Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Android Applications Permissions

As Android is one of the most popular mobile open-source platforms, it is very important to ensure the security and privacy of Android apps. Android has an authorization system that allows developers to announce their applications requiring essential services, and when running such applications, users need to comply with these requirements. Users frequently download applications, giving them infinite permissions easily without thinking about the impact on their privacy. In this paper, we analyze 222 applications manually to grant these permissions to see how they are compatible with user privacy depending on many criteria.
Mean Waiting Time in Large-Scale and Critically Loaded Power of d Load Balancing Systems

Mean field models are a popular tool used to analyse load balancing policies. In some exceptional cases the waiting time distribution of the mean field limit has an explicit form. In other cases it can be computed as the solution of a set of differential equations. In this paper we study the limit of the mean waiting time E[Wλ] as the arrival rate λ approaches 1 for a number of load balancing policies in a large-scale system of homogeneous servers which finish work at a constant rate equal to one and exponential job sizes with mean 1 (i.e. when the system gets close to instability). As E[Wλ] diverges to infinity, we scale with -log(1-λ) and present a method to compute the limit limλ-> 1- -E[Wλ]/l(1-λ). We show that this limit has a surprisingly simple form for the load balancing algorithms considered. More specifically, we present a general result that holds for any policy for which the associated differential equation satisfies a list of assumptions. For the well-known LL(d) policy which assigns an incoming job to a server with the least work left among d randomly selected servers these assumptions are trivially verified. For this policy we prove the limit is given by 1/d-1. We further show that the LL(d,K) policy, which assigns batches of K jobs to the K least loaded servers among d randomly selected servers, satisfies the assumptions and the limit is equal to K/d-K. For a policy which applies LL(di) with probability pi, we show that the limit is given by 1/ ∑i pi di - 1. We further indicate that our main result can also be used for load balancers with redundancy or memory. In addition, we propose an alternate scaling -l(pλ) instead of -l(1-λ), where pλ is adapted to the policy at hand, such that limλ-> 1- -E[Wλ]/l(1-λ)=limλ-> 1- -E[Wλ]/l(pλ), where the limit limλ-> 0+ -E[Wλ]/l(pλ) is well defined and non-zero (contrary to limλ-> 0+ -E[Wλ]/l(1-λ)). This allows to obtain relatively flat curves for -E[Wλ]/l(pλ) for λ ∈ [0,1] which indicates that the low and high load limits can be used as an approximation when λ is close to one or zero. Our results rely on the earlier proven ansatz which asserts that for certain load balancing policies the workload distribution of any finite set of queues becomes independent of one another as the number of servers tends to infinity.
N-Polar GaN Nanowires Seeded by Al Droplets on Si(111)

The polarity of GaN nanowires grown on Si(111) by molecular beam epitaxy is determined by convergent beam electron diffraction. Thick specimens lead to unambiguous results showing their N-polarity. Complementary sample etching indicates that all the nanowires have the same N-polarity and are surrounded by a Ga-polar two-dimensional layer. A correlation with the initial growth stage which uses consecutive exposures of the Si substrate to Al and N is established. Al–Si liquid droplets are formed and produce AlN columnar pedestals playing the role of seeds for the GaN nanowires. On the surrounding Si surface, the GaN layer nucleates with the opposite Ga-polarity.
Multi-attribute Classification of Human Body Images Based on Simplified Two-level Consistency

Multi-attribute classification of human body images is an important research topic in computer vision. It belongs to a type of multi-label classification, which mainly classifies various attributes of people. The attention mechanism is a common method for solving the multi-label classification problem, in which the visual attention consistency method can achieve good performance. However, this kind of visual attention consistency requires manually designing complex mapping relationships between attention graphs. This paper proposes a multi-attribute classification of human body images based on simplified two-level consistency. We first propose a logits consistency matching mechanism, which does not require the manual design of mapping relationships between complex attention graphs and reduces the difficulty to achieve consistency. Afterward, based on this logits consistency, a complete two-level consistency mechanism is designed to further improve the accuracy of the model. In experiments on the multi-attribute classification of human body images benchmarks, the proposed two-level consistency method achieves a mean accuracy of 92.7% and a mAP of 87.1%.
MGF: An Algorithm for Compressed Sensing MRI with Gradient Domain Guided Image Filtering

