Miracle-dz/UniFAR-FTUs huggingface dataset:
Kairos: Practical Intrusion Detection and Investigation using Whole-system Provenance

Provenance graphs are structured audit logs that describe the history of a system's execution. Recent studies have explored a variety of techniques to analyze provenance graphs for automated host intrusion detection, focusing particularly on advanced persistent threats. Sifting through their design documents, we identify four common dimensions that drive the development of provenance-based intrusion detection systems (PIDSes): scope (can PIDSes detect modern attacks that infiltrate across application boundaries? ), attack agnosticity (can PIDSes detect novel attacks without a priori knowledge of attack characteristics? ), timeliness (can PIDSes efficiently monitor host systems as they run? ), and attack reconstruction (can PIDSes distill attack activity from large provenance graphs so that sysadmins can easily understand and quickly respond to system intrusion? ). We present KAIROS, the first PIDS that simultaneously satisfies the desiderata in all four dimensions, whereas existing approaches sacrifice at least one and struggle to achieve comparable detection performance. Kairos leverages a novel graph neural network-based encoder-decoder architecture that learns the temporal evolution of a provenance graph's structural changes to quantify the degree of anomalousness for each system event. Then, based on this fine-grained information, Kairos reconstructs attack footprints, generating compact summary graphs that accurately describe malicious activity over a stream of system audit logs. Using state-of-the-art benchmark datasets, we demonstrate that Kairos outperforms previous approaches.
Surface reflectance analysis of small bodies on different scales

The aim of this thesis is to model the surface reflectance of small bodies using spacecraft data i.e. asteroids Steins and Lutetia. The overall and local variation of brightness from the surface covers with different local geometries are controlled by the optical, structural, and physical properties of the surface material. Two photometric models are frequently utilized to model the scattering of light from planetary and small bodies surfaces, the one of Hapke and the one of Minnaert. In both approaches, the intensity is modeled in terms of viewing and illuminating angles with a number of photometric parameters. The Minnaert model is an empirical photometric function with two parameters, the Minnaert albedo and the limb-darkening coefficient, representing the light scattering behavior across the surface. The Hapke photometric function, is a semi-empirical model and is mathematically more complicated which is composed of five coupled parameters, known as Hapke parameters. This work focuses on the application of the Minnaert and Hapke modeling to find the optimum values of their photometric parameters from the surface reflectance of two asteroids Steins and Lutetia. I used OSIRIS (Optical, Spectroscopic, and Infrared Remote Imaging System) images acquired during Rosetta encounter on September of 2008 and July of 2010 of Steins and Lutetia, respectively.
Surface Tension Between Coexisting Phases of Active Brownian Particles

The confliction between the stable interface in phase-separated active Brownian particles and its negative surface tension, obtained mechanically via the active pressure, has sparked considerable debate about the formula of active surface tension. Based on the intrinsic pressure of active system, we here derive a proper mechanical expression of active surface tension by calculating the work required to create a differential interface area, while remaining interfacial profile invariant (not considered previously). Our expression yields a significantly positive surface tension that increases with the particle actvity, which is supported by mechanical analysis on the interface. Our results thus fundamentally resolve the contradiction related to active surface tension.
Linear shrinkage of sample covariance matrix or matrices under elliptical distributions: a review

This chapter reviews methods for linear shrinkage of the sample covariance matrix (SCM) and matrices (SCM-s) under elliptical distributions in single and multiple populations settings, respectively. In the single sample setting a popular linear shrinkage estimator is defined as a linear combination of the sample covariance matrix (SCM) with a scaled identity matrix. The optimal shrinkage coefficients minimizing the mean squared error (MSE) under elliptical sampling are shown to be functions of few key parameters only, such as elliptical kurtosis and sphericity parameter. Similar results and estimators are derived for multiple population setting and applications of the studied shrinkage estimators are illustrated in portfolio optimization.
Going Deeper with Five-point Stencil Convolutions for Reaction-Diffusion Equations

