Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing Lexical and Syntactic Comprehension in Deaf Signing Adults

Abstract Among the existing sign language assessment tools, only a small number can be used in clinical settings. This contribution aims at presenting three comprehension assessment tests (two lexical and one syntactic) that offer a solid basis to build tools to assess language impairments in deaf signing adults. We provide the material and guidelines, based on psychometric analyses of the items, to make these tests suitable for clinical assessment. They are available for French Sign Language and Italian Sign Language. So far, the three tests were administered to three groups of deaf participants based on age of exposure (AoE) to sign language: native (AoE from birth), early (AoE = from 1 to 5 years), and late (AoE = from 6 to 15 years) signers. The results showed that the three tests are easy for the typical deaf signing population, and therefore, they can be adapted into tests that assess a deaf signing population with language impairments. Moreover, the results of the syntactic test reveal a categorial difference between native and non-native signers and therefore show the need for baselines that mirror the effect of AoE to sign language when assessing language competence, in particular in clinical assessment.
Risk factors for adverse outcomes in twin pregnancies: a narrative review

Abstract Purpose Globally, the incidence of twin pregnancies is rising owing to the use of assisted reproductive technologies (ART), emigration and deferment of pregnancy until advanced maternal age (AMA). While twin pregnancies have higher absolute risks of adverse outcomes, including miscarriage, stillbirth, neonatal death and preterm delivery, the impact of specific exposures and risk factors related to these outcomes may differ between twin pregnancies and singleton pregnancies. Regarding modifiable factors, data are sometimes based on evidence extrapolated from singleton or whole obstetric populations. Therefore, targeted evidence is required to provide care tailored to twin pregnancies to prevent adverse outcomes. We aimed to comprehensively review the association between different risk factors and adverse outcomes in twin pregnancies, including data on chorionicity, and to compare these to singletons. Materials and methods This review examines the risks associated with chorionicity, AMA, body mass index (BMI), socioeconomic and ethnic inequalities, maternal smoking, use of ART, maternal perception of fetal movement, and maternal comorbidities, including hypertensive disorders of pregnancy (HDP) and gestational diabetes mellitus (GDM). Adverse outcomes reported were preterm birth, admission to the neonatal intensive care unit (NICU), stillbirth and neonatal mortality. As such, fetal mortality and morbidity will be under-represented, as pregnancy loss before 22–24 weeks is omitted. Results Monochorionicity increases the risk of stillbirth, NICU admission, and preterm delivery in twin pregnancy. AMA predisposes twin pregnancies to higher risks of mortality, admission to the NICU, and preterm birth than singleton pregnancies do. Conversely, the impact of BMI, socioeconomic inequalities, smoking, ART, and HDP on adverse outcomes appears to be lower in twin pregnancies than in singleton pregnancies. This attenuation might be explained by the higher baseline risk of adverse outcomes such as preterm birth in twin pregnancies. Some exposures, such as ART use and GDM, appear to be “protective” against perinatal mortality in twin pregnancies, despite being established risk factors for adverse outcomes in singleton pregnancies, potentially related to access to specialist care. There is a paucity of evidence available to counsel mothers of twin pregnancies regarding reduced fetal movement. Conclusions Overall, the risk factors for adverse pregnancy outcomes differ between twin and singleton pregnancies. This highlights the need for further studies to examine the association between risk factors and adverse outcomes in twin pregnancies. The resulting data would facilitate tailored guidance for twin pregnancies, contribute to improved antenatal care, and inform wider public health strategies.
Symptoms of posttraumatic stress disorder and adjustment disorder in hematological cancer patients with different treatment regimes

