Miracle-dz/UniFAR-FTUs huggingface dataset:
Genome Wide Transcriptomic Analysis of WRKY Gene Family Response to Biotic Stresses in Malus ×domestica

Apple (Malus × domestica Borkh.) is a perennial woody plant that often suffers from various biological stresses. Many harmful pathogens can infect apple trees and lead to reduced production. We comprehensively identified the WRKY genes in the apple genome and analyzed their expression in response to several biological stressors, including Alternaria alternata, Pythium ultimum, Botryosphaeria dothidea, Erwinia amylovora, Penicillium expansum, Gymnosporangium yamadae, and Apple replant disease. There were 113 MdWRKYs identified in the apple genome. Twenty-two MdWRKYs were differentially expressed in response to at least five pathogens. Promoter sequence analysis showed that these genes carried many defense- and stress-responsive elements, such as MeJA-response elements, salicylic acid-response elements, and W-box elements, in their promoters. Transient expression assays showed that MdWRKY40a and MdWRKY54h played negative roles in defense against B. dothidea infection. WRKY40 and WRKY60 and the MdWKRY33s might play important roles in responding to pathogens and are conserved in some plants. These differentially expressed MdWRKYs might play key roles in the apple response to multiple pathogens.
CMAR_A_248166 4573..4583

School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, India Purpose: Cancer rates are exponentially increasing worldwide and over 15 million new cases are expected in the year 2020 according to the World Cancer Report. To support the clinical diagnosis of the disease, recent technical advancements in digital microscopy have been achieved to reduce the cost and increase the efficiency of the process. Food and Drug Administration (FDA or Agency) has issued the guidelines, in particular, the development of digital whole slide image scanning system. It is very helpful to the computer-aided diagnosis of breast cancer. Methods: Whole slide imaging supported by fluorescence, immunohistochemistry, and multispectral imaging concepts. Due to the high dimension of WSI images and computation, it is a challenging task to find the region of interest (ROI) on a malignant sample image. The unsupervised machine learning and quantitative analysis of malignant sample images are supported by morphological features and shape formulas to find the correct region of interest. Due to computational limitations, it starts to work on small patches, integrate the results, and automated localize or detect the ROI. It is also compared to the handcrafted and automated region of interest provided in the ICIAR2018 dataset. Results: A total of 10 hematoxylins and eosin (H&E) stained malignant breast histology microscopy whole slide image samples are labeled and annotated by two medical experts who are team members of the ICIAR 2018 challenge. After applying the proposed methodology, it is successfully able to localize the malignant patches of WSI sample images and getting the ROI with an average accuracy of 85.5%. Conclusion: With the help of the k-means clustering algorithm, morphological features, and shape formula, it is possible to recognize the region of interest using the whole slide imaging concept.
DMSO_A_286153 801..811

1Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai, People’s Republic of China; 2Department of Preventive Medicine, School of Public Health, Jiamusi University, Jiamusi, Heilongjiang Province, People’s Republic of China; 3Department of Psychosomatic Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, People’s Republic of China; 4Clinical Psychology Department, Wuhan Mental Health Center, Tongji Medical College of Huazhong University of Science & Technology, Wuhan, Hubei Province, People’s Republic of China; 5Department of Medical Statistics, Tongji University School of Medicine, Shanghai, People’s Republic of China
On the Feasibility of Using GANs for Claim Verification- Experiments and Analysis

The research on fact checking and claim verification has been explored using the Fact Extraction and VERification (FEVER) dataset. To supplement this research a Generative Adversarial Network (GAN) based model is used for fact checking on the FEVER dataset. The GAN based model generates synthetic data in an extended feature space of the FEVER dataset and gives leverage to new features. This synthetically generated data is further classified using positive-unlabeled (PU) learning considering supported facts as positive class and are added to the existing training dataset. Bidirectional Encoder Representations from Transformers (BERT) based encoding technique is used for both original and newly generated data to get the text’s underline context. Due to the Information Gain in the synthetically generated data features, better performance is achieved in the fact checking and claim verification task. A thorough analysis of the model selection is done by comparing the GAN based model with BERT based classifier and other standard classifiers.
Animal Face Classification using Dual Deep Convolutional Neural Network

