Miracle-dz/UniFAR-FTUs huggingface dataset:
Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks

Lung cancer is the leading cause of cancer death and early diagnosis is associated with a positive prognosis. Chest X-ray (CXR) provides an inexpensive imaging mode for lung cancer diagnosis. Suspicious nodules are difficult to distinguish from vascular and bone structures using CXR. Computer vision has previously been proposed to assist human radiologists in this task, however, leading studies use down-sampled images and computationally expensive methods with unproven generalization. Instead, this study localizes lung nodules using efficient encoder-decoder neural networks that process full resolution images to avoid any signal loss resulting from down-sampling. Encoder-decoder networks are trained and tested using the JSRT lung nodule dataset. The networks are used to localize lung nodules from an independent external CXR dataset. Sensitivity and false positive rates are measured using an automated framework to eliminate any observer subjectivity. These experiments allow for the determination of the optimal network depth, image resolution and pre-processing pipeline for generalized lung nodule localization. We find that nodule localization is influenced by subtlety, with more subtle nodules being detected in earlier training epochs. Therefore, we propose a novel self-ensemble model from three consecutive epochs centered on the validation optimum. This ensemble achieved a sensitivity of 85% in 10-fold internal testing with false positives of 8 per image. A sensitivity of 81% is achieved at a false positive rate of 6 following morphological false positive reduction. This result is comparable to more computationally complex systems based on linear and spatial filtering, but with a sub-second inference time that is faster than other methods. The proposed algorithm achieved excellent generalization results against an external dataset with sensitivity of 77% at a false positive rate of 7.6.
Maximizing Penetration Testing Success with Effective Reconnaissance Techniques using ChatGPT

Background/Objective: The study investigates the integration of ChatGPT, a generative pretrained transformer language model into the reconnaissance phase of penetration testing. The research aims to enhance the efficiency and depth of information gathering during critical security assessments offering potential improvements to traditional approaches. Research Problem: The research study addresses the challenge of optimizing the reconnaissance phase in penetration testing. It seeks to provide a solution by exploring the capabilities of ChatGPT in extracting valuable data, such as various aspects of the digital footprint or infrastructure of a system or an organization. The scope of the research relies in demonstrating how ChatGPT can contribute to the planning phase of penetration testing, guiding the selection of tactics, tools, and techniques for identifying and mitigating potential risks that could be used to assist with securing Internet accessible assets of a system or an organization. Methodology: The research adopts a case study methodology to assess the effectiveness of ChatGPT in reconnaissance. Tailored questions are formulated to extract specific information relevant to penetration testing. The study highlights the importance of prompt engineering emphasizing the need for carefully constructed questions to ensure usable results. Results: The research showcases the ability of ChatGPT to provide diverse and insightful reconnaissance information. The extracted data includes IP address ranges, domain names, vendor technologies, SSL/TLS ciphers, and network protocols. The information gathering improves efficiency of the reconnaissance phase aiding penetration testers in planning subsequent phases of the assessment. Discussion: The research study extends to the broader field of cybersecurity where artificial intelligence language models can play a valuable role in enhancing the success of reconnaissance in penetration testing. The research suggests that integrating ChatGPT into penetration testing can bring about positive changes in the efficiency and depth of information obtained during reconnaissance. Conclusion: The results of the study determine that incorporating ChatGPT in the reconnaissance phase significantly benefits penetration testers by offering valuable insights and streamlining subsequent assessment planning. The results affirm ChatGPT as a pivotal tool in maximizing success in penetration testing, contributing to ongoing advancements in cybersecurity practices.
Relative Koszul coresolutions and relative Betti numbers