In this paper, we develop an algorithm for magnetic resonance imaging (MRI) reconstruction with gradient domain guided image filtering (called MGF). We first turn a MRI reconstruction problem into two-phase objective functions: in the first phase, a latent image is generated to be a guidance image for gradient domain guided image filtering (GGF), and in the second phase, GGF is used to integrate fine structures of the latent image into the ideal solution, meanwhile, thenorm prior is simply yet effectively imposed on the GGF constraint for the error between latent and ideal images in image gradient domain. Then an efficient optimization scheme is derived to address the proposed model by iteratively alternating latent image reconstruction, GGF andnorm approximations, and ideal image reconstruction. Final experiments on real-valued and complex-valued MR images demonstrate the satisfactory performance of MGF in MRI reconstructions, and our method outperforms several well-known compressed sensing (CS) reconstruction approaches in terms of subjective results and objective assessments.
On the turbulent flow past a realistic open-cell metal foam

Abstract Turbulence is investigated in the lee of an open-cell metal foam layer. In contrast to canonical grids, metal foams are locally irregular but statistically isotropic. The solid matrix is characterised by two lengths, the ligament thickness $d_f$ and the pore diameter $d_p$. A direct numerical simulation is conducted on a realistic metal foam geometry for which $d_f/d_p = 0.14$ and the porous layer thickness is five times the pore diameter. The Reynolds number based on the pore size is ${\textit {Re}}_{d_p} = 4000$, corresponding to a Taylor-scale Reynolds number ${\textit {Re}}_\lambda \approx 80$. Closer to the foam than two pore diameters, the pressure and turbulent transports of turbulent kinetic energy are non-negligible. In the same region, ${\textit {Re}}_\lambda$ undergoes a steep decrease whereas the dissipation coefficient $C_{\epsilon }$ increases like ${\textit {Re}}_\lambda ^{-1}$. At larger distances from the porous layer, the classical grid turbulence situation is recovered, where the mean advection of turbulent kinetic energy equals dissipation. This entails a power-law decay of turbulent quantities and characteristic lengths. The decaying exponents of integral, Taylor and Kolmogorov scales are close to one-half, indicating that the turbulence simulated here differs from Saffman turbulence. Analysis of the scaling exponents of structure functions and the decorrelation length of dissipation reveals that small-scale fluctuations are weakly intermittent.
Low Energy Electron Irradiation Is a Potent Alternative to Gamma Irradiation for the Inactivation of (CAR-)NK-92 Cells in ATMP Manufacturing

Background With increasing clinical use of NK-92 cells and their CAR-modified derivatives in cancer immunotherapy, there is a growing demand for efficient production processes of these “off-the-shelf” therapeutics. In order to ensure safety and prevent the occurrence of secondary tumors, (CAR-)NK-92 cell proliferation has to be inactivated before transfusion. This is commonly achieved by gamma irradiation. Recently, we showed proof of concept that low energy electron irradiation (LEEI) is a new method for NK-92 inactivation. LEEI has several advantages over gamma irradiation, including a faster reaction time, a more reproducible dose rate and much less requirements on radiation shielding. Here, LEEI was further evaluated as a promising alternative to gamma irradiation yielding cells with highly maintained cytotoxic effector function. Methods Effectiveness and efficiency of LEEI and gamma irradiation were analyzed using NK-92 and CD123-directed CAR-NK-92 cells. LEE-irradiated cells were extensively characterized and compared to gamma-irradiated cells via flow cytometry, cytotoxicity assays, and comet assays, amongst others. Results Our results show that both irradiation methods caused a progressive decrease in cell viability and are, therefore, suitable for inhibition of cell proliferation. Notably, the NK-mediated specific lysis of tumor cells was maintained at stable levels for three days post-irradiation, with a trend towards higher activities after LEEI treatment as compared to gamma irradiation. Both gamma irradiation as well as LEEI led to substantial DNA damage and an accumulation of irradiated cells in the G2/M cell cycle phases. In addition, transcriptomic analysis of irradiated cells revealed approximately 12-fold more differentially expressed genes two hours after gamma irradiation, compared to LEEI. Analysis of surface molecules revealed an irradiation-induced decrease in surface expression of CD56, but no changes in the levels of the activating receptors NKp46, NKG2D, or NKp30. Conclusions The presented data show that LEEI inactivates (CAR-)NK-92 cells as efficiently as gamma irradiation, but with less impact on the overall gene expression. Due to logistic advantages, LEEI might provide a superior alternative for the manufacture of (CAR-)NK-92 cells for clinical application.
Radiomics Analysis of Contrast-Enhanced CT for Hepatocellular Carcinoma Grading

