Miracle-dz/UniFAR-FTUs huggingface dataset:
Grouping Bilinear Pooling

Fusion of the extracted high-order features by capturing complex correlation between features to obtain a better representation performs well in visual tasks. As a simple and effective high-order feature interaction representation, the bilinear representation has achieved remarkable results in many visual tasks: ﬁne-grained image classiﬁcation, semantic segmentation and so on. However, bilinear pooling has not been widely used due to the bilinear representation up to hundreds of thousands or even millions of dimensions. In this paper, we propose grouping bilinear pooling (GBP) that the representation captured by GBP can achieve the same effect with less than 0.4% parameters compare with full bilinear representation. This extreme compact representation largely overcomes the high redundancy of the full bilinear representation, the computational cost and storage consumption. It can be used as a plug-and-play module with convenient operation. Comparing with other state-of-the-art approaches, it achieves competitive performance. The effectiveness of the proposed GBP is proved by experiments on the widely used ﬁne-grained recognition datasets.
Marketing Strategy and Consumer Psychology A Quantitative Study of Chinese Luxury Consumption in the Context of New Media

With the steady improvement of China's international status and the increase of national purchasing power, luxury goods have emerged in the Chinese market, and luxury brands have gradually penetrated and integrated into people's lives. However, different luxury brands have different marketing strategies, and consumers also have different acceptance levels for them. This study examines Chinese mainland consumers' complex and rational attitudes towards luxury marketing strategies and content communication channels and consumers' feedback on brand concepts in the context of new media. This study fills a gap in previous studies as surveyed 358 mainland Chinese participants of different ages, compared their opinions on luxury goods and brands with the position of Chinese luxuries in the market, analyzed the possible reasons, and obtained the important predictors of consumers' luxury purchasing power. The data show that luxury consumption tends to be rational. Consumers have a rational view of the role of luxury in public life and widely accept the brand's promotion of traditional advertising and new media. The status of Chinese luxury goods in the fashion industry is steadily improving. Advanced management mode and distinct cultural characteristics make Chinese fashion elements an important factor in buying luxuries. Therefore, this study provides reliable data and large-scale studies to further develop the fashion and luxury industry in the new media era.
Artificial neural network for ECG arryhthmia monitoring

The application of a multilayer perceptron artificial neural network model (ANN) to detect the QRS complex in ECG (electrocardiography) signal processing is presented. The objective is to improve the heart beat detection rate in the presence of severe background noise. An adaptively tuned multilayer perceptron structure is used to model the nonlinear, time-varying background noise. The noise is removed by subtracting the predicted noise from the original signal. Preliminary experimental results indicate that the ANN based approach consistently outperforms the conventional bandpass filtering approach and the linear adaptive filtering approach. Such performance enhancement is most critical toward the development of a practical automated online ECG arrhythmia monitoring system. <<ETX>>
E-MMAD: Multimodal Advertising Caption Generation Based on Structured Information

With multimodal tasks increasingly getting pop-001 ular in recent years, datasets with large scale 002 and reliable authenticity are in urgent demand. 003 Therefore, we present an e-commercial multi-004 modal advertising dataset, E-MMAD, which 005 contains 120 thousand valid data elaborately 006 picked out from 1.3 million real product exam-007 ples in both Chinese and English. Noticeably, it 008 is one of the largest video captioning datasets in 009 this field, in which each example has its product 010 video (around 30 seconds), title, caption and 011 structured information table that is observed to 012 play a vital role in practice. We also introduce 013 a novel task for vision-language research based 014 on E-MMAD: e-commercial multimodal ad-015 vertising caption generation, which requires to 016 use aforementioned product multimodal infor-017 mation to generate textual advertisement. Ac-018 cordingly, we propose a baseline method on the 019 strength of structured information reasoning to 020 solve the demand in reality on this dataset. 021
Developments in Tumor Targeting and Internalizing Peptides

The major inconvenient of the use of cytotoxic drugs in anti-tumor therapies is their poor ability to distinguish tumor cells from the normal cells. Since their discovery about 30 years ago, tumor targeting peptides (TTPs) have become an useful tool for the treatment and diagnosis of cancer, once they are able to recognize specific tumor targets, such as antibodies (Abs) but without their structural disadvantages. Thus, their use in the development of peptide-drug conjugates (PDCs) is promising, although none of these drug delivery systems (DDS) are approved for clinical use yet. The aim of this work was to review the literature about tumor targeting approaches and to organize and summarize the TTPs that contributed in a significant way to the advances of this field, including the development of therapeutic conjugates. In this work, nine peptides with targeting ability, representative of each class of TTPs and promising for a future clinical application as an integral part of drug conjugates were presented - RGD, NGR, F3, Octreotide, LyP-1, Bombesin, Angiopep 2, CREKA and M2pep. This way, structural and functional features, addresses, structure-activity relationship (SAR) studies, internalization capacity and applications in the development of therapeutic conjugates were focused as key-points for each targeting peptide.
Contamination of barley grain with microscopic fungi and their metabolites in Poland in the years 2015–2016

