Miracle-dz/UniFAR-FTUs huggingface dataset:
A 0.25-V 28-nW 58-dB Dynamic Range Asynchronous Delta Sigma Modulator in 130-nm Digital CMOS Process

In this paper, we present a single-bit clock-less asynchronous delta-sigma modulator (ADSM) operating at just 0.25 V power supply. Several circuit approaches were employed to enable such low-voltage operation and maintain high performance. One approach involved utilizing bulk-driven transistors in subthreshold region with transconductance-enhancement topology. Another approach was to employ distributed transistor layout structure to mitigate the effect of low output impedance due to halo drain implants employed in today's digital CMOS process. The ADSM achieved a characteristic center frequency of 630 Hz. It had an effective signal-to-noise-plus-distortion ratio (SNDR) of 58 dB or effective number of bits (ENOB) 9 b and just 28-nW power dissipation. A detailed analytical model capturing the effect of nonidealities of the individual circuit components is also presented for the first time with a close agreement with experimental results.
Wavelet-Based Compressive Sensing for Point Scatterers

Compressive Sensing (CS) allows for the sam- pling of signals at well below the Nyquist rate but does so, usually, at the cost of the suppression of lower amplitude sig- nal components. Recent work suggests that important infor- mation essential for recognizing targets in the radar context is contained in the side-lobes as well, which are often sup- pressed by CS. In this paper we extend existing techniques and introduce new techniques both for improving the accu- racy of CS reconstructions and for improving the separa- bility of scenes reconstructed using CS. We investigate the Discrete Wavelet Transform (DWT), and show how the use of the DWT as a representation basis may improve the ac- curacy of reconstruction generally. Moreover, we introduce the concept of using multiple wavelet-based reconstructions of a scene, given only a single physical observation, to derive reconstructions that surpass even the best wavelet-based CS reconstructions. Lastly, we specifically consider the effect of the wavelet-based reconstruction on classification. This is done indirectly by comparing outputs of different algo- rithms using a variety of separability measures. We show that various wavelet-based CS reconstructions are substan- tially better than conventional CS approaches at inducing (or preserving) separability, and hence may be more useful in classification applications.
Effect of High-Flow Nasal Cannula and Body Position on End-Expiratory Lung Volume: A Cohort Study Using Electrical Impedance Tomography

BACKGROUND: Electrical impedance tomography measures changes in lung impedance, which are mainly related to changes in lung volume. We used electrical impedance tomography to investigate the effects of high-flow nasal cannula (HFNC) and body position on global and regional end-expiratory lung impedance variation (ΔEELI). METHODS: Prospective study with 20 healthy adults. Two periods were defined: the first in supine position and the second in prone position. Each period was divided into 3 phases. In the first and the third phases the subjects were breathing ambient air, and in the second HFNC was implemented. Four regions of interest were defined: 2 ventral and 2 dorsal. For each respiratory cycle, global and regional ΔEELI were measured by electrical impedance tomography and were expressed as a function of the tidal variation of the first stable respiratory cycle (units). RESULTS: HFNC increased global EELI by 1.26 units (95% CI 1.20–1.31, P < .001) in supine position, and by 0.87 units (95% CI 0.82–0.91, P < .001) in prone position. The distribution of ΔEELI was homogeneous in prone position, with no difference between ventral and dorsal lung regions (−0.01 units, 95% CI −0.01 to 0, P = .18), while in supine position a significant difference was found (0.22 units, 95% CI 0.21–0.23, P < .001) with increased EELI in ventral areas. CONCLUSIONS: HFNC increased global EELI in our population, regardless of body position, suggesting an increase in functional residual capacity. Prone positioning was related to a more homogeneous distribution of ΔEELI, while in supine position ΔEELI was higher in the ventral lung regions.
Evaluation of selection in context-free grammar learning systems

Grammatical inference deals with learning of grammars describing languages. Formal grammatical inference aims at identifying families of languages that have a shared property, which can be used to prove ecient learnability of the families formally. In contrast, in empirical grammatical inference research, practical systems are developed that are applied to languages. The eectiveness of these systems is measured by comparing the learned grammar against a Gold standard which indicates the ground truth. From successful empirical learnability results, either shared properties may be identied, leading to further formal learnability results, or modications to the systems may be made, improving practical results. Proper evaluation of empirical systems is, therefore, essential. Here, we evaluate and compare existing state-of-the-art context-free grammar learning systems (and novel systems based on combinations of existing phases) in a standardized evaluation environment (on a corpus of plain natural language sentences), illustrating future directions for empirical grammatical inference research.
A Critical Gating Switch at a Modulatory Site in Neuronal Kir3 Channels

