Miracle-dz/UniFAR-FTUs huggingface dataset:
Parallel Computation of the Burrows-Wheeler Transform of Short Reads Using Prefix Parallelism

The Burrows-Wheeler transform (BWT) of short-read data has unexplored potential utilities, such as for efficient and sensitive variation analysis against multiple reference genome sequences, because it does not depend on any particular reference genome sequence, unlike conventional mapping-based methods. However, since the amount of read data is generally much larger than the size of the reference sequence, computation of the BWT of reads is not easy, and this hampers development of potential applications. For the alleviation of this problem, a new method of computing the BWT of reads in parallel is proposed. The BWT, corresponding to a sorted list of suffixes of reads, is constructed incrementally by successively including longer and longer suffixes. The working data is divided into more than 10,000 “blocks” corresponding to sublists of suffixes with the same prefixes. Thousands of groups of blocks can be processed in parallel while making exclusive writes and concurrent reads into a shared memory. Reads and writes are basically sequential, and the read concurrency is limited to two. Thus, a fine-grained parallelism, referred to as <italic>prefix parallelism</italic>, is expected to work efficiently. The time complexity for processing <inline-formula><tex-math notation="LaTeX">$n$</tex-math><alternatives><inline-graphic xlink:href="kimura-ieq1-2837749.gif"/></alternatives></inline-formula> reads of length <inline-formula><tex-math notation="LaTeX">$\ell$</tex-math><alternatives><inline-graphic xlink:href="kimura-ieq2-2837749.gif"/></alternatives></inline-formula> is <inline-formula><tex-math notation="LaTeX">$O(n\ell ^2)$</tex-math><alternatives><inline-graphic xlink:href="kimura-ieq3-2837749.gif"/></alternatives></inline-formula>. On actual biological DNA sequence data of about 100 Gbp with a read length of 100 bp (base pairs), a tentative implementation of the proposed method took less than an hour on a single-node computer; i.e., it was about three times faster than one of the fastest programs developed so far.
Robust Reconstruction of Elasticity Using Ultrasound Imaging and Multi-Frequency Excitations

Biomedical parameters of tissue can be important indicators for clinical diagnosis. One such parameter that reflects tissue stiffness is elasticity, the imaging of which is called elastography. In this paper, we use displacements from harmonic excitations to solve the inverse problem of elasticity based on a finite-element method (FEM) formulation. This leads to iterative solution of nonlinear and nonconvex problems. In this paper, we show the importance and selection of viable initializations in numerical simulation studies and propose techniques for the fusion of multiple initializations for ideal reconstructions of unknown tissue as well as combining information from excitations at multiple frequencies. Results show that our method leads up to 76% decrease in root-mean-squared error (RMSE) and 9.9 dB increase in contrast-to-noise ratio (CNR) in simulations with noise, when compared to conventional iterative FEM without multiple initializations and frequencies. As the wave patterns in individually selected frequencies may introduce artifacts, a joint inverse-problem solution of multi-frequency excitations is introduced as a robust solution, where CNR improvements of up to 11.9 dB are observed. We also present the methods on a tissue-mimicking gelatin phantom study using mechanical excitation and ultrafast plane-wave ultrasound imaging, where the RMSE was improved by up to 51%. An experiment of ablation via heating an ex-vivo bovine liver shows that reconstruction artifacts are reduced with our proposed method.
Learners' Self Checking and Its Effectiveness in Conceptual Data Modeling Exercises

Conceptual data modeling is an important activity in database design. However, it is difficult for novice learners to master its skills. In the conceptual data modeling, learners are required to detect and correct errors of their artifacts by themselves because modeling tools do not assist these activities. We call such activities self checking, which is also an important process. However, the previous research did not focus on it and/or the data collection of self checks. The data collection of self checks is difficult because self checking is an internal activity and self checks are not usually expressed. Therefore, we developed a method to help learners express their self checks by reflecting on their artifact making processes. In addition, we developed a system, KIfU 3.0, which implements this method. We conducted an evaluation experiment and showed the effectiveness of the method. From the experimental results, we found out that (1) the novice learners conduct self checks during their conceptual data modeling tasks; (2) it is difficult for them to detect errors in their artifacts; (3) they cannot necessarily correct the errors even if they could identify them; and (4) there is no relationship between the numbers of self checks by the learners and the quality of their artifacts. key words: Conceptual data modeling, self checking, artifact-making process, UML
Bas-Relief Modeling from Normal Layers

