Miracle-dz/UniFAR-FTUs huggingface dataset:
The retrograde continence enema in children with spina bifida: not as effective as first thought

Aims: To investigate the effectiveness of Peristeen retrograde continence enema (RCE) in the management of faecal incontinence in children with spina bifida. Methods: We identified an homogenous group of spina bifida patients in whom RCE was initiated (01/2006 – 07/2013). Confidential assessments included: (1) Fecal Incontinence Quality Of Life (FIQOL), (2) St Marks Faecal Incontinence score, (3) Cleveland Clinic Constipation score, and, (4) Neurogenic Bowel Dysfunction score. Results: 11/20 patients (mean age 14.5 ± 5.3 years) were male. 9/20 patients were still using RCE (mean follow-up 4.1 years). Three patients ceased RCE within ten days, six after 4-12 months, and two after 36-48 months. Reasons for cessation included balloon difficulties (n = 4), procedure deemed too difficult (n = 4), and pain (n =3). There were no differences between the groups in length of training time for technique, instillate fluid/volume used, and time taken to perform RCE. There were no differences between the groups for quality of life, faecal incontinence or constipation scores. Conclusions: We demonstrated a high rate of cessation with the RCE in patients with spina bifida. This could not be explained by associated
Advantages of visible-band spectral remote sensing at both satellite and near-surface scales for monitoring the seasonal dynamics of GPP in a Japanese larch forest (Special issue: Remote sensing and GIS research group)

Remotely sensed vegetation indices such as the normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI) have been used to scale up flux-based gross primary production (GPP) measurements. Recently, the use of visible-band (VIS) indices for estimation of GPP has been proposed, and VIS_indices derived from digital cameras have been used for detecting phenological changes. To confirm the utility of remotely sensed VIS_indices for the evaluation of GPP in a Japanese larch forest, we investigated the relationships between flux-based GPP measurements and indices derived from both moderate resolution imaging spectroradiometer (MODIS) data and tower-mounted digital camera images. We evaluated the suitability of both traditional (NDVI and EVI) and VIS_indices (the greenred vegetation index (GRVI) and green ratio (GR)) at both satellite and near-surface scales for GPP estimation. We also used the MODIS data to evaluate the sensitivity of the indices to the effects of a severe forest disturbance. The results showed that VIS_indices had several advantages over the traditional indices: (1) seasonal variations in VIS_indices were more strongly correlated with GPP variations; (2) the vegetation growing season could be easily discriminated from the winter dormant period, because ground surface conditions affect VIS_indices less than they affect traditional indices; (3) the seasonal dynamics of vegetation could be determined at a satellite scale from MODIS data, and possibly even at a canopy scale from digital camera images; and (4) inter-annual variations of VIS_indices were likely to be more sensitive to vegetation changes after a disturbance. These results demonstrate the utility of VIS_indices for estimating GPP at satellite scales and possibly at the canopy scale. We suggest that multi-scale visible-band remote sensing could help our understanding of the ecosystem by improving the temporal and spatial resolutions of satellite data.
Re: Treatment outcomes for patients with metastatic castrate-resistant prostate cancer following docetaxel for hormone sensitive disease.

Methods: Retrospective analysis of patients with mHSPC treated with one or more cycles of D-ADT who were identified from a prospectively maintained multisite prostate cancer database of patients treated in a community or academic centre setting in Australia. The primary endpoint of this study was first line time to treatment failure (1L TTF) for subsequent treatment of metastatic Castrate Resistant Prostate Cancer (mCRPC), with secondary endpoints of PSA reduction >50% and first line time to treatment failure (1L TTF)
Development and initial validation of prescribing quality indicators for patients with chronic kidney disease.

