Miracle-dz/UniFAR-FTUs huggingface dataset:
NRGA: Gravitational Approach for Non-rigid Point Set Registration

Recovery of correspondences between point sets which differ by some non-rigid transformation is an ill-posed problem. Many existing methods underperform on noisy or corrupted input data. In this study, a novel physics-based approach -- Non-Rigid Gravitational Approach (NRGA) -- for non-rigid point set registration is introduced which is robust to the mentioned artifacts. Thereafter, a distributed N-body simulation and iterative Procrustes alignment non-rigidly transform and register the template point set. Furthermore, in the force field evolution, per-point Gaussian curvature serves as a shape matching descriptor whereas the displacement fields are regularized by coherent collective motion. The optimal alignment is referred to as the state of minimum gravitational potential energy between the point sets. A thorough experimental evaluation and comparison are provided with widely used state-of-the-art methods on 2D and 3D data sets. Experiments show NRGA's robustness against uniform outliers and missing data.
Preamplification with dUTP and Cod UNG Enables Elimination of Contaminating Amplicons

Analyzing rare DNA and RNA molecules in limited sample sizes, such as liquid biopsies and single cells, often requires preamplification, which makes downstream analyses particularly sensitive to polymerase chain reaction (PCR) generated contamination. Herein, we assessed the feasibility of performing Cod uracil-DNA N-glycosylase (Cod UNG) treatment in combination with targeted preamplification, using deoxyuridine triphosphate (dUTP) to eliminate carry-over DNA. Cod UNG can be completely and irreversibly heat inactivated, a prerequisite in preamplification methods, where any loss of amplicons is detrimental to subsequent quantification. Using 96 target assays and quantitative real-time PCR, we show that replacement of deoxythymidine triphosphate (dTTP) with dUTP in the preamplification reaction mix results in comparable dynamic range, reproducibility, and sensitivity. Moreover, Cod UNG essentially removes all uracil-containing template of most assays, regardless of initial concentration, without affecting downstream analyses. Finally, we demonstrate that the use of Cod UNG and dUTP in targeted preamplification can easily be included in the workflow for single-cell gene expression profiling. In summary, Cod UNG treatment in combination with targeted preamplification using dUTP provides a simple and efficient solution to eliminate carry-over contamination and the generation of false positives and inaccurate quantification.
Transient selective brain cooling confers neurovascular and functional protection from acute to chronic stages of ischemia/reperfusion brain injury

Ischemic injury can be alleviated by the judicious use of hypothermia. However, the optimal regimens and the temporal kinetics of post-stroke neurovascular responses to hypothermic intervention have not been systematically studied. These gaps slow the clinical translation of hypothermia as an anti-stroke therapy. Here, we characterized the effects of transient selective brain hypothermia (TSBH) from the hyperacute to chronic stages of focal ischemia/reperfusion brain injury induced by transient middle cerebral artery occlusion in mice. A simple cooling device was used to induce TSBH during cerebral ischemia. This treatment reduced mortality from 31.8% to 0% and improved neurological outcomes for at least 35 days post-injury. TSBH mitigated blood–brain barrier leakage during the hyperacute and acute injury stages (1–23 h post-reperfusion). This early protection of the blood–brain barrier was associated with anti-inflammatory phenotypic polarization of microglia/macrophages, reduced production of pro-inflammatory cytokines, and less brain infiltration of neutrophils and macrophages during the subacute injury stage (three days post-reperfusion). TSBH elicited enduring protective effects on both grey and white matter for at least 35 days post-injury and preserved the long-term electrophysiological function of fiber tracts. In conclusion, TSBH ameliorates ischemia/reperfusion injury in the neurovascular unit from hyperacute to chronic injury stages after experimental stroke.
A Geometric Morphometric Examination of Hominoid Third Metacarpal Shape and Its Implications for Inferring the Precursor to Terrestrial Bipedalism