Barley is one of the oldest and most-grown cereals in the world. It owes its health-promoting properties to the presence of nutrients and non-nutrients with strong antioxidant properties. Barley grain is used in the agri-food industry for the production of, among others, feed, beer as well as in the cosmetics industry for care and medicinal purposes. Barley grain during vegetation, stor-age and processing, is exposed to various stress factors that contribute to the deterioration of grain quality, e.g. by the presence of microscopic fungi. The purpose of this study was to determine the level of contamination with microscopic fungi and mycotoxins from the group of trichothecenes in barley grain from Poland in a 2-year cycle (2015–2016). A total of 44 grain samples were tested. The analyzed barley samples had a similar content of tested fungal metabolites, as well as levels of microscopic fungi. The ERG concentration aver-aged 6.82 mg/kg, while in colony forming units this value ranged from 1.35 log CFU/g to over 2.87 log CFU/g. Total trichothecene concentrations were also low and during the two years of the study not exceeding 0,059 mg/kg. DON concentration did not exceed 1250 µg/kg in any of the tested samples. Very significant correlations between the content of trichothecenes and the concentration of ERG indicate that the level of this metabolite is closely related to the content of mycotoxins in the grain.
The Effect of Heavy Smoking on Early Retirement: An Instrumental Variable Approach

The extent to which heavy smoking and early retirement are causally related remains to be determined. To overcome the endogeneity of heavy smoking behaviour, we employ a novel approach by exploiting Mendelian Randomisation and use genetic predisposition to heavy smoking, as measured with a polygenic risk score (PGS), as an instrumental variable. A total of 3578 participants from the English Longitudinal Study of Ageing (mean age 64.41 years) had data on smoking behaviour, employment and a heavy smoking PGS. Heavy smoking was indexed as smoking at least 20 cigarettes a day. Early retirement was classified as retiring before state pension age. Our results show that being a heavy smoker increases significantly the probability of early retirement (  = 0.635, standard error = 0.209, p < 0.001). Results were robust to a battery of robustness checks and a falsification test. Overall, our findings support a causal pathway from heavy smoking to early
Performance-detective: automatic deduction of cheap and accurate performance models

The many configuration options of modern applications make it difficult for users to select a performance-optimal configuration. Performance models help users in understanding system performance and choosing a fast configuration. Existing performance modeling approaches for applications and configurable systems either require a full-factorial experiment design or a sampling design based on heuristics. This results in high costs for achieving accurate models. Furthermore, they require repeated execution of experiments to account for measurement noise. We propose Performance-Detective, a novel code analysis tool that deduces insights on the interactions of program parameters. We use the insights to derive the smallest necessary experiment design and avoiding repetitions of measurements when possible, significantly lowering the cost of performance modeling. We evaluate Performance-Detective using two case studies where we reduce the number of measurements from up to 3125 to only 25, decreasing cost to only 2.9% of the previously needed core hours, while maintaining accuracy of the resulting model with 91.5% compared to 93.8% using all 3125 measurements.
SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser

This work proposes a syntax-enhanced grammatical error correction (GEC) approach named SynGEC that effectively incorporates dependency syntactic information into the encoder part of GEC models. The key challenge for this idea is that off-the-shelf parsers are unreliable when processing ungrammatical sentences. To confront this challenge, we propose to build a tailored GEC-oriented parser (GOPar) using parallel GEC training data as a pivot. First, we design an extended syntax representation scheme that allows us to represent both grammatical errors and syntax in a unified tree structure. Then, we obtain parse trees of the source incorrect sentences by projecting trees of the target correct sentences. Finally, we train GOPar with such projected trees. For GEC, we employ the graph convolution network to encode source-side syntactic information produced by GOPar, and fuse them with the outputs of the Transformer encoder. Experiments on mainstream English and Chinese GEC datasets show that our proposed SynGEC approach consistently and substantially outperforms strong baselines and achieves competitive performance. Our code and data are all publicly available at https://github.com/HillZhang1999/SynGEC.
L EVERAGING N EURAL L ANGUAGE M ODEL FOR A U - TOMATED C ODE Q UALITY I SSUE I DENTIFICATION

