Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Gradient-Free Variational Inference using Policy Search

Inference from complex distributions is a common problem in machine learning needed for many Bayesian methods. We propose an efficient, gradient-free method for learning general GMM approximations of multimodal distributions based on recent insights from stochastic search methods. Our method establishes information-geometric trust regions to ensure efficient exploration of the sampling space and stability of the GMM updates, allowing for efficient estimation of multi-variate Gaussian variational distributions. For GMMs, we apply a variational lower bound to decompose the learning objective into sub-problems given by learning the individual mixture components and the coefficients. The number of mixture components is adapted online in order to allow for arbitrary exact approximations. We demonstrate on several domains that we can learn significantly better approximations than competing variational inference methods and that the quality of samples drawn from our approximations is on par with samples created by state-of-the-art MCMC samplers that require significantly more computational resources.
A Social Utility-Based Dissemination Scheme for Emergency Warning Messages in Vehicular Social Networks

In recent years, many schemes have been proposed to disseminate Emergency Warning Messages (EWMs) in VANETs. However, various problems such as broadcast storm, hidden terminal and connectivity issues still persist to reduce the efficacy of these systems. In this paper, we propose a novel Social Utility-based Dissemination Scheme (SUDS) for Emergency Warning Messages in Vehicular Social Networks (VSNs). We utilize social properties of nodes such as centrality, interests and friendships to mitigate broadcast storm and hidden terminal problems. Furthermore, we employ the hybrid architecture of VSNs to solve connectivity and contact duration-related issues in sparse and high mobility environments. For this purpose, we devise a dual-strategy-based mechanism, where vehicles communicate with each other in a distributed or centralized manner according to the required situation. In order to evaluate the performance of proposed scheme, we have conducted extensive experiments for a highway scenario under varying vehicular density, vehicular speed and distance, in comparison with the state-of-the-art dissemination schemes. Simulation results have demonstrated the superiority of SUDS over the compared protocols in terms of delivery ratio, transmission delay and total number of transmissions. We have also demonstrated the positive effects of hybrid architecture of VSNs on various network parameters.
Inter-Rater and Intra-Rater Reliability of Sonographic Median Nerve and Wrist Measurements

Background: Electrophysiologic studies have been considered the “gold standard” in diagnosing carpal tunnel syndrome (CTS); however, reports of false-negative results, as well as discomfort for the patient during the procedure has paved the use of ultrasound, being a painless and cost-efficient tool, as an alternative means for its diagnosis. Various ultrasound parameters assessing the median nerve and wrist dimensions have been described, but description of landmarks to assess these in a reliable manner has been lacking. Methodology: A systematic search of different databases yielded data regarding ultrasound parameters for CTS diagnosis, the landmarks used, and presence of reliability testing. Based on this, three sonologists discussed the external and sonographic landmarks that will be used in measuring the median nerve measurements, bowing of the flexor retinaculum and the carpal tunnel dimensions. A pilot test with two consecutive healthy participants using the discussed ultrasound parameters was carried out, and results were subjected to inter- and intra-rater reliability testing. Modifications were accordingly made on the acquisition of ultrasound image using external landmarks. The reliability testing proper was done with ten consecutive healthy participants. Results: Based on the systematic review and the pilot study, external landmarks were used to locate the median nerve in the forearm, carpal tunnel inlet and outlet. For the forearm measurement, it was taken 10 cm proximal from the distal palmar crease. The distal palmar crease was the external landmark used for the carpal tunnel inlet, while for the carpal tunnel outlet; it was measured 1 cm distal to the distal palmar crease. Instead of using the inner edge of the hook of hamate and trapezium, the apices of these bones were used as the landmarks in measuring the carpal tunnel outlet dimensions. There was excellent intra-rater reliability (mid-forearm, carpal tunnel inlet and outlet) except for the following: cross-sectional area (CSA) of the median nerve at the carpal tunnel inlet and outlet; and bowing of the flexor retinaculum. All the parameters had an excellent inter-rater reliability measured at the three levels (intraclass correlation [ICC]: Of 0.77–0.99) except for CSA of the median nerve at the levels of the forearm (fair-to-good with ICC of 0.71) and the carpal tunnel inlet (fair-to-good reliability of ICC: 0.43). Conclusion: There was an improved inter- and intra-rater reliability when external landmarks were used instead of sonographic landmarks.
Dropout Training, Data-dependent Regularization, and Generalization Bounds