Objectives To investigate the value of contrast-enhanced computer tomography (CT)-based on radiomics in discriminating high-grade and low-grade hepatocellular carcinoma (HCC) before surgery. Methods The retrospective study including 161 consecutive subjects with HCC which was approved by the institutional review board, and the patients were divided into a training group (n = 112) and test group (n = 49) from January 2013 to January 2018. The least absolute shrinkage and selection operator (LASSO) was used to select the most valuable features to build a support vector machine (SVM) model. The performance of the predictive model was evaluated using the area under the curve (AUC), accuracy, sensitivity, and specificity. Results The SVM model showed an acceptable ability to differentiate high-grade from low-grade HCC, with an AUC of 0.904 in the training dataset and 0.937 in the test dataset, accuracy (92.2% versus 95.7%), sensitivity(82.5% versus 88.0%), and specificity (92.7% versus 95.8%), respectively. Conclusion The machine learning-based radiomics reflects a better evaluating performance in differentiating HCC between low-grade and high-grade, which may contribute to personalized treatment.
Transarterial Chemoembolization in Treatment-Naïve and Recurrent Hepatocellular Carcinoma: A Propensity-Matched Outcome and Risk Signature Analysis

Objectives The purpose of this study was to evaluate the efficacy and safety of transarterial chemoembolization (TACE) in the treatment of patients with treatment-naïve hepatocellular carcinoma (TN-HCC) and recurrent HCC (R-HCC). In addition, risk signature analysis was performed to accurately assess patients’ recurrence and survival. Methods This retrospective study assessed the consecutive medical records of TN-HCC and R-HCC patients from January 2014 to December 2018. In order to reduce the patient selection bias, propensity score matching (PSM) analysis was applied. Conditional inference tree was used to establish a risk signature. Results A total of 401 eligible patients were included in our study, including 346 patients in the TN-HCC group and 55 patients in the R-HCC group. Forty-seven pairs of patients were chosen after the PSM analysis. Before the PSM analysis, the objective tumor regression (ORR) and disease control rate (DCR) of R-HCC patients were better than that of TN-HCC patients; however, after the PSM analysis, there was no significant difference in the ORR and DCR between the two groups (P>0.05). Before the PSM analysis, the median overall survival (OS) and progression-free survival (PFS) in the R-HCC group were significantly greater than those of the TN-HCC group (OS: 24 months vs. 18 months, P =0.004; PFS: 9 months vs. 6 months, P =0.012). However, after the PSM analysis, the median OS and PFS in the R-HCC group were inferior to those in the TN-HCC group (OS: 24 months vs. 33 months, P= 0.0035; PFS: 10 months vs. 12 months, P = 0.01). The conditional inference tree divided patients into different subgroups according to tumor size, BCLC stage, and TACE sessions and shared different hazards ratio to recurrence or survival. Conclusion Patients with R-HCC treated with TACE achieved satisfactory results, although survival after the PSM analysis was not as good as in the TN-HCC group. In addition, risk signature based on conditional inference tree analysis can more accurately predict the recurrence and survival in both groups of patients.
Real‐Time Frequency Adjustment of Images and Videos

