UniASM: Binary Code Similarity Detection without Fine-tuning

Binary code similarity detection (BCSD) is widely used in various binary analysis tasks such as vulnerability search, malware detection, clone detection, and patch analysis. Recent studies have shown that the learning-based binary code embedding models perform better than the traditional feature-based approaches. In this paper, we proposed a novel transformer-based binary code embedding model, named UniASM, to learn representations of the binary functions. We designed two new training tasks to make the spatial distribution of the generated vectors more uniform, which can be used directly in BCSD without any fine-tuning. In addition, we proposed a new tokenization approach for binary functions, increasing the token's semantic information while mitigating the out-of-vocabulary (OOV) problem. The experimental results show that UniASM outperforms state-of-the-art (SOTA) approaches on the evaluation dataset. We achieved the average scores of recall@1 on cross-compilers, cross-optimization-levels and cross-obfuscations are 0.72, 0.63, and 0.77, which is higher than existing SOTA baselines. In a real-world task of known vulnerability searching, UniASM outperforms all the current baselines.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Inverse problem with continuous parameters for solar oscillations.

Solar seismology is a relatively new field in solar astrophysics. It provides us a way of "looking" into the deep interior of the Sun. The goal of solar seismology is to derive information about the internal structure of the Sun from the observed properties of solar oscillations. This is called the inverse problem of solar seismology. This project is to explore a new set of methods and algorithms to solve the inverse problem. The continuous orthonormalization (CON) method and the adjoint method adapted by Rosenwald can be used to compute the eigenfrequency sensitivities to the solar structure parameters in a very efficient way. In this work, the computational algorithm for using these methods has been modified and improved. Continuous parametrization for the internal structure of the Sun is introduced. The solar interior is subdivided into sections, and polynomial fits are applied to the solar structure parameters in each section. The eigenfrequency sensitivities to these polynomial coefficients--the continuous parameters--are computed. These sensitivities can be used to predict the change in solar eigenfrequencies when the structure parameters are perturbed (with the necessary physical constraints satisfied). The inverse problem for the solar internal structure is formulated by using these sensitivities. The generalized inverse technique is used to solve the nonlinear inverse problem in an iterative process. Observed data of low-degree g-modes have been used for a preliminary inversion. The nonlinearity of the solar seismic inverse problem is demonstrated. A nonlinear inversion process has been successfully performed and the results analysed. The inversion results indicate that the standard solar model is a good approximation of the real Sun. Only relatively small perturbations to the model are needed to explain the frequency deviations between observation and theory

Emergency Relief Routing Models for Injured Victims Considering Equity and Priority.

International audienceIn humanitarian aid, emergency relief routing optimization needs to consider equity and priority issues. Different from the general path selection optimization, this paper builds two models differentiated by considerations on the identical and diverse injured degrees, where the relative deprivation cost is proposed as one of the decision-making objectives to emphasize equity, and the in-transit tolerable suffering duration is employed as a type of time window constraint to highlight rescue priority. After proving the NP-hardness of our models, we design a meta-heuristic algorithm based on the ant colony optimization to accelerate the convergence speed, which is more efficient than the commonly-used genetic algorithm. Taking 2017 Houston Flood as a case, we find results by performing the experimental comparison and sensitivity analysis. First, our models have advantages in the fairness of human sufferings mitigation. Second, the role of the in-transit tolerable suffering time window cannot be ignored in humanitarian relief solutions. Various measures are encouraged to extend this type of time window for achieving better emergency relief. Finally, our proposed hybrid transportation strategy aiming at diverse injured degrees stably outperforms the separated strategy, both in operational cost control and psychological sufferings alleviation, especially when relief supplies are limited.<br/

Emergency Relief Routing Models for Injured Victims Considering Equity and Priority.

