Miracle-dz/UniFAR-FTUs huggingface dataset:
Relationship between manifold smoothness and adversarial vulnerability in deep learning with local errors

Artificial neural networks can achieve impressive performances, and even outperform humans in some specific tasks. Nevertheless, unlike biological brains, the artificial neural networks suffer from tiny perturbations in sensory input, under various kinds of adversarial attacks. It is therefore necessary to study the origin of the adversarial vulnerability. Here, we establish a fundamental relationship between geometry of hidden representations (manifold perspective) and the generalization capability of the deep networks. For this purpose, we choose a deep neural network trained by local errors, and then analyze emergent properties of trained networks through the manifold dimensionality, manifold smoothness, and the generalization capability. To explore effects of adversarial examples, we consider independent Gaussian noise attacks and fast-gradient-sign-method (FGSM) attacks. Our study reveals that a high generalization accuracy requires a relatively fast power-law decay of the eigen-spectrum of hidden representations. Under Gaussian attacks, the relationship between generalization accuracy and power-law exponent is monotonic, while a non-monotonic behavior is observed for FGSM attacks. Our empirical study provides a route towards a final mechanistic interpretation of adversarial vulnerability under adversarial attacks.
Frequency of the rs 14035 polymorphism of RAN gen in recurrent pregnancy loss: A case-control study

Abstract Background Genetic factors could account for recurrent pregnancy loss (RPL). The RAN gene is a member of the "large RAS family" and a small GTPase that is essential for the translocation of Ribonucleic acid (RNA) and proteins through the nuclear pore. Mutation in the RAN constitutive gene could stop DNA synthesis and alter the expression of genes in the uterus, likely playing a role in recurrent miscarriage. Objective The aim was to investigate the frequency of RAN (rs 14035) polymorphism in women with RPL compared with women without abortion history. Materials and Methods In this case-control study, 100 women with at least two consecutive miscarriages before the 20th wk of gestation and having spouses with karyotype and normal sperm parameters as the case group and 100 women with no history of abortion and having at least one successful pregnancy and normal delivery as the control group. The groups were age matched (20-40 yr). The rs 14035 polymorphism of RAN gene was investigated by Polymerase Chain Reaction-Restriction Fragment Length poly morphism technique and the frequency of which was compared between the two groups. Results The frequency of TT, TC, and CC genotypes of RAN gene polymorphism in the case group were 9%, 40%, and 51%, respectively, and in the control group were 11%, 38%, and 51%, respectively. There was no significant difference in the genotypes between two groups (p = 0.882). Conclusion According to our results, it seems that RAN polymorphism (rs 14035) is not associated with the risk of RPL in this study population.
A Nested Decomposition Method and Its Application for Coordinated Operation of Hierarchical Electrical Power Grids

Multilevel, multiarea, and hierarchically interconnected electrical power grids confront substantial challenges with the increasing integration of many volatile energy resources. The traditional isolated operation of interconnected power grids is uneconomical due to a lack of coordination; it may result in severe accidents that affect operational safety. However, the centralized operation of interconnected power grids is impractical, considering the operational independence and information privacy of each power grid. This paper proposes a nested decomposition method for the coordinated operation of hierarchical electrical power grids, which can achieve global optimization by iterating among upper- and lower-level power grids with exchange of boundary information alone. During each iteration, a projection function, which embodies the optimal objective value of a lower-level power grid projected onto its boundary variable space, is computed with second-order exactness. Thus, the proposed method can be applied widely to nonlinear continuous optimizations and can converge much more rapidly than existing decomposition methods. We conducted numerical tests of coordinated operation examples with a trilevel power grid that demonstrate the validity and performance of the proposed method.
Automatically Generating Codes from Graphical Screenshots Based on Deep Autocoder

During software front-end development, the work to convert Graphical User Interface(GUI) image to the corresponding front-end code is an inevitable tedious work. There have been some attempts to make this work to be automatic. However, the GUI code generated by these models is not accurate due to the lack of attention mechanism guidance. To solve this problem, we propose PixCoder based on an artificially supervised attention mechanism. The approach is to train a neural network to predict the style sheets in the input GUI image and then output a vector. PixCoder generate the GUI code targeting specific platform according to the output vector. The experimental results have shown the accuracy of the GUI code generated by PixCoder is over 95%.
Insecticide distribution model in human tissues viewing worker's health monitoring programs

