Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey of Graph Cohomology and Perturbative Methods

This is a set of seminar notes which survey several different connections between Gromov-Witten invariants and Chern-Simons theory. Using perturbation Chern-Simons theory, Bar-Natan, Kontsevich and others were able to construct a 3 manifold an invariant that captures the contributions of the second term of Witten’s Chern-Simons expansion. This is sometimes called the “two-loop contribution” to Chern-Simons theory, and the resulting invariant can be computed without using perturbative techniques. in [m]imic these constructions using graph cohomology in Morse theory, and find that the Feynman diagrams that appear in our gaugetheoretic construction show up again in the Morse theory. By viewing Morse theory as Lagrangian Floer theory of the zero section, we’ll see the conjectured “Large N -duality” relating the Chern-Simons potential function to Gromov-Witten invariants in the cotangent bundle. Our primary source for this is [Fuk96a].
HIV Care Prioritization using Phylogenetic Branch Length

In HIV epidemics, the structure of the transmission network can be dictated by just a few individuals. Public health intervention, such as ensuring people living with HIV adhere to antiretroviral therapy (ART) and are continually virally-suppressed, can help control the spread of the virus. However, such intervention requires utilizing the limited public health resource allocations. As a result, the ability to determine which individuals are most at-risk of transmitting HIV could allow public health officials to focus their limited resources on these individuals. Molecular epidemiology suggests an approach: prioritizing people living with HIV based on patterns of transmission inferred from their sampled viral sequences. In this paper, we introduce ProACT (Prioritization using AnCesTral edge lengths), a phylogenetic approach for prioritizing individuals living with HIV. ProACT uses a simple idea: ordering individuals by their terminal branch length in the phylogeny of their virus. In simulations and also on a dataset of HIV-1 subtype B pol sequences obtained in San Diego, we show that this simple strategy improves the effectiveness of prioritization compared to state-of-the-art methods that rely on monitoring the growth of transmission clusters defined based on genetic distance.
EFFECT OF SALT LOADING ON METABOLIC CHANGES IN OVARIECTOMIZED RATS

Background: Menopause, a natural step in woman`s life aging process, is associated with increased risk of metabolic diseases. Salt, is an essential micronutrient, commonly added to food. However, studies addressing the metabolic effects of high salt intake are controversial and limited. Objective: The present study was designed to determine the changes imposed by increased salt consumption on glucose and lipid homeostasis in ovariectomized rats that mimics the estrogen-deprived condition in postmenopausal women. Material and Methods: Thirty six adult female albino Wister rats were allocated into 3 equal groups: Control group, ovariectomized group, and high salt ovariectomized group which received high salt (2%NaCl) solution for 2 months. Rats were subjected to estimation of body weight (BW), body mass index (BMI), waist circumference (WC), and visceral fat weight. Glucose uptake by diaphragm and glucose output by kidneys were determined. Also, levels of glucose, lipid profile, malondialdehyde, catalase, insulin, estradiol and tumor necrosis factor-α were measured. Pancreatic and hepatic tissues were examined histopathologically. Also, caspase-3 was assessed in pancreas by immunohistochemistry. Results: High salt ovariectomized group showed significant decrease in final WC, and significant increase in serum insulin, HOMA-IR, serum levels of MDA and TNF-α compared to the ovariectomized group. There were significant increases in plasma levels of glucose, triglycerides, total cholesterol, LDL-C, catalase, and AI, and significant decrease in glucose uptake by diaphragm, and plasma HDL-C in high salt group compared to control group. As compared to ovariectomized group, the pancreas in salt loaded group showed decreased number of islets of Langerhans and fatty degeneration together with increased caspase-3. Liver histology was also worsened, and hepatocytes showed ballooning, steatosis and inflammatory infiltration together with massive amounts of collagen fibers around portal tract. Conclusion: High salt intake exacerbated the hyperglycemia and insulin resistance, intensified pancreatic apoptosis and hepatic fatty, and degenerative changes. These effects could be explained by salt loading-induced exacerbation and exaggeration of oxidative and inflammatory influences of ovariectomy.
The Quality of Blood is not Affected by Drone Transport: An Evidential Study of the Unmanned Aerial Vehicle Conveyance of Transfusion Material in Japan

