Miracle-dz/UniFAR-FTUs huggingface dataset:
Technical Report: Hardening Code Obfuscation Against Automated Attacks

Software obfuscation is a crucial technology to protect intellectual property and manage digital rights within our society. Despite its huge practical importance, both commercial and academic state-of-the-art obfuscation methods are vulnera-ble to a plethora of automated deobfuscation attacks, such as symbolic execution, taint analysis, or program synthesis. While several enhanced obfuscation techniques were recently proposed to thwart taint analysis or symbolic execution, they either impose a prohibitive runtime overhead or can be removed in an automated way (e. g., via compiler optimizations). In general, these techniques suffer from focusing on a single attack vector, allowing an attacker to switch to other, more effective techniques, such as program synthesis. In this work, we present L OKI , an approach for software obfuscation that is resilient against all known automated deobfuscation attacks. To this end, we use and efﬁciently combine multiple techniques, including a generic approach to synthesize formally veriﬁed expressions of arbitrary complexity. Contrary to state-of-the-art approaches that rely on a few hardcoded generation rules, our expressions are more diverse and harder to pattern match against. Even the most recent state-of-the-art research on Mixed-Boolean Arithmetic (MBA) deobfuscation fails to simplify them. Moreover, L OKI protects against previously unaccounted attack vectors such as program synthesis, for which it reduces the success rate to merely 19%. In a comprehensive evaluation, we show that our design incurs signiﬁcantly less overhead while providing a much stronger protection level compared to existing works.
TOD: GPU-accelerated Outlier Detection via Tensor Operations

Outlier detection (OD) is a key machine learning task for finding rare and deviant data samples, with many time-critical applications such as fraud detection and intrusion detection. In this work, we propose TOD, the first tensor-based system for efficient and scalable outlier detection on distributed multi-GPU machines. A key idea behind TOD is decomposing complex OD applications into a small collection of basic tensor algebra operators. This decomposition enables TOD to accelerate OD computations by leveraging recent advances in deep learning infrastructure in both hardware and software. Moreover, to deploy memory-intensive OD applications on modern GPUs with limited on-device memory, we introduce two key techniques. First, provable quantization speeds up OD computations and reduces its memory footprint by automatically performing specific floating-point operations in lower precision while provably guaranteeing no accuracy loss. Second, to exploit the aggregated compute resources and memory capacity of multiple GPUs, we introduce automatic batching , which decomposes OD computations into small batches for both sequential execution on a single GPU and parallel execution across multiple GPUs. TOD supports a diverse set of OD algorithms. Evaluation on 11 real-world and 3 synthetic OD datasets shows that TOD is on average 10.9X faster than the leading CPU-based OD system PyOD (with a maximum speedup of 38.9X), and can handle much larger datasets than existing GPU-based OD systems. In addition, TOD allows easy integration of new OD operators, enabling fast prototyping of emerging and yet-to-discovered OD algorithms.
Hyperspectral Image Denoising via Multi-modal and Double-weighted Tensor Nuclear Norm

—Hyperspectral images (HSIs) usually suffer from different types of pollution. This severely reduces the quality of HSIs and limits the accuracy of subsequent processing tasks. HSI denoising can be modeled as a low-rank tensor denoising problem. Tensor nuclear norm (TNN) induced by tensor singular value decomposition plays an important role in this problem. In this letter, we ﬁrst reconsider three inconspicuous but crucial phenomenons in TNN. In the Fourier transform domain of HSIs, different frequency slices (FS) contain different information; different singular values (SVs) of each FS also represent different information. The two physical phenomenons lie not only in the spectral mode but also in the spatial modes. Then based on them, we propose a multi-modal and double-weighted TNN. It can adaptively shrink the FS and SVs according to their physical meanings in all modes of HSIs. In the framework of the alternating direction method of multipliers, we design an effective alternating iterative strategy to optimize our proposed model. Denoised experiments on both synthetic and real HSI datasets demonstrate their superiority against related methods.
Self-Sovereign Identity: A Systematic Review, Mapping and Taxonomy