International audienceIn humanitarian aid, emergency relief routing optimization needs to consider equity and priority issues. Different from the general path selection optimization, this paper builds two models differentiated by considerations on the identical and diverse injured degrees, where the relative deprivation cost is proposed as one of the decision-making objectives to emphasize equity, and the in-transit tolerable suffering duration is employed as a type of time window constraint to highlight rescue priority. After proving the NP-hardness of our models, we design a meta-heuristic algorithm based on the ant colony optimization to accelerate the convergence speed, which is more efficient than the commonly-used genetic algorithm. Taking 2017 Houston Flood as a case, we find results by performing the experimental comparison and sensitivity analysis. First, our models have advantages in the fairness of human sufferings mitigation. Second, the role of the in-transit tolerable suffering time window cannot be ignored in humanitarian relief solutions. Various measures are encouraged to extend this type of time window for achieving better emergency relief. Finally, our proposed hybrid transportation strategy aiming at diverse injured degrees stably outperforms the separated strategy, both in operational cost control and psychological sufferings alleviation, especially when relief supplies are limited.<br/

A Novel Virus Capable of Intelligent Program Infection through Software Framework Function Recognition

Viruses are one of the main threats to the security of today’s cyberspace. With the continuous development of virus and artificial intelligence technologies in recent years, the intelligentization of virus technology has become a trend. It is of urgent significance to study and combat intelligent viruses. In this paper, we design a new type of confirmatory virus from the attacker’s perspective that can intelligently infect software frameworks. We aim for structural software as the target and use BCSD (binary code similarity detection) to identify the framework. By incorporating a software framework functional structure recognition model in the virus, the virus is enabled to intelligently recognize software framework functions in executable files. This paper evaluates the BCSD model that is suitable for a virus to carry and constructs a lightweight BCSD model with a knowledge distillation technique. This research proposes a software framework functional structure recognition algorithm, which effectively reduces the recognition precision’s dependence on the BCSD model. Finally, this study discusses the next researching direction of intelligent viruses. This paper aims to provide a reference for the research of detection technology for possible intelligent viruses. Consequently, focused and effective defense strategies could be proposed and the technical system of malware detection could be reinforced

Rheumatic Symptoms Following Coronavirus Disease 2019 (COVID-19): A Chronic Post–COVID-19 Condition

Background!#!Detailed characteristics of rheumatic symptoms of coronavirus disease 2019 (COVID-19) were still unknown. We aim to investigate the proportions, characteristics, and risk factors of this condition.!##!Methods!#!In this prospective, longitudinal cohort study, discharged patients with COVID-19 were interviewed face-to-face at 12 months after symptom onset. Rheumatic symptoms following COVID-19 included newly occurring joint pain and/or joint swelling. The risk factors of developing rheumatic symptoms were identified by multivariable logistic regression analysis.!##!Results!#!In total, 1296 of 2469 discharged patients with COVID-19 were enrolled in this study. Among them, 160 (12.3% [95% confidence interval {CI}, 10.6%-14.3%]) suffered from rheumatic symptoms following COVID-19 at 12-month follow-up. The most frequently involved joints were the knee joints (38%), followed by hand (25%) and shoulder (19%). Rheumatic symptoms were independent of the severity of illness and corticosteroid treatment during the acute phase, while elderly age (odds ratio [OR], 1.22 [95% CI, 1.06-1.40]) and female sex (OR, 1.58 [95% CI, 1.12-2.23]) were identified as the risk factors for this condition.!##!Conclusions!#!Our investigation showed a considerable proportion of rheumatic symptoms following COVID-19 in discharged patients, which highlights the need for continuing attention. Notably, rheumatic symptoms following COVID-19 were independent of the severity of illness and corticosteroid treatment during the acute phase

Droplet Microfluidics for the Production of Microparticles and Nanoparticles

Droplet microfluidics technology is recently a highly interesting platform in material fabrication. Droplets can precisely monitor and control entire material fabrication processes and are superior to conventional bulk techniques. Droplet production is controlled by regulating the channel geometry and flow rates of each fluid. The micro-scale size of droplets results in rapid heat and mass-transfer rates. When used as templates, droplets can be used to develop reproducible and scalable microparticles with tailored sizes, shapes and morphologies, which are difficult to obtain using traditional bulk methods. This technology can revolutionize material processing and application platforms. Generally, microparticle preparation methods involve three steps: (1) the formation of micro-droplets using a microfluidics generator; (2) shaping the droplets in micro-channels; and (3) solidifying the droplets to form microparticles. This review discusses the production of microparticles produced by droplet microfluidics according to their morphological categories, which generally determine their physicochemical properties and applications

