Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of miRNA-155 Expression on Treatment Outcome in Adult Acute Myeloid Leukemia Patients

Background: Acute myeloid leukemia is a heterogeneous hematologic malignancy associated with gene mutations, chromosomal rearrangements, deregulation of gene expression and epigenetic modifications. The treatment outcome of AML is highly variable signifying the heterogeneous nature of the disease. Aim of the Study: To evaluate miRNA-155 expression level as a prognostic marker for adult patients with acute myeloid leukemia. Patients and Methods: 101 subjects were included in this study. They were classified into 2 groups, patient group (61 adult patients with newly diagnosed acute myeloid leukemia) and control group (40 apparently healthy adult subjects). miRNA-155 expression was assessed using real time PCR using QIAGEN, miScript, Quanti Tect and Rotor-isc (QIAGEN Group) PAXgene (pre Analytix Gmbh). Samples were either peripheral blood or bone marrow aspiration sample. Results: Roc curve detected 2.85 as best fit value of miRNA-155 for discriminating patients from healthy controls with sensitivity 92.3%, specificity 88.5%, AUC 0.98 and CI (0.96 - 0.99) (p < 0.001). The 75th percentile value of the patient group was taken as prognostic cut off value with a value < 9.8 as low miRNA-155 and a value ≥ 9.8 as high miRNA-155. The expression level of miRNA-155 was significantly higher in AML patients than in controls (p = 0.002). Patients with high miRNA expression had a significantly higher white blood cells count (p = 0.002), bone marrow blasts (p = 0.006) and peripheral blood blasts (p = 0.006) compared to patients with low miRNA-155 expression. Patients with poor cytogentics had a significantly higher level of miRNA-155 expression compared to patients with favorable cytogentics (p = 0.007). The complete remission rate was significantly higher in patients with low miRNA-155 expression compared to those with high expression (86.4% and 23.5%, consequently, p = 0.004). The disease free survival was significantly shorter in patients with high miRNA-155 expression compared to those with low expression (median 12 months, 95% CI (7.4 - 15.5) and median notreached, consquenly, p = 0.001). The overall survival was significantly higher (p = 0.002) in patients with low miRNA-155 expression (median overall survival was not reached) compared to patients with high miRNA-155 expression (median overall survival 14.5 months, 95% CI; 10.2 - 17.6). Conclusion: The expression level of miR-155 was significantly higher in AML patients than in control groups and high miRNA-155 expression level was significantly associated with poor cytogenetics, poor response to therapy and shorter disease free and overall survival conferring a poor outcome.
Perbandingan Local Binary Pattern untuk Klasifikasi Sel Darah Putih

In recent year, a lot of researches try to overcome problem in recognition and classify white blood cells to help hematologists diagnose white blood cells disease such blood cancer, leukemia and AIDS. This paper compares several methods Local Binary Pattern such as Local Binary Pattern Uniform, Local Binary Pattern Rotation Invariant and Local Binary Pattern Rotation Invariant Uniform to classify five types of white blood cells using two classifier: Support Vector Machine and K-Nearest Neighbour. Index Terms—LBP, LBP-U, LBP-RI, LBP-RIU, white blood cells
A Review on Security in MANET

Networks formed for specific applications are called ad-hoc networks. In an ad-hoc model wireless devices can communicate with each other without the need of any central entity. All the devices that are in range of each other can discover and communicate to each other. Ad-hoc networks are flexible in every way, means they can be constructed, partitioned or merged with any other of the type on the go. In ad-hoc network, nodes are mobile in nature and uses wireless communication. In this case these are called as Mobile Ad-hoc Network (MANET). For these networks to find peer-to-peer path between working nodes, several protocols have been proposed. These routing protocols are supine to attack by the malicious nodes. The need of time is to detect and prevent the attacks caused by the malicious nodes without abruption in network services. In this paper, we present the study about various threats in security of MANETS and their detection and prevention techniques. Keywords— Ad-hoc networks, MANET, Wireless network, malicious node, Routing Protocols, Security
Meningkatkan Kinerja Tenaga Penjualan Melalui Kapabilitas Dinamik Mengikat Pelanggan (Studi Empirik Pada Tenaga Penjualan Industri Kosmetik Indonesia)

