Miracle-dz/UniFAR-FTUs huggingface dataset:
Graph-Regularized Fast Low-Rank Matrix Approximation Using The NystrÖM Method for Clustering

In recovering low-dimensional representations of high-dimensional data, graph or manifold-regularized schemes have been investigated as a key tool in many areas to preserve the neighborhood structure of the data set. In spite of its effectiveness, these methods are often not tractable in practice, because graph structures of data lead to a large matrix (e.g., affinity of Laplacian matrix) and the methods require eigenanalysis of it interactively. In this paper, we propose an efficient low-rank matrix approximation that regularized by graph information derived from row and column range spaces of data. To deal with high computational complexity issue, we leverage the Nyström method, which has been universally used to approximate low-rank component of Symmetric Positive Semi-Definite (SPSD) matrices with sampling. Moreover, we devise a Clustered Nystrom extension with QR decomposition to efficiently aggregate more information from samples and to accurately approximate low-rank structure. We compare the performance of the proposed algorithm with other several general algorithms in clustering experiments on benchmark dataset. Our experimental results show that our method has a favorable running speed while the accuracy of our proposed method is better or comparable to the competing methods.
Family History of Premature Coronary Artery Disease (P-CAD)—A Non-Modifiable Risk Factor? Dietary Patterns of Young Healthy Offspring of P-CAD Patients: A Case-Control Study (MAGNETIC Project)

Dietary habits of healthy offspring with a positive family history of premature coronary artery disease (P-CAD) have not been studied so far. The aim of this study was twofold: (1) to identify dietary patterns in a sample of young healthy adults with (cases) and without (controls) family history of P-CAD, and (2) to study the association between dietary patterns and family history of P-CAD. The data came from the MAGNETIC case-control study. The participants were healthy adults aged 18–35 years old, with (n = 351) and without a family history of P-CAD (n = 338). Dietary data were collected with food frequency questionnaire FFQ-6. Dietary patterns (DP) were derived using principal component analysis (PCA). The associations between the adherence to DPs and family history of P-CAD were investigated using logistic regression. Two models were created: crude and adjusted for age, sex, smoking status, place of residence, financial situation, education, and physical activity at leisure time. Three DPs were identified: ‘prudent’, ‘westernized traditional’ and ‘dairy, breakfast cereals, and treats’. In both crude and adjusted models, subjects with family history of P-CAD showed higher adherence by 31% and 25% to ‘westernized traditional’ DP (odds ratio (OR) 1.31, 95% confidence interval (95% CI): 1.12–1.53; p < 0.005; per 1 unit of standard deviation (SD) of DP score and adjOR 1.25, 95% CI: 1.06–1.48; p = 0.007; per 1 unit of SD of DP score, respectively). Young healthy adults with family history of P-CAD present unfavorable dietary patterns and are potentially a target group for CAD primary prevention programs.
Advanced Age Redistributes Positive but Not Negative Leg Joint Work during Walking

Introduction Advanced age brings a distal-to-proximal redistribution of positive joint work during walking that is relevant to walking performance and economy. It is unclear whether negative joint work is similarly redistributed in old age. Negative work can affect positive work through elastic energy return in gait. We determined the effects of age, walking speed, and grade on positive and negative joint work in young and older adults. Methods Bilateral ground reaction force and marker data were collected from healthy young (age = 22.5 yr, n = 18) and older (age = 76.0 yr, n = 22) adults walking on a split-belt instrumented treadmill at 1.1, 1.4, and 1.7 m·s−1 at each of three grades (0%, 10%, and −10%). Subjects also performed maximal voluntary eccentric, isometric, and concentric contractions for the knee extensors (120°·s−1, 90°·s−1, and 0°·s−1) and plantarflexors (90°·s−1, 30°·s−1, and 0°·s−1). Results Compared with young adults, older adults exhibited a distal-to-proximal redistribution of positive leg joint work during level (P < 0.001) and uphill (P < 0.001) walking, with larger differences at faster walking speeds. However, the distribution of negative joint work was unaffected by age during level (P = 0.150) and downhill (P = 0.350) walking. Finally, the age-related loss of maximal voluntary knee extensor (P < 0.001) and plantarflexor (P = 0.001) strength was smaller during an eccentric contraction versus concentric contraction for the knee extensors (P < 0.001) but not for the plantarflexors (P = 0.320). Conclusion The distal-to-proximal redistribution of positive joint work during level and uphill walking is absent for negative joint work during level and downhill walking. Exercise prescription should focus on improving ankle muscle function while preserving knee muscle function in older adults trying to maintain their independence.
Hybrid On-Chip Communication Architectures for Heterogeneous Manycore Systems

