Miracle-dz/UniFAR-FTUs huggingface dataset:
A Transfer Learning Based Model for Text Readability Assessment in German

Text readability assessment has a wide range of applications for different target people, from language learners to people with disabilities. The fast pace of textual content production on the web makes it impossible to measure text complexity without the benefit of machine learning and natural language processing techniques. Although various research addressed the readability assessment of English text in recent years, there is still room for improvement of the models for other languages. In this paper, we proposed a new model for text complexity assessment for German text based on transfer learning. Our results show that the model outperforms more classical solutions based on linguistic features extraction from input text. The best model is based on the BERT pre-trained language model achieved the Root Mean Square Error (RMSE) of 0.483.
Further results on the divisibility of $q$-trinomial coefficients

We study divisibility for the q -trinomial coeﬃcients τ 0 ( n, m, q ), T 0 ( n, m, q ) and T 1 ( n, m, q ), which were ﬁrst introduced by Andrews and Baxter. In particular, we completely determine τ 0 ( an, bn, q ), T 0 ( an, bn, q ) and T 1 ( an, bn, q ) modulo the square of the cyclotomic polynomial Φ n ( q ) for ( a, b ) = ( m, m − 1).
Sepiolite and ZIF-67 co-modified PAN/PVdF-HFP nanofiber separators for advanced Li-ion batteries

Electrospun PAN/PVdF-HFP membranes have the potential to be used as separators for Li-ion batteries owing to their good mechanical properties and high chemical stability. However, the application of PAN/PVdF-HFP separators has been hampered by their poor electrochemical performances. In this study, semi-aligned PAN/PVdF-HFP nanofiber separators have been fabricated by an electrospinning technology. Sepiolite and ZIF-67 co-modification was employed to enhance the physical properties of the PAN/PVdF-HFP separators. The test cells with the as-prepared composite separator showed better electrochemical performance than the commercial and raw PAN/PVdF-HFP separators.
Antibiotic stewardship to reduce inappropriate antibiotic prescribing in integrated academic health-system urgent care clinics

Abstract Objective: To develop and implement antibiotic stewardship activities in urgent care targeting non–antibiotic-appropriate acute respiratory tract infections (ARIs) that also reduces overall antibiotic prescribing and maintains patient satisfaction. Patients and setting: Patients and clinicians at the urgent care clinics of an integrated academic health system. Intervention and methods: The stewardship activities started in fiscal 2020 and included measure development, comparative feedback, and clinician and patient education. We measured antibiotic prescribing in fiscal years 2019, 2020, and 2021 for the stewardship targets, potential diagnosis-shifting visits, and overall. We also collected patient satisfaction data for ARI visits. Results: From FY19 to FY21, 576,609 patients made 1,358,816 visits to 17 urgent care clinics, including 105,781 visits for which stewardship measures were applied and 149,691 visits for which diagnosis shifting measures were applied. The antibiotic prescribing rate decreased for stewardship-measure visits from 34% in FY19 to 12% in FY21 (absolute change, −22%; 95% confidence interval [CI], −23% to −22%). The antibiotic prescribing rate decreased for diagnosis-shifting visits from 63% to 35% (−28%; 95% CI, −28% to −27%), and the antibiotic prescribing rate decreased overall from 30% to 10% (−20%; 95% CI, −20% to −20%). The patient satisfaction rate increased from 83% in FY19 to 89% in FY20 and FY21. There was no significant association between antibiotic prescribing rates of individual clinicians and ARI visit patient satisfaction. Conclusions: Although it was affected by the COVID-19 pandemic, an ambulatory antimicrobial stewardship program that focused on improving non–antibiotic-appropriate ARI prescribing was associated with decreased prescribing for (1) the stewardship target, (2) a diagnosis shifting measure, and (3) overall antibiotic prescribing. Patient satisfaction at ARI visits increased over time and was not associated with clinicians’ antibiotic prescribing rates.
Computer vision-based system for early diagnosis of stereoscopic vision alterations