This study aims to build a new concept (Novelty), Dynamic Customer Bonding Capability (DCBC) to bridge the inconclusive research that is between customer orientation and salesperson performance. The results of previous studies to date still can not be concluded in a deterministic so it becomes a research gap that must be solved. The new concepts in this study were analyzed using a relevant analysis knife in depth and empirically tested the salespeople of the cosmetics industry in Indonesia. This research method uses the Structural Equation Model (SEM) with research object of cosmetics industry sales force in Indonesia. There are 209 complete questionnaires generated from this research for further on if as a basis for drawing conclusions. Sampling technique using purposive sampling where the sample is taken based on predetermined criteria first. Empirical research test in this research use seven hypothesis. After the test model using AMOS 22 software found five hypotheses have a significant effect that is the higher the customer orientation of the salespeople the higher the dynamic customer bonding capability, the higher the portfolio of competitive experience the salespeople the higher the collaborative capability, the higher the collaborative capability of the salespeople, the higher dynamic customer bonding capability, the higher the collaborative capability the higher the salespeople performance, and the higher the dynamic customer bonding capability the higher the salespeople performance. The two hypotheses proved insignificant that the higher the customer orientation of the salespeople the higher the collaborative capability and the higher the portfolio of competitive experience of the salespeople the higher the dynamic customer bonding capability. The results of empirical research indicate that novelty dynamic customer bonding capability able to bridge the research gap between customer orientation and salespeople performance. Novelty in this study provides implications in the development of marketing science to enrich the body of the knowledge and provide implications for business practices in improving the performance of salespeople.
The Garnaut Climate Change Review

Professor Ross Garnaut was commissioned by all of the Governments of Australia's Federation to examine the impacts of climate change on Australia and to recommend policy frameworks to improve the prospects of sustainable prosperity. The Garnaut Climate Change Review is one of the most important reports to be published in Australia for many years. It examines the impacts of climate change on the Australian economy, the costs of adaptation and mitigation, and the international context in which climate change is experienced and negotiated. It analyses the elements of an appropriate international policy response, and the challenges that face Australia in playing its proportionate part in that response. The Garnaut Climate Change Review is highly relevant to the global problem that is climate change. It considers what policies the international community should adopt in responding to climate change, and urges humanity to act now, and in concert, to develop the required policy response in time.
Geometry and solutions of an epidemic SIS model permitting fluctuations and quantization

Some recent works reveal that there are models of differential equations for the mean and variance of infected individuals that reproduce the SIS epidemic model at some point. This stochastic SIS epidemic model can be interpreted as a Hamiltonian system, therefore we wondered if it could be geometrically handled through the theory of Lie--Hamilton systems, and this happened to be the case. The primordial result is that we are able to obtain a general solution for the stochastic/ SIS-epidemic model (with fluctuations) in form of a nonlinear superposition rule that includes particular stochastic solutions and certain constants to be related to initial conditions of the contagion process. The choice of these initial conditions will be crucial to display the expected behavior of the curve of infections during the epidemic. We shall limit these constants to nonsingular regimes and display graphics of the behavior of the solutions. As one could expect, the increase of infected individuals follows a sigmoid-like curve. Lie--Hamiltonian systems admit a quantum deformation, so does the stochastic SIS-epidemic model. We present this generalization as well. If one wants to study the evolution of an SIS epidemic under the influence of a constant heat source (like centrally heated buildings), one can make use of quantum stochastic differential equations coming from the so-called quantum deformation.
Future CEvNS experiments as probes of lepton unitarity and light sterile neutrinos

We determine the sensitivities of short-baseline coherent elastic neutrino-nucleus scattering (CE$\nu$NS) experiments using a pion decay at rest neutrino source as a probe for non-unitarity in the lepton sector, expected in low-scale type-I seesaw schemes. We also identify the best configuration for probing light-sterile neutrinos at future ton-scale liquid argon CE$\nu$NS experiments, estimating the projected sensitivities on the sterile neutrino parameters. Possible experimental setups at the Spallation Neutron Source, Lujan facility and the European Spallation Source are discussed.
A Wavelength/Route Reservation Scheme Using Signaling Protocol in WDM Networks