The widespread adoption of big data has led to the search for highperformance and low-power computational platforms. Emerging heterogeneous manycore processing platforms consisting of CPU and GPU cores along with various types of accelerators offer power and area-efficient trade-offs for running these applications. However, heterogeneous manycore architectures need to satisfy the communication and memory requirements of the diverse computing elements that conventional Network-on-Chip (NoC) architectures are unable to handle effectively. Further, with increasing system sizes and level of heterogeneity, it becomes difficult to quickly explore the large design space and establish the appropriate design trade-offs. To address these challenges, machine learning-inspired heterogeneous manycore system design is a promising research direction to pursue. In this paper, we highlight various salient features of heterogeneous manycore architectures enabled by emerging interconnect technologies and machine learning techniques.
Estimating and Optimizing BTl Aging Effects: From Physics to CAD

Transistor aging due to Bias Temperature Instability (BTI) is a crucial degradation that affects the reliability of circuits over time. Aging-aware circuit design flows do virtually not exist yet and even research is in its infancy. In this work, we demonstrate how the deleterious effects BTI-induced degradations can be modeled from physics, where they do occur, all the way up to the system level, where they finally take place and affect the delay and power of circuits. To achieve that, degradation-aware cell libraries, that properly capture the impact of BTI not only on the delay of standard cells but also on their static and dynamic power, are created. Unlike state of the art, which solely models the impact of BTI on the threshold voltage of transistors $(V_{th})$, we are the first to model the other key transistor parameters degraded by BTI like carrier mobility ($\mu$), sub-threshold slope ($SS$), and gate-drain capacitance $(C_{gd})$. Our cell libraries are compatible with existing commercial CAD tools. Employing the mature algorithms in such tools, enables designers – after importing our cell libraries – to accurately estimate the overall impact of aging on changing the delay and/or power of any circuit, despite its complexity. We demonstrate that $\Delta V_{th}$ alone (as done in state of the art) is insufficient to correctly model the impact of BTI either on delay or power of circuits. On the one hand, neglecting BTl-induced $\mu$ and $C_{gd}$ degradations leads to underestimating the impact that BTI has on increasing the delay of circuits. Hence, designers will employ narrower timing guardbands in which reliability of circuits during lifetime cannot be sustained. On the other hand, neglecting BTI-induced $SS$ degradation leads to overestimating the impact that BTI has on static power reduction. Hence, the potential benefit of circuits from BTI will be exaggerated.
Substantial Increase in Compliance with Saturated Fatty Acid Intake Recommendations after One Year Following the American Heart Association Diet

