Miracle-dz/UniFAR-FTUs huggingface dataset:
Minimum Distance and Trapping Set Analysis of Protograph-Based LDPC Convolutional Codes

Low-density parity-check (LDPC) convolutional codes have been shown to be capable of achieving capacity-approaching performance with iterative message-passing decoding. In the first part of this paper, using asymptotic methods to obtain lower bounds on the free distance to constraint length ratio, we show that several ensembles of regular and irregular LDPC convolutional codes derived from protograph-based LDPC block codes have the property that the free distance grows linearly with respect to the constraint length, i.e., the ensembles are asymptotically good. In particular, we show that the free distance to constraint length ratio of the LDPC convolutional code ensembles exceeds the minimum distance to block length ratio of the corresponding LDPC block code ensembles. A large free distance growth rate indicates that codes drawn from the ensemble should perform well at high signal-to-noise ratios under maximum-likelihood decoding. When suboptimal decoding methods are employed, there are many factors that affect the performance of a code. Recently, it has been shown that so-called trapping sets are a significant factor affecting decoding failures of LDPC codes over the additive white Gaussian noise channel with iterative message-passing decoding. In the second part of this paper, we study the trapping sets of the asymptotically good protograph-based LDPC convolutional codes considered earlier. By extending the theory presented in part one and using similar bounding techniques, we show that the size of the smallest non-empty trapping set grows linearly with the constraint length for these ensembles.
A Deep Connection Between the Vapnik–Chervonenkis Entropy and the Rademacher Complexity

In this paper, we derive a deep connection between the Vapnik-Chervonenkis (VC) entropy and the Rademacher complexity. For this purpose, we first refine some previously known relationships between the two notions of complexity and then derive new results, which allow computing an admissible range for the Rademacher complexity, given a value of the VC-entropy, and vice versa. The approach adopted in this paper is new and relies on the careful analysis of the combinatorial nature of the problem. The obtained results improve the state of the art on this research topic.
Snap-on robotic wrist module for enhanced dexterity in endoscopic surgery

Burgeoning transendoscopic procedures, such as endoscopic submucosal dissection (ESD), provide a promising means of treating early-stage gastric neoplasia in a minimally-invasive way. However, the remote locations of these lesions, coupled with their origination in the submucosal layers of the gastrointestinal tract, often lead to extreme technical, cognitive and ergonomic challenges which combat the widespread applicability and adoption of these techniques. Among these challenges is achieving the in vivo dexterity required to retract and dissect tissue. By leveraging workspace and force data obtained through clinical studies, we developed a modular, disposable, distally-mounted actuator (an `active endcap') that can augment an endoscopist's distal dexterity in ways that are not achievable with the endoscope's built-in degrees-of-freedom. The device consists of a flexible articulating `exoskeleton' manufactured via printed-circuit MEMS (PCMEMS) which engages and deflects electrosurgical tools that are passed through the endoscopic working channel. Embedded proprioceptive sensing is implemented on-board using distributed LED/phototransistor pairs and the principle of light intensity modulation (LIM). The distal degree-of-freedom is actuated using shape memory alloy (SMA) technology, and the actuation transmission system is fully contained within a 1-inch-long end cap that can be mounted on the distal end of the endoscope, thereby obviating the need for a mechanical connection to a proximal source. Proof-of-concept tests demonstrate that the actuator adds over 50 degrees of distal articulation to existing tools and can generate 450 mN of lateral force which has been clinically determined to be sufficient for performing circumferential incisions in ESD.
Plasma and urine amino acid profiles in a healthy adult population of Singapore.

INTRODUCTION The analysis of amino acids in plasma and urine was introduced in Singapore when a laboratory for the investigation of inherited metabolic disorders was established by the Ministry of Health. Reference ranges are required for interpreting test results and making diagnoses. Initially, reference ranges established for Caucasians were used as there were no local data and we were unable to find data obtained by the same analytical method for Asian populations. This was not considered an ideal and long-term solution, as Singaporeans may have amino acid concentrations quite different from those of Caucasians due to genetic factors, dietary difference, environment, and other influences. This study was therefore undertaken when a number of healthy laboratory personnel volunteered to provide specimens for the study. MATERIALS AND METHODS Sixty healthy male and female laboratory workers not on any form of medication were recruited. They consisted of 24 males (range, 23 to 58 years) and 36 females (range, 20 to 60 years), with a mean age of 38.7 years. Non-fasting random blood and urine specimens were collected on ice. Removal of protein and peptides from heparinised plasma and urine was achieved by ultrafiltration through protein-exclusion membrane. Amino acid analysis on the ultrafiltrate was performed by a dedicated Beckman 6300 Amino Acid Analyzer using a cation exchange resin column and post-column colour reaction with ninhydrin reagent. Urine creatinine was measured by a Beckman LX 20 PRO Analyzer. Results for urine amino acids were expressed as micromol/mmol of creatinine. RESULTS Reference ranges for 32 amino acids in blood plasma and 36 amino acids in urine were calculated by a non-parametric method using the SPSS statistical calculation software. The ranges cover 95% of the population and the low and high limits of each reference range represent the 2.5th percentile and 97.5th percentile of the frequency distribution respectively. CONCLUSIONS We observed differences in the reference ranges of several plasma and urine amino acids between Singaporean and Caucasian populations. Moreover, the list of urine amino acids for Caucasian population is incomplete. We have therefore discontinued the use of reference values established for Caucasians and adopted the results of this study for our patient diagnostic work.
Quantitative Competitive Reverse Transcription-PCR for Quantification of Dengue Virus RNA

