Miracle-dz/UniFAR-FTUs huggingface dataset:
FocalErrorNet: Uncertainty-aware focal modulation network for inter-modal registration error estimation in ultrasound-guided neurosurgery

In brain tumor resection, accurate removal of cancerous tissues while preserving eloquent regions is crucial to the safety and outcomes of the treatment. However, intra-operative tissue deformation (called brain shift) can move the surgical target and render the pre-surgical plan invalid. Intra-operative ultrasound (iUS) has been adopted to provide real-time images to track brain shift, and inter-modal (i.e., MRI-iUS) registration is often required to update the pre-surgical plan. Quality control for the registration results during surgery is important to avoid adverse outcomes, but manual verification faces great challenges due to difficult 3D visualization and the low contrast of iUS. Automatic algorithms are urgently needed to address this issue, but the problem was rarely attempted. Therefore, we propose a novel deep learning technique based on 3D focal modulation in conjunction with uncertainty estimation to accurately assess MRI-iUS registration errors for brain tumor surgery. Developed and validated with the public RESECT clinical database, the resulting algorithm can achieve an estimation error of 0.59+-0.57 mm.
Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performance

Many studies have proposed machine-learning (ML) models for malware detection and classification, reporting an almost-perfect performance. However, they assemble ground-truth in different ways, use diverse static- and dynamic-analysis techniques for feature extraction, and even differ on what they consider a malware family. As a consequence, our community still lacks an understanding of malware classification results: whether they are tied to the nature and distribution of the collected dataset, to what extent the number of families and samples in the training dataset influence performance, and how well static and dynamic features complement each other. This work sheds light on those open questions by investigating the impact of datasets, features, and classifiers on ML-based malware detection and classification. For this, we collect the largest balanced malware dataset so far with 67k samples from 670 families (100 samples each), and train state-of-the-art models for malware detection and family classification using our dataset. Our results reveal that static features perform better than dynamic features, and that combining both only provides marginal improvement over static features. We discover no correlation between packing and classification accuracy, and that missing behaviors in dynamically-extracted features highly penalise their performance. We also demonstrate how a larger number of families to classify makes the classification harder, while a higher number of samples per family increases accuracy. Finally, we find that models trained on a uniform distribution of samples per family better generalize on unseen data.
Likely, Light, and Accurate Context-Free Clusters-based Trajectory Prediction

Autonomous systems in the road transportation network require intelligent mechanisms that cope with uncertainty to foresee the future. In this paper, we propose a multi-stage probabilistic approach for trajectory forecasting: trajectory transformation to displacement space, clustering of displacement time series, trajectory proposals, and ranking proposals. We introduce a new deep feature clustering method, underlying self-conditioned GAN, which copes better with distribution shifts than traditional methods. Additionally, we propose novel distance-based ranking proposals to assign probabilities to the generated trajectories that are more efficient yet accurate than an auxiliary neural network. The overall system surpasses context-free deep generative models in human and road agents trajectory data while performing similarly to point estimators when comparing the most probable trajectory.
Approaching ideal rectification in superconducting diodes through multiple Andreev reflections

We analyze the rectification properties of voltage-biased Josephson junctions exhibiting the superconducting diode effect. Taking into account multiple Andreev reflection (MAR) processes in our scattering theory, we consider a short weak link of arbitrary transparency between two superconductors with finite Cooper pair momentum $2q$. In equilibrium, the diode efficiency is bounded from above in this model, with maximal efficiency $\eta_0\approx 0.4$. Out of equilibrium, we find a rich subharmonic structure in the current-voltage curve. For high transparency and low bias voltage $V$, the rectification efficiency $\eta(V)$ approaches the ideal value $\eta=1$ for $q\xi\to 1$ (with coherence length $\xi$).
Knot Theory and Error-Correcting Codes

This paper builds a novel bridge between algebraic coding theory and mathematical knot theory, with applications in both directions. We give methods to construct error-correcting codes starting from the colorings of a knot, describing through a series of results how the properties of the knot translate into code parameters. We show that knots can be used to obtain error-correcting codes with prescribed parameters and an efficient decoding algorithm.
Abelian fields and the Brumer-Stark conjecture

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Spatial and temporal metrology of intense attosecond pulses

