Miracle-dz/UniFAR-FTUs huggingface dataset:
Distribution-Consistent Modal Recovering for Incomplete Multimodal Learning

Recovering missing modality is popular in incomplete multimodal learning because it usually benefits downstream tasks. However, the existing methods often directly estimate missing modalities from the observed ones by deep neural networks, lacking consideration of the distribution gap between modalities, resulting in the inconsistency of distributions between the recovered and the true data. To mitigate this issue, in this work, we propose a novel recovery paradigm, Distribution-Consistent Modal Recovering (DiCMoR), to transfer the distributions from available modalities to missing modalities, which thus maintains the distribution consistency of recovered data. In particular, we design a class-specific flow based modality recovery method to transform cross-modal distributions on the condition of sample class, which could well predict a distribution-consistent space for missing modality by virtue of the invertibility and exact density estimation of normalizing flow. The generated data from the predicted distribution is integrated with available modalities for the task of classification. Experiments show that DiCMoR gains superior performances and is more robust than existing state-of-the-art methods under various missing patterns. Visualization results show that the distribution gaps between recovered modalities and missing modalities are mitigated. Codes are released at https://github.com/mdswyz/DiCMoR.
Design of Majority Decoded Codes and Decoding Algorithm Based on Error Propagation Analysis

Modern digital communication systems are subject to very strict requirements for the reliability of information transmission. To ensure these requirements the methods of error-correction coding are applied. In this paper, we propose a method and software to design majority decoding codes and multi-threshold decoders to reduce the effect of decoding error propagation. Simulation and simulation results show that iterative multi-threshold decoding with selected parameters allows for near-optimal decoding of self-orthogonal codes with linear implementation complexity, demonstrating good error correction ability at low noise levels. The proposed codes and corresponding decoding circuits provide gains of tenths of a decibel when the noise level increases.
Stakeholder perspectives on the effects of beach hut development alond the Dutch coast : a qualitative study to assess scenarios for the preservation of important coastal dune functions

The Dutch coastline largely consists of dunes. These dunes offer several important functions to the society. These functions include flood protection, nature, recreation, etc. Recently controversies have emerged due to an increasing trend of beach hut constructions along the coastline. As the construction of new beach huts on a large scale is seen as a new development, it brings uncertainties on what the effects will be on the many dune functions. Furthermore different perceptions may be causing a problem for coastal managers on how to manage beach hut development. Some stakeholders are in it for financial reasons while others expect deterioration of the coastal dune functions. If coastal managers are unable to properly address this problem, the increasing trend of beach hut constructions may lead to increased conflicts between stakeholders and even deterioration of the valued coastal dune functions. In this thesis, I investigate how the different perspectives regarding beach hut development that are held by the different actors influence the actions taken by the governmental institutions. To this end I elicit the perspectives of relevant stakeholders.
Self-help Support Groups: Choices in Participation Among Women Facing Systemic Lupus Erythematosus (SLE)

An abstract of the thesis of Maria A. Pfeifer for the Master of Urban Studies presented December 2, 2005. Title: Self-Help Support Groups: Choices in Participation Among Women Facing Systemic Lupus Erythematosus (SLE) This research study explored the experiences of 19 women who had been diagnosed with, or were still seeking the diagnosis of SLE (lupus) and their decisions regarding support group participation. The aim of this study was to evaluate the variety of factors influencing their choices in types and sources of support, their coping strategies and the reasons behind their decisions to either choose or not choose lupus support groups as a viable support resource. Those women identified as support groups attendees recalled a more emotion-focused response to their diagnosis and showed stronger reliance on seeking emotional forms of support. Conversely, those women who chose not to participate in groups (non-attendees) utilized more problem-focused strategies when they received their news of the illness and indicated more reliance on instrumental forms of support. Additionally, the women who do not attend support groups did not seem to have more social support from outside sources, but did show a tendency to utilize relationship-focused coping more than other forms of coping strategies overall. Both groups showed a heavy reliance on their medical providers for both emotional and instrumental forms of support suggesting this source as an important factor in individual choices in coping strategies and support sources. The decisions to attend or not attend differed only in the strategies they relied on and specific group structure, timing and locations. The results of this study supports earlier research in the types and sources of social support used in adapting to a chronic illness. This study also encourages incorporating individual support services through medical providers and the development of programs that acknowledge individual coping and support needs.
Artificial Intelligence in International Arbitration – A Spotlight on the Options