ABSTRACT A quantitative competitive reverse transcription-PCR assay was developed to quantify dengue virus RNA in this study. The main features include a primer pair targeting a highly conserved region in the capsid and the addition of competing RNA that contains an internal deletion to provide a stringent internal control for quantification. It can be utilized to quantify RNA isolated from the four dengue virus serotypes but not RNA isolated from other flaviviruses, including Japanese encephalitis virus and hepatitis C virus, both prevalent in Asia. It can also be used to quantify dengue virus RNA isolated from the plasma of infected individuals. The sensitivity of the assay was estimated to be 10 to 50 copies of RNA per reaction, and twofold differences in virus titer are distinguishable. This assay is a convenient, sensitive, and accurate method for quantification and can be used to further understanding of the pathogenesis of dengue virus infection.
On the use of Machine Learning Methods for Speech and Voicing Classification

This work examines the effectiveness of machine learning (ML) classifiers on the problems of voice activity detection and voicing classification. A wide range of ML classifiers are considered and include parametric, probabilistic and non-probabilistic, artificial neural networks and regression. Evaluations are carried out in both stationary and non-stationary noise types at signal-to-noise ratios down to 0dB. In comparison to conventional methods the ML methods are found to be significantly more robust with multilayer perceptrons, Gaussian mixture models and Rotation Forest giving consistently best performance.
Actor Network and Conduct: The Discipline and Practices of Knowledge Management

The works of Gillian Rose in philosophy and social theory provide an alternative perspective on the debates around Actor-Network Theory (ANT), which goes beyond the choice between `radical epistemology' versus `radical politics'. This paper outlines some salient elements of her work, in particular the concept of `diremption `—forced splitting—and `the broken middle' between law and ethics. The paper builds on this work in arguing that a focus on conduct, defined as both discipline and practices, can remain critical of the contemporary world whilst aware of the problems of representation and explanation addressed within ANT
Openness to diversity, trust and conflict in multicultural organizations

Abstract The purpose of this study was to explore the relation between openness to diversity in terms of linguist, visible, value and informational heterogeneity and group processes depicted by group trust and group conflict. We collected data using a web-based survey among 498 academics in multicultural departments. The departments were located in four different Danish universities. We found openness to value diversity and informational diversity to have a positive association with group trust and a negative association with group conflict. Openness to linguistic diversity had positive relationships with group trust. The findings of this study suggest that openness to diversity should be highly prioritized by human resource managers in demographically diverse organizations. Possible interventions include organizational culture change programs, diversity training at individual and team levels as well as rewarding group oriented behavior.
JAABA : An interactive machine learning system for biologists

“Big data” problems are everywhere in neuroscience, the result of new technologies that generate huge, complex data sets, in particular large image and video data [1]. Because of the scale of these data sets, machine vision and learning methods are necessary to extract meaning and understanding. Ideally, biologists and computer scientists could work together, with biologists creating annotated data sets indicating the problem and desired solution, and computer scientists developing algorithms to automatically reproduce these manual annotations. Unfortunately, such a clean separation of work is usually not possible. Scientific research is by nature an iterative process, and it is often difficult a priori to formulate a mathematical criterion to be optimized. Animal behavior annotation is a prime example of this. The behavioral “vocabulary” of animals is unknown. For a specific behavior category, it is difficult to draw the boundaries of that category (e.g. how many steps backwards must a fly take to be considered “backing up”? ). Finally, some types of mismatches from manual labels are more important than others. To address this problem, we have developed the Janelia Automatic Animal Behavior Annotator (JAABA), an intuitive, interactive system that allows biologists to use machine learning in closed loop without assistance from machine learning experts [2]. Our system allows biologists to quickly and efficiently train behavior classifiers. With this system, biologists can define new behaviors from patterns they find in the data, or adapt existing behavior classifiers to new environments and types of animals. JAABA has been used in several very different biology projects, in flies, mice, and larvae.
International Journal of Parallel, Emergent and Distributed Systems