The deubiquitinating enzyme UCHL1 is a favorable prognostic marker in neuroblastoma as it promotes neuronal differentiation

Abstract Background Neuroblastoma (NB) is the most common pediatric solid tumor that originates from neural crest-derived sympathoadrenal precursor cells that are committed to development of sympathetic nervous system. The well differentiated histological phenotype of NB tumor cells has been reportedly associated with favorable patient outcome. Retinoic acid (RA) can effectively induce NB cell differentiation, thereby being used in the clinic as a treatment agent for inducing the differentiation of high-risk NB. However, the underlying molecular mechanisms of regulating differentiation remain elusive. Methods The correlation between clinical characteristics, survival and the deubiquitinating enzyme ubiquitin C-terminal hydrolase 1 (UCHL1) expression were assessed using a neuroblastic tumor tissue microarray, and then validated in three independent patient datasets. The different expression of UCHL1 in ganglioneuroblastoma, ganglioneuroma and NB was detected by immunohistochemistry, mass spectra and immunoblotting analysis, and the correlation between UCHL1 expression and the differentiated histology was analyzed, which was also validated in three independent patient datasets. Furthermore, the roles of UCHL1 in NB cell differentiation and proliferation and the underlying mechanisms were studied by using short hairpin RNA and its inhibitor LDN57444 in vitro. Results Based on our neuroblastic tumor tissue microarrays and three independent validation datasets (Oberthuer, Versteeg and Seeger), we identified that UCHL1 served as a prognostic marker for better clinical outcome in NB. We further demonstrated that high UCHL1 expression was associated with NB differentiation, indicated by higher UCHL1 expression in ganglioneuroblastomas/ganglioneuromas and well-differentiated NB than poorly differentiated NB, and the positive correlation between UCHL1 and differentiation markers. As expected, inhibiting UCHL1 by knockdown or LDN57444 could significantly inhibit RA-induced neural differentiation of NB tumor cells, characterized by decreased neurite outgrowth and neural differentiation markers. This effect of UCHL1 was associated with positively regulating RA-induced AKT and ERK1/2 signaling activation. What’s more, knockdown of UCHL1 conferred resistance to RA-induced growth arrest. Conclusion Our findings identify a pivotal role of UCHL1 in NB cell differentiation and as a prognostic marker for survival in patients with NB, potentially providing a novel therapeutic target for NB

Identification of MYCN non-amplified neuroblastoma subgroups points towards molecular signatures for precision prognosis and therapy stratification

Background: Despite the extensive study of MYCN-amplified neuroblastomas, there is a significant unmet clinical need in MYCN non-amplified cases. In particular, the extent of heterogeneity within the MYCN non-amplified population is unknown.Methods: A total of 1566 samples from 16 datasets were identified in Gene Expression Omnibus (GEO) and ArrayExpress. Characterisation of the subtypes was analysed by ConsensusClusterPlus. Independent predictors for subgrouping were constructed from the single sample predictor based on the multiclassPairs package. Findings were verified using immunohistochemistry and CIBERSORTx analysis.Results: We demonstrate that MYCN non-amplified neuroblastomas are heterogeneous and can be classified into 3 subgroups based on their transcriptional signatures. Within these groups, subgroup_2 has the worst prognosis and this group shows a 'MYCN' signature that is potentially induced by the overexpression of Aurora Kinase A (AURKA); whilst subgroup_3 is characterised by an 'inflamed' gene signature. The clinical implications of this subtype classification are significant, as each subtype demonstrates a unique prognosis and vulnerability to investigational therapies. A total of 420 genes were identified as independent subgroup predictors with average balanced accuracy of 0.93 and 0.84 for train and test datasets, respectively.Conclusion: We propose that transcriptional subtyping may enhance precision prognosis and therapy stratification for patients with MYCN non-amplified neuroblastomas