Miracle-dz/UniFAR-FTUs huggingface dataset:
Circular RNA hsa_circ_0002124 promotes hepatocellular carcinoma cell proliferation through the MAPK pathway

Background Hsa_circ_0002124, which was first reported in 2013, is derived from NuSAP1. However, its role in hepatocellular carcinoma (HCC) and its regulatory mechanisms remain to be investigated. Methods First, hsa_circ_0002124 was structurally validated via specific convergent and divergent primer amplification. The hsa_circ_0002124 expression in the liver cancer tissues and multiple HCC cell lines were determined using qPCR. Further, the cell functions of hsa_circ_0002124 in HCC cells were examined using knockdown and overexpressed hsa_circ_0002124 in 97H cells. The cell proliferation was assessed using MTS assay, cell proliferation and invasion capacities were evaluated using Transwell culture system, and cell cycle progression and apoptosis were analyzed using flow cytometry. Further, GO and KEGG analyses were performed to uncover the key function and pathways in HCC. The interaction networks between hsa_circ_0002124 and its downstream miRNAs and genes were constructed using Cytoscape software. The key protein expressions (p-JNK, JNK, p-ERK, ERK, p-P38, P38, and c-Myc) of the MAPK pathway in 97H cells with knockdown and overexpressed hsa_circ_0002124 treatments were detected using Western blotting. Results Hsa_circ_0002124 was highly expressed in the HCC cells and liver cancer tissues. Moreover, the knockdown hsa_circ_0002124 in 97H cells resulted in the repression of cell proliferation, cell invasion, and migration, with simultaneous promotion of cell apoptosis and cell cycle transformation. The opposing situations of cell function could be detected in overexpressed hsa_circ_0002124 in 97H cells. KEGG and interaction network analysis of hsa_circ_0002124 indicated that hsa_circ_0002124 could be a molecular sponge of miRNAs, which regulates the key protein expressions in the MAPK pathway. The p-JNK/JNK, p-ERK/ERK, p-P38/P38, and c-Myc expressions in knockdown hsa_circ_0002124-treated 97H cells were significantly lower than in normal 97H cells, whereas these expressions in overexpressed hsa_circ_0002124-treated 97H cells were significantly higher than in mock vector-treated 97H cells. Conclusions Hsa_circ_0002124 could be a potential biomarker for the early diagnosis and treatment of HCC.
Measurement Bias in the Financial Performance of Family Firms

Notwithstanding the impressive body of research focusing on the financial performance of family-led enterprises over their non-family counterparts, no aggregate conclusion can be extrapolated from the existing data. In this article, we show that performance inference is biased and depends exclusively on the combined choice of financial measurements and family taxonomy; any general conclusion drawn from a specific pair of metrics should be interpreted with caution. We analyze the cross-combinations of eighteen variants of family firms with twenty financial metrics to show that family attributes greatly influence hybrid performance measures, display a notable effect on accounting-based metrics but have almost no impact on market returns. These peculiar results are ascribed to the fact that family businesses tend to favor creditors and directors over investors.
Natural Disasters and Domestic Violence: A study of the 2015 Nepal earthquake

This study explores the link between exposure to an earthquake and the incidence of intimate partner violence using two rounds of Demographic and Health Surveys data in Nepal. Using a differences-in-differences estimation, we find that exposure to the earthquake lead to a statistically and economically significant increase in the incidence of intimate partner violence in urban areas, which is attributable to the increase in stress felt by the victims. We argue that the heterogeneity of the impact between the urban and rural areas would be partly due to the differences in the reconstruction processes and assistance provided.
Resilience and Path Dependency: Income Distribution Effects of GDP in Colombia

