Miracle-dz/UniFAR-FTUs huggingface dataset:
Male sterility at extreme temperatures: a significant but neglected phenomenon for understanding Drosophila climatic adaptations

The thermal range for viability is quite variable among Drosophila species and it has long been known that these variations are correlated with geographic distribution: temperate species are on average more cold tolerant but more heat sensitive than tropical species. At both ends of their viability range, sterile males have been observed in all species investigated so far. This symmetrical phenomenon restricts the temperature limits within which permanent cultures can be kept in the laboratory. Thermal heat sterility thresholds are very variable across species from 23 °C in heat sensitive species up to 31 °C in heat tolerant species. In Drosophila melanogaster, genetic variations are observed among geographic populations. Tropical populations are more tolerant to heat induced sterility and recover more rapidly than temperate ones. A genetic analysis revealed that about 50% of the difference observed between natural populations was due to the Y chromosome. Natural populations have not reached a selection limit, however: thermal tolerance was still increased by keeping strains at a high temperature, close to the sterility threshold. On the low temperature side, a symmetrical reverse phenomenon seems to exist: temperate populations are more tolerant to cold than tropical ones. Compared to Mammals, drosophilids exhibit two major differences: first, male sterility occurs not only at high temperature, but also at a low temperature; second, sterility thresholds are not evolutionarily constrained, but highly variable. Altogether, significant and sometimes major genetic variations have been observed between species, between geographic races of the same species, and even between strains kept in the laboratory under different thermal regimes. In each case, it is easily argued that the observed variations correspond to adaptations to climatic conditions, and that male sterility is a significant component of fitness and a target of natural selection.
Understanding the Effect of Data Augmentation on Knowledge Distillation

Knowledge distillation (KD) requires sufficient data to transfer knowledge from large-scale teacher models to small-scale student models. Therefore, data augmentation has been widely used to mitigate the shortage of data under specific scenarios. Classic data augmentation techniques, such as synonym replacement and k-nearest-neighbors, are initially designed for fine-tuning. To avoid severe semantic shifts and preserve task-specific labels, those methods prefer to change only a small proportion of tokens (e.g., changing 10% tokens is generally the best option for fine-tuning). However, such data augmentation methods are sub-optimal for knowledge distillation since the teacher model could provide label distributions and is more tolerant to semantic shifts. We first observe that KD prefers as much data as possible, which is different from fine-tuning that too much data will not gain more performance. Since changing more tokens leads to more semantic shifts, we use the proportion of changed tokens to reflect semantic shift degrees. Then we find that KD prefers augmented data with a larger semantic shift degree (e.g., changing 30% tokens is generally the best option for KD) than fine-tuning (changing 10% tokens). Besides, our findings show that smaller datasets prefer larger degrees until the out-of-distribution problem occurs (e.g., datasets with less than 10k inputs may prefer the 50% degree, and datasets with more than 100k inputs may prefer the 10% degree). Our work sheds light on the preference difference in data augmentation between fine-tuning and knowledge distillation and encourages the community to explore KD-specific data augmentation methods.
Design and Development of IoT and Deep Ensemble Learning Based Model for Disease Monitoring and Prediction