Abstract Background Symptoms of posttraumatic stress disorder (PTSD) and adjustment disorder (AjD) are common in hematological cancer patients as they face severe stressors during their serious disease and often intensive treatment, such as stem cell transplantation (SCT). Aims of the present study were to provide frequency and risk factors for PTSD and AjD based on updated diagnostic criteria that are lacking to date. Material and methods In a cross-sectional study, hematological cancer patients were assessed for stressor-related symptoms via validated self-report questionnaires based on updated criteria for PTSD (PCL-5) and AjD (ADMN-20). Frequency and symptom severity were estimated among the total sample and SCT subgroups (allogeneic, autologous, no SCT). SCT subgroups were compared using Chi-squared-tests and ANOVAs. Linear regression models investigated sociodemographic and medical factors associated with symptomatology. Results In total, 291 patients were included (response rate: 58%). 26 (9.3%), 66 (23.7%) and 40 (14.2%) patients met criteria for cancer-related PTSD, subthreshold PTSD and AjD, respectively. Symptom severity and frequency of criteria-based PTSD and AjD did not differ between SCT subgroups (all p > 0.05). Factors associated with elevated symptomatology were younger age (PTSD: p < 0.001; AjD: p = 0.02), physical comorbidity (PTSD: p < 0.001; AjD: p < 0.001) and active disease (PTSD: p = 0.12; AjD: p = 0.03). Conclusion Based on new criteria, a considerable part of hematological cancer patients reports PTSD and AjD symptoms. Younger patients and patients with physical symptom burden might be particularly at risk and need to be monitored closely to enable effective treatment at an early stage.
Lookbehind-SAM: k steps back, 1 step forward

Sharpness-aware minimization (SAM) methods have gained increasing popularity by formulating the problem of minimizing both loss value and loss sharpness as a minimax objective. In this work, we increase the efficiency of the maximization and minimization parts of SAM's objective to achieve a better loss-sharpness trade-off. By taking inspiration from the Lookahead optimizer, which uses multiple descent steps ahead, we propose Lookbehind, which performs multiple ascent steps behind to enhance the maximization step of SAM and find a worst-case perturbation with higher loss. Then, to mitigate the variance in the descent step arising from the gathered gradients across the multiple ascent steps, we employ linear interpolation to refine the minimization step. Lookbehind leads to a myriad of benefits across a variety of tasks. Particularly, we show increased generalization performance, greater robustness against noisy weights, as well as improved learning and less catastrophic forgetting in lifelong learning settings.
Deep Dive into the Language of International Relations: NLP-based Analysis of UNESCO’s Summary Records

Cultural heritage is an arena of international relations that interests all states worldwide. The inscription process on the UNESCO World Heritage List and the UNESCO Representative List of the Intangible Cultural Heritage of Humanity often leads to tensions and conflicts among states. This research addresses these challenges by developing automatic tools that provide valuable insights into the decision-making processes regarding inscriptions to the two lists mentioned above. We propose innovative topic modelling and tension detection methods based on UNESCO's summary records. Our analysis achieved a commendable accuracy rate of 72% in identifying tensions. Furthermore, we have developed an application tailored for diplomats, lawyers, political scientists, and international relations researchers that facilitates the efficient search of paragraphs from selected documents and statements from specific speakers about chosen topics. This application is a valuable resource for enhancing the understanding of complex decision-making dynamics within international heritage inscription procedures.
Simulation–extrapolation for bias correction with exposure uncertainty in radiation risk analysis utilizing grouped data

In observational epidemiological studies, the exposure that is received by an individual often cannot be precisely observed, resulting in measurement error, and a common approach to dealing with measurement error is regression calibration (RC). Use of RC, which requires assumptions about the distribution of unknown error‐free (true) variables, leads to concern about the possibility of bias due to misspecification of that distribution. The simulation–extrapolation (SIMEX) method, in contrast, does not require a distributional assumption. However, analyses of large cohorts may be performed by using grouped or person‐year data, and application of SIMEX to grouped data is not straightforward, particularly when there is a mixture of classical and Berkson measurement errors. We compared RC and SIMEX with grouped data analyses to assess robustness of the RC method to misspecification of the true dose distribution. We also applied SIMEX assuming mixtures of classical and Berkson errors and compared the results with those obtained by using RC for classical error only. SIMEX had less bias than RC and performed well regardless of the true dose distribution, whereas RC based on a misspecified true dose distribution showed greater bias than when based on the correctly specified true dose distribution.
A symmetry algebra in double-scaled SYK