Let $G$ be a generator and a cogenerator in the category of finitely generated right $A$-modules for a finite-dimensional algebra $A$ over a filed $\Bbbk$, and $\mathcal{I}$ the additive closure of $G$. We will define a $\mathcal{I}$-relative Koszul coresolution $\mathcal{K}^{\bullet}(V)$ of an indecomposable direct summand $V$ of $G$, and show that for a finitely generated $A$-module $M$, the $\mathcal{I}$-relative $i$-th Betti number for $M$ at $V$ is given as the $\Bbbk$-dimension of the $i$-th homology of the $\mathcal{I}$-relative Koszul complex $\mathcal{K}^{\bullet}_V(M):=\operatorname{Hom}_A(\mathcal{K}^{\bullet}(V),M)$ of $M$ at $V$ for all $i \ge 0$. This is applied to investigate the minimal interval resolution/coresolution of a persistence module $M$, e.g., to check the interval decomposability of $M$, and to compute the interval approximation of $M$.
Local character expansions via positive depth Barbasch-Moy theory

In this paper we extend Barbasch and Moy's results about the local character expansion of depth-$0$ representations to arbitrary depth. This leads to the definition of a graded linearized version of the Kawanaka wavefront set which we use to prove that for unramified groups, the toral supercuspidal representations attached to unramified tori have singleton geometric wavefront sets provided the residue characteristic is greater than $3(h-1)$ where $h$ is the Coxeter number for $\mathbb G$. In the process we introduce the notion of the asymptotic cone in the context of geometric invariant theory and prove it is irreducible in the case of a reductive group acting on its Lie algebra via the adjoint action using Borho's theory of $\mathbb G$-sheets.
Discovering How Agents Learn Using Few Data

Decentralized learning algorithms are an essential tool for designing multi-agent systems, as they enable agents to autonomously learn from their experience and past interactions. In this work, we propose a theoretical and algorithmic framework for real-time identification of the learning dynamics that govern agent behavior using a short burst of a single system trajectory. Our method identifies agent dynamics through polynomial regression, where we compensate for limited data by incorporating side-information constraints that capture fundamental assumptions or expectations about agent behavior. These constraints are enforced computationally using sum-of-squares optimization, leading to a hierarchy of increasingly better approximations of the true agent dynamics. Extensive experiments demonstrated that our approach, using only 5 samples from a short run of a single trajectory, accurately recovers the true dynamics across various benchmarks, including equilibrium selection and prediction of chaotic systems up to 10 Lyapunov times. These findings suggest that our approach has significant potential to support effective policy and decision-making in strategic multi-agent systems.
Energy Discrepancies: A Score-Independent Loss for Energy-Based Models

Energy-based models are a simple yet powerful class of probabilistic models, but their widespread adoption has been limited by the computational burden of training them. We propose a novel loss function called Energy Discrepancy (ED) which does not rely on the computation of scores or expensive Markov chain Monte Carlo. We show that ED approaches the explicit score matching and negative log-likelihood loss under different limits, effectively interpolating between both. Consequently, minimum ED estimation overcomes the problem of nearsightedness encountered in score-based estimation methods, while also enjoying theoretical guarantees. Through numerical experiments, we demonstrate that ED learns low-dimensional data distributions faster and more accurately than explicit score matching or contrastive divergence. For high-dimensional image data, we describe how the manifold hypothesis puts limitations on our approach and demonstrate the effectiveness of energy discrepancy by training the energy-based model as a prior of a variational decoder model.
Non-stationary astrophysical stochastic gravitational-wave background: A new probe to the high redshift population of binary black holes

The astrophysical Stochastic Gravitational Wave Background (SGWB) originates from the mergers of compact binary objects that are otherwise undetected as individual events, along with other sources such as supernovae, magnetars, etc. The individual GW signal is time-varying over a time scale that depends on the chirp mass of the coalescing binaries. Another timescale that plays a role is the timescale at which the sources repeat, which depends on the merger rate. The combined effect of these two leads to a breakdown of the time-translation symmetry of the observed SGWB and a correlation between different frequency modes in the signal covariance matrix of the SGWB. Using an ensemble of SGWB due to binary black hole coalescence, calculated using simulations of different black hole mass distributions and merger rates, we show how the structure of the signal covariance matrix varies. This structure in the signal covariance matrix brings additional information about the sources on top of the power spectrum. We show that there is a significant improvement in the Figure of Merit by using this additional information in comparison to only power spectrum estimation for the LIGO-Virgo-KAGRA (LVK) network of detectors with the design sensitivity noise with two years of observation. The inclusion of the off-diagonal correlation in the covariance of the SGWB in the data analysis pipelines will be beneficial in the quest for the SGWB signal in LVK frequency bands as well as in lower frequencies and in getting an insight into its origin.
Faster Rectangular Matrix Multiplication by Combination Loss Analysis

