Miracle-dz/UniFAR-FTUs huggingface dataset:
Noisy Label Learning for Security Defects

Data-driven software engineering processes, such as vulnerability prediction heavily rely on the quality of the data used. In this paper, we observe that it is infeasible to obtain a noise-free security defect dataset in practice. Despite the vulnerable class, the non-vulnerable modules are difficult to be verified and determined as truly exploit free given the limited manual efforts available. It results in uncertainty, introduces labeling noise in the datasets and affects conclusion validity. To address this issue, we propose novel learning methods that are robust to label impurities and can leverage the most from limited label data; noisy label learning. We investigate various noisy label learning methods applied to soft-ware vulnerability prediction. Specifically, we propose a two-stage learning method based on noise cleaning to identify and remediate the noisy samples, which improves AUC and recall of baselines by up to 8.9% and 23.4%, respectively. Moreover, we discuss several hurdles in terms of achieving a performance upper bound with semi-omniscient knowledge of the label noise. Overall, the experimental results show that learning from noisy labels can be effective for data-driven software and security analytics.
A Study of Multihop mmW Aerial Backhaul Links

The main contribution of this paper is to analyze a long networked flying platform (NFP)-based millimeter wave (mmWave) backhaul link that is offered as a cost effective and easy to deploy solution to connect a disaster or remote area to the nearest core network. The same network model can be considered for interlinking Low Earth Orbit (LEO) satellite constellations with different geometry and channel characteristics. For this aim, we characterize the backhaul channel as a function of realistic physical parameters such as heights and distances of obstacles along the route, flight altitude and the intensity of NFPs' vibrations, the actual 3D antenna pattern, etc. For the characterized channel, we derive an analytical closed-form expression for the outage probability. Finally, using the obtained results, we provide a fast algorithm for the optimal parameter design of the considered system that minimizes the cost.
Low-light Image and Video Enhancement via Selective Manipulation of Chromaticity

Image acquisition in low-light conditions suffers from poor quality and significant degradation in visual aesthetics. This affects the visual perception of the acquired image and the performance of various computer vision and image processing algorithms applied after acquisition. Especially for videos, the additional temporal domain makes it more challenging, wherein we need to preserve quality in a temporally coherent manner. We present a simple yet effective approach for low-light image and video enhancement. To this end, we introduce"Adaptive Chromaticity", which refers to an adaptive computation of image chromaticity. The above adaptivity allows us to avoid the costly step of low-light image decomposition into illumination and reflectance, employed by many existing techniques. All stages in our method consist of only point-based operations and high-pass or low-pass filtering, thereby ensuring that the amount of temporal incoherence is negligible when applied on a per-frame basis for videos. Our results on standard lowlight image datasets show the efficacy of our algorithm and its qualitative and quantitative superiority over several state-of-the-art techniques. For videos captured in the wild, we perform a user study to demonstrate the preference for our method in comparison to state-of-the-art approaches.
CHARACTER STACKS ARE PORC COUNT

Abstract We compute the number of points over finite fields of the character stack associated to a compact surface group and a reductive group with connected centre. We find that the answer is a polynomial on residue classes (PORC). The key ingredients in the proof are Lusztig’s Jordan decomposition of complex characters of finite reductive groups and Deriziotis’s results on their genus numbers. As a consequence of our main theorem, we obtain an expression for the E-polynomial of the character stack.
Geometric Assumptions in Hydrodynamic Modeling of Coronal and Flaring Loops

In coronal loop modeling, it is commonly assumed that the loops are semicircular with a uniform cross-sectional area. However, observed loops are rarely semicircular, and extrapolations of the magnetic field show that the field strength decreases with height, implying that the cross-sectional area expands with height. We examine these two assumptions directly, to understand how they affect the hydrodynamic and radiative response of short, hot loops to strong, impulsive electron beam heating events. Both the magnitude and rate of area expansion impact the dynamics directly, and an expanding cross section significantly lengthens the time for a loop to cool and drain, increases upflow durations, and suppresses sound waves. The standard T ∼ n 2 relation for radiative cooling does not hold with expanding loops, which cool with relatively little draining. An increase in the eccentricity of loops, on the other hand, only increases the draining timescale, and is a minor effect in general. Spectral line intensities are also strongly impacted by the variation in the cross-sectional area because they depend on both the volume of the emitting region as well as the density and ionization state. With a larger expansion, the density is reduced, so the lines at all heights are relatively reduced in intensity, and because of the increase of cooling times, the hottest lines remain bright for significantly longer. Area expansion is critical to accurate modeling of the hydrodynamics and radiation, and observations are needed to constrain the magnitude, rate, and location of the expansion—or lack thereof.
Metric Entropy Duality and the Sample Complexity of Outcome Indistinguishability