The American Heart Association (AHA) dietary guidelines recommend 30–35% of energy intake (%E) be from total fat, <7%E from saturated fatty acids (SFA), and <1%E from trans fatty acid (TFA). This study evaluates the effect of AHA dietary counselling on fat intake. Between 2009 and 2014, 119 obese adults with metabolic syndrome (MetS), (71% women, average 52.5 years of age, and 34.9 kg/m2 of body mass index), received individual and group counselling on the AHA diet, over a one-year study period. Each participant attended 2 individual sessions (months 1 and 12) and 12 group sessions, at one-month intervals. At baseline and one-year, we collected three random 24-h diet recalls (two weekdays and one weekend day). Fat intake patterns over time were analyzed using paired-t test and linear mixed-effect models. There was significant variation on SFA and TFA intake per meal, being highest at dinner, in restaurants, and on weekends. Over the one-year study period, daily intake of total fat, SFA, and TFA decreased by 27%, 37% and 41%, respectively (p-value < 0.01, each). Correspondingly, the percentage of participants complying with AHA’s recommendations, increased from 25.2% to 40.2% for total fat (p-value = 0.02); from 2.5% to 20.7% for SFA (p-value < 0.01); and from 45.4% to 62% for TFA (p-value = 0.02). Additionally, SFA intake for all meal types at home decreased significantly (p-value < 0.05, each). AHA dietary counselling significantly increased the compliance with AHA dietary guidelines, with an eightfold increase in compliance in SFA intake. Nonetheless, ~80% of our participants still exceeded the recommended SFA intake. Substantial efforts are needed to encourage low-SFA and low-TFA food preparation at home, with strong public health policies to decrease SFA and TFA in restaurants and prepared foods.
A Quasi-Newton Algorithm on the Orthogonal Manifold for NMF with Transform Learning

Nonnegative matrix factorization (NMF) is a popular method for audio spectral unmixing. While NMF is traditionally applied to off-the-shelf time-frequency representations based on the short-time Fourier or Cosine transforms, the ability to learn transforms from raw data attracts increasing attention. However, this adds an important computational overhead. When assumed orthogonal (like the Fourier or Cosine transforms), learning the transform yields a non-convex optimization problem on the orthogonal matrix manifold. In this paper, we derive a quasi-Newton method on the manifold using sparse approximations of the Hessian. Experiments on synthetic and real audio data show that the proposed algorithm outperforms state-of-the-art first-order and coordinate-descent methods by orders of magnitude in terms of speed. A Python package for fast TL-NMF is released online at https://github.com/pierreablin/tlnmf.
A Multidimensional and Multiscale Model for Pressure Analysis in a Reservoir-Pipe-Valve System

Reservoir-pipe-valve (RPV) systems are widely used in many industrial processes. The pressure in an RPV system plays an important role in the safe operation of the system, especially during the sudden operations such as rapid valve opening or closing. To investigate the pressure response, with particular interest in the pressure fluctuations in an RPV system, a multidimensional and multiscale model combining the method of characteristics (MOC) and computational fluid dynamics (CFD) method is proposed. In the model, the reservoir is modeled as a zero-dimensional virtual point, the pipe is modeled as a one-dimensional system using the MOC, and the valve is modeled using a three-dimensional CFD model. An interface model is used to connect the multidimensional and multiscale model. Based on the model, a transient simulation of the turbulent flow in an RPV system is conducted in which not only the pressure fluctuation in the pipe but also the detailed pressure distribution in the valve is obtained. The results show that the proposed model is in good agreement when compared with a high fidelity CFD model used to represent both large-scale and small-scale spaces. As expected, the proposed model is significantly more computationally efficient than the CFD model. This demonstrates the feasibility of analyzing complex RPV systems within an affordable computational time.
Visualizing tags over time

We consider the problem of visualizing the evolution of tags within the Flickr (flickr.com) online image sharing community. Any user of the Flickr service may append a tag to any photo in the system. Over the past year, users have on average added over a million tags each week. Understanding the evolution of these tags over time is therefore a challenging task. We present a new approach based on a characterization of the most interesting tags associated with a sliding interval of time. An animation provided via Flash in a web browser allows the user to observe and interact with the interesting tags as they evolve over time.New algorithms and data structures are required to support the efficient generation of this visualization. We combine a novel solution to an interval covering problem with extensions to previous work on score aggregation in order to create an efficient backend system capable of producing visualizations at arbitrary scales on this large dataset in real time.
The role of microtubules in the regulation of epithelial junctions

