Search

The comparison analysis of the most popular tools to extract features from network traffic is conducted in this paper. Feature extraction plays a crucial role in Intrusion Detection Systems (IDS) because it helps to transform huge raw network data into meaningful and manageable features for analysis and detection of malicious activities. The good choice of feature extraction tool is an essential step in construction of Artificial Intelligence-based Intrusion Detection Systems (AI-IDS), which can help to enhance the efficiency, accuracy, and scalability of such systems., Comment: Paper Submitted to ITTAP 2024 CEUR-WS, see https://ceur-ws.org/Vol-3896/paper1.pdf

The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provide new avenues for improving the safety and robustness of our models. In particular, our models can reason about our safety policies in context when responding to potentially unsafe prompts, through deliberative alignment. This leads to state-of-the-art performance on certain benchmarks for risks such as generating illicit advice, choosing stereotyped responses, and succumbing to known jailbreaks. Training models to incorporate a chain of thought before answering has the potential to unlock substantial benefits, while also increasing potential risks that stem from heightened intelligence. Our results underscore the need for building robust alignment methods, extensively stress-testing their efficacy, and maintaining meticulous risk management protocols. This report outlines the safety work carried out for the OpenAI o1 and OpenAI o1-mini models, including safety evaluations, external red teaming, and Preparedness Framework evaluations.

Expert systems are effective tools for automatically identifying energy efficiency potentials in manufacturing, thereby contributing significantly to global climate targets. These systems analyze energy data, pinpoint inefficiencies, and recommend optimizations to reduce energy consumption. Beyond systematic approaches for developing expert systems, there is a pressing need for simple and rapid software implementation solutions. Expert system shells, which facilitate the swift development and deployment of expert systems, are crucial tools in this process. They provide a template that simplifies the creation and integration of expert systems into existing manufacturing processes. This paper provides a comprehensive comparison of existing expert system shells regarding their suitability for improving energy efficiency, highlighting significant gaps and limitations. To address these deficiencies, we introduce a novel expert system shell, implemented in Jupyter Notebook, that provides a flexible and easily integrable solution for expert system development., Comment: 6 pages, 3 figures, preprint for conference contribution

We develop a robust structure theory for multiple ergodic averages of commuting transformations along Hardy sequences of polynomial growth. We then apply it to derive a number of novel results on joint ergodicity, recurrence and convergence. Specifically, we construct a suitable generalization of Host-Kra and box seminorms that quantitatively controls the aforementioned averages subject to necessary nondegeneracy conditions on the Hardy sequences. Combining this with a variant of the seminorm smoothing argument, we obtain Host-Kra seminorm estimates for averages along all pairwise independent Hardy sequences. In conjunction with joint ergodicity criteria and classical equidistribution results, these estimates yield a number of novel joint ergodicity results that reach far beyond the state of the art. In particular, we prove joint ergodicity for (a) pairwise independent Hardy sequences and weakly mixing transformations, (b) strongly independent Hardy sequences and ergodic transformations, (c) irrationally strongly independent Hardy sequences and totally ergodic transformations. We also positively resolve the joint ergodicity classification problem for all pairwise independent Hardy sequences, of which the aforementioned three families are special cases. Lastly, we use these joint ergodicity results to provide new recurrence results for multidimensional patterns along strongly independent Hardy sequences. While building on recent technical advances (e.g. PET coefficient tracking schemes and joint ergodicity criteria), our work introduces a number of technical developments of its own, including the theory of generalized box seminorms, an ergodic version of the quantitative concatenation argument, improved simultaneous Taylor approximations for Hardy sequences, and a generalization of the seminorm smoothing argument., Comment: 105 pages. Comments welcome!