Over the past several years, the arbitration lawyers and arbitrators have shown an inclination towards the shifting of traditional arbitral processes to artificial intelligence (AI). There are various tools through which AI has been introduced to the arbitral process. Predictive coding is one such method that is currently being employed in international arbitration. There have been discussions going on how arbitration can be benefited from the use of predictive coding and the possible influence it can have on the arbitral process from a dispute resolution standpoint. Despite the discussions on predictive coding and artificial intelligence in international arbitration, very limited literature is available on predictive coding and artificial intelligence for arbitration. Given the importance of predictive coding in international arbitration, this article is aimed at examining the use of predictive coding in international arbitration and how likely it is to influence e-discovery in international arbitration. Analysis of the current scenario indicates that the selection and scope of predictive coding are clearly to be decided by the parties and arbitrators from case-to-case basis.
Association between Lipoprotein(a) and diabetic nephropathy in patients with type 2 diabetes

Background Diabetic nephropathy (DN) is one of the most prevalent and severe microvascular complications of type 2 diabetes (T2DM). However, little is currently known about the pathogenesis and its associated risk factors in DN. The present study aims to investigate the potential risk factors of DN in patients with T2DM. Methods A total of 6,993 T2DM patients, including 5,089 participants with DN and 1,904 without DN, were included in this cross-sectional study. Comparisons between the two groups (DN vs. non-DN) were carried out using Student’s t-test, Mann-Whitney U-test, or Pearson’s Chi-squared test. Spearman’s correlation analyses were performed to assess the correlations of serum lipids and indicators of renal impairment. Logistic regression models were applied to assess the relationship between blood lipid indices and the presence of DN. Results T2DM patients with DN were older, and had a longer duration of diagnosed diabetes compared to those without DN. Of note, the DN patients also more likely develop metabolic disorders. Among all serum lipids, Lipoprotein(a) [Lp(a)] was the most significantly correlated indicators of renal impairment. Moreover, univariate logistic regression showed that elevated Lp(a) level was associated with an increased risk of DN. After adjusted for confounding factors, including age, gender, duration of T2DM, BMI, SBP, DBP and lipid-lowering drugs usage, Lp(a) level was independently positively associated with the risk of DN [odds ratio (OR):1.115, 95% confidence interval (CI): 1.079-1.151, P=6.06×10-11]. Conclusions Overall, we demonstrated that serum Lp(a) level was significantly positively associated with an increased risk of DN, indicating that Lp(a) may have the potential as a promising target for the diagnosis and treatment of diabetic nephropathy.
Glaucoma detection model by exploiting multi-region and multi-scan-pattern OCT images with dynamical region score

Currently, deep learning-based methods have achieved success in glaucoma detection. However, most models focus on OCT images captured by a single scan pattern within a given region, holding the high risk of the omission of valuable features in the remaining regions or scan patterns. Therefore, we proposed a multi-region and multi-scan-pattern fusion model to address this issue. Our proposed model exploits comprehensive OCT images from three fundus anatomical regions (macular, middle, and optic nerve head regions) being captured by four scan patterns (radial, volume, single-line, and circular scan patterns). Moreover, to enhance the efficacy of integrating features across various scan patterns within a region and multiple regional features, we employed an attention multi-scan fusion module and an attention multi-region fusion module that auto-assign contribution to distinct scan-pattern features and region features adapting to characters of different samples, respectively. To alleviate the absence of available datasets, we have collected a specific dataset (MRMSG-OCT) comprising OCT images captured by four scan patterns from three regions. The experimental results and visualized feature maps both demonstrate that our proposed model achieves superior performance against the single scan-pattern models and single region-based models. Moreover, compared with the average fusion strategy, our proposed fusion modules yield superior performance, particularly reversing the performance degradation observed in some models relying on fixed weights, validating the efficacy of the proposed dynamic region scores adapted to different samples. Moreover, the derived region contribution scores enhance the interpretability of the model and offer an overview of the model’s decision-making process, assisting ophthalmologists in prioritizing regions with heightened scores and increasing efficiency in clinical practice.
A whole new Mercury: MESSENGER reveals a dynamic planet at the last frontier of the inner solar system

The MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) mission yielded a wealth of information about the innermost planet. For the first time, visible images of the entire planet, absolute altimetry measurements and a global gravity field, measurements of Mercury's surface composition, magnetic field, exosphere, and magnetosphere taken over more than four Earth years are available. From these data, two overarching themes emerge. First, multiple data sets and modeling efforts point toward a dynamic ancient history. Signatures of graphite in the crust suggest solidification of an early magma ocean, image data show extensive volcanism and tectonic features indicative of subsequent global contraction, and low‐altitude measurements of magnetic fields reveal an ancient magnetic field. Second, the present‐day Mercury environment is far from quiescent. Convective motions in the outer core support a modern magnetic field whose strength and geometry are unique among planets with global magnetic fields. Furthermore, periodic and aperiodic variations in the magnetosphere and exosphere have been observed, some of which couple to the surface and the planet's deep interior. Finally, signatures of geologically recent volatile activity at the surface have been detected. Mercury's early history and its present‐day environment have common elements with the other inner solar system bodies. However, in each case there are also crucial differences and these likely hold the key to further understanding of Mercury and terrestrial planet evolution. MESSENGER's exploration of Mercury has enabled a new view of the innermost planet, and more importantly has set the stage for much‐needed future exploration.
Renal insufficiency in breast cancer patients; a review study