The usefulness of machine learning techniques for understanding source code and assisting with software engineering tasks have been demonstrated by recent progress in the community. More specifically, language models (LMs) on code have been originally developed based on n-grams. One of the limitations of such models is the Out-Of-Vocabulary issue which abounds in code-related applications. Recent advances in deep learning have provided new and more powerful tools for source code modeling. For instance, RNN and transformer based models have been built to learn embeddings of codes for various downstream tasks such as code completion, summarization and translation. However, the power of neural language models has not been well leveraged for improving the quality of code. We develop a single framework based on the unifying concept of neural language modeling to identify four different types of code quality or stylistic issues that may hurt the readability, understandability and maintainability of code. We have built an automated tool to spot such issues in real code repositories from GitHub, and the recommendations by our tool are well accepted by developers. Finally, we show that by fine tuning the language model trained in cross-project setting on a specific repository, the resulting "local" model not only has better model performance but can help reduce false positives.
D EEP L EARNING M ODEL FOR F LEXIBLE AND E FFI - CIENT P ROTEIN -L IGAND D OCKING

Protein-ligand docking is an essential tool in structure-based drug design with applications ranging from virtual high-throughput screening to pose prediction for lead optimization. Most docking programs for pose prediction are optimized for re-docking to an existing co-crystalized protein structure ignoring protein flexibility. In real-world drug design applications, however, protein flexibility is an essential feature of the ligand-binding process. Here we present a deep learning model for flexible protein-ligand docking based on the prediction of an intermolecular Euclidean distance matrix (EDM), making the typical use of search algorithms obsolete. Our method introduces a new approach for the reconstruction of ligand poses in Cartesian coordinates, utilizing EDM completion and restrained energy-based optimization. The model was trained on a large-scale dataset of protein-ligand complexes and evaluated on standardized test sets. Our model generates high quality poses for a diverse set of protein and ligand structures and outperforms comparable docking methods.
Monetary Policy since the Financial Crisis: Why Interest Rates Need to Be Low 1

Central banks around the world lowered interest rates to almost zero and took exceptional measures in response to the financial crisis. It has been claimed that these policies have unintended side effects while yielding little benefit for the real economy. In particular, a long period with low interest rates may induce unsustainable asset-price developments and financial instability. These concerns need to be taken seriously. Currently, however, there is little evidence that the unintended side effects are dominating the benefits of the expansive monetary policy. Nevertheless, an exit from the low-interest-rate environment will be challenging. Central banks should focus on price stability as their main target.
Single-View Reconstruction of a Manhattan World from Line Segments

Single-view reconstruction (SVR) is a fundamental method in computer vision. Often used for reconstructing human-made environments, the Manhattan world assumption presumes that planes in the real world exist in mutually orthogonal directions. Accordingly, this paper addresses an automatic SVR algorithm for Manhattan worlds. A method for estimating the directions of planes using graph-cut optimization is proposed. After segmenting an image from extracted line segments, the data cost function and smoothness cost function for graph-cut optimization are defined by considering the directions of the line segments and neighborhood segments. Furthermore, segments with the same depths are grouped during a depth-estimation step using a minimum spanning tree algorithm with the proposed weights. Experimental results demonstrate that, unlike previous methods, the proposed method can identify complex Manhattan structures of indoor and outdoor scenes and provide the exact boundaries and intersections of planes.
Ternarized TCN for μJ/Inference Gesture Recognition from DVS Event Frames

—Dynamic Vision Sensors (DVS) offer the opportunity to scale the energy consumption in image acquisition proportion-ally to the activity in the captured scene by only transmitting data when the captured image changes. Their potential for energy-proportional sensing makes them highly attractive for severely energy-constrained sensing nodes at the edge. Most approaches to the processing of DVS data employ Spiking Neural Networks to classify the input from the sensor. In this paper, we propose an alternative, event frame-based approach to the classiﬁcation of DVS video data. We assemble ternary video frames from the event stream and process them with a fully ternarized Temporal Convolutional Network which can be mapped to CUTIE, a highly energy-efﬁcient Ternary Neural Network accelerator. The network mapped to the accelerator achieves a classiﬁcation accuracy of 94 . 5% , matching the state of the art for embedded implementations. We implement the processing pipeline in a modern 22nm FDX technology and perform post-synthesis power simulation of the network running on the system, achieving an inference energy of 1 . 7 µ J , which is 647 × lower than previously reported results based on Spiking Neural Networks.
on the treatment of genital

