Miracle-dz/UniFAR-FTUs huggingface dataset:
Fabrication and Characterization of Scaffolds of Poly(ε-caprolactone)/Biosilicate® Biocomposites Prepared by Generative Manufacturing Process

Scaffolds of poly(ε-caprolactone) (PCL) and their biocomposites with 0, 1, 3, and 5 wt. % Biosilicate® were fabricated by the generative manufacturing process coupled with a vertical miniscrew extrusion head to application for restoration of bone tissue. Their morphological characterization indicated the designed 0°/90° architecture range of pore sizes and their interconnectivity is feasible for tissue engineering applications. Mechanical compression tests revealed an up to 57% increase in the stiffness of the scaffold structures with the addition of 1 to 5 wt. % Biosilicate® to the biocomposite. No toxicity was detected in the scaffolds tested by in vitro cell viability with MC3T3-E1 preosteoblast cell line. The results highlighted the potential application of scaffolds fabricated with poly(ε-caprolactone)/Biosilicate® to tissue engineering.
AgentBuddy: A Contextual Bandit based Decision Support System for Customer Support Agents

In this short paper, we present early insights from a Decision Support System for Customer Support Agents (CSAs) serving customers of a leading accounting software. The system is under development and is designed to provide suggestions to CSAs to make them more productive. A unique aspect of the solution is the use of bandit algorithms to create a tractable human-in-the-loop system that can learn from CSAs in an online fashion. In addition to discussing the ML aspects, we also bring out important insights we gleaned from early feedback from CSAs. These insights motivate our future work and also might be of wider interest to ML practitioners.
Knowledge-Embedded Routing Network for Scene Graph Generation

To understand a scene in depth not only involves locating/recognizing individual objects, but also requires to infer the relationships and interactions among them. However, since the distribution of real-world relationships is seriously unbalanced, existing methods perform quite poorly for the less frequent relationships. In this work, we find that the statistical correlations between object pairs and their relationships can effectively regularize semantic space and make prediction less ambiguous, and thus well address the unbalanced distribution issue. To achieve this, we incorporate these statistical correlations into deep neural networks to facilitate scene graph generation by developing a Knowledge-Embedded Routing Network. More specifically, we show that the statistical correlations between objects appearing in images and their relationships, can be explicitly represented by a structured knowledge graph, and a routing mechanism is learned to propagate messages through the graph to explore their interactions. Extensive experiments on the large-scale Visual Genome dataset demonstrate the superiority of the proposed method over current state-of-the-art competitors.
Internet opt-in and opt-out: investigating the roles of frames, defaults and privacy concerns

This paper studies the solicitation process of consumers' consent - should consumers be requested to explicitly disapprove the use of their personal data (opt-out), or to acknowledge and permit the use of such data (opt-in)? Although these two actions may serve the same functional purpose (i.e., grant approval to the use of the supplied information), various regulatory and industry bodies have exhibited opposing attitudes towards them. The European Union Data Directive (1995) endorses the opt-in approach, whereas the Direct Marketing Association (DMA) recommends an opt-out procedure for consumers to remove their data from future uses. The two approaches can be operationalized via different option frames and preference elicitations. We illustrate how different permutation of frames and default preferences can affect the level of consumer participation and investigate the moderating role of privacy concern on these corollaries.
Learning Hierarchical Teaching in Cooperative Multiagent Reinforcement Learning