The signiﬁcant proliferation of research papers in both conferences and journals has made it difﬁcult for researchers to easily access relevant scholarly papers for academic learning. This has been a substantial problem for many researchers. Conferences, in comparison with journals, have an aspect of social learning and networking, which leads to personal familiarisation through various interactions among researchers. In this paper, we improve the social awareness of conference participants by proposing a novel folksonomy-based paper recommendation algorithm, called socially aware recommendation of scholarly papers (SARSP). SARSP recommends papers issued by active participants (APs), to other Group Proﬁle Participants at the same conference based on preference similarity of their research interests. In addition, SARSP computes the social ties between an AP and other conference participants to effectively generate social recommendations of scholarly papers. We evaluate our proposed algorithm using a real-world data-set. Our experimental results conﬁrm that SARSP has signiﬁcant improvement over other existing methods.
Synchronizability of EEG-Based Functional Networks in Early Alzheimer's Disease

Recently graph theory and complex networks have been widely used as a mean to model functionality of the brain. Among different neuroimaging techniques available for constructing the brain functional networks, electroencephalography (EEG) with its high temporal resolution is a useful instrument of the analysis of functional interdependencies between different brain regions. Alzheimer's disease (AD) is a neurodegenerative disease, which leads to substantial cognitive decline, and eventually, dementia in aged people. To achieve a deeper insight into the behavior of functional cerebral networks in AD, here we study their synchronizability in 17 newly diagnosed AD patients compared to 17 healthy control subjects at no-task, eyes-closed condition. The cross-correlation of artifact-free EEGs was used to construct brain functional networks. The extracted networks were then tested for their synchronization properties by calculating the eigenratio of the Laplacian matrix of the connection graph, i.e., the largest eigenvalue divided by the second smallest one. In AD patients, we found an increase in the eigenratio, i.e., a decrease in the synchronizability of brain networks across delta, alpha, beta, and gamma EEG frequencies within the wide range of network costs. The finding indicates the destruction of functional brain networks in early AD.
Adapting to Unavoidable Loss During Adulthood

Older adults are expected to better differentiate functional from dysfunctional ways of dealing with unavoidable loss. We examined whether age-differential effects of strategies on affect could be found in a simulation task concerning unavoidable loss management. In an experimental study, we confronted 83 healthy young, middle-aged, and old adults with video vignettes of a person suffering from multiple sclerosis. Participants engaged in a thinking-aloud task on managing their life after learning about the fictitious diagnosis. Only the older participants’ striving for improvement was associated with an increase in negative affect after the thinking-aloud task, whereas their reorientation strategies led to enhanced affective well-being. Findings revealed age-differential effects of mentally simulating unavoidable loss.
Optimal Auctions for Partially Rational Bidders

We investigate the problem of revenue optimal mechanism design [Myerson, 1981] under the context of the partial rationality model, where buyers randomize between two modes: rational and irrational. When a buyer is irrational (can be thought of as lazy), he acts according to certain fixed strategies, such as bidding his true valuation. The seller cannot observe the buyer's valuation, or his rationality mode, but treat them as random variables from known distributions. The seller's goal is to design a single-shot auction that maximizes her expected revenue. A minor generalization as it may seem, our findings are in sharp contrast to Myerson's theory on the standard rational bidder case. In particular, we show that, even for the simplest setting with one buyer, direct value revelation loses generality. However, we do show that, in terms of revenue, the optimal value-revelation and type-revelation mechanisms are equivalent. In addition, the posted-price mechanism is no longer optimal. In fact, the more complicated the mechanism, the higher the revenue. For the case where there are multiple bidders with IID uniform valuations, we show that when the irrational buyers are truthful, first price auction yields more revenue than second price auction.
Non-rigid 3D model retrieval using multi-scale local features

The number of available non-rigid 3D models in various areas increases steadily. The local features are more effective than global features for the search of these non-rigid 3D models. Global descriptors fail to consistently compensate for the intra-class variability of non-rigid 3D models. To solve this problem, we propose a non-rigid 3D model retrieval method based on multi-scale local features. Firstly, we extract keypoints at multiple scales automaticlly. Then, the Heat Kernel Signature (HKS) local descriptors are computed for each keypoint. However, the HKS descriptors are sensitive to scale. In order to solve this problem, the HKS descriptors are put into the Bag-of-Features (BOF) framework. In the BOF framework, we use a kind of histogram equalization technique to make our feature descriptor robust to model scaling. Experimental results on two public benchmarks show that our algorithm can achieve satisfactory retrieval performance for the non-rigid 3D models.
Efficient MD Coding Core Selection to Reduce the Bandwidth Consumption

