Miracle-dz/UniFAR-FTUs huggingface dataset:
Isolated Perfused Rat Livers to Quantify the Pharmacokinetics and Concentrations of Gd-BOPTA

With recent advances in liver imaging, the estimation of liver concentrations is now possible following the injection of hepatobiliary contrast agents and radiotracers. However, how these images are generated remains partially unknown. Most experiments that would be helpful to increase this understanding cannot be performed in vivo. For these reasons, we investigated the liver distribution of the magnetic resonance (MR) contrast agent gadobenate dimeglumine (Gd-BOPTA, MultiHance®, Bracco Imaging) in isolated perfused rat livers (IPRLs). In IPRL, we developed a new set up that quantifies simultaneously the Gd-BOPTA compartment concentrations and the transfer rates between these compartments. Concentrations were measured either by MR signal intensity or by count rates when the contrast agent was labelled by [153Gd]. With this experimental model, we show how the Gd-BOPTA hepatocyte concentrations are modified by temperature and liver flow rates. We define new pharmacokinetic parameters to quantify the canalicular transport of Gd-BOPTA. Finally, we present how transfer rates generate Gd-BOPTA concentrations in rat liver compartments. These findings better explain how liver imaging with hepatobiliary radiotracers and contrast agents is generated and improve the image interpretation by clinicians.
Deterministic Factorization of Sparse Polynomials with Bounded Individual Degree

In this paper we study the problem of deterministic factorization of sparse polynomials. We show that if f is an n-variate polynomial with s monomials, with individual degrees of its variables bounded by d, then f can be deterministically factored in time s^poly(d) log n. Prior to our work, the only efficient factoring algorithms known for this class of polynomials were randomized, and other than for the cases of d=1 and d=2, only exponential time deterministic factoring algorithms were known. A crucial ingredient in our proof is a quasi-polynomial sparsity bound for factors of sparse polynomials of bounded individual degree. In particular we show if f is an s-sparse polynomial in n variables, with individual degrees of its variables bounded by d, then the sparsity of each factor of f is bounded by s^O(d^2 log n). This is the first nontrivial bound on factor sparsity for d>2. Our sparsity bound uses techniques from convex geometry, such as the theory of Newton polytopes and an approximate version of the classical Carathéodory's Theorem. Our work addresses and partially answers a question of von zur Gathen and Kaltofen (JCSS 1985) who asked whether a quasi-polynomial bound holds for the sparsity of factors of sparse polynomials.
Robust Empirical Optimization is Almost the Same As Mean-Variance Optimization

Abstract We formulate a distributionally robust optimization problem where the deviation of the alternative distribution is controlled by a ϕ -divergence penalty in the objective, and show that a large class of these problems are essentially equivalent to a mean–variance problem. We also show that while a “small amount of robustness” always reduces the in-sample expected reward, the reduction in the variance, which is a measure of sensitivity to model misspecification, is an order of magnitude larger.
ATP reduction by MgtC and Mg2+ homeostasis by MgtA and MgtB enables Salmonella to accumulate RpoS upon low cytoplasmic Mg2+ stress

RpoS is one of several alternative sigma factors known to alter gene expression profiles by RpoS‐associated RNA polymerase in response to a variety of stresses. The enteric bacteria Salmonella enterica and Escherichia coli accumulate RpoS under low Mg2+ concentrations via a common mechanism in which the PhoP regulator activates expression of antiadaptor proteins that, by sequestering the adaptor RssB, prevent RpoS degradation by the protease ClpXP. Here, we demonstrate that this genetic program alone does not fully support RpoS accumulation when cytoplasmic Mg2+ concentration drops to levels that impair protein synthesis. Under these circumstances, only S. enterica continues RpoS accumulation in a manner dependent on other PhoP‐activated programs (i.e. ATP reduction by the MgtC protein and Mg2+ import by the MgtA and MgtB transporters) that maintain translation homeostasis. Moreover, we provide evidence that the mgtC gene, which is present in S. enterica but not in E. coli, is responsible for the differences in RpoS accumulation between these two bacterial species. Our results suggest that bacteria possess a mechanism to control RpoS accumulation responding to cytoplasmic Mg2+ levels, the difference of which causes distinct RpoS accumulation in closely related bacterial species.
Audience-Retention-Rate-Aware Caching and Coded Video Delivery With Asynchronous Demands