Physics-informed neural networks have been widely applied to partial differential equations with great success because the physics-informed loss essentially requires no observations or discretization. However, it is difficult to optimize model parameters, and these parameters must be trained for each distinct initial condition. To overcome these challenges in second-order reaction-diffusion type equations, a possible way is to use five-point stencil convolutional neural networks (FCNNs). FCNNs are trained using two consecutive snapshots, where the time step corresponds to the step size of the given snapshots. Thus, the time evolution of FCNNs depends on the time step, and the time step must satisfy its CFL condition to avoid blow-up solutions. In this work, we propose deep FCNNs that have large receptive fields to predict time evolutions with a time step larger than the threshold of the CFL condition. To evaluate our models, we consider the heat, Fisher's, and Allen-Cahn equations with diverse initial conditions. We demonstrate that deep FCNNs retain certain accuracies, in contrast to FDMs that blow up.
HyperCoil-Recon: A Hypernetwork-based Adaptive Coil Configuration Task Switching Network for MRI Reconstruction

Parallel imaging, a fast MRI technique, involves dynamic adjustments based on the configuration i.e. number, positioning, and sensitivity of the coils with respect to the anatomy under study. Conventional deep learning-based image reconstruction models have to be trained or fine-tuned for each configuration, posing a barrier to clinical translation, given the lack of computational resources and machine learning expertise for clinicians to train models at deployment. Joint training on diverse datasets learns a single weight set that might underfit to deviated configurations. We propose, HyperCoil-Recon, a hypernetwork-based coil configuration task-switching network for multi-coil MRI reconstruction that encodes varying configurations of the numbers of coils in a multi-tasking perspective, posing each configuration as a task. The hypernetworks infer and embed task-specific weights into the reconstruction network, 1) effectively utilizing the contextual knowledge of common and varying image features among the various fields-of-view of the coils, and 2) enabling generality to unseen configurations at test time. Experiments reveal that our approach 1) adapts on the fly to various unseen configurations up to 32 coils when trained on lower numbers (i.e. 7 to 11) of randomly varying coils, and to 120 deviated unseen configurations when trained on 18 configurations in a single model, 2) matches the performance of coil configuration-specific models, and 3) outperforms configuration-invariant models with improvement margins of ∼ 1 dB / 0.03 and 0.3 dB / 0.02 in PSNR / SSIM for knee and brain data. Our code is available at https://github.com/sriprabhar/HyperCoil-Recon
Competitions in AI - Robustly Ranking Solvers Using Statistical Resampling

Solver competitions play a prominent role in assessing and advancing the state of the art for solving many problems in AI and beyond. Notably, in many areas of AI, competitions have had substantial impact in guiding research and applications for many years, and for a solver to be ranked highly in a competition carries considerable weight. But to which extent can we expect competition results to generalise to sets of problem instances different from those used in a particular competition? This is the question we investigate here, using statistical resampling techniques. We show that the rankings resulting from the standard interpretation of competition results can be very sensitive to even minor changes in the benchmark instance set used as the basis for assessment and can therefore not be expected to carry over to other samples from the same underlying instance distribution. To address this problem, we introduce a novel approach to statistically meaningful analysis of competition results based on resampling performance data. Our approach produces confidence intervals of competition scores as well as statistically robust solver rankings with bounded error. Applied to recent SAT, AI planning and computer vision competitions, our analysis reveals frequent statistical ties in solver performance as well as some inversions of ranks compared to the official results based on simple scoring.
Inflation in symmergent metric-Palatini gravity