Bas-relief is characterized by its unique presentation of intrinsic shape properties and/or detailed appearance using materials raised up in different degrees above a background. However, many bas-relief modeling methods could not manipulate scene details well. We propose a simple and effective solution for two kinds of bas-relief modeling (i.e., structure-preserving and detail-preserving) which is different from the prior tone mapping alike methods. Our idea originates from an observation on typical 3D models, which are decomposed into a piecewise smooth base layer and a detail layer in normal field. Proper manipulation of the two layers contributes to both structure-preserving and detail-preserving bas-relief modeling. We solve the modeling problem in a discrete geometry processing setup that uses normal-based mesh processing as a theoretical foundation. Specifically, using the two-step mesh smoothing mechanism as a bridge, we transfer the bas-relief modeling problem into a discrete space, and solve it in a least-squares manner. Experiments and comparisons to other methods show that (i) geometry details are better preserved in the scenario with high compression ratios, and (ii) structures are clearly preserved without shape distortion and interference from details.
Prediction of Perceived Steering Wheel Operation Force by Muscle Activity

Humans feel forces or weights while grasping and manipulating an object. There is a difference between the physical and perceived forces because the physical characteristics of an object and/or human psychophysical characteristics affect perceived force. Sense of effort plays an important role in deciding the movement made by humans. In this study, we propose a computational method to predict the perceived force by evaluating the muscle activity as a function of effort in the operation of a steering wheel based on a 3D-musculoskeletal model simulation. We found that the perceived-force characteristics depend on the driving posture, though the applied force is the same. We evaluated the results, and showed that the mean of the absolute error is 1.78 N for the experiments conducted on four different vehicles in commercially available.
Words with weight: The construction of obesity in eating disorders research

In current public health discourse, obesity is conceptualized as a disease epidemic, with treatment being weight loss. The pursuit of weight loss as a treatment for the “disease” of obesity is in direct contradiction to the history of research in eating disorders, which has demonstrated the risks for the development of eating disorders. In this study, we critically examined the eating disorder literature to explore this contradiction. We analyzed 30 of the top-cited articles in the eating disorder literature between 1994 and 2011, asking: how is the concept of obesity examined in eating disorder research? We identified tensions related to body mass index and the perceived associated risks of lower or higher body mass index, assumptions of the “causes” of fatness (i.e. overeating and inactivity), and the anti-diet voice challenging the prescription of dieting for those in fat bodies. In our analysis, we highlight the problematics of, for instance, prescribing a body mass index range of 20–24 in eating disorder recovery, how many studies in eating disorders do not problematize the presumption that a higher body mass index is necessarily associated with ill health, and a lack of cultural sensitivity and acknowledgment of intersectional spaces of belonging. We discuss these themes in the context of biomedical discourses of obesity contributing to the cultural thin ideal. We argue that biomedical discourses on obesity contribute to the thin ideal nuanced against discourses of healthism that permeate our society. Rather than an ideal of emaciation, it is an ideal of a healthy, productive person, often constructed as morally superior. The moral panic around obesity is evident throughout the eating disorder literature, which is a concern given that we would hope that the aim of eating disorder treatment would be to promote wellness for all—not only those who are thin.
An Integrated Framework for Identifying Mutated Driver Pathway and Cancer Progression

Next-generation sequencing (NGS) technologies provide amount of somatic mutation data in a large number of patients. The identification of mutated driver pathway and cancer progression from these data is a challenging task because of the heterogeneity of interpatient. In addition, cancer progression at the pathway level has been proved to be more reasonable than at the gene level. In this paper, we introduce an integrated framework to identify mutated driver pathways and cancer progression (iMDPCP) at the pathway level from somatic mutation data. First, we use uncertainty coefficient to quantify mutual exclusivity on gene driver pathways and develop a computational framework to identify mutated driver pathways based on the adaptive discrete differential evolution algorithm. Then, we construct cancer progression model for driver pathways based on the Bayesian Network. Finally, we evaluate the performance of iMDPCP on real cancer somatic mutation datasets. The experimental results indicate that iMDPCP is more accurate than state-of-the-art methods according to the enrichment of KEGG pathways, and it also provides new insights on identifying cancer progression at the pathway level.
Effects of AR Display Context Switching and Focal Distance Switching on Human Performance

In augmented reality (AR) environments, information is often distributed between real world and virtual contexts, and often appears at different distances from the user. Therefore, to integrate the information, users must repeatedly switch context and refocus the eyes. To focus at different distances, the user's eyes must accommodate, which when done repeatedly can cause eyestrain and degrade task performance. An experiment was conducted that examined switching context and focal distance between a real and an AR environment, using a text-based visual search task and a monocular optical see-through AR display. Both context switching and focal distance switching resulted in significantly reduced performance. In addition, repeatedly performing the task caused visual fatigue to steadily increase. Performance was particularly poor for virtual text presented at optical infinity, and for target letters that participants tried to read before their eyes had completely accommodated to a new focal distance. The results show that context switching and focal distance switching are important AR user interface design issues.
Large Scale Image Segmentation with Structured Loss Based Deep Learning for Connectome Reconstruction