Abstract This study examines the effect of GDP per capita on the Gini index, which measures income concentration, in Colombia. The methodology used is an econometric analysis of time series with data extracted from the Inter-American Development Bank and the World Bank. The econometric results suggest that, at least during the period studied here, there is no evidence that GDP per capita has been an explanatory variable of the behaviour of income distribution in Colombia. The results also align with the understanding that the problem of inequality in the distribution of income is not merely economic but concerns persistent matters such as political and historical issues.
Can Non-Cognitive Skills Explain The Gender Wage Gap In Russia? An Unconditional Quantile Regression Approach

Non-cognitive skills are widely recognized in economics as an important factor that affects various individual outcomes, including wages and employment. Non-cognitive skills can also serve as an additional explanation for the gender wage gap. This paper disentangles the complex relationship between non-cognitive skills and the gender wage gap based on Russian data. Data are collected from a nationally representative Russian survey RLMS-HSE and include detailed information on individuals aged 20–60. We use the Big Five factor model, locus of control, and attitudes towards risk to represent non-cognitive skills. Our findings suggest that non-cognitive skills account for up to 8 per cent of the gender wage gap, although significant variation is observed with different measures of personality and across the wage distribution. We conclude that personality traits are noteworthy but not exhaustive factors in the gender wage gap, and there are other unobserved factors which researchers have yet to identify.
The German Data Service Center for Business and Organizational Data (DSC-BO)

Beyond organizational studies there is an increasing awareness in economics and social sciences that the level “organizations” must be included for the explanation of various social and economic phenomena such as labor markets, educational systems, individual life-courses and social inequalities. However, despite its substantive relevance, up to now there has been a lack of in-depth organizational data in research. Data that allow the analysis of emerging questions are needed in those areas that treat organizations as important contexts and actors of economic and social processes. In Germany, there are only a few data sets publicly available for secondary research at the organizational level, such as the “IAB Establishment Panel” from the Institute for Employment Research (IAB) and various types of official data from the Federal Statistical Office Germany (Liebig, 2009, 6). All of these data sets include a limited amount of information and typically lack information on issues such as business culture or management styles (Liebig, 2009, 7). Moreover in the vast majority of data produced in empirical organizational research projects financed by public or private research funding, the level of “organizations” is usually not available to secondary users at all. These data are generally characterized by comparatively small sample sizes and a strong orientation towards organization-related research topics such as the description and explanation of organizational strategies, internal processes, industrial relations, etc. (Liebig, 2009, 8).
Quadratically optimal equilibrium points of weakly potential bi-matrix games

In this paper we show without using any fixed-point theorem argument, the existence of quadratically optimal equilibrium points of weakly potential bi-matrix games and quadratically optimal symmetric equilibrium points for those weakly potential square bi-matrix games which have potential matrices that are two-way matrices. The existence results are obtained as an optimal solution of a quadratic programming problem and hence constitute a refinement of the usual solution concepts for the subclass of games we consider.
Equilibrium thermodynamics and mass transfer parameters estimation of the synthesis products of piperonal from essential oil of Piper hispidinervum C. DC by reversed phase liquid chromatography

The synthetic route applied to essential oil of Piper hispidinervum leads to the formation of a multicomponent mixture composed, mainly by piperonal, safrole, isosafrole, and terpinolene. In this paper, it was studied the adsorption phenomena of these compounds using acetonitrile/water 70/30 (v/v) as mobile phase and C18 as stationary phase. Piperonal is the component that has the lowest affinity and terpinolene, the greatest with stationary phase. The internal resistance to mass transfer controls the overall mass transfer and parallel diffusion model describes the intra-particle transport mechanism. Key-words: multicomponent separation, long pepper, adsorption, mass transfer, C18.
Open-Set Recognition: A Good Closed-Set Classifier is All You Need

