Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentiment Analysis: It’s Complicated!

Sentiment analysis is used as a proxy to measure human emotion, where the objective is to categorize text according to some predefined notion of sentiment. Sentiment analysis datasets are typically constructed with gold-standard sentiment labels, assigned based on the results of manual annotations. When working with such annotations, it is common for dataset constructors to discard “noisy” or “controversial” data where there is significant disagreement on the proper label. In datasets constructed for the purpose of Twitter sentiment analysis (TSA), these controversial examples can compose over 30% of the originally annotated data. We argue that the removal of such data is a problematic trend because, when performing real-time sentiment classification of short-text, an automated system cannot know a priori which samples would fall into this category of disputed sentiment. We therefore propose the notion of a “complicated” class of sentiment to categorize such text, and argue that its inclusion in the short-text sentiment analysis framework will improve the quality of automated sentiment analysis systems as they are implemented in real-world settings. We motivate this argument by building and analyzing a new publicly available TSA dataset of over 7,000 tweets annotated with 5x coverage, named MTSA. Our analysis of classifier performance over our dataset offers insights into sentiment analysis dataset and model design, how current techniques would perform in the real world, and how researchers should handle difficult data.
Priority-Aware VM Allocation and Network Bandwidth Provisioning in Software-Defined Networking (SDN)-Enabled Clouds

In the multi-tenant architecture of cloud computing, different applications have different requirements and priorities. In the case of network congestion within a data center, failure of transferring data on time may cause a significant performance degradation of an application. It may result in a severe failure for a critical application that needs a certain level of QoS satisfaction, therefore, efficient resource provisioning techniques are vital to ensure transferring the high-priority data over the other traffics even in network congestion. In Software-Defined Networking (SDN)-enabled clouds, this is possible by reconfiguring network flows dynamically adapting to network traffics. In this paper, we propose priority-aware resource placement algorithms considering both host and network resources. Our priority-aware VM allocation (PAVA) algorithm places VMs of the high-priority application to closely connected hosts to reduce the chance of network congestion caused by other tenants. The required bandwidth of a critical application is also guaranteed by bandwidth allocation with a configuration of priority queues on each networking device in a data center network managed by SDN controller. Our experiment results show that the combination of proposed approaches can allocate sufficient resources for high-priority applications to meet the application’s QoS requirement in a multi-tenant cloud data center.
Fast Approximate Nearest Neighbor Search With The Navigating Spreading-out Graph

Approximate nearest neighbor search (ANNS) is a fundamental problem in databases and data mining. A scalable ANNS algorithm should be both memory-efficient and fast. Some early graph-based approaches have shown attractive theoretical guarantees on search time complexity, but they all suffer from the problem of high indexing time complexity. Recently, some graph-based methods have been proposed to reduce indexing complexity by approximating the traditional graphs; these methods have achieved revolutionary performance on million-scale datasets. Yet, they still can not scale to billion-node databases. In this paper, to further improve the search-efficiency and scalability of graph-based methods, we start by introducing four aspects: (1) ensuring the connectivity of the graph; (2) lowering the average out-degree of the graph for fast traversal; (3) shortening the search path; and (4) reducing the index size. Then, we propose a novel graph structure called Monotonic Relative Neighborhood Graph (MRNG) which guarantees very low search complexity (close to logarithmic time). To further lower the indexing complexity and make it practical for billion-node ANNS problems, we propose a novel graph structure named Navigating Spreading-out Graph (NSG) by approximating the MRNG. The NSG takes the four aspects into account simultaneously. Extensive experiments show that NSG outperforms all the existing algorithms significantly. In addition, NSG shows superior performance in the E-commercial scenario of Taobao (Alibaba Group) and has been integrated into their billion-scale search engine.
Observer and Adaptive Fuzzy Control Design for Nonlinear Strict-Feedback Systems With Unknown Virtual Control Coefficients

This paper considers the problems of designing a robust observer and developing a backstepping-based adaptive fuzzy control scheme for a class of strict-feedback systems, in which the virtual control coefficients are unknown. By using convex combination method, a robust fuzzy observer has been constructed to estimate the unmeasurable system state variables. Further, an observer-based adaptive fuzzy control scheme has been proposed. During the controller design procedure, fuzzy logic systems are used to model the unknown nonlinear functions, adaptive technique and backstepping are combined to construct the ideal virtual and the real laws. The proposed adaptive fuzzy output feedback controller guarantees that the tracking error converges to a small neighborhood of the origin and all the signals in the adaptive closed-loop system are bounded. Simulation results are provided to demonstrate the effectiveness of the presented approach.
A performance evaluation of defect detection by using Denoising AutoEncoder Generative Adversarial Networks