Inwardly rectifying potassium channels enforce tight control of resting membrane potential in excitable cells. The Kir3.2 channel, a member of the Kir3 subfamily of G-protein-activated potassium channels (GIRKs), plays several roles in the nervous system, including key responsibility in the GABAB pathway of inhibition, in pain perception pathways via opioid receptors, and is also involved in alcoholism. PKC phosphorylation acts on the channel to reduce activity, yet the mechanism is incompletely understood. Using the heterologous Xenopus oocyte system combined with molecular dynamics simulations, we show that PKC modulation of channel activity is dependent on Ser-196 in Kir3.2 such that, when this site is phosphorylated, the channel is less sensitive to PKC inhibition. This reduced inhibition is dependent on an interaction between phospho-Ser (SEP)-196 and Arg-201, reducing Arg-201 interaction with the sodium-binding site Asp-228. Neutralization of either SEP-196 or Arg-201 leads to a channel with reduced activity and increased sensitivity to PKC inhibition. This study clarifies the role of Ser-196 as an allosteric modulator of PKC inhibition and suggests that the SEP-196/Arg-201 interaction is critical for maintaining maximal channel activity. SIGNIFICANCE STATEMENT The inwardly rectifying potassium 3.2 (Kir3.2) channel is found principally in neurons that regulate diverse brain functions, including pain perception, alcoholism, and substance addiction. Activation or inhibition of this channel leads to changes in neuronal firing and chemical message transmission. The Kir3.2 channel is subject to regulation by intracellular signals including sodium, G-proteins, ethanol, the phospholipid phosphatidylinositol bis-phosphate, and phosphorylation by protein kinases. Here, we take advantage of the recently published structure of Kir3.2 to provide an in-depth molecular view of how phosphorylation of a specific residue previously thought to be the target of PKC promotes channel gating and acts as an allosteric modulator of PKC-mediated inhibition.
Effects of team-skills guidance on accounting students with lone wolf tendencies

ABSTRACT We investigate whether short-term in-class team-skills guidance impacts the perceptions of accounting students with lone wolf tendencies on team work, and peer evaluation systems adopted in team work. We find that students with greater lone wolf tendencies see fewer benefits from engaging in team work and are also less comfortable with peer evaluation systems. In terms of team-skills guidance, we find that students who are exposed to this are more aware of shortcomings in their teams and are more concerned about there being collusion in peer evaluation ratings. In terms of interaction effects, we find that the team-skills guidance explored in our study results in students with greater lone wolf tendencies perceiving one positive benefit from engaging in team work: they find working on the project to be easier than students not exposed to this team-skills guidance. Students with greater lone wolf tendencies who undergo team-skills guidance are also more concerned that friendship and popularity may distort the reliability of peer evaluation. Among students with lesser lone wolf tendencies, we find that team-skills guidance results in the perception of fewer benefits from engaging in team work on a number of dimensions when compared to students not exposed to team-skills guidance.
Learning Distributed Document Representations for Multi-label Document Categorization

Multi-label Document Categorization, the task of automatically assigning a text document into one or more categories has various real-world applications such as categorizing news articles, tagging Web pages, maintaining medical patient records and organizing digital libraries among many others. Statistical Machine Learning approaches to document categorization have focused on multi-label learning algorithms such as Support Vector Machines, k-Nearest Neighbors, Logistic Regression, Neural Networks, Naive Bayes, Generative Probabilistic Models etc. while the input to such algorithms i.e. the vector representation for documents has traditionally been used as the bag-of-words model. Though the usage of simple bag-of-words representation gives surprisingly accurate results, it suffers from sparsity, high-dimensionality, lack of similarity measures along with other drawbacks such as the inability to encode word ordering and contextual information in which the words occur. Encoding contextual information about words in documents is crucial to capture the correct semantic content of the highly complex and ambiguous human language. Our work is focused on learning continuous distributed vector representations for documents by embedding all the documents in the same low-dimensional space such that documents that are similar in their semantic content have similar vector representations. To tackle the issues in bag-of-words representation model, we present an unsupervised neural network model that uses the document vector to predict words in the document along with using the contextual information in which the word occurs and jointly learns distributed document and word representations. We develop a modified version of the logistic regression algorithm to learn similar distributed representations for categories to perform the document categorization task. We show that our model gives state-of-the-art results on the standard Reuters-21578 dataset, improving the bag-of-words model by 9% and previous state-of-the-art by 3.26% in terms of the F1 Score. We also show the effectiveness of our model in imputing missing categories on the Wikipedia articles against the bag-of-words representations. As we embed documents, categories and words in the same low-dimensional space our model can also estimate semantic similarities between them. We qualitatively demonstrate that the learned representations capture the semantic dependencies between categories and words which is not directly observed in the data.
Functional Analyses of Mycobacterial Lipoprotein Diacylglyceryl Transferase and Comparative Secretome Analysis of a Mycobacterial lgt Mutant