The ability to identify whether or not a test sample belongs to one of the semantic classes in a classifier's training set is critical to practical deployment of the model. This task is termed open-set recognition (OSR) and has received significant attention in recent years. In this paper, we first demonstrate that the ability of a classifier to make the 'none-of-above' decision is highly correlated with its accuracy on the closed-set classes. We find that this relationship holds across loss objectives and architectures, and further demonstrate the trend both on the standard OSR benchmarks as well as on a large-scale ImageNet evaluation. Second, we use this correlation to boost the performance of a maximum logit score OSR 'baseline' by improving its closed-set accuracy, and with this strong baseline achieve state-of-the-art on a number of OSR benchmarks. Similarly, we boost the performance of the existing state-of-the-art method by improving its closed-set accuracy, but the resulting discrepancy with the strong baseline is marginal. Our third contribution is to present the 'Semantic Shift Benchmark' (SSB), which better respects the task of detecting semantic novelty, in contrast to other forms of distribution shift also considered in related sub-fields, such as out-of-distribution detection. On this new evaluation, we again demonstrate that there is negligible difference between the strong baseline and the existing state-of-the-art. Project Page: https://www.robots. ox.ac. uk/~vgg/research/osr/
On the computational equivalence of co-NP refutations of a matrix being a P-matrix

A P-matrix is a square matrix X such that all principal submatrices of X have positive determinant. Such matrices appear naturally in instances of the linear complementarity problem, where these are precisely the matrices for which the corresponding linear complementarity problem has a unique solution for any input vector. Testing whether or not a square matrix is a P-matrix is co-NP complete, so while it is possible to exhibit polynomiallysized witnesses for the fact that a matrix is not a P-matrix, it is believed that there is no efficient way to prove that a given matrix is a P-matrix. We will show that several well known witnesses for the fact that a matrix is not a P-matrix are computationally equivalent, so that we are able to convert between them in polynomial time, answering a question raised in [2]. A P-matrix is a matrix with the property that all of its principal minors are strictly positive. If X is symmetric, then X is a P-matrix if and only if X is positive semidefinite, meaning that all of its eigenvalues are positive. An immediate consequence is that we can check if a symmetric matrix is a P-matrix in polynomial time. On the other hand, for nonsymmetric matrices, being a P-matrix is a much more difficult property to understand, and indeed, it is co-NP complete to check that general (nonsymmetric) matrix is a P-matrix [1]. The P-matrix property has connections to a number of different problems in computer science and linear algebra; [4] gives one of the earliest definitions of the P-matrix property for its connection to a property of arrangements of vectors in Euclidean space, where certain convex cones spanned by these vectors divide space up into disjoint pieces. Stickney and Watson connected this property to the linear-complementarity problem in [6], which is a quadratic optimization problem of the form Find w, z ∈ R subject to w−Mz = q w, z ≥ 0 w · z = 0. (LCP(M ,q)) The problem data is (M , z) ∈ R×R and w, z ∈ R are the optimization variables. This problem has connections to a number of problems in applied mathematics. This linear complementarity problem has a unique solution for any choice of q if and only if M is a P-matrix, though it is not yet known if there is a polynomial time algorithm for finding this unique solution even if M is a P-matrix. While it is not easy to check that a matrix is a P-matrix, we can still give some equivalent conditions for being a P-matrix. The three equivalent definitions we will be interested in here will be 1. Every principal submatrix of M has positive determinant. 2. For every vector x ∈ R so that x 6= 0, x i(M x)i > 0 for all i ∈ [n]. 3. The matrix M defines a unique sink orientation on the hypercube for every vector q ∈ R. We will define item 3 precisely in section 1.3. A unique sink orientation (USO) is a type of orientation on a hypercube graph which models the combinatorial properties of linear optimization problems on the hypercube polytope. USO’s are of interest because it has been suggested that they may provide a path towards constructing strongly
Parallel Batched Interpolation Search Tree