ABSTRACT Stereopsis is the three-dimensional perception capability, which is possible when binocular vision is present. Development of binocular vision system ends around 7-year-old, and after this time brain connections are already set, therefore, it will be difficult to recover stereopsis. Early diagnosis of Stereoscopic Visual Alterations (SVA) in the childhood is paramount to receive an appropriate treatment as soon as possible. Currently, to detect SVA, ophthalmologists commonly carry out tests based on cards. All these tests are based on the random-dot-stereogram principle, with different seconds of arc images, which allows doctors to graduate stereopsis. Some limits of this tests have been identified, such as monocular clues (e.g., the contours of the objects), or the non-standardized range levels depending on the test. This paper presents a novel concept of measuring stereopsis based on computer vision techniques. The system detects SVA in patients and calculates the degree of the perceived depth. As early diagnosis of SVA may suppose a therapeutic possibility, this platform is aimed at children by using stereoscopic models with varied and attractive designs. To validate the proposal, an early-stage prototype has been implemented and an objective evaluation of the measurement accuracy and reliability has been carried out with satisfactory results.
Particpants' Proceedings on the Workshop: Types for Program Analysis

As a satellite meeting of the TAPSOFT'95 conference we organized a small workshop on program analysis. The title of the workshop, ``Types for Program Analysis´´, was motivated by the recent trend of letting the presentation and development of program analyses be influenced by annotated type systems, effect systems, and more general logical systems. The contents of the workshop was intended to be somewhat broader; consequently the call for participation listed the following areas of interest: - specification of specific analyses for programming languages, - the role of effects, polymorphism, conjunction/disjunction types, dependent types etc.in specification of analyses, - algorithmic tools and methods for solving general classes of type-based analyses, - the role of unification, semi-unification etc. in implementations of analyses, - proof techniques for establishing the safety of analyses, - relationship to other approaches to program analysis, including abstract interpretation and constraint-based methods, - exploitation of analysis results in program optimization and implementation. The submissions were not formally refereed; however each submission was read by several members of the program committee and received detailed comments and suggestions for improvement. We expect that several of the papers, in slightly revised forms, will show up at future conferences. The workshop took place at Aarhus University on May 26 and May 27 and lasted two half days.
QT-Routenet: Improved GNN generalization to larger 5G networks by fine-tuning predictions from queueing theory

– In order to promote the use of machine learning in 5G, the International Telecommunication Union (ITU) proposed in 2021 the second edition of the ITU AI/ML in 5G challenge, with over 1600 participants from 82 countries. This work details the second place solution overall, which is also the winning solution of the Graph Neural Networking Challenge 2021. We tackle the problem of generalization when applying a model to a 5G network that may have longer paths and larger link capacities than the ones observed in training. To achieve this, we propose to ﬁrst extract robust features related to Queueing Theory (QT), and then ﬁne-tune the analytical baseline prediction using a modiﬁcation of the Routenet Graph Neural Network (GNN) model. The proposed solution generalizes much better than simply using Routenet, and manages to reduce the analytical baseline’s 10.42 mean absolute percent error to 1.45 (1.27 with an ensemble). This suggests that making small changes to an approximate model that is known to be robust can be an eﬀective way to improve accuracy without compromising generalization.
Estimating Exposure to Information on Social Networks

This paper considers the problem of estimating exposure to information in a social network. Given a piece of information (e.g., a URL of a news article on Facebook, a hashtag on Twitter), our aim is to find the fraction of people on the network who have been exposed to it. The exact value of exposure to a piece of information is determined by two features: the structure of the underlying social network and the set of people who shared the piece of information. Often, both features are not publicly available (i.e., access to the two features is limited only to the internal administrators of the platform) and difficult to be estimated from data. As a solution, we propose two methods to estimate the exposure to a piece of information in an unbiased manner: a vanilla method which is based on sampling the network uniformly and a method which non-uniformly samples the network motivated by the Friendship Paradox. We provide theoretical results which characterize the conditions (in terms of properties of the network and the piece of information) under which one method outperforms the other. Further, we outline extensions of the proposed methods to dynamic information cascades (where the exposure needs to be tracked in real-time). We demonstrate the practical feasibility of the proposed methods via experiments on multiple synthetic and real-world datasets.
A single natural RNA modification can destabilize a U•A-T-rich RNA•DNA-DNA triple helix