ABSTRACT Preprolipopoprotein diacylglyceryl transferase (Lgt) is the gating enzyme of lipoprotein biosynthesis, and it attaches a lipid structure to the N-terminal part of preprolipoproteins. Using Lgt from Escherichia coli in a BLASTp search, we identified the corresponding Lgt homologue in Mycobacterium tuberculosis and two homologous (MSMEG_3222 and MSMEG_5408) Lgt in Mycobacterium smegmatis. M. tuberculosis lgt was shown to be essential, but an M. smegmatis ΔMSMEG_3222 mutant could be generated. Using Triton X-114 phase separation and [14C]palmitic acid incorporation, we demonstrate that MSMEG_3222 is the major Lgt in M. smegmatis. Recombinant M. tuberculosis lipoproteins Mpt83 and LppX are shown to be localized in the cell envelope of parental M. smegmatis but were absent from the cell membrane and cell wall in the M. smegmatis ΔMSMEG_3222 strain. In a proteomic study, 106 proteins were identified and quantified in the secretome of wild-type M. smegmatis, including 20 lipoproteins. All lipoproteins were secreted at higher levels in the ΔMSMEG_3222 mutant. We identify the major Lgt in M. smegmatis, show that lipoproteins lacking the lipid anchor are secreted into the culture filtrate, and demonstrate that M. tuberculosis lgt is essential and thus a validated drug target.
Minimal Residual Disease-Based Risk Stratification in Chinese Childhood Acute Lymphoblastic Leukemia by Flow Cytometry and Plasma DNA Quantitative Polymerase Chain Reaction

Minimal residual disease, or MRD, is an important prognostic indicator in childhood acute lymphoblastic leukemia. In ALL-IC-BFM 2002 study, we employed a standardized method of flow cytometry MRD monitoring for multiple centers internationally using uniformed gating, and determined the relevant MRD-based risk stratification strategies in our local patient cohort. We also evaluated a novel method of PCR MRD quantitation using peripheral blood plasma. For the bone marrow flow MRD study, patients could be stratified into 3 risk groups according to MRD level using a single time-point at day-15 (Model I) (I-A: <0.1%, I-B: 0.1–10%, I-C: >10%), or using two time-points at day-15 and day-33 (Model II) (II-A: day-15<10% and day-33<0.01%, II-B: day-15≥10% or day-33≥0.01% but not both, II-C: day-15≥10% and day-33≥0.01%), which showed significantly superior prediction of relapse (p = .00047 and <0.0001 respectively). Importantly, patients with good outcome (frequency: 56.0%, event-free survival: 90.1%) could be more accurately predicted by Model II. In peripheral blood plasma PCR MRD investigation, patients with day-15-MRD≥10−4 were at a significantly higher risk of relapse (p = 0.0117). By multivariate analysis, MRD results from both methods could independently predict patients’ prognosis, with 20–35-fold increase in risk of relapse for flow MRD I-C and II-C respectively, and 5.8-fold for patients having plasma MRD of ≥10−4. We confirmed that MRD detection by flow cytometry is useful for prognostic evaluation in our Chinese cohort of childhood ALL after treatment. Moreover, peripheral blood plasma DNA MRD can be an alternative where bone marrow specimen is unavailable and as a less invasive method, which allows close monitoring.
When the default Hadoop job scheduling policy is applied , the task of a single job typically runs on the same

Cloud computing has become increasingly popular model for delivering applications hosted in large data centers as subscription oriented services. Hadoop is a popular system supporting the MapReduce function, which plays a crucial role in cloud computing. The resources required for executing jobs in a large data center vary according to the job type. In Hadoop, jobs are scheduled by default on a first-come-first-served basis, which may unbalance resource utilization. This paper proposes a job scheduler called the job allocation scheduler (JAS), designed to balance resource utilization. For various job workloads, the JAS categorizes jobs and then assigns tasks to a CPU-bound queue or an I/O-bound queue. However, the JAS exhibited a locality problem, which was addressed by developing a modified JAS called the job allocation scheduler with locality (JASL). The JASL improved the use of nodes and the performance of Hadoop in heterogeneous computing environments. Finally, two parameters were added to the JASL to detect inaccurate slot settings and create a dynamic job allocation scheduler with locality (DJASL). The DJASL exhibited superior performance than did the JAS, and data locality similar to that of the JASL.
Organization of Participation in the Meetings of Alcoholics Anonymous

AA meetings are an arena of mutual help for recovering substance abusers. They are characteristically formal interactions in which turns are pre-allocated to parties. Through the analysis of audio-recordings of interactions, I have shown that the formality of interaction is members' collaborative achievement. The opening rituals of a meeting are members' method to mark the boundary between muruiane talk and the specific institutional sphere so that parties may move from conversational turn-taking to formally arranged turn-taking. As a collaborative achievement, the format of meeting interaction is an enabling structure that allows parties to design their turns so that they may talk into being the institution of mutual help. Participants orient to the pre-allocated time-slots as cm aspect of the format of AA meeting interaction that allows them to construct their turns in collaboration with recipients. AA members use the specific format of their meeting interaction to share their experiences and to establish egalitarian relationships with each other.
A new method for pitch prediction from spectral envelope and its application in voice conversion