Duan, Wu and Zhou (FOCS 2023) recently obtained the improved upper bound on the exponent of square matrix multiplication $\omega<2.3719$ by introducing a new approach to quantify and compensate the ``combination loss"in prior analyses of powers of the Coppersmith-Winograd tensor. In this paper we show how to use this new approach to improve the exponent of rectangular matrix multiplication as well. Our main technical contribution is showing how to combine this analysis of the combination loss and the analysis of the fourth power of the Coppersmith-Winograd tensor in the context of rectangular matrix multiplication developed by Le Gall and Urrutia (SODA 2018).
The Algorithmic Phase Transition of Random Graph Alignment Problem

We study the graph alignment problem over two independent Erd\H{o}s-R\'enyi graphs on $n$ vertices, with edge density $p$ falling into two regimes separated by the critical window around $p_c=\sqrt{\log n/n}$. Our result reveals an algorithmic phase transition for this random optimization problem: polynomial-time approximation schemes exist in the sparse regime, while statistical-computational gap emerges in the dense regime. Additionally, we establish a sharp transition on the performance of online algorithms for this problem when $p$ lies in the dense regime, resulting in a $\sqrt{8/9}$ multiplicative constant factor gap between achievable and optimal solutions.
Anisotropic distribution of scalar in stably stratified turbulence

We derive the anisotropic mean-square scalar (or temperature variance) spectrum and the buoyancy (or heat) flux for a stably stratified turbulent fluid by assuming the buoyancy terms in the governing dynamical equations as perturbations of the otherwise isotropic dynamics. This allows us to investigate their anisotropic distribution across scales larger and smaller than the Ozmidov scale. The ensuing 1D vertical wavenumber temperature spectrum at large scales is found to be in good agreement with stratospheric observations. The polar plot of the angle-dependent expression for temperature variance signifies the enhanced anisotropy with decreasing Froude number. It also indicates that with increasing stratification, the anisotropy persists down to smaller and smaller scales. In addition, we also display various polar plots for the components of buoyancy current, showing their anisotropic distribution.
The Two Faces of NKG2D

One of the defining "hallmarks" of cancer is metastasis, i.e. the ability of tumor cells to advance from their site of origin, invade surrounding tissue, enter the circulation, and establish new tumor growth at distant body parts. In order to do that, certain traits need to be developed in a process called cancer cell plasticity, which is regulated by the epithelial mesenchymal transition (EMT) and the interrelated acquisition of certain cancer stem cell-like features.
The Atacama Cosmology Telescope: Galactic Dust Structure and the Cosmic PAH Background in Cross-correlation with WISE