A practical animal face classification system that classifies animals in image and video data is considered as a pivotal topic in machine learning. In this research, we are proposing a novel method of fully connected dual Deep Convolutional Neural Network (DCNN), which extracts and analyzes image features on a large scale. With the inclusion of the state of the art Batch Normalization layer and Exponential Linear Unit (ELU) layer, our proposed DCNN has gained the capability of analyzing a large amount of dataset as well as extracting more features than before. For this research, we have built our dataset containing ten thousand animal faces of ten animal classes and a dual DCNN. The significance of our network is that it has four sets of convolutional functions that work laterally with each other. We used a relatively small amount of batch size and a large number of iteration to mitigate overfitting during the training session. We have also used image augmentation to vary the shapes of the training images for the better learning process. The results demonstrate that, with an accuracy rate of 92.0%, the proposed DCNN outruns its counterparts while causing less computing costs.
Survey On Credit Card Fraud Detection using Face Authentication

: With the popularization of on-line looking, dealings fraud is growing seriously. Therefore, the study on fraud detection is attention-grabbing and important. a vital manner of detective work fraud is to extract the behavior profiles (BPs) of users supported their historical dealings records, so to verify if AN incoming dealings may be a fraud or not seeable of their bits per second. Markov process models square measure widespread to represent bits per second of users that is effective for those users whose dealings behaviors square measure stable comparatively. However, with the event and popularization of on-line looking, it's a lot of convenient for users to consume via the net that diversifies the dealings behaviors of users. Therefore, Markov process models square measure unsuitable for the illustration of those behaviors. During this paper, we have a tendency to propose logical graph of BP (LGBP) that may be a total order-based model to represent the relation of attributes of dealings records. Supported LGBP and users dealings records, we are able to cipher a path-based transition chance from AN attribute to a different one. Here we are able to find Face by mistreatment viola jones and LBP acknowledge formula for face detection as we use invisible keyword sequence for authentication of OTP. The keyword sequence modification when. At constant time, we have a tendency to outline. A data entropy-based diversity constant so as to characterize the variety of dealings behaviors of a user. We have a tendency to additionally track fraud user with location by mackintosh address of the user laptop portable computer} or computer that have last dealings with success. Additionally, we have a tendency to outline a state transition chance matrix to capture temporal options of transactions of a user. Consequently, we are able to construct a BP for every user so use it to verify if AN incoming dealings may be a fraud or not. Our experiments over a true information set illustrate that our methodology is healthier than 3 progressive ones.
How old users are? Community analysis.

The presented work is devoted to the development of an approach to the age inference for users on the online social network VK.com. This approach is based on an analysis of the communities that the user is a member of. The proposed method is based on the hypothesis that users join some of those communities that contain data about class, graduation from school, data about the group at the university, etc. and those that have users’ peers as members. The proposed approach allows for the age inference. The obtained results can benefit research on online social networks, as well as the task of discovering a user vulnerability through social networks.
Game Theory-Based Minimization of the Ostracism Risk in Construction Companies

Strategic and managemerial decision-making in an organization can have a crucial effect for the whole entity; however, it rarely involves the organization’s employees evenly at the different organizational levels. The result is—what is addressed in this paper as—the ostracism risk, namely the risk accruing from the lack of satisfaction of underprivileged employees’ groups during the decision-making process. The ostracism risk could jeopardize the organization’s integrity and therefore requires effective treatment. This paper aims at verifying a conceptual approach, which is proposed as a methodology for assessing the probability of organizational cooperation when deciding under risk, thus minimizing ostracism risk. The proposed approach is based on organizational and human resources management (HRM) theories and is contextualized for construction through the understanding of systems theory. The proposed methodology presents a potential modelling via game theory of a medium-sized construction company that is organized according to Mintzberg’s organizational model. The utilization of the bounded Pareto distribution is presented as an approach of the model’s probabilistic processing, and the potential for estimating the probabilities to adopt a favorable cooperational decision is verified. The paper concludes with the reference to the next steps required for the methodology’s validation and further improvement.
EARLY PREDICTION OF ACEDEMIC PERFORMANCE USING MACHINE LEARNING APPROACH

Predicting Students performance heretofore can be extremely helpful for educational institutions to improve their instructing quality. This paper proposes to predict students performance by thinking about their scholarly subtleties. Educational associations are extraordinary and assume utmost signiﬁcant job for the improvement of any country.As Education changes the lives of people, families, networks, social orders, nations and at last the world! This is the reason we live agreeable lives today. Presently a day’s training isn’t restricted to just the homeroom instructing however it goes past that like Online Education System, Web-based Education System, Seminars, Workshops, MOOC course turns into It’s all the more testing to Predict understudy’s performance in view of the colossal main part of information put away in the conditions of Educational databases, Learning Management databases. Students’ performance can be assessed with the assistance of different accessible techniques. It is advancing territory of concentrate that accentuation on different strategies like characterization, prediction, include determination. It is utilized on learning records or information identiﬁed with training to predict the students’ performance and learning conduct by extricating the hidden knowledge.
Deep Neural Network-Based Survival Analysis for Skin Cancer Prediction in Heart Transplant Recipients