Heterogeneous knowledge naturally arises among different agents in cooperative multiagent reinforcement learning. As such, learning can be greatly improved if agents can effectively pass their knowledge on to other agents. Existing work has demonstrated that peer-to-peer knowledge transfer, a process referred to as action advising, improves team-wide learning. In contrast to previous frameworks that advise at the level of primitive actions, we aim to learn high-level teaching policies that decide when and what high-level action (e.g., sub-goal) to advise a teammate. We introduce a new learning to teach framework, called hierarchical multiagent teaching (HMAT). The proposed framework solves difficulties faced by prior work on multiagent teaching when operating in domains with long horizons, delayed rewards, and continuous states/actions by leveraging temporal abstraction and deep function approximation. Our empirical evaluations show that HMAT accelerates team-wide learning progress in difficult environments that are more complex than those explored in previous work. HMAT also learns teaching policies that can be transferred to different teammates/tasks and can even teach teammates with heterogeneous action spaces.
Accelerating Generalized Linear Models with MLWeaving: A One-Size-Fits-All System for Any-precision Learning

Learning from the data stored in a database is an important function increasingly available in relational engines. Methods using lower precision input data are of special interest given their overall higher efficiency. However, in databases, these methods have a hidden cost: the quantization of the real value into a smaller number is an expensive step. To address this issue, we present ML-Weaving, a data structure and hardware acceleration technique intended to speed up learning of generalized linear models over low precision data. MLWeaving provides a compact in-memory representation that enables the retrieval of data at any level of precision. MLWeaving also provides a highly efficient implementation of stochastic gradient descent on FPGAs and enables the dynamic tuning of precision, instead of using a fixed precision level during learning. Experimental results show that MLWeaving converges up to 16 x faster than low-precision implementations of first-order methods on CPUs.
On the Sub-Packetization Size and the Repair Bandwidth of Reed-Solomon Codes

Reed-Solomon (RS) codes are widely used in distributed storage systems. In this paper, we study the repair bandwidth and sub-packetization size of RS codes. The repair bandwidth is defined as the amount of transmitted information from surviving nodes to a failed node. The RS code can be viewed as a polynomial over a finite field <inline-formula> <tex-math notation="LaTeX">$GF(q^\ell)$ </tex-math></inline-formula> evaluated at a set of points, where <inline-formula> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula> is called the sub-packetization size. Smaller bandwidth reduces the network traffic in distributed storage, and smaller <inline-formula> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula> facilitates the implementation of RS codes with lower complexity. Recently, Guruswami and Wootters proposed a repair method for RS codes when the evaluation points are the entire finite field. While the sub-packetization size can be arbitrarily small, the repair bandwidth is higher than the minimum storage regenerating (MSR) bound. Tamo, Ye, and Barg achieved the MSR bound but the sub-packetization size grows faster than the exponential function of the number of the evaluation points. In this paper, we present code constructions and repair schemes that extend these results to accommodate different sizes of the evaluation points. In other words, we design schemes that provide points in between. These schemes provide a flexible tradeoff between the sub-packetization size and the repair bandwidth. In addition, we generalize our schemes to manage multiple failures.
Joint Learning of Brain Lesion and Anatomy Segmentation from Heterogeneous Datasets

Brain lesion and anatomy segmentation in magnetic resonance images are fundamental tasks in neuroimaging research and clinical practice. Given enough training data, convolutional neuronal networks (CNN) proved to outperform all existent techniques in both tasks independently. However, to date, little work has been done regarding simultaneous learning of brain lesion and anatomy segmentation from disjoint datasets. In this work we focus on training a single CNN model to predict brain tissue and lesion segmentations using heterogeneous datasets labeled independently, according to only one of these tasks (a common scenario when using publicly available datasets). We show that label contradiction issues can arise in this case, and propose a novel adaptive cross entropy (ACE) loss function that makes such training possible. We provide quantitative evaluation in two different scenarios, benchmarking the proposed method in comparison with a multi-network approach. Our experiments suggest that ACE loss enables training of single models when standard cross entropy and Dice loss functions tend to fail. Moreover, we show that it is possible to achieve competitive results when comparing with multiple networks trained for independent tasks.
Generating Difficult SAT Instances by Preventing Triangles