O objetivo desse trabalho foi avaliar as caracteristicas de 39 inseticidas quanto a sua distribuicao e acumulacao em tecidos humanos como tambem avaliar o grau de similaridade entre os inseticidas quanto a sua distribuicao nos tecidos. Para tanto, foram utilizadas as caracteristicas fisico-quimicas dos compostos e o conteudo de lipideos teciduais para calcular a distribuicao dos inseticidas entre os tecidos estudados. Os inseticidas selecionados para o presente trabalho foram alguns daqueles que se encontram registrados no Brasil para uso agricola. No calculo da distribuicao dos inseticidas entre os tecidos foi utilizado o modelo de fugacidade nivel I em musculos, visceras, pele, gordura, sangue, figado, rins e intestinos. As caracteristicas fisico- quimicas dos inseticidas e os conteudos de lipidio dos tecidos foram usados para calcular a distribuicao dos inseticidas entre os tecidos. O coeficiente de particao octanol-agua, a solubilidade em agua e o conteudo de lipidio dos tecidos determinaram a distribuicao de um inseticida entre tecidos humanos. Foram realizadas analises de agrupamento, para identificar grupos de inseticidas com distribuicao similar nos tecidos. 90% dos 39 inseticidas apresentaram distribuicao em gordura maior do que 50%. A analise de agrupamento indicou tres grupos de inseticidas: no primeiro o percentual acumulado na gordura variou de 70 - 86%, no segundo de 44 - 58% e no terceiro de 9 - 19%. Os resultados obtidos podem contribuir para os programas de monitoramento da saude do trabalhador rural.
Dispersal capacity in the Mediterranean corn borer, Sesamia nonagrioides

Corn (Zea mays L.) borers are the primary target of Bacillus thuringiensis Berliner (Bt) transgenic maize. Management of corn borer resistance to Bt requires information on larval and adult dispersal capacities, a feature that is particularly unknown in Sesamia nonagrioides Lefèbvre (Lepidoptera: Noctuidae), the most damaging corn borer in Spain. Larval dispersal was studied over a 3 year period by infesting plants with egg masses and dissecting the neighbouring plants 7, 14, and 32 days later to measure larval dispersal at several ages. The number and age of larvae were recorded in the dissected plants. Only mature larvae dispersed in significant numbers; they moved at least to rows adjacent to those containing the infested plant, and down the row five plants. The percentage of larvae that dispersed from the infested plant was density‐dependent. Adult dispersal was studied with directional light and pheromone uni‐traps over 5 and 3 year periods, respectively. Directional light traps were placed in the margins between Bt and non‐Bt maize fields, half oriented towards each of the two kinds of maize field. Pheromone traps were placed in the Bt and non‐Bt fields at increasing distances (0–100 m) from the border. The numbers of males and females caught in directional light traps were not different in traps oriented towards Bt or non‐Bt fields, but the number of males caught in the third flight in Bt fields was lower than in non‐Bt fields. These results suggest that males from adjacent Bt and non‐Bt fields mate indiscriminately with females emerging in any of the two kinds of maize fields. However, male movement in the third flight may not be sufficient to randomly distribute males between the two fields.
Spectral rigidity of non-Hermitian symmetric random matrices near the Anderson transition