We study the problem of generalization guarantees for dropout training. A general framework is first proposed for learning procedures with random perturbation on model parameters. The generalization error is bounded by sum of two offset Rademacher complexities: the main term is Rademacher complexity of the hypothesis class with minus offset induced by the perturbation variance, which characterizes data-dependent regularization by the random perturbation; the auxiliary term is offset Rademacher complexity for the variance class, controlling the degree to which this regularization effect can be weakened. For neural networks, we estimate upper and lower bounds for the variance induced by truthful dropout, a variant of dropout that we propose to ensure unbiased output and fit into our framework, and the variance bounds exhibits connection to adaptive regularization methods. By applying our framework to ReLU networks with one hidden layer, a generalization upper bound is derived with no assumptions on the parameter norms or data distribution, with O(1/n) fast rate and adaptivity to geometry of data points being achieved at the same time.
Accuracy of Parental Reporting of Preschoolers’ Dietary Intake Using an Online Self-Administered 24-h Recall

Parents are typically relied upon to report young children’s dietary intake. However, there has been limited research assessing the accuracy of such reports captured using novel dietary assessment tools. The purpose of the current study was to assess the validity of the web-based Automated Self-Administered 24-h Dietary Assessment Tool (ASA24-Canada) for capturing dietary intake among children aged two-five years (n = 40), using parental proxy reporting. The study was conducted in a daycare setting, allowing for standardization of foods and drinks offered and direct observation of intake. Parental-reported intake was compared to true intake for lunch and dinner, as well as an afternoon snack, on a single day. Each eating occasion, including plate waste, was unobtrusively documented. Parents were not present for lunch or the afternoon snack, but joined their children at the daycare centre for the dinner meal. The following day, parents reported their children’s intake from the previous 24-h period using ASA24-Canada. For the eating occasions assessed, parents reported exact or close matches for 79.2% (82.3% for lunch, 81.2% for the snack, and 77.4% for dinner) of the foods and beverages truly consumed by children. Estimates of intake for energy and macronutrients examined (carbohydrates, fat, and protein) based on parental reports were higher than those based on true (observed) intake. Our findings suggest that parents are able to report what their preschool children eat and drink relatively accurately. However, the accuracy of portion size estimates is low. Strategies to enhance portion size reporting are needed to improve parental proxy reporting.
Nelfinavir Induces Endoplasmic Reticulum Stress and Sensitizes Renal Cancer Cells to TRAIL

Background/Aim: Induction of endoplasmic reticulum (ER) stress is a novel strategy for cancer treatment. The human immunodeficiency virus protease inhibitor nelfinavir was recently shown to induce ER stress, but its anti-neoplastic activity has never been investigated in renal cancer, as far as we are aware. Materials and Methods: Using renal cancer cells (769-P, 786-O, Caki-2), the ability of nelfinavir to induce ER stress and sensitize them to tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) was tested. Results: Nelfinavir caused apoptosis and inhibited renal cancer growth in a dose-dependent fashion. It also suppressed colony formation significantly. Nelfinavir induced ER stress and increased the expression of TRAIL death receptor (DR) 4 and DR5, sensitizing the cancer cells to TRAIL. This sensitization was blocked by human recombinant DR4/Fc and DR5/Fc chimeric protein, confirming that the sensitization was due to increased expression of both DR4 and DR5. Conclusion: Nelfinavir induces ER stress in renal cancer cells and sensitizes them to TRAIL.
Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph

Analyzing human multimodal language is an emerging area of research in NLP. Intrinsically this language is multimodal (heterogeneous), sequential and asynchronous; it consists of the language (words), visual (expressions) and acoustic (paralinguistic) modalities all in the form of asynchronous coordinated sequences. From a resource perspective, there is a genuine need for large scale datasets that allow for in-depth studies of this form of language. In this paper we introduce CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI), the largest dataset of sentiment analysis and emotion recognition to date. Using data from CMU-MOSEI and a novel multimodal fusion technique called the Dynamic Fusion Graph (DFG), we conduct experimentation to exploit how modalities interact with each other in human multimodal language. Unlike previously proposed fusion techniques, DFG is highly interpretable and achieves competative performance when compared to the previous state of the art.
On Learning Better Embeddings from Chinese Clinical Records: Study on Combining In-Domain and Out-Domain Data

High quality word embeddings are of great significance to advance applications of biomedical natural language processing. In recent years, a surge of interest on how to learn good embeddings and evaluate embedding quality based on English medical text has become increasing evident, however a limited number of studies based on Chinese medical text, particularly Chinese clinical records, were performed. Herein, we proposed a novel approach of improving the quality of learned embeddings using out-domain data as a supplementary in the case of limited Chinese clinical records. Moreover, the embedding quality evaluation method was conducted based on Medical Conceptual Similarity Property. The experimental results revealed that selecting good training samples was necessary, and collecting right amount of out-domain data and trading off between the quality of embeddings and the training time consumption were essential factors for better embeddings.
Boundedness, Stabilization, and Pattern Formation Driven by Density-Suppressed Motility

We are concerned with the following density-suppressed motility model: $u_t=\Delta (\gamma(v) u)+\mu u(1-u); v_t=\Delta v+ u-v,$ in a bounded smooth domain $\Omega\subset \mathbb{R}^2$ with homogeneous Neumann boundary conditions, where the motility function $\gamma(v)\in C^3([0,\infty))$, $\gamma (v)>0$, $\gamma'(v)<0$ for all $v\geq 0$, $\lim_{v \to \infty}\gamma(v)=0$, and $\lim_{v \to \infty}\frac{\gamma'(v)}{\gamma(v)}$ exists. The model is proposed to advocate a new possible mechanism: density-suppressed motility can induce spatio-temporal pattern formation through self-trapping. The major technical difficulty in the analysis of above density-suppressed motility model is the possible degeneracy of diffusion from the condition $\lim_{v \to \infty}\gamma(v)=0$. In this paper, by treating the motility function $\gamma(v)$ as a weight function and employing the method of weighted energy estimates, we derive the a priori $L^\infty$-bound of $v$ to rule out the degeneracy and establish the global existenc.
A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval

In this study, we investigate learning-to-rank and query refinement approaches for information retrieval in the pharmacogenomic domain. The goal is to improve the information retrieval process of biomedical curators, who manually build knowledge bases for personalized medicine. We study how to exploit the relationships between genes, variants, drugs, diseases and outcomes as features for document ranking and query refinement. For a supervised approach, we are faced with a small amount of annotated data and a large amount of unannotated data. Therefore, we explore ways to use a neural document auto-encoder in a semi-supervised approach. We show that a combination of established algorithms, feature-engineering and a neural auto-encoder model yield promising results in this setting.
Principal-Agent Problem with Common Agency Without Communication

In this paper, we consider a problem of contract theory in which several Principals hire a common Agent and we study the model in the continuous time setting. We show that optimal contracts should satisfy some equilibrium conditions and we reduce the optimisation problem of the Principals to a system of coupled Hamilton-Jacobi-Bellman (HJB) equations. We provide conditions ensuring that for risk-neutral Principals, the system of coupled HJB equations admits a solution. Further, we apply our study in a more specific linear-quadratic model where two interacting Principals hire one common Agent. In this continuous time model, we extend the result of Bernheim and Whinston (1986) in which the authors compare the optimal effort of the Agent in a non-cooperative Principals model and that in the aggregate model, by showing that these two optimisations coincide only in the first best case. We also study the sensibility of the optimal effort and the optimal remunerations with respect to appetence parameters and the correlation between the projects.
Safety of an Oncolytic Myxoma Virus in Dogs with Soft Tissue Sarcoma