Recent studies suggest noncoding RNAs interact with genomic DNA, forming RNA•DNA-DNA triple helices, as a mechanism to regulate transcription. One way cells could regulate the formation of these triple helices is through RNA modifications. With over 140 naturally occurring RNA modifications, we hypothesize that some modifications stabilize RNA•DNA-DNA triple helices while others destabilize them. Here, we focus on a pyrimidine-motif triple helix composed of canonical U•A-T and C•G-C base triples. We employed electrophoretic mobility shift assays and microscale thermophoresis to examine how 11 different RNA modifications at a single position in an RNA•DNA-DNA triple helix affect stability: 5-methylcytidine (m5C), 5-methyluridine (m5U or rT), 3-methyluridine (m3U), pseudouridine (Ψ), 4-thiouridine (s4U), N6-methyladenosine (m6A), inosine (I), and each nucleobase with 2′-O-methylation (Nm). Compared to the unmodified U•A-T base triple, some modifications have no significant change in stability (Um•A-T), some have ∼2.5-fold decreases in stability (m5U•A-T, Ψ•A-T, and s4U•A-T), and some completely disrupt triple helix formation (m3U•A-T). To identify potential biological examples of RNA•DNA-DNA triple helices controlled by an RNA modification, we searched RMVar, a database for RNA modifications mapped at single-nucleotide resolution, for lncRNAs containing an RNA modification within a pyrimidine-rich sequence. Using electrophoretic mobility shift assays, the binding of DNA-DNA to a 22-mer segment of human lncRNA Al157886.1 was destabilized by ∼1.7-fold with the substitution of m5C at known m5C sites. Therefore, the formation and stability of cellular RNA•DNA-DNA triple helices could be influenced by RNA modifications.
3D Concept Grounding on Neural Fields

In this paper, we address the challenging problem of 3D concept grounding (i.e. segmenting and learning visual concepts) by looking at RGBD images and reasoning about paired questions and answers. Existing visual reasoning approaches typically utilize supervised methods to extract 2D segmentation masks on which concepts are grounded. In contrast, humans are capable of grounding concepts on the underlying 3D representation of images. However, traditionally inferred 3D representations (e.g., point clouds, voxelgrids, and meshes) cannot capture continuous 3D features flexibly, thus making it challenging to ground concepts to 3D regions based on the language description of the object being referred to. To address both issues, we propose to leverage the continuous, differentiable nature of neural fields to segment and learn concepts. Specifically, each 3D coordinate in a scene is represented as a high-dimensional descriptor. Concept grounding can then be performed by computing the similarity between the descriptor vector of a 3D coordinate and the vector embedding of a language concept, which enables segmentations and concept learning to be jointly learned on neural fields in a differentiable fashion. As a result, both 3D semantic and instance segmentations can emerge directly from question answering supervision using a set of defined neural operators on top of neural fields (e.g., filtering and counting). Experimental results show that our proposed framework outperforms unsupervised/language-mediated segmentation models on semantic and instance segmentation tasks, as well as outperforms existing models on the challenging 3D aware visual reasoning tasks. Furthermore, our framework can generalize well to unseen shape categories and real scans.
FD-GATDR: A Federated-Decentralized-Learning Graph Attention Network for Doctor Recommendation Using EHR

