Miracle-dz/UniFAR-FTUs huggingface dataset:
An Octavalent Lyme Disease Vaccine Induces Antibodies That Recognize All Incorporated ospC Type-Specific Sequences

Lyme disease is the most common vector-borne disease in North America and Europe and, if untreated, has significant arthritic, cardiac, dermatological and neurological sequelae. There is no currently available human Lyme disease vaccine. Outer surface protein C, because of its antigenicity, protective ability, and expression characteristics has emerged as a promising second generation vaccine candidate; however, significant sequence heterogeneity has impeded its development. Analyses of OspC sequences have revealed the existence of stable phylogenetic clusters or types, and the type-defining sequence variation within OspC occurs in defined domains of the protein. Recent data indicating that immunodominant, and potentially protective OspC epitopes are located in these hypervariable regions has allowed development of a tetravalent, epitope-based, chimeric vaccine. In this report, we have extended that previously described tetravalent construct to include four additional OspC types. We demonstrate that the construct is highly immunogenic, and elicits type-specific antibodies that recognize each of the eight incorporated OspC type-specific epitopes. Antibody raised to the octavalent construct readily binds to the surface of strains expressing each component OspC type, indicating that the incorporated epitopes are presented on the surface of intact cells. In addition, the construct elicits antibody isotypes associated with complement-dependent bactericidal activity. These results represent an important step forward in the design of a broadly protective polyvalent OspC-based Lyme disease vaccine.
Higher order superintegrability, Painlevé transcendents and representations of polynomial algebras

Résumé. In recent years, progress toward the classification of superintegrable systems with higher order integrals of motion has been made. In particular, a complete classification of all exotic potentials with a third or a fourth order integrals, and allowing separation of variables in Cartesian coordinates. All doubly exotic potentials with a fifth order integral have also been completely classified. It has been demonstrated how the Chazy class of third order differential equations plays an important role in solving determining equations. Moreover, taking advantage of various operator algebras defined as Abelian, Heisenberg, Conformal and Ladder case of operator algebras, we re-derived these models. These new techniques also provided further examples of superintegrable Hamiltonian with integrals of arbitrary order. It has been conjectured that all quantum superintegrable potentials that do not satisfy any linear equation satisfy nonlinear equations having the Painlevé property. In addition, it has been discovered that their integrals naturally generate finitely generated polynomial algebras and the representations can be exploited to calculate the energy spectrum. For certain very interesting cases associated with exceptional orthogonal polynomials, these algebraic structures do not allow to calculate the full spectrum and degeneracies. It has been demonstrated that alternative sets of integrals which can be build and used to provide a complete solution. This this allow to make another conjecture i.e. that higher order superintegrable systems can be solved algebraically, they require alternative set of integrals than the one provided by a direct approach.
Rapid evolution towards heavy metal resistance by mountain birch around two subarctic copper–nickel smelters

Adaptations to pollution among long‐lived trees have rarely been documented, possibly because of their long reproductive cycles and the evolutionarily short timescales of anthropogenic pollution. Here, I present the results of a greenhouse experiment that suggest rapid evolutionary adaptation of mountain birch [Betula pubescens subsp. czerepanovii (Orlova) Hämet‐Ahti] to heavy metal (HM) stress around two copper–nickel smelters in NW Russia. The adaptation incurs a cost with reduced performance of adapted seedlings in pristine conditions. The industrial barrens around the studied smelters are extremely high‐stress sites with low seed germination and survival. It is likely that strong natural selection has eliminated all sensitive genotypes within one or two generations, with only the most tolerant individuals persisting and producing adapted seeds in the individual barrens. The results were similar from around both smelters, suggesting parallel evolution towards HM resistance.
Emergence of a Unique Penicillin-Resistant Streptococcus pneumoniae Serogroup 35 Strain

ABSTRACT We analyzed seven Streptococcus pneumoniae serogroup 35 isolates by pulsed-field gel electrophoresis of the genome and pbp2b gene nucleotide sequences. Three penicillin-susceptible strains and one penicillin-intermediate-resistant strain exhibited 100% identity to prototype R6. Two resistant strains and one other intermediate strain differed from them and contained a unique sequence.
Leveraging Wikipedia for Ontology Pattern Population from Text

Traditional approaches to populating ontology design patterns from unstructured text often involve using a dictionary, rules, or machine learning approaches that have been established based on a training set of annotated documents. While these approaches are quite effective in many cases, performance can suffer over time as the nature of the text documents changes to reflect advances in the domain of interest. This is particularly true when attempting to populate patterns related to fast-changing domains such as technology, medicine, or law. This paper explores the use of Wikipedia as a source of continually updated background knowledge to facilitate ontology pattern population as the domain changes over time.
Tips on Haskell

