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Dysregulation of the tumour suppressor gene Adenomatous Polyposis Coli (APC) is a canonical step in colorectal cancer development. Curiously, most colorectal tumours carry biallelic mutations that result in only partial loss of APC function, suggesting that a "just-right" level of APC inactivation, and hence Wnt signalling, provides the optimal conditions for tumorigenesis. Mutational processes act variably across the APC gene, which could contribute to the bias against complete APC inactivation. Thus the selective consequences of partial APC loss are unclear. Here we propose a mathematical model to quantify the tumorigenic effect of biallelic APC genotypes, controlling for somatic mutational processes. Analysing sequence data from >2500 colorectal cancers, we find that APC genotypes resulting in partial protein function confer about 50 times higher probability of progressing to cancer compared to complete APC inactivation. The optimal inactivation level varies with anatomical location and additional mutations of Wnt pathway regulators. We use this context dependency to assess the regulatory effect of secondary Wnt drivers in combination with APC in vivo, and provide evidence that mutant AMER1 combines with APC genotypes that lead to relatively low Wnt. The fitness landscape of APC inactivation is consistent across microsatellite unstable and POLE-deficient colorectal cancers and tumours in patients with Familial Adenomatous Polyposis, suggesting a general "just-right" optimum, and pointing to Wnt hyperactivation as a potential cancer vulnerability., Comment: Main: 32 pages, 7 figures Supplements: 17 pages, 7 figures

For matrix model and QFT, we discuss how dual gravitational geometry emerges from matrix degrees of freedom (specifically, adjoint scalars in super Yang-Mills theory) and how operator algebra that describes an arbitrary region of the bulk geometry can be constructed. We pay attention to the subtle difference between the notions of wave packets that describe low-energy excitations: QFT wave packet associated with the spatial dimensions of QFT, matrix wave packet associated with the emergent dimensions from matrix degrees of freedom, and bulk wave packet which is a combination of QFT and matrix wave packets. In QFT, there is an intriguing interplay between QFT wave packet and matrix wave packet that connects quantum entanglement and emergent geometry. We propose that the bulk wave packet is the physical object in QFT that describes the emergent geometry from entanglement. This proposal sets a unified view on two seemingly different mechanisms of holographic emergent geometry: one based on matrix eigenvalues and the other based on quantum entanglement. Further intuition comes from the similarity to a traversable wormhole discussed as the dual description of the coupled SYK model by Maldacena and Qi: the bulk can be seen as an eternal traversable wormhole connecting boundary regions., Comment: 39 pages

The recently discovered ferroelectric nematic (NF) liquid crystals (LC) have been reported to show an extraordinarily large value of the real part of the dielectric constant at low frequencies. However, it was argued by Clark et al in Physical Review Research 6, 013195 (2024) that what was measured was the capacitance of the insulating layer at LC/electrode surface and not that of the liquid crystal. Here we describe the results of dielectric spectroscopy measurements of an NF material in cells with variable thickness of the insulating layers. Our measurements quantitatively verify the model by Clark et al. Additionally, our measurements in cells with bare conducting indium tin oxide surface provide a crude estimate of epsilon perpendicular~100 in the NF phase., Comment: 17 pages, 6 figures, Presented in Workshop on Ferroelectric Nematic Liquid Crystals in Ljubljana

We propose a general approach to find an optimal representation of a quantum many body wave function for a given error margin via global fermionic mode optimization. The stationary point on a fixed rank matrix product state manifold is obtained via a joint optimization on the Grassman manifold [Phys. Rev. Lett. 117, 210402] together with swap gates controlled permutations. The minimization of the global quantity, the block entropy area, guarantees that the method fulfills all criteria with respect to partial derivatives. Numerical results via large scale density matrix renormalization group simulations on strongly correlated molecular systems and two-dimensional fermionic lattice models are discussed., Comment: 5 pages, 4 figures