Many oncolytic viruses that are efficacious in murine cancer models are ineffective in humans. The outcomes of oncolytic virus treatment in dogs with spontaneous tumors may better predict human cancer response and improve treatment options for dogs with cancer. The objectives of this study were to evaluate the safety of treatment with myxoma virus lacking the serp2 gene (MYXVΔserp2) and determine its immunogenicity in dogs. To achieve these objectives, dogs with spontaneous soft tissue sarcomas were treated with MYXVΔserp2 intratumorally (n = 5) or post-operatively (n = 5). In dogs treated intratumorally, clinical scores were recorded and tumor biopsies and swabs (from the mouth and virus injection site) were analyzed for viral DNA at multiple time-points. In all dogs, blood, urine, and feces were frequently collected to evaluate organ function, virus distribution, and immune response. No detrimental effects of MYXVΔserp2 treatment were observed in any canine cancer patients. No clinically significant changes in complete blood profiles, serum chemistry analyses, or urinalyses were measured. Viral DNA was isolated from one tumor swab, but viral dissemination was not observed. Anti-MYXV antibodies were occasionally detected. These findings provide needed safety information to advance clinical trials using MYXVΔserp2 to treat patients with cancer.
Freshwater copepod carcasses as pelagic microsites of dissimilatory nitrate reduction to ammonium

ABSTRACT A considerable fraction of freshwater zooplankton was recently found to consist of dead specimens that sink to the lake bottom. Such carcasses host intense microbial activities that may promote oxygen depletion at the microscale. Therefore, we tested the hypothesis that sinking zooplankton carcasses are microsites of anaerobic nitrogen cycling that contribute to pelagic fixed‐nitrogen loss even in the presence of ambient oxygen. Incubation experiments were performed with the ubiquitous copepods Eudiaptomus sp. and Megacyclops gigas at different ambient oxygen levels that sinking carcasses encounter during their descent in stratified lakes. 15N‐stable‐isotope incubations revealed intense carcass‐associated anaerobic nitrogen cycling only at low ambient oxygen levels (<25% air saturation). Dissimilatory nitrate reduction to ammonium (DNRA) dominated over denitrification and thus the potential for fixed‐nitrogen loss was low. Consistent with this partitioning of anaerobic nitrogen cycling, the relative abundance of the carcass‐associated marker gene for DNRA (nrfA) was ˜20‐400 times higher than that for denitrification (nirS). Additionally, the relative nrfA and nirS abundances were ˜90‐180 times higher on copepod carcasses than in lake water. This functional distinctiveness of carcass‐associated bacterial communities was further substantiated by 16S rDNA‐based fingerprinting. We conclude that the unique bacterial communities and microenvironments provided by zooplankton carcasses influence pelagic nitrogen cycling in lakes, but mainly at seasonally low ambient O2 levels in the bottom water.
Postoperative Regional Analgesia Is Effective in Preserving Perforin-Expressing Lymphocytes in Patients After Total Knee Replacement