Abstract In a conventional two-way wavelength reservation protocol, there is a problem that traffic load is concentrated on specific links since the shortest path is always used for the route between source and destination nodes. In this paper, we propose a new two-way wavelength reservation protocol using the multi-route signaling protocol. In our proposed protocol, source nodes collect two information which are the link distance and the frequency of use for wavelengths when a connection request occured. And destination nodes select the proper couple of route and wavelength which has the lowest cost calculated by the two collected information, and initiate the wavelength reservation. By computer simulations,we show the blocking probability can be reduced less than 15 % by our proposed signaling scheme.
Graph Convolutional Networks for Hyperspectral Image Classification

Convolutional neural networks (CNNs) have been attracting increasing attention in hyperspectral (HS) image classification due to their ability to capture spatial–spectral feature representations. Nevertheless, their ability in modeling relations between the samples remains limited. Beyond the limitations of grid sampling, graph convolutional networks (GCNs) have been recently proposed and successfully applied in irregular (or nongrid) data representation and analysis. In this article, we thoroughly investigate CNNs and GCNs (qualitatively and quantitatively) in terms of HS image classification. Due to the construction of the adjacency matrix on all the data, traditional GCNs usually suffer from a huge computational cost, particularly in large-scale remote sensing (RS) problems. To this end, we develop a new minibatch GCN (called miniGCN hereinafter), which allows to train large-scale GCNs in a minibatch fashion. More significantly, our miniGCN is capable of inferring out-of-sample data without retraining networks and improving classification performance. Furthermore, as CNNs and GCNs can extract different types of HS features, an intuitive solution to break the performance bottleneck of a single model is to fuse them. Since miniGCNs can perform batchwise network training (enabling the combination of CNNs and GCNs), we explore three fusion strategies: additive fusion, elementwise multiplicative fusion, and concatenation fusion to measure the obtained performance gain. Extensive experiments, conducted on three HS data sets, demonstrate the advantages of miniGCNs over GCNs and the superiority of the tested fusion strategies with regard to the single CNN or GCN models. The codes of this work will be available at https://github.com/danfenghong/IEEE_TGRS_GCN for the sake of reproducibility.
Model projective twists and generalised lantern relations

We use Picard-Lefschetz theory to introduce a new local model for the planar projective twists $\tau_{\mathbb{A}\mathbb{P}^2} \in \mathrm{Symp}_{ct}(T^*\mathbb{A}\mathbb{P}^2), \ \mathbb{A} \in \{ \mathbb{R}, \mathbb{C} \}$. In each case, we construct an exact Lefschetz fibration $\pi\colon T^*\mathbb{A}\mathbb{P}^2\to \mathbb{C}$ with three singular fibres, and define a compactly supported symplectomorphism $\varphi \in \mathrm{Symp}_{ct}(T^*\mathbb{A}\mathbb{P}^2)$ on the total space. Given two disjoint Lefschetz thimbles $\Delta_{\alpha},\Delta_{\beta} \subset T^*\mathbb{A}\mathbb{P}^2$, we compute the Floer cohomology groups $\mathrm{HF}(\varphi^k(\Delta_{\alpha}), \Delta_{\beta}; \mathbb{Z}/2\mathbb{Z})$ and verify (partially for $\mathbb{C}\mathbb{P}^2$) that $\varphi$ is indeed isotopic to (a power of) the projective twist in its local model. The constructions we present are governed by generalised lantern relations, which provide an isotopy between the total monodromy of a Lefschetz fibration and a fibred twist along an $S^1$-fibred coisotropic submanifold of the smooth fibre. We also use these relations to generate non-exact fillings for the contact manifolds $(ST^*\mathbb{C}\mathbb{P}^2, \xi_{std}), (ST^*\mathbb{R}\mathbb{P}^3,\xi_{std})$, and to study two classes of monotone Lagrangian submanifolds of $(T^*\mathbb{C}\mathbb{P}^2, d\lambda_{\mathbb{C}\mathbb{P}^2})$.
Bi-level Optimization for Support Vector Machines