We present a cross-correlation analysis between 1′ resolution total intensity and polarization observations from the Atacama Cosmology Telescope (ACT) at 150 and 220 GHz and 15″ mid-infrared photometry from the Wide-field Infrared Survey Explorer (WISE) over 107 12. °5 × 12. °5 patches of sky. We detect a spatially isotropic signal in the WISE×ACT TT cross-power spectrum at 30σ significance that we interpret as the correlation between the cosmic infrared background at ACT frequencies and polycyclic aromatic hydrocarbon (PAH) emission from galaxies in WISE, i.e., the cosmic PAH background. Within the Milky Way, the Galactic dust TT spectra are generally well described by power laws in ℓ over the range 103 < ℓ < 104, but there is evidence both for variability in the power-law index and for non-power-law behavior in some regions. We measure a positive correlation between WISE total intensity and ACT E-mode polarization at 1000 < ℓ ≲ 6000 at >3σ in each of 35 distinct ∼100 deg2 regions of the sky, suggesting that alignment between Galactic density structures and the local magnetic field persists to subparsec physical scales in these regions. The distribution of TE amplitudes in this ℓ range across all 107 regions is biased to positive values, while there is no evidence for such a bias in the TB spectra. This work constitutes the highest-ℓ measurements of the Galactic dust TE spectrum to date and indicates that cross-correlation with high-resolution mid-infrared measurements of dust emission is a promising tool for constraining the spatial statistics of dust emission at millimeter wavelengths.
IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation

Knowledge Graph Embedding (KGE) models are used to learn continuous representations of entities and relations. A key task in the literature is predicting missing links between entities. However, Knowledge Graphs are not just sets of links but also have semantics underlying their structure. Semantics is crucial in several downstream tasks, such as query answering or reasoning. We introduce the subgraph inference task, where a model has to generate likely and semantically valid subgraphs. We propose IntelliGraphs, a set of five new Knowledge Graph datasets. The IntelliGraphs datasets contain subgraphs with semantics expressed in logical rules for evaluating subgraph inference. We also present the dataset generator that produced the synthetic datasets. We designed four novel baseline models, which include three models based on traditional KGEs. We evaluate their expressiveness and show that these models cannot capture the semantics. We believe this benchmark will encourage the development of machine learning models that emphasize semantic understanding.
Multipartite entanglement groups

We propose to define multipartite entanglement of pure states as transformations acting on some parts of a system that can be undone by transformations acting on other parts. This leads to a definition of multipartite entanglement in terms of groups, namely certain quotients of the stabilizer group and its subgroups. We analyze properties of these entanglement groups and show that they lead to restrictions which give a precise meaning to monogamy of entanglement. We use these groups to propose a finite classification of entanglement types in multi-partite quantum systems and we show that this characterization of entanglement underlies several well-known quantum tasks.
Interacting Local Topological Markers: A one-particle density matrix approach for characterizing the topology of interacting and disordered states

While topology is a property of a quantum state itself, most existing methods for characterizing the topology of interacting phases of matter require direct knowledge of the underlying Hamiltonian. We offer an alternative by utilizing the one-particle density matrix formalism to extend the concept of the Chern, chiral, and Chern-Simons markers to include interactions. The one-particle density matrix of a free-fermion state is a projector onto the occupied bands, defining a Brillouin zone bundle of the given topological class. This is no longer the case in the interacting limit, but as long as the one-particle density matrix is gapped, its spectrum can be adiabatically flattened, connecting it to a topologically equivalent projector. The corresponding topological markers thus characterize the topology of the interacting phase. Importantly, the one-particle density matrix is defined in terms of a given state alone, making the local markers numerically favorable, and providing an invaluable tool for characterizing topology of interacting systems when only the state itself is available. To demonstrate the practical use of the markers we use the chiral marker to identify the topology of the ground state of the Majorana-XYZ model as well as the midspectrum eigenstates of the Ising-Majorana chain across the transition between the ergodic and many-body localized phases.
DeepIPCv2: LiDAR-powered Robust Environmental Perception and Navigational Control for Autonomous Vehicle

We present DeepIPCv2, an autonomous driving model that perceives the environment using a LiDAR sensor for more robust drivability, especially when driving under poor illumination conditions where everything is not clearly visible. DeepIPCv2 takes a set of LiDAR point clouds as the main perception input. Since point clouds are not affected by illumination changes, they can provide a clear observation of the surroundings no matter what the condition is. This results in a better scene understanding and stable features provided by the perception module to support the controller module in estimating navigational control properly. To evaluate its performance, we conduct several tests by deploying the model to predict a set of driving records and perform real automated driving under three different conditions. We also conduct ablation and comparative studies with some recent models to justify its performance. Based on the experimental results, DeepIPCv2 shows a robust performance by achieving the best drivability in all driving scenarios. Furthermore, we will upload the codes to https://github.com/oskarnatan/DeepIPCv2.
NLOS Dies Twice: Challenges and Solutions of V2X for Cooperative Perception