This paper deals with the estimation of pitch from only spectral envelope information. The proposed method uses a Gaussian Mixture Model (GMM) to characterize the joint distribution of the spectral envelope parameters and pitchnormalized values. During the learning stage, the model parameters are estimated by means of the EM algorithm. Then, a regression is made which enables the determination of a pitch prediction function from spectral envelope coefficients. Some results are presented which show the accuracy of the proposed method in terms of pitch prediction. Finally, the application of this method in a voice conversion system is described.
Sharing and Reusing Context Information in Ubiquitous Computing Environments

In highly dynamic environments it is not enough to model the user in order to provide proactive and personalized services. User features, preferences and needs change depending on different contextual aspects such as physical, social and computational conditions. Taking context into account in these environments implies coping with high openness and dynamicity of users and devices. Moreover, context modeling and context management is a complex task performed repeatedly in distributed environments, and users constantly share information about current activities, location, social events, goals, etc. In different applications. There is huge context information scattered over user's applications and devices that can be taken advantage of to provide more accurate adaptation and personalization. In this paper, we analyze the literature solutions with a focus on context information interoperability. We aim to identify basic requirements to perform the complex task of sharing and reusing context information between heterogeneous context providers and context consumers.
A 3-D object shape recovering system for active scenes using colour disordered patterns

A new 3-D data recovering system based on structured light-colour projection for active scenes is presented in this paper. With the use of a light projector and a single camera the system is capable of easily solving the correspondence problem when a 6-connectivity colour disordered pattern illuminates the scene. Since the pattern is handled as a set, the correspondence solution is based on set algebra operators. False matchings and errors are also detected and effectively solved. Experimentation and results show the goodness of the method.
Verifying influenza and pneumococcal immunization status of children in 2009-2010 from primary care practice records and from the North Carolina Immunization Registry.

BACKGROUND The North Carolina Immunization Registry (NCIR) has been available since 2004. We sought to measure its utilization among practices that provide primary care for children who are enrolled in a prospective influenza surveillance study. METHODS This study included children aged 0.5-17 years who presented with fever or acute respiratory symptoms to an emergency department or inpatient setting in Winston-Salem, North Carolina, from September 1, 2009, through May 19, 2010. Study team members verified influenza and pneumococcal immunization status by requesting records from each child's primary care practice and by independently reviewing the NCIR. We assessed agreement of nonregistry immunization medical records with NCIR data using the kappa statistic. RESULTS Fifty-six practices confirmed the immunization status of 292 study-enrolled children. For most children (238/292, 82%), practices verified the child's immunizations by providing a copy of the NCIR record. For 54 children whose practices verified their immunizations by providing practice records alone, agreement with the NCIR by the kappa statistic was 0.6-0.7 for seasonal and monovalent H1N1 influenza vaccines and 0.8-0.9 for pneumococcal conjugate and polysaccharide vaccines. A total of 221 (98%) of 226 enrolled children younger than 6 years of age had 2 or more immunizations documented in the NCIR. LIMITATIONS NCIR usage may vary in other regions of North Carolina. CONCLUSION More than 95% of children younger than 6 years of age had 2 or more immunizations documented in the NCIR; thus, the Centers for Disease Control and Prevention 2010 goal for immunization information systems was met in this population. We found substantial agreement between practice records and the NCIR for influenza and pneumococcal immunizations in children.
EFFICACY AND SAFETY OF RITUXIMAB IN B-CELL POST-TRANSPLANT LYMPHOPROLIFERATIVE DISORDERS: RESULTS OF A PROSPECTIVE MULTICENTRE PHASE II STUDY Running head: Rituximab in PTLD

Sylvain Choquet, MD , Veronique Leblond, MD, Raoul Herbrecht, MD, Gerard Socie, MD, PhD, Anne-Marie Stoppa, MD, Peter Vandenberghe, MD, PhD, Alain Fischer, MD, PhD, Franck Morschhauser, MD, Gilles Salles, MD, PhD, Walter Feremans, MD, PhD, Etienne Vilmer, MD, Marie-Noelle Peraldi, MD, PhD, Philippe Lang, MD, Yvon Lebranchu, MD, Eric Oksenhendler, MD, Jeanne Luce Garnier, MD, PhD, Thierry Lamy, MD, PhD, Arnaud Jaccard, MD, Augustin Ferrant, MD, PhD, Fritz Offner, MD, Olivier Hermine,