Unmanned aerial vehicles (UAVs), or drones, are used in Rwanda for transfusion transport, but they have not yet been used in Japan. This technology holds promise for transporting medical supplies during disasters or to remote places where the terrain makes it difficult to travel by land. One of the difficulties in using UAVs is the temperature-control requirements for red blood cell (RBC) solutions, i.e., 2 °C to 6 °C according to Japanese regulations. This study aimed to describe the effectiveness of UAV-based transport of RBC solution. For testing, we gradually increased the UAV travel distance, monitored the temperature of the RBC solution, and conducted laboratory tests to check the integrity of the blood sample. Lactate dehydrogenase (LD) was used as a hemolytic index to indicate the effect of the UAV flight on the blood samples. The UAV was able to exceed 7 km of travel distance despite the relatively heavy load needed for the RBC solution storage. The LD level was not significantly different between the flight and non-flight (control) samples. However, we were not able to completely maintain a temperature of 2 °C to 6 °C; nonetheless, the deviation was within the safe range.
Is Better Access to Markets a Substitute for Educated Parents? Evidence from Rural India*

This paper focuses on a particularly important channel, human capital investment, through which domestic market integration may affect intergenerational economic mobility. Using household data from India Human Development Survey, our empirical analysis focuses on the rural households. We use two sources of plausibly exogenous variations: (1) historical road infrastructure, and (2) crow-fly distance to the Golden Quadrilateral road network. The estimates from the OLS and 2SLS regressions for the average effect show that better market access improves childrens schooling attainment. More importantly, the interaction of fathers education with market access is negative; better market access thus works as a substitute for a higher educated father. The estimates from IV quantile regressions using the control function approach show that better market access weakens intergenerational persistence the most for the children in the middle of the conditional distribution.
Macroeconomic Crisis and Individual Firm Performance

This paper considers financial, operational, solvency, and performance ratios, in order to detect when there were balance sheets’ variations related to the 1994 Mexican currency crisis. Quarterly results for 88 non-financial Mexican companies that survived the crisis are used, and tests for structural change are performed. Findings show that generally firms’ balance sheets deteriorated between the fourth quarters of 1993 and 1995, which points the possibility of corporate roots of the macroeconomic crisis. Although in most cases firms’ balance sheets improved after the crisis, the recovery was partial and gradual, and overall this episode was prejudicial even for surviving companies. JEL codes: F3, GO, G3
Stable Reduction Methods of Linear Dynamic Systems in Frequency Domain

In many practical situations a fairly complex and high order model is obtained from theoretical considerations. The complexity often makes it difficult to obtain a good understanding of the behavior of a system. The exact analysis of most of high order system is both tedious and costly; it poses a great challenge to both system analyst and control engineer. The preliminary design and optimization of such systems can often be accomplished with greater ease if a low order linear model is derived which provides a good approximation to the system. In this paper an analysis is presented by use of four different Model Order Reduction Methods viz Routh Approximation Method, Routh Stability Method, Differentiation Method and Stability Equation Method in Time & Frequency Domain. The results are quite encouraging and preserving the stability and other characteristic parameters of an 8 order system reduced to 3 & 2 order system. KeywordsModel Order Reduction Techniques; Routh Approximation; Routh Stability Array; Stable Model; Differentiation; Stability Equation Method.
FACULTY OF MATERIALS SCIENCE AND TECHNOLOGY IN TRNAVA SLOVAK UNIVERSITY OF TECHNOLOGY IN BRATISLAVA

The available researches that referred mobile platforms fragmentation problem showed that current solutions, although many, converge into several groups that have common advantages and disadvantages and are based on the same principle: code ones, run anywhere. Thus, the new approach that would enable the developers to use native development environments was necessary. This paper introduces an ontology oriented approach that is based on methodological interoperability. Important assumption of the ongoing research presented in this paper is that during the development process the development team should use the same methodology and the same approach while developing for multiple mobile platforms. This will enhance this process with the artifacts reuse possibility according to the ontologically described knowledge.
Understanding the Global Status of Particulate Matter with Respect to Research Topics and Research Networks

Atmospheric pollution caused by particulate matter (PM), which has been identified as a carcinogen, is a major concern in South Korea, with various PM emission sources identified, including vehicles and power plants. The Korean government recently announced plans to increase research and development (R&D) expenditure to reduce PM levels in the atmosphere. In this study, the research topics and research networks for PM technology are summarized to help policymakers to more efficiently plan future R&D investment. A topic model is implemented to identify the main keywords from Science Citation Index (SCI) papers related to PM, and social network analysis is used to understand research collaboration between different countries or institutions. The results reveal that China and the USA published the largest number of SCI papers related to PM research, while South Korea was ranked in the top five globally. Unlike China and South Korea, developed countries such as the USA, Germany, and France have been carrying out PM research on climate change, ozone, NH3, nanoparticles, and the health effects on pregnant women. The primary research networks are centered on China and the USA, while South Korea has weak collaboration with other countries or institutions. More effort is thus required to improve strategic R&D investment and the research network within Korea.
Preliminary results from a large sequencing project in a pig breeding program