Self-Sovereign Identity (SSI) is an identity model centered on the user. The user maintains and controls their data in this model. When a service provider requests data from the user, the user sends it directly to the service provider, bypassing third-party intermediaries. Thus, SSI reduces identity providers’ involvement in the identification, authentication, and authorization, thereby increasing user privacy. Additionally, users can share portions of their personal information with service providers, significantly improving user privacy. This identity model has drawn the attention of researchers and organizations worldwide, resulting in an increase in both scientific and non-scientific literature on the subject. This study conducts a comprehensive and rigorous systematic review of the literature and a systematic mapping of theoretical and practical advances in SSI. We identified and analyzed evidence from reviewed materials to address four research questions, resulting in a novel SSI taxonomy used to categorize and review publications. Additionally, open challenges are discussed along with recommendations for future work.
Design of Planar and Conformal, Passive, Lossless Metasurfaces That Beamform

A general technique for synthesizing both planar and conformal beamforming metasurfaces is presented that utilizes full-wave modeling techniques and rapid optimization methods. The synthesized metasurfaces consist of a patterned metallic cladding supported by a finite-size grounded dielectric substrate. The metasurfaces are modeled using integral equations which accurately account for mutual coupling and the metasurface's finite dimensions. The synthesis technique consists of three phases: a direct solve phase to obtain an initial metasurface design with complex-valued impedances satisfying the desired far-field beam specifications, a subsequent optimization phase that converts the complex-valued impedances to purely reactive ones, and a final patterning phase to realize the purely reactive impedances as a patterned metallic cladding. The optimization phase introduces surface waves which facilitate passivity. The metasurface is optimized using gradient descent with a semi-analytic gradient obtained using the adjoint variable method. Three examples are presented: a low-profile directly-fed metasurface antenna with near perfect aperture efficiency, a scanned-beam reflectarray design with controlled sidelobes, and a conformal metasurface reflectarray. The far-field and near-field performance of the metasurfaces are verified and the bandwidth and loss tolerance of the metasurfaces are investigated.
Modified Thermal Balloon Endometrial Ablation for Treatment of Heavy Menstrual Bleeding

Objectives: The objective of this study was to determine the efficacy of modified thermal balloon ablation using Foley's catheter in the treatment of heavy menstrual bleeding (HMB). Materials and Methods: Twelve patients with HMB aged 35–55 years underwent modified thermal balloon ablation using Foley's catheter. Patients were selected after complete clinical evaluation and investigations. The procedure was undertaken in the operation theater under general anesthesia/intravenous sedation. Three cycles of modified thermal balloon ablation using Foley's catheter were performed to ablate the endometrium. The time given to each cycle was 7 min. All the cycles were performed in the same setting. The main outcome measures that were studied were reduction in the menstrual flow, the need for further treatment, and relief of dysmenorrhea if present. Outcome measure regarding reduction in menstrual flow was statistically analyzed using Fisher's exact test. Statistical significance was determined at a level of P < 0.05. Results: Eighty-two percent of patients experienced a reasonable reduction in menstrual blood flow at 3-month follow-up. Eighteen percent observed no change in bleeding pattern and needed further treatment after failure of the procedure. Forty-two percent of patients complained of minor side effects such as cramp lower abdominal pain and fever. Rupture of balloon during the procedure occurred in only one case (8%). Conclusion: Modified thermal balloon ablation with Foley's catheter can be a promising management of HMB in resource-poor settings. It is a cost-effective alternative to the original endometrial ablation techniques.
Med-SRNet: GAN-Based Medical Image Super-Resolution via High-Resolution Representation Learning