With the rapidly increasing reliance on advances in IoT, we persist towards pushing technology to new heights. From ordering food online to gene editing-based personalized healthcare, disruptive technologies like ML and AI continue to grow beyond our wildest dreams. Early detection and treatment through AI-assisted diagnostic models have outperformed human intelligence. In many cases, these tools can act upon the structured data containing probable symptoms, offer medication schedules based on the appropriate code related to diagnosis conventions, and predict adverse drug effects, if any, in accordance with medications. Utilizing AI and IoT in healthcare has facilitated innumerable benefits like minimizing cost, reducing hospital-obtained infections, decreasing mortality and morbidity etc. DL algorithms have opened up several frontiers by contributing towards healthcare opportunities through their ability to understand and learn from different levels of demonstration and generalization, which is significant in data analysis and interpretation. In contrast to ML which relies more on structured, labeled data and domain expertise to facilitate feature extractions, DL employs human-like cognitive abilities to extract hidden relationships and patterns from uncategorized data. Through the efficient application of DL techniques on the medical dataset, precise prediction, and classification of infectious/rare diseases, avoiding surgeries that can be preventable, minimization of over-dosage of harmful contrast agents for scans and biopsies can be reduced to a greater extent in future. Our study is focused on deploying ensemble deep learning algorithms and IoT devices to design and develop a diagnostic model that can effectively analyze medical Big Data and diagnose diseases by identifying abnormalities in early stages through medical images provided as input. This AI-assisted diagnostic model based on Ensemble Deep learning aims to be a valuable tool for healthcare systems and patients through its ability to diagnose diseases in the initial stages and present valuable insights to facilitate personalized treatment by aggregating the prediction of each base model and generating a final prediction.
Implicit Subgoal Planning with Variational Autoencoders for Long-Horizon Sparse Reward Robotic Tasks

The challenges inherent to long-horizon tasks in robotics persist due to the typical inefficient exploration and sparse rewards in traditional reinforcement learning approaches. To alleviate these challenges, we introduce a novel algorithm, Variational Autoencoder-based Subgoal Inference (VAESI), to accomplish long-horizon tasks through a divide-and-conquer manner. VAESI consists of three components: a Variational Autoencoder (VAE)-based Subgoal Generator, a Hindsight Sampler, and a Value Selector. The VAE-based Subgoal Generator draws inspiration from the human capacity to infer subgoals and reason about the final goal in the context of these subgoals. It is composed of an explicit encoder model, engineered to generate subgoals, and an implicit decoder model, designed to enhance the quality of the generated subgoals by predicting the final goal. Additionally, the Hindsight Sampler selects valid subgoals from an offline dataset to enhance the feasibility of the generated subgoals. The Value Selector utilizes the value function in reinforcement learning to filter the optimal subgoals from subgoal candidates. To validate our method, we conduct several long-horizon tasks in both simulation and the real world, including one locomotion task and three manipulation tasks. The obtained quantitative and qualitative data indicate that our approach achieves promising performance compared to other baseline methods. These experimental results can be seen in the website \url{https://sites.google. com/view/vaesi/home}.
DECOrrelated feature space partitioning for distributed sparse regression

Fitting statistical models is computationally challenging when the sample size or the dimension of the dataset is huge. An attractive approach for down-scaling the problem size is to first partition the dataset into subsets and then fit using distributed algorithms. The dataset can be partitioned either horizontally (in the sample space) or vertically (in the feature space). While the majority of the literature focuses on sample space partitioning, feature space partitioning is more effective when $p\gg n$. Existing methods for partitioning features, however, are either vulnerable to high correlations or inefficient in reducing the model dimension. In this paper, we solve these problems through a new embarrassingly parallel framework named DECO for distributed variable selection and parameter estimation. In DECO, variables are first partitioned and allocated to $m$ distributed workers. The decorrelated subset data within each worker are then fitted via any algorithm designed for high-dimensional problems. We show that by incorporating the decorrelation step, DECO can achieve consistent variable selection and parameter estimation on each subset with (almost) no assumptions. In addition, the convergence rate is nearly minimax optimal for both sparse and weakly sparse models and does NOT depend on the partition number $m$. Extensive numerical experiments are provided to illustrate the performance of the new framework.
Short Term Load Forecasting using Artificial Neural Network