Multiple distribution trees and multiple description (MD) coding are highly robust since they provide redundancy both in network paths and data. However, MD coded streaming includes a redundant information, which results in additional bandwidth consumptions in entire distribution trees. In this paper, we deploy core nodes in distribution tree, and give a role of MD coding to each core node, instead of a source node then we show how amount of bandwidth consumption can be reduced. Since the problem of finding an optimal set of core nodes is proved to be NP-hard, an intuitional heuristic-based algorithm is proposed. The simulation results show that our heuristic algorithm reduces the bandwidth consumptions by about 25% in the hierarchical topology compared to the MD coding in source node only.
Adamant Virgins, Potential Nonvirgins and Nonvirgins

Abstract Most researchers studying premarital sexual behavior have focused on virgins and nonvirgins as distinct entities and have generally ignored important subgroups. Using data obtained from 514 single high school and college women, this study analyzed variables differentiating among adamant virgins, potential nonvirgins and nonvirgins. Adamant virgins were defined as those who had not experienced intercourse and were not likely to engage in premarital intercourse (PMI). Women who had not experienced PMI but were likely to engage in PMI were defined as potential nonvirgins. Variables significantly related (p < .05) to virginity status in chi‐square analyses were age, religiosity, career aspirations, parental acceptance of PMI, peer acceptance of PMI, peer experience with PMI, dating commitment and dating frequency. Using discriminant analysis, it was found that peer experience with PMI was the most important predictor of virginity status, followed in importance by dating commitment and religiosity. Th.
Robust stability condition for a class of time-delay plants with uncertainty

In this paper, we consider the robust stability condition for single-input/single-output time-delay plants with new class of uncertainties. First of all, we define a class of uncertainties to be considered. The necessary and sufficient robust stability condition for time-delay plants with such class of uncertainties is presented. The relation between the time-delay plant and the nominal time-delay plant that is included in such class of uncertainties is clarified. By using this relation, we will show the necessary and sufficient robust stability condition for the time-delay plants with varying number of right half plane poles.
Analysis of equivalence relation in joint sparse recovery

The joint sparse recovery problem is a generalization of the single measurement vector problem which is widely studied in Compressed Sensing and it aims to recovery a set of jointly sparse vectors. i.e. have nonzero entries concentrated at common location. Meanwhile l_p-minimization subject to matrices is widely used in a large number of algorithms designed for this problem. Therefore the main contribution in this paper is two theoretical results about this technique. The first one is to prove that in every multiple systems of linear equation, there exists a constant p* such that the original unique sparse solution also can be recovered from a minimization in l_p quasi-norm subject to matrices whenever 0< p<p*. The other one is to show an analysis expression of such p*. Finally, we display the results of one example to confirm the validity of our conclusions.
Peptide Length Extension Skews the Minor HA-1 Antigen Presentation toward Activated Dendritic Cells but Reduces Its Presentation Efficiency

Minor histocompatibility Ags (mHags) are important targets of the graft-versus-leukemia effect after HLA-matched allogeneic stem cell transplantation. mHags are HLA-restricted polymorphic peptides expressed on normal and leukemia cells. Vaccination with hematopoiesis-restricted mHag peptides, such as HA-1, may boost the graft-versus-leukemia effect. However, some animal studies indicate that peptides exactly reflecting immunogenic T cell epitopes (short peptides [SPs]) induce tolerance that is potentially due to systemic Ag spreading. Peptide length extension (long peptides [LPs]) may optimize immune responses by restricting and prolonging Ag presentation on dendritic cells (DCs). In this study, we compared the in vitro characteristics and T cell-stimulatory capacities of a human 30-mer HA-1 LP with the 9-mer HA-1 SP. DCs presented the HA-1 LP and SP and expanded HA-1–specific cytotoxic T cell lines. As hypothesized, HA-1 LP presentation, but not SP presentation, was largely restricted to activated DCs and was nearly absent on other hematopoietic cells. However, DCs presented the HA-1 LP 2–3 log levels less efficiently than the SP. Finally, the decay of HA-1 LP and SP presentation on DCs was comparable. We conclude that HA-1 LP and SP differ in their in vitro characteristics and that only comparative clinical studies after allogeneic stem cell transplantation may reveal the optimal HA-1 vaccine.
Federated Tensor Factorization for Computational Phenotyping

Tensor factorization models offer an effective approach to convert massive electronic health records into meaningful clinical concepts (phenotypes) for data analysis. These models need a large amount of diverse samples to avoid population bias. An open challenge is how to derive phenotypes jointly across multiple hospitals, in which direct patient-level data sharing is not possible (e.g., due to institutional policies). In this paper, we developed a novel solution to enable federated tensor factorization for computational phenotyping without sharing patient-level data. We developed secure data harmonization and federated computation procedures based on alternating direction method of multipliers (ADMM). Using this method, the multiple hospitals iteratively update tensors and transfer secure summarized information to a central server, and the server aggregates the information to generate phenotypes. We demonstrated with real medical datasets that our method resembles the centralized training model (based on combined datasets) in terms of accuracy and phenotypes discovery while respecting privacy.