BACKGROUND Quality assessment is a key element for improving the quality of care. Currently, a comprehensive indicator set for measuring the quality of medication treatment in patients with chronic kidney disease (CKD) is lacking. Our aim was to develop and validate a set of prescribing quality indicators (PQIs) for CKD care, and to test the feasibility of applying this set in practice. METHODS Potential indicators were based on clinical practice guidelines and evaluated using the RAND/UCLA Appropriateness Method. This is a structured process in which an expert panel assesses the validity of the indicators. Feasibility was tested in a Dutch primary care database including >4500 diabetes patients with CKD. RESULTS An initial list of 22 PQIs was assessed by 12 experts. After changing 10 PQIs, adding 2 and rejecting 8, a final list of 16 indicators was accepted by the expert panel as valid. These PQIs focused on the treatment of hypertension, albuminuria, mineral and bone disorder, statin prescribing and possible unsafe medication. The indicators were successfully applied to measure treatment quality in the primary care database, but for some indicators the number of eligible patients was too small for reliable calculation. Results showed that there was room for improvement in the treatment quality of this population. CONCLUSIONS We developed a set of 16 PQIs for measuring the quality of treatment in CKD patients, which had sufficient content and face validity as well as operational feasibility. These PQIs can be used to point out priority areas for improvement.
Efficient Multiplicative-to-Additive Function from Joye-Libert Cryptosystem and Its Application to Threshold ECDSA

Threshold ECDSA receives interest lately due to its widespread adoption in blockchain applications. A common building block of all leading constructions involves a secure conversion of multiplicative shares into additive ones, which is called the multiplicative-to-additive (MtA) function. MtA dominates the overall complexity of all existing threshold ECDSA constructions. Specifically, O(n2) invocations of MtA are required in the case of n active signers. Hence, improvement of MtA leads directly to significant improvements for all state-of-the-art threshold ECDSA schemes. In this paper, we design a novel MtA by revisiting the Joye-Libert (JL) cryptosystem. Specifically, we revisit JL encryption and propose a JL-based commitment, then give efficient zero-knowledge proofs for JL cryptosystem which are the first to have standard soundness. Our new MtA offers the best time-space complexity trade-off among all existing MtA constructions. It outperforms state-of-the-art constructions from Paillier by a factor of 1.85 to 2 in bandwidth and 1.2 to 1.7 in computation. It is 7X faster than those based on Castagnos-Laguillaumie encryption only at the cost of 2X more bandwidth. While our MtA is slower than OT-based constructions, it saves 18.7X in bandwidth requirement. In addition, we also design a batch version of MtA to further reduce the amortised time and space cost by another 25%.
Short-term treatment outcomes and dropout risk in males and females with eating disorders

This study compared treatment outcomes between males and females with eating disorders (ED) and analyzed clinical predictors of treatment outcome. Our sample consisted of 131 male and 131 female ED patients who underwent cognitive-behavioral therapy (CBT) treatment. ED severity, personality and psychopathology were assessed using standard instruments. We found that the risk of dropout was higher for males with BN than for females with BN, and that males with BN and OSFED were more likely to obtain full remission in comparison with their female counterparts. Predictive models of treatment outcome indicated that higher scores in novelty seeking was a shared factor associated with higher risk of dropout and not obtaining full remission for both men and women with ED. However, only in men, younger age and lower scores in reward dependence predicted higher dropout. Contrastingly, higher persistence scores were predictors of full remission. This study reinforces the effectiveness of using outpatient CBT as treatment as usual for men with ED. Nonetheless, placing greater emphasis on strategies targeting gender specific issues could enhance outcomes.
German Compound Splitting Using the Compound Productivity of Morphemes

In this work, we present a novel compound splitting method for German by capturing the compound productivity of morphemes. We use a giga web corpus to create a lexicon and decompose noun compounds by computing the probabilities of compound elements as bound and free morphemes. Furthermore, we provide a uniformed evaluation of several unsupervised approaches and morphological analysers for the task. Our method achieved a high F1 score of 0.92, which was a comparable result to state-of-the-art methods.
Improving business performance through developing a corporate culture