This review summarizes new treatments from the last seven years employed for the treatment of genital warts caused by human papillomavirus (HPV). Imquimod 3.75% is a new agent with fewer side effects and perhaps a better dosing schedule than imquimod 5%, but is not more effective. Sinecatechins/Polyphenon E 15%, a novel extract from green tea can be effective against genital warts but requires three times a day dosing and is not more effective than existing treatments; the treatment course is 12-16 weeks. Photodynamic therapy combined with other destructive modalities might increase the cure rate for genital warts. The quadrivalent vaccine against HPV 6, 11, 16, 18 is decreasing the incidence of warts in the western world but the evidence does not support vaccination as a treatment for those already infected by HPV. Hyperthermia and immunomodulators might be positive additions to the armamentarium of clinicians. In sum, there are new tools that physicians can use but none is really a great advance over what was available a decade ago. mode of action of sinecatechins ointment 15% in the clearance of genital and perianal warts is unknown. In vitro, sinecatechins had anti-oxidative activity; the clinical significance of this finding is unknown The efficacy of sinecatechins in the treatment of external genital and perianal warts was assessed in 2 randomized, double-blind, vehicle-controlled studies designed to investigate the safety and efficacy of sinecatechins ointment (10 and 15% concentrations) in the treatment of immunocompetent patients 18 years of age and older with external genital and perianal warts. The side effects were mostly mild. This study was carried out in the United States and in other cities worldwide. There were detailed exclusion criteria and inclusion criteria used in these clinical trials. The clinical subjects in this study applied the ointment 3 times daily for up to 16 weeks or until complete clearance of all warts (baseline and new warts occurring during treatment). Over both studies the median baseline wart area was 51 mm 2 (range 12 to 585 mm 2 ) and the median baseline number of warts was 6 (range 2 to 30). The primary efficacy outcome measure was the response rate, defined as the proportion of patients with complete clinical (visual) clearance of all external genital and perianal warts (baseline and new) by week 16. cervical dysplasia, pelvic pain, cutaneous facial rash, and staphylococcemia. In a dermal sensitization study of sinecatechins ointment in healthy volunteers, hypersensitivity (type IV) was observed in 5 out of 209 subjects (2.4%) under occlusive conditions. Sinecatechins are indicated for use 3 times per day to all external genital and perianal warts. Treatment should be continued until all warts are cleared but should not be continued for greater than 16 weeks. Sinecatechins is not a cure for external genital and perianal warts and new warts may develop during or after a course of treatment. The safety of treating external genital and perianal warts for greater than 16 weeks or for multiple treatment courses has not been established.
Antimicrobial comparative competence of phytochemical extract from Calendula officinalis and Citrus sinensis

-----------------------------------------------------------------------------------------------------------------------------------ABSTRACT The selected plant materials are known such as COMF (Calendula officinalis: Marigold flowers) and CSOFP (Citrus sinensis: Orange fruits peel) have good antimicrobial efficiency. Selected organic solvents such as chloroform (Chl.), ethanol (Eth.), methanol (Met.) and water (Wat) were selected for extraction of phytochemicals. The different kinds of phytochemicals have been used for each in the soxhlet extraction. In which the total quantity of 3.180 g from 100 g of COMF and total 3.090 g from 100 g of CSOFP samples has been used for phytochemicals extraction. The growth inhibition of microbial on NAM by disc diffusion methods using and MIC (minimum inhibitor concentration) has been compared with standard medicine.
Association Rules of Comorbidities in Dementia by Using Korea National Hospital Discharge In-depth Injury Survey Data

This study aims to find out the associative relationship between dementia and comorbidities. To conduct this study, we used KNHDIS(Korea National Hospital Discharge In-depth Injury Survey) data from 2009 to 2018 provided by the KDCA(Korean Disease Control and Prevention Agency) annually. We used MySQL for data preprocessing and R for data analysis. As a result of applying the Apriori algorithm criteria of support(≥ 0.01), confidence(≥ 0.6), and lift(>1), seventeen rules related to dementia were discovered. The diseases associated with dementia were diabetes mellitus, hypertension, disorders of lipoprotein metabolism, glomerular disorders in diabetes mellitus, renal diseases, cardiovascular disease, cerebrovascular disease, and other urinary system disorders. This study can be utilized as primary data for the care of patients with dementia and provides implications for improving effective dementia prevention policies.
The Clinical and Prognostic Significance of Triple-Negative Status Invasive Breast Cancer in a Sample of Iraqi Women

Introduction: Triple-negative breast cancer (TNBC) is a type of breast cancer that is negative for estrogen receptor, progesterone receptor, and HER2. This study aimed to evaluate the clinical outcomes and short-term prognosis of this type of cancer compared with other subtypes of breast cancer. Methods: A cross-sectional study was conducted on 108 women with invasive breast cancer. The patients were divided into a TNBC or a non-TNBC group. Multiple clinicopathological variables, diagnostic modality reports, progression-free survival, and overall survival were assessed. Results: The results showed no statistically significant difference between the TNBC group and the non-TNBC group in age, BMI, or family history of breast cancer. In the TNBC group, compared with the non-TNBC group, the presence grade 3 tumor and lymphovascular invasion were significantly higher ( P = 0.02 and P = 0.014, respectively). Progression-free survival rates in the TNBC and non-TNBC groups were 81.4% and 90.9%, and overall survival in the two groups were 93.8% and 93%, respectively. Conclusion: There was a significant difference between the two groups in the form of histologic grade and presence of lymphovascular invasion. Non-significant differences in progression-free survival and overall survival between the two groups may be due to the short duration of follow-up.