Most of the current literature on coded caching focus on a static scenario in which a fixed number of users synchronously place their requests from a content library, and the performance is measured in terms of the latency in satisfying all of these requests. In practice, however, users start watching an online video content asynchronously over time, and often abort watching a video before it is completed. The latter behavior is captured by the notion of audience retention rate, which measures the portion of a video content watched on average. In order to bring coded caching one step closer to practice, asynchronous user demands are considered in this paper by allowing user demands to arrive randomly over time, and both the popularity of video files, and the audience retention rates are taken into account. A decentralized partial coded delivery (PCD) scheme is proposed, and two cache allocation schemes are employed; namely homogeneous cache allocation (HoCA) and heterogeneous cache allocation (HeCA), which allocate users’ caches among different chunks of the video files in the library. Numerical results validate that the proposed PCD scheme, either with HoCA or HeCA, outperforms conventional uncoded caching as well as the state-of-the-art decentralized caching schemes, which consider only the file popularities, and are designed for synchronous demand arrivals. An information-theoretical lower bound on the average delivery rate is also presented.
Novelty Goes Deep. A Deep Neural Solution To Document Level Novelty Detection

The rapid growth of documents across the web has necessitated finding means of discarding redundant documents and retaining novel ones. Capturing redundancy is challenging as it may involve investigating at a deep semantic level. Techniques for detecting such semantic redundancy at the document level are scarce. In this work we propose a deep Convolutional Neural Networks (CNN) based model to classify a document as novel or redundant with respect to a set of relevant documents already seen by the system. The system is simple and do not require any manual feature engineering. Our novel scheme encodes relevant and relative information from both source and target texts to generate an intermediate representation which we coin as the Relative Document Vector (RDV). The proposed method outperforms the existing state-of-the-art on a document-level novelty detection dataset by a margin of ∼5% in terms of accuracy. We further demonstrate the effectiveness of our approach on a standard paraphrase detection dataset where paraphrased passages closely resemble to semantically redundant documents.
The importance of context in the automatic classification of email as records of business value: A pilot study

Despite the growth of alternative means of electronic messaging, email continues to be a crucial business communication tool. In many organizations, it is common to find a large proportion of business-critical data in email form. However, as the volume of business-critical email continues to grow, conventional approaches for managing paper-based records of business value are increasingly unsuited to the digital realities. Yet effective approaches to automate the management of email are essential to ensure organizational efficiency, accountability and regulatory compliance. Our research aims to develop a model that characterizes information managers' context-specific email triage strategies for identifying emails of business value so that it can be applied to automatically classify email records. We conducted a pilot study of two information managers' email triage practices in two different contexts and develop machine-learning models of lexical and non-lexical features that are involved in the appraisal of business value. An experiment with a machine learning algorithm trained on about two hundred emails in two different business contexts indicates that the automation of email triage for identifying records of business value is highly context dependent and that automated classifiers must be trained to recognize "business value" specifically for that context.
Neural Transition-based String Transduction for Limited-Resource Setting in Morphology

We present a neural transition-based model that uses a simple set of edit actions (copy, delete, insert) for morphological transduction tasks such as inflection generation, lemmatization, and reinflection. In a large-scale evaluation on four datasets and dozens of languages, our approach consistently outperforms state-of-the-art systems on low and medium training-set sizes and is competitive in the high-resource setting. Learning to apply a generic copy action enables our approach to generalize quickly from a few data points. We successfully leverage minimum risk training to compensate for the weaknesses of MLE parameter learning and neutralize the negative effects of training a pipeline with a separate character aligner.
Neural Collaborative Ranking

Recommender systems are aimed at generating a personalized ranked list of items that an end user might be interested in. With the unprecedented success of deep learning in computer vision and speech recognition, recently it has been a hot topic to bridge the gap between recommender systems and deep neural network. And deep learning methods have been shown to achieve state-of-the-art on many recommendation tasks. For example, a recent model, NeuMF, first projects users and items into some shared low-dimensional latent feature space, and then employs neural nets to model the interaction between the user and item latent features to obtain state-of-the-art performance on the recommendation tasks. NeuMF assumes that the non-interacted items are inherent negative and uses negative sampling to relax this assumption. In this paper, we examine an alternative approach which does not assume that the non-interacted items are necessarily negative, just that they are less preferred than interacted items. Specifically, we develop a new classification strategy based on the widely used pairwise ranking assumption. We combine our classification strategy with the recently proposed neural collaborative filtering framework, and propose a general collaborative ranking framework called Neural Network based Collaborative Ranking (NCR). We resort to a neural network architecture to model a user's pairwise preference between items, with the belief that neural network will effectively capture the latent structure of latent factors. The experimental results on two real-world datasets show the superior performance of our models in comparison with several state-of-the-art approaches.
Retention failures in cement- and screw-retained fixed restorations on dental implants in partially edentulous arches: A systematic review with meta-analysis