Ordered set (and map) is one of the most used data type. In addition to standard set operations, like insert, delete and contains, it can provide set-set operations such as union, intersection, and difference. Each of these set-set operations is equivalent to batched operations: the data structure should process a set of operations insert, delete, and contains. It is obvious that we want these"large"operations to be parallelized. Typically, these sets are implemented with the trees of logarithmic height, such as 2-3 tree, Treap, AVL tree, Red-Black tree, etc. Until now, little attention was devoted to data structures that work better but under several restrictions on the data. In this work, we parallelize Interpolation Search Tree which serves each request from a smooth distribution in doubly-logarithmic time. Our data structure of size $n$ performs a batch of $m$ operations in $O(m \log\log n)$ work and poly-log span.
Action-Sufficient State Representation Learning for Control with Structural Constraints

Perceived signals in real-world scenarios are usually high-dimensional and noisy, and finding and using their representation that contains essential and sufficient information required by downstream decision-making tasks will help improve computational efficiency and generalization ability in the tasks. In this paper, we focus on partially observable environments and propose to learn a minimal set of state representations that capture sufficient information for decision-making, termed \textit{Action-Sufficient state Representations} (ASRs). We build a generative environment model for the structural relationships among variables in the system and present a principled way to characterize ASRs based on structural constraints and the goal of maximizing cumulative reward in policy learning. We then develop a structured sequential Variational Auto-Encoder to estimate the environment model and extract ASRs. Our empirical results on CarRacing and VizDoom demonstrate a clear advantage of learning and using ASRs for policy learning. Moreover, the estimated environment model and ASRs allow learning behaviors from imagined outcomes in the compact latent space to improve sample efficiency.
Fetal Gender Identification using Machine and Deep Learning Algorithms on Phonocardiogram Signals

Phonocardiogram (PCG) signal analysis is a critical, widely-studied technology to noninvasively analyze the heart's mechanical activity. Through evaluating heart sounds, this technology has been chiefly leveraged as a preliminary solution to automatically diagnose Cardiovascular diseases among adults; however, prenatal tasks such as fetal gender identification have been relatively less studied using fetal Phonocardiography (FPCG). In this work, we apply common PCG signal processing techniques on the gender-tagged Shiraz University Fetal Heart Sounds Database and study the applicability of previously proposed features in classifying fetal gender using both Machine Learning and Deep Learning models. Even though PCG data acquisition's cost-effectiveness and feasibility make it a convenient method of Fetal Heart Rate (FHR) monitoring, the contaminated nature of PCG signals with the noise of various types makes it a challenging modality. To address this problem, we experimented with both static and adaptive noise reduction techniques such as Low-pass filtering, Denoising Autoencoders, and Source Separators. We apply a wide range of previously proposed classifiers to our dataset and propose a novel ensemble method of Fetal Gender Identification (FGI). Our method substantially outperformed the baseline and reached up to 91% accuracy in classifying fetal gender of unseen subjects.
Quantifying Cognitive Factors in Lexical Decline

Abstract We adopt an evolutionary view on language change in which cognitive factors (in addition to social ones) affect the fitness of words and their success in the linguistic ecosystem. Specifically, we propose a variety of psycholinguistic factors—semantic, distributional, and phonological—that we hypothesize are predictive of lexical decline, in which words greatly decrease in frequency over time. Using historical data across three languages (English, French, and German), we find that most of our proposed factors show a significant difference in the expected direction between each curated set of declining words and their matched stable words. Moreover, logistic regression analyses show that semantic and distributional factors are significant in predicting declining words. Further diachronic analysis reveals that declining words tend to decrease in the diversity of their lexical contexts over time, gradually narrowing their ‘ecological niches’.
Sanctuary lost: a cyber-physical warfare in space