In this paper, we study the cosmological inflation phenomenon in symmergent gravity theory. Symmergent gravity is a novel framework which merges gravity and the standard model (SM) so that the gravity emerges from the matter loops and restores the broken gauge symmetries along the way. Symmergent gravity is capable of inducing the gravitational constant G and the quadratic curvature coefficient cO from the loop corrections of the matter sector in a flat space-time. In the event that all the matter fields, including the beyond the standard model (BSM) sector, are mass degenerate, the vacuum energy can be expressed in terms of G and cO . The parameter which measures the deviation from the mass degeneracy is dubbed α̂. The parameters, cO and α̂, of symmergent gravity convey the information about the fermion and boson balance in the matter (SM+BSM) sector in number and in mass, respectively. In our analysis, we have investigated the space of the symmergent parameters cO and α̂ wherein they produce results that comply with the inflationary observables ns , r, and dns /d ln k. We have shown that the vacuum energy together with the quadratic curvature term arising in the symmergent gravity prescription are capable of inflating the universe provided that the quadratic curvature coefficient cO is negative (which corresponds to fermion dominance in number in the matter sector) and the deviation from the mass degeneracy in the matter sector is minute for both boson mass dominance and fermion mass dominance cases.
Speaker Recognition Using Isomorphic Graph Attention Network Based Pooling on Self-Supervised Representation

The emergence of self-supervised representation (i.e., wav2vec 2.0) allows speaker-recognition approaches to process spoken signals through foundation models built on speech data. Nevertheless, effective fusion on the representation requires further investigating, due to the inclusion of fixed or sub-optimal temporal pooling strategies. Despite of improved strategies considering graph learning and graph attention factors, non-injective aggregation still exists in the approaches, which may influence the performance for speaker recognition. In this regard, we propose a speaker recognition approach using Isomorphic Graph ATtention network (IsoGAT) on self-supervised representation. The proposed approach contains three modules of representation learning, graph attention, and aggregation, jointly considering learning on the self-supervised representation and the IsoGAT. Then, we perform experiments for speaker recognition tasks on VoxCeleb1\&2 datasets, with the corresponding experimental results demonstrating the recognition performance for the proposed approach, compared with existing pooling approaches on the self-supervised representation.
CiFHER: A Chiplet-Based FHE Accelerator with a Resizable Structure

Fully homomorphic encryption (FHE) is in the spotlight as a definitive solution for privacy, but the high computational overhead of FHE poses a challenge to its practical adoption. Although prior studies have attempted to design ASIC accelerators to mitigate the overhead, their designs require excessive amounts of chip resources (e.g., areas) to contain and process massive data for FHE operations. We propose CiFHER, a chiplet-based FHE accelerator with a resizable structure, to tackle the challenge with a cost-effective multi-chip module (MCM) design. First, we devise a flexible architecture of a chiplet core whose configuration can be adjusted to conform to the global organization of chiplets and design constraints. The distinctive feature of our core is a recomposable functional unit providing varying computational throughput for number-theoretic transform (NTT), the most dominant function in FHE. Then, we establish generalized data mapping methodologies to minimize the network overhead when organizing the chips into the MCM package in a tiled manner, which becomes a significant bottleneck due to the technology constraints of MCMs. Also, we analyze the effectiveness of various algorithms, including a novel limb duplication algorithm, on the MCM architecture. A detailed evaluation shows that a CiFHER package composed of 4 to 64 compact chiplets provides performance comparable to state-of-the-art monolithic ASIC FHE accelerators with significantly lower package-wide power consumption while reducing the area of a single core to as small as 4.28mm$^2$.
Multicontact semilattices

We characterize those join semilattices with a multicontact relation which can be multicontact- and semilattice-embedded into a Boolean algebra with the multicontact given by overlap, equivalently, into a distributive lattice with additive multicontact, still equivalently, into the multicontact semilattice associated to some topological space. A similar characterization is presented for embeddability into Boolean algebras or distributive lattices with a not necessarily overlap nor additive multicontact.
The Project Highlight of Japan's Lunar Explorer KAGUYA (SELENE)