When creating benchmarks for SAT solvers, we need SAT instances that are easy to build but hard to solve. A recent development in the search for such methods has led to the Balanced SAT algorithm, which can create k-SAT instances with m clauses of high difficulty, for arbitrary k and m. In this paper we introduce the No-Triangle SAT algorithm, a SAT instance generator based on the cluster coefficient graph statistic. We empirically compare the two algorithms by fixing the arity and the number of variables, but varying the number of clauses. The hardest instances that we find are produced by No-Triangle SAT. Furthermore, difficult instances from No-Triangle SAT have a different number of clauses than difficult instances from Balanced SAT, potentially allowing a combination of the two methods to find hard SAT instances for a larger array of parameters.
Non-Invasive Attributes Significance in the Risk Evaluation of Heart Disease Using Decision Tree Analysis

Non-invasive attributes are low cost, easy to identify attributes that can have significant influence in the risk evaluation of heart disease. This research formulates a combination of non-invasive attributes that can be used in the risk evaluation of heart disease. It uses the decision tree data mining technique to identify the significance of different single, combined, and calculated non-invasive attribute combinations in the risk evaluation of heart disease against the benchmark Cleveland heart disease dataset and a larger Canberra hospital heart disease dataset. For each non-invasive attribute combination, 10-fold cross-validation is applied to ensure reliable performance measures. Different equations of non-invasive attributes on the Canberra dataset show that the best combination in the risk evaluation of heart disease is age, sex, resting blood pressure and Rohrer’s Index equation with a mean accuracy and standard deviation of 73.8% and 4.9% respectively.
Computing Three-Dimensional Constrained Delaunay Refinement Using the GPU

We propose the first GPU algorithm for the 3D constrained Delaunay refinement problem. For an input of a piecewise linear complex G and a constant B, it produces, by adding Steiner points, a constrained Delaunay triangulation conforming to G and containing tetrahedra mostly with radiusedge ratios smaller than B. Our implementation of the algorithm shows that it can be an order of magnitude faster than the best CPU software while using similar quantities of Steiner points to produce triangulations of comparable qualities. It thus reduces the computing time of triangulation refinement from possibly an hour to a few seconds or minutes for possible use in interactive applications.
Effects of Phase II Comprehensive Cardiac Rehabilitation on Coronary Plaque Volume After Acute Coronary Syndrome.

The present study aimed to determine the effects of phase II (PII) comprehensive cardiac rehabilitation (CR) on coronary plaque volume in patients after acute coronary syndrome (ACS). We assigned 46 patients with ACS who had undergone standard phase I CR into groups who proceeded with PII-CR (PII-CR; n = 21) and those who did not (non-PII-CR; n = 25). We then measured anthropometric parameters and daily physical activity using a pedometer for up to 60 days. The isokinetic strength of the knee extensor and flexor muscles and exercise tolerance were tested and non-culprit lesions were analyzed using volumetric intravascular ultrasound at baseline and 6 months later.Baseline characteristics did not significantly differ between the two groups and exercise tolerance was significantly improved in both. Waist size and fat weight were significantly decreased, and muscle strength was significantly increased in the PII-CR group but not in the non-PII-CR group. The percent change in plaque volume (primary endpoint) did not differ significantly between the two groups. The percent change in plaque volume was significantly and negatively correlated with daily physical activity.Although risk factors, muscle strength, and exercise tolerance were improved by PII-CR, plaque regression did not differ significantly between the two study groups. A significant correlation between percent change in coronary plaque volume and physical activity was observed. A comprehensive phase II-CR, including frequent supervised exercise sessions and a program encouraging an increase in daily physical activity, may reduce plaque volume in patients after ACS (UMIN000006038).
Efficacy of biological drugs in short-duration versus long-duration inflammatory bowel disease: a protocol for a systematic review and an individual-patient level meta-analysis of randomised controlled trials