1 The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush Campus, EH25 9RG Midlothian, Scotland, UK roger.ros@roslin.ed.ac.uk (Corresponding Author) 2 Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, SE-750 07, Uppsala, Sweden Genus plc, 100 Bluegrass Commons Blvd., Suite 2200, Hendersonville, TN 37075, USA Genus plc, 1525 River Road, DeForest, WI 53532, USA
Lipidomic Analysis of Cells and Extracellular Vesicles from High- and Low-Metastatic Triple-Negative Breast Cancer

Extracellular vesicles (EVs) are lipid bilayer nanovesicles secreted from almost all cells including cancer. Cancer-derived EVs contribute to cancer progression and malignancy via educating the surrounding normal cells. In breast cancer, epidemiological and experimental observations indicated that lipids are associated with cancer malignancy. However, lipid compositions of breast cancer EVs and their contributions to cancer progression are unexplored. In this study, we performed a widely targeted quantitative lipidomic analysis in cells and EVs derived from high- and low-metastatic triple-negative breast cancer cell lines, using supercritical fluid chromatography fast-scanning triple-quadrupole mass spectrometry. We demonstrated the differential lipid compositions between EVs and cells of their origin, and between high- and low-metastatic cell lines. Further, we demonstrated EVs from highly metastatic breast cancer accumulated unsaturated diacylglycerols (DGs) compared with EVs from lower-metastatic cells, without increasing the amount in cells. The EVs enriched with DGs could activate the protein kinase D signaling pathway in endothelial cells, which can lead to stimulated angiogenesis. Our results indicate that lipids are selectively loaded into breast cancer EVs to support tumor progression.
Gaussian Smoothen Semantic Features (GSSF) - Exploring the Linguistic Aspects of Visual Captioning in Indian Languages (Bengali) Using MSCOCO Framework

In this work, we have introduced Gaussian Smoothen Semantic Features (GSSF) for Better Semantic Selection for Indian regional language-based image captioning and introduced a procedure where we used the existing translation and English crowd-sourced sentences for training. We have shown that this architecture is a promising alternative source, where there is a crunch in resources. Our main contribution of this work is the development of deep learning architectures for the Bengali language (is the fifth widely spoken language in the world) with a completely different grammar and language attributes. We have shown that these are working well for complex applications like language generation from image contexts and can diversify the representation through introducing constraints, more extensive features, and unique feature spaces. We also established that we could achieve absolute precision and diversity when we use smoothened semantic tensor with the traditional LSTM and feature decomposition networks. With better learning architecture, we succeeded in establishing an automated algorithm and assessment procedure that can help in the evaluation of competent applications without the requirement for expertise and human intervention.
Lake Ice Detection from Sentinel-1 SAR with Deep Learning

Abstract. Lake ice, as part of the Essential Climate Variable (ECV) lakes, is an important indicator to monitor climate change and global warming. The spatio-temporal extent of lake ice cover, along with the timings of key phenological events such as freeze-up and break-up, provide important cues about the local and global climate. We present a lake ice monitoring system based on the automatic analysis of Sentinel-1 Synthetic Aperture Radar (SAR) data with a deep neural network. In previous studies that used optical satellite imagery for lake ice monitoring, frequent cloud cover was a main limiting factor, which we overcome thanks to the ability of microwave sensors to penetrate clouds and observe the lakes regardless of the weather and illumination conditions. We cast ice detection as a two class (frozen, non-frozen) semantic segmentation problem and solve it using a state-of-the-art deep convolutional network (CNN). We report results on two winters (2016–17 and 2017–18) and three alpine lakes in Switzerland. The proposed model reaches mean Intersection-over-Union (mIoU) scores >90% on average, and >84% even for the most difficult lake. Additionally, we perform cross-validation tests and show that our algorithm generalises well across unseen lakes and winters.
Learning Individually Fair Classifier with Causal-Effect Constraint

Machine learning is increasingly being used in various applications that make decisions for individuals. For such applications, we need to strike a balance between achieving good prediction accuracy and making fair decisions with respect to a sensitive feature (e.g., race or gender), which is difficult in complex real-world scenarios. Existing methods measure the unfairness in such scenarios as {\it unfair causal effects} and constrain its mean to zero. Unfortunately, with these methods, the decisions are not necessarily fair for all individuals because even when the mean unfair effect is zero, unfair effects might be positive for some individuals and negative for others, which is discriminatory for them. To learn a classifier that is fair for all individuals, we define unfairness as the {\it probability of individual unfairness} (PIU) and propose to solve an optimization problem that constrains an upper bound on PIU. We theoretically illustrate why our method achieves individual fairness. Experimental results demonstrate that our method learns an individually fair classifier at a slight cost of prediction accuracy.
Raydrop : a universal droplet generator based on a non-embedded co-flow-focusing