For complex nonlinear systems, it is challenging to design algorithms that are fast, scalable, and give an accurate approximation of the stability region. This paper proposes a sampling-based approach to address these challenges. By extending the parametrization of quadratic Lyapunov functions with the system dynamics and formulating an $\ell_1$ optimization to maximize the invariant set over a grid of the state space, we arrive at a computationally efficient algorithm that estimates the domain of attraction (DOA) of nonlinear systems accurately by using only linear programming. The scalability of the Lyapunov function synthesis is further improved by combining the algorithm with ADMM-based parallelization. To resolve the inherent approximative nature of grid-based techniques, a small-scale nonlinear optimization is proposed. The performance of the algorithm is evaluated and compared to state-of-the-art solutions on several numerical examples.

The number of people accessing online services is increasing day by day, and with new users, comes a greater need for effective and responsive cyber-security. Our goal in this study was to find out if there are common patterns within the most widely used programming languages in terms of security issues and fixes. In this paper, we showcase some statistics based on the data we extracted for these languages. Analyzing the more popular ones, we found that the same security issues might appear differently in different languages, and as such the provided solutions may vary just as much. We also found that projects with similar sizes can produce extremely different results, and have different common weaknesses, even if they provide a solution to the same task. These statistics may not be entirely indicative of the projects' standards when it comes to security, but they provide a good reference point of what one should expect. Given a larger sample size they could be made even more precise, and as such a better understanding of the security relevant activities within the projects written in given languages could be achieved.

Bug prediction aims at finding source code elements in a software system that are likely to contain defects. Being aware of the most error-prone parts of the program, one can efficiently allocate the limited amount of testing and code review resources. Therefore, bug prediction can support software maintenance and evolution to a great extent. In this paper, we propose a function level JavaScript bug prediction model based on static source code metrics with the addition of a hybrid (static and dynamic) code analysis based metric of the number of incoming and outgoing function calls (HNII and HNOI). Our motivation for this is that JavaScript is a highly dynamic scripting language for which static code analysis might be very imprecise; therefore, using a purely static source code features for bug prediction might not be enough. Based on a study where we extracted 824 buggy and 1943 non-buggy functions from the publicly available BugsJS dataset for the ESLint JavaScript project, we can confirm the positive impact of hybrid code metrics on the prediction performance of the ML models. Depending on the ML algorithm, applied hyper-parameters, and target measures we consider, hybrid invocation metrics bring a 2-10% increase in model performances (i.e., precision, recall, F-measure). Interestingly, replacing static NOI and NII metrics with their hybrid counterparts HNOI and HNII in itself improves model performances; however, using them all together yields the best results.

The rapid rise of cyber-crime activities and the growing number of devices threatened by them place software security issues in the spotlight. As around 90% of all attacks exploit known types of security issues, finding vulnerable components and applying existing mitigation techniques is a viable practical approach for fighting against cyber-crime. In this paper, we investigate how the state-of-the-art machine learning techniques, including a popular deep learning algorithm, perform in predicting functions with possible security vulnerabilities in JavaScript programs. We applied 8 machine learning algorithms to build prediction models using a new dataset constructed for this research from the vulnerability information in public databases of the Node Security Project and the Snyk platform, and code fixing patches from GitHub. We used static source code metrics as predictors and an extensive grid-search algorithm to find the best performing models. We also examined the effect of various re-sampling strategies to handle the imbalanced nature of the dataset. The best performing algorithm was KNN, which created a model for the prediction of vulnerable functions with an F-measure of 0.76 (0.91 precision and 0.66 recall). Moreover, deep learning, tree and forest based classifiers, and SVM were competitive with F-measures over 0.70. Although the F-measures did not vary significantly with the re-sampling strategies, the distribution of precision and recall did change. No re-sampling seemed to produce models preferring high precision, while re-sampling strategies balanced the IR measures.