In this paper, we discuss a method to detect defects in industrial products by using Denoising AutoEncoder Generative Adversarial Networks. In previous methods, a defective area is detected by restoring a defective product image which added an artificial defect to a non-defective product image by Denoising AutoEncoder (DAE). Therefore, a defective area is detected by subtracted image of them. We discuss whether further accuracy improvement is possible by introducing a framework of adversarial learning to DAE in order to restore a defective image to a non-defective image clearer.
PHARMacy‐based interdisciplinary program for patients with Chronic Heart Failure (PHARM‐CHF): rationale and design of a randomized controlled trial, and results of the pilot study

We report the rationale and design of a community PHARMacy‐based prospective randomized controlled interdisciplinary study for ambulatory patients with Chronic Heart Failure (PHARM‐CHF) and results of its pilot study. The pilot study randomized 50 patients to a pharmacy‐based intervention or usual care for 12 months. It demonstrated the feasibility of the design and showed reduced systolic blood pressure in the intervention group as indicator for improved medication adherence. The main study will randomize patients ≥60 years on stable pharmacotherapy including at least one diuretic and a history of heart failure hospitalization within 12 months. The intervention group will receive a medication review at baseline followed by regular dose dispensing of the medication, counselling regarding medication use and symptoms of heart failure. The control patients are unknown to the pharmacy and receive usual care. The primary efficacy endpoint is medication adherence, pre‐specified as a significant difference of the proportion of days covered between the intervention and control group within 365 days following randomization using pharmacy claims data for three CHF medications (angiotensin‐converting enzyme inhibitors or angiotensin receptor blockers, beta‐blockers, and mineralocorticoid receptor antagonists). The primary composite safety endpoint is days lost due to blindly adjudicated unplanned cardiovascular hospitalizations or death. Overall, 248 patients shall be randomized. The minimum follow‐up is 12 months with an expected mean of 24 months. Based on the feasibility demonstrated in the pilot study, the randomized PHARM‐CHF trial will test whether an interdisciplinary pharmacy‐based intervention can safely improve medication adherence and will estimate the potential impact on clinical endpoints.
Virtual Character Facial Expressions Influence Human Brain and Facial EMG Activity in a Decision-Making Game

We examined the effects of the emotional facial expressions of a virtual character (VC) on human frontal electroencephalographic (EEG) asymmetry (putatively indexing approach/withdrawal motivation), facial electromyographic (EMG) activity (emotional expressions), and social decision making (cooperation/defection). In a within-subjects design, the participants played the Iterated Prisoner's Dilemma game with VCs with different dynamic facial expressions (predefined or dependent on the participant's electrodermal and facial EMG activity). In general, VC facial expressions elicited congruent facial muscle activity. However, both frontal EEG asymmetry and facial EMG activity elicited by an angry VC facial expression varied as a function of preceding interactional events (human collaboration/defection). Pre-decision inner emotional-motivational processes and emotional facial expressions were dissociated, suggesting that human goals influence pre-decision frontal asymmetry, whereas display rules may affect (pre-decision) emotional expressions in human-VC interaction. An angry VC facial expression, high pre-decision corrugator EMG activity, and relatively greater left frontal activation predicted the participant's decision to defect. Both post-decision frontal asymmetry and facial EMG activity were related to reciprocal cooperation. The results suggest that the justifiability of VC emotional expressions and the perceived fairness of VC actions influence human emotional responses.
Combining Character and Word Information in Neural Machine Translation Using a Multi-Level Attention

Natural language sentences, being hierarchical, can be represented at different levels of granularity, like words, subwords, or characters. But most neural machine translation systems require the sentence to be represented as a sequence at a single level of granularity. It can be difficult to determine which granularity is better for a particular translation task. In this paper, we improve the model by incorporating multiple levels of granularity. Specifically, we propose (1) an encoder with character attention which augments the (sub)word-level representation with character-level information; (2) a decoder with multiple attentions that enable the representations from different levels of granularity to control the translation cooperatively. Experiments on three translation tasks demonstrate that our proposed models outperform the standard word-based model, the subword-based model, and a strong character-based model.
Toxicokinetics-Variations due to Genetics or Interactions : Basics and Examples

Individual variations in the pharmacological or toxicological responses to the same drug dose may be caused by a variety of factors such as body mass, age, sex, kidney and liver function, drug-drug (food-drug) interactions, and genetic variability [1]. Detailed knowledge of the metabolism of drugs (of abuse) allows to predict possible interactions with other xenobiotics because of e.g. inhibition or induction of individual metabolic isoenzymes by poisons, drugs (of abuse), alcohol, tobacco smoke, or food ingredients [6, 12, 13]. This knowledge is a prerequisite for understanding pharmaco-/toxicokinetics and pharmacogenetic variations, for evidence-based case interpretation, for toxicological risk assessment, for developing toxicological analysis procedures, and for understanding pitfalls in drug testing.
Group B Streptococci: Declining Incidence in Infants in Germany