High-resolution (HR) medical imaging data provide more anatomical details of human body, which facilitates early-stage disease diagnosis. But it is challenging to get clear HR medical images because of the limiting factors, such as imaging systems, imaging environments, and human factors. This work presents a novel medical image super-resolution (SR) method via high-resolution representation learning based on generative adversarial network (GAN), namely, Med-SRNet. We use GAN as backbone of SR considering the advantages of GAN that can significantly reconstruct the visual quality of the images, and the high-frequency details of the images are more realistic in the image SR task. Furthermore, we employ the HR network (HRNet) in GAN generator to maintain the HR representations and repeatedly use multi-scale fusions to strengthen HR representations for facilitating SR. Moreover, we adopt deconvolution operations to recover high-quality HR representations from all the parallel lower resolution (LR) streams with the aim to yield richer aggregated features, instead of simple bilinear interpolation operations used in HRNetV2. When evaluated on a home-made medical image dataset and two public COVID-19 CT datasets, the proposed Med-SRNet outperforms other leading edge methods, which obtains higher peak signal to noise ratio (PSNR) values and structural similarity (SSIM) values, i.e., maximum improvement of 1.75 and minimum increase of 0.433 on the PSNR metric for “Brain” test sets under 8× and maximum improvement of 0.048 and minimum increase of 0.016 on the SSIM metric for “Lung” test sets under 8× compared with other methods.
Biological and ergonomic risks of radiology technicians in a conventional radiology service

Abstract Introduction: Radiology technicians are exposed to occupational risks in their daily work. Among these, there are biological and ergonomic risks. Objectives: To describe the biological and ergonomic risks in the work process of radiology technicians in a conventional radiology service. Methods: This was a qualitative, descriptive, and exploratory research. Data collection was conducted through semi-structured interviews with 12 radiology technicians, participant observation, and consensual validation with nine workers. Content analysis was based on the assumptions of the thematic analysis. Results: Exposure to body fluids, contamination of work instruments by biological agents, no disinfection, and no use of personal protective equipment were found to be biological risks. Excessive weight manipulation, inappropriate physical structure, night work, and work instruments that do conform to the principles of body mechanics were identified as ergonomic risks. Conclusions: The mentioned occupational risks are related to the work conditions of the health service and to inadequate body positioning of radiology technicians.
Multiple RNA cleavage pathways in mammalian cells

The sequence content of mouse L cell pre-rRNA was examined by RNA gel transfer and blot hybridization. Nuclear RNAs were separated by agarose gel electrophoresis, transferred to diazo-paper, and hybridized to twelve different restriction fragments that are compleaentary to various sections of 45S pre- rRNA. An abundant new 34S pre-rRNA and less abundant new 37S, 26S and 17S pre- rRNAs were detected. The presence of these new pre-rRNAs suggests the existence of at least two new pre-rRNA cleavage pathways. 34S and 26S pre- rRNAs were also detected in HeLa cells suggesting that these new cleavage path- ways are characteristic of maimualian cells. pre-rRNA.
Adaptive modeling of plate structures

The primary goal of the thesis is to provide some answers to the questions related to the key steps in the process of adaptive modeling of plates. Since the adaptivity depends on reliable error estimates, a large part of the thesis is related to the derivation of computational procedures for discretization error estimates as well as model error estimates. A practical comparison of some of the established discretization error estimates is made. Special attention is paid to what is called equilibrated residuum method, which has a potential to be used both for discretization error and model error estimates. It should be emphasized that the model error estimates are quite hard to obtain, in contrast to the discretization error estimates. The concept of model adaptivity for plates is in this work implemented on the basis of equilibrated residuum method and hierarchic family of plate finite element models.The finite elements used in the thesis range from thin plate finite elements to thick plate finite elements. The latter are based on a newly derived higher order plate theory, which includes through the thickness stretching. The model error is estimated by local element-wise computations. As all the finite elements, representing the chosen plate mathematical models, are re-derived in order to share the same interpolation bases, the difference between the local computations can be attributed mainly to the model error. This choice of finite elements enables effective computation of the model error estimate and improves the robustness of the adaptive modeling. Thus the discretization error can be computed by an independent procedure.Many numerical examples are provided as an illustration of performance of the derived plate elements, the derived discretization error procedures and the derived modeling error procedure. Since the basic goal of modeling in engineering is to produce an effective model, which will produce the most accurate results with the minimum input data, the need for the adaptive modeling will always be present. In this view, the present work is a contribution to the final goal of the finite element modeling of plate structures: a fully automatic adaptive procedure for the construction of an optimal computational model (an optimal finite element mesh and an optimal choice of a plate model for each element of the mesh) for a given plate structure.
A Stochastic Parameter Regression Approach for Time-Varying Relationship between Gold and Silver Prices