In the past decade, with the development of big data technology, an increasing amount of patient information has been stored as electronic health records (EHRs). Leveraging these data, various doctor recommendation systems have been proposed. Typically, such studies process the EHR data in a flat-structured manner, where each encounter was treated as an unordered set of features. Nevertheless, the heterogeneous structured information such as service sequence stored in claims shall not be ignored. This paper presents a doctor recommendation system with time embedding to reconstruct the potential connections between patients and doctors using heterogeneous graph attention network. Besides, to address the privacy issue of patient data sharing crossing hospitals, a federated decentralized learning method based on a minimization optimization model is also proposed. The graph-based recommendation system has been validated on a EHR dataset. Compared to baseline models, the proposed method improves the AUC by up to 6.2%. And our proposed federated-based algorithm not only yields the fictitious fusion center's performance but also enjoys a convergence rate of O(1/T).
DLCSS: Dynamic Longest Common Subsequences

Autonomous driving is a key technology towards a brighter, more sustainable future. To enable such a future, it is necessary to utilize autonomous vehicles in shared mobility models. However, to evaluate, whether two or more route requests have the potential for a shared ride, is a compute-intensive task, if done by rerouting. In this work, we propose the Dynamic Longest Common Subsequences algorithm for fast and cost-efficient comparison of two routes for their compatibility, dynamically only incorporating parts of the routes which are suited for a shared trip. Based on this, one can also estimate, how many autonomous vehicles might be necessary to fulfill the local mobility demands. This can help providers to estimate the necessary fleet sizes, policymakers to better understand mobility patterns and cities to scale necessary infrastructure.
Structure PLP-SLAM: Efficient Sparse Mapping and Localization using Point, Line and Plane for Monocular, RGB-D and Stereo Cameras

This paper presents a visual SLAM system that uses both points and lines for robust camera localization, and simultaneously performs a piece-wise planar reconstruction (PPR) of the environment to provide a structural map in real-time. One of the biggest challenges in parallel tracking and mapping with a monocular camera is to keep the scale consistent when reconstructing the geometric primitives. This further introduces difficulties in graph optimization of the bundle adjustment (BA) step. We solve these problems by proposing several run-time optimizations on the reconstructed lines and planes. Our system is able to run with depth and stereo sensors in addition to the monocular setting. Our proposed SLAM tightly incorporates the semantic and geometric features to boost both frontend pose tracking and backend map optimization. We evaluate our system exhaustively on various datasets, and show that we outperform state-of-the-art methods in terms of trajectory precision. The code of PLP-SLAM has been made available in open-source for the research community (https://github.com/PeterFWS/Structure-PLP-SLAM).
On Post-Quantum Perfect Forward Secrecy in 6G

—The standardized Authentication and Key Agreement protocol for 5G networks (also known as 5G AKA) has several security and privacy vulnerabilities. For example, the 5G AKA does not undertake perfect forward secrecy . In this paper, we propose a novel quantum-safe authentication and key agreement protocol for future generation of mobile communication networks (6G). Our protocol has several privacy and security properties, e.g., it is resistant against linkability attacks and it is quantum-safe. We use the Kyber algorithm, chosen by NIST to become a standard and NIST Round 4 candidate algorithms to analyze the performance of our protocol. The results for communication and computation costs show that utilizing our protocol is feasible in practice. We further prove the security of our protocol by utilizing the well-known formal veriﬁer ProVerif .
@Western Scholarship@Western

Prostate segmentation on T2w MRI is important for several diagnostic and therapeutic procedures for prostate cancer. Manual segmentation is time-consuming, labor-intensive, and subject to high interobserver variability. This study investigated the suitability of computer-assisted segmentation algorithms for clinical translation, based on measurements of interoperator variability and measurements of the editing time required to yield clinically acceptable segmentations. A multioperator pilot study was performed under three pre-and postediting conditions: manual, semiautomatic, and automatic segmentation. We recorded the required editing time for each segmentation and measured the editing magnitude based on five different spatial metrics. We recorded average editing times of 213, 328
Microscopic dynamics and Bose–Einstein condensation in liquid helium