- Development of artificial neural network for short term load forecasting is presented in this paper. Load forecasting is the process for prediction of electric load. Accurate load forecasting affects the economic operation and reliability of system up to great extent. If the generation is not adequate to accomplish the demand, it will lead the problem of unbalanced supply and in case of surplus generation the generating company will have to bear loss . For the optimal power system operation and planning appropriate evaluation of present and future electric load is needed. Many electrical utilities are routinely forecasting load power based on conventional methods. However, since the relationship between load power and factors influencing load power is non-linear, it is difficult to identify its non-linearity by using conventional methods. To overcome these problems load forecasting method using Artificial Neural Network (ANN) with adaptive learning rate is being used in this paper . The ANN model was trained by Levenberg Marquardt. The data used for training is collected from Gwalior region of MPSEB. The model for short term load forecasting was design and implemented with MATLAB. The result was calculated by Mean Absolute Percentage Error (MAPE) of 3.329 for the forecasted day.
A Longitudinal Study of Health-Related Quality of Life of First Stroke Survivors’ Close Relatives

Background and Purpose:Mental and social burdens of stroke patients’ relatives have been investigated repeatedly, but there is little information about their quality of life (QoL). The purpose of the study was to describe the health-related QoL of stroke patients’ relatives at 3 points in time after the stroke. Method: Sixty-four closest relatives of patients suffering from a disabling stroke were questioned at admission to inpatient rehabilitation as well as 6 months and 1 year after discharge with the SF36 (short form 36-item questionnaire) and about probable covariates. Results: During inpatient rehabilitation relatives’ perception of their physical role as well as of all mental health scales was significantly lower than the normative data. We found improvements in all scales except physical functioning between inpatient rehabilitation and 6 months after discharge. Deteriorations between 6 months and 1 year occurred in physical functioning, role physical, social functioning, role emotional and mental health. Conclusions: Changes in perceived health-related QoL indicate that relatives go through different stages while coping with the chronic illness of their family member during rehabilitation and in the first year after discharge. These findings may aid support and counseling.
AMSI-Based Detection of Malicious PowerShell Code Using Contextual Embeddings

PowerShell is a command-line shell, supporting a scripting language. It is widely used in organizations for configuration management and task automation but is also increasingly used for launching cyber attacks against organizations, mainly because it is pre-installed on Windows machines and exposes strong functionality that may be leveraged by attackers. This makes the problem of detecting malicious PowerShell code both urgent and challenging. Microsoft's Antimalware Scan Interface (AMSI), built into Windows 10, allows defending systems to scan all the code passed to scripting engines such as PowerShell prior to its execution. In this work, we conduct the first study of malicious PowerShell code detection using the information made available by AMSI. We present several novel deep-learning based detectors of malicious PowerShell code that employ pretrained contextual embeddings of words from the PowerShell "language". A contextual word embedding is able to project semantically-similar words to proximate vectors in the embedding space. A known problem in the cybersecurity domain is that labeled data is relatively scarce, in comparison with unlabeled data, making it difficult to devise effective supervised detection of malicious activity of many types. This is also the case with PowerShell code. Our work shows that this problem can be mitigated by learning a pretrained contextual embedding based on unlabeled data. We trained and evaluated our models using real-world data, collected using AMSI. The contextual embedding was learnt using a large corpus of unlabeled PowerShell scripts and modules collected from public repositories. Our performance analysis establishes that the use of unlabeled data for the embedding significantly improved the performance of our detectors. Our best-performing model uses an architecture that enables the processing of textual signals from both the character and token levels and obtains a true-positive rate of nearly 90% while maintaining a low false-positive rate of less than 0.1%.
Radioactive analysis and radiological hazards of sand in Weifang , China

Background: The ac vity concentra ons of 226 Ra, 232 Th and 40 K in sand used as building material in Weifang of China were inves gated for evalua ng the radia on hazard. Materials and Methods: Sand samples were collected from Weifang and their radioac vity levels were measured using gamma-ray spectrometry. The radia on hazard for residents was assessed by radium equivalent ac vity (Raeq), indoor air absorbed dose rate (D), annual effec ve dose (AED) and excess life me cancer risk (ELCR). Results: The ac vity concentra ons of 226 Ra, 232 Th and 40 K ranged from 11.7 to 23.0, 33.6 to 126.1 and 353.2 to 924.8 Bq kg −1 with averages of 15.5, 70.3 and 802.9 Bq kg -1 , respec vely. All Raeq values were lower than the limit of 370 Bq kg -1 . The mean value of D was higher than the world popula on-weighted average of 84 nGy h -1 , while the mean AED and ELCR values were below the interna onally accepted values. Conclusions: The use of sand in construc on of dwellings is considered to be safe for inhabitants.
A spectral-based clustering algorithm for categorical data using data summaries