In this thesis, we studied the gold and silver relationship using stochastic-parameter regression models. We formulated their time-varying relationship as a state-space model and used the Kalman filter algorithm to estimate the stochastic regression parameters for gold and silver prices. The data set used in this thesis covers 31 years using the London fix prices between January 1969 and December 2000. The start date was selected as the first full year silver prices were included in the London fix prices. Our stochastic parameter regression model explained well the time-varying relationship between gold and silver prices. As a special case of the stochastic parameter regression model, we also fitted the random walk, the random walk with drift model and random coefficient model. The random walk with drift model appeared to have the closest fit with 12-month forecast errors minimal among those four models considered in this thesis.
Financial incentives for quitting smoking in pregnancy: Are they cost‐effective?

Aims To evaluate whether adding financial incentives to usual care is cost-effective in encouraging pregnant women to quit tobacco smoking, compared to usual care alone. Design Cost-effectiveness analysis (CEA) and cost-utility analysis (CUA) from a healthcare provider's perspective, embedded in the Smoking Cessation in Pregnancy Incentives Trial (CPIT III). Long-term analyses were conducted from the same perspective, using an existing Markov model over a lifetime horizon. Setting Seven maternity smoking cessation sites in Scotland, England and Northern Ireland in the United Kingdom. Participants In the short-term analysis CPIT III participants were assessed: women 16 years or older, self-reporting as smokers, less than 24 weeks pregnant and English speaking (n=944). The same population was used for the lifetime analysis, plus their infants. Measurements Costs include financial incentive vouchers and postage, cessation support and nicotine replacement therapy and neonatal stays. The outcome measure was biochemically verified quit rate for the CEA and quality adjusted life-years (QALY) for CUA. Costs are presented in 2020 GBP sterling. Data for the lifetime analysis came from the trial and was combined with data from published literature embedded in the model, reporting incremental cost per quitter and QALY. A 3.5% discount rate was applied. Findings The short-term incremental cost per quitter was 4,400GBP, uncertainty in QALY gains resulted in a cost per QALY of 150,000GBP. Results of sensitivity analyses confirm these results. The long-term analysis combined costs and outcomes for mother and infants, results show a cost saving of 37GBP (-35 to 106) and increase in QALYs of 0.171 (0.124 to 0.229). These findings indicate that, over a lifetime, financial incentives are cost saving and improve health outcomes. Conclusions Offering up to 400GBP financial incentives, in addition to usual care, to support pregnant women to stop smoking is cost-effective over a lifetime for mother and infants.
Environmental change and the rate of phenotypic plasticity

With rapid and less predictable environmental change emerging as the ‘new norm’, understanding how individuals tolerate environmental stress via plastic, often reversible changes to the phenotype (i.e., reversible phenotypic plasticity, RPP), remains a key issue in ecology. Here, we examine the potential for better understanding how organisms overcome environmental challenges within their own lifetimes by scrutinizing a somewhat overlooked aspect of RPP, namely the rate at which it can occur. Although recent advances in the field provide indication of the aspects of environmental change where RPP rates may be of particular ecological relevance, we observe that current theoretical models do not consider the evolutionary potential of the rate of RPP. Whilst recent theory underscores the importance of environmental predictability in determining the slope of the evolved reaction norm for a given trait (i.e., how much plasticity can occur), a hitherto neglected possibility is that the rate of plasticity might be a more dynamic component of this relationship than previously assumed. If the rate of plasticity itself can evolve, as empirical evidence foreshadows, rates of plasticity may have the potential to alter the level predictability in the environment as perceived by the organism and thus influence the slope of the evolved reaction norm. However, optimality in the rate of phenotypic plasticity, its evolutionary dynamics in different environments and influence of constraints imposed by associated costs remain unexplored and may represent fruitful avenues of exploration in future theoretical and empirical treatments of the topic. We conclude by reviewing published studies of RPP rates, providing suggestions for improving the measurement of RPP rates, both in terms of experimental design and in the statistical quantification of this component of plasticity.
Antigenic landscapes on Staphylococcus aureus pore-forming toxins reveal insights into specificity and cross-neutralization