Group B streptococcus (GBS) is a leading cause of morbidity and mortality in newborns worldwide. From 2000 to 2008, national guidelines in Germany recommended intrapartum antibiotic prophylaxis for pregnant women displaying risk factors (eg, perinatal anogenital GBS colonization, rupture of the membranes ≥18 hours before birth) for the vertical transmission of GBS to their children. In 2008, these guidelines were revised to advocate universal, culture-based screening for GBS colonization among all pregnant women between 35 and 37 weeks of gestation. For the period 2009–2010, our prospective active surveillance study assessed the incidence of invasive GBS infections in infants 0–90 days of age in Germany. We did this by means of a capture–recapture analysis of 2 separate, independent systems (pediatric reporting versus laboratory reporting). We compared our results with those from a previous study by employing an equivalent design (2001–2003). We detected a 32% reduction in GBS incidence, from 0.47 per 1000 live births (n = 679) in 2001–2003 to 0.34 per 1000 live births (n = 450) in 2009–2010. This decline primarily is tied to a reduced number of GBS cases in children under 1 week of age. In 2009–2010, the ratio of early-onset disease to late-onset disease reversed from 1.52 (206:136), as determined in 2001–2003, to 0.75 (92:122). This study is the first to assess changes in the incidence of invasive GBS in Germany after the implementation of the guidelines for intrapartum prophylaxis for pregnant women colonized with GBS.
A Walk through the Graveyard : Which Insurance Companies Have to Leave the Market ?

This paper analyzes insurance companies that left the market in 2003-2013. In a sample of 4,655 insurers, 146 of which failed, we find that technical efficiency negatively and business volatility positively correlates with failure probability. Firm growth has a Ushaped non-linear relationship with the failure probability. We classify insurers taken over by other firms as a special type of failure, because they show different symptoms from other failures (i.e. higher efficiency and profitability). Moreover, we document that the warning signals from failure indicators become stronger as the time to the failure event approaches. The findings are robust across the 2008 financial crisis. Our research relies on a large dataset, a long time period, a cross-country design, and is innovative in using new insurer failure models relying on business volatility measures and rare event logistic regressions.
Analyzing 3D cell data of optical diffraction tomography through volume rendering

Optical diffraction tomography (ODT) constitutes a novel approach for acquiring cell images since it is capable of capturing morphology of a living cell without chemical treatment. ODT is an interferometric technique that measures the 3D refractive index (RI) distribution of optically transparent samples such as biological cells. Unlike other cell imaging modalities, naïve ODT data do not contain encoded information about the cellular properties such as labeled protein in fluorescent microscopic data or the fixed border of a cell wall in scanning electron microscopic data. Therefore, specifying the region of interest in the raw image is an important and challenging task for a quantitative analysis of ODT cell data. We propose an interactive interface for reconstructing 3D shape of cells from ODT image data based on intervening visualization results of the cell data to guide the investigator observing the overall blueprint of cell morphology. The cell organelles are segmented based on the lookup table referring corresponding transfer function items adopted from volume rendering technique. The final shape of the cell is then constructed as mesh models from the segmentation results for further quantitative analyses. In this paper, we also demonstrate the various modeling options accounting characteristics of target cell.
CRRN: Multi-scale Guided Concurrent Reflection Removal Network

Removing the undesired reflections from images taken through the glass is of broad application to various computer vision tasks. Non-learning based methods utilize different handcrafted priors such as the separable sparse gradients caused by different levels of blurs, which often fail due to their limited description capability to the properties of real-world reflections. In this paper, we propose the Concurrent Reflection Removal Network (CRRN) to tackle this problem in a unified framework. Our proposed network integrates image appearance information and multi-scale gradient information with human perception inspired loss function, and is trained on a new dataset with 3250 reflection images taken under diverse real-world scenes. Extensive experiments on a public benchmark dataset show that the proposed method performs favorably against state-of-the-art methods.
5G multi-antenna V2V channel modeling with a 3D game engine

The automotive industry is currently conceiving the future cars of the 2020s, which are expected to combine multiple antenna elements distributed across the car body in order to enhance their communication capabilities. This paper describes a novel approach for channel characterization that uses the ray tracing capabilities of the Unity 3D game engine on a vehicle-to-vehicle scenario supported by fifth-generation mobile communications. First, a validation process was carried out, which satisfactorily validated the path loss results of our Unity approach with respect to channel measurements available in the literature for V2V communication at 5.2 GHz and 5.75 GHz. Then, path loss results were obtained for a comprehensive set of involved vehicle arrangements considering multiple transmission and reception antenna combinations at 5.9 GHz. The results suggest that, in future 5G V2V scenarios, the selection of the antenna combination providing the minimum path loss in a particular situation, could maximize the received signal level, thus playing an essential role in ensuring the best possible channel conditions for vehicular communications.
Accelerated Development of the First-Order Central Auditory Neurons With Spontaneous Activity