ABSTRACT The cytoskeleton is crucially important for the assembly of cell-cell junctions and the homeostatic regulation of their functions. Junctional proteins act, in turn, as anchors for cytoskeletal filaments, and as regulators of cytoskeletal dynamics and signalling proteins. The cross-talk between junctions and the cytoskeleton is critical for the morphogenesis and physiology of epithelial and other tissues, but is not completely understood. Microtubules are implicated in the delivery of junctional proteins to cell-cell contact sites, in the differentiation and spatial organization of the cytoplasm, and in the stabilization of the barrier and adhesive functions of junctions. Here we focus on the relationships between microtubules and junctions of vertebrate epithelial cells. We highlight recent discoveries on the molecular underpinnings of microtubule-junction interactions, and report new data about the interaction of cingulin and paracingulin with microtubules. We also propose a possible new role of junctions as “molecular sinks” for microtubule-associated signalling proteins.
An approach to generate noncontact ACL-injury prone situations on a computer using kinematic data of non-injury situations and Monte Carlo simulation

Abstract ACL-injuries are one of the most common knee injuries in noncontact sports. Kinematic data of injury prone situations provide important information to study the underlying ACL-injury mechanisms. However, these data are rare. In this work an approach is presented to generate injury prone situations for noncontact ACL-injuries on a computer. The injury prone situations are generated by a musculoskeletal simulation model using kinematic data of a non-injury situation and the method of Monte Carlo simulation. The approach is successfully applied to generate injury prone landings in downhill ski racing. The characteristics of the obtained injury prone landings are consistent with video recordings of injury cases.
Template-based multilingual football reports generation using Wikidata as a knowledge base

This paper presents a new version of a football reports generation system called PASS. The original version generated Dutch text and relied on a limited hand-crafted knowledge base. We describe how, in a short amount of time, we extended PASS to produce English texts, exploiting machine translation and Wikidata as a large-scale source of multilingual knowledge.
Cardiovascular Disease as a Result of the Interactions Between Obesity, Climate Change, and Inflammation: The COCCI Syndemic

Abstract Obesity and climate change conspire to create an environment in which subclinical vascular inflammation leads to progressive atherosclerosis, which contributes to the number 1 cause of global mortality: cardiovascular disease. The syndemic model requires 2 or more diseases or contributors to disease (such as obesity and climate change) clustering within a specific population in addition to the associated societal and social factors, ultimately creating an environment supportive of a greater adverse interaction. This article explores the syndemic of obesity and climate change as a driver for cardiovascular disease.
Online One-class Classification for Intrusion Detection Based on the Mahalanobis Distance

Machine learning techniques have been very popular in the past decade for their ability to detect hidden patterns in large volumes of data. Researchers have been developing online intrusion detection al- gorithms based on these techniques. In this paper, we propose an online one-class classification approach based on the Mahalanobis distance which takes into account the covariance in each feature direction and the differ- ent scaling of the coordinate axes. We define the one-class problem by two concentric hyperspheres enclosing the support vectors of the description. We update the classifier at each time step. The tests are conducted on real data.
Discovery of Relationships Between Long Non-Coding RNAs and Genes in Human Diseases Based on Tensor Completion

Thousands of long non-coding RNAs (lncRNAs) are encoded by mammalian genomes and play important roles in various biological processes, including the regulation of gene transcription. Through these relationships, lncRNAs can participate in proliferation, differentiation, and cytoprotective programs, which implies their critical roles in human diseases, especially cancers. Therefore, there is an urgent need to study the relationships between lncRNAs and genes in human diseases, which will help uncover the mechanisms underlying disease progression. In this paper, we explore the relationships between lncRNAs and genes in various diseases through a tensor completion-based approach (TCA). The results of performance evaluation suggest that TCA can obtain a significantly better performance than the baseline method. Moreover, top ranked relationships with highest average scores in all diseases corroborate the effectiveness of TCA and the reliability of the predicted results. Case study of hepatocellular carcinoma (HCC) indicates that the elements of the top ranked relationships may be functionally implicated in HCC. Furthermore, three lncRNAs (HULC, MALAT1, and BANCR) and gene HOXB7 are found to be important in HCC, which are consistent with the newest reports and existing literatures.
Size-Dependent and Property-Independent Passive Microdroplet Sorting by Droplet Transfer on Dot Rails