Multi-agent multi-lidar sensor fusion between connected vehicles for cooperative perception has recently been recognized as the best technique for minimizing the blind zone of individual vehicular perception systems and further enhancing the overall safety of autonomous driving systems. This technique relies heavily on the reliability and availability of vehicle-to-everything (V2X) communication. In practical sensor fusion application scenarios, the non-line-of-sight (NLOS) issue causes blind zones for not only the perception system but also V2X direct communication. To counteract underlying communication issues, we introduce an abstract perception matrix matching method for quick sensor fusion matching procedures and mobility-height hybrid relay determination procedures, proactively improving the efficiency and performance of V2X communication to serve the upper layer application fusion requirements. To demonstrate the effectiveness of our solution, we design a new simulation framework to consider autonomous driving, sensor fusion and V2X communication in general, paving the way for end-to-end performance evaluation and further solution derivation.
Layered controller synthesis for dynamic multi-agent systems

In this paper we present a layered approach for multi-agent control problem, decomposed into three stages, each building upon the results of the previous one. First, a high-level plan for a coarse abstraction of the system is computed, relying on parametric timed automata augmented with stopwatches as they allow to efficiently model simplified dynamics of such systems. In the second stage, the high-level plan, based on SMT-formulation, mainly handles the combinatorial aspects of the problem, provides a more dynamically accurate solution. These stages are collectively referred to as the SWA-SMT solver. They are correct by construction but lack a crucial feature: they cannot be executed in real time. To overcome this, we use SWA-SMT solutions as the initial training dataset for our last stage, which aims at obtaining a neural network control policy. We use reinforcement learning to train the policy, and show that the initial dataset is crucial for the overall success of the method.
BittyBuzz: A Swarm Robotics Runtime for Tiny Systems

Swarm robotics is an emerging field of research which is increasingly attracting attention thanks to the advances in robotics and its potential applications. However, despite the enthusiasm surrounding this area of research, software development for swarm robotics is still a tedious task. That fact is partly due to the lack of dedicated solutions, in particular for low-cost systems to be produced in large numbers and that can have important resource constraints. To address this issue, we introduce BittyBuzz, a novel runtime platform: it allows Buzz, a domain-specific language, to run on microcontrollers while maintaining dynamic memory management. BittyBuzz is designed to fit a flash memory as small as 32 kB (with usable space for scripts) and work with as little as 2 kB of RAM. In this work, we introduce the BittyBuzz implementation, its differences from the original Buzz virtual machine, and its advantages for swarm robotics systems. We show that BittyBuzz is successfully integrated with three robotic platforms with minimal memory footprint and conduct experiments to show computation performance of BittyBuzz. Results show that BittyBuzz can be effectively used to implement common swarm behaviors on microcontroller-based systems.
Gravitational Landau levels and the chiral anomaly

A popular physical picture of the mechanism behind the four-dimensional chiral anomaly is provided by the massless Dirac equation in the presence of constant electric and magnetic background ﬁelds. The magnetic ﬁeld creates highly degenerate Landau levels, the lowest of which is gapless. Any parallel component of the electric ﬁeld drives a spectral ﬂow in the gapless mode that causes particles to emerge from, or disappear into, the Dirac sea. Seeking a similar picture for the gravitational contribution to the chiral anomaly, we consider the massless Dirac equation in a background spacetime that creates gravitational Landau levels. We ﬁnd that in this case the resulting spectral ﬂow, with its explicit particle production, accounts for only a small part of the anomalous creation of chiral charge. The balance is provided by the vacuum expectation charge arising from the spectral asymmetry.