Miracle-dz/UniFAR-FTUs huggingface dataset:
Differential expression profile analysis of miRNAs with HER-2 overexpression and intervention in breast cancer cells

Receptor tyrosine-protein kinase erbB-2 (HER-2) as a member of the human epidermal growth factor receptor family, plays a key role in the development and the progression of certain aggressive types of breast cancer, and has become an important biomarker and target of therapy for breast cancer. Recently, liquid biopsy biomarker of tumor cells, such as miRNAs, has been validated and approved as a useful prognostic method for cancer, and however of which in breast cancer is not fully elucidated. To address it, human breast cancer cell stains of MCF-7, BT474, MCF-7/HER-2, BT474/HER-2 (siRNA) were chosen, and the mRNA and protein expression level of HER-2 was analyzed by real-time quantitative polymerase chain reaction (RT-qPCR) and Western Blot respectively. Subsequently, the differential expression profile of miRNA was analyzed by miRNA gene-chip with bioinformatics analysis. As expected, the mRNA and protein expression level of HER-2 was significantly increased with HER-2 overexpression in MCF-7 cells, and significantly decreased with HER-2 intervention in BT474 cells. Significantly, a total of 217 miRNAs with 123 up-regulated miRNAs and 94 down-regulated miRNAs was identified with HER-2 overexpression in MCF-7 cells, and also a total of 374 miRNAs with 121 up-regulated miRNAs and 253 down-regulated miRNAs were identified with HER-2 intervention in BT474 cells. Wherein, 10 miRNAs were up-regulated with HER-2 overexpression in MCF-7 cells, and down-regulated with HER-2 intervention in BT474 cells synchronously, and 17 miRNAs were down-regulated with HER-2 overexpression in MCF-7 cells, and up-regulated with HER-2 intervention in BT474 cells synchronously, and the expression level of hsa-miR-877-3p was consistent with gene-chip results, and not only provided a significant reference on the differential expression of miRNAs in breast cancer cells and its culturing supernatant, and also provided a significant potentially liquid biopsy biomarker for breast cancer in clinic.
Working paper 04-04 June 2004 ON RISK MANAGEMENT DETERMINANTS : WHAT REALLY MATTERS ?

We investigate the determinants of the risk management decision for an original dataset of North American gold mining firms. We propose explanations based on the firm’s financial characteristics, managerial risk aversion and internal corporate governance mechanisms. We develop a theoretical model in which the debt and the hedging decisions are made simultaneously. Our model suggests that more hedging does not always lead to a higher debt capacity when the firm holds a standard debt contract, while hedging is an increasing function of the firm’s financial distress costs. We then test the predictions of our model. To estimate our system of simultaneous Tobit equations, we extend, to panel data, the minimum distance estimator proposed by Lee (1995). We obtain that financial distress costs, information asymmetry, separation between the posts of CEO and chairman of the board positions and managerial risk aversion are important determinants of the decision to hedge whereas the composition of the board of directors has no impact in such decision. Also, our results do not support the conclusion that firms hedge in order to increase their debt capacity which seems to confirm our model’s prediction.
Government Debt and Capital Structure Decisions : International Evidence

Our paper investigates the impact of government debt on corporate financing decisions. An increase in government debt supply might reduce corporate debt if investors prefer to maintain a relatively stable proportion of debt and equity securities. Using data on 40 countries between 1990-2014, we document a negative relation between government debt and corporate leverage. This relation holds for both levels and changes of debt, and after controlling for countryand year-fixed effects as well as country-level controls. Our firm-level analysis shows that the effect is more prominent when it is easier for corporations to adjust their capital structure, for example, for larger and more profitable firms. In order to address potential endogeneity issues, we use the introduction of the Euro currency as a quasi-natural experiment that changes the demand for local government bonds in countries adopting the Euro currency. Our findings suggest that government debt crowds out corporate debt. JEL Classification: E44, E50, G11, G38
A Review of Behaviometric Techniques for User Authentication

Biometrics is an authentication mechanism classified into physical biometrics and behavioural biometrics. Physical biometrics uses the physical attributes of the individual to verify the claimed identity. Behavioural Biometrics or behaviometrics uses measurable behavioural patterns of device usage to verify the identity of the individual. Physical biometrics would require a special hardware device to capture the characteristics of the claimed identity for verification purposes but behaviometric does not require one in most cases. Keystroke dynamics and mouse dynamics have been researched most frequently for the purpose of static authentication, periodic authentication and continuous authentication of users. In this paper we make a comparative survey on the effectiveness of keystroke dynamics, Keystroke sound, mouse dynamics and mouse gesture dynamics individually as a defense-in-depth mechanism for authentication purposes.
Achieving Machine Reasoning for Math Problems Through Step-By-Step Supervision Signal