A fully passive microdroplet sorting method is presented in this paper. On the rails with dot patterns, the droplets were sorted in different ways depending on their size. However, the effect of droplet properties on the threshold size of the sorting was eliminated. The droplet positions on two railways and the Laplace pressure of the droplets on the dot patterns allowed selective droplet transfer according to size. Different gaps between the rails altered the threshold size of the transfer. However, the threshold size was independent of the droplet’s surface tension and viscosity because the droplet transfer utilized only the droplet position and Laplace pressure without lateral flow to sort targets. This feature has a high potential for bio/chemical applications requiring categorization of droplet targets consisting of various mixtures as pre- or post-elements.
Space Bounds for a Game of Graphs

We study a one-person game played by placing pebbles, according to certain rules, on the vertices of a directed graph. In [3] it was shown that for each graph with n vertices and maximum in-degree d , there is a pebbling strategy which requires at most c(d) n/log n pebbles. Here we show that this bound is tight to within a constant factor. We also analyze a variety of pebbling algorithms, including one which achieves the 0(n/log n) bound.
Joint hop-by-hop and receiver-driven interest control protocol for content-centric networks

Content-centric networking (CCN) advocates a new transport model tailored to named-data communication. Three features distinguish CCN transport from the TCP/IP model: unique endpoint at the receiver, pull-based data retrieval in a point to multi-point fashion and in-path caching. The definition of transport control mechanisms is of fundamental importance within the CCN architectural design and beyond, in the broader scope of information-centric networks. In this work, we propose a joint Hop-by-hop and Receiver-driven Interest Control Protocol (HR-ICP) to regulate user requests (Interests) either at the receiver and at intermediate nodes via Interest shaping. We prove that HR-ICP is stable and converges to an efficient and max-min fair equilibrium. Compared to controlling traffic only at the receiver, HR-ICP accelerates congestion reaction and reduces the loss rate, as we show by means of CCN packet-level simulations. In different network scenarios, we highlight the advantages of our solution in terms of faster convergence to the optimal throughput, robustness against misbehaving receivers and flow protection of delay-sensitive applications.
Normocalcemic Primary Hyperparathyroidism: A Comparison with the Hypercalcemic Form in a Tertiary Referral Population

Abstract Normocalcemic primary hyperparathyroidism (NPHPT) is a formally recognized variant of primary hyperparathyroidism (PHPT), characterized by normal total and ionized serum calcium concentrations and elevated parathyroid hormone (PTH) levels, in the absence of secondary causes for hyperparathyroidism. NPHPT has been studied previously, but data are limited and confounded. We aimed to compare the clinical and biochemical data of normocalcemic and hypercalcemic subjects in a hospital-based population. We retrospectively analysed the medical records of 131 subjects diagnosed with PHPT at the university hospital Brussels (UZ Brussel) between January 1st 2007 and December 31st 2016, including 25 normocalcemic and 106 hypercalcemic subjects. The mean values of age, BMI, sex, serum 25-OH vitamin D levels and urinary phosphate excretion were comparable between both groups. Subjects diagnosed with NPHPT had significantly lower plasma PTH levels, lower urinary calcium excretion and lower serum creatinine levels compared to the hypercalcemic subjects with PHPT. Corresponding eGFR values were higher in the normocalcemic group. Normocalcemic subjects (NPHPT) presented with a high prevalence of nephrolithiasis (36%), fragility fractures (12%) and osteoporosis (25%). Clinical manifestations and BMD measurements revealed no statistically significant differences between both groups. Our data show a relative prevalence of 19% NPHPT in PHPT. NPHPT may present the earliest form of PHPT with an extension in time, but is not an indolent disease state. Normocalcemic subjects should be managed as hypercalcemic subjects with PHPT. Further research regarding the pathophysiology and natural course of NPHPT is required.