The popularity and wide adoption of JavaScript both at the client and server side makes its code analysis more important than ever before. Most of the algorithms for vulnerability analysis, coding issue detection, or type inference rely on the call graph representation of the underlying program. Despite some obvious advantages of dynamic analysis, static algorithms should also be considered for call graph construction as they do not require extensive test beds for programs and their costly execution and tracing. In this paper, we systematically compare five widely adopted static algorithms - implemented by the npm call graph, IBM WALA, Google Closure Compiler, Approximate Call Graph, and Type Analyzer for JavaScript tools - for building JavaScript call graphs on 26 WebKit SunSpider benchmark programs and 6 real-world Node.js modules. We provide a performance analysis as well as a quantitative and qualitative evaluation of the results. We found that there was a relatively large intersection of the found call edges among the algorithms, which proved to be 100 precise. However, most of the tools found edges that were missed by all others. ACG had the highest precision followed immediately by TAJS, but ACG found significantly more call edges. As for the combination of tools, ACG and TAJS together covered 99% of the found true edges by all algorithms, while maintaining a precision as high as 98%. Only two of the tools were able to analyze up-to-date multi-file Node.js modules due to incomplete language features support. They agreed on almost 60% of the call edges, but each of them found valid edges that the other missed.

Newer technologies - programming languages, environments, libraries - change very rapidly. However, various internal and external constraints often prevent projects from quickly adopting to these changes. Customers may require specific platform compatibility from a software vendor, for example. In this work, we deal with such an issue in the context of the C++ programming language. Our industrial partner is required to use SDKs that support only older C++ language editions. They, however, would like to allow their developers to use the newest language constructs in their code. To address this problem, we created a source code transformation framework to automatically backport source code written according to the C++11 standard to its functionally equivalent C++03 variant. With our framework developers are free to exploit the latest language features, while production code is still built by using a restricted set of available language constructs. This paper reports on the technical details of the transformation engine, and our experiences in applying it on two large industrial code bases and four open-source systems. Our solution is freely available and open-source.

Here we present a continuation of the work that was based on video observations of the Tau Her 2022 outburst from the McDonald Observatory, Texas, US. On the night of the maximum in 2022 we detected 626 individual Tau Her meteors, for which we estimated photovisual magnitudes and analysed their distribution on the sky to determine the radiant position in an innovative way. The derived population index is $5.56 \pm 1.83$, while the radiant position for the mid-time of the observations is: $\mathrm{RA} = 209.71 ^\circ \pm 1.01 ^\circ$, $\mathrm{Dec} = 27.73 ^\circ \pm 0.07 ^\circ$. Both measurements are in good agreement with other results in the literature, although our population index seems to be higher than most of the published values. We speculate this might be due to the higher sensitivity of our equipment to fainter meteors.

As part of an intensive effort to observe the predicted 2022 Tau Herculids outburst, we recorded almost 800 individual meteor streaks on May 30th and 31st, 2022, using a high-sensitivity Sony~$\alpha$7 camera. The video recordings were obtained under perfect conditions at the McDonald Observatory, Texas, USA. The meteor sample is dominated by the predicted Tau Herculids shower, however, we also noted significant activity of sporadic meteors and other possible weak showers. We found that the time of the maximum activity matched very well the predictions, while we note the large fraction of faint meteors that were not detectable visually. We determined the radiant, and the time evolution of the activities and currently we are working on the determination of the brightness statistics.

The emergence of advanced neural networks has opened up new ways in automated code generation from conceptual models, promising to enhance software development processes. This paper presents a preliminary evaluation of GPT-4-Vision, a state-of-the-art deep learning model, and its capabilities in transforming Unified Modeling Language (UML) class diagrams into fully operating Java class files. In our study, we used exported images of 18 class diagrams comprising 10 single-class and 8 multi-class diagrams. We used 3 different prompts for each input, and we manually evaluated the results. We created a scoring system in which we scored the occurrence of elements found in the diagram within the source code. On average, the model was able to generate source code for 88% of the elements shown in the diagrams. Our results indicate that GPT-4-Vision exhibits proficiency in handling single-class UML diagrams, successfully transforming them into syntactically correct class files. However, for multi-class UML diagrams, the model's performance is weaker compared to single-class diagrams. In summary, further investigations are necessary to exploit the model's potential completely.