Over the last decades, space has grown from a purely scientific struggle, fueled by the desire to demonstrate superiority of one regime over the other, to an anchor point of the economies of essentially all developed countries. Many businesses depend crucially on satellite communication or data acquisition, not only for defense purposes, but increasingly also for day-to-day applications. However, although so far space faring nations refrained from extending their earth-bound conflicts into space, this critical infrastructure is not as invulnerable as common knowledge suggests. In this paper, we analyze the threats space vehicles are exposed to and what must change to mitigate them. In particular, we shall focus on cyber threats, which may well be mounted by small countries and terrorist organizations, whose incentives do not necessarily include sustainability of the space domain and who may not be susceptible to the threat of mutual retaliation on the ground. We survey incidents, highlight threats and raise awareness from general preparedness for accidental faults, which is already widely spread within the space community, to preparedness and tolerance of both accidental and malicious faults (such as targeted attacks by cyber terrorists and nation-state hackers).
GraPE: fast and scalable Graph Processing and Embedding

Graph Representation Learning methods opened new possibilities for addressing complex, real-world problems represented by graphs. However, many graphs used in these applications comprise millions of nodes and billions of edges and are beyond the capabilities of current methods and software implementations. We present GRAPE , a software resource for graph processing and representation learning that is able to scale with big graphs by using specialized and smart data structures, algorithms, and a fast parallel implementation. When compared with state of the art software resources, GRAPE shows an improvement of orders of magnitude in empirical space and time complexity, as well as a substantial and statistically signiﬁcant improvement in edge prediction and node label prediction performance. Furthermore, GRAPE provides over 80 , 000 graphs from the literature and other sources, standardized interfaces allowing a straightforward integration of third-party libraries, 61 node embedding methods, 25 inference models, and 3 modular pipelines to allow a FAIR and reproducible comparison of methods and libraries for graph processing and embedding.
Multi-Channel Narrow-Band Deep Speech Separation with Full-Band Permutation Invariant Training

This paper addresses the problem of multi-channel multi-speech separation based on deep learning techniques. In the short time Fourier transform domain, we propose an end-to-end narrow-band network that directly takes as input the multi-channel mixture signals of one frequency, and outputs the separated signals of this frequency. In narrow-band, the spatial information (or inter-channel difference) can well discriminate between speakers at different positions. This information is intensively used in many narrow-band speech separation methods, such as beamforming and clustering of spatial vectors. The proposed network is trained to learn a rule to automatically exploit this information and perform speech separation. Such a rule should be valid for any frequency, thence the network is shared by all frequencies. In addition, a full-band permutation invariant training criterion is proposed to solve the frequency permutation problem encountered by most narrow-band methods. Experiments show that, by focusing on deeply learning the narrow-band information, the proposed method outperforms the oracle beamforming method and the state-of-the-art deep learning based method.
Early Melanoma Diagnosis With Sequential Dermoscopic Images

Dermatologists often diagnose or rule out early melanoma by evaluating the follow-up dermoscopic images of skin lesions. However, existing algorithms for early melanoma diagnosis are developed using single time-point images of lesions. Ignoring the temporal, morphological changes of lesions can lead to misdiagnosis in borderline cases. In this study, we propose a framework for automated early melanoma diagnosis using sequential dermoscopic images. To this end, we construct our method in three steps. First, we align sequential dermoscopic images of skin lesions using estimated Euclidean transformations, extract the lesion growth region by computing image differences among the consecutive images, and then propose a spatio-temporal network to capture the dermoscopic changes from aligned lesion images and the corresponding difference images. Finally, we develop an early diagnosis module to compute probability scores of malignancy for lesion images over time. We collected 179 serial dermoscopic imaging data from 122 patients to verify our method. Extensive experiments show that the proposed model outperforms other commonly used sequence models. We also compared the diagnostic results of our model with those of seven experienced dermatologists and five registrars. Our model achieved higher diagnostic accuracy than clinicians (63.69% vs. 54.33%, respectively) and provided an earlier diagnosis of melanoma (60.7% vs. 32.7% of melanoma correctly diagnosed on the first follow-up images). These results demonstrate that our model can be used to identify melanocytic lesions that are at high-risk of malignant transformation earlier in the disease process and thereby redefine what is possible in the early detection of melanoma.