We study the spectral rigidity of the non-Hermitian analog of the Anderson model suggested by Tzortzakakis, Makris, and Economou (TME). This is a $L\ifmmode\times\else\texttimes\fi{}L\ifmmode\times\else\texttimes\fi{}L$ tightly bound cubic lattice, where both real and imaginary parts of onsite energies are independent random variables uniformly distributed between $\ensuremath{-}W/2$ and $W/2$. The TME model may be used to describe a random laser. In a recent paper we proved that this model has the Anderson transition at $W={W}_{c}\ensuremath{\simeq}6$ in three dimension. Here we numerically diagonalize TME $L\ifmmode\times\else\texttimes\fi{}L\ifmmode\times\else\texttimes\fi{}L$ cubic lattice matrices and calculate the number variance of eigenvalues in a disk of their complex plane. We show that on the metallic side $Wl6$ of the Anderson transition, complex eigenvalues repel each other as strongly as in the complex Ginibre ensemble only in a disk containing ${N}_{c}(L,W)$ eigenvalues. We find that ${N}_{c}(L,W)$ is proportional to $L$ and grows with decreasing $W$ similarly to the number of energy levels ${N}_{c}$ in the Thouless energy band of the Anderson model.
COVID-19 Pandemic: How to Use Artificial Intelligence to Choose Non-Vulnerable Workers for Positions with the Highest Possible Levels of Exposure to the Novel Coronavirus

The COVID-19 pandemic has affected people globally; nowadays several countries are facing a major change in daily life due to universal quarantining, closed schools, social isolation, and shelter-in-place orders. In addition, this pandemic caused an economic crisis. International Labour Organization (ILO) reported that not only COVID-19 is a serious threat to public health but also the economic and social disruption of this crisis threatens the long-term livelihoods and wellbeing of millions of people. In this short paper, we introduce our hypothesis on using artificial intelligence (AI) technology to predict which employees are most vulnerable to the infections caused by the novel coronavirus (SARS-CoV-2). Such a system can be used in a wide variety of work places such as libraries, banks, drugstores, and hotel receptions to reduce the risk of severe infections in employees as well as to ensure the safety of labor force and the sustainability of businesses and jobs.
Identification of Chemoresistance-Associated Key Genes and Pathways in High-Grade Serous Ovarian Cancer by Bioinformatics Analyses

Purpose High-grade serous ovarian cancer (HGSOC) is the leading cause of death among gynecological malignancies. This is mainly attributed to its high rates of chemoresistance. To date, few studies have investigated the molecular mechanisms underlying this resistance to treatment in ovarian cancer patients. In this study, we aimed to explore these molecular mechanisms using bioinformatics analysis. Methods We analyzed microarray data set GSE51373, which included 16 platinum-sensitive HGSOC samples and 12 platinum-resistant control samples. Differentially expressed genes (DEGs) were identified using RStudio. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using DAVID, and a DEG-associated protein–protein interaction (PPI) network was constructed using STRING. Hub genes in the PPI network were identified, and the prognostic value of the top ten hub genes was evaluated. MGP, one of the hub genes, was verified by immunohistochemistry. Results All samples were confirmed to be of high quality. A total of 109 DEGs were identified, and the top ten enriched GO terms and four KEGG pathways were obtained. Specifically, the PI3K-AKT signaling pathway and the Rap1 signaling pathway were identified as having significant roles in chemoresistance in HGSOC. Furthermore, based on the PPI network, KIT, FOXM1, FGF2, HIST1H4D, ZFPM2, IFIT2, CCNO, MGP, RHOBTB3, and CDC7 were identified as hub genes. Five of these hub genes could predict the prognosis of HGSOC patients. Positive immunostaining signals for MGP were observed in the chemoresistant samples. Conclusion Taken together, the findings of this study may provide novel insights into HGSOC chemoresistance and identify important therapeutic targets.
Discovery of diffuse optical emission lines from the inner Galaxy: Evidence for LI(N)ER-like gas

Optical emission line observations from the inner Galaxy show that the Milky Way hosts the closest known example of a LI(N)ER. Optical emission lines are used to categorize galaxies into three groups according to their dominant central radiation source: active galactic nuclei, star formation, or low-ionization (nuclear) emission regions [LI(N)ERs] that may trace ionizing radiation from older stellar populations. Using the Wisconsin H-Alpha Mapper, we detect optical line emission in low-extinction windows within eight degrees of Galactic Center. The emission is associated with the 1.5-kiloparsec-radius “Tilted Disk” of neutral gas. We modify a model of this disk and find that the hydrogen gas observed is at least 48% ionized. The ratio [NII] λ6584 angstroms/Hα λ6563 angstroms increases from 0.3 to 2.5 with Galactocentric radius; [OIII] λ5007 angstroms and Hβ λ4861 angstroms are also sometimes detected. The line ratios for most Tilted Disk sightlines are characteristic of LI(N)ER galaxies.
Understanding More About Human and Machine Attention in Deep Neural Networks

