Miracle-dz/UniFAR-FTUs huggingface dataset:
INTELLIGENT EYE USING AI

: Experiencing the surrounding, experiencing the nature around for blinds is impossible like that of a normal person. Extracting information from an image or a scene is done by visualizing the picture thoroughly by men, which is a tedious and time consuming task. So we had developed an application which helps the people to get the information of an image in fraction of seconds without even looking at it, using object recognition and voice assistance. our system is so unique that it uses the yolo V3 to detect multiple objects in one shot, which is one of the fastest algorithms to detect the objects. And builds the relationship between the objects detected with the help of RNN, generates a brief paragraph which describes the whole scene in the image using NLP and the audio output is given with the help of pytts package.
Efficacy and Safety of a Combination of Shenmai Injection plus Chemotherapy for theTreatment of LungCancer: AMeta-Analysis

Science and Technology Department of Guilin Medical University, Guilin 541001, Guangxi Zhuang Autonomous Region, China First Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi Zhuang Autonomous Region, China Department of Pathophysiology, Xuzhou Medical University, Xuzhou 221009, China Laboratory of Clinical and Experimental Pathology, Xuzhou Medical University, Xuzhou 221009, China Department of Physiology, Xuzhou Medical University, Xuzhou 221009, China
AdVeil: A Private Targeted-Advertising Ecosystem

This paper presents AdVeil, a privacy-preserving advertising ecosystem with formal guarantees for end users. AdVeil is built around an untrusted advertising network which is responsible for brokering the display of advertisement to users. This ad network targets relevant ads to users without learning any of the users’ personal information in the process. Our targeting protocol combines private information retrieval with standard, locality-sensitive hashing based techniques for nearest neighbor search. By running ad targeting in this way, users of AdVeil have full control over and transparency into the contents of their targeting profile. AdVeil additionally supports private metrics for ad interactions, allowing the ad network to correctly charge advertisers and pay websites for publishing ads. This is done without the ad network learning which user interacted with an ad, only that some honest user did. AdVeil achieves this using an anonymizing proxy (e.g., Tor) to transit batched user reports along with unlinkable anonymous tokens to certify the authenticity of each report. We build a prototype implementation of AdVeil which we evaluate on a range of parameters to demonstrate the applicability of AdVeil to a real-world deployment. Our evaluation shows that AdVeil scales to ad networks with millions of ads, using state-of-the-art single-server private information retrieval. A selection of ads from a database of 1 million ads can be targeted to a user in approximately 4.5 seconds with a single 32-core server, and can be parallelized further with more servers. Targeting is performed out-of-band (e.g., on a weekly basis) while ad delivery happens in real time as users browse the web. Verifying report validity (for fraud prevention) requires less than 300 microseconds of server computation per report.
Detection Moleculer Of Putative 18S rRNA Gen Protozoa Trichodina sp. Infected Larvae Gurami (Osphronemus gouramy L) in Balai Benih Ikan Kutasari Purbalingga Central Java

Diterima : 19/02/2021 Disetujui : 10/04/2021 Abstract Trichodina spp. are ectoparasitic pathogens of ciliata group that commonly infect both freshwater and marine fish, including gouramy fish. As a result of infection of Trichodina spp. this will lead to inhibition of fish growth and decreased fish production, resulting in low fish selling value. The rate of occurrence of Trichodina spp. that infects gurami can reach 100%. Research has been conducted to determine which one Trichodina spp. Protozoa that infects the gouramy seeds of BBI (Fish Seed Center) Kutasari Purbalingga following detection of 18S RNA gene. Gene detection method used in this research is Polymerase Chain Reaction (PCR) is a technique of DNA synthesis and amplification in vitro. This research is done following these methodes: (1) sampling of Gurami fish with purposive sampling which obtained from BBI Kutasari Purbalingga, (2) isolation of Trichodina spp. , (3). Preparation of Trichodina spp. sample and its identification, and (4). Molecular character obervation following detection of 18S rRNA gene. This study obtained 10% percentage of detection of 18S rRNA genes of the species of Trichodina paraheterodentata that infect on the gouramy fish of Purbalingga. The percentage rate of detection of these genes is low when compared with the results of the detection of 18S rRNA Trichodina paraheterodentata gene that infects gouramy fish in Banjarnegara.
Filgrastim Versus Pegfilgrastim for Neutropenia Prevention in Children with Solid Tumors: A Randomized Trial