Most commercial microfluidic droplet generators rely on the planar flow-focusing configuration implemented in polymer or glass chips. The planar geometry, however, suffers from many limitations and drawbacks, such as the need of specific coatings or the use of dedicated surfactants, depending on the fluids in play. On the contrary, and thanks to their axisymmetric geometry, glass capillary-based droplet generators are a priori not fluid-dependent. Nevertheless, they have never reached the market because their assembly requires art-dependent and not scalable fabrication techniques. Here we present a new device, called Raydrop, based on the alignment of two capillaries immersed in a pressurized chamber containing the continuous phase. The dispersed phase exits one of the capillaries through a 3D-printed nozzle, placed in front of the extraction capillary for collecting the droplets. This non-embedded implementation of an axisymmetric flow-focusing is referred to {\it co-flow-focusing}. Experimental results demonstrate the universality of the device in terms of the variety of fluids that can be emulsified, as well as the range of droplet radii that can be obtained, without neither the need of surfactant nor coating. Additionally, numerical computations of the Navier-Stokes equations based on the quasi-steadiness assumption are shown to correctly predict the droplet radius in the dripping regime and the dripping-jetting transition when varying the geometrical and fluid parameters. The monodispersity ensured by the dripping regime, the robustness of the fabrication technique, the optimization capabilities from the numerical modeling and the universality of the configuration confer to the Raydrop technology a very high potential in the race towards high-throughput droplet generation processes.
Speech-To-Singing Conversion in an Encoder-Decoder Framework

In this paper our goal is to convert a set of spoken lines into sung ones. Unlike previous signal processing based methods, we take a learning based approach to the problem. This allows us to automatically model various aspects of this transformation, thus overcoming dependence on specific inputs such as high quality singing templates or phoneme-score synchronization information. Specifically, we propose an encoder–decoder framework for our task. Given time-frequency representations of speech and a target melody contour, we learn encodings that enable us to synthesize singing that preserves the linguistic content and timbre of the speaker while adhering to the target melody. We also propose a multi-task learning based objective to improve lyric intelligibility. We present a quantitative and qualitative analysis of our framework.
Development of Conditional Random Field Insert for UNet-based Zonal Prostate Segmentation on T2-Weighted MRI

Purpose: A conventional 2D UNet convolutional neural network (CNN) architecture may result in ill-defined boundaries in segmentation output. Several studies imposed stronger constraints on each level of UNet to improve the performance of 2D UNet, such as SegNet. In this study, we investigated 2D SegNet and a proposed conditional random field insert (CRFI) for zonal prostate segmentation from clinical T2-weighted MRI data. Methods: We introduced a new methodology that combines SegNet and CRFI to improve the accuracy and robustness of the segmentation. CRFI has feedback connections that encourage the data consistency at multiple levels of the feature pyramid. On the encoder side of the SegNet, the CRFI combines the input feature maps and convolution block output based on their spatial local similarity, like a trainable bilateral filter. For all networks, 725 2D images (i.e., 29 MRI cases) were used in training; while, 174 2D images (i.e., 6 cases) were used in testing. Results: The SegNet with CRFI achieved the relatively high Dice coefficients (0.76, 0.84, and 0.89) for the peripheral zone, central zone, and whole gland, respectively. Compared with UNet, the SegNet+CRFIs segmentation has generally higher Dice score and showed the robustness in determining the boundaries of anatomical structures compared with the SegNet or UNet segmentation. The SegNet with a CRFI at the end showed the CRFI can correct the segmentation errors from SegNet output, generating smooth and consistent segmentation for the prostate. Conclusion: UNet based deep neural networks demonstrated in this study can perform zonal prostate segmentation, achieving high Dice coefficients compared with those in the literature. The proposed CRFI method can reduce the fuzzy boundaries that affected the segmentation performance of baseline UNet and SegNet models.
Learning Adaptive Loss for Robust Learning with Noisy Labels

Robust loss minimization is an important strategy for handling robust learning issue on noisy labels. Current robust loss functions, however, inevitably involve hyperparameter(s) to be tuned, manually or heuristically through cross validation, which makes them fairly hard to be generally applied in practice. Besides, the non-convexity brought by the loss as well as the complicated network architecture makes it easily trapped into an unexpected solution with poor generalization capability. To address above issues, we propose a meta-learning method capable of adaptively learning hyperparameter in robust loss functions. Specifically, through mutual amelioration between robust loss hyperparameter and network parameters in our method, both of them can be simultaneously finely learned and coordinated to attain solutions with good generalization capability. Four kinds of SOTA robust loss functions are attempted to be integrated into our algorithm, and comprehensive experiments substantiate the general availability and effectiveness of the proposed method in both its accuracy and generalization performance, as compared with conventional hyperparameter tuning strategy, even with carefully tuned hyperparameters.