Purpose: This systematic review aimed to evaluate retention failures in cement- and screw-retained fixed restorations on dental implants in partially edentulous arches. Methods: The relevant articles were retrieved from MEDLINE (PubMed), Cochrane Library, and EBSCO electronic databases for articles published from January 1995 to January 2016 and were restricted to randomized controlled trials and retrospective and prospective studies on human subjects that were reported in English. A further hand search was conducted on individual journals and reference list of the articles found. Reviewed studies which reported retention failures in fixed implant-supported prostheses using screw and cement retention mechanism. Information on the type and nature of restoration, as well as different luting cement, were also collected. Results: Thirty-three articles were finalized, 20 short-term clinical studies (up to 5 years) and 13 long-term studies (≥5 years). Out of 33 studies, 16 studies were included in meta-analysis, 8 in short-term and 8 in long-term studies. The results of the meta-analysis for short-term studies showed statistically significant difference between cement-retained and screw-retained prosthesis, with the forest plot favoring cement-retained prostheses (risk ratio [RR]: 0.26; confidence interval [CI]: 0.09–0.74; P < 0.0001; I2 = 79%). In long-term studies, the forest plot revealed statistically significant difference between both retention systems favoring cement-retained prostheses (RR: 0.31; CI: 0.13–0.76; P = 0.03; I2 = 56%). Conclusion: Analysis of the short- and long-term studies shows lesser retention failures with cement-retained prostheses when compared to screw-retained prostheses. Further, multicentric, high-quality randomized controlled studies with long-term observations and modified cementation protocols can yield higher grades of recommendation to avoid retention failures.
Butterfly Effects in Frame Semantic Parsing: impact of data processing on model ranking

Knowing the state-of-the-art for a particular task is an essential component of any computational linguistics investigation. But can we be truly confident that the current state-of-the-art is indeed the best performing model? In this paper, we study the case of frame semantic parsing, a well-established task with multiple shared datasets. We show that in spite of all the care taken to provide a standard evaluation resource, small variations in data processing can have dramatic consequences for ranking parser performance. This leads us to propose an open-source standardized processing pipeline, which can be shared and reused for robust model comparison.
Forensic investigation through data remnants on Hadoop big data storage system

Forensic examiners are in an uninterrupted battle with criminals in the use of Big Data technology. The underlying storage system is the main scene to trace the criminal activities. Big Data Storage System is identified as an emerging challenge to digital forensics. Thus, it requires the development of a sound methodology to investigate Big Data Storage System. Since the use of Hadoop as Big Data Storage System continues to grow rapidly, investigation process model for forensic analysis on Hadoop Storage and attached client devices is compulsory. Moreover, forensic analysis on Hadoop Big Data Storage System may take additional time without knowing where the data remnants can reside. In this paper, a new forensic investigation process model for Hadoop Big Data Storage System is proposed and discovered data remnants are presented. By conducting forensic research on Hadoop Big Data Storage System, the resulting data remnants assist the forensics examiners and practitioners for generating the evidences
Motoneuron Wnts regulate neuromuscular junction development

The neuromuscular junction (NMJ) is a synapse between motoneurons and skeletal muscles to control motor behavior. Unlike extensively investigated postsynaptic differentiation, less is known about mechanisms of presynaptic assembly. Genetic evidence of Wnt in mammalian NMJ development was missing due to the existence of multiple Wnts and their receptors. We show when Wnt secretion is abolished from motoneurons by mutating the Wnt ligand secretion mediator (Wls) gene, mutant mice showed muscle weakness and neurotransmission impairment. NMJs were unstable with reduced synaptic junctional folds and fragmented AChR clusters. Nerve terminals were swollen; synaptic vesicles were fewer and mislocated. The presynaptic deficits occurred earlier than postsynaptic deficits. Intriguingly, these phenotypes were not observed when deleting Wls in muscles or Schwann cells. We identified Wnt7A and Wnt7B as major Wnts for nerve terminal development in rescue experiments. These observations demonstrate a necessary role of motoneuron Wnts in NMJ development, in particular presynaptic differentiation.
Using a Virion Assembly-Defective Dengue Virus as a Vaccine Approach