Introduction: KAGUYA(SELENE) is the most sophisticated lunar exploration mission in the post-Apollo Era and consists of the main orbiter and two small satellites – the Relay satellite (OKINA) and the VRAD satellite (OUNA). SELENE was successful launched on September 14, 2007 at Tanegashima Space Center of JAXA and it was entered polar orbit with about 100km altitude from October 18, 2007. KAGUYA had been in nominal operation during De-cember 21, 2007 and October 31, 2008 and has been in extended operation by early summer, 2009. By using KAGUYA existing observation data, KAGUYA already provided several initial scientific results and public outreach. In addition, KAGUYA Level-2 processed data (standard product) will be archived in " Level-2 Data Archive and Distribution System " (L2DB) at SOAC (SELENE Operation and Analysis Center) at Sagamihara-campus in JAXA. Users can search and download Level-2 processed data via Web browser. SELENE data will be available for users through the internet from November 2009. This paper describes the overview of KAGUYA system, ground segment, highlight of operation during 10 month nominal operation phase, public promotion result and future expected operation plan. KAGUYA OVERVIEW: KAGUYA consists of a main orbiting satellite at about 100km altitude and two sub-satellites (Relay Satellite named " OKINA " and VRAD Satellite named " OUNA ") in lunar polar orbit[1]. The main orbiter weight at launch is about 2.9 tons and the size of its main body is 2.1m× 2.1m×4.8m. The mass of the sub-satellites is about 50kg, and these are spin-stabilized. When separating from the main orbiter, spin rotation power is added. This sub-satellite separation mechanism which gives the rotational and the translational force simultaneously was originally developed for JAXA's mi-cro-lab satellite. The 2 sub-satellites are separated from the main orbiter after being injected into the Moon circulation orbit. The perilune altitude at the separation is 100km. The aposelene altitude of OKINA at the separation is 2400km, which is determined to measure the gravity field anomaly on the far side of the Moon through relaying the Main orbiter s-band signal effectively. OUNA's aposelene altitude is 800km and is selected for the low order
Hilbert-Schmidt operators and the conjugate of a complex Hilbert space: Dirac's bra-ket formalism revisited

We reveal in detail how the definition of the inner product on a given complex Hilbert space - usually used in mathematics (where linearity is assumed in the first component and semilinearity in the second) - directly links to Dirac's powerful bra-ket formalism in quantum physics. To this end, we just have to make use of the conjugate of a complex Hilbert space (by which an analysis of semilinear operators can be handled by means of linear operator theory) and re-apply the theorem of Fr\'{e}chet-Riesz accordingly. Applications are specified, including a self-contained and simple description of the tensor product of two complex Hilbert spaces $H \otimes K$ (answering a related question of B. K. Driver) and a purely linear algebraic description of the quantum teleportation process (Example 3.8). In doing so, we provide an explicit construction of a canonical isometric isomorphism between the Hilbert spaces $H \otimes (K \otimes L)$ and $(H \otimes K) \otimes L$ (Theorem 3.7).
IDiff-Face: Synthetic-based Face Recognition through Fizzy Identity-Conditioned Diffusion Models

The availability of large-scale authentic face databases has been crucial to the significant advances made in face recognition research over the past decade. However, legal and ethical concerns led to the recent retraction of many of these databases by their creators, raising questions about the continuity of future face recognition research without one of its key resources. Synthetic datasets have emerged as a promising alternative to privacy-sensitive authentic data for face recognition development. However, recent synthetic datasets that are used to train face recognition models suffer either from limitations in intra-class diversity or cross-class (identity) discrimination, leading to less optimal accuracies, far away from the accuracies achieved by models trained on authentic data. This paper targets this issue by proposing IDiff-Face, a novel approach based on conditional latent diffusion models for synthetic identity generation with realistic identity variations for face recognition training. Through extensive evaluations, our proposed synthetic-based face recognition approach pushed the limits of state-of-the-art performances, achieving, for example, 98.00% accuracy on the Labeled Faces in the Wild (LFW) benchmark, far ahead from the recent synthetic-based face recognition solutions with 95.40% and bridging the gap to authentic-based face recognition with 99.82% accuracy *.
Automatically measuring speech fluency in people with aphasia: first achievements using read-speech data