We review fundamental problems involved in liquid theory including both classical and quantum liquids. Understanding classical liquids involves exploring details of their microscopic dynamics and its consequences. Here, we apply the same general idea to quantum liquids. We discuss momentum condensation in liquid helium which is consistent with microscopic dynamics in liquids and high mobility of liquid atoms. We propose that mobile transit atoms accumulate in the finite-energy state where the transit speed is close to the speed of sound. In this state, the transit energy is close to the oscillatory zero-point energy. In momentum space, the accumulation operates on a sphere with the radius set by interatomic spacing and corresponds to zero net momentum. We show that this picture is supported by experiments, including the measured kinetic energy of helium atoms below the superfluid transition and sharp peaks of scattered intensity at predicted energy. We discuss the implications of this picture including the macroscopic wave function and superfluidity.
The miniJPAS survey: The role of group environment in quenching the star formation

The miniJPAS survey has observed $\sim 1$ deg$^2$ on the AEGIS field with 60 bands (spectral resolution of $R \sim 60$) in order to demonstrate the capabilities of the Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS) that will map $\sim 8000$ deg$^2$ of the northern sky in the next years. This paper shows the power of J-PAS to detect low mass groups and characterise their galaxy populations up to $z \sim 1$. We use the spectral energy distribution fitting code BaySeAGal to derive the stellar population properties of the galaxy members in 80 groups at $z \leq 0.8$ previously detected by the AMICO code, as well as for a galaxy field sample retrieved from the whole miniJPAS sample. We identify blue, red, quiescent, and transition galaxy populations through their rest-frame (extinction corrected) colour, stellar mass ($M_\star$) and specific star formation rate. We measure their abundance as a function of $M_\star$ and environment. We find: (i) The fraction of red and quiescent galaxies in groups increases with $M_\star$ and it is always higher in groups than in the field. (ii) The quenched fraction excess (QFE) in groups strongly increases with $M_\star$, (from a few percent to higher than 60% in the mass range $10^{10} - 3 \times 10 ^{11}$ $M_\odot$. (iii) The abundance excess of transition galaxies in groups shows a modest dependence with $M_\star$ (iv) The fading time scale is very short ($<1.5$ Gyr), indicating that the star formation declines very rapidly in groups. (v) The evolution of the galaxy quenching rate in groups shows a modest but significant evolution since $z\sim0.8$, compatible with an evolution with constant $QFE=0.4$, previously measured for satellites in the nearby Universe, and consistent with a scenario where the low-mass star-forming galaxies in clusters at $z= 1-1.4$ are environmentally quenched.
A Near-Optimal Primal-Dual Method for Off-Policy Learning in CMDP

As an important framework for safe Reinforcement Learning, the Constrained Markov Decision Process (CMDP) has been extensively studied in the recent literature. However, despite the rich results under various on-policy learning settings, there still lacks some essential understanding of the offline CMDP problems, in terms of both the algorithm design and the information theoretic sample complexity lower bound. In this paper, we focus on solving the CMDP problems where only offline data are available. By adopting the concept of the single-policy concentrability coefficient $C^*$, we establish an $\Omega\left(\frac{\min\left\{|\mathcal{S}||\mathcal{A}|,|\mathcal{S}|+I\right\} C^*}{(1-\gamma)^3\epsilon^2}\right)$ sample complexity lower bound for the offline CMDP problem, where $I$ stands for the number of constraints. By introducing a simple but novel deviation control mechanism, we propose a near-optimal primal-dual learning algorithm called DPDL. This algorithm provably guarantees zero constraint violation and its sample complexity matches the above lower bound except for an $\tilde{\mathcal{O}}((1-\gamma)^{-1})$ factor. Comprehensive discussion on how to deal with the unknown constant $C^*$ and the potential asynchronous structure on the offline dataset are also included.