Many flaviviruses are significant human pathogens that pose global threats to public health. Although licensed vaccines are available for yellow fever, Japanese encephalitis, tick-borne encephalitis, and dengue viruses, new approaches are needed to develop improved vaccines. Using dengue virus as a model, we developed a vaccine platform using a virion assembly-defective virus. We show that such an assembly-defective virus could be rescued to higher titers and infect cells for a single round. Mice immunized with the assembly-defective virus were protected from wild-type-virus infection. This vaccine approach could be applicable to other viral pathogens. ABSTRACT Dengue virus (DENV) is the most prevalent mosquito-transmitted viral pathogen in humans. The recently licensed dengue vaccine has major weaknesses. Therefore, there is an urgent need to develop improved dengue vaccines. Here, we report a virion assembly-defective DENV as a vaccine platform. DENV containing an amino acid deletion (K188) in nonstructural protein 2A (NS2A) is fully competent in viral RNA replication but is completely defective in virion assembly. When trans-complemented with wild-type NS2A protein, the virion assembly defect could be rescued, generating pseudoinfectious virus (PIVNS2A) that could initiate single-round infection. The trans-complementation efficiency could be significantly improved through selection for adaptive mutations, leading to high-yield PIVNS2A production, with titers of >107 infectious-focus units (IFU)/ml. Mice immunized with a single dose of PIVNS2A elicited strong T cell immune responses and neutralization antibodies and were protected from wild-type-virus challenge. Collectively, the results proved the concept of using assembly-defective virus as a vaccine approach. The study also solved the technical bottleneck in producing high yields of PIVNS2A vaccine. The technology could be applicable to vaccine development for other viral pathogens. IMPORTANCE Many flaviviruses are significant human pathogens that pose global threats to public health. Although licensed vaccines are available for yellow fever, Japanese encephalitis, tick-borne encephalitis, and dengue viruses, new approaches are needed to develop improved vaccines. Using dengue virus as a model, we developed a vaccine platform using a virion assembly-defective virus. We show that such an assembly-defective virus could be rescued to higher titers and infect cells for a single round. Mice immunized with the assembly-defective virus were protected from wild-type-virus infection. This vaccine approach could be applicable to other viral pathogens.
Two Local Models for Neural Constituent Parsing

Non-local features have been exploited by syntactic parsers for capturing dependencies between sub output structures. Such features have been a key to the success of state-of-the-art statistical parsers. With the rise of deep learning, however, it has been shown that local output decisions can give highly competitive accuracies, thanks to the power of dense neural input representations that embody global syntactic information. We investigate two conceptually simple local neural models for constituent parsing, which make local decisions to constituent spans and CFG rules, respectively. Consistent with previous findings along the line, our best model gives highly competitive results, achieving the labeled bracketing F1 scores of 92.4% on PTB and 87.3% on CTB 5.1.
Dynamic Feature Selection with Attention in Incremental Parsing

One main challenge for incremental transition-based parsers, when future inputs are invisible, is to extract good features from a limited local context. In this work, we present a simple technique to maximally utilize the local features with an attention mechanism, which works as context- dependent dynamic feature selection. Our model learns, for example, which tokens should a parser focus on, to decide the next action. Our multilingual experiment shows its effectiveness across many languages. We also present an experiment with augmented test dataset and demon- strate it helps to understand the model’s behavior on locally ambiguous points.
Reach/Grasp Times with Lateral Reach Obstructions

Abstract When moving to grasp an object having adjacent obstacles that limit the space available for placing the fingers, the time for the reach/grasp is dependent on the distance of reaching and the space available for finger placement. Here we model the time taken in terms of these variables and develop mathematical models for the reach and grasp phases of the task and the location of obstacles. Data show that the movement to the target may be made under visual control and that, when the obstacles are close to the target object, a visually-controlled movement is made that is modeled by a modified form of Fitts' law. The time for the two components of the reach/grasp appear to be independent and linearly additive.
Enhancement of Electrical Characteristics and Stability of Amorphous Si-Sn-O Thin Film Transistors with SiOx Passivation Layer

In this research, a passivated methodology was proposed for achieving good electrical characteristics for back-channel-etch (BCE) typed amorphous Si-Sn-O thin film transistors (a-STO TFTs). This methodology implied that the thermal annealing (i.e., pre-annealing) should be carried out before deposition of a SiOx passivation layer. The pre-annealing played an important role in affecting device performance, which did get rid of the contamination of the lithography process. Simultaneously, the acceptor-like sub-gap density of states (DOS) of devices was extracted for further understanding the reason for improving device performance. It found that the SiOx layer could reduce DOS of the device and successfully protect the device from surroundings. Finally, a-STO TFT applied with this passivated methodology could possess good electrical properties including a saturation mobility of 4.2 ± 0.2 cm2/V s, a low threshold voltage of 0.00 V, a large on/off current ratio of 6.94 × 108, and a steep subthreshold swing of 0.23 V/decade. The threshold voltage slightly shifted under bias stresses and recovered itself to its initial state without any annealing procedure, which was attributed to the charge trapping in the bulk dielectric layers or interface. The results of this study indicate that a-STO TFT could be a robust candidate for realizing a large-size and high-resolution display.