We present a novel spectral-based algorithm for clustering categorical data that combines attribute relationship and dimension reduction techniques found in Principal Component Analysis (PCA) and Latent Semantic Indexing (LSI). The new algorithm uses data summaries that consist of attribute occurrence and co-occurrence frequencies to create a set of vectors each of which represents a cluster. We refer to these vectors as "candidate cluster representatives. " The algorithm also uses spectral decomposition of the data summaries matrix to project and cluster the data objects in a reduced space. We refer to the algorithm as SCCADDS (Spectral-based Clustering algorithm for CAtegorical Data using Data Summaries). SCCADDS differs from other spectral clustering algorithms in several key respects. First, the algorithm uses the attribute categories similarity matrix instead of the data object similarity matrix (as is the case with most spectral algorithms that find the normalized cut of a graph of nodes of data objects). SCCADDS scales well for large datasets since in most categorical clustering applications the number of attribute categories is small relative to the number of data objects. Second, non-recursive spectral-based clustering algorithms typically require K-means or some other iterative clustering method after the data objects have been projected into a reduced space. SCCADDS clusters the data objects directly by comparing them to candidate cluster representatives without the need for an iterative clustering method. Third, unlike standard spectral-based algorithms, the complexity of SCCADDS is linear in terms of the number of data objects. Results on datasets widely used to test categorical clustering algorithms show that SCCADDS produces clusters that are consistent with those produced by existing algorithms, while avoiding the computation of the spectra of large matrices and problems inherent in methods that employ the K-means type algorithms.
Labeling circular focus regions based on a tractable case of maximum weight independent set of rectangles

We introduce a new model for labeling point features in a circular focus region that a user can define in a map. In this model, each label is an axis-aligned rectangle that touches the boundary of the focus region with one of its corners. A straight-line segment (the leader of the label) connects that corner with the corresponding point feature to ensure a clear graphical association. We require that every leader points towards the center of the focus region, which improves legibility and, in particular, avoids intersecting leaders. Our model allows only one possible position for each label. Consequently, but also more generally due to limited map space, labeling all points without overlapping labels can easily become impossible. Therefore, we compute a conflict-free labeling with the maximal number of labels or with the maximal total weight, where the weight of each label corresponds to the importance of its corresponding point. Our problems are special cases of the NP-hard problem Maximum Weight Independent Set of Rectangles (MWISR). We show that MWISR can be solved efficiently if the upper-left corners of all rectangles (of potentially different heights) lie on a monotonically ascending curve. This allows us to compute optimal labelings efficiently, e.g., in O(n log n) time for unit-height labels. We achieve a real-time performance with an implementation in JavaScript that runs in a browser.
RESISTANCE TO PEAR PSYLLA NYMPHAL FEEDING OF GERMPLASM FROM CENTRAL EUROPE