Introduction Crohn’s disease (CD) and ulcerative colitis (UC) are remitting–relapsing inflammatory diseases often culminating in disease complications and/or need for surgery. Biologic monoclonal antibody drugs (‘Biologics’) are efficacious for both diseases, but there are no systematic assessments of their efficacy if administered early after disease onset (‘top-down’ strategy) vis-à-vis later in the course of disease (‘step-up’ approach). Methods and analysis Electronic databases (MEDLINE, EMBASE/EMBASE classic Cochrane CENTRAL register of controlled trials, the Cochrane IBD Group Specialised Trials Register and Clinicaltrials.gov registry) will be searched to identify all randomised placebo-controlled clinical trials of food and drug administration (FDA)-approved biologics for CD and UC (by March 2016). Two independent reviewers will screen identified papers, extract data and assess the risk of bias according to the Cochrane Handbook for Systematic Reviews of Interventions. Individual-patient-level data (IPD) will be extracted from the identified trials through data-sharing platforms for pharmaceutical companies’ sponsored trials and by contacting principal investigators of independent investigator-initiated trials. We will analyse induction of remission in patients with early-disease (<18 months since disease onset) versus patients with longer disease duration, using a generalised linear mixed effect model and by a two-stage approach using coefficient for the treatment-by-subgroup interaction within each trial. We will perform receiver operator curve analysis of optimal disease duration for response. Analyses will be separate for CD and UC. This first-of-its-kind meta-analysis at IPD level of interaction of disease duration with the response to biologics in UC and CD may elucidate the impact of early initiation of biologics, which is of paramount importance for clinical practice and management strategies of inflammatory bowel disease. Ethics and dissemination This meta-analysis was approved by the Ethics Committee of the First Affiliated Hospital of Sun Yat-sen University. Findings will be published in peer-reviewed journal and disseminated via scientific meetings and links with organisations. PROSPERO registration number CRD42018041961.
L-Cysteine-Derived H2S Promotes Microglia M2 Polarization via Activation of the AMPK Pathway in Hypoxia-Ischemic Neonatal Mice

We have reported previously that L-cysteine-derived hydrogen sulfide (H2S) demonstrates a remarkable neuroprotective effect against hypoxia-ischemic (HI) insult in neonatal animals. Here, we assessed some of the mechanisms of this protection as exerted by L-cysteine. Specifically, we examined the capacity for L-cysteine to stimulate microglial polarization of the M2 phenotype and its modulation of complement expression in response to HI in neonatal mice. L-cysteine treatment suppressed the production of inflammatory cytokines, while dramatically up-regulating levels of anti-inflammatory cytokines in the damaged cortex. This L-cysteine administration promoted the conversion of microglia from an inflammatory M1 to an anti-inflammatory M2 phenotype, an effect which was associated with inhibiting the p38 and/or JNK pro-inflammatory pathways, nuclear factor-κB activation and a decrease in HI-derived levels of the C1q, C3a and C3a complement receptor proteins. Notably, blockade of H2S-production clearly prevented L-cysteine-mediated M2 polarization and complement expression. L-cysteine also inhibited neuronal apoptosis as induced by conditioned media from activated M1 microglia in vitro. We also show that L-cysteine promoted AMP-activated protein kinase (AMPK) activation and the AMPK inhibitor abolished these anti-apoptotic and anti-inflammatory effects of L-cysteine. Taken together, our findings demonstrate that L-cysteine-derived H2S attenuated neuronal apoptosis after HI and suggest that these effects, in part, result from enhancing microglia M2 polarization and modulating complement expression via AMPK activation.
Application of a novel sex independent anthropometric index, termed angle index, in relation to type 2 diabetes: a Trinidadian case–control study