The third metacarpal has been a focus of study when examining questions surrounding early hominin locomotion since this bone is adapted to the diverse range of positional behaviors performed by extant hominoids. The shape of this bone is potentially under strong selective pressure related to the biomechanical demands of terrestrial knuckle‐walking, arboreal clambering, and brachiation performed by extant hominoids since the hand directly interacts with the substrate during the performance of these movements. The objective of the present study was to explore shape variation of the third metacarpal and examine how different parts of the bone discriminated between hominoid genera that perform these different locomotor behaviors. In addition to examining general interspecies variation, shape analysis was applied to testing the knuckle‐walking hypothesis for human evolution. Fourteen 3D landmark coordinates were collected on hominoid third metacarpals, and principal component analysis and Procrustes distances were used to examine metacarpal shape. Comparable measurements were collected on fossilized third metacarpals of Australopithecus afarensis as an early hominin test case for examining the knuckle‐walking hypothesis. Analyses that included landmarks collected on both ends of the bone distinguished humans from great apes and presented a strong functional signal related to suspensory locomotion among nonhuman hominoids, whereas the distal articular surface provided the strongest knuckle‐walking signal. The shapes of Australopithecus afarensis metacarpals examined in the current study did not provide evidence for a trajectory of shape change in early hominin evolution that started from a metacarpal adapted for terrestrial knuckle‐walking. Anat Rec, 302:983–998, 2019. © 2018 Wiley Periodicals, Inc.
Flare: Optimizing Apache Spark with Native Compilation for Scale-Up Architectures and Medium-Size Data

In recent years, Apache Spark has become the de facto standard for big data processing. Spark has enabled a wide audience of users to process petabyte-scale workloads due to its flexibility and ease of use: users are able to mix SQL-style relational queries with Scala or Python code, and have the resultant programs distributed across an entire cluster, all without having to work with low-level parallelization or network primitives. However, many workloads of practical importance are not large enough to justify distributed, scale-out execution, as the data may reside entirely in main memory of a single powerful server. Still, users want to use Spark for its familiar interface and tooling. In such scale-up scenarios, Spark's performance is suboptimal, as Spark prioritizes handling data size over optimizing the computations on that data. For such medium-size workloads, performance may still be of critical importance if jobs are computationally heavy, need to be run frequently on changing data, or interface with external libraries and systems (e.g., TensorFlow for machine learning). We present Flare, an accelerator module for Spark that delivers order of magnitude speedups on scale-up architectures for a large class of applications. Inspired by query compilation techniques from main-memory database systems, Flare incorporates a code generation strategy designed to match the unique aspects of Spark and the characteristics of scale-up architectures, in particular processing data directly from optimized file formats and combining SQL-style relational processing with external frameworks such as TensorFlow.
Endoscopic third ventriculostomy and ventriculoperitoneal shunt for patients with noncommunicating hydrocephalus

Background: The surgical methods of endoscopic third ventriculostomy (ETV) and ventriculoperitoneal shunt (VS) for patients with noncommunicating hydrocephalus have rapidly increased in the past 2 decades. However, there is controversy regarding the efficacy and safety of these 2 surgical methods for noncommunicating hydrocephalus. The purpose of this study was to identify whether ETV is safer and more efficacious than VS for patients with noncommunicating hydrocephalus. Methods: We performed electronic searches in PubMed, Embase, China National Knowledge Internet, and the Cochrane Library to identify studies published up to February 03, 2018. The study summary results included improvement of symptoms, major complications, hematoma, infection, reoperation, mortality, duration of surgery, and hospital stay. Odds ratios (ORs) or standard mean differences (SMDs) with 95% confidence intervals (CIs) were calculated using random-effects models. Results: We identified 10 observational studies (4 prospective and 6 retrospective studies) with data collected from 2017 patients with noncommunicating hydrocephalus. First, there was no significant difference between ETV and VS for symptom improvement (OR: 0.83; 95%CI: 0.46–1.50; P = .534). Second, ETV was associated with lower incidence of major complications when compared with VS (OR: 0.31; 95%CI: 0.17–0.56; P < .001). Third, ETV has little or no significant effect on hematoma (OR: 0.65; 95%CI: 0.22–1.92; P = .433) and mortality (OR: 0.90; 95%CI: 0.11–7.72; P = .926). Fourth, ETV were associated with lower incidence of infection (OR: 0.20; 95%CI: 0.06–0.69; P = .010) and reoperation (OR: 0.22; 95%CI: 0.08–0.56; P = .002). Finally, patients who received ETV had shorter duration of surgery (SMD: -1.71; 95%CI: -3.16 to -0.27; P = .020) and hospital stay (SMD: −0.91; 95%CI: −1.45 to −0.38; P = .001). Conclusions: This meta-analysis provides robust evidence that ETV has greater benefits in terms of major complications, infection, reoperation, duration of surgery, and hospital stay than VS for patients with noncommunicating hydrocephalus.
XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning

A new semi-supervised ensemble algorithm called XGBOD (Extreme Gradient Boosting Outlier Detection) is proposed, described and demonstrated for the enhanced detection of outliers from normal observations in various practical datasets. The proposed framework combines the strengths of both supervised and unsupervised machine learning methods by creating a hybrid approach that exploits each of their individual performance capabilities in outlier detection. XGBOD uses multiple unsupervised outlier mining algorithms to extract useful representations from the underlying data that augment the predictive capabilities of an embedded supervised classifier on an improved feature space. The novel approach is shown to provide superior performance in comparison to competing individual detectors, the full ensemble and two existing representation learning based algorithms across seven outlier datasets.
Linguistic Regularities Between Words and Their Definitions

In the field of natural language processing we use distributed vector representations to capture both semantic and morphological information of a word, which is a basic unit of language that possesses meaning. Naturally, these are composed into phrases and sentences, which we can represent as vectors by sentence modeling methods. While a meaning of a word is partly captured in its representation, it is also easily available in the form of a definition, which is essentially a phrase or a sentence. As we can represent both sentences and words by vectors, in this paper we are studying regularities between pair of words and their definitions.
Hybrid Beamforming Design for Multiuser Massive MIMO-OFDM Systems

A multiuser hybrid beamforming system with orthogonal frequency division multiplexing (OFDM) is considered. We propose an alternating optimization algorithm based on manifold optimization to maximize the performance in terms of the system spectral efficiency. Moreover, two simplified methods with low complexity are also proposed which are of practical relevance for massive MIMO. The proposed methods are verified by simulation results to have smaller spectral efficiency gaps with the fully-digital beamforming than the scheme in another recent research. With the proposed designs, significant cost savings are obtained with only marginal loss in performance.
Investigation of Ondansetron, Haloperidol, and Dexmedetomidine Efficacy for Prevention of Postoperative Nausea and Vomiting In Patients with Abdominal Hysterectomy

AIM: This study was aimed to compare the effects of ondansetron, haloperidol, and dexmedetomidine for reducing postoperative nausea and vomiting (PONV) after laparoscopic cholecystectomy. METHODS: This randomised clinical trial study was performed on p.patients who were candidates for abdominal hysterectomy referring to Taleghani hospital in Arak. In this study, 114 patients with abdominal hysterectomy were randomly assigned to three groups (ondansetron, haloperidol, and dexmedetomidine) using the cubull randomisation method. RESULTS: The results revealed a significant difference between the three groups of ondansetron, haloperidol and dexmedetomidine in terms of scorpion vomiting in recovery, 2 and 4 hours after surgery, and vomiting score was significantly lower in the ondansetron group compared with the other two groups (P = 0.04; P = 0.02; P = 0.001). There was a significant difference between the three groups of e ondansetron, haloperidol and dexmedetomidine regarding the mean dose of metoclopramide in mg for 24 hours after surgery. Therefore, the dosage of dexmedetomidine in the ondansetron group was less than the other two groups (P = 0.001). CONCLUSION: these three drugs are effective in reducing PONV in patients undergoing a hysterectomy. However, the effect of ondansetron was found to be more than the other two drugs in reducing PONV.
Nonnegative Matrix Factorization Using Autoencoders And Exponentiated Gradient Descent