Background: Speech and language pathologists (SLPs) often relyon judgements of speech fluency for diagnosing or monitoringpatients with aphasia. However, such subjective methods havebeen criticised for their lack of reliability and their clinical cost interms of time. Aims: This study aims at assessing the relevance of a signalprocessingalgorithm, initially developed in the field of language acquisition, for the automatic measurement of speech fluency in people with aphasia (PWA). Methods&Procedures: Twenty-nine PWA and five control participantswere recruited via non-profit organizations and SLP networks. All participants were recorded while reading out loud a set ofsentences taken from the French version of the Boston Diagnostic Aphasia Examination. Three trained SLPs assessed the fluency of each sentence on a five-point qualitative scale. A forward-backward divergence segmentation and a clustering algorithm were used to compute, for each sentence, four automatic predictors of speech fluency: pseudo-syllable rate, speech ratio, rate of silent breaks, and standard deviation of pseudo-syllable length. The four predictors were finally combined into multivariate regression models (a multiplelinear regression - MLR, and two non-linear models) to predict the average SLP ratings of speech fluency, using a leave-one speaker-out validation scheme. Outcomes&Results: All models achieved accurate predictions of speech fluency ratings, with average root-mean-square errors as low as 0.5. The MLR yielded a correlation coefficient of 0.87 with reference ratings at the sentence level, and of 0.93 when aggregating the data for each participant. The inclusion of an additional predictor sensitive to repetitions improved further the predictions with a correlation coefficient of 0.91 at the sentence level, and of 0.96 at the participant level. Conclusions: The algorithms used in this study can constitute a cost-effective and reliable tool for the assessment of the speech fluency of patients with aphasia in read-aloud tasks. Perspectives for the assessment of spontaneous speech are discussed.
InstantAvatar: Efficient 3D Head Reconstruction via Surface Rendering

Recent advances in full-head reconstruction have been obtained by optimizing a neural field through differentiable surface or volume rendering to represent a single scene. While these techniques achieve an unprecedented accuracy, they take several minutes, or even hours, due to the expensive optimization process required. In this work, we introduce InstantAvatar, a method that recovers full-head avatars from few images (down to just one) in a few seconds on commodity hardware. In order to speed up the reconstruction process, we propose a system that combines, for the first time, a voxel-grid neural field representation with a surface renderer. Notably, a naive combination of these two techniques leads to unstable optimizations that do not converge to valid solutions. In order to overcome this limitation, we present a novel statistical model that learns a prior distribution over 3D head signed distance functions using a voxel-grid based architecture. The use of this prior model, in combination with other design choices, results into a system that achieves 3D head reconstructions with comparable accuracy as the state-of-the-art with a 100x speed-up.
WhaleVis: Visualizing the History of Commercial Whaling

Whales are an important part of the oceanic ecosystem. Although historic commercial whale hunting a.k. a. whaling has severely threatened whale populations, whale researchers are looking at historical whaling data to inform current whale status and future conservation efforts. To facilitate this, we worked with experts in aquatic and fishery sciences to create WhaleVis—an interactive dashboard for the commercial whaling dataset maintained by the International Whaling Commission (IWC). We characterize key analysis tasks among whale researchers for this database, most important of which is inferring spatial distribution of whale populations over time. In addition to facilitating analysis of whale catches based on the spatio-temporal attributes, we use whaling expedition details to plot the search routes of expeditions. We propose a model of the catch data as a graph, where nodes represent catch locations, and edges represent whaling expedition routes. This model facilitates visual estimation of whale search effort and in turn the spatial distribution of whale populations normalized by the search effort—a well known problem in fisheries research. It further opens up new avenues for graph analysis on the data, including more rigorous computation of spatial distribution of whales normalized by the search effort, and enabling new insight generation. We demonstrate the use of our dashboard through a real life use case.