The latest statistics and estimations indicated that breast cancer occurs in one-fourth of women worldwide and is responsible for one in six cancer deaths among women. A bilateral relationship seems to exist between breast cancer pathophysiology and kidney failure. Consideration should be given to this relationship when selecting a treatment protocol. This paper reviews the association between these two factors in breast cancer patients. There are many aspects to consider in the association between breast cancer and renal insufficiency. A breast cancer patient with normal kidney function is at risk of developing kidney failure due to paraneoplastic syndromes, hypercalcemia, and in rare cases, tumor lysis syndrome (TLS). In defining the optimal treatment protocol for each breast cancer patient, clinicians should consider the patient’s basal glomerular filtration rate (GFR). Frequent renal clearance monitoring and taking immediate action at the time GFR begins to decrease will lower the rate of kidney failure in breast cancer patients. Lastly, patients with chronic kidney disease who are recently diagnosed with breast cancer may have higher morbidity and mortality compared to breast cancer patients with normal GFR. Further investigation is needed to lower morbidity and mortality in such patients.
Pengaruh Sarana Prasarana Dan Pemanfaatan Teknologi Informasi Dalam Pembelajaran Terhadap Mutu Pendidikan Di Sekolah Menengah Pertama Negeri Kabupaten Lombok Utara Tahun 2022

Penelitian ini dilakukan untuk menemukan pengaruh sarana prasarana dan pemanfaatan teknologi informasi dalam pembelajaran terhadap mutu pendidikan. Tujuan penelitian ini adalah untuk menganalisis dan menemukan perbedaan mutu pendidikan di SMP Negeri Kabupaten Lombok Utara yang memiliki perbedaan sarana prasarana dan pemanfaatan teknologi informasi dalam pembelajaran. Penelitian ini menggunakan pendekatan deskriptif kuantitatif dengan metode penelitian kausal komparatif. Populasi pada penelitian ini terdiri dari 270 orang guru ASN yang tersebar di 16 SMP Negeri Kabupaten Lombok Utara. Teknik pengambilan sampel menggunakan proportional random sampling pada tiap sub populasi dan sampel ditentukan secara random. Sampel pada penelitian ini berjumlah 160 orang guru. Uji hipotesis menggunakan Analisys of Varians (ANOVA). Hasil penelitian menunjukkan: 1) Sarana prasarana tergolong cukup (69,04%); 2) Pemanfaatan teknologi informasi dalam pembelajaran tergolong cukup (68,51%); 3) Mutu pendidikan tergolong baik (81,04%); 4) Sarana prasarana berpengaruh positif dan signifikan terhadap mutu pendidikan (p ≤ 0,005); 5) Pemanfaatan teknologi informasi dalam pembelajaran berpengaruh positif dan signifikan terhadap mutu pendidikan (p ≤ 0,005); dan 6) Tidak terdapat pengaruh interaksi sarana prasarana dan pemanfaatan teknologi informasi dalam pembelajaran terhadap peningkatan mutu pendidikan (p > 0,005).
Evaluation of effects of agri-environmental measures on rangeland degradation in two less favoured areas in Portugal

Evaluation of effects of agri-environmental measures on rangeland degradation in two less-favoured areas in Portugal Nadia Manuela Jones In the past decades there have been significant land use changes in Portugal. After the integration of Portugal in the EU, farmers have been able to benefit from EU policy measures, which were initially mainly aimed at supporting farmer’s income. It soon became apparent that these land use changes led to both intensification and abandonment of land, which were detrimental to the environment in various ways, in particular to higher soil erosion hazards and to an increased incidence of wildfire. The thesis assesses the impact of agro-environmental policies on agro-ecosystem goods and services and land degradation at farm and regional levels in Portugal. It identifies the main land use changes, provides insight in the role of past policy measures targeting the preservation of extensive grazing in marginal areas, and explores the options for the improvement of future policy measures. First an historical review is provided of land use changes in Portugal and their implications for land degradation and conservation. Thereafter an analysis is made of the farming systems in two less favoured areas in Centro and Alentejo regions, which showed an increasing focus on livestock and rangeland activities. Subsequently an assessment is made of the role of two specific EU agri-environmental measures (AEM) that preserve extensive grazing in these two less-favoured areas in Portugal. Thereby attention is paid to the uptake of these AEMs and to their effects on preserving reduced stocking rates and sufficient soil cover. An analysis is also made of the AEM payments and other EU subsidy flows, and their changes over the period 2005-2009, and of the effectiveness of the financial incentives offered through the two specific AEMS for preserving extensive grazing in the two research areas. Finally a normative analysis is made, whereby the impact of agri-environmental policy is targeted. Through scenario analysis of different combinations of policy measures for the two research areas, the impacts on extensive livestock farm production, soil erosion risk and wildfire hazard are assessed. A final discussion about the respective research results is presented in the synthesis of the thesis.
Prinsip Ubat-Ubatan Nabawi Menurut Mahmud Nazhim Al-Nasimi