Heart-transplant recipients are at high risk of developing skin cancer, while Squamous Cell Carcinoma (SCC) and Basal Cell Carcinoma (BCC) are commonly detected. This paper utilized the database from the United Network for Organ Sharing (UNOS) to study the incidence rate of SCC and BCC among heart transplant recipients. Cox proportional hazards model and two deep neural network-based models were studied, and their performance were compared. In addition, Lasso regression, Chi-square test, and Wilcoxon signed-rank test were applied to identify key risk factors. The neural network-based survival models showed better accuracy compared to the standard Cox regression model, which indicates the advantage of deep learning approaches in survival analysis and risk prediction for post-transplant skin cancer.This study investigates the performance of deep learning (DL) models in clinical applications for predicting the risk of skin cancer in heart transplant recipients. The DL models outperform the standard models in assessing the incidence rate of skin cancer across different time spans.
Model Predictive Control versus Traditional Relay Control in a High Energy Efficiency Greenhouse

The sustainable agriculture cultivation in greenhouses is constantly evolving thanks to new technologies and methodologies able to improve the crop yield and to solve the common concerns which occur in protected environments. In this paper, an MPC-based control system has been realized in order to control the indoor air temperature in a high efficiency greenhouse. The main objective is to determine the optimal control signals related to the water mass flow rate supplied by a heat pump. The MPC model allows a predefined temperature profile to be tracked with an energy saving approach. The MPC has been implemented as a multiobjective optimization model that takes into account the dynamic behavior of the greenhouse in terms of energy and mass balances. The energy supply is provided by a ground coupled heat pump (GCHP) and by the solar radiation while the energy losses related to heat transfers across the glazed envelope. The proposed MPC method was applied in a smart innovative greenhouse located in Italy, and its performances were compared with a traditional reactive control method in terms of deviation of the indoor temperature in respect to the desired one and in terms of electric power consumption. The results demonstrated that, for a time horizon of 20 h, in a greenhouse with dimensions 15.3 and 9.9 m and an average height of 4.5 m, the proposed MPC approach saved about 30% in electric power compared with a relay control, guaranteeing a consistent and reliable temperature profile in respect to the predefined tracked one.
The Impact of Digitalisation on Personal Income Taxes

This paper considers the potential impact upon personal income taxes (PIT) of the acceleration of digitalisation and remote working due to the COVID-19 pandemic. The societal changes brought about by the digitalisation of the economy affect all areas of the tax system. Until now, however, most attention has centred on corporate income taxes, and to a lesser extent VAT. What has been comparatively neglected is how the spread of remote working – and particularly inter-jurisdictional remote working – has the potential to trigger very significant challenges to PIT systems. In this paper we argue that some remote working is set to continue post-pandemic, and that some of it is likely to be inter-jurisdictional. These mobile workers are likely to be at the top of the income distribution, and thus even assuming a small number of mobile workers revenues loses can be significant. In the UK we estimate that the overall revenue loss – i.e. combined PIT and SSCs revenues – could amount to between £6.5 billion and £32.5 billion. This new mobility of workers at the top of the income distribution is also likely to have distributional impact, as well as significant spillovers in other taxes, in particular for CIT and for consumption taxes. These challenges are particularly problematic for PIT systems -like that in the UK- which are heavily reliant on PITs as total percentage of revenue, as well as on a relatively small number of high-income – and now potentially mobile - taxpayers. We conclude that the challenges of adapting our tax systems to a digital economy are far from over; indeed, they have just started.
Leakage of Dataset Properties in Multi-Party Machine Learning

Secure multi-party machine learning allows several parties to build a model on their pooled data to increase utility while not explicitly sharing data with each other. We show that such multi-party computation can cause leakage of global dataset properties between the parties even when parties obtain only black-box access to the final model. In particular, a ``curious'' party can infer the distribution of sensitive attributes in other parties' data with high accuracy. This raises concerns regarding the confidentiality of properties pertaining to the whole dataset as opposed to individual data records. We show that our attack can leak population-level properties in datasets of different types, including tabular, text, and graph data. To understand and measure the source of leakage, we consider several models of correlation between a sensitive attribute and the rest of the data. Using multiple machine learning models, we show that leakage occurs even if the sensitive attribute is not included in the training data and has a low correlation with other attributes or the target variable.
Rare and Different: Anomaly Scores from a combination of likelihood and out-of-distribution models to detect new physics at the LHC