ABSTRACT Staphylococcus aureus carries an exceptional repertoire of virulence factors that aid in immune evasion. Previous single-target approaches for S. aureus-specific vaccines and monoclonal antibodies (mAbs) have failed in clinical trials due to the multitude of virulence factors released during infection. Emergence of antibiotic-resistant strains demands a multi-target approach involving neutralization of different, non-overlapping pathogenic factors. Of the several pore-forming toxins that contribute to S. aureus pathogenesis, efforts have largely focused on mAbs that neutralize α-hemolysin (Hla) and target the receptor-binding site. Here, we isolated two anti-Hla and three anti-Panton-Valentine Leukocidin (LukSF-PV) mAbs, and used a combination of hydrogen deuterium exchange mass spectrometry (HDX-MS) and alanine scanning mutagenesis to delineate and validate the toxins’ epitope landscape. Our studies identified two novel, neutralizing epitopes targeted by 2B6 and CAN6 on Hla that provided protection from hemolytic activity in vitro and showed synergy in rodent pneumonia model against lethal challenge. Of the anti-LukF mAbs, SA02 and SA131 showed specific neutralization activity to LukSF-PV while SA185 showed cross-neutralization activity to LukSF-PV, γ-hemolysin HlgAB, and leukotoxin ED. We further compared these antigen-specific mAbs to two broadly neutralizing mAbs, H5 (targets Hla, LukSF-PV, HlgAB, HlgCB, and LukED) and SA185 (targeting LukSF-PV, HlgAB, and LukED), and identified molecular level markers for broad-spectrum reactivity among the pore-forming toxins by HDX-MS. To further underscore the need to target the cross-reactive epitopes on leukocidins for the development of broad-spectrum therapies, we annotated Hla sequences isolated from patients in multiple countries for genomic variations within the perspective of our defined epitopes.
Prognostic Value of Pretreatment Skeletal Muscle Mass Index in Esophageal Cancer Patients: A Meta-Analysis

Abstract The prognostic role of pretreatment skeletal muscle mass index (SMI) has been verified in several types of cancers. However, it remains unclear whether pretreatment SMI is a valuable prognostic indicator in esophageal cancer. The aim of the present study was to identify the prognostic value of pretreatment SMI in esophageal cancer. PubMed, EMBASE and Web of Science databases were searched for relevant studies up to November 10, 2021. The hazard ratios (HRs) with 95% confidence intervals (CIs) were combined to assess the association of pretreatment SMI with the overall survival (OS) and disease-free survival (DFS) of esophageal cancer patients. In total, 17 studies involving 2441 patients were included in this meta-analysis. The pooled results demonstrated that a lower SMI was significantly associated with poorer OS (HR = 1.18, 95% CI: 1.09–1.27, P < 0.001) and DFS (HR = 1.78, 95% CI: 1.10–2.88, P = 0.019). In addition, subgroup analysis based on treatment (surgery vs. nonsurgery), tumor type (squamous cell carcinoma vs. adenocarcinoma) and cutoff value of SMI showed similar results. The present findings demonstrated that pretreatment SMI is an independent prognostic indicator for esophageal cancer patients, and patients with a lower pretreatment SMI are more likely to have a worse prognosis. However, additional prospective high-quality studies are needed to verify the above findings.