Background Pain and surgical stress cause a pro-inflammatory response followed by downregulation of the immune response, which can increase the incidence of postoperative complications, such as infections or prolonged wound healing. T lymphocytes and natural killer (NK) cells have cytotoxic potential and are crucial components of cellular immunity, which is important for maintenance of immune balance. The aim of this study was to analyze the effects of 3 types of postoperative analgesia on the preservation and cytotoxic potential of T lymphocytes, NK cells, and their subpopulations, as well as NKT cells, in patients after total knee replacement (TKR) to find the most effective analgesic technique for mitigating immune suppression. Material/Methods Forty-eight patients scheduled for TKR were randomly allocated to Group 1 (patients received epidural analgesia), Group 2 (patients received sciatic and femoral nerve block), or Group 3 (patients received multimodal systemic analgesia). Pain intensity was assessed at rest and on movement before, immediately after, and at 24 and 72 h after surgery. Blood samples were collected at the same time points and peripheral blood mononuclear cells were isolated. The frequencies of T lymphocytes, NK cells, and NKT cells, as well as their perforin expression, were simultaneously detected and analyzed by flow cytometry. Results Patients in Group 1 and Group 2 experienced less severe pain than those in Group 3. The frequencies and perforin levels of T lymphocytes, their subsets, and NKT cells were significantly lower in Group 3 than in Group 1 and Group 2. Conclusions The present study confirmed that regional analgesia is more effective in maintaining cell-mediated immunity and perforin expression in peripheral blood lymphocytes in patients after TKR.
Complete Genome Sequencing and Comparative Genomic Analysis of Helicobacter Apodemus Isolated From the Wild Korean Striped Field Mouse (Apodemus agrarius) for Potential Pathogenicity

Citation: Kim J, Kim W, An J-U, Suh JG, Seong JK, Jeon B-Y and Cho S (2018) Complete Genome Sequencing and Comparative Genomic Analysis of Helicobacter Apodemus Isolated From the Wild Korean Striped Field Mouse (Apodemus agrarius) for Potential Pathogenicity. Front. Pharmacol. 9:838. doi: 10.3389/fphar.2018. 00838 Complete Genome Sequencing and Comparative Genomic Analysis of Helicobacter Apodemus Isolated From the Wild Korean Striped Field Mouse (Apodemus agrarius) for Potential Pathogenicity
CoreCavity: interactive shell decomposition for fabrication with two-piece rigid molds

Molding is a popular mass production method, in which the initial expenses for the mold are offset by the low per-unit production cost. However, the physical fabrication constraints of the molding technique commonly restrict the shape of moldable objects. For a complex shape, a decomposition of the object into moldable parts is a common strategy to address these constraints, with plastic model kits being a popular and illustrative example. However, conducting such a decomposition requires considerable expertise, and it depends on the technical aspects of the fabrication technique, as well as aesthetic considerations. We present an interactive technique to create such decompositions for two-piece molding, in which each part of the object is cast between two rigid mold pieces. Given the surface description of an object, we decompose its thin-shell equivalent into moldable parts by first performing a coarse decomposition and then utilizing an active contour model for the boundaries between individual parts. Formulated as an optimization problem, the movement of the contours is guided by an energy reflecting fabrication constraints to ensure the moldability of each part. Simultaneously the user is provided with editing capabilities to enforce aesthetic guidelines. Our interactive interface provides control of the contour positions by allowing, for example, the alignment of part boundaries with object features. Our technique enables a novel workflow, as it empowers novice users to explore the design space, and it generates fabrication-ready two-piece molds that can be used either for casting or industrial injection molding of free-form objects.
Learning Semantic Representations for Unsupervised Domain Adaptation

It is important to transfer the knowledge from label-rich source domain to unlabeled target domain due to the expensive cost of manual labeling efforts. Prior domain adaptation methods address this problem through aligning the global distribution statistics between source domain and target domain, but a drawback of prior meth-ods is that they ignore the semantic information contained in samples, e.g., features of backpack-s in target domain might be mapped near features of cars in source domain. In this paper, we present moving semantic transfer network, which learn semantic representations for unlabeled target samples by aligning labeled source centroid and pseudo-labeled target centroid. Features in same class but different domains are expected to be mapped nearby, resulting in an improved target classiﬁcation accuracy. Moving average centroid alignment is cautiously designed to compensate the insufﬁcient categorical information within each mini batch. Experiments testify that our model yields state of the art results on standard datasets.