We propose a new method to define anomaly scores and apply this to particle physics collider events. Anomalies can be either rare, meaning that these events are a minority in the normal dataset, or different, meaning they have values that are not inside the dataset. We quantify these two properties using an ensemble of One-Class Deep Support Vector Data Description models, which quantifies differentness, and an autoregressive flow model, which quantifies rareness. These two parameters are then combined into a single anomaly score using different combination algorithms. We train the models using a dataset containing only simulated collisions from the Standard Model of particle physics and test it using various hypothetical signals in four different channels and a secret dataset where the signals are unknown to us. The anomaly detection method described here has been evaluated in a summary paper where it performed very well compared to a large number of other methods. The method is simple to implement and is applicable to other datasets in other fields as well.
Midisuperspace foam and the cosmological constant

Wheeler’s conjectured ‘spacetime foam’—large quantum fluctuations of spacetime at the Planck scale—could have important implications for quantum gravity, perhaps even explaining why the cosmological constant seems so small. Here I explore this problem in a midisuperspace model consisting of metrics with local spherical symmetry. Classically, an infinite class of ‘foamy’ initial data can be constructed, in which cancellations between expanding and contracting regions lead to a small average expansion even if Λ is large. Quantum mechanically, the model admits corresponding stationary states, for which the probability current is also nearly zero. These states appear to describe a self-reproducing spacetime foam with very small average expansion, effectively hiding the cosmological constant.
Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples

Evaluating robustness of machine-learning models to adversarial examples is a challenging problem. Many defenses have been shown to provide a false sense of robustness by causing gradient-based attacks to fail, and they have been broken under more rigorous evaluations. Although guidelines and best practices have been suggested to improve current adversarial robustness evaluations, the lack of automatic testing and debugging tools makes it difficult to apply these recommendations in a systematic manner. In this work, we overcome these limitations by: (i) categorizing attack failures based on how they affect the optimization of gradient-based attacks, while also unveiling two novel failures affecting many popular attack implementations and past evaluations; (ii) proposing six novel indicators of failure, to automatically detect the presence of such failures in the attack optimization process; and (iii) suggesting a systematic protocol to apply the corresponding fixes. Our extensive experimental analysis, involving more than 15 models in 3 distinct application domains, shows that our indicators of failure can be used to debug and improve current adversarial robustness evaluations, thereby providing a first concrete step towards automatizing and systematizing them. Our open-source code is available at: https://github.com/pralab/IndicatorsOfAttackFailure.
Integrated information as a common signature of dynamical and information-processing complexity

The apparent dichotomy between information-processing and dynamical approaches to complexity science forces researchers to choose between two diverging sets of tools and explanations, creating conflict and often hindering scientific progress. Nonetheless, given the shared theoretical goals between both approaches, it is reasonable to conjecture the existence of underlying common signatures that capture interesting behavior in both dynamical and information-processing systems. Here, we argue that a pragmatic use of integrated information theory (IIT), originally conceived in theoretical neuroscience, can provide a potential unifying framework to study complexity in general multivariate systems. By leveraging metrics put forward by the integrated information decomposition framework, our results reveal that integrated information can effectively capture surprisingly heterogeneous signatures of complexity—including metastability and criticality in networks of coupled oscillators as well as distributed computation and emergent stable particles in cellular automata—without relying on idiosyncratic, ad hoc criteria. These results show how an agnostic use of IIT can provide important steps toward bridging the gap between informational and dynamical approaches to complex systems.
Proof-of-Work Cryptocurrencies: Does Mining Technology Undermine Decentralization?

Does the proof-of-work consensus protocol serve its intended purpose of supporting decentralized cryptocurrency mining? To address this question, we develop a game-theoretical model in which miners first invest in hardware to improve the efficiency of their operations and then compete for mining rewards in a rent-seeking game. We show that centralization grows with heterogeneity in mining costs, but hardware capacity constraints prevent the most efficient miners from monopolizing the mining process. Investment leads to a more decentralized network unless larger miners have a significant comparative advantage in acquiring new hardware. Our model generates empirically supported implications: (i) mining centralization is countercyclical with respect to mining reward, and (ii) a change in mining reward leads to a less-than-proportional change in hash rates. This paper was accepted by David Simchi-Levi, Special Issue of Management Science: Blockchains and crypto economics. Supplemental Material: The data file is available at https://doi.org/10.1287/mnsc.2023. 4840 .