This thesis deals with an efficient approach for learning the optimal hyper-parameters for Support Vector Machines (SVMs). The common method to determine hyper-parameters is grid search. Grid search typically involves the definition of a discretized ”grid” of possible parameter values with a certain resolution and a search for the values that result in the minimal validation error of the learned model. A major limitation of grid search is that the search space grows exponentially in the parameters which makes the approach only practical for determining very few hyper-parameters. Additionally, grid search operates on discrete parameter values which leads to suboptimal solutions. In this thesis we develop an approach to use bi-level optimization for learning the optimal hyper-parameters and solve both major shortcomings of grid search in an efficient and elegant way. Bi-level learning is an optimization method where one optimization problem has another optimization problem as its constraint. The goal of the bi-level program is to find optimal hyper-parameters such that the validation error (the higher level objective) is minimized, while the optimal training problem is solved for the underlying SVM (the lower level objective). We use Lagrange multipliers to solve the bi-level problem and formulate the solution for several variants of the SVM (linear, kernel, multiple kernel). We can show that, using this method, the model selection problem (i.e. selection of hyper-parameters) can be solved also for a large number of hyper-parameters. The bilevel approach exploits the continuity of the hyper-parameters which allows for better solutions than with grid search. In the experiments, we investigate different properties of the bi-level approach and try to give insights into the advantages of this method. We find that highly parametrized kernel SVMs perform best compared to simpler models which is a clear advantage of bi-level optimization against grid search for model selection.
Rank varieties and 𝜋-points for elementary supergroup schemes

<p>We develop a support theory for elementary supergroup schemes, over a field of positive characteristic <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="p greater-than-or-slanted-equals 3"> <mml:semantics> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>⩾<! -- ⩾ --></mml:mo> <mml:mn>3</mml:mn> </mml:mrow> <mml:annotation encoding="application/x-tex">p\geqslant 3</mml:annotation> </mml:semantics> </mml:math> </inline-formula>, starting with a definition of a <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="pi"> <mml:semantics> <mml:mi>π<! -- π --></mml:mi> <mml:annotation encoding="application/x-tex">\pi</mml:annotation> </mml:semantics> </mml:math> </inline-formula>-point generalising cyclic shifted subgroups of Carlson for elementary abelian groups and <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="pi"> <mml:semantics> <mml:mi>π<! -- π --></mml:mi> <mml:annotation encoding="application/x-tex">\pi</mml:annotation> </mml:semantics> </mml:math> </inline-formula>-points of Friedlander and Pevtsova for finite group schemes. These are defined in terms of maps from the graded algebra <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="k left-bracket t comma tau right-bracket slash left-parenthesis t Superscript p Baseline minus tau squared right-parenthesis"> <mml:semantics> <mml:mrow> <mml:mi>k</mml:mi> <mml:mo stretchy="false">[</mml:mo> <mml:mi>t</mml:mi> <mml:mo>,</mml:mo> <mml:mi>τ<! -- τ --></mml:mi> <mml:mo stretchy="false">]</mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:msup> <mml:mi>t</mml:mi> <mml:mi>p</mml:mi> </mml:msup> <mml:mo>−<! -- − --></mml:mo> <mml:msup> <mml:mi>τ<! -- τ --></mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:annotation encoding="application/x-tex">k[t,\tau ]/(t^p-\tau ^2)</mml:annotation> </mml:semantics> </mml:math> </inline-formula>, where <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="t"> <mml:semantics> <mml:mi>t</mml:mi> <mml:annotation encoding="application/x-tex">t</mml:annotation> </mml:semantics> </mml:math> </inline-formula> has even degree and <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="tau"> <mml:semantics> <mml:mi>τ<! -- τ --></mml:mi> <mml:annotation encoding="application/x-tex">\tau</mml:annotation> </mml:semantics> </mml:math> </inline-formula> has odd degree. The strength of the theory is demonstrated by classifying the parity change invariant localising subcategories of the stable module category of an elementary supergroup scheme. </p>
Computation of a 30750-Bit Binary Field Discrete Logarithm