We prove a lower bound for the exponent of the relative class group $\mathrm{Pic}^0 X_1/\phi^* \mathrm{Pic}^0 X_2$ for a covering of curves $X_1 \to X_2$ over a finite field $\mathbb{F}_q$. The results improve on the existing best bounds (due to Stichtenoth) in the case $X_2=\mathbb{P}^1$, when the relative class group equals the class group of the function field $\mathbb{F}_q(X_1)$, and are completely new for the genuinely relative situation., Comment: v1: 9 pages, comments welcome

We present strain tuning of excitonic emission in monolayer MoSe$_2$ by using a high-temperature physical vapor deposition (PVD). The use of two amorphous substrates, Si$_{3}$N$_{4}$ and SiO$_{2}$, provides two setpoints to induce distinct amounts of \textit{biaxial} tensile strain determined by a thermal expansion mismatch between the monolayer and the substrate. The tuning rate of the $A$-exciton transition energy is found to be 103 meV/\% by photoluminescence (PL), which represents the highest value realized by biaxial strain in transition metal dichalcogenides. The biaxial nature of the tensile strain is confirmed by polarization-resolved second harmonic generation, which reveals unperturbed in-plane three-fold symmetry of the monolayer. Furthermore, a softening of $A_\mathrm{1g}$ out-of-plane lattice vibration is identified in the Raman spectroscopy, which is known to be insignificant for uniaxial strain. Concomitantly, PL mapping of our PVD monolayers demonstrates (i) larger strain occurs in the interior of the mono-domain islands compared to the edges and (ii) the absence of island-size dependence in the magnitude of induced strain. Our results demonstrate an effective path towards strain engineering of excitons by using growth substrates, which holds great promise as a building block for future optoelectronic applications.

Traditional segmentation networks approach anatomical structures as standalone elements, overlooking the intrinsic hierarchical connections among them. This study introduces Softmax for Arbitrary Label Trees (SALT), a novel approach designed to leverage the hierarchical relationships between labels, improving the efficiency and interpretability of the segmentations. This study introduces a novel segmentation technique for CT imaging, which leverages conditional probabilities to map the hierarchical structure of anatomical landmarks, such as the spine's division into lumbar, thoracic, and cervical regions and further into individual vertebrae. The model was developed using the SAROS dataset from The Cancer Imaging Archive (TCIA), comprising 900 body region segmentations from 883 patients. The dataset was further enhanced by generating additional segmentations with the TotalSegmentator, for a total of 113 labels. The model was trained on 600 scans, while validation and testing were conducted on 150 CT scans. Performance was assessed using the Dice score across various datasets, including SAROS, CT-ORG, FLARE22, LCTSC, LUNA16, and WORD. Among the evaluated datasets, SALT achieved its best results on the LUNA16 and SAROS datasets, with Dice scores of 0.93 and 0.929 respectively. The model demonstrated reliable accuracy across other datasets, scoring 0.891 on CT-ORG and 0.849 on FLARE22. The LCTSC dataset showed a score of 0.908 and the WORD dataset also showed good performance with a score of 0.844. SALT used the hierarchical structures inherent in the human body to achieve whole-body segmentations with an average of 35 seconds for 100 slices. This rapid processing underscores its potential for integration into clinical workflows, facilitating the automatic and efficient computation of full-body segmentations with each CT scan, thus enhancing diagnostic processes and patient care.

A $P$-polynomial corner, for $P \in \mathbb{Z}[z]$ a polynomial, is a triple of points $(x,y),\; (x+P(z),y),\; (x,y+P(z))$ for $x,y,z \in \mathbb{Z}$. In the case where $P$ has an integer root of multiplicity $1$, we show that if $A \subseteq [N]^2$ does not contain any nontrivial $P$-polynomial corners, then $$|A| \ll_P \frac{N^2}{(\log\log\log N)^c}$$ for some absolute constant $c>0$. This simultaneously generalizes a result of Shkredov about corner-free sets and a recent result of Peluse, Sah, and Sawhney about sets without $3$-term arithmetic progressions of common difference $z^2-1$. The main ingredients in our proof are a multidimensional quantitative concatenation result from our companion paper arXiv:2407.08636 and a novel degree-lowering argument for box norms.

Using PET and quantitative concatenation techniques, we establish box-norm control with the "expected" directions for counting operators for general multidimensional polynomial progressions, with at most polynomial losses in the parameters. Such results are often useful first steps towards obtaining explicit upper bounds on sets lacking instances of given such progressions. In the companion paper arXiv:2407.08637, we complete this program for sets in $[N]^2$ lacking nondegenerate progressions of the form $(x, y), (x + P(z), y), (x, y + P(z))$, where $P \in \mathbb{Z}[z]$ is any fixed polynomial with an integer root of multiplicity $1$.

