Pluvio: Assembly Clone Search for Out-of-domain Architectures and Libraries through Transfer Learning and Conditional Variational Information Bottleneck

The practice of code reuse is crucial in software development for a faster and more efficient development lifecycle. In reality, however, code reuse practices lack proper control, resulting in issues such as vulnerability propagation and intellectual property infringements. Assembly clone search, a critical shift-right defence mechanism, has been effective in identifying vulnerable code resulting from reuse in released executables. Recent studies on assembly clone search demonstrate a trend towards using machine learning-based methods to match assembly code variants produced by different toolchains. However, these methods are limited to what they learn from a small number of toolchain variants used in training, rendering them inapplicable to unseen architectures and their corresponding compilation toolchain variants. This paper presents the first study on the problem of assembly clone search with unseen architectures and libraries. We propose incorporating human common knowledge through large-scale pre-trained natural language models, in the form of transfer learning, into current learning-based approaches for assembly clone search. Transfer learning can aid in addressing the limitations of the existing approaches, as it can bring in broader knowledge from human experts in assembly code. We further address the sequence limit issue by proposing a reinforcement learning agent to remove unnecessary and redundant tokens. Coupled with a new Variational Information Bottleneck learning strategy, the proposed system minimizes the reliance on potential indicators of architectures and optimization settings, for a better generalization of unseen architectures. We simulate the unseen architecture clone search scenarios and the experimental results show the effectiveness of the proposed approach against the state-of-the-art solutions.Comment: 13 pages and 4 figures. 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

When bladder and brain collide: Is there a gender difference in the relationship between urinary incontinence, chronic depression, and anxiety?

In longitudinal and cross-sectional studies, depression and anxiety have been associated with urinary incontinence (UI) in women. However, this association has not been studied in men. Utilizing data from the 2008 Turkish Health Studies Survey conducted by the Turkish Statistical Institute, we analyzed 13,830 participants aged 15 years and above. We investigated the association of UI with psychological discomfort in both sexes using multivariable logistic regression. High psychological discomfort significantly correlated with UI in males (OR 2.30, 95% CI 1.43–3.71) and females (OR 2.78, 95% CI 1.80–4.29). Anxiety increased UI likelihood in females (OR 2.36, 95% CI 1.61–3.46) and males (OR 2.37, 95% CI 1.10–5.13). Depression related significantly to UI in females (OR 2.54, 95% CI 1.81–3.58) but not males (OR 1.63, 95% CI 0.71–3.76). Antidepressant and anxiolytic use was not significantly related to UI in either gender. Anxiety and psychological discomfort contribute to UI in both genders. While depression significantly correlates with UI in females, it does not show the same magnitude and significance in males. Antidepressant and anxiolytic use did not significantly influence the association. These findings underscore the psychological distress-UI link, advocating a holistic approach for managing UI in individuals with mental health conditions

Model-Driven End-to-End Learning for Integrated Sensing and Communication

Integrated sensing and communication (ISAC) is envisioned to be one of the pillars of 6G. However, 6G is also expected to be severely affected by hardware impairments. Under such impairments, standard model-based approaches might fail if they do not capture the underlying reality. To this end, data-driven methods are an alternative to deal with cases where imperfections cannot be easily modeled. In this paper, we propose a model-driven learning architecture for joint single- target multi-input multi-output (MIMO) sensing and multi-input single-output (MISO) communication. We compare it with a standard neural network approach under complexity constraints. Results show that under hardware impairments, both learning methods yield better results than the model-based standard baseline. If complexity constraints are further introduced, model- driven learning outperforms the neural-network-based approach. Model-driven learning also shows better generalization performance for new unseen testing scenario

End-to-End Learning for Integrated Sensing and Communication

Integrated sensing and communication (ISAC) aims to unify radar and communication systems through a combination of joint hardware, joint waveforms, joint signal design, and joint signal processing. At high carrier frequencies, where ISAC is expected to play a major role, joint designs are challenging due to several hardware limitations. Model-based approaches, while powerful and flexible, are inherently limited by how well the models represent reality. Under model deficit, data-driven methods can provide robust ISAC performance. We present a novel approach for data-driven ISAC using an auto-encoder (AE) structure. The approach includes the proposal of the AE architecture, a novel ISAC loss function, and the training procedure. Numerical results demonstrate the power of the proposed AE, in particular under hardware impairments

6G Positioning and Sensing Through the Lens of Sustainability, Inclusiveness, and Trustworthiness

6G promises a paradigm shift in which positioning and sensing are inherently integrated, enhancing not only the communication performance but also enabling location- and context-aware services. Historically, positioning and sensing have been viewed through the lens of cost and performance trade-offs, implying an escalated demand for resources, such as radio, physical, and computational resources, for improved performance. However, 6G goes beyond this traditional perspective to encompass a set of broader values, namely sustainability, inclusiveness, and trustworthiness. This paper aims to: (i) shed light on these important value indicators and their relationship with the conventional key performance indicators, and (ii) unveil the dual nature of 6G in relation to these key value indicators (i.e., ensuring operation according to the values and enabling services that affect the values)

Dipolar quantum solids emerging in a Hubbard quantum simulator

In quantum mechanical many-body systems, long-range and anisotropic interactions promote rich spatial structure and can lead to quantum frustration, giving rise to a wealth of complex, strongly correlated quantum phases. Long-range interactions play an important role in nature; however, quantum simulations of lattice systems have largely not been able to realize such interactions. A wide range of efforts are underway to explore long-range interacting lattice systems using polar molecules, Rydberg atoms, optical cavities, and magnetic atoms. Here, we realize novel quantum phases in a strongly correlated lattice system with long-range dipolar interactions using ultracold magnetic erbium atoms. As we tune the dipolar interaction to be the dominant energy scale in our system, we observe quantum phase transitions from a superfluid into dipolar quantum solids, which we directly detect using quantum gas microscopy with accordion lattices. Controlling the interaction anisotropy by orienting the dipoles enables us to realize a variety of stripe ordered states. Furthermore, by transitioning non-adiabatically through the strongly correlated regime, we observe the emergence of a range of metastable stripe-ordered states. This work demonstrates that novel strongly correlated quantum phases can be realized using long-range dipolar interaction in optical lattices, opening the door to quantum simulations of a wide range of lattice models with long-range and anisotropic interactions

Transgenic overexpression of VEGF-C induces weight gain and insulin resistance in mice

Obesity comprises great risks for human health, contributing to the development of other diseases such as metabolic syndrome, type 2 diabetes and cardiovascular disease. Previously, obese patients were found to have elevated serum levels of VEGF-C, which correlated with worsening of lipid parameters. We recently identified that neutralization of VEGF-C and -D in the subcutaneous adipose tissue during the development of obesity improves metabolic parameters and insulin sensitivity in mice. To test the hypothesis that VEGF-C plays a role in the promotion of the metabolic disease, we used K14-VEGF-C mice that overexpress human VEGF-C under control of the keratin-14 promoter in the skin and monitored metabolic parameters over time. K14-VEGF-C mice had high levels of VEGF-C in the subcutaneous adipose tissue and gained more weight than wildtype littermates, became insulin resistant and had increased ectopic lipid accumulation at 20 weeks of age on regular mouse chow. The metabolic differences persisted under high-fat diet induced obesity. These results indicate that elevated VEGF-C levels contribute to metabolic deterioration and the development of insulin resistance, and that blockade of VEGF-C in obesity represents a suitable approach to alleviate the development of insulin resistance.Peer reviewe