We present a method combining affinity prediction with region agglomeration, which improves significantly upon the state of the art of neuron segmentation from electron microscopy (EM) in accuracy and scalability. Our method consists of a 3D U-Net, trained to predict affinities between voxels, followed by iterative region agglomeration. We train using a structured loss based on Malis, encouraging topologically correct segmentations obtained from affinity thresholding. Our extension consists of two parts: First, we present a quasi-linear method to compute the loss gradient, improving over the original quadratic algorithm. Second, we compute the gradient in two separate passes to avoid spurious gradient contributions in early training stages. Our predictions are accurate enough that simple learning-free percentile-based agglomeration outperforms more involved methods used earlier on inferior predictions. We present results on three diverse EM datasets, achieving relative improvements over previous results of 27, 15, and 250 percent. Our findings suggest that a single method can be applied to both nearly isotropic block-face EM data and anisotropic serial sectioned EM data. The runtime of our method scales linearly with the size of the volume and achieves a throughput of $\sim$∼ 2.6 seconds per megavoxel, qualifying our method for the processing of very large datasets.
Young children with significant developmental delay differentiate home observed attachment behaviour towards their parents.

BACKGROUND The hallmark of attachment is that contact, proximity and relief from stress are sought from specific individuals, laying important groundwork for healthy socioemotional functioning. This study investigated the extent to which differentiated attachment behaviour can be observed in young children with significant developmental delay (DD). METHOD Video-taped observations of the parent-child and stranger-child interaction were conducted at home and complemented with questionnaires in 20 families with a child with significant DD (age 2-7 years with an average DD of 49 months). RESULTS Children displayed more intense and persistent contact-seeking, contact-maintaining and resistant behaviour in the episodes with their parent compared to the episodes with the stranger. Parent-reported secure attachment behaviour was slightly more characteristic towards mother compared to father. CONCLUSIONS Even children with significant DD develop differentiated attachment behaviour. Detailed observations may support parents in identifying the interactions that make the attachment relationship with their child special.
Predictor-Based Extended-State-Observer Design for Consensus of MASs With Delays and Disturbances

In this paper, we study output feedback leader–follower consensus problem for multiagent systems subject to external disturbances and time delays in both input and output. First, we consider the linear case and a novel predictor-based extended state observer is designed for each follower with relative output information of the neighboring agents. Then, leader–follower consensus protocols are proposed which can compensate the delays and disturbances efficiently. In particular, the proposed observer and controller do not contain any integral term of the past control input and hence are easy to implement. Consensus analysis is put in the framework of Lyapunov–Krasovskii functionals and sufficient conditions are derived to guarantee that the consensus errors converge to zero asymptotically. Then, the results are extended to nonlinear multiagent systems with nonlinear disturbances. Finally, the validity of the proposed design is demonstrated through a numerical example of network-connected unmanned aerial vehicles.
A Smart Capsule With a Hydrogel-Based pH-Triggered Release Switch for GI-Tract Site-Specific Drug Delivery

In this paper, we present a smart capsule that can release its payload after a predetermined/adjustable delay subsequent to passing from the stomach into the small intestine. The described capsule (9 mm × 22 mm) comprises a pH-sensitive hydrogel-based switch, an electronic compartment containing a capacitor charged to 2.7 V, and a drug reservoir capped by a taut fusible thread intertwined with a nichrome wire. The nichrome wire, capacitor, and pH-responsive electrical switch are connected in series. The pH transition the capsule encounters when it enters the small intestine triggers controlled swelling of the pH-responsive hydrogel, which pushes a conductive elastic membrane to close an electrical switch. This initiates a sequence of events, i.e., the discharge of the capacitor, heating the nichrome wire, breakage of the fusible thread, and release of the payload stored in the capsule reservoir through the unlatched cap. The time lag between initiation of hydrogel swelling (by the near-neutral pH of the small intestine) and payload release is controlled by the deflection of the conductive elastic membrane and the gap separating the contacts. The release time can be set to within ±5 min after one hour in the small intestine (start of the swelling) increasing to ±40 min after 4 h.
Character Feature Learning for Named Entity Recognition

SUMMARY The deep neural named entity recognition model automatically learns and extracts the features of entities and solves the problem of the traditional model relying heavily on complex feature engineering and obscure professional knowledge. This issue has become a hot topic in recent years. Existing deep neural models only involve simple character learning and extraction methods, which limit their capability. To further explore the performance of deep neural models, we propose two character feature learning models based on convolution neural network and long short-term memory network. These two models consider the local semantic and position features of word characters. Experiments conducted on the CoNLL-2003 dataset show that the proposed models outperform traditional ones and demonstrate excellent performance.
A Hierarchical Bayesian Model for Personalized Survival Predictions