The double-scaled limit of the Sachdev-Ye-Kitaev (SYK) model takes the number of fermions and their interaction number to infinity in a coordinated way. In this limit, two entangled copies of the SYK model have a bulk description of sorts known as the “chord Hilbert space”. We analyze a symmetry algebra acting on this Hilbert space, generated by the two Hamiltonians together with a two-sided operator known as the chord number. This algebra is a deformation of the JT gravitational algebra, and it contains a subalgebra that is a deformation of the \mathfrak{sl}_2𝔰𝔩2 near-horizon symmetries. The subalgebra has finite-dimensional unitary representations corresponding to matter moving around in a discrete Einstein-Rosen bridge. In a semiclassical limit the discreteness disappears and the subalgebra simplifies to \mathfrak{sl}_2𝔰𝔩2, but with a non-standard action on the boundary time coordinate. One can make the action of \mathfrak{sl}_2𝔰𝔩2 algebra more standard at the cost of extending the boundary circle to include some “fake” portions. Such fake portions also accommodate certain subtle states that survive the semi-classical limit, despite oscillating on the scale of discreteness. We discuss applications of this algebra, including sub-maximal chaos, the traversable wormhole protocol, and a two-sided OPE.
Continual Learning in Predictive Autoscaling

Predictive Autoscaling is used to forecast the workloads of servers and prepare the resources in advance to ensure service level objectives (SLOs) in dynamic cloud environments. However, in practice, its prediction task often suffers from performance degradation under abnormal traffics caused by external events (such as sales promotional activities and applications' re-configurations), for which a common solution is to re-train the model with data of a long historical period, but at the expense of high computational and storage costs. To better address this problem, we propose a replay-based continual learning method, i.e., Density-based Memory Selection and Hint-based Network Learning Model (DMSHM), using only a small part of the historical log to achieve accurate predictions. First, we discover the phenomenon of sample overlap when applying replay-based continual learning in prediction tasks. In order to surmount this challenge and effectively integrate new sample distribution, we propose a density-based sample selection strategy that utilizes kernel density estimation to calculate sample density as a reference to compute sample weight, and employs weight sampling to construct a new memory set. Then we implement hint-based network learning based on hint representation to optimize the parameters. Finally, we conduct experiments on public and industrial datasets to demonstrate that our proposed method outperforms state-of-the-art continual learning methods in terms of memory capacity and prediction accuracy. Furthermore, we demonstrate remarkable practicability of DMSHM in real industrial applications.
Micrometer-size spatial superpositions for the QGEM protocol via screening and trapping

The quantum gravity-induced entanglement of masses (QGEM) protocol for testing quantum gravity using entanglement witnessing utilizes the creation of spatial quantum superpositions of two neutral, massive matter-wave interferometers kept adjacent to each other, separated by a distance d. The mass and the spatial superposition should be such that the two quantum systems can entangle solely via the quantum nature of gravity. Despite being charge-neutral, there are many electromagnetic backgrounds that can also entangle the systems, such as the dipole-dipole interaction, and the Casimir-Polder interaction. To minimize electromagnetic-induced interactions between the masses it is pertinent to isolate the two superpositions by a conducting plate. However, the conducting plate will also exert forces on the masses and hence the trajectories of the two superpositions would be affected. To minimize this effect, we propose to trap the two interferometers such that the trapping potential dominates over the attraction between the conducting plate and the matter-wave interferometers. The superpositions can still be created via the Stern-Gerlach effect in the direction parallel to the plate, where the trapping potential is negligible. The combination of trapping and shielding provides a better parameter space for the parallel configuration of the experiment, where the requirement on the size of the spatial superposition, to witness the entanglement between the two masses purely due to their quantum nature of gravity, decreases by at least two orders of magnitude as compared to the original protocol paper.
Quantum electrodynamic corrections to cyclotron states in a Penning trap