Attosecond pulses are short enough to capture the electronic dynamics in atoms and molecules. The work presented in this thesis deals with the generation and optimization of attosecond extreme-ultraviolet (XUV) pulses. The goal is to progress towards the realization of time-resolved nonlinear experiments at the intense XUV beamline of the Lund High-Power Laser Facility.The XUV flux was optimized using a loose focusing geometry. This led to a formalization of scaling laws for high-order harmonic generation (HHG) and more generally for nonlinear optics in gases, e.g., filamentation. A high intensity on target was achieved by focusing the high XUV flux using a pair of toroidal mirrors in a Wolter-like configuration.Spatial properties of the high-order harmonics were studied in details. Wavefront measurements of the harmonics were performed, both in the far field and after the refocusing optics, as well as spectrally resolved measurements. The origin of XUV aberrations is discussed, and the variation of the harmonic divergence depending on the generation position relative to the fundamental focus is studied.The intense XUV beamline and its first nonlinear experiments are presented. An interferometer to split-and-delay the beam was developed. Tests were performed, showing the capacity to provide attosecond resolution fortime-resolved experiments in the beamline.The high XUV intensity on target combined with the attosecond interferometer opens the door to perform XUV-pump XUV-probe experiments. (Less)
AI Art Curation: Re-imagining the city of Helsinki in occasion of its Biennial

Art curatorial practice is characterized by the presentation of an art collection in a knowledgeable way. Machine processes are characterized by their capacity to manage and analyze large amounts of data. This paper envisages AI curation and audience interaction to explore the implications of contemporary machine learning models for the curatorial world. This project was developed for the occasion of the 2023 Helsinki Art Biennial, entitled New Directions May Emerge. We use the Helsinki Art Museum (HAM) collection to re-imagine the city of Helsinki through the lens of machine perception. We use visual-textual models to place indoor artworks in public spaces, assigning fictional coordinates based on similarity scores. We transform the space that each artwork inhabits in the city by generating synthetic 360 art panoramas. We guide the generation estimating depth values from 360 panoramas at each artwork location, and machine-generated prompts of the artworks. The result of this project is an AI curation that places the artworks in their imagined physical space, blurring the lines of artwork, context, and machine perception. The work is virtually presented as a web-based installation on this link http://newlyformedcity.net/, where users can navigate an alternative version of the city while exploring and interacting with its cultural heritage at scale.
Consistency of logistic classifier in abstract Hilbert spaces

We study the asymptotic behavior of the logistic classifier in an abstract Hilbert space and require realistic conditions on the distribution of data for its consistency. The number kn of estimated parameters via maximum quasi-likelihood is allowed to diverge so that kn/n → 0 and nτ kn → ∞, where n is the number of observations and τkn is the variance of the last principal component of data used for estimation. This is the only result on the consistency of the logistic classifier we know so far when the data are assumed to come from a Hilbert space.
Thermal Transport in Disordered Materials

ABSTRACT We review the status of research on thermal/phonon transport in disordered materials. The term disordered materials is used here to encompass both structural and compositional disorder. It includes structural deviations ranging from an ideal crystal with disordered arrangements of defects all the way to fully amorphous materials, as well as crystals with impurities up through multi-component random alloys. Both types of disorder affect phonons by breaking the symmetry of an idealized crystal and changing their character/mode shapes. These effects have important implications with regard to phonon–phonon interactions, phonon transport and phonon interactions with other quantum particles, which are being actively investigated. Herein, we synthesize the current theoretical understanding, identify the aspects of the problem that require more work, and pose open questions. Abbreviations: BTE: Boltzmann transport equation; DFT: Density functional theory; EPP: Eigenvector periodicity parameter; FAFDTR: Fiber-aligned frequency domain thermoreflectance; GK: Green–Kubo; GKMA: Green–Kubo modal analysis; HCACF: Heat current autocorrelation function; IXS: Inelastic X-ray scattering; LD: Lattice dynamics; LJ: Lennard–Jones; MD: Molecular dynamics; MFP: Mean free path; NEMD: Non-equilibrium molecular dynamics; NMD: Normal-mode dynamics; PDL: Propagon, diffuson, locon; PGM: Phonon gas model; PR: Participation ratio; SCLD: Supercell lattice dynamics; SED: Spectral energy density; TDTR: Time-domain thermoreflectance; VCA: Virtual crystal approximation;
Regular Synthesis Method of a Complete Class of Ternary Bent-Sequences and Their Nonlinear Properties

A regular synthesis method of the full class of ternary bent-sequences based on their representation in the form of 3-bent-squares is developed. The formula for the nonlinearity estimation of ternary sequences, based on their VilenkinChristenson spectral coefficients is introduced. The distribution of nonlinearity of the full code of length N=9 is researched.
Galaxy and Mass Assembly (GAMA): galaxy pairwise velocity dispersion