We study the problem of personalizing survival estimates of patients in heterogeneous populations for clinical decision support. The desiderata are to improve predictions by making them personalized to the patient-at-hand, to better understand diseases and their risk factors, and to provide interpretable model outputs to clinicians. To enable accurate survival prognosis in heterogeneous populations we propose a novel probabilistic survival model which flexibly captures individual traits through a hierarchical latent variable formulation. Survival paths are estimated by jointly sampling the location and shape of the individual survival distribution resulting in patient-specific curves with quantifiable uncertainty estimates. An understanding of model predictions is paramount in medical practice where decisions have major social consequences. We develop a personalized interpreter that can be used to test the effect of covariates on each individual patient, in contrast to traditional methods that focus on population average effects. We extensively validated the proposed approach in various clinical settings, with a special focus on cardiovascular disease.
Prevalence of Antibiotic Resistance in Helicobacter pylori: A Systematic Review and Meta-analysis in World Health Organization Regions.

BACKGROUND & AIMS In 2017, the World Health Organization (WHO) designated clarithromycin-resistant Helicobacter pylori a high priority for antibiotic research and development. However, there are no clear data on the global distribution of resistance or its clinical effects. We performed a systematic review and meta-analysis to assess the distribution of H pylori resistance to commonly used antibiotics and to measure the association between antibiotic resistance and treatment failure. METHODS We searched publication databases for studies that assessed rates of H pylori resistance to clarithromycin, metronidazole, levofloxacin, amoxicillin, or tetracycline. Pooled estimates of primary and secondary resistance and 95% confidence intervals (CIs) were grouped by WHO region. The association between antibiotic resistance and treatment failure was measured by extracting data on treatment efficacy in patients with resistant and susceptible isolates and pooling odds ratios with 95% CIs. RESULTS We identified 178 studies, comprising 66,142 isolates from 65 countries. Primary and secondary resistance rates to clarithromycin, metronidazole, and levofloxacin were ≥15% in all WHO regions, except primary clarithromycin resistance in the Americas (10%; 95% CI, 4%-16%) and South-East Asia region (10%; 95% CI, 5%-16%) and primary levofloxacin resistance in the European region (11%; 95% CI, 9%-13%). There was considerable heterogeneity (I2 > 75%) among all analyses-this might have resulted from the grouping of resistance rates by country. Increasing antibiotic resistance was observed in most WHO regions. Resistance to clarithromycin was significantly associated with failure of clarithromycin-containing regimens (odds ratio, 6.97; 95% CI, 5.23-9.28; P < .001). CONCLUSIONS Resistance of H pylori to antibiotics has reached alarming levels worldwide, which has a great effect on efficacy of treatment. Local surveillance networks are required to select appropriate eradication regimens for each region.
Teaching Semi-Supervised Classifier via Generalized Distillation

Semi-Supervised Learning (SSL) is able to build reliable classifier with very scarce labeled examples by properly utilizing the abundant unlabeled examples. However, existing SSL algorithms often yield unsatisfactory performance due to the lack of supervision information. To address this issue, this paper formulates SSL as a Generalized Distillation (GD) problem, which treats existing SSL algorithm as a learner and introduces a teacher to guide the learner? s training process. Specifically, the intelligent teacher holds the privileged knowledge that ? explains? the training data but remains unknown to the learner, and the teacher should convey its rich knowledge to the imperfect learner through a specific teaching function. After that, the learner gains knowledge by ? imitating? the output of the teaching function under an optimization framework. Therefore, the learner in our algorithm learns from both the teacher and the training data, so its output can be substantially distilled and enhanced. By deriving the Rademacher complexity and error bounds of the proposed algorithm, the usefulness of the introduced teacher is theoretically demonstrated. The superiority of our algorithm to the related state-of-the-art methods has also been empirically demonstrated by the experiments on different datasets with various sources of privileged knowledge.
A Two-Stage Multiobjective Evolutionary Algorithm for Multiobjective Multidepot Vehicle Routing Problem With Time Windows

This paper proposes a multiobjective multidepot vehicle routing problem with time windows and designs some real-world test instances. It develops a two-stage multiobjective evolutionary algorithm (TS-MOEA) for dealing with the problem. Stage I of our proposed algorithm focuses on finding extreme solutions, and forms a coarse Pareto front, while stage II extends the found extreme solutions for approximating the whole Pareto front. The two-stage strategy provides a new method to balance convergence and diversity. Moreover, a hybrid neighborhood structure is designed for solution improvement. Experimental result shows that TS-MOEA significantly outperforms two other representative algorithms.