Pegfilgrastim; Chemotherapy; Malignancy; Filgrastim ABSTRACT: Prophylaxis of chemotherapy-induced neutropenia by granulocyte stimulatory factors (GCSFs) has a significant effect on reducing the complications of chemotherapy. The aim of this study was to compare effects of filgrastim and pegfilgrastim (two types of GCSFs) for neutropenia prevention in children with malignancies. This crossover study was carried out in children who were admitted to oncology ward of Amir Kabir Hospital, Arak, Iran. Patients were randomly divided into 3 groups each with 30 participants. Filgrastim (group A), pegfilgrastim (group B) were injected subcutaneously 10 μg/kg/day and 100 μg/kg as a single dose, respectively and patients in group C had no medical treatment. Washout period was 30 days. Cell blood were checked at beginning and at 3, 7, 14 days of the treatment. The mean age in group A was 6.4 ±3.5 years, the group B was 6.4 ± 3.5 and the group C was 6.2 ± 1.8. The mean Absolute Neutrophil Count (ANC) was similar in all three groups prior to chemotherapy. After receiving the last dose of chemotherapy, the mean ANC was not significantly different in 3 groups (p = 0.217), and only 2 cases of mild neutropenia were seen in group B. On the 14th day, the ratio of neutropenia was different in 3 groups, and this difference was significant (p = 0.000) but there was no significant difference between the ratio of neutropenia in group A and group B. (p = 0.524). 20% of cases in group C and then 16.7% in group B were treated due to delayed neutropenia and this difference was significant (p = 0.026). Pegfligrastim was associated with better clinical response and fewer side effects as compared to filgrastim in children with solid tumors. Due to efficacy and acceptable safety profile, pegfligrastim can be a better choice. There was no significant difference between the costs of the three groups (0.064)
Efficacy and Safety Evaluation of Human Reovirus Type 3 in Immunocompetent Animals: Racine and Nonhuman Primates

Purpose: Human reovirus type 3 has been proposed to kill cancer cells with an activated Ras signaling pathway. The purpose of this study was to investigate the efficacy of reovirus in immunocompetent glioma animal models and safety/toxicity in immunocompetent animals, including nonhuman primates. Experimental Design: Racine glioma cells 9L and RG2 were implanted s.c. or intracranially in Fisher 344 rats with or without reovirus antibodies, followed by treatment of reovirus. To study whether reovirus kills contralateral tumors in the brain and to determine viral distribution, we established an in situ dual tumor model followed by reovirus intratumoral inoculation only into the ipsilateral tumor. To evaluate neurotoxicity/safety of reovirus, Cynomolgus monkeys and immunocompetent rats were given intracranially with reovirus, and pathological examination and/or behavioral studies were done. Viral shedding and clinical biochemistry were systematically studied in monkeys. Results: Intratumorally given reovirus significantly suppressed the growth of both s.c. and intracranially tumors and significantly prolonged survival. The presence of reovirus-neutralizing antibodies did not abort the reovirus’ antitumor effect. Reovirus inhibited glioma growth intracranially in the ipsilateral but not the contralateral tumors; viral load in ipsilateral tumors was 15 to 330-fold higher than the contralateral tumors. No encephalitis or behavioral abnormalities were found in monkeys and rats given reovirus intracranially. No treatment-related clinical biochemistry changes or diffuse histopathological abnormality were found in monkeys inoculated intracranially with Good Manufacturing Practice prepared reovirus. Microscopic changes were confined to the region of viral inoculation and were dose related, suggesting reovirus intracranially was well tolerated in nonhuman primates. Conclusions: These data show the efficacy and safety of reovirus when it is used in the treatment of gliomas in immunocompetent hosts. Inoculation of reovirus into the brain of nonhuman primates did not produce significant toxicities.
A Survey on Brain Tumor Segmentation and Clustering using clustering techniques