Abstract We describe preliminary measurements of the pairwise velocity dispersion (PVD) of galaxies in the Galaxy and Mass Assembly (GAMA) survey as a function of projected separation and galaxy luminosity. Due to the faint magnitude limit (r < 19.8) and highly-complete spectroscopic sampling of the GAMA survey, we are able to measure the PVD to smaller scales and for lower-luminosity galaxies than previous SDSS-based work. We see no strong scale-dependence at most luminosities in the quasi-linear regime. We observe an apparent drop in PVD towards very small scales (below ≈ 0.1h −1 Mpc), but this could in part be due to a restriction of the streaming model employed. At intermediate scales, the PVD is highest (~ 500 km/s) at intermediate luminosities, dropping at both fainter and brighter luminosities.
All-Sky Microwave Radiance Assimilation in NCEP’s GSI Analysis System

AbstractThe capability of all-sky microwave radiance assimilation in the Gridpoint Statistical Interpolation (GSI) analysis system has been developed at the National Centers for Environmental Prediction (NCEP). This development effort required the adaptation of quality control, observation error assignment, bias correction, and background error covariance to all-sky conditions within the ensemble–variational (EnVar) framework. The assimilation of cloudy radiances from the Advanced Microwave Sounding Unit-A (AMSU-A) microwave radiometer for ocean fields of view (FOVs) is the primary emphasis of this study.In the original operational hybrid 3D EnVar Global Forecast System (GFS), the clear-sky approach for radiance data assimilation is applied. Changes to data thinning and quality control have allowed all-sky satellite radiances to be assimilated in the GSI. Along with the symmetric observation error assignment, additional situation-dependent observation error inflation is employed for all-sky conditions. Mo.
Unscented and square‐root unscented Kalman filters for quaternionic systems

Proper construction of an unscented Kalman filter (UKF) for unit quaternionic systems is not straightforward due to the incompatibility between the algebraic properties of the unit quaternions and the common real vector space operations (additions and scalar multiplications) needed in the steps of a filter algorithm. This work studies, in detail, all UKFs and square‐root UKFs for quaternionic systems proposed in the literature. First, we classify the algorithms according to the preservation of the unity norm of the quaternion variables. Second, we propose two new algorithms: the quaternionic additive unscented Kalman filter (QuAdUKF) and a square‐root variant of it. The QuAdUKF encompasses all known UKFs for quaternionic systems of the literature preserving, in all steps, the norm of the unit quaternion variables. Besides, it can also yield new UKFs with this norm preservation property. The QuAdUKF's square‐root variant has better properties in comparison with all the square‐root UKFs for quaternionic systems of the literature. Numerical experiments for a spacecraft attitude estimation problem illustrate the theoretical results.
Linear proportional–integral control for skin-friction reduction in a turbulent channel flow

In the present study, we apply a proportional (P)–integral (I) feedback control to a turbulent channel flow for skin-friction reduction. The instantaneous wall-normal velocity at a sensing plane above the wall is measured as a sensing parameter, and blowing/suction is provided at the wall based on the PI control. The performance of PI controls is estimated by the change in the skin friction while varying the sensing plane location $y_{s}$ and the proportional and integral feedback gains ( $\unicode[STIX]{x1D6FC}$ and $\unicode[STIX]{x1D6FD}$ respectively). The opposition control proposed by Choi et al. (J. Fluid Mech., vol. 262, 1994, pp. 75–110) corresponds to a P control with $\unicode[STIX]{x1D6FC}=1$ . When the sensing plane is located close to the wall ( $y_{s}^{+}\lesssim 10$ ), PI controls result in greater skin-friction reductions than corresponding P controls. The root-mean-square (r.m.s.) sensing velocity fluctuations, considered as the control error, approach zero with successful PI controls, but do not with P controls. Successful PI controls reduce the strength of near-wall coherent structures and the r.m.s. velocity fluctuations above the wall apart from those near the wall due to the control input. The frequency spectra of the sensing velocity show that the I component of PI controls significantly reduces the energy at low frequencies, much more than P controls do. Proportional–integral controls are also applied to a linearized flow model having transient growth of disturbances. The performance of PI controls for a linearized flow model is very similar to that for a turbulent channel flow, i.e. the low-frequency components of disturbances are significantly reduced by the I component of PI controls, and the transient energy growth is suppressed more than by P controls.
Coupled FCT-HP for Analytical Solutions of the Generalized Timefractional Newell-Whitehead-Segel Equation

This paper considers the generalized form of the time-fractional Newell-Whitehead-Segel model (TFNWSM) with regard to exact solutions via the application of Fractional Complex Transform (FCT) coupled with He’s polynomials method of solution. This is applied to two forms of the TFNWSM viz: nonlinear and linear forms of the time-fractional NWSM equation whose derivatives are based on Jumarie’s sense. The results guarantee the reliability and efficiency of the proposed method with less computation time while still maintaining high level of accuracy.
Transaction-aware heterogeneous graph embedding for recommendation