In today’s competitive environment, global competition has forced many companies to seek ways in which to improve their manufacturing performance. Describes a case study of a small/medium subcontract jobbing shop situated in the industrial heart of North West England, and explains how, despite considerable setbacks and hardship, the company managed to develop, integrate and sustain a number of key enablers into their corporate culture. Describes the organizational culture within which an approach to leadership, continuous improvement, innovation and employee development has allowed the entire workforce to continue their quest for total quality. Concludes by describing how these key enablers have considerably improved the organization’s performance and contributed towards its success and growth, resulting in a positive proactive culture based on best practices.
Contradicting effects of subjective economic and cultural values on ocean protection willingness: preliminary evidence of 42 countries

: Coastal protection is crucial to human development since the ocean has many values associated with the economy, ecosystem, and culture. However, most ocean-protecting efforts are currently ineffective due to the burdens of finance, lack of appropriate management, and international cooperation regimes. For aiding bottom-up initiatives for ocean protection support, this study employed the Mindsponge Theory to examine how the public’s perceived economic and cultural values influence their willingness to support actions to protect the ocean. Analyzing the European-Union-Horizon-2020-funded dataset of 709 respondents from 42 countries, we discovered that perceived economic values have negative effects on the tendency of ocean protection supports (i.e., food, transportation, renewable energy, oil and gas, and recreation). In contrast, certain perceived cultural values can help increase the willingness to do so (i.e., mental well-being and sense of identity). However, the effects of perceived cultural values are only moderately reliable. These findings suggest that designing cultural information delivery campaigns can help promote coastal reserve supports, such as fundraisings and preserving the oceans from the community.
From Micro to Macro : Understanding User Preferences from Different Perspectives for Personalized Recommendation

In recent years, recommender systems have become widely utilized by businesses across industries. Given a set of users, items, and observed user-item interactions, these systems learn user preferences by collective intelligence, and deliver proper items under various contexts to improve user engagements and merchant profits. Collaborative Filtering is the most popular method for recommender systems. The principal idea of Collaborative Filtering is that users might be interested in the items that are preferred by users with similar preferences. Therefore, learning user preferences is the core technique of Collaborative Filtering. In this report, we first present an overview of several state-of-the-art Collaborative Filtering models. Afterwards, we discuss three perspectives to rethink the recommendation problem and some related work that attempts to improve recommender systems from these perspectives. Finally, we point out some potential opportunities for the future work.
Measurement and structural invariance of cognitive ability tests after computer-based training

Ability tests are core elements in performance research as well as in applied contexts and are increasingly carried out using computer-based versions. In the last few decades a whole training and coaching industry has developed to prepare individuals for computer-based assessments. Evidence suggests that such commercial training programs can result in score gains in ability tests, thereby creating an advantage for those who can afford it and challenging the fairness of ability assessment. As a consequence, several authors recommended freely offering training software to all participants to increase measurement fairness. However, it is still an open question whether the unsupervised use of training software could have an impact on the measurement properties of ability tests. The goal of the present study is to fill this gap by examining the subjects’ ability scores for measurement and structural invariance across different amounts of computer-based training. Structural equation modeling was employed in a sample of 15,752 applicants who participated in high-stakes assessments with computer-based ability tests. Across different training amounts, our analyses supported measurement and structural invariance of ability scores. In conclusion, free training software is a means that provides fair preparation opportunities without changing the measurement properties of the tests.
Segmentation by watersheds

The watershed algorithm is a method for image segmentation widely used in the area of mathematical morphology. In this paper we (cid:12)rst address the problem of how to de(cid:12)ne watersheds. It is pointed out that various existing de(cid:12)nitions are not equivalent. In particular we explain the di(cid:11)erences between the recursive de(cid:12)nition, a modi(cid:12)cation of this de(cid:12)nition necessary to avoid relabelling of watershed pixels, and the de(cid:12)nition based on shortest paths with respect to a certain grey-value distance function. The sequential implementation of both algorithms is discussed. Finally we sketch parallel implementations of the two watershed algorithms on a MIMD ring-architecture, and a Cray J932 shared memory computer, respectively.
Robust Online Covariance and Sparse Precision Estimation Under Arbitrary Data Corruption