- This work deals with the implementation of Simple Algorithm for detection of range and shape of tumor in brain MR images and identifies stage of tumor from the given area of tumor. Tumor is an uncontrolled growth of tissues in any part of the body. Tumors are of different types and they have different Characteristics and different treatment. As it is known, brain tumor is inherently serious and life-threatening because of its character in the limited space of the intracranial cavity (space formed inside the skull). Most Research in developed countries show that the number of people who have brain tumors were died due to the fact of inaccurate detection. Generally, CT scan or MRI that is directed into intracranial cavity produces a complete image of brain. After researching a lot statistical analysis which is based on those people whose are affected in brain tumor some general Risk factors and Symptoms have been discovered. The development of technology in science day night tries to develop new methods of treatment. This image is visually examined by the physician for detection & diagnosis of brain tumor. However this method accurate determines the accurate of stage & size of tumor and identifies stage of tumor from the area of tumor. This work uses segmentation of brain tumor based on the k-means and fuzzy c-means algorithms. This method allows the segmentation of tumor tissue with accuracy and reproducibility comparable to manual segmentation. In addition, it also reduces the time for analysis and identifies stage of tumor from the given area of tumor. Finally implement a system using java to identify stage of tumor which is easier, cost reducible and time savable.
Robust Quad-Tree based Registration on Whole Slide Images

: Abstract The registration of whole slide images (WSIs) provides the basis for many subsequent processing steps in digital pathology. For instance, the registration of immunohistochemistry (IHC) and hematoxylin & eosin (H&E)-stained WSIs is usually the ﬁrst step in guiding IHC diagnostic procedures. Still, many registration methods operate poorly on WSIs. Reasons for this include the WSI size, ﬂuctuating image quality or elastic tissue deformations. Multiple prior methods are further specialised towards a speciﬁc image modality, such as histology or cytology, or rely on a speciﬁc preparation
So Different Yet So Alike! Constrained Unsupervised Text Style Transfer

Automatic transfer of text between domains has become popular in recent times. One of its aims is to preserve the semantic content while adapting to the target domain. However, it does not explicitly maintain other attributes between the source and translated text: e.g., text length and descriptiveness. Maintaining constraints in transfer has several downstream applications, including data augmentation and debiasing. We introduce a method for such constrained unsupervised text style transfer by introducing two complementary losses to the generative adversarial network (GAN) family of models. Unlike the competing losses used in GANs, we introduce cooperative losses where the discriminator and the generator cooperate and reduce the same loss. The first is a contrastive loss and the second is a classification loss — aiming to regularize the latent space further and bring similar sentences closer together. We demonstrate that such training retains lexical, syntactic and domain-specific constraints between domains for multiple benchmark datasets, including ones where more than one attribute change. We show that the complementary cooperative losses improve text quality, according to both automated and human evaluation measures.
The effect of political polarization on social distance stances in the Brazilian COVID-19 scenario

The COVID-19 pandemic changed the routine and concerns of people around the world since 2020. The alarming contagious rate and the lack of treatment or vaccine evoked different reactions to controlling and mitigating the virus's contagious. In this paper, we developed a case study on the Brazilian COVID scenario, investigating the influence of the political polarization in the pro/against stances of social isolation, represented in Twitter by two groups referred to as the Cloroquiners and Quarenteners. We analyzed these groups according to multiple dimensions: a) concerns expressed by each group and main arguments representing each stance; b) techniques to automatically infer from users political orientation, c) network analysis and community detection to characterize their behavior as a social network group and d) analysis of linguistic characteristics to identify psychological aspects. We propose combining two topic modeling techniques, LDA and BERTopics, to understand each stance's concerns in different granularity levels. Our main findings confirm that Cloroquiners are right-wing partisans, whereas Quarenteners are more related to the left-wing. Cloroquiners and Quarenteners' political polarization influences the arguments of economy and life and a stronger support/opposition to the president. As a group, the network of Cloroquiners is more closed and connected, and Quarenteners have a more diverse political engagement with a community of users polarized only with left-wing politicians and his supporters. In terms of psychological aspects, polarized groups come together on cognitive issues and negative emotions.
Beliefs asymmetry and price stability in a cobweb model