Human visual system can selectively attend to parts of a scene for quick perception, a biological mechanism known as Human attention. Inspired by this, recent deep learning models encode attention mechanisms to focus on the most task-relevant parts of the input signal for further processing, which is called Machine/Neural/Artificial attention. Understanding the relation between human and machine attention is important for interpreting and designing neural networks. Many works claim that the attention mechanism offers an extra dimension of interpretability by explaining where the neural networks look. However, recent studies demonstrate that artificial attention maps do not always coincide with common intuition. In view of these conflicting evidence, here we make a systematic study on using artificial attention and human attention in neural network design. With three example computer vision tasks (i.e., salient object segmentation, video action recognition, and fine-grained image classification), diverse representative backbones (i.e., AlexNet, VGGNet, ResNet) and famous architectures (i.e., Two-stream, FCN), corresponding real human gaze data, and systematically conducted large-scale quantitative studies, we quantify the consistency between artificial attention and human visual attention and offer novel insights into existing artificial attention mechanisms by giving preliminary answers to several key questions related to human and artificial attention mechanisms. Overall results demonstrate that human attention can benchmark the meaningful ‘ground-truth’ in attention-driven tasks, where the more the artificial attention is close to human attention, the better the performance; for higher-level vision tasks, it is case-by-case. It would be advisable for attention-driven tasks to explicitly force a better alignment between artificial and human attention to boost the performance; such alignment would also improve the network explainability for higher-level computer vision tasks.
Temporary alterations to milking frequency, immediately post-calving, modified the expression of genes regulating milk synthesis and apoptosis in the bovine mammary gland

Temporary changes to milking frequency during early lactation can elicit lactation-long changes in milk production. This study tested whether altered milking frequency, immediately post-calving, would affect mammary gene expression during and/or following treatment. Multiparous, grazing dairy cows (n = 150) were allocated to one of five treatments post-calving: milking thrice-daily (3X) for three or six weeks, and twicedaily (2X) thereafter; milking once-daily (1X) for three or six weeks, and 2X thereafter; or milking 2X for the entire lactation. Mammary tissue was biopsied from 12 cows per treatment at three, six and nine weeks postcalving and expression of milk synthesis and apoptosis genes measured using quantitative PCR. The milk fat, lactose and protein genes were down-regulated (P <0.05) in cows milked 1X compared with cows milked 2X. At nine weeks post-calving, cows milked 1X for three weeks had reduced mRNA levels of milk synthesis genes compared with cows milked 1X for six weeks (P <0.05). The expression of apoptosis genes was upregulated (P <0.05) in cows milked 1X relative to 2X, during and post-treatment. There were no gene expression changes during 3X milking compared with cows milked 2X. Changes in gene expression observed in cows temporarily milked 1X reflect physiological changes consistent with longer-term reductions in milksolids yield.
Contextualized Spoken Word Representations from Convolutional Autoencoders

A lot of work has been done recently to build sound language models for the textual data, but not much such has been done in the case of speech/audio type data. In the case of text, words can be represented by a unique fixed-length vector. Such models for audio type data can not only lead to great advances in the speech-related natural language processing tasks but can also reduce the need for converting speech to text for performing the same. This paper proposes a novel model architecture that produces syntactically, contextualized, and semantically adequate representation of varying length spoken words. The performance of the spoken word embeddings generated by the proposed model was validated by (1) inspecting the vector space generated, and (2) evaluating its performance on the downstream task of next spoken word prediction in a speech.
Clinical and Morphological Study of Single and Twin Pregnancies Placenta