We introduce a new autoencoder-based algorithm for non-negative matrix factorization. Instead of stochastic gradient descent-based update rules, our approach employs exponentiated gradient decent which, unlike the former, inherently guarantees the non-negativity of basis vectors when they are initialized to non-negative values. Moreover, we explore the potential of our autoencoder-based non-negative matrix factorization model for clustering applications, and show that it can learn hierarchical factorizations, each of which corresponding to a different and meaningful clustering. Further, we show that adding a supervised loss at intermediate layers results in more diverse representations at the different layers of the NMF hierarchy. We provide extensive empirical evaluations on text and image datasets and compare our proposed model to two alternative approaches, including another autoencoder-based algorithm.
Over‐discretized SENSE reconstruction and B0 correction for accelerated non‐lipid‐suppressed 1H FID MRSI of the human brain at 9.4 T

The aim of this work was to use post‐processing methods to improve the data quality of metabolite maps acquired on the human brain at 9.4 T with accelerated acquisition schemes. This was accomplished by combining an improved sensitivity encoding (SENSE) reconstruction with a B0 correction of spatially over‐discretized magnetic resonance spectroscopic imaging (MRSI) data.
Multimodal Deep Neural Network with Image Sequence Features for Video Captioning

In this paper, we propose MDNNiSF (Multimodal Deep Neural Network with image Sequence Features) for generating a sentence description of a given video clip. A recently proposed model, S2VT, uses a stack of two LSTMs to solve the problem and demonstrated high METEOR. However, experiments show that S2VT sometimes produces inaccurate sentences, which is quite natural due to the challenging nature of learning relationships between visual and textual contents. A possible reason is that the video caption data were still small for the purpose. We try to circumvent this flaw by integrating S2VT with NeuralTalk2, which is for image captioning and known to generate an accurate description due to its capability of learning alignments between text fragments to image fragments. Experiments using two video caption data, MSVD and MSRVTT, demonstrate the effectiveness of our MDNNiSF over S2VT. For example, MDNNiSF achieved METEOR 0.344, which is 21.5% higher than S2VT, with MSVD.
Towards the Learning of Weighted Multi-label Associative Classifiers

Because of the ability to capture the correlation between features and labels, association rules have been applied to multi-label classification. However, existing multi-label associative classification algorithms usually exploit association rules using heuristic strategies. Moreover, only the covering association rules whose feature set is a subset of the testing instance are considered. Discarding any mined rules may diminish the performance of the classifier, especially when some rules only differ from the testing instance by a few insignificant features. In this paper we propose Weighted Multi-label Associative Classifiers (WMAC) that leverage an extended set of association rules with overlapping features with the testing instance to learn a universal weight vector for features. For this purpose, we embed the set of rules into a linear model and weigh the association rules by its confidence. Empirical results on diversified datasets clearly demonstrate that WMAC outperforms other well-established multi-label classification algorithms.
Hand Gesture Recognition and Real-time Game Control Based on A Wearable Band with 6-axis Sensors

Human-computer interaction introduces critical open door with the proceeds with improvement of wearable gadgets. Gesture recognition through smart devices is becoming a popular research direction. This paper proposes a hand gesture recognition and real-time game control system that is capable of continues human-computer interaction in view of an off-the-rack business wearable wristband. We utilize three-axis accelerator and gyroscope sensors embedded in smart band to collect hand motion information and use Kinect camera capture video information for manual segmentation during the training model phase. A continuous gesture segmentation algorithm based on sliding window and DTW algorithm is developed to detect meaningful gestures in the real-time game control stage. In addition, an android game named Fly Birds which is controlled by gesture recognition result is presented to simulate real-time human-computer interaction. Then, we classify the data in the window using common classifiers. Finally, our experimental results show that, we can accurately identify the designed gestures during the stage of static gesture recognition, and we also achieve a perfect interactive effect in the process of dynamic real-time game control. The experiment outcomes will advance the ascent of human-PC cooperation in view of hand gesture recognition and related applications will rise in vast numbers.
Does monetary reward operantly enhance pain sensitivity over time? An experiment in healthy individuals