We give the first sample complexity characterizations for outcome indistinguishability, a theoretical framework of machine learning recently introduced by Dwork, Kim, Reingold, Rothblum, and Yona (STOC 2021). In outcome indistinguishability, the goal of the learner is to output a predictor that cannot be distinguished from the target predictor by a class $D$ of distinguishers examining the outcomes generated according to the predictors' predictions. In the distribution-specific and realizable setting where the learner is given the data distribution together with a predictor class $P$ containing the target predictor, we show that the sample complexity of outcome indistinguishability is characterized by the metric entropy of $P$ w.r. t. the dual Minkowski norm defined by $D$, and equivalently by the metric entropy of $D$ w.r. t. the dual Minkowski norm defined by $P$. This equivalence makes an intriguing connection to the long-standing metric entropy duality conjecture in convex geometry. Our sample complexity characterization implies a variant of metric entropy duality, which we show is nearly tight. In the distribution-free setting, we focus on the case considered by Dwork et al. where $P$ contains all possible predictors, hence the sample complexity only depends on $D$. In this setting, we show that the sample complexity of outcome indistinguishability is characterized by the fat-shattering dimension of $D$. We also show a strong sample complexity separation between realizable and agnostic outcome indistinguishability in both the distribution-free and the distribution-specific settings. This is in contrast to distribution-free (resp. distribution-specific) PAC learning where the sample complexity in both the realizable and the agnostic settings can be characterized by the VC dimension (resp. metric entropy).
High-density disc reflection spectroscopy of low-mass active galactic nuclei

The standard alpha-disc model predicts an anti-correlation between the density of the inner accretion disc and the black hole mass times square of the accretion rate, as seen in higher mass (MBH > 106M⊙) active galactic nuclei (AGNs). In this work, we test the predictions of the alpha-disc model and study the properties of the inner accretion flow for the low-mass end (MBH ≈ 105 − 6M⊙) of AGNs. We utilize a new high-density disc reflection model where the density parameter varies from ne = 1015 to 1020 cm−3 and apply it to the broadband X-ray (0.3−10 keV) spectra of the low-mass AGN sample. The sources span a wide range of Eddington fractions and are consistent with being sub-Eddington or near-Eddington. The X-ray spectra reveal a soft X-ray excess below ∼1.5 keV which is well modeled by high-density reflection from an ionized accretion disc of density ne ∼ 1018 cm−3 on average. The results suggest a radiation pressure-dominated disc with an average of 70 per cent fraction of the disc power transferred to the corona, consistent with that observed in higher mass AGNs. We show that the disc density higher than 1015 cm−3 can result from the radiation pressure compression when the disc surface does not hold a strong magnetic pressure gradient. We find tentative evidence for a drop in black hole spin at low-mass regimes.
Onception: Active Learning with Expert Advice for Real World Machine Translation

Active learning can play an important role in low-resource settings (i.e., where annotated data is scarce), by selecting which instances may be more worthy to annotate. Most active learning approaches for Machine Translation assume the existence of a pool of sentences in a source language, and rely on human annotators to provide translations or post-edits, which can still be costly. In this article, we apply active learning to a real-world human-in-the-loop scenario in which we assume that: (1) the source sentences may not be readily available, but instead arrive in a stream; (2) the automatic translations receive feedback in the form of a rating, instead of a correct/edited translation, since the human-in-the-loop might be a user looking for a translation, but not be able to provide one. To tackle the challenge of deciding whether each incoming pair source–translations is worthy to query for human feedback, we resort to a number of stream-based active learning query strategies. Moreover, because we do not know in advance which query strategy will be the most adequate for a certain language pair and set of Machine Translation models, we propose to dynamically combine multiple strategies using prediction with expert advice. Our experiments on different language pairs and feedback settings show that using active learning allows us to converge on the best Machine Translation systems with fewer human interactions. Furthermore, combining multiple strategies using prediction with expert advice outperforms several individual active learning strategies with even fewer interactions, particularly in partial feedback settings.
Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained Environments

Many real-world scenarios in which DNN-based recognition systems are deployed have inherently fine-grained attributes (e.g., bird-species recognition, medical image classification). In addition to achieving reliable accuracy, a critical subtask for these models is to detect Out-of-distribution (OOD) inputs. Given the nature of the deployment environment, one may expect such OOD inputs to also be fine-grained w.r. t. the known classes (e.g., a novel bird species), which are thus extremely difficult to identify. Unfortunately, OOD detection in fine-grained scenarios remains largely underexplored. In this work, we aim to fill this gap by first carefully constructing four large-scale fine-grained test environments, in which existing methods are shown to have difficulties. Particularly, we find that even explicitly incorporating a diverse set of auxiliary outlier data during training does not provide sufficient coverage over the broad region where fine-grained OOD samples locate. We then propose Mixture Outlier Exposure (MixOE), which mixes ID data and training outliers to expand the coverage of different OOD granularities, and trains the model such that the prediction confidence linearly decays as the input transitions from ID to OOD. Extensive experiments and analyses demonstrate the effectiveness of MixOE for building up OOD detector in finegrained environments. The code is available at https://github.com/zjysteven/MixOE.
Roles and user characteristics as driving forces of information use in the Dutch parliament