Multi-step reasoning problems pose major challenges to traditional supervised learning approaches. Solving math questions in a variety of different topics falls into this category. While approaches exist that solve math expressions, they rely on encoded explicit prior knowledge. In this work, we aim to improve algebraic generalization by including intermediate steps to shape the loss landscape to train deep learning models without any explicit domain-specific prior knowledge. We create code to generate intermediate steps for a variety of math problems and test on linear regression, LSTM, and the Transformer.
Object Tracking Using a Many-Core Embedded System Laercio Minozzo

Object localization and tracking is essential for many practical applications, such as mancomputer interaction, security and surveillance, robot competitions, and Industry 4.0. Because of the large amount of data present in an image, and the algorithmic complexity involved, this task can be computationally demanding, mainly for traditional embedded systems, due to their processing and storage limitations. This calls for investigation and experimentation with new approaches, as emergent heterogeneous embedded systems, that promise higher performance, without compromising energy efficiency. This work explores several real-time color-based object tracking techniques, applied to images supplied by a RGB-D sensor attached to different embedded platforms. The main motivation was to explore an heterogeneous Parallella board with a 16-core Epiphany coprocessor, to reduce image processing time. Another goal was to confront this platform with more conventional embedded systems, namely the popular Raspberry Pi family. In this regard, several processing options were pursued, from low-level implementations specially tailored to the Parallella, to higher-level multi-platform approaches. The results achieved allow to conclude that the programming effort required to efficiently use the Epiphany co-processor is considerable. Also, for the selected case study, the performance attained was bellow the one offered by simpler approaches running on quad-core Raspberry Pi boards.
Proofs That Count Azadeh Farzan

Counting arguments are among the most basic proof methods in mathematics. Within the field of formal verification, they are useful for reasoning about programs with infinite control, such as programs with an unbounded number of threads, or (concurrent) programs with recursive procedures. While counting arguments are common in informal, hand-written proofs of such programs, there are no fully automated techniques to construct counting arguments. The key questions involved in automating counting arguments are: how to decide what should be counted? , and how to decide when a counting argument is valid? In this paper, we present a technique for automatically constructing and checking counting arguments, which includes novel solutions to these questions.
Optimal Stopping With Regret

This paper studies an optimal stopping problem in which the decision maker is subject to anticipated regret. Once the decision maker stopped she observes future outcomes. Regret arises, because the decision maker evaluates her outcome against the ideal future outcome. I propose a new way to model regret over the future and show that it considerably affects the decision maker’s behavior. I fully characterize the solution of the model and show that stopping occurs later than in a model without regret. However, not in a monotone way. I also provide comparative statics for the main parameters. More generally I view this model as a game against nature and provide an application to political economy. ⇤Aalto University. email: christian.krestel@aalto.fi 1
Survey on Market Rate Prediction Based Algorithms

: Financial exchange forecast has consistently grabbed the eye of numerous investigators and analysts. Well known hypotheses propose that securities exchanges are basically an irregular walk and it is a trick's down to attempt to foresee them. Foreseeing stock costs is a difficult issue in itself in light of the quantity of factors which are included. For the time being, the market carries on like a democratic machine however in the more extended term, it acts like a gauging machine and thus there is extension for anticipating the market developments for a more drawn out time allotment. Utilization of AI strategies and different calculations for stock value investigation and estimating is a zone that shows incredible guarantee. In this paper, we first overview a compact audit of financial exchanges and scientific categorization of securities exchange expectation techniques.
Mixicles : Simple Private Decentralized Finance Ari Juels ?

We show how to use oracles to build simple, privacy-preserving decentralized finance (DeFi) instruments we call Mixicles. Mixicles ingest input payments from two or more parties and yield payouts that are conditioned on oracle reports. They are designed to provide privacy for both the terms and outcomes of the financial instruments they execute. At the same time, Mixicles can support rigorous regulatory and auditing requirements. The most appealing feature of Mixicles is their avoidance of expensive cryptography and complicated contract structures. Mixicles are conceptually simple, and have low on-chain and off-chain resource consumption. We present an example implementation to show how they work and demonstrate their benefits.
A Brief Introduction to Polar Codes Notes for Introduction to Error-Correcting Codes