We present a technique for real‐time adjustment of spatial frequencies in images and videos. Our method allows for both decreasing and increasing of frequencies, and is orthogonal to image resizing. Thus, it can be used to automatically adjust spatial frequencies to preserve the appearance of structured patterns during image downscaling and upscaling. By pre‐computing the image's space‐frequency decomposition and its unwrapped phases, these operations can be performed in real time, thanks to our novel mathematical perspective on frequency manipulation of digital images: interpreting the problem through the theory of instantaneous frequencies and phase unwrapping. To make this possible, we introduce an algorithm for the simultaneous phase unwrapping of several unordered frequency components, which also deals with the frequency‐sign ambiguity of real signals. As such, our method provides theoretical and practical improvements to the concept of spectral remapping, enabling real‐time performance and improved color handling. We demonstrate its effectiveness on a large number of images subject to frequency adjustment. By providing real‐time control over the spatial frequencies associated with structured patterns, our technique expands the range of creative and technical possibilities for image and video processing.
Spatiotemporal Texture Reconstruction for Dynamic Objects Using a Single RGB‐D Camera

This paper presents an effective method for generating a spatiotemporal (time‐varying) texture map for a dynamic object using a single RGB‐D camera. The input of our framework is a 3D template model and an RGB‐D image sequence. Since there are invisible areas of the object at a frame in a single‐camera setup, textures of such areas need to be borrowed from other frames. We formulate the problem as an MRF optimization and define cost functions to reconstruct a plausible spatiotemporal texture for a dynamic object. Experimental results demonstrate that our spatiotemporal textures can reproduce the active appearances of captured objects better than approaches using a single texture map.
Deep HDR estimation with generative detail reconstruction

We study the problem of High Dynamic Range (HDR) image reconstruction from a Standard Dynamic Range (SDR) input with potential clipping artifacts. Instead of building a direct model that maps from SDR to HDR images as in previous work, we decompose an input SDR image into a base (low frequency) and detail layer (high frequency), and treat reconstructing these two layers as two separate problems. We propose a novel architecture that comprises individual components specially designed to handle both tasks. Specifically, our base layer reconstruction component recovers low frequency content and remaps the color gamut of the input SDR, whereas our detail layer reconstruction component, which builds upon prior work on image inpainting, hallucinates missing texture information. The output HDR prediction is produced by a final refinement stage. We present qualitative and quantitative comparisons with existing techniques where our method achieves state‐of‐the‐art performance.
Cyclostationary Gaussian noise: theory and synthesis

Stationary Gaussian processes have been used for decades in the context of procedural noises to model and synthesize textures with no spatial organization. In this paper we investigate cyclostationary Gaussian processes, whose statistics are repeated periodically. It enables the modeling of noises having periodic spatial variations, which we call “cyclostationary Gaussian noises”. We adapt to the cyclostationary context several stationary noises along with their synthesis algorithms: spot noise, Gabor noise, local random‐phase noise, high‐performance noise, and phasor noise. We exhibit real‐time synthesis of a variety of visual patterns having periodic spatial variations.
Coherent Mark‐based Stylization of 3D Scenes at the Compositing Stage

We present a novel temporally coherent stylized rendering technique working entirely at the compositing stage. We first generate a distribution of 3D anchor points using an implicit grid based on the local object positions stored in a G‐buffer, hence following object motion. We then draw splats in screen space anchored to these points so as to be motion coherent. To increase the perceived flatness of the style, we adjust the anchor points density using a fractalization mechanism. Sudden changes are prevented by controlling the anchor points opacity and introducing a new order‐independent blending function. We demonstrate the versatility of our method by showing a large variety of styles thanks to the freedom offered by the splats content and their attributes that can be controlled by any G‐buffer.
SENIES: DNA Shape Enhanced Two-Layer Deep Learning Predictor for the Identification of Enhancers and Their Strength