The auxiliary information describing users and items are widely used in the model of recommendation increasingly.Heterogeneous graph,as a effective means to incorporate these information, has been widely used in the modelling of the auxiliary information of the users and items.Existing models usually fail to capture relevance of user and its high-order neighbors,likewise the item.Besides,existing models represent the user without considering the effect of predicted item.To address the above issues,we encode high-order semantic relationships into user and item representations by information propagation along the graph.Besides,we design co-attention neural network to generate the transaction-aware embedding of both user and item to better consider the impact of different items to users.In all,we propose a transaction-aware heterogeneous graph embedding for recommendation(TA-HGRec). Experimental with thress real datasets showed that it achieved significant improvement over existing state-of-the-art recommendation methods.
A Dynamic Attributes-driven Graph Attention Network Modeling on Behavioral Finance for Stock Prediction

Stock prediction is a challenging task due to multiple influencing factors and complex market dependencies. Traditional solutions are based on a single type of information. With the success of multi-source information in different fields, the combination of different types of information such as numerical and textual information has become a promising option. Although multi-source information provides rich multi-view information, how to mine and construct structured relationships from them is a difficult problem. Specifically, most existing methods usually extract features from commonly used multi-source information as predictive information sources, without further pre-constructing stock relationship graphs with dependencies using broader information. More importantly, they typically treat each stock as an isolated forecasting, or employ stock market correlations based on a fixed predefined graph structure, but current methods are not sensitive enough to aggregate the attribute features extracted from multi-source information and stock relationship graph, to obtain the dynamic update of market relations and relationship strength. The stock market is highly temporally, and the attributes of nodes are affected by the time perception of other attributes, which is not fully considered. To address these problems, we propose a novel dynamic attributes-driven graph attention networks incorporating sentiment (DGATS) information, transaction data, and text data. Inspired by behavioral finance, we separately extract sentiment information as a factor of technical indicators, and further realize the early fusion of technical indicators and textual data through Kronecker product-based tensor fusion. In particular, by LSTM and temporal attention network, the short-term and long-term transition features are gradually grasped from the local composition of the fused stock trading sequence. Furthermore, real-time intra-market dependencies and key attributes information are captured with graph networks, enabling dynamic updates of relationships and relationship strengths in predefined graphs. Experiments on the real datasets show that the architecture can outperform the previous methods in prediction performance.
Zero-knowledge proofs for secure computation

In this thesis, we study zero-knowledge proofs, a cryptographic primitive that allows to prove a statement while yielding nothing beyond its truth, and their applications to secure computation. Specifically, we first introduce a new type of zero-knowledge proofs, called implicit zero-knowledge arguments, that stands between two existing notions, interactive zeroknowledge proofs and non-interactive zero-knowledge proofs. Our new notion provides the same efficiency benefits than the latter when used to design roundefficient secure computation protocols, but it can be built from essentially the same cryptographic assumptions than the former, which allows to get improved efficiency and security guarantees. Second, we revisit a zero-knowledge proof system that is particularly useful for secure computation protocols manipulating integers, and show that the known security analysis can be improved to base the proof system on a more wellstudied assumption. Eventually, we introduce a new method to build zero-knowledge proof systems over the integers, which particularly improves over existing methods in a client-server model, where a weak client executes a secure computation protocol with a powerful server.
Attention-Based BiLSTM For Malware Family Classification

Due to API calls being the most prominent characteristic of malicious software, this paper uses Windows API call sequences as features to classify malware families. A BiLSTM model based on attention mechanism is proposed. First, to address the problem of significantly different sample lengths, an algorithm for preprocessing Windows API call sequences of different lengths is improved, referred to as RD in this paper. RD can effectively remove duplicate APIs and reduce the length of API call sequences, and experimental results show that this preprocessing algorithm can improve the classification accuracy. Then, considering the temporal nature of API calls, this paper uses a BiLSTM model that can perceive contextual information and integrates an attention mechanism to improve the model's performance. Experimental results show that the attention-based BiLSTM model outperforms other models.
Malware Detection in Internet of Things Devices Based on Association Models

In recent years, attackers have shifted aggressively targeting Internet of Things devices. In this paper, we propose the association IoT malware detection model. Before being associated, the model goes through two processing phases, each having two types of static and dynamic features. The process consists of 3 main steps: (1) the files are extracted static feature (grayscale image) and dynamic feature (system call through V-Sandbox sandbox), (2) features are preprocessed and fed into the learning models; for the grayscale image feature, a convolutional neural network (CNN) is used; for the system call graph feature, traditional machine learning algorithms are used; (3) the results from the two learning models are combined by late fusion to decide the final prediction label for the input files. The performance of the proposed method was evaluated, and its detection accuracy was 99.14% better than in the static analysis and dynamic analysis, which had 99.06% and 98.08% detection accuracy, respectively.