Against the backdrop of the European Union's commitment to achieve climate neutrality by 2050, efforts to improve energy efficiency are being intensified. The manufacturing industry is a key focal point of these endeavors due to its high final electrical energy demand, while simultaneously facing a growing shortage of skilled workers crucial for meeting established goals. Expert systems (ESs) offer the chance to overcome this challenge by automatically identifying potential energy efficiency improvements and thereby playing a significant role in reducing electricity consumption. This paper systematically reviews state-of-the-art approaches of ESs aimed at improving energy efficiency in industry, with a focus on manufacturing. The literature search yields 1692 results, of which 54 articles published between 1987 and 2023 are analyzed in depth. These publications are classified according to the system boundary, manufacturing type, application perspective, application purpose, ES type, and industry. Furthermore, we examine the structure, implementation, utilization, and development of ESs in this context. Through this analysis, the review reveals research gaps, pointing toward promising topics for future research., Comment: 23 pages, 7 figures, journal

Parameterized Quantum Circuits (PQCs) have been acknowledged as a leading strategy to utilize near-term quantum advantages in multiple problems, including machine learning and combinatorial optimization. When applied to specific tasks, the parameters in the quantum circuits are trained to minimize the target function. Although there have been comprehensive studies to improve the performance of the PQCs on practical tasks, the errors caused by the quantum noise downgrade the performance when running on real quantum computers. In particular, when the quantum state is transformed through multiple quantum circuit layers, the effect of the quantum noise happens cumulatively and becomes closer to the maximally mixed state or complete noise. This paper studies the relationship between the quantum noise and the diffusion model. Then, we propose a novel diffusion-inspired learning approach to mitigate the quantum noise in the PQCs and reduce the error for specific tasks. Through our experiments, we illustrate the efficiency of the learning strategy and achieve state-of-the-art performance on classification tasks in the quantum noise scenarios.

Background. Radiomic features, derived from a region of interest (ROI) in medical images, are valuable as prognostic factors. Selecting an appropriate ROI is critical, and many recent studies have focused on leveraging multiple ROIs by segmenting analogous regions across patients - such as the primary tumour and peritumoral area or subregions of the tumour. These can be straightforwardly incorporated into models as additional features. However, a more complex scenario arises for example in a regionally disseminated disease, when multiple distinct lesions are present. Aim. This study aims to evaluate the feasibility of integrating radiomic data from multiple lesions into survival models. We explore strategies for incorporating these ROIs and hypothesise that including all available lesions can improve model performance. Methods. While each lesion produces a feature vector, the desired result is a unified prediction. We propose methods to aggregate either the feature vectors to form a representative ROI or the modeling results to compute a consolidated risk score. As a proof of concept, we apply these strategies to predict distant metastasis risk in a cohort of 115 non-small cell lung cancer patients, 60% of whom exhibit regionally advanced disease. Two feature sets (radiomics extracted from PET and PET interpolated to CT resolution) are tested across various survival models using a Monte Carlo Cross-Validation framework. Results. Across both feature sets, incorporating all available lesions - rather than limiting analysis to the primary tumour - consistently improved the c-index, irrespective of the survival model used. Conclusion. Lesions beyond the primary tumour carry information that should be utilised in radiomics-based models to enhance predictive ability., Comment: To be submitted to Computer Methods and Programs in Biomedicine

Many challenging questions about the Grothendieck-Teichmueller group, $GT$, are motivated by the fact that this group receives the injective homomorphism (called the Ihara embedding) from the absolute Galois group, $G_Q$, of rational numbers. Although the question about the surjectivity of the Ihara embedding is a very challenging problem, in this paper, we construct a family of finite non-abelian quotients of $GT$ that receive surjective homomorphisms from $G_Q$. We also assemble these finite quotients into an infinite (non-abelian) profinite quotient of $GT$. We prove that the natural homomorphism from $G_Q$ to the resulting profinite group is also surjective. We give an explicit description of this profinite group. To achieve these goals, we used the groupoid $GTSh$ of $GT$-shadows for the gentle version of the Grothendieck-Teichmueller group. This groupoid was introduced in the recent paper by the second author and J. Guynee and the set $Ob(GTSh)$ of objects of $GTSh$ is a poset of certain finite index normal subgroups of the Artin braid group on 3 strands. We introduce a sub-poset $Dih$ of $Ob(GTSh)$ related to the family of dihedral groups and call it the dihedral poset. We show that each element $K$ of $Dih$ is the only object of its connected component in $GTSh$. Using the surjectivity of the cyclotomic character, we prove that, if the order of the dihedral group corresponding to $K$ is a power of 2, then the natural homomorphism from $G_Q$ to the finite group $GTSh(K, K)$ is surjective. We introduce the Lochak-Schneps conditions on morphisms of $GTSh$ and prove that each morphism of $GTSh$ with the target $K$ in $Dih$ satisfies the Lochak-Schneps conditions. Finally, we conjecture that the natural homomorphism from $G_Q$ to the finite group $GTSh(K, K)$ is surjective for every object $K$ of the dihedral poset., Comment: comments are welcome