Objective To develop a novel sex independent anthropometric index, termed as angle index, related to type 2 diabetes. Design Case–control. Participants The study comprised 121 participants and were divided into two groups. One group had no form of diabetes and served as controls (n=50). The other group had the condition of type 2 diabetes (n=71). 31% (n=37) of the subjects were male and 69% (n=84) were female. 62% (n=75) of the subjects were of East Indian ethnicity, 28% (n=34) were of African ethnicity and 10% (n=12) were of mixed ethnicity. Setting Participants of the study were from the island of Trinidad, located in the Caribbean. Patients in the study were selected at random from hospital records. Primary outcome measure It was hypothesised that the mean angle index of patients with type 2 diabetes would be higher than the mean angle index of patients without type 2 diabetes. Results Patients with type 2 diabetes had a significantly higher angle index value as compared with controls (p<0.001). Angle index was the superior sex independent anthropometric index in relation to type 2 diabetes (area under the curve=0.72; p<0.001) as compared with other sex independent variables. Angle index correlated with glycated haemoglobin (rs=0.28, p=0.003) and fasting blood glucose (rs=0.31, p=0.001) levels. Patients with type 2 diabetes were four times more likely to have an angle index greater than 184° (OR 4.2, 95% CI 1.8 to 9.9) as compared with controls. Conclusion Angle index was a superior sex independent index for discriminating between patients with and without type 2 diabetes, as compared with waist circumference, abdominal volume index, conicity index, blood pressure readings, triglyceride levels and very low-density lipoprotein levels.
Prevention of Radial Oxygen Loss Is Associated With Exodermal Suberin Along Adventitious Roots of Annual Wild Species of Echinochloa

Internal aeration is crucial for root growth under waterlogged conditions. Some wetland plants have a structural barrier that impedes oxygen leakage from the basal part of roots called a radial oxygen loss (ROL) barrier. The ROL barrier reduces loss of oxygen transported via the aerenchyma to the root tips, enabling root growth into anoxic soil. The roots of some plants develop an ROL barrier under waterlogged conditions, while they remain leaky to oxygen under well-drained or aerated conditions. The main components of the inducible ROL barrier are thought to be suberin and lignin deposited at the outer cellular space (apoplast) in the outer part of roots. On the other hand, a few wetland plants including a species of Echinochloa form a constitutive ROL barrier, i.e., it is formed even in the absence of waterlogging. However, little is known about the components of constitutive ROL barriers. An ROL barrier is considered to be a characteristic of wetland species because it has not been found in any non-wetland species so far. Here, we examined whether Echinochloa species from non-waterlogged fields also form an inducible or constitutive ROL barrier. We found that three species of Echinochloa from non-waterlogged fields constitutively developed an ROL barrier under aerated conditions. Over 85% of their root exodermis cells were covered with suberin lamellae and had well-developed Casparian strips. These substances inhibited the infiltration of an apoplastic tracer (periodic acid), suggesting that the ROL barrier can also prevent the entry of phytotoxic compounds from the soil. Unlike the other Echinochloa species, E. oryzicola, which mainly inhabits rice paddies, was found to lack a constitutive ROL barrier under aerated conditions. Although close to 90% of its sclerenchyma was well lignified, it leaked oxygen from the basal part of roots. A high percentage (55%) of the root exodermis cells were not fortified with suberin lamellae. These results suggest that suberin is an important component of constitutive ROL barriers.
Opportunities and Challenges for Molecular Understanding of Ciliopathies–The 100,000 Genomes Project

Cilia are highly specialized cellular organelles that serve multiple functions in human development and health. Their central importance in the body is demonstrated by the occurrence of a diverse range of developmental disorders that arise from defects of cilia structure and function, caused by a range of different inherited mutations found in more than 150 different genes. Genetic analysis has rapidly advanced our understanding of the cell biological basis of ciliopathies over the past two decades, with more recent technological advances in genomics rapidly accelerating this progress. The 100,000 Genomes Project was launched in 2012 in the UK to improve diagnosis and future care for individuals affected by rare diseases like ciliopathies, through whole genome sequencing (WGS). In this review we discuss the potential promise and medical impact of WGS for ciliopathies and report on current progress of the 100,000 Genomes Project, reviewing the medical, technical and ethical challenges and opportunities that new, large scale initiatives such as this can offer.