In developing sensory systems, elaborate morphological connectivity between peripheral cells and first-order central neurons emerges via genetic programming before the onset of sensory activities. However, how the first-order central neurons acquire the capacity to interface with peripheral cells remains elusive. By making patch-clamp recordings from mouse brainstem slices, we found that a subset of neurons in the cochlear nuclei, the first central station to receive peripheral acoustic impulses, exhibits spontaneous firings (SFs) as early as at birth, and the fraction of such neurons increases during the prehearing period. SFs are reduced but not eliminated by a cocktail of blockers for excitatory and inhibitory synaptic inputs, implicating the involvement of intrinsic pacemaker channels. Furthermore, we demonstrate that these intrinsic firings (IFs) are largely driven by hyperpolarization- and cyclic nucleotide-gated channel (HCN) mediated currents (Ih), as evidenced by their attenuation in the presence of HCN blockers or in neurons from HCN1 knockout mice. Interestingly, genetic deletion of HCN1 cannot be fully compensated by other pacemaker conductances and precludes age-dependent up regulation in the fraction of spontaneous active neurons and their firing rate. Surprisingly, neurons with SFs show accelerated development in excitability, spike waveform and firing pattern as well as synaptic pruning towards mature phenotypes compared to those without SFs. Our results imply that SFs of the first-order central neurons may reciprocally promote their wiring and firing with peripheral inputs, potentially enabling the correlated activity and crosstalk between the developing brain and external environment.
Improved method of reducing mismatching in manifold template matching

Template matching is highly versatile because its underlying principle is very simple. We speeded up conventional rotary template matching by using manifolds. Manifolds are trajectories that are drawn in multi-dimensional space by using multiple images. However, this method has a problem in that it may incur mismatching by changing the shapes of input image manifolds. Therefore, we reduced mismatching with the previous method by not only focusing on the template manifold's shape but also the manifold's space. We reduced mismatching by creating a feature space that was not excessively affected by shape changes to input image manifolds. The feature space was created so that the template manifold and negative samples were separated in multi-dimensional space. Our new method in experiments using actual images demonstrated a recognition rate of 95.3% and a processing time of 4.7s.
Poultry as an Experimental Animal Model in Medical Research and Pharmaceutical Industry

During the past few years there has been a notable increase in the demand for poultry meat and egg production due to its low cost, good nutritional profile and suitability for further processing. Moreover, current forecasts and projection studies have predicted that the expansion of the poultry market will continue in the future. This growing demand has led to progressive improvements in genetic selection to produce fast-growing birds [1-3] and increase egg production from egg type chicken [4]. On the other hand, in recent years poultry were used not only as experimental animal model in pharmaceutical and medical research but also in in the pharmaceutical industry because of its advantages such as their short breeding time, simple management, and high productivity are of interest for recombinant therapeutic production in their eggs [5]. Also, Chick embryos are a significant historical research model in basic and applied sciences. Poultry eggs and cell lines derived from embryonated eggs have found wide application in industry and biotechnology. Avian derived cell lines are an alternative for virus production because they provide a simple, flexible system that can be controlled for an extended time with the capability for rapid vaccine production during a pandemic [5]. It is also strange to use viruses that infect poultry a pathogen causing economic losses in chickens in the treatment of human disease such as Infectious bursal disease virus (IBDV) [6] and the chicken anemia virus (CAV) [7]. Also, many human diseases were studded by using poultry such as cancer, nonalcoholic fatty liver disease (NAFLD), Atherosclerosis [8-11]. Recent advances originated by using transgenic approach highlight the use of avians as animal models in the medical research and pharmaceutical industry.
Diagnosis and management of meibomian gland dysfunction: optometrists' perspective

: Meibomian gland dysfunction (MGD) is commonly encountered among eye care professionals. Our understanding of the pathophysiology for the development of MGD has greatly expanded in recent years, which helped increase awareness of the disease. Despite increased awareness, it is essential for eye care professionals to make a conscious effort to properly examine the meibomian glands through gland expression even for asymptomatic patients. At minimum, early management should include patient education and supportive therapy such as warm compresses, lid hygiene, and gland expression. As patients become more symptomatic and as the ocular surface becomes more affected, employing additional therapeutic management is recommended and may include oral omega-3 essential fatty acids, topical azithromycin, oral tetracycline, and topical anti-inflammatories to improve clinical signs and patient comfort. In addition to treatment of MGD, clinicians should be mindful of the comorbid conditions of MGD and simultaneously manage them in conjunction of MGD treatment.