Figure 1: Factor Graph In particular, consider a setup where (U1, U2) are two equiprobable bits that are encoded into (X1, X2) = (U1 ⊕ U2, U2). Then, (X1, X2) are mapped to (Y1, Y2) by two independent BMS channels with transition probabilities P(Y1 = y |X1 = x) = P(Y2 = y |X2 = x) = W (y|x). The Tanner graph for this setup is shown in Figure 1. Since the mapping from (U1, U2) to (X1, X2) is invertible, one finds that
Multiorgan Drug Action of Levosimendan in Critical Illnesses

Cardiotonic drugs mainly include digitalis, catecholamines, phosphodiesterase inhibitors, and calcium sensitizers, which have been successively discovered and applied in clinical practice. However, there are only a few new drugs available in this field, and the selection is very limited. Digitalis, catecholamines, and phosphodiesterase inhibitors increase myocardial contractility by increasing intracellular concentrations of cyclic adenosine monophosphate (cAMP) and Ca2+, and this increase in intracellular calcium ion concentration enhances myocardial oxygen consumption and causes arrhythmia. For these reasons, the research focus on positive inotropic agents has shifted from calcium mobilization to calcium sensitization. Intracellular calcium sensitizers are more effective and safer drugs because they do not increase the intracellular concentration of calcium ions. However, only three calcium sensitizers have been fully developed and used in the past three decades. One of these drugs, levosimendan, has multiple molecular targets and exerts its pharmacological effects by not only increasing myocardial contractility, but also enhancing respiratory muscle function and liver and kidney protection, and it is useful for patients with severe sepsis and septic shock. Recently, more than 60 randomized controlled clinical trials of levosimendan have been reported; however, these clinical trials have occasionally shown different findings. This article reviews the research progress of levosimendan in critical illnesses in recent years.
A guanxi shibboleth based security infrastructure for e-social science

An e-Social Science infrastructure generally has security requirements to protect their restricted resources or services. As a widely accepted authentication and authorization technology, Shibboleth supports the sharing of resources on interinstitutional federation. Guanxi is an open source implementation of the Shibboleth protocol and architecture. In this paper, we propose a security infrastructure for e-social science based on the Guanxi Shibboleth. This security infrastructure presents two main features. Firstly, Guanxi Shibboleth is integrated into the user-friendly Sakai collaborative and learning environment which provides an ideal place for users to access a variety of federation resources in line with the Shibboleth authentication model. Secondly, PERMIS technology is used to enhance the authorization mechanisms thus enabling a policy-driven, role-based, fine-grained access control. As a result, the security infrastructure presents the advantages of Guanxi Shibboleth, PERMIS and Sakai, and it has been applied to e-Social Science application. We believe this security infrastructure provides a promising authentication and authorization solution for e-social science applications as well as applications in other
Project End-to-End Publication Using the Bittorrent P 2 P Filesharing Protocol 2006-2007

Bittorrent is a promising peer-to-peer network that always allows for fast download speeds despite the number of peers downloading the file. Currently, there exist tools to make . torrent files, tools to ”track” the peers downloading the file, tools to host . torrent files, and tools to initially upload the file. This project aims to unify this process by making an end-to-end software suite that simplifies the process of publishing a file on the Bittorrent network for download. The key to this will be automating and streamlining the process from the perspective of the user. It will involve a complete implementation of the Bittorrent protocol, including encoding torrent files, peer-to-tracker and peer-to-peer communication, and a greater understanding of the benefits and detriments of the Bittorrent protocol.
جئاتنةاكاحم ضيفختل ةردقلا ةبسن لدعمل ُةيلاعلا ُةمقلا ( PAPR ) لدـعمو تبلا أِطخ ةبسن ( BER ) تمتقيرط نع ةبساحلا مادختساب ِبوساحلاب ةاكاحملا جمارب MATLAB

One of the major drawbacks of OFDM is high Peak-to-Average Power Ratio (PAPR) which can result in poor power efficiency and serious distortion in the transmitter amplifier. In this paper, the advantages of two different approaches to PAPR reduction are exploited in order to reduce the PAPR more significantly. The first approach is based on clipping and filtering which provides a high PAPR reduction at the cost of signal distortion. The second approach (Partial Transmit Sequence PTS or Selected Mapping SLM methods) results in no distortion. The performance of the three proposed combined methods(Clipping And Filtering with PTS scheme, SLM with Clipping And Filtering scheme, and PTS with Clipping And Filtering scheme)are evaluated on the PAPR distribution function and on the Bit Error Rate as a function of Signal to Noise Ratio in Additive White Gaussian Noise Channel. The simulation results show that the proposed PTS with Clipping And Filtering scheme provides more PAPR reduction without degradation in the BER performance as compared to the other two proposed scheme (Clipping And Filtering with PTS scheme and SLM with Clipping And Filtering scheme). The simulation results of PAPR reduction and BER performances are simulated using MATLAB R2009a computer simulation software.
A review of data acquisition and difficulties in sensor module of biometric systems