We analyze the leading and higher-order quantum electrodynamic corrections to the energy levels for a single electron bound in a Penning trap, including the Bethe logarithm correction due to virtual excitations of the reference quantum cyclotron state. The effective coupling parameter alpha_c in the Penning trap is identified as the square root of the ratio of the cyclotron frequency, converted to an energy via multiplication by the Planck constant, to the electron rest mass energy. We find a large, state-independent, logarithmic one-loop self-energy correction of order alpha alpha_c^4 m c^2 ln(alpha_c^(-2)), where m is the electron rest mass and c is the speed of light. Furthermore, we find a state-independent"trapped"Bethe logarithm. We also obtain a state-dependent higher-order logarithmic self-energy correction of order alpha alpha_c^6 m c^2 ln(alpha_c^(-2)). In the high-energy part of the bound-state self energy, we need to consider terms with up to six magnetic interaction vertices inside the virtual photon loop.
Mirror symmetric Gamma conjecture for Fano and Calabi-Yau manifolds

The mirror symmetric Gamma conjecture roughly speaking says that the Gamma class of a manifold determines the asymptotics of (exponential) periods of the mirror. We recast the method in [Iri11] in a more general context and show that the mirror symmetric Gamma conjecture for a Fano manifold F implies, via Laplace transformation, that for the total space K_F of the canonical bundle or for anticanonical sections in F. More generally, we discuss the mirror symmetric Gamma conjecture for the total space of a sum of anti-nef line bundles over F or for nef complete intersections in F.
Resume Evaluation through Latent Dirichlet Allocation and Natural Language Processing for Effective Candidate Selection

In this paper, we propose a method for resume rating using Latent Dirichlet Allocation (LDA) and entity detection with SpaCy. The proposed method first extracts relevant entities such as education, experience, and skills from the resume using SpaCy's Named Entity Recognition (NER). The LDA model then uses these entities to rate the resume by assigning topic probabilities to each entity. Furthermore, we conduct a detailed analysis of the entity detection using SpaCy's NER and report its evaluation metrics. Using LDA, our proposed system breaks down resumes into latent topics and extracts meaningful semantic representations. With a vision to define our resume score to be more content-driven rather than a structure and keyword match driven, our model has achieved 77% accuracy with respect to only skills in consideration and an overall 82% accuracy with all attributes in consideration. (like college name, work experience, degree and skills)
Gastrointestinal Mucosal Problems Classification with Deep Learning

Gastrointestinal mucosal changes can cause cancers after some years and early diagnosing them can be very useful to prevent cancers and early treatment. In this article, 8 classes of mucosal changes and anatomical landmarks including Polyps, Ulcerative Colitis, Esophagitis, Normal Z-Line, Normal Pylorus, Normal Cecum, Dyed Lifted Polyps, and Dyed Lifted Margin were predicted by deep learning. We used neural networks in this article. It is a black box artificial intelligence algorithm that works like a human neural system. In this article, Transfer Learning (TL) based on the Convolutional Neural Networks (CNNs), which is one of the well-known types of neural networks in image processing is used. We compared some famous CNN architecture including VGG, Inception, Xception, and ResNet. Our best model got 93% accuracy in test images. At last, we used our model in some real endoscopy and colonoscopy movies to classify problems.
Single Proxy Synthetic Control