Placental morphology is very important in both single and multiple pregnancies. It can dictate certain aspects such as: fibrin depositions, calcifications, infarctions, type of vascularization, which can be directly related to placental weight and implicitly to foetal weight, both in single and twin pregnancy. Our study highlighted the macroscopic morphological aspects and through the classical and immunohistochemical colours the microscopic placental morphological aspects, both in single and in dichorionic diamniotic twin pregnancy and showed that the placenta of the foetuses from the twin pregnancy has a higher vascular density compared to the single pregnancy, and the areas of placental fusion are poor in blood vessels, but rich in fibrin depositions, calcifications and placental infarctions. We also pointed out that maternal weight can increase with age, foetal weight can be directly proportional to maternal weight, as well as placental weight is directly proportional to foetal weight and implicitly to maternal weight, but in terms of vascularization, we observed that there is an inversely proportional connection between placental, foetal weight and vascular density.
Phytochemical Investigation and Molecular Profiling by p21 and NF-κB of Chorisia crispiflora Hexane Extract in Human Breast Cancer Cells in Vitro.

Aims: The current study targets two main aims; 1 aim is the phytochemical investigation of the hexane extract of Chorisia crispiflora leaves. The 2 aim is the evaluation of the invitro cytotoxic activity of the extract then examination of the molecular mechanisms underlying this cytotoxic effect. Study Design: Isolation and identification of the compounds, cytotoxic activity investigation on breast cancer cell line and molecular mechanisms underlying the cytotoxic extract of Chorisia crispiflora which may interfere with several cell signaling pathways and insert anti-cancer effects through the suppression of NF-κB or activation of p21, on breast cancer cell lines MCF-7. Place and Duration of Study: Faculty of Pharmacy, Ain Shams University and National Research Article British Journal of Pharmaceutical Research, 3(1): 78-89, 2013 79 Cancer Institute, Cairo University, Egypt. The study was completed within 10 months. Methodology: n-hexane extract was tested against breast cancer cell line then investigated for its effect on NF-κB, p21 and DNA fragmentation. The compounds isolated were identified using different spectroscopic techniques. Results: In this regard, three main compounds were isolated; β-sitosterol 1, β-sitosterol 3-glucoside 2 and stigmasterol 3-glucoside 3. The extract exhibited IC50 values of 7 and 4.2 μg/ml following 48 and 72 hrs of treatment; indicating its significant in-vitro cytotoxic activity. This cytotoxic activity was proven to be mediated through down regulation of NFκB. Conclusion: Our results suggest that n-hexane extract has potent cytotoxic effect on MCF7 cells in addition to down regulation of NF-κB. These findings consequently merit further exploration of the extract in subsequent in-vivo studies and later in controlled clinical trials.
Experimenting with At-Home General Chemistry Laboratories During the COVID-19 Pandemic

During the COVID-19 pandemic, an at-home laboratory program was created and implemented for a section of the general chemistry course at the University of Southern California. The experiments were designed to only utilize safe household items and no special equipment. These laboratory activities, spanning over 4 weeks, focused on concepts usually covered in the final one-third of our second-semester chemistry laboratory, including pH, acid–base titrations, buffers, solubility, phase equilibria, and thermodynamics. In this article, we describe the design of the laboratories and our experience with this experiment, while also providing an assessment on how similar activities could be integrated profitably into a regular general chemistry course.
Polarization of Climate Politics Results from Partisan Sorting: Evidence from Finnish Twittersphere

Prior research shows that public opinion on climate politics sorts along partisan lines. However, they leave open the question of whether climate politics and other politically salient issues exhibit tendencies for issue alignment, which the political polarization literature identifies as among the most deleterious aspects of polarization. Using a network approach and social media data from the Twitter platform, we study polarization of public opinion toward climate politics and ten other politically salient topics during the 2019 Finnish elections as the emergence of opposing groups in a public forum. We find that while climate politics is not particularly polarized compared to the other topics, it is subject to partisan sorting and issue alignment within the universalist-communitarian dimension of European politics that arose following the growth of right-wing populism. Notably, climate politics is consistently aligned with the immigration issue, and temporal trends indicate that this phenomenon will likely persist.