Gaussian graphical models are widely used to represent correlations among entities but remain vulnerable to data corruption. In this work, we introduce a modified trimmed-inner-product algorithm to robustly estimate the covariance in an online scenario even in the presence of arbitrary and adversarial data attacks. At each time step, data points, drawn nominally independently and identically from a multivariate Gaussian distribution, arrive. However, a certain fraction of these points may have been arbitrarily corrupted. We propose an online algorithm to estimate the sparse inverse covariance (i.e., precision) matrix despite this corruption. We provide the error-bound and convergence properties of the estimates to the true precision matrix under our algorithms.
Classification of tastants: A deep learning based approach

Predicting the taste of molecules is of critical importance in the food and beverages, flavor, and pharmaceutical industries for the design and screening of new tastants. In this work, we have built deep learning models to classify sweet, bitter, and umami molecules– the three basic tastes whose sensation is mediated by G protein‐coupled receptors. An extensive dataset containing 1466 bitter, 1764 sweet, and 238 umami tastants was curated from existing literature. We analyzed the chemical characteristics of the molecules, with special focus on the presence of different functional groups. A deep neural network model based on molecular descriptors and a graph neural network model were trained for taste prediction. The class imbalance due to fewer umami molecules was tackled using special sampling techniques. Both models show comparable performance during evaluation, but the graph‐based model can learn task‐specific representations from the molecular structure without requiring handcrafted features. We further explain the deep neural network predictions using Shapley additive explanations. Finally, we demonstrated the applicability of the models by screening bitter, sweet, and umami molecules from a large food database. This study develops an in‐silico approach to classify molecules based on their taste by leveraging the recent progress in deep learning, which can serve as a powerful tool for tastant design.
Asynchronous Task Plan Refinement for Multi-Robot Task and Motion Planning

This paper explores general multi-robot task and motion planning, where multiple robots in close proximity manipulate objects while satisfying constraints and a given goal. In particular, we formulate the plan refinement problem--which, given a task plan, finds valid assignments of variables corresponding to solution trajectories--as a hybrid constraint satisfaction problem. The proposed algorithm follows several design principles that yield the following features: (1) efficient solution finding due to sequential heuristics and implicit time and roadmap representations, and (2) maximized feasible solution space obtained by introducing minimally necessary coordination-induced constraints and not relying on prevalent simplifications that exist in the literature. The evaluation results demonstrate the planning efficiency of the proposed algorithm, outperforming the synchronous approach in terms of makespan.
On level line fluctuations of SOS surfaces above a wall

We study the low temperature $(2+1)$D Solid-On-Solid model on $[[1, L ]]^2$ with zero boundary conditions and non-negative heights (a floor at height $0$). Caputo et al. (2016) established that this random surface typically admits either $\mathfrak h $ or $\mathfrak h+1$ many nested macroscopic level line loops $\{\mathcal L_i\}_{i\geq 0}$ for an explicit $\mathfrak h\asymp \log L$, and its top loop $\mathcal L_0$ has cube-root fluctuations: e.g., if $\rho(x)$ is the vertical displacement of $\mathcal L_0$ from the bottom boundary point $(x,0)$, then $\max \rho(x) = L^{1/3+o(1)}$ over $x\in I_0:=L/2+[[-L^{2/3},L^{2/3}]]$. It is believed that rescaling $\rho$ by $L^{1/3}$ and $I_0$ by $L^{2/3}$ would yield a limit law of a diffusion on $[-1,1]$. However, no nontrivial lower bound was known on $\rho(x)$ for a fixed $x\in I_0$ (e.g., $x=\frac L2$), let alone on $\min\rho(x)$ in $I_0$, to complement the bound on $\max\rho(x)$. Here we show a lower bound of the predicted order $L^{1/3}$: for every $\epsilon>0$ there exists $\delta>0$ such that $\min_{x\in I_0} \rho(x) \geq \delta L^{1/3}$ with probability at least $1-\epsilon$. The proof relies on the Ornstein--Zernike machinery due to Campanino-Ioffe-Velenik, and a result of Ioffe, Shlosman and Toninelli (2015) that rules out pinning in Ising polymers with modified interactions along the boundary. En route, we refine the latter result into a Brownian excursion limit law, which may be of independent interest. We further show that in a $ K L^{2/3}\times K L^{2/3}$ box with boundary conditions $\mathfrak h-1,\mathfrak h,\mathfrak h,\mathfrak h$ (i.e., $\mathfrak h-1$ on the bottom side and $\mathfrak h$ elsewhere), the limit of $\rho(x)$ as $K,L\to\infty$ is a Ferrari--Spohn diffusion.
MOVIN: Real‐time Motion Capture using a Single LiDAR