This paper reports on the computation of a discrete logarithm in the finite field F 2 30750 \mathbb {F}_{2^{30750}} , breaking by a large margin the previous record, which was set in January 2014 by a computation in F 2 9234 \mathbb {F}_{2^{9234}} . The present computation made essential use of the elimination step of the quasi-polynomial algorithm due to Granger, Kleinjung and Zumbrägel, and is the first large-scale experiment to truly test and successfully demonstrate its potential when applied recursively, which is when it leads to the stated complexity. It required the equivalent of about 2900 2900 core years on a single core of an Intel Xeon Ivy Bridge processor running at 2.6 GHz, which is comparable to the approximately 3100 3100 core years expended for the discrete logarithm record for prime fields, set in a field of bit-length 795 795 , and demonstrates just how much easier the problem is for this level of computational effort. In order to make the computation feasible we introduced several innovative techniques for the elimination of small degree irreducible elements, which meant that we avoided performing any costly Gröbner basis computations, in contrast to all previous records since early 2013. While such computations are crucial to the L ( 1 4 + o ( 1 ) ) L(\frac 1 4 + o(1)) complexity algorithms, they were simply too slow for our purposes. Finally, this computation should serve as a serious deterrent to cryptographers who are still proposing to rely on the discrete logarithm security of such finite fields in applications, despite the existence of two quasi-polynomial algorithms and the prospect of even faster algorithms being developed.
Newcomer Candidate: Characterizing Contributions of a Novice Developer to GitHub

To attract, onboard, and retain any newcomer in Open Source Software (OSS) projects is vital to their livelihood. Recent studies conclude that OSS projects risk failure due to abandonment and poor participation of newcomers. Evidence suggests more new users are joining GitHub, however, the extent to which they contribute to OSS projects is unknown. In this study, we coin the term ‘newcomer candidate’ to describe new users to the GitHub platform. Our objective is to track and characterize their initial contributions. As a preliminary survey, we collected 208 newcomer candidate contributions in GitHub. Using this dataset, we then plan to track their contributions to reveal insights. We will use a mixed-methods approach, i.e., quantitative and qualitative, to identify whether or not newcomer candidates practice social coding, the kinds of their contributions, projects they target, and the proportion that they eventually onboard to an OSS project.
DANA

Motion sensors embedded in wearable and mobile devices allow for dynamic selection of sensor streams and sampling rates, enabling several applications, such as power management and data-sharing control. While deep neural networks (DNNs) achieve competitive accuracy in sensor data classification, DNN architectures generally process incoming data from a fixed set of sensors with a fixed sampling rate, and changes in the dimensions of their inputs cause considerable accuracy loss, unnecessary computations, or failure in operation. To address these limitations, we introduce a dimension-adaptive pooling (DAP) layer that makes DNNs flexible and more robust to changes in sensor availability and in sampling rate. DAP operates on convolutional filter maps of variable dimensions and produces an input of fixed dimensions suitable for feedforward and recurrent layers. Further, we propose a dimension-adaptive training (DAT) procedure for enabling DNNs that use DAP to better generalize over the set of feasible data dimensions at inference time. DAT comprises the random selection of dimensions during the forward passes and optimization with accumulated gradients of several backward passes. Combining DAP and DAT, we show how to transform existing non-adaptive DNNs into a Dimension-Adaptive Neural Architecture (DANA), while keeping the same number of parameters. Compared to existing approaches, our solution provides better average classification accuracy over the range of possible data dimensions at inference time and does not require up-sampling or imputation, thus reducing unnecessary computations. Experimental results on seven datasets (four benchmark real-world datasets for human activity recognition and three synthetic datasets) show that DANA prevents significant losses in classification accuracy of the state-of-the-art DNNs and, compared to baselines, it better captures correlated patterns in sensor data under dynamic sensor availability and varying sampling rates.