Pear psylla (Cacopsylla pyricola Foërster) is the most serious pest of the European pear (Pyrus communis L.) in the production regions of Europe and North America. Nine plant germplasm introductions of pear from Central Europe recently released from plant quarantine were evaluated for nymphal feeding antixenosis, a primary mode of resistance known to be correlated with ovipositional antixenosis and antibiosis (i.e. nymphal mortality and reduced developmental rate). Five single-shoot budded trees were grown in the greenhouse without pesticide application. The top two youngest fully expanded leaves were infested with ten 2nd and 3rd instar nymphs from a young laboratory colony. The number of surviving nymphs and the number of actively feeding nymphs was recorded after two days. Three cultivars from Yugoslavia, ‘Karamanka’, ‘Jerisbasma’, and ‘Vodenjac’, plus NY 10353 (a resistant control of P. ussuriensis Max. ancestry) were significantly more resistant to nymphal feeding than the susceptible control, ‘Bartlett’, and the cultivars ‘Junsko Zlato’, ‘Lida’, ‘Orlovskaya’, ‘Oranzhevaja’, ‘Nadejda’, and the selection IV 12. These objective data confirm field observations made in Europe of the Yugoslavian cultivars, and increases the number of psylla-resistant genotypes available for breeding new cultivars. INTRODUCTION The pear psylla (Cacopsylla pyricola Foërster syn., Psylla pyricola Foërster, P. pyri L., and P. pyrisuga Foerster) is a serious insect pest of the cultivated European pear (Pyrus communis L.) in the production regions of North America and Europe. The insect has rapidly developed resistance to most of the pesticides employed for control and biological controls alone have rarely provided sufficient control for commercial production. The annual cost of chemical control exceeds $300 per acre. Cultivars with durable resistance would enhance the economic and environmental sustainability of pear production by reducing producer costs and pesticide use. These species of pear psylla and their host apparently coevolved in Europe. Early investigations of western European cultivars identified only a single moderately resistant cultivar, the old Italian pear ‘Spina Carpi’ (Quarta and Puggioni, 1985). Pomologists and breeders have been collecting wild germplasm and landrace cultivars in order to identify diverse sources of resistance for use in the development of new, but psylla-resistant, cultivars. The present research is a continuation of an investigation of the Pyrus gene pool of eastern and central Europe as a source of resistance (Bell, 1991, 1992; Bell and Stuart, 1990; Puterka et al., 1993). MATERIALS AND METHODS Germplasm Nine recent germplasm introductions of pear from central Europe, plus ‘Bartlett’ as a susceptible control and NY 10353 (a P. ussuriensis × P. communis BC1 hybrid) as a resistant control (Table 1). Proc. XXVI IHC – Genetics and Breeding of Tree Fruits and Nuts
Some further evidence on the role of effective corporate income taxes as determinant of foreign direct investment in central and east European countries

FOREIGN DIRECT INVESTMENT (FDI) ACTIVITY of multinational enterprises (MNEs) has grown substantially during the last decades. In line with this trend, a substantial interest to empirically investigate the determinants of FDI has emerged (Bloningen, 2005). Clearly taxes, corporate income taxes in particular, are among the many possible drivers of FDI. Conceptually the profi tability of an investment is expected to decrease with increases in taxes. Empirically, the negative relationship between the corporate income tax burden and FDI has been broadly confi rmed. Empirical evidence for FDI between the most advanced OECD countries is summarized by DeMooij and Ederveen (2003, 2005). From these two studies, a median tax rate elasticity (semi-elasticity) of around -3 can be derived. However, the magnitude of the effect of taxes on FDI usually varies substantially depending on the measure of corporate income tax burden (e.g., statutory tax rate, backward-looking effective tax rate, forward-looking effective tax rate), the measurement of FDI (e.g., aggregate FDI data from balance of payments, property, plant and equipment) and the type of dataset (time series, cross-section, panel data, discrete choice models) used (DeMooij and Ederveen, 2005; Devereux and Griffi th, 2002). Concerning the choice of FDI data one has to rely on offi cial data (see Devereux, 2006 for an overview) but with respect to proxies for the corporate income tax burden one has to calculate measures according to theoretical guidelines (see OECD 2000 for an overview of approaches). Empirical evidence concerning FDI and taxation in the Central and East European Countries (CEECs) is much scarcer than for the more advanced OECD countries. In Bellak et al. (2005), we survey existing studies on the role of taxes as a driver of FDI in CEECs and fi nd a median tax rate elasticity of about -1.5. A notable feature of the SOME FURTHER EVIDENCE ON THE ROLE OF EFFECTIVE CORPORATE INCOME TAXES AS DETERMINANTS OF FOREIGN DIRECT INVESTMENT IN CENTRAL AND EAST EUROPEAN COUNTRIES*