Recent advancements in technology have brought forth new forms of interactive applications, such as the social metaverse, where end users interact with each other through their virtual avatars. In such applications, precise full‐body tracking is essential for an immersive experience and a sense of embodiment with the virtual avatar. However, current motion capture systems are not easily accessible to end users due to their high cost, the requirement for special skills to operate them, or the discomfort associated with wearable devices. In this paper, we present MOVIN, the data‐driven generative method for real‐time motion capture with global tracking, using a single LiDAR sensor. Our autoregressive conditional variational autoencoder (CVAE) model learns the distribution of pose variations conditioned on the given 3D point cloud from LiDAR. As a central factor for high‐accuracy motion capture, we propose a novel feature encoder to learn the correlation between the historical 3D point cloud data and global, local pose features, resulting in effective learning of the pose prior. Global pose features include root translation, rotation, and foot contacts, while local features comprise joint positions and rotations. Subsequently, a pose generator takes into account the sampled latent variable along with the features from the previous frame to generate a plausible current pose. Our framework accurately predicts the performer's 3D global information and local joint details while effectively considering temporally coherent movements across frames. We demonstrate the effectiveness of our architecture through quantitative and qualitative evaluations, comparing it against state‐of‐the‐art methods. Additionally, we implement a real‐time application to showcase our method in real‐world scenarios. MOVIN dataset is available at https://movin3d.github. io/movin_pg2023/https://movin3d.github. io/movin_pg2023/">https://movin3d.github. io/movin_pg2023/.
Performance of Graph Neural Networks for Point Cloud Applications

Graph Neural Networks (GNNs) have gained significant momentum recently due to their capability to learn on unstructured graph data. Dynamic GNNs (DGNNs) are the current state-of-the-art for point cloud applications; such applications (viz. autonomous driving) require real-time processing at the edge with tight latency and memory constraints. Conducting performance analysis on such DGNNs, thus, becomes a crucial task to evaluate network suitability. This paper presents a profiling analysis of EdgeConv-based DGNNs applied to point cloud inputs. We assess their inference performance in terms of end-to-end latency and memory consumption on state-of-the-art CPU and GPU platforms. The EdgeConv layer has two stages: (1) dynamic graph generation using k-Nearest Neighbors (kNN) and, (2) node feature updation. The addition of dynamic graph generation via kNN in each (EdgeConv) layer enhances network performance compared to networks that work with the same static graph in each layer; such performance enhancement comes, however, at the added computational cost associated with the dynamic graph generation stage (via kNN algorithm). Understanding its costs is essential for identifying the performance bottleneck and exploring potential avenues for hardware acceleration. To this end, this paper aims to shed light on the performance characteristics of EdgeConv-based DGNNs for point cloud inputs. Our performance analysis on a state-of-the-art EdgeConv network for classification shows that the dynamic graph construction via kNN takes up upwards of 95% of network latency on the GPU and almost 90% on the CPU. Moreover, we propose a quasi-Dynamic Graph Neural Network (qDGNN) that halts dynamic graph updates after a specific depth within the network to significantly reduce the latency on both CPU and GPU whilst matching the original networks inference accuracy.