Synthetic control methods are widely used to estimate the treatment effect on a single treated unit in time-series settings. A common approach for estimating synthetic controls is to regress the treated unit's pre-treatment outcome on those of untreated units via ordinary least squares. However, this approach can perform poorly if the pre-treatment fit is not near perfect, whether the weights are normalized or not. In this paper, we introduce a single proxy synthetic control approach, which views the outcomes of untreated units as proxies of the treatment-free potential outcome of the treated unit, a perspective we leverage to construct a valid synthetic control. Under this framework, we establish alternative identification and estimation methodologies for synthetic controls and for the treatment effect on the treated unit. Notably, unlike a proximal synthetic control approach which requires two types of proxies for identification, ours relies on a single type of proxy, thus facilitating its practical relevance. Additionally, we adapt a conformal inference approach to perform inference about the treatment effect, obviating the need for a large number of post-treatment data. Lastly, our framework can accommodate time-varying covariates and nonlinear models. We demonstrate the proposed approach in a simulation study and a real-world application.
A One-Parameter Diagnostic Classification Model with Familiar Measurement Properties

Diagnostic classification models (DCMs) are psychometric models designed to classify examinees according to their proficiency or non-proficiency of specified latent characteristics. These models are well-suited for providing diagnostic and actionable feedback to support formative assessment efforts. Several DCMs have been developed and applied in different settings. This study proposes a DCM with functional form similar to the 1-parameter logistic item response theory model. Using data from a large-scale mathematics education research study, we demonstrate that the proposed DCM has measurement properties akin to the Rasch and 1-parameter logistic item response theory models, including test score sufficiency, item-free and person-free measurement, and invariant item and person ordering. We discuss the implications and limitations of these developments, as well as directions for future research.
A hybrid approach for improving U-Net variants in medical image segmentation

Medical image segmentation is vital to the area of medical imaging because it enables professionals to more accurately examine and understand the information offered by different imaging modalities. The technique of splitting a medical image into various segments or regions of interest is known as medical image segmentation. The segmented images that are produced can be used for many different things, including diagnosis, surgery planning, and therapy evaluation. In initial phase of research, major focus has been given to review existing deep-learning approaches, including researches like MultiResUNet, Attention U-Net, classical U-Net, and other variants. The attention feature vectors or maps dynamically add important weights to critical information, and most of these variants use these to increase accuracy, but the network parameter requirements are somewhat more stringent. They face certain problems such as overfitting, as their number of trainable parameters is very high, and so is their inference time. Therefore, the aim of this research is to reduce the network parameter requirements using depthwise separable convolutions, while maintaining performance over some medical image segmentation tasks such as skin lesion segmentation using attention system and residual connections.
Synthesis of possible room temperature superconductor LK-99: Pb9Cu(PO4)6O

The quest for room temperature superconductors has been teasing scientists and physicists, since its inception in 1911 itself. Several assertions have already been made about room temperature superconductivity, but have never been verified or reproduced across the labs. The cuprates were the earliest high transition temperature (T c) superconductors, and it seems that copper has done the magic once again. In July 2023, a Korean group synthesized a lead apatite based compound LK-99, showing a T c of above 400 K (Lee et al 2023 arXiv: 2307.12008; 2023 arXiv: 2307.12037; Lee et al 2023 J. Korean Cryst. Growth Cryst. 33 61). The signatures of superconductivity in the compound are very promising, in terms of resistivity (ρ = 0) and diamagnetism at T c. Although, the heat capacity (C p) did not show the obvious transition at T c. Inspired by the interesting claims of the above room temperature superconductivity in LK-99, in this article, we report the synthesis of polycrystalline samples of LK-99, by following the same heat treatment as reported in Lee et al (2023 arXiv: 2307.12008; 2023 arXiv: 2307.12037) using a two-step precursor method. The phase is confirmed through x-ray diffraction measurements, performed after each heat treatment. The room temperature diamagnetism is not evidenced by the levitation of a permanent magnet over the sample or vice versa. The isothermal magnetization measurement at 280 K shows that as synthesized sample of LK-99 is paramagnetic. Further measurements for the confirmation of bulk superconductivity in variously synthesized samples are underway. Our results on the present LK-99 sample, synthesized at 925 °C, as of now do not confirm the appearance of bulk superconductivity at room temperature. Further studies with different heat treatments are underway.