This guide seeks to emulate the style used in Tofte’s summary [6] of StandardML. It seeks to outline some essential pieces of syntax one needs to know in order to use Haskell, and loosely describes their semantics. The coarse nature of this guide makes it better suited for readers familiar with the functional paradigm who want a quick outline of the language, or who want to do a rapid revision of Haskell syntax. Thompson’s book [5] on Haskell is recommended for more expansive coverage, includingmethods of proof for Haskell programs.
SANQ: A Simulation Framework for Architecting Noisy Intermediate-Scale Quantum Computing System

To bridge the gap between limited hardware access and the huge demand for experiments for Noisy Intermediate-Scale Quantum (NISQ) computing system study, a simulator which can capture the modeling of both the quantum processor and its classical control system to realize early-stage evaluation and design space exploration, is naturally invoked but still missing. This paper presents SANQ, a Simulation framework for Architecting NISQ computing system. SANQ consists of two components, 1) an optimized noisy quantum computing (QC) simulator with flexible error modeling accelerated by eliminating redundant computation, and 2) an architectural simulation infrastructure to construct behavior models for evaluating the control systems. SANQ is validated with existing NISQ quantum processor and control systems to ensure simulation accuracy. It can capture the variance on the QC device and simulate the timing behavior precisely (<1% and 10% error for various real control systems). Several potential applications are proposed to show that SANQ could benefit the future design of NISQ compiler, architecture, etc.
Representation Similarity Analysis for Efficient Task Taxonomy & Transfer Learning

Transfer learning is widely used in deep neural network models when there are few labeled examples available. The common approach is to take a pre-trained network in a similar task and finetune the model parameters. This is usually done blindly without a pre-selection from a set of pre-trained models, or by finetuning a set of models trained on different tasks and selecting the best performing one by cross-validation. We address this problem by proposing an approach to assess the relationship between visual tasks and their task-specific models. Our method uses Representation Similarity Analysis (RSA), which is commonly used to find a correlation between neuronal responses from brain data and models. With RSA we obtain a similarity score among tasks by computing correlations between models trained on different tasks. Our method is efficient as it requires only pre-trained models, and a few images with no further training. We demonstrate the effectiveness and efficiency of our method to generating task taxonomy on Taskonomy dataset. We next evaluate the relationship of RSA with the transfer learning performance on Taskonomy tasks and a new task: Pascal VOC semantic segmentation. Our results reveal that models trained on tasks with higher similarity score show higher transfer learning performance. Surprisingly, the best transfer learning result for Pascal VOC semantic segmentation is not obtained from the pre-trained model on semantic segmentation, probably due to the domain differences, and our method successfully selects the high performing models.
Sparse Neural Attentive Knowledge-based Models for Grade Prediction

Grade prediction for future courses not yet taken by students is important as it can help them and their advisers during the process of course selection as well as for designing personalized degree plans and modifying them based on their performance. One of the successful approaches for accurately predicting a student's grades in future courses is Cumulative Knowledge-based Regression Models (CKRM). CKRM learns shallow linear models that predict a student's grades as the similarity between his/her knowledge state and the target course. A student's knowledge state is built by linearly accumulating the learned provided knowledge components of the courses he/she has taken in the past, weighted by his/her grades in them. However, not all the prior courses contribute equally to the target course. In this paper, we propose a novel Neural Attentive Knowledge-based model (NAK) that learns the importance of each historical course in predicting the grade of a target course. Compared to CKRM and other competing approaches, our experiments on a large real-world dataset consisting of $\sim$1.5 grades show the effectiveness of the proposed NAK model in accurately predicting the students' grades. Moreover, the attention weights learned by the model can be helpful in better designing their degree plans.
Building Knowledge Base through Deep Learning Relation Extraction and Wikidata

Many AI agent tasks require domain specific knowledge graph (KG) that is compact and complete. We present a methodology to build domain specific KG by merging output from deep learning-based relation extraction from free text and existing knowledge database such as Wikidata. We first form a static KG by traversing knowledge database constrained by domain keywords. Very large high-quality training data set is then generated automatically by matching Common Crawl data with relation keywords extracted from knowledge database. We describe the training data generation process in detail and subsequent experiments with deep learning approaches to relation extraction. The resulting model is used to generate new triples from free text corpus and create a dynamic KG. The static and dynamic KGs are then merged into a new KB satisfying the requirement of specific knowledge-oriented AI tasks such as question answering, chatting, or intelligent retrieval. The proposed methodology can be easily transferred to other domains or languages.
Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory

Deep learning based knowledge tracing model has been shown to outperform traditional knowledge tracing model without the need for human-engineered features, yet its parameters and representations have long been criticized for not being explainable. In this paper, we propose Deep-IRT which is a synthesis of the item response theory (IRT) model and a knowledge tracing model that is based on the deep neural network architecture called dynamic key-value memory network (DKVMN) to make deep learning based knowledge tracing explainable. Specifically, we use the DKVMN model to process the student's learning trajectory and estimate the student ability level and the item difficulty level over time. Then, we use the IRT model to estimate the probability that a student will answer an item correctly using the estimated student ability and the item difficulty. Experiments show that the Deep-IRT model retains the performance of the DKVMN model, while it provides a direct psychological interpretation of both students and items.
Automatic design of domain-specific instructions for low-power processors

This paper explores hardware specialization of low-power processors to improve performance and energy efficiency. Our main contribution is an automated framework that analyzes instruction sequences of applications within a domain at the loop body level and identifies exactly and partially-matching sequences across applications that can become custom instructions. Our framework transforms sequences to a new code abstraction, a Merging Diagram, that improves similarity identification, clusters alike groups of potential custom instructions to effectively reduce the search space, and selects merged custom instructions to efficiently exploit the available customizable area. For a set of 11 media applications, our fast framework generates instructions that significantly improve the energy-delay product and speed-up, achieving more than double the savings as compared to a technique analyzing sequences within basic blocks. This paper shows that partially-matched custom instructions, which do not significantly increase design time, are crucial to achieving higher energy efficiency at limited hardware areas.
Agent and Task Modeling at Honeywell

Introduction Modeling the activities, knowledge, and requirements of agents (both human and automated) has been recognized as a recurrent theme in research conducted by members of the Software and Systems Technology Area (SSTA) at the Honeywell Technology Center (HTC). In fact, we have formed a special interest group that aims to collate and clarify the broad range of task and agent modeling experiences of the hundred-odd SSTA members. This paper gives a high-level overview of the various functions agent modeling has played in our research, as exemplified in several projects. We hope that this effort will eventually lead to fruitful cooperative activities with other researchers pursuing related work.
Educational Inequalities in Exit from Paid Employment among Dutch Workers: The Influence of Health, Lifestyle and Work

Background Individuals with lower socioeconomic status are at increased risk of involuntary exit from paid employment. To give sound advice for primary prevention in the workforce, insight is needed into the role of mediating factors between socioeconomic status and labour force participation. Therefore, it is aimed to investigate the influence of health status, lifestyle-related factors and work characteristics on educational differences in exit from paid employment. Methods 14,708 Dutch employees participated in a ten-year follow-up study during 1999–2008. At baseline, education, self-perceived health, lifestyle (smoking, alcohol, sports, BMI) and psychosocial (demands, control, rewards) and physical work characteristics were measured by questionnaire. Employment status was ascertained monthly based on tax records. The relation between education, health, lifestyle, work-characteristics and exit from paid employment through disability benefits, unemployment, early retirement and economic inactivity was investigated by competing risks regression analyses. The mediating effects of these factors on educational differences in exit from paid employment were tested using a stepwise approach. Results Lower educated workers were more likely to exit paid employment through disability benefits (SHR:1.84), unemployment (SHR:1.74), and economic inactivity (SHR:1.53) but not due to early retirement (SHR:0.92). Poor or moderate health, an unhealthy lifestyle, and unfavourable work characteristics were associated with disability benefits and unemployment, and an unhealthy lifestyle with economic inactivity. Educational differences in disability benefits were explained for 40% by health, 31% by lifestyle, and 12% by work characteristics. For economic inactivity and unemployment, up to 14% and 21% of the educational differences could be explained, particularly by lifestyle-related factors. Conclusions There are educational differences in exit from paid employment, which are partly mediated by health, lifestyle and work characteristics, particularly for disability benefits. Health promotion and improving working conditions seem important measures to maintain a productive workforce, particularly among workers with a low education.
A Balance of BMP and Notch Activity Regulates Neurogenesis and Olfactory Nerve Formation

Although the function of the adult olfactory system has been thoroughly studied, the molecular mechanisms regulating the initial formation of the olfactory nerve, the first cranial nerve, remain poorly defined. Here, we provide evidence that both modulated Notch and bone morphogenetic protein (BMP) signaling affect the generation of neurons in the olfactory epithelium and reduce the number of migratory neurons, so called epithelioid cells. We show that this reduction of epithelial and migratory neurons is followed by a subsequent failure or complete absence of olfactory nerve formation. These data provide new insights into the early generation of neurons in the olfactory epithelium and the initial formation of the olfactory nerve tract. Our results present a novel mechanism in which BMP signals negatively affect Notch activity in a dominant manner in the olfactory epithelium, thereby regulating neurogenesis and explain why a balance of BMP and Notch activity is critical for the generation of neurons and proper development of the olfactory nerve.
Joint attention and symbolic play in young children with autism: a randomized controlled intervention study.

