects for multipulse sequences using sub-nanosecond population inversion. From this, we determine that the reduced fidelity and non-Markovianity is due to coherent interactions of the electron spin with the nuclear spin environment. Ultimately, we identify a potentially realizable regime of nanosecond control duration for high-fidelity multipulse sequences. These results provide key insights into the fundamental limits of quantum information processing using electron spin defects in diamond.
A Peer-to-Peer Energy Trading Framework in Distribution Systems Considering Network Constraints

With the widespread adoption of Renewable Energy Sources (RESs) in low-voltage distribution systems, opportunities for energy trading among peers have emerged. In particular, the advent of distributed ledgers and blockchain technologies has catalyzed the application of Peer-to-Peer (P2P) economic concepts in decentralized, small-scale energy trading. This paper focuses on the critical physical layer aspects of transactions within the context of P2P energy trading, with a specific emphasis on addressing network constraints. Key challenges include maintaining margins for over/under voltage, voltage balance, and preventing congestion, all of which must be upheld during P2P energy exchanges. To address these challenges, we propose a novel analytical approach tailored to distribution networks. Furthermore, we introduce the Block Double Auction (BDA) mechanism as the P2P market mechanism for determining the acceptance or rejection of P2P transactions. The effectiveness of our proposed method is validated using the IEEE 33-node distribution test system, demonstrating its robust capabilities.
Robust Recommender System: A Survey and Future Directions

With the rapid growth of information, recommender systems have become integral for providing personalized suggestions and overcoming information overload. However, their practical deployment often encounters"dirty"data, where noise or malicious information can lead to abnormal recommendations. Research on improving recommender systems' robustness against such dirty data has thus gained significant attention. This survey provides a comprehensive review of recent work on recommender systems' robustness. We first present a taxonomy to organize current techniques for withstanding malicious attacks and natural noise. We then explore state-of-the-art methods in each category, including fraudster detection, adversarial training, certifiable robust training against malicious attacks, and regularization, purification, self-supervised learning against natural noise. Additionally, we summarize evaluation metrics and common datasets used to assess robustness. We discuss robustness across varying recommendation scenarios and its interplay with other properties like accuracy, interpretability, privacy, and fairness. Finally, we delve into open issues and future research directions in this emerging field. Our goal is to equip readers with a holistic understanding of robust recommender systems and spotlight pathways for future research and development.
A Micro-Macro parallel-in-time Implementation for the 2D Navier-Stokes Equations

In this paper the Micro-Macro Parareal algorithm was adapted to PDEs. The parallel-in-time approach requires two meshes of different spatial resolution in order to compute approximations in an iterative way to a predefined reference solution. When fast convergence in few iterations can be accomplished the algorithm is able to generate wall-time reduction in comparison to the serial computation. We chose the laminar flow around a cylinder benchmark on 2-dimensional domain which was simulated with the open-source software OpenFoam. The numerical experiments presented in this work aim to approximate states local in time and space and the diagnostic lift coefficient. The Reynolds number is gradually increased from 100 to 1,000, before the transition to turbulent flows sets in. After the results are presented the convergence behavior is discussed with respect to the Reynolds number and the applied interpolation schemes.
Echelles d'équivalence et différentiel spatial de pauvreté et d'inégalité au Burkina Faso

Based on the household survey carried out by 1994_95, the present study analyzes the relation between the equivalence scales and the spatial poverty in Burkina Faso. Several conclusions are highlighted. Firstly,the estimate of an Engel's curve generates a value of the parameter of scale 2 of 0,53, and a relative child cost for the age group 0_4 years of (=0,6. Secondly, the effect of this new equivalence scale EQ1 – compared to EQ0 where 2 = ( =1– is to raise all measurements of poverty,but the impact appears unequal according to the sectors and regions –reduction in rural poverty compared to urban poverty, and relative variation of the poverty according to geographical areas. Thirdly, the expression of household needs with EQ1 appreciably modifies the characteristics of poverty according to the demography of the groups and areas : (i) inversion of the incidence of poverty of the households according to the sex of the head, female households becoming poorer ;(ii) households having many children are not necessarily poorest –except in the large cities ; (iii) the consideration of EQ1 strongly increases the incidence of poverty in households comprising only the elderly. Fourthly, the statistic 0, testing the null hypothesis of poverty differences according to sectors and areas according to the value of ( and 2, indicates that relative targeting of areas in terms of reduction of poverty with EQ1 is less clear, and diverges somewhat from that which prevails in presence of EQ0. This conclusion is reinforced by the analysis of stochastic second-order dominance. Fifthly, the examination of the sensibility of the incidence of the relative regional poverty according to the variation of the parameters ( and 2 shows : (i)an increase in rural poverty compared to urban poverty almost twice higher when the cœfficient of scale 2 varies from 0,2 to 1 ; (ii) a weak variation of rural-urban relative poverty with (, for a given value of 2 ; (iii) a relative incidence of poverty in terms of national deprivation non-independent of 2. Sixthly, by using the decomposition of the index of Gini proposed by Yitzhaki and Lerman, the study shows that the increase of 2 induces at the same time an increase in the national inequality - even if there is a minimal value of 2 beyond which the variation of Gini changes sign –, and a rise in the weight of between-group inequality – between rural and urban areas – in the explanation of Gini. Furthermore, these trends also characterize each sector - although, for each area, the quasi-totality of Gini is explained by within-group inequality. (Full text in French)
MuLanTTS: The Microsoft Speech Synthesis System for Blizzard Challenge 2023

In this paper, we present MuLanTTS, the Microsoft end-to-end neural text-to-speech (TTS) system designed for the Blizzard Challenge 2023. About 50 hours of audiobook corpus for French TTS as hub task and another 2 hours of speaker adaptation as spoke task are released to build synthesized voices for different test purposes including sentences, paragraphs, homographs, lists, etc. Building upon DelightfulTTS, we adopt contextual and emotion encoders to adapt the audiobook data to enrich beyond sentences for long-form prosody and dialogue expressiveness. Regarding the recording quality, we also apply denoise algorithms and long audio processing for both corpora. For the hub task, only the 50-hour single speaker data is used for building the TTS system, while for the spoke task, a multi-speaker source model is used for target speaker fine tuning. MuLanTTS achieves mean scores of quality assessment 4.3 and 4.5 in the respective tasks, statistically comparable with natural speech while keeping good similarity according to similarity assessment. The excellent and similarity in this year's new and dense statistical evaluation show the effectiveness of our proposed system in both tasks.