Prophetic medicine presents various methods of treatment and disease prevention recommended by the Prophet Muhammad (peace be upon him). He is seen advising the use of specific natural ingredients such as honey, dates, and black seed (al-ḥabbat al-sawdāʾ) to his companions as a solution to their health issues. In recent times, some individuals have exploited the sanctity associated with the words of the Prophet Muhammad (peace be upon him) for the purpose of promoting their products containing ingredients mentioned in the hadith. Certain traders who sell these ingredients directly have also been observed to publicise their businesses using the same modus operandi. Moreover, some general statements from the sayings of the Prophet (peace be upon him) have been used indiscriminately to capture the interest of buyers without relying on the understanding of Islamic scholars and modern medical experts when dealing with relevant hadith. The purpose of this article is to highlight the fundamentals of prophetic medicine as described in Maḥmūd Nāẓim al-Nasīmī ‘s book al-Ṭibb al-Nabawī wa al-ꜥIlm al-Ḥadīth. The analysis is carried out through a deductive approach and a comparison of the principles he proposed with the commentary of Islamic scholars in their hadith lecture works. The study found that al-Nasīmī’s claims about prophetic medicine, like the idea that it includes both revelation and ijtihad (independent reasoning), are in line with what some early Islamic scholars thought.
The TGF-β Family in Glioblastoma

Members of the transforming growth factor β (TGF-β) family have been implicated in the biology of several cancers. In this review, we focus on the role of TGFβ and bone morphogenetic protein (BMP) signaling in glioblastoma. Glioblastoma (GBM) is the most common malignant brain tumor in adults; it presents at a median age of 64 years, but can occur at any age, including childhood. Unfortunately, there is no cure, and even patients undergoing current treatments (surgical resection, radiotherapy, and chemotherapy) have a median survival of 15 months. There is a great need to identify new therapeutic targets to improve the treatment of GBM patients. TGF-βs signaling promotes tumorigenesis in glioblastoma, while BMPs suppress tumorigenic potential by inducing tumor cell differentiation. In this review, we discuss the actions of TGF-βs and BMPs on cancer cells as well as in the tumor microenvironment, and their use in potential therapeutic intervention.
Emotion Recognition of Humans using modern technology of AI: A Survey

This comprehensive investigation and evaluation of the subject matter of emotion recognition is presented, focusing on the broader implications for society at large. The study utilizes a thorough analysis of scholarly literature and practical observations to establish a theoretical framework that facilitates comprehension of the topic under investigation. The findings have considerable implications for future investigations and pragmatic applications. Emotion recognition holds immense importance in diverse domains such as human-computer interaction and healthcare. The analysis of techniques used for emotional recognition includes facial expression assessment, speech patterns analysis, physiological signal interpretation, music perception, and written expression evaluation. The study presents a comprehensive overview of research methodologies commonly used in emotion recognition, discussing datasets, feature extraction techniques, and classification algorithms. The analysis of challenges and limitations pertaining to emotion recognition systems, including privacy concerns, is also discussed. Performance evaluation is analyzed through various methods, including machine assessment and self-report. The significance of continued investigation within the domains encompassing data integration across diverse modalities, the creation of robust classification algorithms, and the exploration of the intricate connection between the brain and affective states is underscored.
Investigating Self-supervised Learning for Predicting Stress and Stressors from Passive Sensing

The application of machine learning (ML) techniques for well-being tasks has grown in popularity due to the abundance of passively-sensed data generated by devices. However, the performance of ML models are often limited by the cost associated with obtaining ground truth labels and the variability of well-being annotations. Self-supervised representations learned from large-scale unlabeled datasets have been shown to accelerate the training process, with subsequent fine-tuning to the specific downstream tasks with a relatively small set of annotations. In this paper, we investigate the potential and effectiveness of self-supervised pre-training for well-being tasks, specifically predicting both workplace daily stress as well as the most impactful stressors. Through a series of experiments, we find that self-supervised methods are effective when predicting on unseen users, relative to supervised baselines. Scaling both data size and encoder depth, we observe the superior performance obtained by self-supervised methods, further showcasing their utility for well-being applications. In addition, we present future research directions and insights for applying self-supervised representation learning on well-being tasks.