We investigate the emergent factorization of Hilbert space in the low-energy description of matrix models, addressing key aspects of the black hole information paradox. We examine the collective description for the low-energy sector of $SU(N)$ matrix model, characterized by a factorized Hilbert space composed of a finite number of boxes and anti-boxes. This factorization leads us to examine the emergence of thermofield dynamics (TFD) state in the low energy sector from a fine-tuned state. In addition, we study the collective Hamiltonian of the $U(N)$ matrix model for the semi-classical description of "particle-hole" fluctuations around a background Young tableau. Our investigation of these matrix models elucidates a concrete mechanism for constructing the truncated algebra of accessible observables, thereby facilitating an understanding of black hole complementarity. In the context of the black hole information paradox, we discuss the origin of the island appearing inside the black hole and provide a reinterpretation of the recent proposal -- the holography of information., Comment: 9 pages, 9 figures; v2. reference added, typo corrected, minor revision

A new type of non-invasive deep-brain stimulation is conceived and demonstrated by computer simulations. The process is based on spatio-temporal Fourier synthesis using multiple electrode pairs with sinusoidal current drive to limit skin sensations and concentrate the stimulus power to a small spatial volume and into large rare spikes in the times domain, while the signal power at the skin is steady and small. Exotic time signals are also shown, such as the cases of high-frequency prime harmonics, quasi-random and chirping stimulations. The first one is able to generate sharp spikes with low frequency while its carriers are high-frequency harmonics that easily conducts via the skin and brain tissue. Open questions are, among others, the best shapes and timing of spikes. The answers require experimental tests and explorations in animal models and human subjects., Comment: Accepted for publication in Fluctuation and Noise Letters. US patent was filed

In this paper we present YOLOX-ViT, a novel object detection model, and investigate the efficacy of knowledge distillation for model size reduction without sacrificing performance. Focused on underwater robotics, our research addresses key questions about the viability of smaller models and the impact of the visual transformer layer in YOLOX. Furthermore, we introduce a new side-scan sonar image dataset, and use it to evaluate our object detector's performance. Results show that knowledge distillation effectively reduces false positives in wall detection. Additionally, the introduced visual transformer layer significantly improves object detection accuracy in the underwater environment. The source code of the knowledge distillation in the YOLOX-ViT is at https://github.com/remaro-network/KD-YOLOX-ViT.

Information propagation in the one-dimensional infinite temperature Hubbard model with a dissipative particle sink at the end of a semi-infinite chain is studied. In the strongly interacting limit, the two-site mutual information and the operator entanglement entropy exhibit a rich structure with two propagating information fronts and superimposed interference fringes. A classical reversible cellular automaton model quantitatively captures the transport and the slow, classical part of the correlations, but fails to describe the rapidly propagating information jet. The fast quantum jet resembles coherent free particle propagation, with the accompanying long-ranged interference fringes that are exponentially damped by short-ranged spin correlations in the many-body background., Comment: 5 pages, 3 figures, supplemental material included

We present statistical analysis of visual meteor data taken with networks of meteor cameras operating in Hungary between 2020 and 2023. We use three different camera systems: a set of traditional MetRec-based video cameras, a self-developed automated DSLR camera system and a network of newly installed AllSky7 camera stations. Similarities and differences between the data produced by the three systems, aimed at recording different types of meteor phenomena, are presented and discussed., Comment: accepted for publication in WGN, the Journal of the International Meteor Organization (IMO)