The advancement of quantum photonic technologies relies on the ability to precisely control the degrees of freedom of optically active states. Here, we realize real-time, room-temperature tunable strong plasmon-exciton coupling in 2D semiconductor monolayers enabled by a general approach that combines strain engineering plus force- and voltage-adjustable plasmonic nanocavities. We show that the exciton energy and nanocavity plasmon resonance can be controllably toggled in concert by applying pressure with a plasmonic nanoprobe, allowing in operando control of detuning and coupling strength, with observed Rabi splittings >100 meV. Leveraging correlated force spectroscopy, nano-photoluminescence (nano-PL) and nano-Raman measurements, augmented with electromagnetic simulations, we identify distinct polariton bands and dark polariton states, and map their evolution as a function of nanogap and strain tuning. Uniquely, the system allows for manipulation of coupling strength over a range of cavity parameters without dramatically altering the detuning. Further, we establish that the tunable strong coupling is robust under multiple pressing cycles and repeated experiments over multiple nanobubbles. Finally, we show that the nanogap size can be directly modulated via an applied DC voltage between the substrate and plasmonic tip, highlighting the inherent nature of the concept as a plexcitonic nano-electro-mechanical system (NEMS). Our work demonstrates the potential to precisely control and tailor plexciton states localized in monolayer (1L) transition metal dichalcogenides (TMDs), paving the way for on-chip polariton-based nanophotonic applications spanning quantum information processing to photochemistry., Comment: 17 pages, 4 figures

Following closely the analysis performed by Andrew C. Fowler to derive the first canonical equation for nonlinear dune dynamics, but considering some appropriate changes of variables, suitable scalings, and by neglecting higher order terms, we obtain an adaptation of the aforementioned equation, which contains an additional term, to describe dune morphodynamics., Comment: 15 pages

A useful result is that if a bounded complex-valued path is Riemann-integrable, then its modulus is also Riemann-integrable. The extension of this last result to bounded paths taking values in a normed space is affirmed, as being true, in [3]. However, we show that if a bounded path taking values in a normed space is barely Riemann-integrable, then it is not really guaranteed that the norm of this path is also Riemann-integrable., Comment: 4 pages

Localized states in two-dimensional (2D) transition metal dichalcogenides (TMDCs) have been the subject of intense study, driven by potential applications in quantum information science. Despite the rapidly growing knowledge surrounding these emitters, their microscopic nature is still not fully understood, limiting their production and application. Motivated by this challenge, and by recent theoretical and experimental evidence showing that nanowrinkles generate localized room-temperature emitters, we demonstrate a method to intentionally induce wrinkles with collections of stressors, showing that long-range wrinkle direction and position are controllable with patterned array design. Nano-photoluminescence (nano-PL) imaging combined with detailed strain modeling based on measured wrinkle topography establishes a correlation between wrinkle properties, particularly shear strain, and localized exciton emission. Beyond the array-induced super-wrinkles, nano-PL spatial maps further reveal that the strain environment around individual stressors is heterogeneous due to the presence of fine wrinkles that are less deterministic. Detailed nanoscale hyperspectral images uncover a wide range of low-energy emission peaks originating from these fine wrinkles, and show that the states can be tightly confined to regions < 10 nm, even in ambient conditions. These results establish a promising potential route towards realizing room temperature quantum emission in 2D TMDC systems., Comment: 5 figures