Identifying enhancers is a critical task in bioinformatics due to their primary role in regulating gene expression. For this reason, various computational algorithms devoted to enhancer identification have been put forward over the years. More features are extracted from the single DNA sequences to boost the performance. Nevertheless, DNA structural information is neglected, which is an essential factor affecting the binding preferences of transcription factors to regulatory elements like enhancers. Here, we propose SENIES, a DNA shape enhanced deep learning predictor, to identify enhancers and their strength. The predictor consists of two layers where the first layer is for enhancer and non-enhancer identification, and the second layer is for predicting the strength of enhancers. Apart from two common sequence-derived features (i.e., one-hot and k-mer), DNA shape is introduced to describe the 3D structures of DNA sequences. Performance comparison with state-of-the-art methods conducted on public datasets demonstrates the effectiveness and robustness of our predictor. The code implementation of SENIES is publicly available at https://github.com/hlju-liye/SENIES.
Staring at the naked Goddess. Unraveling structure and reactivity of Artemis endonuclease interacting with a DNA double strand

Artemis is an endonuclease responsible for breaking hairpin DNA strand during immune system adaptation and maturation as well as the processing of potentially toxic DNA lesions. Thus, Artemis may be an important target in the development of anticancer therapy, both for the sensitization of radiotherapy and for immunotherapy. Despite its importance its structure has been resolved only recently, and important questions concerning the arrangement of its active center, the interaction with the DNA substrate, or the catalytic mechanism remain unanswered. In this contribution, by performing extensive molecular dynamic simulation, both classically and at hybrid quantum mechanics/ molecular mechanics level, we evidence the stable interaction modes of Artemis with a model DNA strand. We also analyze the catalytic cycle providing the free energy profile and key transition states for the DNA cleavage reaction.
In an “ecological-belonging” intervention to reduce inequities in STEM, context matters

Doubts about belonging in the classroom are often shouldered disproportionately by students from historically marginalized groups, which can lead to underperformance. Ecological-belonging interventions use a classroom-based activity to instill norms that adversity is normal, temporary and surmountable. Building on prior studies, we sought to identify the conditions under which such interventions are effective. In a chemistry course (Study 1), students from underrepresented ethnic backgrounds underperformed relative to their peers in the absence of the intervention. This performance gap was eliminated by the intervention. In an introductory biology course (Study 2), there were no large performance gaps in the absence of the intervention, and the intervention had no effect. Study 2 also explored the role of the instructor that delivers the intervention. The intervention boosted scores in classrooms of instructors with a fixed (versus growth-oriented) intelligence mindset. Our results suggest that ecological-belonging interventions are more effective in more threatening classroom contexts.
Genome-wide association mapping of transcriptome variation in Mimulus guttatus indicates differing patterns of selection on cis- versus trans-acting mutations

We measured the floral bud transcriptome of 151 fully sequenced lines of Mimulus guttatus from one natural population. Thousands of single nucleotide polymorphisms (SNPs) are implicated as transcription regulators, but there is a striking difference in the Allele Frequency Spectrum (AFS) of cis-acting and trans-acting mutations. Cis-SNPs have intermediate frequencies (consistent with balancing selection) while trans-SNPs exhibit a rare-alleles model (consistent with purifying selection). This pattern only becomes clear when transcript variation is normalized on a gene-to-gene basis. If a global normalization is applied, as is typically in RNAseq experiments, asymmetric transcript distributions combined with “rarity disequilibrium” produce a super-abundance of false positives for trans-acting SNPs. To explore the cause of purifying selection on trans-acting mutations, we identified gene expression modules as sets of co-expressed genes. The extent to which trans-acting mutations influence modules is a strong predictor of allele frequency. Mutations altering expression of genes with high “connectedness” (those that are highly predictive of the representative module expression value) have the lowest allele frequency. The expression modules can also predict whole-plant traits such as flower size. We find that a substantial portion of the genetic (co)variance among traits can be described as an emergent property of genetic effects on expression modules.