The recent advance in autonomous underwater robotics facilitates autonomous inspection tasks of offshore infrastructure. However, current inspection missions rely on predefined plans created offline, hampering the flexibility and autonomy of the inspection vehicle and the mission's success in case of unexpected events. In this work, we address these challenges by proposing a framework encompassing the modeling and verification of mission plans through Behavior Trees (BTs). This framework leverages the modularity of BTs to model onboard reactive behaviors, thus enabling autonomous plan executions, and uses BehaVerify to verify the mission's safety. Moreover, as a use case of this framework, we present a novel AI-enabled algorithm that aims for efficient, autonomous pipeline camera data collection. In a simulated environment, we demonstrate the framework's application to our proposed pipeline inspection algorithm. Our framework marks a significant step forward in the field of autonomous underwater robotics, promising to enhance the safety and success of underwater missions in practical, real-world applications. https://github.com/remaro-network/pipe_inspection_mission

This paper presents SubPipe, an underwater dataset for SLAM, object detection, and image segmentation. SubPipe has been recorded using a \gls{LAUV}, operated by OceanScan MST, and carrying a sensor suite including two cameras, a side-scan sonar, and an inertial navigation system, among other sensors. The AUV has been deployed in a pipeline inspection environment with a submarine pipe partially covered by sand. The AUV's pose ground truth is estimated from the navigation sensors. The side-scan sonar and RGB images include object detection and segmentation annotations, respectively. State-of-the-art segmentation, object detection, and SLAM methods are benchmarked on SubPipe to demonstrate the dataset's challenges and opportunities for leveraging computer vision algorithms. To the authors' knowledge, this is the first annotated underwater dataset providing a real pipeline inspection scenario. The dataset and experiments are publicly available online at https://github.com/remaro-network/SubPipe-dataset

We present a hybrid numerical approach to simulate quantum many body problems on two spatial dimensional quantum lattice models via the non-Abelian ab initio version of the density matrix renormalization group method on state-of-the-art high performance computing infrastructures. We demonstrate for the two dimensional spinless fermion model and for the Hubbard model on torus geometry that altogether several orders of magnitude in computational time can be saved by performing calculations on an optimized basis and by utilizing hybrid CPU-multiGPU parallelization. At least an order of magnitude reduction in computational complexity results from mode optimization, while a further order of reduction in wall time is achieved by massive parallelization. Our results are measured directly in FLOP and seconds. A detailed scaling analysis of the obtained performance as a function of matrix ranks and as a function of system size up to $12\times 12$ lattice topology is discussed. Our CPU-multiGPU model also tremendously accelerates the calculation of the one- and two-particle reduced density matrices, which can be used to construct various order parameters and trace quantum phase transitions with high fidelity., Comment: 12 pages, 11 figures

In this paper, we extend an available neural network verification technique to support a wider class of piece-wise linear activation functions. Furthermore, we extend the algorithms, which provide in their original form exact respectively over-approximative results for bounded input sets represented as start sets, to allow also unbounded input set. We implemented our algorithms and demonstrated their effectiveness in some case studies., Comment: In Proceedings FMAS 2023, arXiv:2311.08987

Neural Cellular Automata (NCA) are a powerful combination of machine learning and mechanistic modelling. We train NCA to learn complex dynamics from time series of images and PDE trajectories. Our method is designed to identify underlying local rules that govern large scale dynamic emergent behaviours. Previous work on NCA focuses on learning rules that give stationary emergent structures. We extend NCA to capture both transient and stable structures within the same system, as well as learning rules that capture the dynamics of Turing pattern formation in nonlinear Partial Differential Equations (PDEs). We demonstrate that NCA can generalise very well beyond their PDE training data, we show how to constrain NCA to respect given symmetries, and we explore the effects of associated hyperparameters on model performance and stability. Being able to learn arbitrary dynamics gives NCA great potential as a data driven modelling framework, especially for modelling biological pattern formation., Comment: For videos referenced in appendix, see: https://github.com/AlexDR1998/NCA/tree/main/Videos