Biometrics refers to the recognition of individuals based on their physiological and/or behavioral characteristics. The biometric traits which may be considered for the authentication of a person are face, hand geometry, finger print, vein, iris, etc. A competent selection of a sensor, its mechanism and adaptability is required, as the absence of these will leave the biometric sensor deceptive to information sensing. Selecting a sensor for a biometric application from the large number of available sensors with different technologies always brought the issue of performance and accuracy. Therefore, various error rates and sensibility contention differentiate the available biometric sensors. This paper presents the difficulties faced in the sensor module of the biometric system and the incomparable alternatives on the basis of availability of information at sensor module of the various systems.
IMPROVEMENT OF ENERGY EFFICIENCY USING CO-OPERATIVE SPECTRUM SENSING IN COGNITIVE RADIO NETWORKS

A cognitive radio (CR) is an wireless network technology that can be programmed and configured to detect the communication channels are in use and which are not. Cooperative sensing is a technique to boost up the detection performance, so that the secondary users interact with each other to sense the spectrum and to find the spectrum holes. The goal is to detect the spectrum holes and to assign each SU a sensing channel with a maximum probability of being empty. The existing system involves in spectrum learning and graph theory based approach consumes more energy. To minimize the energy consumption we introduce gazer based cognitive radio networks which provides more freedom for frequency sharing paradigm. Index Terms : spectrum sensing, gazer, frequency, sharing, energy etc.,
Venus ’ atmosphere-insights from interior models

Most of Venus' history is hidden from us, since its surface is covered by relatively young material. Different mechanisms have been suggested in the literature to explain why Venus evolved in such a different way than Earth including models that link the planetary interior to the evolving atmosphere. It is still not clear how ancient Venus' present-day atmosphere is, and if its composition and mass was set already during the earliest stages of planet accretion and magma ocean degassing, if it changed substantially over time, or if the planet could have had at least for some time much more hospitable surface conditions than today. To answer these questions, different disciplines need to work together to combine insights from geological observations, geochemical and atmosphere chemistry studies, and interior modeling and degassing.
High Step Up Converter Using Voltage Multiplier Cells ( VMC )

This paper introduces a new family of dc–dc converters based on the three state switching cell and voltage multiplier cells. A brief literature review is presented to demonstrate some advantages and inherent\ limitations of several topologies that are typically used in voltage step-up applications. In order to verify the operation principle of this family, the boost converter is chosen and investigated in detail. The behavior of the converter is analyzed through an extensive theoretical analysis, while its performance is investigated by experimental results obtained from a 1-kW laboratory prototype and relevant issues are discussed. The analyzed converter can be applied in uninterruptible power supplies, fuel cell systems, and is also adequate to operate as a high-gain boost stage with cascaded inverters in renewable energy systems. Furthermore, it is suitable in cases where dc voltage step-up is demanded, such as electrical fork-lift, audio amplifiers, and many other applications.
University of Southern Denmark Blue Carbon Storage Capacity of Temperate Eelgrass ( Zostera marina )

Despite the importance of coastal ecosystems for the global carbon budgets, knowledge of their carbon storage capacity and the factors driving variability in storage capacity is still limited. Here we provide an estimate on the magnitude and variability of carbon stocks within a widely distributed marine foundation species throughout its distribution area in temperate Northern Hemisphere. We sampled 54 eelgrass (Zostera marina) meadows, spread across eight ocean margins and 36° of latitude, to determine abiotic and biotic factors influencing organic carbon (Corg) stocks in Zostera marina sediments. The Corg stocks (integrated over 25-cm depth) showed a large variability and ranged from 318 to 26,523 g C/m with an average of 2,721 g C/m. The projected Corg stocks obtained by extrapolating over the top 1 m of sediment ranged between 23.1 and 351.7 Mg C/ha, which is in line with estimates for other seagrasses and other blue carbon ecosystems. Most of the variation in Corg stocks was explained by five environmental variables (sediment mud content, dry density and degree of sorting, and salinity and water depth), while plant attributes such as biomass and shoot density were less important to Corg stocks. Carbon isotopic signatures indicated that at most sites <50% of the sediment carbon is derived from seagrass, which is lower than reported previously for seagrass meadows. The high spatial carbon storage variability urges caution in extrapolating carbon storage capacity between geographical areas as well as within and between seagrass species. RÖHR ET AL. 1 Global Biogeochemical Cycles