This work analyses the role of asymmetry in beliefs for price dynamics in a cobweb model with heterogeneous expectations and evolutionary selection of predictors. While heterogeneous but symmetric beliefs result in the rational expectations equilibrium price, the effect of asymmetry depends on whether predictors on one side or the other of rationality have a larger support. A support skewed towards predictors that are anchored to past prices can be destabilizing, and the interaction with the evolutionary selection mechanism can lead to complex dynamics in prices; a support skewed towards predictors that overshoot price changes leads instead to price stability, irrespective of the underlying evolutionary dynamics. The design of the set of beliefs allowed to compete on the market is thus crucial for the possible outcomes of the model. One could interpret a skewed support in terms of sentiments, intended as one-sided systematic biases in expectations.
Supplementary material to "A Novel Pathway of Atmospheric Sulfate Formation Through Carbonate Radical"

Abstract. Carbon dioxide is considered an inert gas that rarely participates in atmospheric chemical reactions. However, we show here that CO2 is involved in some important photo-oxidation reactions in the atmosphere through the formation of carbonate radicals (CO3∙-). This potentially active intermediate CO3∙- is routinely overlooked in atmospheric chemistry regarding its effect on sulfate formation. Present work demonstrates that SO2 uptake coefficient is enhanced by 17 times on mineral dust particles driven by CO3∙-. It can be produced through two routes over mineral dust surfaces: i) hydroxyl radical + CO32-; ii) holes (h+) + CO32-. Employing a suite of laboratory investigations of sulfate formation in the presence of carbonate radical on the model and authentic dust particles, field measurements of sulfate and (bi)carbonate ions within ambient PM, together with density functional theory (DFT) calculations for single electron transfer processes in terms of CO3∙--initiated S(IV) oxidation, a new role of carbonate radical in atmospheric chemistry is elucidated.
Lattice-Based 3-Dimensional Wireless Sensor Deployment

With the wide application of wireless sensor networks (WSNs) in real space, there are numerous studies on 3D sensor deployments. In this paper, the k -connectivity theoretical model of fixed and random nodes in regular lattice-based deployment was proposed to study the coverage and connectivity of sensor networks with regular lattice in 3D space. The full connectivity range and cost of the deployment with sensor nodes fixed in the centers of four regular lattices were quantitatively analyzed. The optimal single lattice coverage model and the ratio of the communication range to the sensing range r c / r s were investigated when the deployment of random nodes satisfied the k -connectivity requirements for full coverage. In addition, based on the actual sensing model, the coverage, communication link quality, and reliability of different lattice-based deployment models were determined in this study.
Preparation of Porous Titania by Sol–Gel Method Using Alkylketene Dimers as a Template

Risa Takahashi1, Hideyuki Matsumoto2, Takafumi Horie3 and Keigo Matsuda1,2 1 Department of Chemistry and Chemical Engineering, Graduate School of Science and Engineering, Yamagata University, 4-3-16 Jonan, Yonezawa-shi, Yamagata 992-8510, Japan 2 Renewable Energy Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-2-9 Machiikedai, Koriyama-shi, Fukushima 963-0298, Japan 3 Department of Chemical Science and Engineering, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe-shi, Hyogo 657-8501, Japan
The Interface Theory of Perception and the Science of Consciousness