IMPACT This article looks at how the roles and personal characteristics of politicians affect their use of accounting information. The findings in this article suggest that it is very likely that the quality of debates in parliament correlate with use of the accounting information provided. Political parties should consider age and experience in their selection of candidates and in terms of whether to retire MPs, because age and experience do seem to affect the extent to which politicians substantiate their statements and decisions with hard accounting information.
Experimental and analytical study on the behavior of hybrid GFRP/steel bars in reinforced concrete deep beams

Abstract The deep beam is one of the essential members of high-rise buildings structures, so the deep beams are used as a transfer girder; in walls water structures, the deep beam behavior is different from the slender beam behavior; the deep beam plane section before does not remain plane after bending. In recent years, the use of FRP as a composite material in reinforced concrete structures has been growing up to cover problems by weight of structure buildings, corrosion, repairing, and construction cost. This paper presents an experimental, analytical study to assign the variation of mechanical properties of reinforced concrete deep beams using vertical and horizontal GFRP stirrups. This paper investigates the mechanical properties of test specimens for deep beams reinforced in shear with GFRP or steel bars as web reinforcement. The deep beams are reinforced with glass fiber reinforced polymer (GFRP) in various ratios as a web reinforcement configuration (0, 0.25%, and 0.40%) rather than traditional steel web reinforcement. All tested specimens have the same span to depth ratio of 0.40 (a/d); the primary and secondary reinforcement is steel bars. The web reinforcement ratio significantly affected deep beams’ load capacity and mechanical behavior. The GFRP enhancement the mechanical behavior of the reinforced concrete deep. Increasing the GFRP web reinforcement ratio enhances the deep beam load capacity. The test results compared with the traditional ACI design method strut-tie model to demonstrate the effect of web reinforcement ratio on deep beam load capacity and strut width. The test results have been verified by ABAQUS 6.13.
Assessment of Standing and Recumbent Restraint Positions and the Significance of Rumenotomy Techniques Based on the Profiles of Serum Amyloid a in Kano Brown Goats

Abstract Standing and lateral recumbency positions have been used as a standard approach for most surgical procedures for large and small ruminants, respectively, without appropriate attention to the associated surgical stress. The study aimed to assess the level of surgical stress in Kano Brown goats (KBGs) based on their serum amyloid A (SAA) profiles by undergoing rumenotomy in lateral recumbency (Rumen Skin Clamp Fixation-RSCF and Stay Suture Rumenotomy-SSR) and a standing position restraint. A total of 24 KBGs were equally allocated by number and sex in groups A, B, C, and D. Groups A and B underwent RSCF and SSR in lateral recumbency, respectively, while group D underwent a rumenotomy in a standing position performed in a custom made mobile small ruminant surgical chute. Animals in groups A, B, and D were diagnosed with rumen foreign bodies, whereas group C was used as a control with a negative diagnosis. At 48 h post-rumenotomy, the mean SAA concentration in group B (137.88±66.87 g/L) was significantly higher (P<0.05) than the value in group C (34.59±0.57 g/L). The females in group B had a significantly higher (P<0.05) mean concentration of SAA at 48 hours (210.15±123.73 g/L) than groups C (35.18±0.08 g/L) and D (48.35±12.15 g/L). In group A, at 24 hours, SAA concentration (115.61±20.96 µg/L) was significantly higher (P<0.05) than that of group B (31.51±2.59 µg/L) and group C (34.86±3.21 µg/L) in males. Rumenotomy in the standing restraint position was associated with minimal surgical stress, hence it’s recommended over the conventional lateral recumbent position techniques.
Hyperthyroidism in the Domestic Cat (Felis Catus): Informed Treatment Choice Based on Survival Analysis