We construct a model of Pauli spin operators with all-to-all 4-local interactions by replacing Majorana fermions in the SYK model with spin operators. Equivalently, we replace fermions with hard-core bosons. We study this model numerically and compare the properties with those of the SYK model. We observe a striking quantitative coincidence between the spin model and the SYK model, which suggests that this spin model is strongly chaotic and, perhaps, can play some role in holography. We also discuss the path-integral approach with multi-local fields and the possibility of quantum simulations. This model may be an interesting target for quantum simulations because Pauli spins are easier to implement than fermions on qubit-based quantum devices., Comment: 35 pages, 16 figures; version accepted for publication in JHEP

A deeper understanding of pedestrian dynamics is essential to improve crowd flows in public spaces such as train stations. It is essential to understand both the physical and the psychological processes present in this context. However, current research on train boarding behavior is limited in scope and mainly focuses on how group level variables such as number of boarders/deboarders influence train boarding efficiency. Viewing pedestrian dynamics through a psychological lens is important for a detailed understanding of the train boarding context and to recognize target areas for improving crowd flows. At Dutch train stations, boarders follow a social norm of waiting at the train door until deboarding is complete. Although people generally adhere to this norm, the way it is executed may not be optimal for deboarding efficiency. We investigate how waiting boarders form a deboarding channel (a corridor where deboarders exit the train) which is a macroscopic structure formed by pedestrians, and how this channel in turn influences the efficiency of deboarding. Analyzing a dataset with 3278 boarding events at Utrecht Centraal Station in the Netherlands from 2017 - 2020 (a subset of a trajectory dataset that captures 100,000 trajectories per day), we found that higher numbers of boarders and a higher ratio of boarders to deboarders, reduced the width of the deboarding channel, and a lower width was associated with lower deboarding efficiency. These results shift the focus from group level variables to identifying macroscopic structures that are formed when pedestrians interact within a social system and provide specific target areas where nudges/behavioral interventions could be implemented.

Freestanding slender fluid filaments of room temperature ferroelectric nematic liquid crystals are described. They are stabilized either by internal electric fields of bound charges formed due to polarization splay, or by external voltage applied between suspending wires. The phenomenon is similar to those observed in dielectric fluids, such as deionized water, except that in ferroelectric nematic materials the voltages required are 3 orders of magnitudes smaller and the aspect ratio is much higher. The observed ferroelectric fluid threads are not only unique and novel, but also offer measurements of basic physical quantities, such as the ferroelectric polarization and viscosity. Ferroelectric nematic fluid threads may have practical applications in nano-fluidic micron-size logic devices, switches, and relays., Comment: 18 pages, 5 figures

Online news outlets are grappling with the moderation of user-generated content within their comment section. We present a recommender system based on ranking class probabilities to support and empower the moderator in choosing featured posts, a time-consuming task. By combining user and textual content features we obtain an optimal classification F1-score of 0.44 on the test set. Furthermore, we observe an optimum mean NDCG@5 of 0.87 on a large set of validation articles. As an expert evaluation, content moderators assessed the output of a random selection of articles by choosing comments to feature based on the recommendations, which resulted in a NDCG score of 0.83. We conclude that first, adding text features yields the best score and second, while choosing featured content remains somewhat subjective, content moderators found suitable comments in all but one evaluated recommendations. We end the paper by analyzing our best-performing model, a step towards transparency and explainability in hybrid content moderation.

Representation learning has become a crucial area of research in machine learning, as it aims to discover efficient ways of representing raw data with useful features to increase the effectiveness, scope and applicability of downstream tasks such as classification and prediction. In this paper, we propose a novel method to generate representations for time series-type data. This method relies on ideas from theoretical physics to construct a compact representation in a data-driven way, and it can capture both the underlying structure of the data and task-specific information while still remaining intuitive, interpretable and verifiable. This novel methodology aims to identify linear laws that can effectively capture a shared characteristic among samples belonging to a specific class. By subsequently utilizing these laws to generate a classifier-agnostic representation in a forward manner, they become applicable in a generalized setting. We demonstrate the effectiveness of our approach on the task of ECG signal classification, achieving state-of-the-art performance., Comment: 28 pages, 1 figure, 1 table