BACKGROUND Delays and deficits in joint attention and symbolic play constitute two important developmental problems in young children with autism. These areas of deficit have been well studied in autism but have rarely been the focus of treatment efforts (see Kasari, Freeman, & Paparella, 2001). In this study, we examine the efficacy of targeted interventions of joint attention and symbolic play. METHODS Participants were 58 children with autism aged 3 and 4 years (46 boys). Children were randomized to a joint attention intervention, a symbolic play intervention, or control group. Interventions were conducted 30 minutes daily for 5-6 weeks. Both structured assessments of joint attention and play skills and mother-child interactions were collected pre and post intervention by independent assessors. RESULTS Results indicate that both intervention groups improved significantly over the control group on certain behaviors. Children in the joint attention intervention initiated significantly more showing and responsiveness to joint attention on the structured joint attention assessment and more child-initiated joint attention in the mother-child interaction. The children in the play group showed more diverse types of symbolic play in interaction with their mothers and higher play levels on both the play assessment and in interaction with their mothers. CONCLUSIONS This randomized controlled trial provides promising data on the specificity and generalizability of joint attention and play interventions for young children with autism. Future studies need to examine the long-term effects of these early interventions on children's development.
Configuration interaction method for out-of-equilibrium correlated many-body systems -- admixing nonequilibrium quasiparticle excitations to density matrix

We develop configuration interaction method to treat far from equilibrium quantum many-body systems. The concept of embedding (buffer zones between the reservoirs and the correlated quantum system) is used to derive an exact master equation for the reduced density matrix. To enable the use of field theoretic methods we employ the superoperator techniques in Liouville-Fock space. Within the suggested method the exact steady-state density matrix is represented as a linear combination all possible nonequilibrium quasiparticle excitations built on the appropriate reference state. As an example we consider the electron transport through the system with electron-phonon interaction. Using approximate (truncated) expansion of the density matrix we obtain the linear system of equations for two-quasiparticle amplitudes. Then we compute the current and compare the result with other approaches. The current conserving property of the method is proved. Configuration interaction method for out-of-equilibrium correlated many-body systems 2
Optical Flow Techniques for Facial Expression Analysis: Performance Evaluation and Improvements

Optical flow techniques are becoming increasingly performant and robust when estimating motion in a scene, but their performance has yet to be proven in the area of facial expression recognition. In this work, a variety of optical flow approaches are evaluated across multiple facial expression datasets, so as to provide a consistent performance evaluation. Additionally, the strengths of multiple optical flow approaches are combined in a novel data augmentation scheme. Under this scheme, increases in average accuracy of up to 6% (depending on the choice of optical flow approaches and dataset) have been achieved.
System structure for software fault tolerance

Presents and discusses the rationale behind a method for structuring complex computing systems by the use of what is termed `recovery blocks,' `conversations,' and `fault-tolerant interfaces. ' The aim is to facilitate the provision of dependable error detection and recovery facilities which can cope with errors caused by residual design inadequacies, particularly in the system software, rather than merely the occasional malfunctioning of hardware components.
Wing shaping concepts using distributed propulsion

Purpose – The purpose of this research is to explore innovative aircraft concepts that use flexible wings and distributed propulsion to significantly reduce fuel burn of future transport aircraft by exploiting multidisciplinary interactions. Design/methodology/approach – Multidisciplinary analysis and trajectory optimization are used to evaluate the mission performance benefits of flexible wing distributed propulsion aircraft concepts. Findings – The flexible wing distributed propulsion aircraft concept was shown to achieve a 4 per cent improvement in L/D over a mission profile consisting of a minimum fuel climb, minimum fuel cruise and continuous descent. Practical implications – The technologies being investigated may lead to mission adaptive aircraft that can minimize drag, and thus fuel burn, throughout the flight envelope. Originality/value – The aircraft concepts being explored seek to create synergistic interactions between disciplines for reducing fuel burn while capitalizing on the potential bene.