In his book “Galileo’s Error”, Philip Goff lays out what he calls “foundations for a new science of consciousness”, which are decidedly anti-physicalist (panpsychist), motivated by a critique of Galileo’s distinction into knowable objective and unknowable subjective properties and Arthur Eddington’s argument for the limitation of purely structural (physical) knowledge. Here we outline an alternative theory, premised on the Interface Theory of Perception, that too subscribes to a “post-Galilean” research programme. However, interface theorists disagree along several lines. 1. They note that Galileo’s distinction should be replaced by a truly non-dual account, referring to a difference of degree only. 2. They highly appreciate the role of mathematics, in particular when it comes to actually engaging scientifically with consciousness. Some notable features of the interface theory are its skepticism towards our epistemic capacities and its rejection of the existence of a public, mind-independent reality. In addition, some interface theorists further employ a thin concept of “conscious agency” to ground their theory. The interface theory leaves open many of the problems of consciousness science (e.g. what is a “self”? ) as questions for further (scientific, mathematical) research.
BNPmix: An R Package for Bayesian Nonparametric Modeling via Pitman-Yor Mixtures

BNPmix is an R package for Bayesian non-parametric multivariate density estimation, clustering, and regression, using Pitman-Yor mixture models, a flexible and robust generalization of the popular class of Dirichlet process mixture models. A variety of model specifications and state-of-the-art posterior samplers are implemented. In order to achieve computational efficiency, all sampling methods are written in C++ and seamless integrated into R by means of the Rcpp and RcppArmadillo packages. BNPmix exploits the ggplot2 capabilities and implements a series of generic functions to plot and print summaries of posterior densities and induced clustering of the data.
Early Childhood Human Capital Formation at Scale

Can governments leverage existing service-delivery platforms to scale early childhood development (ECD) programs? We experimentally study a large-scale home-visiting intervention providing materials and counseling --- integrated into Bangladesh's national nutrition program without extra financial incentives for the service providers (SPs). We find SPs partially substituted away from nutritional to ECD counseling. Intent-to-treat estimates show the program improved child's cognitive (0.17 SD), language (0.23 SD), and socio-emotional developments (0.12-0.14 SD). Wasting and underweight rates also declined. Improved maternal agency, complementary parental investments, and higher take-up of the pre-existing nutrition program were important mechanisms. We estimate a sizeable internal rate-of-return of 19.6
Deep Learning for Exotic Option Valuation

A common approach to valuing exotic options involves choosing a model and then determining its parameters to fit the volatility surface as closely as possible. This is referred to as the model calibration approach (MCA). A disadvantage of MCA is that some information in the volatility surface is lost during the calibration process, and the prices of exotic options will not in general be consistent with those of plain vanilla options. The authors consider an alternative approach in which the structure of the user’s preferred model is preserved, but points on the volatility surface are features input to a neural network. They refer to this as the volatility feature approach (VFA) model. The authors conduct experiments showing that VFA can be expected to outperform MCA for the volatility surfaces encountered in practice. Once the upfront computational time has been invested in developing the neural network, the valuation of exotic options using VFA is very fast.
Semi-Automatic Annotation For Visual Object Tracking

We propose a semi-automatic bounding box annotation method for visual object tracking by utilizing temporal information with a tracking-by-detection approach. For detection, we use an off-the-shelf object detector which is trained iteratively with the annotations generated by the proposed method, and we perform object detection on each frame independently. We employ Multiple Hypothesis Tracking (MHT) to exploit temporal information and to reduce the number of false-positives which makes it possible to use lower objectness thresholds for detection to increase recall. The tracklets formed by MHT are evaluated by human operators to enlarge the training set. This novel incremental learning approach helps to perform annotation iteratively. The experiments performed on AUTH Multidrone Dataset reveal that the annotation workload can be reduced up to 96% by the proposed approach. Resulting uav_detection_2 annotations and our codes are publicly available at github.com/aybora/Semi-Automatic-Video-Annotation-OGAM.
Intersection spaces and multiple transverse recurrence

We study multiple recurrence properties along separated cross sections for pmp actions of unimodular lcsc group on Polish spaces. We establish a multiple transverse recurrence theorem under the assumption that sufficiently large powers of the return time set are Delone sets. Typical examples of such situations arise from the theory of uniform approximate lattices.