Aim Operant conditioning has long been believed to influence the pain experience through a psychological reward pathway. This study was formulated to test the hypothesis that pain sensitivity may be enhanced >3 months if a monetary reward works as a reinforcement. Methods Forty healthy subjects volunteered to participate in this study. The subjects repeatedly underwent pain testing via mechanical stimuli, and they rolled dice three (or six) times to gain money at the following five time points: baseline, three reinforcement sessions, and last session. The payoff was determined by roll of the dice. The subjects were instructed to roll the dice into a masked stand three times per session and informed that no one monitored the number of dice actually appeared. The subjects were also informed that they could roll the dice another three times when they reported strong pain during reinforcement sessions. Results The amount of individual payoff had significantly increased at last session compared with the values obtained at baseline; however, no changes were identified in terms of the pain ratings for mechanical stimuli during all sessions. Conclusion The results suggest that the psychological reward pathway does not always involve pain perception, and it is difficult to conclude whether pain sensitivity is operantly changed through the monetary reward in healthy individuals. Further investigation is required.
How AD can help solve differential-algebraic equations

A characteristic feature of differential-algebraic equations is that one needs to find derivatives of some of their equations with respect to time, as part of the so-called index reduction or regularization, to prepare them for numerical solution. This is often done with the help of a computer algebra system. We show in two significant cases that it can be done efficiently by pure algorithmic differentiation. The first is the Dummy Derivatives method; here we give a mainly theoretical description, with tutorial examples. The second is the solution of a mechanical system directly from its Lagrangian formulation. Here, we outline the theory and show several non-trivial examples of using the ‘Lagrangian facility’ of the Nedialkov–Pryce initial-value solver DAETS, namely a spring-mass-multi-pendulum system; a prescribed-trajectory control problem; and long-time integration of a model of the outer planets of the solar system, taken from the DETEST testing package for ODE solvers.
Question Classification by Convolutional Neural Networks Embodying Subword Information

Question Classification is a core module of Question Answering paradigm. Development of classification models based on neural networks showed that convolutional architectures allow obtaining uppermost results for this task. In particular, this type of approach avoids extracting features from questions, by treating text as a sequence of words, and transforming each word in a dense vector, named word embedding. Among different techniques to learn word embeddings, a recent approach takes into account also subword information, which could be very useful for morphologically rich languages. In this paper, a Question Classification approach based on word embedding using subword information and Convolutional Neural Networks is proposed, in order to improve classification accuracy. In particular, questions taken from a TRC dataset are considered, and a comparison between English and Italian languages is reported, by highlighting eventual improvements obtained by initializing word embeddings with advanced vectors learned in an unsupervised manner using skip- gram model and comprising character-based information.
Characterization of the complete mitochondrial genome of Parabreviscolexniepini Xi et al., 2018 (Cestoda, Caryophyllidea)

Abstract Parabreviscolexniepini is a recently described caryophyllidean monozoic tapeworm from schizothoracine fish on the Tibetan Plateau. In the present study, the complete mitochondrial genome of P.niepini is determined for the first time. The mitogenome is 15,034 bp in length with an A+T content of 59.6%, and consists of 12 protein-encoding genes, 22 tRNA genes, two rRNA genes, and two non-coding regions. The secondary structure of tRNAs exhibit the conventional cloverleaf structure, except for trnS1(AGN) and trnR, which lack DHU arms. The anti-codon of trnS1(AGN) in the mitogenome of P.niepini is TCT. The two major non-coding regions, 567 bp and 1428 bp in size, are located between trnL2 and cox2, trnG and cox3, respectively. The gene order of P.niepini shows a consistent pattern with other caryophyllideans. Phylogenetic analysis based on mitogenomic data indicates that P.niepini has a close evolutionary relationship with tapeworms Breviscolexorientalis and Atractolytocestushuronensis.