Abstract Hyperthyroidism is the most frequently diagnosed endocrine disorder in cats. Therapy may include pharmacological, surgical (thyroidectomy), radioactive (iodine), and dietary treatment. The choice of treatment is believed to be strongly influenced by the veterinarian’s experience, level of education, and knowledge of the current scientific literature. The history of survival rates can affect the decision for treatment by both the veterinarian and the owner. This study aimed to explore the longevity in cats diagnosed with hyperthyroidism and to identify significant variables which affect survival rates by using retrospective data from the practice. A multivariate Cox regression was applied with the following results: surgical thyroidectomy and methimazole medication produced similar longevity (median 23.5 months, P>0.05); Domestic Short Hair cats survived longer than pure breeds (median 27.2 vs 9.4 months, P<0.05); as do cats without chronic renal disease (median 28.1 vs 6.2 months, P<0.001); and those with low activities of alanine aminotransferase (median 27.1 vs 17.0 months, P<0.01). Hyperthyroidism is comorbid with renal diseases, but no cumulative effect was found on survivability. There was no difference in survival rates between surgical and pharmaceutical treatment, therefore the discussion of treatment options with owners can focus on other factors (e.g., cost, owners’ compliance, cats’ tolerance to medication, presence of comorbidities). We propose that surgery may be the preferred treatment when the survival rates are expected to be higher than one year. This would avoid high costs and potential side effects of medication.
Computational Mirror Cup and Saucer Art

In the mirror cup and saucer art created by artists Yul Cho and Sang-Ha Cho, part of the saucer is directly visible to the viewer, while the other part of the saucer is occluded and can only be seen as a reflection through a mirror cup. Thus, viewers see an image directly on the saucer and another image on the mirror cup; however, the existing art design is limited to wavelike saucers. In this work, we propose a general computational framework for mirror cup and saucer art design. As input, we take from the user one image for the direct view, one image for the reflected view, and the base shape of the saucer. Our algorithm then generates a suitable saucer shape by deforming the input shape. We formulate this problem as a constrained optimization for the saucer surface. Our framework solves for the fine geometry details on the base shape along with its texture, such that when a mirror cup is placed on the saucer, the user-specified images are observed as direct and reflected views. Through extensive experiments, we demonstrate the effectiveness of our framework and the great design flexibility that it offers to users. We further validate the produced art pieces by fabricating the colored saucers using three-dimensional printing.
Hybrid Classification and Clustering Algorithm on Recent Android Malware Detection

With the explosion in the popularity of smartphones over the previous decade, mobile malware appears to be unavoidable. Because Android is an open platform that is fast dominating other rival platforms (e.g. iOS) in the mobile smart device industry, Android malware has been much more widespread. Recent Android malware developers have more advanced capabilities when building their malicious apps, which make the apps themselves much more difficult to detect using conventional methods. In our paper, we proposed a hybrid machine learning classification and clustering algorithm to detect recent Android malware. The proposed algorithm performs better than the state-of-art algorithms with both F1-score and recall of 0.9944. More importantly, the top features returned by our algorithm clearly explain the important factors in the detection task. They can not only be used for enhanced Android malware detection but also quicker white-box analysis by means of more interpretable results.
OPTN/SRTR 2020 Annual Data Report: Heart

As we enter the third year of the new adult heart allocation policy, we are faced with the new challenges of the COVID‐19 pandemic. In 2020, new listings (adult and pediatric) decreased slightly, with 4000 new listings in 2020, compared with 4087 in 2019; however, the number of adult heart transplants performed continued to increase, to 3715 in 2020. The number of pediatric heart transplants declined from 509 in 2019 to 465 in 2020. One‐year and six‐month posttransplant mortality rates in adult recipients have increased slightly since 2015 but have not significantly changed over the past decade. Overall, posttransplant mortality rates for adult recipients were 7.4% at six months and 9.4% at one year for transplants in 2019, 14.0% at three years for transplants in 2017, and 19.1% at five years for transplants in 2015. Although shorter‐term posttransplant mortality rates have slightly increased, there has been a steady downward trend in longer‐term mortality. Mortality rates for pediatric recipients were 5.7% at six months and 8.1% at one year for transplants in 2019, 11.6% at three years for transplants in 2017, and 15.2% at five years for transplants in 2015.
A review and experimental evaluation of deep learning methods for MRI reconstruction

Following the success of deep learning in a wide range of applications, neural network-based machine-learning techniques have received significant interest for accelerating magnetic resonance imaging (MRI) acquisition and reconstruction strategies. A number of ideas inspired by deep learning techniques for computer vision and image processing have been successfully applied to nonlinear image reconstruction in the spirit of compressed sensing for accelerated MRI. Given the rapidly growing nature of the field, it is imperative to consolidate and summarize the large number of deep learning methods that have been reported in the literature, to obtain a better understanding of the field in general. This article provides an overview of the recent developments in neural-network based approaches that have been proposed specifically for improving parallel imaging. A general background and introduction to parallel MRI is also given from a classical view of k-space based reconstruction methods. Image domain based techniques that introduce improved regularizers are covered along with k-space based methods which focus on better interpolation strategies using neural networks. While the field is rapidly evolving with plenty of papers published each year, in this review, we attempt to cover broad categories of methods that have shown good performance on publicly available data sets. Limitations and open problems are also discussed and recent efforts for producing open data sets and benchmarks for the community are examined.